From ada4e426e87120023a6edcdd1b519652b7ff1708 Mon Sep 17 00:00:00 2001 From: Kyle Mathews Date: Fri, 16 Oct 2026 13:13:52 -0700 Subject: [PATCH 01/10] blog: draft TanStack DB 1.0 RC1 oracle essay --- ...tack-db-1.0-rc1-going-all-in-on-oracles.md | 147 ++++++++++++++++++ 1 file changed, 147 insertions(+) create mode 100644 src/blog/tanstack-db-1.0-rc1-going-all-in-on-oracles.md diff --git a/src/blog/tanstack-db-1.0-rc1-going-all-in-on-oracles.md b/src/blog/tanstack-db-1.0-rc1-going-all-in-on-oracles.md new file mode 100644 index 000000000..44fb12a99 --- /dev/null +++ b/src/blog/tanstack-db-1.0-rc1-going-all-in-on-oracles.md @@ -0,0 +1,147 @@ +--- +title: 'TanStack DB 1.0 RC1: Going All In on Oracles' +published: 2026-10-16 +draft: true +excerpt: 'TanStack DB 1.0 RC1 is here. How oracles helped me find whole classes of bugs and rethink programming with agents.' +library: db +authors: + - Kyle Mathews +--- + +_Agents make the models practical to write. The models help us find and fix whole classes of bugs._ + +I'm delighted to announce on behalf of the core DB maintainers, myself, Sam Willis, Kevin De Porre, and the entire DB community, that TanStack DB 1.0 RC1 is here! + +After 1.5 years of development and betas and many discussions with users, the API is powerful and stable. We expect to ship 1.0 in a few short weeks. + +Since our [0.6 release](https://tanstack.com/blog/tanstack-db-0.6-app-ready-with-persistence-and-includes), we have shipped [server rendering and deep integration with TanStack Start](https://tanstack.com/db/latest/docs/guides/ssr), worked through a long run of correctness problems, and [made DB much faster for applications with many simultaneous live queries](https://github.com/TanStack/db/pull/1987). + +This post covers what shipped for RC1 and what happened when I tried to fix every known DB bug. Oracles turned that apparently finite task into a search for whole classes of failures, changing how I think about programming and the frameworks, infrastructure, and developer tools agents build on. + +If you haven't used it yet, TanStack DB is a reactive client data framework that makes backend data feel local. Records from your API or sync engine become typed collections. Components query them for joined, filtered live results. Optimistic edits appear immediately and reconcile with the server later. + +Perhaps the biggest difference from earlier client-side databases is that you can adopt TanStack DB incrementally. Historically, getting reactive queries, optimistic mutations, and offline support often meant adopting a new database and sync architecture. With TanStack DB, you can start with your existing REST APIs and [TanStack Query](https://tanstack.com/query/latest) setup, immediately gain fast incremental relational queries, and progressively add [local persistence](https://tanstack.com/db/latest/docs/guides/sqlite-persistence), [durable offline mutations](https://tanstack.com/db/latest/docs/guides/offline-transactions), or real-time sync as your needs evolve. Because the application-facing query model stays the same, you can go from a conventional web application to a fully offline-capable, local-first app without rewriting everything along the way. + +We began building DB in early 2025 at [ElectricSQL](https://electric-sql.com/), which incubated the project for its first year and a half. We [shipped the first beta that July](https://tanstack.com/blog/tanstack-db-0.1-the-embedded-client-database-for-tanstack-query). Since [Neon acquired us in August](https://neon.com/blog/electrifying-neon-building-the-realtime-backend), it has continued to back DB and we released yesterday [Neon Realtime](https://neon.com/docs/realtime/overview) with [TanStack DB support](https://neon.com/docs/realtime/sdks/typescript#tanstack-db). + +Adoption is accelerating, with teams using TanStack DB to build everything from developer tools to consumer mobile apps. Prisma uses DB in [Prisma Studio](https://github.com/prisma/studio), its open-source database editor. Unkey uses it extensively across its [developer dashboard](https://github.com/unkeyed/unkey/tree/main/web/apps/dashboard), and Skylight uses DB and its offline capabilities in its [React Native family calendar app](https://www.youtube.com/watch?v=HSUKoa4JoRw). + +Across its packages, DB now has [almost 3 million weekly NPM downloads](https://tanstack.com/db/latest). Compared to [weekly downloads of React](https://tanstack.com/stats/npm?packageGroups=%5B%7B%22packages%22%3A%5B%7B%22name%22%3A%22%40tanstack%2Fdb%22%7D%5D%7D%2C%7B%22packages%22%3A%5B%7B%22name%22%3A%22react%22%2C%22hidden%22%3Atrue%7D%5D%2C%22color%22%3A%22%2361DAFB%22%2C%22baseline%22%3Atrue%2C%22baselineLabel%22%3A%22React%22%7D%5D&range=1825-days&transform=none&binType=weekly&viewMode=history&chartType=line&barSort=value&bucketOffset=0&playbackIntervalMs=350&playbackLoop=false&playbackPlaying=false&showDataMode=all&normalizeBaseline=true&showBaseline=false&showLegend=false&height=400), downloads of the core DB package have grown roughly tenfold over the past year, to about 0.7% of React's downloads. + +After server rendering landed a few months ago, I thought the remaining work before 1.0 was fairly straightforward: fix every known bug. I'd fire up my agents and set them loose and be done in a few weeks. Before and during that work, though, I tried some new ideas for making the coding environment more productive for agent swarms. The new environment has proved astonishingly effective at finding new bugs and undefined behavior. My short bug-fixing expedition has now stretched to hundreds of PRs and months of intense work hardening DB's core abstractions for 1.0. Since the first PR in this push, the repository has gained roughly 330,000 lines of tests. + +I've worked on open-source libraries for a long time and previously found it hard to find bugs proactively before users provide reproductions. Now essentially every new agent session stumbles on multiple new classes of bugs. After months of fixes, many of these bugs are now so obscure that I suspect no one has encountered them in the wild. Yet they keep coming. Increasingly, my job is to keep agents from fixing _too much_—chasing hypothetical edge cases or adding complexity to solve problems DB never promised to solve. + +## Crafting a playable environment for agents + +The basic problem coding agents present is: when implementation gets cheap, what is left for programmers to do? My answer is _crafting a playable environment for agents_. Programmers shape the _design space_ for swarms of agents: what they can see, which moves they can make, how to judge the results, and what each round passes to the next. + +Over the past few months, I've made the DB repository _playable_ for fresh agent sessions. An agent can find what a part of DB promises, try a small change or challenge that promise, and see the consequences quickly. I can run many short investigations in parallel. What one session discovers becomes the starting point for the next. + +I use one recurring loop: start with a report or design question, ask what general behavior ought to hold, search for counterexamples, then revise the implementation, the rule, or how we verify it. I decide the product promise when it isn't settled. Agents do much of the searching. A bug fix improves DB; a clearer rule or stronger verification improves what later agents can discover. + +Oracles have become the most powerful form of verification in that loop. **Each oracle judges one small part of DB against an independently stated law about what users should observe.** Many use a small reference model to predict what should happen across legal sequences of operations, then compare that prediction with production's public behavior. A unit test can keep one reported failure from returning. An oracle keeps asking whether the rule behind that failure holds across other cases. DB now has **161 oracle test files across 14 packages**, covering [collection change messages](https://github.com/TanStack/db/blob/main/packages/db/tests/change-event-history-oracle.test.ts), [nested live-query includes](https://github.com/TanStack/db/blob/main/packages/db/tests/query/includes-cross-formulation-oracle.property.test.ts), and [cursor pagination](https://github.com/TanStack/db/blob/main/packages/query-db-collection/tests/cursor-pagination.oracle.test.ts), among other behaviors. Sometimes an agent finds that an oracle missed a bug. Strengthening that judge gives later investigations a better instrument. + +I've written and heavily iterated on [oracle-authoring](https://github.com/TanStack/db/blob/main/.agents/skills/oracle-authoring/SKILL.md) and [oracle-review](https://github.com/TanStack/db/blob/main/.agents/skills/oracle-review/SKILL.md) SKILLs, plus a rigorous [14-part rubric](https://github.com/TanStack/db/blob/main/docs/contributing/oracle-rubric.md) that agents use to evaluate and improve their oracles. + +## The bug list kept growing + +No software survives contact with reality. Software becomes battle-tested when many people use it in different ways and maintainers fix what those uses reveal. That maturation normally takes years. + +We made things harder for ourselves with DB's ambitious and radically simple APIs. As our [0.5 post](https://tanstack.com/blog/tanstack-db-0.5-query-driven-sync) put it: “You describe what data you need. DB handles the fetching, caching, and updating.” A single query shapes what DB asks the backend to load across thousands of potential loading scenarios. Our promise that client queries drive data loading and sync has to hold across on-demand queries, pagination, [persistence and offline use](https://tanstack.com/blog/tanstack-db-0.6-app-ready-with-persistence-and-includes), optimistic edits, retries, and reconnection. Consider an issue board showing the ten highest-priority open issues. Complete one, and the replacement may be the eleventh issue, which the client has never loaded. Add pages and offline restarts, and the possible interactions multiply. Some failures are ordinary bugs. Others reveal undefined behavior or push data-loading complexity back into application code. + +When I started tackling our seemingly finite bug list, I surveyed open issues and PRs, grouped related reports, and asked agents what work would solve each entire cluster. One result was the [framework-adapter RFC](https://github.com/TanStack/db/issues/1623) and a [conformance suite](https://github.com/TanStack/db/pull/1636) to make the same public behavior hold across framework adapters. An agent working through another cluster suggested oracle tests. They spread through the RFC work and across DB because each investigation exposed nearby questions the original report hadn't asked. I've merged 350 bug-fixing PRs since starting this work. + +One sequence of fixes makes this loop concrete. A user report led to an oracle, which agents then tried to break. Each blind spot led to a stronger oracle that exposed bugs the previous version couldn't see. The implementation improved, but so did the instrument for investigating it. + +A user [reported a bug](https://github.com/TanStack/db/issues/1901) with collection change messages. They deleted a row and reinserted a replacement with the same key in one turn. A direct read returned the replacement, but a subscriber replaying the messages could end with no row at all. An application maintaining a cache from those messages could silently lose data. + +[PR #1902](https://github.com/TanStack/db/pull/1902) fixed the immediate failure and added an oracle for the broader promise: **a subscriber applying every change message should end up with the same rows as a direct read.** Its reference model is a plain map of keyed rows. It generates legal sequences of inserts, updates, and deletes, runs them both one at a time and batched together, and compares three results: what the map predicts, what DB returns from a direct read, and what a subscriber reconstructs from DB's messages. That checks the reported delete-and-reinsert sequence alongside nearby sequences involving other starting rows, keys, and operation orders. A future change has to preserve the agreement across all of them. + +Then an agent tried removing branches from the collection code to see which ones the tests actually needed. One removal passed the oracle and produced a better change message than the original branch. The oracle had a blind spot: its simulated subscriber used the same map operation for an **insert** and an **update**. It could reconstruct the right final rows even when DB sent an update for a row the subscriber had already deleted. A real subscriber that applies updates only to existing rows would reject it or lose the row. + +I strengthened the rule the subscriber checks as it replays each message: **insert requires an absent key; update and delete require a present key.** The old code then failed 26 of 378 histories already reaching that path. [PR #1995](https://github.com/TanStack/db/pull/1995) made DB send an insert when the key became visible again. The same generated histories now check both the final rows _and_ whether every message was valid at the moment a subscriber received it. The original oracle could catch a missing row; the stronger one can catch a broken message even when the final rows happen to match. + +A review exposed another gap. While reviewing [PR #2004](https://github.com/TanStack/db/pull/2004), CodeRabbit found that replacing the server's source rows during an optimistic edit could send **two inserts for the same key**. The existing subscriber setup filtered out the second insert before the oracle saw it. For [PR #2005](https://github.com/TanStack/db/pull/2005), an agent added a subscriber seeded with the current visible rows that received the raw change messages. After every batch, it checks whether each message is valid for the rows that subscriber actually holds, then compares the subscriber's reconstructed rows with the independent model. The old code failed seven cases. The fix published each key once, and the stronger oracle went on to find another invalid insert when a pending delete crossed an optimistic insert. The new observation point made an entire path of previously hidden failures visible. + +The original report was fixed in #1902. The stronger oracle still found invalid updates and duplicate inserts. Fixing the reported sequence became the start of an investigation into the broader contract. + +Other investigations require me to decide the promise before an oracle can enforce it. During a code simplification audit, an agent found a case where an ordered query's row changed value A → B → A while notifications were held, but also moved behind another row. DB sent no callback when it released the result. A subscribed component could keep showing the old order. I chose the last result subscribers **actually saw** as the comparison point: the private value excursion needs no update, but the public move needs a notification. The oracle for [PR #1933](https://github.com/TanStack/db/pull/1933) runs that held sequence through a real ordered query, checks that nothing is published during the hold, and compares the final rows and callbacks with a small model that sorts the source rows. Neighboring cases distinguish a pure move from a lasting value change. The old implementation missed the final callback; the repaired one passes those checks. + +This is how I pursue a whole class of bugs. I turn the report into a law, generate related histories, and check the public behavior at the point where a user or subscriber would notice the difference. I also test the instrument against the old implementation or a plausible wrong change. When another path reveals a blind spot, I expand what the oracle drives or observes. That stronger instrument stays in the repository for the next agent. + +## Look for cranes + +I've been reading Daniel Dennett's [_Darwin's Dangerous Idea_](https://www.simonandschuster.com/books/Darwins-Dangerous-Idea/Daniel-C-Dennett/9780684824710). Darwin's startling claim, in Dennett's account, was that natural selection, with no foresight, could produce eyes, brains, and other astonishingly intricate adaptations. Many people doubted that a blind process could do this and looked for an outside intelligence to supply the design. Dennett calls that explanatory move a **skyhook**: it lifts the finished complexity into place from above, like the _deus ex machina_ of Greek theater, where stage machinery lowers a god to resolve a plot. To explain how complexity actually develops, we need mechanisms that work within the process. + +Dennett calls these evolutionary mechanisms **cranes**—construction cranes. Both skyhooks and cranes lift; the difference is that a crane stands on the ground and works from what is already there. In evolution, a crane makes more possibilities reachable for the next round of variation and selection. Sexual reproduction, for example, shuffles inherited variants into new combinations, opening paths that mutation in a single lineage would reach much more slowly. A new crane often builds on older cranes. The path to complexity runs through accumulating mechanisms to speedily explore and test the design space. + +The skyhook story in agentic programming is that a sufficiently intelligent model, given a repository, will infer every unstated rule and deliver the right system in one leap. One-shot coding makes that easy to believe for familiar tasks, but those successes stand on compilers, types, libraries, conventions, examples, tests, and a vast history of solved problems in the model's training. Every substantial software project runs into questions that history hasn't settled. Features interact in unexpected ways; new uses reveal missing guarantees; performance work opens up alternative implementations. There is no finished answer for an agent to retrieve. The work is to search the design space, decide what should hold, and test candidate answers. Agents need cranes that make that search productive and preserve what each investigation learns. + +For agents working on DB itself, an oracle is a crane because it turns a reported failure into a repeatable search over a law. [Fast-check](https://fast-check.dev/) generates legal sequences of operations and shrinks a failure into a short counterexample. An agent can run proposed fixes and simpler implementations through the same instrument and see where their behavior diverges. The oracle makes many neighboring cases cheap to explore and bad candidates easy to reject. + +I've set up the repository so a fresh agent can use these instruments without reconstructing months of design decisions. [Project guidelines](https://github.com/TanStack/db/blob/main/AGENTS.md) point it to the glossary, architecture documents, and [coverage map](https://github.com/TanStack/db/blob/main/docs/contributing/oracle-coverage.md), which identifies the oracle responsible for each behavior and its limits. The oracle files put the law beside the small model, generated histories, production path, and comparison, so an agent can read the promise and run an experiment in the same place. The [authoring](https://github.com/TanStack/db/blob/main/.agents/skills/oracle-authoring/SKILL.md) and [review](https://github.com/TanStack/db/blob/main/.agents/skills/oracle-review/SKILL.md) guides teach agents to strengthen that experiment; hostile reviewers follow the same paths to find plausible wrong implementations. A new session starts with a map of the system, runnable experiments, and ways to challenge the experiments themselves. + +Framework conformance tests and benchmarks add feedback for shared behavior and speed. I've also used [TLA+](https://lamport.org/tla/tla.html) to explore the possible sequences of a proposed [query-scoped subscription design](https://github.com/TanStack/db/issues/1935). + +I shape the _fitness landscape_ generally by editing the [AGENTS.md](https://github.com/TanStack/db/blob/main/AGENTS.md). For example I say "all PRs must be net negative for production code". This pushes agents hard to avoid bloated and duplicative implementations. + +That extensive machinery I've developed matters because DB's API opened a huge design space. On-demand queries, pagination, persistence, optimistic writes, and synchronization meet in combinations we never enumerated when designing it. Together, these instruments help us build a richer theory of how clients should load, update, reconcile, and present data. + +Paradoxically, the bug list keeps growing because our ability to find bugs keeps improving. Three months and many billions of tokens have produced more than a pile of fixes: agents now investigate DB with instruments that get sharper as they use them. That change in their working environment has mattered far more than any model upgrade I've seen. + +## Superhistory + +An afternoon can compress what used to be weeks of software history. Venkatesh Rao calls this agent-mediated compression of experience [“artificial time”](https://contraptions.venkateshrao.com/p/superhistory-not-superintelligence). This release push feels like a software-development version of his “superhistory.” I spend much of the day in what feels like a succession of intense design meetings, with fresh evidence arriving 100x faster than I could have gathered it myself. + +Building software starts as an act of invention, but much of its maturation is discovery. DB's API created a world with rules whose consequences we didn't fully know. As users try it and agents probe its combinations, unexpected interactions reveal ambiguities, missing guarantees, and layers of architecture we hadn't anticipated. Some consequences follow from existing promises; others call for new rules. + +That's what superhistory feels like from inside: questions that normally accumulate across years are arriving in months. Implementation work clears quickly, leaving me in a steady stream of design decisions. I spend much more of my day deciding what DB should guarantee and whether a fix simplifies the system or adds unnecessary machinery. Programming has become more intellectually demanding for me even as writing the code has become cheaper. + +Artificial time changes the economics of rigor. An oracle needs a model, generated histories, a production driver, and continuing review as the system changes. Since this push began, DB has gained roughly 330,000 lines of tests—probably more code than I wrote in my entire life before AI. Agents make that work practical. + +## The world DB creates for your agents + +Good programming abstractions create smaller, more consistent worlds for the code above them. A filesystem gives programs files and paths instead of disk sectors. TCP gives them an ordered stream even when the network drops or reorders packets. A programming language gives them functions and types instead of registers and jump instructions. Each settles a mass of lower-level questions so the next layer can make more ambitious moves. + +DB creates a simpler world of client data for application developers and their agents. An agent building an application can work with typed collections backed by TanStack Query over an existing API, a sync engine, or local persistence. It can join issues with projects, show one team's open work, and edit optimistically without building a separate endpoint and cache path for every screen. DB keeps the live results current while handling on-demand fetching, pagination, and reconciliation with the server. + +For that world to exist, DB must deliver on its guarantees. A joined, filtered view stays current as its underlying records change. An optimistic edit appears immediately, then remains when persistence succeeds or rolls back when it fails. With on-demand sync, the query can shape backend requests so a screen loads only the rows it needs. Those promises still have to hold when pagination, offline persistence, and server updates interact—and at the speed a demanding application needs. The last three months of work have been about making this simplified world reliable enough to build on. + +When you start a new application, you can experiment more freely by building on DB's mature data mechanics. You and your agents can focus on behaviors you want to create instead of rebuilding query, cache, and sync behavior for each screen. + +The oracles I've described live in DB's repository. They help me and other contributors strengthen DB; applications benefit from the guarantees they protect. DB's guarantees are an example of what frameworks, infrastructure, and developer tools offer: a detailed theory of how some part of software should work. Their APIs and runtime behavior already shape applications. Coding agents open another route for that knowledge: it can guide the code an agent writes. [SKILLs](https://agentskills.io/home) are a first step, explaining which abstractions to use, what the system guarantees, and where the application has a decision to make. + +I'm exploring how to expose those guarantees directly to agents building applications, beyond written guidance. A framework could provide executable rules, diagnostics, and conformance tests that reach into application code. An agent building a custom sync adapter, for instance, could run it against DB's change-message rules and get a short failing history if it sends an update for a key the subscriber has never seen. + +These are cranes for userland: mechanisms that work inside the application-building process, helping agents explore and construct the next layer on top of a framework. The next generation of frameworks, infrastructure, and developer tools will create rich, lawful, playable worlds for applications and equip coding agents to build within them. + +Here's one idea we're exploring for where to take TanStack DB next: write ordinary server functions to query and mutate Postgres, and DB compiles them into the collections and actions your client uses. + +```tsx +// Server: define a query +const todos = query({ + async handler(req, res) { + return res.json(await db.select().from(todo)) + }, +}) + +// Server: define a mutation +const addTodo = mutation({ + async handler(req, res) { + return res.json(await db.insert(todo).values(req.body).returning()) + }, +}) + +// Client: use the generated collection and action +const { data } = useLiveQuery(todos) +addTodo({ id: crypto.randomUUID(), text: 'Hello world' }) +``` + +_Simplified sketch of an experimental API._ + +DB also generates the data-loading and mutation wiring. When a mutation runs, the client tells the server which query collections are live. The server determines which need updating and returns fresh results with the mutation response. + +Making this work broadly means understanding enough of Postgres's vast language to preserve query meaning, determine which reads a write affects, and reconcile client state correctly. A few years ago, I wouldn't have considered attempting the larger project. The last three months have changed the economics: agents can explore that space, find where the design breaks, and carry stronger instruments into the next investigation. That makes an extraordinarily demanding abstraction tractable enough to pursue. + +I began this push with a finite bug list. It became a continuing process of deciding what DB should promise, finding consequences of those decisions, and giving the next agent stronger equipment for the search. TanStack DB 1.0 RC1 is the result so far. [Try it](https://tanstack.com/db/latest/docs/quick-start) in a demanding application and tell us [what breaks](https://github.com/TanStack/db/issues) or [what DB makes needlessly hard to build](https://github.com/TanStack/db/discussions). Those reports are how the next layer of design begins. From bfab34aea220973d50d9c9c70a66b3e6211d4e80 Mon Sep 17 00:00:00 2001 From: Kyle Mathews Date: Thu, 22 Oct 2026 15:24:06 -0600 Subject: [PATCH 02/10] docs: sync RC1 oracle essay with latest revisions --- ...tack-db-1.0-rc1-going-all-in-on-oracles.md | 64 ++++++++++++------- 1 file changed, 40 insertions(+), 24 deletions(-) diff --git a/src/blog/tanstack-db-1.0-rc1-going-all-in-on-oracles.md b/src/blog/tanstack-db-1.0-rc1-going-all-in-on-oracles.md index 44fb12a99..35b9dde02 100644 --- a/src/blog/tanstack-db-1.0-rc1-going-all-in-on-oracles.md +++ b/src/blog/tanstack-db-1.0-rc1-going-all-in-on-oracles.md @@ -14,15 +14,15 @@ I'm delighted to announce on behalf of the core DB maintainers, myself, Sam Will After 1.5 years of development and betas and many discussions with users, the API is powerful and stable. We expect to ship 1.0 in a few short weeks. -Since our [0.6 release](https://tanstack.com/blog/tanstack-db-0.6-app-ready-with-persistence-and-includes), we have shipped [server rendering and deep integration with TanStack Start](https://tanstack.com/db/latest/docs/guides/ssr), worked through a long run of correctness problems, and [made DB much faster for applications with many simultaneous live queries](https://github.com/TanStack/db/pull/1987). +Since our [0.6 release](https://tanstack.com/blog/tanstack-db-0.6-app-ready-with-persistence-and-includes), we have shipped [server rendering and deep integration with TanStack Start](https://tanstack.com/db/latest/docs/guides/ssr), worked through a long run of correctness problems closing every known bug, added new collection adapters for Neon Realtime and Convex, and [made DB much faster for applications with many simultaneous live queries](https://github.com/TanStack/db/pull/1987). -This post covers what shipped for RC1 and what happened when I tried to fix every known DB bug. Oracles turned that apparently finite task into a search for whole classes of failures, changing how I think about programming and the frameworks, infrastructure, and developer tools agents build on. +Trying to fix every known TanStack DB bug changed how I think about programming. Programming is the iterative work of drawing out design ideas, exploring their consequences, and developing them into a coherent working system. LLMs give us access to an enormous repertoire of ideas and make implementing and investigating them dramatically cheaper. Each investigation can leave the system capable of attempting something more ambitious. This post follows how oracles helped that happen while preparing DB for 1.0—and what it suggests about the future of frameworks, infrastructure, and developer tools. If you haven't used it yet, TanStack DB is a reactive client data framework that makes backend data feel local. Records from your API or sync engine become typed collections. Components query them for joined, filtered live results. Optimistic edits appear immediately and reconcile with the server later. Perhaps the biggest difference from earlier client-side databases is that you can adopt TanStack DB incrementally. Historically, getting reactive queries, optimistic mutations, and offline support often meant adopting a new database and sync architecture. With TanStack DB, you can start with your existing REST APIs and [TanStack Query](https://tanstack.com/query/latest) setup, immediately gain fast incremental relational queries, and progressively add [local persistence](https://tanstack.com/db/latest/docs/guides/sqlite-persistence), [durable offline mutations](https://tanstack.com/db/latest/docs/guides/offline-transactions), or real-time sync as your needs evolve. Because the application-facing query model stays the same, you can go from a conventional web application to a fully offline-capable, local-first app without rewriting everything along the way. -We began building DB in early 2025 at [ElectricSQL](https://electric-sql.com/), which incubated the project for its first year and a half. We [shipped the first beta that July](https://tanstack.com/blog/tanstack-db-0.1-the-embedded-client-database-for-tanstack-query). Since [Neon acquired us in August](https://neon.com/blog/electrifying-neon-building-the-realtime-backend), it has continued to back DB and we released yesterday [Neon Realtime](https://neon.com/docs/realtime/overview) with [TanStack DB support](https://neon.com/docs/realtime/sdks/typescript#tanstack-db). +We began building DB in early 2025 at [ElectricSQL](https://electric-sql.com/), which incubated the project for its first year and a half. We [shipped the first beta that July](https://tanstack.com/blog/tanstack-db-0.1-the-embedded-client-database-for-tanstack-query). Since [Neon acquired us in August](https://neon.com/blog/electrifying-neon-building-the-realtime-backend), it has continued to back DB and we released recently [Neon Realtime](https://neon.com/docs/realtime/overview) with [TanStack DB support](https://neon.com/docs/realtime/sdks/typescript#tanstack-db). Adoption is accelerating, with teams using TanStack DB to build everything from developer tools to consumer mobile apps. Prisma uses DB in [Prisma Studio](https://github.com/prisma/studio), its open-source database editor. Unkey uses it extensively across its [developer dashboard](https://github.com/unkeyed/unkey/tree/main/web/apps/dashboard), and Skylight uses DB and its offline capabilities in its [React Native family calendar app](https://www.youtube.com/watch?v=HSUKoa4JoRw). @@ -34,13 +34,11 @@ I've worked on open-source libraries for a long time and previously found it har ## Crafting a playable environment for agents -The basic problem coding agents present is: when implementation gets cheap, what is left for programmers to do? My answer is _crafting a playable environment for agents_. Programmers shape the _design space_ for swarms of agents: what they can see, which moves they can make, how to judge the results, and what each round passes to the next. +I've been reshaping DB's repository so fresh agents can find the relevant design decisions, experiment, and get clear feedback. That's what I mean by a _playable_ environment: agents can explore possibilities and recognize when a proposed design doesn't hold up. -Over the past few months, I've made the DB repository _playable_ for fresh agent sessions. An agent can find what a part of DB promises, try a small change or challenge that promise, and see the consequences quickly. I can run many short investigations in parallel. What one session discovers becomes the starting point for the next. +I use one recurring loop: start with a bug report or design question, work out what should hold across the whole class of cases, and ask agents to search for counterexamples. I make the product decisions; agents do much of the searching. What we learn stays in the repository so the next investigation can start from it. -I use one recurring loop: start with a report or design question, ask what general behavior ought to hold, search for counterexamples, then revise the implementation, the rule, or how we verify it. I decide the product promise when it isn't settled. Agents do much of the searching. A bug fix improves DB; a clearer rule or stronger verification improves what later agents can discover. - -Oracles have become the most powerful form of verification in that loop. **Each oracle judges one small part of DB against an independently stated law about what users should observe.** Many use a small reference model to predict what should happen across legal sequences of operations, then compare that prediction with production's public behavior. A unit test can keep one reported failure from returning. An oracle keeps asking whether the rule behind that failure holds across other cases. DB now has **161 oracle test files across 14 packages**, covering [collection change messages](https://github.com/TanStack/db/blob/main/packages/db/tests/change-event-history-oracle.test.ts), [nested live-query includes](https://github.com/TanStack/db/blob/main/packages/db/tests/query/includes-cross-formulation-oracle.property.test.ts), and [cursor pagination](https://github.com/TanStack/db/blob/main/packages/query-db-collection/tests/cursor-pagination.oracle.test.ts), among other behaviors. Sometimes an agent finds that an oracle missed a bug. Strengthening that judge gives later investigations a better instrument. +Oracles have become the most powerful form of verification in that loop. **Each oracle judges one small part of DB against an independently stated law about what users should observe.** Many use a small reference model to predict what should happen across legal sequences of operations, then compare that prediction with production's public behavior. A unit test can keep one reported failure from returning. An oracle keeps asking whether the rule behind that failure holds across other cases. DB now has **176 oracle test files across 15 packages**, covering [collection change messages](https://github.com/TanStack/db/blob/main/packages/db/tests/change-event-history-oracle.test.ts), [nested live-query includes](https://github.com/TanStack/db/blob/main/packages/db/tests/query/includes-cross-formulation-oracle.property.test.ts), and [cursor pagination](https://github.com/TanStack/db/blob/main/packages/query-db-collection/tests/cursor-pagination.oracle.test.ts), among other behaviors. Sometimes an agent finds that an oracle missed a bug. Strengthening that judge gives later investigations a better instrument. I've written and heavily iterated on [oracle-authoring](https://github.com/TanStack/db/blob/main/.agents/skills/oracle-authoring/SKILL.md) and [oracle-review](https://github.com/TanStack/db/blob/main/.agents/skills/oracle-review/SKILL.md) SKILLs, plus a rigorous [14-part rubric](https://github.com/TanStack/db/blob/main/docs/contributing/oracle-rubric.md) that agents use to evaluate and improve their oracles. @@ -50,9 +48,9 @@ No software survives contact with reality. Software becomes battle-tested when m We made things harder for ourselves with DB's ambitious and radically simple APIs. As our [0.5 post](https://tanstack.com/blog/tanstack-db-0.5-query-driven-sync) put it: “You describe what data you need. DB handles the fetching, caching, and updating.” A single query shapes what DB asks the backend to load across thousands of potential loading scenarios. Our promise that client queries drive data loading and sync has to hold across on-demand queries, pagination, [persistence and offline use](https://tanstack.com/blog/tanstack-db-0.6-app-ready-with-persistence-and-includes), optimistic edits, retries, and reconnection. Consider an issue board showing the ten highest-priority open issues. Complete one, and the replacement may be the eleventh issue, which the client has never loaded. Add pages and offline restarts, and the possible interactions multiply. Some failures are ordinary bugs. Others reveal undefined behavior or push data-loading complexity back into application code. -When I started tackling our seemingly finite bug list, I surveyed open issues and PRs, grouped related reports, and asked agents what work would solve each entire cluster. One result was the [framework-adapter RFC](https://github.com/TanStack/db/issues/1623) and a [conformance suite](https://github.com/TanStack/db/pull/1636) to make the same public behavior hold across framework adapters. An agent working through another cluster suggested oracle tests. They spread through the RFC work and across DB because each investigation exposed nearby questions the original report hadn't asked. I've merged 350 bug-fixing PRs since starting this work. +When I started tackling our seemingly finite bug list, I surveyed open issues and PRs, grouped related reports, and asked agents what work would solve each entire cluster. One result was the [framework-adapter RFC](https://github.com/TanStack/db/issues/1623) and a [conformance suite](https://github.com/TanStack/db/pull/1636) to make the same public behavior hold across framework adapters. An agent working through another cluster suggested oracle tests. They quickly spread as I saw how effective they are. I've merged 350 bug-fixing PRs since starting this work. -One sequence of fixes makes this loop concrete. A user report led to an oracle, which agents then tried to break. Each blind spot led to a stronger oracle that exposed bugs the previous version couldn't see. The implementation improved, but so did the instrument for investigating it. +### How one oracle got stronger A user [reported a bug](https://github.com/TanStack/db/issues/1901) with collection change messages. They deleted a row and reinserted a replacement with the same key in one turn. A direct read returned the replacement, but a subscriber replaying the messages could end with no row at all. An application maintaining a cache from those messages could silently lose data. @@ -64,9 +62,7 @@ I strengthened the rule the subscriber checks as it replays each message: **inse A review exposed another gap. While reviewing [PR #2004](https://github.com/TanStack/db/pull/2004), CodeRabbit found that replacing the server's source rows during an optimistic edit could send **two inserts for the same key**. The existing subscriber setup filtered out the second insert before the oracle saw it. For [PR #2005](https://github.com/TanStack/db/pull/2005), an agent added a subscriber seeded with the current visible rows that received the raw change messages. After every batch, it checks whether each message is valid for the rows that subscriber actually holds, then compares the subscriber's reconstructed rows with the independent model. The old code failed seven cases. The fix published each key once, and the stronger oracle went on to find another invalid insert when a pending delete crossed an optimistic insert. The new observation point made an entire path of previously hidden failures visible. -The original report was fixed in #1902. The stronger oracle still found invalid updates and duplicate inserts. Fixing the reported sequence became the start of an investigation into the broader contract. - -Other investigations require me to decide the promise before an oracle can enforce it. During a code simplification audit, an agent found a case where an ordered query's row changed value A → B → A while notifications were held, but also moved behind another row. DB sent no callback when it released the result. A subscribed component could keep showing the old order. I chose the last result subscribers **actually saw** as the comparison point: the private value excursion needs no update, but the public move needs a notification. The oracle for [PR #1933](https://github.com/TanStack/db/pull/1933) runs that held sequence through a real ordered query, checks that nothing is published during the hold, and compares the final rows and callbacks with a small model that sorts the source rows. Neighboring cases distinguish a pure move from a lasting value change. The old implementation missed the final callback; the repaired one passes those checks. +The original report was fixed in #1902. The stronger oracle still found invalid updates and duplicate inserts. Each round forced me to spell out more of what collection change messages should guarantee, then teach the oracle to enforce it. This is how I pursue a whole class of bugs. I turn the report into a law, generate related histories, and check the public behavior at the point where a user or subscriber would notice the difference. I also test the instrument against the old implementation or a plausible wrong change. When another path reveals a blind spot, I expand what the oracle drives or observes. That stronger instrument stays in the repository for the next agent. @@ -74,15 +70,15 @@ This is how I pursue a whole class of bugs. I turn the report into a law, genera I've been reading Daniel Dennett's [_Darwin's Dangerous Idea_](https://www.simonandschuster.com/books/Darwins-Dangerous-Idea/Daniel-C-Dennett/9780684824710). Darwin's startling claim, in Dennett's account, was that natural selection, with no foresight, could produce eyes, brains, and other astonishingly intricate adaptations. Many people doubted that a blind process could do this and looked for an outside intelligence to supply the design. Dennett calls that explanatory move a **skyhook**: it lifts the finished complexity into place from above, like the _deus ex machina_ of Greek theater, where stage machinery lowers a god to resolve a plot. To explain how complexity actually develops, we need mechanisms that work within the process. -Dennett calls these evolutionary mechanisms **cranes**—construction cranes. Both skyhooks and cranes lift; the difference is that a crane stands on the ground and works from what is already there. In evolution, a crane makes more possibilities reachable for the next round of variation and selection. Sexual reproduction, for example, shuffles inherited variants into new combinations, opening paths that mutation in a single lineage would reach much more slowly. A new crane often builds on older cranes. The path to complexity runs through accumulating mechanisms to speedily explore and test the design space. +Dennett calls these evolutionary mechanisms **cranes**—construction cranes. Both skyhooks and cranes lift; the difference is that a crane works from solid support. It helps you reach upwards to build a new layer. Once that layer is built, it can support new cranes that reach higher and build further layers. In evolution, a crane makes more possibilities reachable for the next round of variation and selection. Sexual reproduction, for example, shuffles inherited variants into new combinations, opening paths that mutation in a single lineage would reach much more slowly. _The path to complexity runs through accumulating mechanisms to speedily explore and test the design space_. The skyhook story in agentic programming is that a sufficiently intelligent model, given a repository, will infer every unstated rule and deliver the right system in one leap. One-shot coding makes that easy to believe for familiar tasks, but those successes stand on compilers, types, libraries, conventions, examples, tests, and a vast history of solved problems in the model's training. Every substantial software project runs into questions that history hasn't settled. Features interact in unexpected ways; new uses reveal missing guarantees; performance work opens up alternative implementations. There is no finished answer for an agent to retrieve. The work is to search the design space, decide what should hold, and test candidate answers. Agents need cranes that make that search productive and preserve what each investigation learns. -For agents working on DB itself, an oracle is a crane because it turns a reported failure into a repeatable search over a law. [Fast-check](https://fast-check.dev/) generates legal sequences of operations and shrinks a failure into a short counterexample. An agent can run proposed fixes and simpler implementations through the same instrument and see where their behavior diverges. The oracle makes many neighboring cases cheap to explore and bad candidates easy to reject. +For agents working on DB itself, an oracle is a crane because it turns a reported failure into a repeatable search over a law. [Fast-check](https://fast-check.dev/) generates legal sequences of operations and shrinks a failure into a short counterexample. An agent can run proposed fixes and simpler implementations through the same instrument and see where their behavior diverges. The oracle makes many neighboring cases cheap to explore and bad candidates easy to reject. A more precise oracle gives me more freedom to try a simpler algorithm or a faster implementation; benchmarks tell me whether a performance experiment actually improved things. Those experiments can expose further gaps in what we've specified or verified. I've set up the repository so a fresh agent can use these instruments without reconstructing months of design decisions. [Project guidelines](https://github.com/TanStack/db/blob/main/AGENTS.md) point it to the glossary, architecture documents, and [coverage map](https://github.com/TanStack/db/blob/main/docs/contributing/oracle-coverage.md), which identifies the oracle responsible for each behavior and its limits. The oracle files put the law beside the small model, generated histories, production path, and comparison, so an agent can read the promise and run an experiment in the same place. The [authoring](https://github.com/TanStack/db/blob/main/.agents/skills/oracle-authoring/SKILL.md) and [review](https://github.com/TanStack/db/blob/main/.agents/skills/oracle-review/SKILL.md) guides teach agents to strengthen that experiment; hostile reviewers follow the same paths to find plausible wrong implementations. A new session starts with a map of the system, runnable experiments, and ways to challenge the experiments themselves. -Framework conformance tests and benchmarks add feedback for shared behavior and speed. I've also used [TLA+](https://lamport.org/tla/tla.html) to explore the possible sequences of a proposed [query-scoped subscription design](https://github.com/TanStack/db/issues/1935). +Framework conformance tests and benchmarks add feedback for shared behavior and speed. I often use [TLA+](https://lamport.org/tla/tla.html) to explore potential changes to complex designs. I shape the _fitness landscape_ generally by editing the [AGENTS.md](https://github.com/TanStack/db/blob/main/AGENTS.md). For example I say "all PRs must be net negative for production code". This pushes agents hard to avoid bloated and duplicative implementations. @@ -92,31 +88,47 @@ Paradoxically, the bug list keeps growing because our ability to find bugs keeps ## Superhistory -An afternoon can compress what used to be weeks of software history. Venkatesh Rao calls this agent-mediated compression of experience [“artificial time”](https://contraptions.venkateshrao.com/p/superhistory-not-superintelligence). This release push feels like a software-development version of his “superhistory.” I spend much of the day in what feels like a succession of intense design meetings, with fresh evidence arriving 100x faster than I could have gathered it myself. +An afternoon with coding agents can compress what used to be weeks of software history. Venkatesh Rao calls this agent-mediated compression of experience [“artificial time”](https://contraptions.venkateshrao.com/p/superhistory-not-superintelligence). I spend much of the day in what feels like a succession of intense design meetings, with fresh evidence arriving 100x faster than I could have gathered it myself. Building software starts as an act of invention, but much of its maturation is discovery. DB's API created a world with rules whose consequences we didn't fully know. As users try it and agents probe its combinations, unexpected interactions reveal ambiguities, missing guarantees, and layers of architecture we hadn't anticipated. Some consequences follow from existing promises; others call for new rules. -That's what superhistory feels like from inside: questions that normally accumulate across years are arriving in months. Implementation work clears quickly, leaving me in a steady stream of design decisions. I spend much more of my day deciding what DB should guarantee and whether a fix simplifies the system or adds unnecessary machinery. Programming has become more intellectually demanding for me even as writing the code has become cheaper. +That's what artificial time feels like from inside: questions that normally accumulate across years are arriving in months. Implementation work clears quickly, leaving me in a steady stream of design decisions. I spend much more of my day deciding what DB should guarantee and whether a fix simplifies the system or adds unnecessary machinery. Programming has become more intellectually demanding for me even as writing the code has become cheaper. + +Artificial time changes the economics of rigor. Oracles add lots of rigor but are demanding to write and very verbose. Since this push began, DB has gained roughly 330,000 lines of tests—probably more code than I wrote in my entire life before AI. Agents make that work practical. + +Expensive engineering doesn't always come with a business model that can fund it. Client-side infrastructure like DB can be useful to huge numbers of applications without offering an obvious way to charge for that value. Bringing the cost of implementation and design discovery down makes a thoroughly engineered framework possible on a much smaller budget. + +## What source code can't tell an agent + +A codebase is an incomplete account of the software we want to build. It records what the implementation does today, but doesn't reliably tell an agent which behaviors are promises, which are accidents, or why I chose one rule over another. More intelligence cannot reconstruct a decision we haven't made or captured. -Artificial time changes the economics of rigor. An oracle needs a model, generated histories, a production driver, and continuing review as the system changes. Since this push began, DB has gained roughly 330,000 lines of tests—probably more code than I wrote in my entire life before AI. Agents make that work practical. +Design develops through many successive adaptations. Each decision has to fit what's already there, and its consequences reveal further questions to work through. Programmers have always drawn on accumulated design knowledge to make those decisions—recognizing parallels, adapting ideas, and discovering where they stop fitting. LLMs give coding agents a super fast search interface into that history. I use them to explore how those ideas apply to DB's particular problems and gradually develop a more complete and coherent design. This has become the bulk of programming for me. + +Design is immensely complicated. Establishing or improving a single oracle can take hours of investigation between me and my agents: figuring out which distinctions matter, what the system should promise, and whether our model actually expresses that promise. A small executable model can embody a great deal of intellectual work. Those 176 oracle files represent a huge accumulated investment in understanding how this framework should behave. + +Each oracle distills a small part of DB's behavior into an explicit model, with the reasoning behind its rules written beside it. Verifying implementations is one powerful use of that account; helping future programmers and agents understand and extend the system is another. + +An agent working on a change will often run afoul of an oracle and get told its design is wrong. It then reads the oracle carefully to understand the design it needs to respect and revises its approach. Hours of earlier design work guide a later agent at the moment it needs that understanding. + +Design that can talk back. ## The world DB creates for your agents Good programming abstractions create smaller, more consistent worlds for the code above them. A filesystem gives programs files and paths instead of disk sectors. TCP gives them an ordered stream even when the network drops or reorders packets. A programming language gives them functions and types instead of registers and jump instructions. Each settles a mass of lower-level questions so the next layer can make more ambitious moves. -DB creates a simpler world of client data for application developers and their agents. An agent building an application can work with typed collections backed by TanStack Query over an existing API, a sync engine, or local persistence. It can join issues with projects, show one team's open work, and edit optimistically without building a separate endpoint and cache path for every screen. DB keeps the live results current while handling on-demand fetching, pagination, and reconciliation with the server. +DB creates a simpler world for client data for application developers and their agents. An agent building an application can work with typed collections backed by TanStack Query over an existing API, a sync engine, or local persistence. It can join issues with projects, show one team's open work, and edit optimistically without building a separate endpoint and cache path for every screen. DB keeps the live results current while handling on-demand fetching, pagination, and reconciliation with the server. For that world to exist, DB must deliver on its guarantees. A joined, filtered view stays current as its underlying records change. An optimistic edit appears immediately, then remains when persistence succeeds or rolls back when it fails. With on-demand sync, the query can shape backend requests so a screen loads only the rows it needs. Those promises still have to hold when pagination, offline persistence, and server updates interact—and at the speed a demanding application needs. The last three months of work have been about making this simplified world reliable enough to build on. When you start a new application, you can experiment more freely by building on DB's mature data mechanics. You and your agents can focus on behaviors you want to create instead of rebuilding query, cache, and sync behavior for each screen. -The oracles I've described live in DB's repository. They help me and other contributors strengthen DB; applications benefit from the guarantees they protect. DB's guarantees are an example of what frameworks, infrastructure, and developer tools offer: a detailed theory of how some part of software should work. Their APIs and runtime behavior already shape applications. Coding agents open another route for that knowledge: it can guide the code an agent writes. [SKILLs](https://agentskills.io/home) are a first step, explaining which abstractions to use, what the system guarantees, and where the application has a decision to make. +Frameworks, infrastructure, and developer tools embody detailed theories of their domains. Their APIs shape applications; that knowledge can also guide the agents writing them. [SKILLs](https://agentskills.io/home) are a first step. The oracles I've described verify DB itself. DB should also provide oracles for custom collections, so developers and their agents can verify that they sync data correctly and get a short failing history when they don't. -I'm exploring how to expose those guarantees directly to agents building applications, beyond written guidance. A framework could provide executable rules, diagnostics, and conformance tests that reach into application code. An agent building a custom sync adapter, for instance, could run it against DB's change-message rules and get a short failing history if it sends an update for a key the subscriber has never seen. +The framework can also supply instruments for application-specific requirements. DB could expose how much data a query loads or how often it refreshes; an application could set the budgets that matter for its users. The framework supplies the instruments, and the application supplies the requirements. Agents then have both explicit expectations and evidence to guide their changes. These are cranes for userland: mechanisms that work inside the application-building process, helping agents explore and construct the next layer on top of a framework. The next generation of frameworks, infrastructure, and developer tools will create rich, lawful, playable worlds for applications and equip coding agents to build within them. -Here's one idea we're exploring for where to take TanStack DB next: write ordinary server functions to query and mutate Postgres, and DB compiles them into the collections and actions your client uses. +Here's another idea we're exploring for where to take TanStack DB next: write ordinary server functions to query and mutate Postgres, and DB compiles them into the collections and actions your client uses. ```tsx // Server: define a query @@ -144,4 +156,8 @@ DB also generates the data-loading and mutation wiring. When a mutation runs, th Making this work broadly means understanding enough of Postgres's vast language to preserve query meaning, determine which reads a write affects, and reconcile client state correctly. A few years ago, I wouldn't have considered attempting the larger project. The last three months have changed the economics: agents can explore that space, find where the design breaks, and carry stronger instruments into the next investigation. That makes an extraordinarily demanding abstraction tractable enough to pursue. -I began this push with a finite bug list. It became a continuing process of deciding what DB should promise, finding consequences of those decisions, and giving the next agent stronger equipment for the search. TanStack DB 1.0 RC1 is the result so far. [Try it](https://tanstack.com/db/latest/docs/quick-start) in a demanding application and tell us [what breaks](https://github.com/TanStack/db/issues) or [what DB makes needlessly hard to build](https://github.com/TanStack/db/discussions). Those reports are how the next layer of design begins. +There's a useful name for what these cranes improve: _evolvability_, a system's capacity to support further useful development. Autonomy asks how much work an agent can do without me. Evolvability asks what new work becomes possible because of what we've learned. An agent arriving tomorrow can attempt something more ambitious using the same LLM because it inherits better cranes. + +My days are full of decisions about what matters, which promises to make, and when an investigation has gone far enough. Each answer can become part of the machinery that makes further development possible. That's what makes me excited about where programming is going: the possibility of much more robust, delightful, and varied software, including ideas that used to cost too much to pursue. + +I began this push with a finite bug list. TanStack DB 1.0 RC1 is the result so far. [Try it](https://tanstack.com/db/latest/docs/quick-start) in a demanding application and tell us [what breaks](https://github.com/TanStack/db/issues) or [what DB makes needlessly hard to build](https://github.com/TanStack/db/discussions). Those reports are how the next layer of design begins. From dbd97d4db4b6f43472e03f4289075540db8b63a6 Mon Sep 17 00:00:00 2001 From: Kyle Mathews Date: Sat, 24 Oct 2026 12:24:30 -0600 Subject: [PATCH 03/10] docs: sync latest TanStack DB RC1 essay --- ...tack-db-1.0-rc1-going-all-in-on-oracles.md | 163 ++++++++++++------ 1 file changed, 109 insertions(+), 54 deletions(-) diff --git a/src/blog/tanstack-db-1.0-rc1-going-all-in-on-oracles.md b/src/blog/tanstack-db-1.0-rc1-going-all-in-on-oracles.md index 35b9dde02..cf8d6872b 100644 --- a/src/blog/tanstack-db-1.0-rc1-going-all-in-on-oracles.md +++ b/src/blog/tanstack-db-1.0-rc1-going-all-in-on-oracles.md @@ -8,21 +8,28 @@ authors: - Kyle Mathews --- -_Agents make the models practical to write. The models help us find and fix whole classes of bugs._ - I'm delighted to announce on behalf of the core DB maintainers, myself, Sam Willis, Kevin De Porre, and the entire DB community, that TanStack DB 1.0 RC1 is here! After 1.5 years of development and betas and many discussions with users, the API is powerful and stable. We expect to ship 1.0 in a few short weeks. Since our [0.6 release](https://tanstack.com/blog/tanstack-db-0.6-app-ready-with-persistence-and-includes), we have shipped [server rendering and deep integration with TanStack Start](https://tanstack.com/db/latest/docs/guides/ssr), worked through a long run of correctness problems closing every known bug, added new collection adapters for Neon Realtime and Convex, and [made DB much faster for applications with many simultaneous live queries](https://github.com/TanStack/db/pull/1987). -Trying to fix every known TanStack DB bug changed how I think about programming. Programming is the iterative work of drawing out design ideas, exploring their consequences, and developing them into a coherent working system. LLMs give us access to an enormous repertoire of ideas and make implementing and investigating them dramatically cheaper. Each investigation can leave the system capable of attempting something more ambitious. This post follows how oracles helped that happen while preparing DB for 1.0—and what it suggests about the future of frameworks, infrastructure, and developer tools. +After a quick introduction to DB and this release, the post has eight sections: + +1. [From unit tests to oracles](#from-unit-tests-to-oracles): what an oracle is and how it helps explore whole categories of bugs. +2. [Crafting a playable environment for agents](#crafting-a-playable-environment-for-agents): making DB's design knowledge available to coding agents. +3. [The bug list kept growing](#the-bug-list-kept-growing): what I did and learned while making DB epically production ready. +4. [A recipe for designing an oracle](#a-recipe-for-designing-an-oracle): how to build one, and how it relates to formal verification. +5. [Look for cranes](#look-for-cranes): why the tools around a coding agent matter so much. +6. [Superhistory](#superhistory): how agents change the pace and economics of programming. +7. [Drawing out the design](#drawing-out-the-design): how agents help develop and refine DB's design. +8. [The world DB creates for your agents](#the-world-db-creates-for-your-agents): what this means for frameworks and application developers. If you haven't used it yet, TanStack DB is a reactive client data framework that makes backend data feel local. Records from your API or sync engine become typed collections. Components query them for joined, filtered live results. Optimistic edits appear immediately and reconcile with the server later. -Perhaps the biggest difference from earlier client-side databases is that you can adopt TanStack DB incrementally. Historically, getting reactive queries, optimistic mutations, and offline support often meant adopting a new database and sync architecture. With TanStack DB, you can start with your existing REST APIs and [TanStack Query](https://tanstack.com/query/latest) setup, immediately gain fast incremental relational queries, and progressively add [local persistence](https://tanstack.com/db/latest/docs/guides/sqlite-persistence), [durable offline mutations](https://tanstack.com/db/latest/docs/guides/offline-transactions), or real-time sync as your needs evolve. Because the application-facing query model stays the same, you can go from a conventional web application to a fully offline-capable, local-first app without rewriting everything along the way. +Perhaps the biggest difference between TanStack DB and from earlier client-side databases is that you can adopt TanStack DB incrementally. Historically, getting reactive queries, optimistic mutations, and offline support often meant adopting a new database and sync architecture. With TanStack DB, you can start with your existing REST APIs and [TanStack Query](https://tanstack.com/query/latest) setup, immediately gain fast incremental relational queries, and progressively add [local persistence](https://tanstack.com/db/latest/docs/guides/sqlite-persistence), [durable offline mutations](https://tanstack.com/db/latest/docs/guides/offline-transactions), or real-time sync as your needs evolve. Because the application-facing query model stays the same, you can go from a conventional web application to a fully offline-capable, local-first app without rewriting everything along the way. -We began building DB in early 2025 at [ElectricSQL](https://electric-sql.com/), which incubated the project for its first year and a half. We [shipped the first beta that July](https://tanstack.com/blog/tanstack-db-0.1-the-embedded-client-database-for-tanstack-query). Since [Neon acquired us in August](https://neon.com/blog/electrifying-neon-building-the-realtime-backend), it has continued to back DB and we released recently [Neon Realtime](https://neon.com/docs/realtime/overview) with [TanStack DB support](https://neon.com/docs/realtime/sdks/typescript#tanstack-db). +We began building DB in early 2025 at [ElectricSQL](https://electric-sql.com/), which incubated the project for its first year and a half. We [shipped the first beta that July](https://tanstack.com/blog/tanstack-db-0.1-the-embedded-client-database-for-tanstack-query). Since [Neon acquired us in August](https://neon.com/blog/electrifying-neon-building-the-realtime-backend), it has continued to back DB and we recently released [Neon Realtime](https://neon.com/docs/realtime/overview) with [TanStack DB support](https://neon.com/docs/realtime/sdks/typescript#tanstack-db). Adoption is accelerating, with teams using TanStack DB to build everything from developer tools to consumer mobile apps. Prisma uses DB in [Prisma Studio](https://github.com/prisma/studio), its open-source database editor. Unkey uses it extensively across its [developer dashboard](https://github.com/unkeyed/unkey/tree/main/web/apps/dashboard), and Skylight uses DB and its offline capabilities in its [React Native family calendar app](https://www.youtube.com/watch?v=HSUKoa4JoRw). @@ -30,105 +37,151 @@ Across its packages, DB now has [almost 3 million weekly NPM downloads](https:// After server rendering landed a few months ago, I thought the remaining work before 1.0 was fairly straightforward: fix every known bug. I'd fire up my agents and set them loose and be done in a few weeks. Before and during that work, though, I tried some new ideas for making the coding environment more productive for agent swarms. The new environment has proved astonishingly effective at finding new bugs and undefined behavior. My short bug-fixing expedition has now stretched to hundreds of PRs and months of intense work hardening DB's core abstractions for 1.0. Since the first PR in this push, the repository has gained roughly 330,000 lines of tests. -I've worked on open-source libraries for a long time and previously found it hard to find bugs proactively before users provide reproductions. Now essentially every new agent session stumbles on multiple new classes of bugs. After months of fixes, many of these bugs are now so obscure that I suspect no one has encountered them in the wild. Yet they keep coming. Increasingly, my job is to keep agents from fixing _too much_—chasing hypothetical edge cases or adding complexity to solve problems DB never promised to solve. +I've worked on open-source libraries for a long time and previously found it hard to find bugs proactively before users provide reproductions. Now essentially every new agent session stumbles on multiple new classes of bugs. Many of which are now so obscure that I suspect no one has encountered them in the wild. Yet they keep coming. Increasingly, my job is to keep agents from fixing _too much_—chasing hypothetical edge cases or adding complexity to solve problems DB never promised to solve. + +## From unit tests to oracles + +Every software test needs something to try and a way to tell whether the result is right. That second part—the answer key—is called an **oracle**. + +In a typical unit test, I write down a specific example and its expected result: start with three open tasks, mark one complete, and check that the list contains the other two. That expected list is the oracle for that test. I supply the answer by hand. + +The model-based oracles I'm using calculate expected answers for many different examples. Suppose a screen shows the ten highest-priority open tasks. DB's production code uses complex machinery to keep that list current efficiently as tasks change. The model trades efficiency for simplicity: take all the tasks, remove the completed ones, sort by priority, and take the first ten. It rebuilds the whole answer from scratch after every change—a few straightforward lines of code can describe what a much more complex implementation should produce. + +Give the model and DB the same changes, then compare their results. The simple model becomes an independent answer key for the optimized production code. + +```mermaid +flowchart TD + A["Same sequence of changes"] --> M["Simple model"] + A --> D["Real DB"] + M --> E["Expected result"] + D --> R["Actual result"] + E --> C["Compare"] + R --> C +``` + +The answer key can come from a small model I write or an existing tool that's already been thoroughly tested. A built-in JavaScript `Map` can track which keyed rows should exist after a sequence of inserts, updates, and deletes. SQLite can tell us what a query should return—I've used it to validate DB's query behavior. In each case, we have an independent way to calculate the expected answer and compare it with production. + +Now agents can automatically try thousands of sequences—adding tasks, changing priorities, completing and reopening them—while the model calculates the answer after each change. A reported bug becomes the starting point for exploring a whole category of failures: different starting data, different changes, different orders. + +**The model gives us an answer key for cases we never wrote out by hand.** ## Crafting a playable environment for agents -I've been reshaping DB's repository so fresh agents can find the relevant design decisions, experiment, and get clear feedback. That's what I mean by a _playable_ environment: agents can explore possibilities and recognize when a proposed design doesn't hold up. +An agent reading DB's implementation can see what it does today. To make good changes, it also needs to understand which behaviors are promises, which are accidents, and why I chose one rule over another. -I use one recurring loop: start with a bug report or design question, work out what should hold across the whole class of cases, and ask agents to search for counterexamples. I make the product decisions; agents do much of the searching. What we learn stays in the repository so the next investigation can start from it. +I've been reshaping DB's repository into a "playable" environment where fresh agents can find those design decisions, experiment, and get clear feedback. Each oracle captures a small part of DB's intended behavior, with the reasoning behind its rules written beside the model. -Oracles have become the most powerful form of verification in that loop. **Each oracle judges one small part of DB against an independently stated law about what users should observe.** Many use a small reference model to predict what should happen across legal sequences of operations, then compare that prediction with production's public behavior. A unit test can keep one reported failure from returning. An oracle keeps asking whether the rule behind that failure holds across other cases. DB now has **176 oracle test files across 15 packages**, covering [collection change messages](https://github.com/TanStack/db/blob/main/packages/db/tests/change-event-history-oracle.test.ts), [nested live-query includes](https://github.com/TanStack/db/blob/main/packages/db/tests/query/includes-cross-formulation-oracle.property.test.ts), and [cursor pagination](https://github.com/TanStack/db/blob/main/packages/query-db-collection/tests/cursor-pagination.oracle.test.ts), among other behaviors. Sometimes an agent finds that an oracle missed a bug. Strengthening that judge gives later investigations a better instrument. +I use one recurring loop to develop that understanding: start with a bug report or design question and work out the rules for the whole class of cases: -I've written and heavily iterated on [oracle-authoring](https://github.com/TanStack/db/blob/main/.agents/skills/oracle-authoring/SKILL.md) and [oracle-review](https://github.com/TanStack/db/blob/main/.agents/skills/oracle-review/SKILL.md) SKILLs, plus a rigorous [14-part rubric](https://github.com/TanStack/db/blob/main/docs/contributing/oracle-rubric.md) that agents use to evaluate and improve their oracles. +- Which actions are allowed, and which should be rejected? +- What should a user see after each action, and when? +- What should happen if actions overlap, fail, or arrive in a different order? + +I encode that reasoning in the oracle's model, turning the design decisions into precise, executable rules the codebase must satisfy. Then agents search for counterexamples and verify their fixes against those rules across generated cases. The goal is to fix the entire class of bugs, with the model continuing to enforce the design as the codebase changes. + +I make the product decisions; agents do much of the searching. + +DB now has **176 oracle test files across 15 packages**, covering [the messages sent when data changes](https://github.com/TanStack/db/blob/main/packages/db/tests/change-event-history-oracle.test.ts), [how queries assemble related records](https://github.com/TanStack/db/blob/main/packages/db/tests/query/includes-cross-formulation-oracle.property.test.ts), and [which rows pagination returns](https://github.com/TanStack/db/blob/main/packages/query-db-collection/tests/cursor-pagination.oracle.test.ts), among other behaviors. The repository's [coverage map](https://github.com/TanStack/db/blob/main/docs/contributing/oracle-coverage.md) helps agents find the relevant oracle. + +An agent working on a change will often run afoul of an oracle and get told its design is wrong. It then reads the oracle carefully to understand the design it needs to respect and revises its approach. Hours of earlier design work guide a later agent at the moment it needs that understanding. + +Design that can talk back. ## The bug list kept growing No software survives contact with reality. Software becomes battle-tested when many people use it in different ways and maintainers fix what those uses reveal. That maturation normally takes years. -We made things harder for ourselves with DB's ambitious and radically simple APIs. As our [0.5 post](https://tanstack.com/blog/tanstack-db-0.5-query-driven-sync) put it: “You describe what data you need. DB handles the fetching, caching, and updating.” A single query shapes what DB asks the backend to load across thousands of potential loading scenarios. Our promise that client queries drive data loading and sync has to hold across on-demand queries, pagination, [persistence and offline use](https://tanstack.com/blog/tanstack-db-0.6-app-ready-with-persistence-and-includes), optimistic edits, retries, and reconnection. Consider an issue board showing the ten highest-priority open issues. Complete one, and the replacement may be the eleventh issue, which the client has never loaded. Add pages and offline restarts, and the possible interactions multiply. Some failures are ordinary bugs. Others reveal undefined behavior or push data-loading complexity back into application code. +For DB, becoming battle-tested means making the promise that backend data feels local hold up under all those different uses. Components query collections for live results, and edits appear immediately. Behind that simple experience, DB coordinates records arriving from the server, changes made on the client, and updates to what's on screen. -When I started tackling our seemingly finite bug list, I surveyed open issues and PRs, grouped related reports, and asked agents what work would solve each entire cluster. One result was the [framework-adapter RFC](https://github.com/TanStack/db/issues/1623) and a [conformance suite](https://github.com/TanStack/db/pull/1636) to make the same public behavior hold across framework adapters. An agent working through another cluster suggested oracle tests. They quickly spread as I saw how effective they are. I've merged 350 bug-fixing PRs since starting this work. +Loading data a page at a time, saving it across restarts, and supporting offline edits make that coordination much harder. An application can restart with an edit still waiting to reach the server. If a screen has loaded only the ten highest-priority open tasks, completing one means fetching the next task from the server. Each feature introduces more interactions that need to work correctly together. The application still uses the same simple API, but making that abstraction reliable becomes an immense testing problem. ### How one oracle got stronger -A user [reported a bug](https://github.com/TanStack/db/issues/1901) with collection change messages. They deleted a row and reinserted a replacement with the same key in one turn. A direct read returned the replacement, but a subscriber replaying the messages could end with no row at all. An application maintaining a cache from those messages could silently lose data. +Collections notify other code—their subscribers—about changes with insert, update, and delete messages. A user [reported a bug](https://github.com/TanStack/db/issues/1901) where they deleted a row and reinserted a replacement with the same key in one turn. A direct read returned the replacement, but a subscriber replaying the messages could end with no row at all. An application maintaining a cache from those messages could lose data. -[PR #1902](https://github.com/TanStack/db/pull/1902) fixed the immediate failure and added an oracle for the broader promise: **a subscriber applying every change message should end up with the same rows as a direct read.** Its reference model is a plain map of keyed rows. It generates legal sequences of inserts, updates, and deletes, runs them both one at a time and batched together, and compares three results: what the map predicts, what DB returns from a direct read, and what a subscriber reconstructs from DB's messages. That checks the reported delete-and-reinsert sequence alongside nearby sequences involving other starting rows, keys, and operation orders. A future change has to preserve the agreement across all of them. +[PR #1902](https://github.com/TanStack/db/pull/1902) fixed the immediate failure and added an oracle for the broader promise: **a subscriber applying every change message should end up with the same rows as a direct read.** A plain map of keyed rows supplied the expected state. Across generated changes, applied individually and in batches, the oracle compared that state with both DB's direct reads and the rows a subscriber reconstructed from its messages. -Then an agent tried removing branches from the collection code to see which ones the tests actually needed. One removal passed the oracle and produced a better change message than the original branch. The oracle had a blind spot: its simulated subscriber used the same map operation for an **insert** and an **update**. It could reconstruct the right final rows even when DB sent an update for a row the subscriber had already deleted. A real subscriber that applies updates only to existing rows would reject it or lose the row. +Later, an agent tried removing branches from the collection code to see which ones the tests actually needed. One removal produced a better change message, yet both the old and new code passed the oracle. Its simulated subscriber treated an **insert** and an **update** as the same map operation. An update could recreate a row the subscriber had already deleted. The final rows looked right, even though a real subscriber that updates only existing rows would reject the message or lose the replacement. -I strengthened the rule the subscriber checks as it replays each message: **insert requires an absent key; update and delete require a present key.** The old code then failed 26 of 378 histories already reaching that path. [PR #1995](https://github.com/TanStack/db/pull/1995) made DB send an insert when the key became visible again. The same generated histories now check both the final rows _and_ whether every message was valid at the moment a subscriber received it. The original oracle could catch a missing row; the stronger one can catch a broken message even when the final rows happen to match. +I strengthened the rule: **insert requires an absent key; update and delete require a present key.** The old code then failed 26 of 378 histories already reaching that path. [PR #1995](https://github.com/TanStack/db/pull/1995) made DB send an insert when the key became visible again. Agreement on final rows was no longer enough; every message had to be valid when received. -A review exposed another gap. While reviewing [PR #2004](https://github.com/TanStack/db/pull/2004), CodeRabbit found that replacing the server's source rows during an optimistic edit could send **two inserts for the same key**. The existing subscriber setup filtered out the second insert before the oracle saw it. For [PR #2005](https://github.com/TanStack/db/pull/2005), an agent added a subscriber seeded with the current visible rows that received the raw change messages. After every batch, it checks whether each message is valid for the rows that subscriber actually holds, then compares the subscriber's reconstructed rows with the independent model. The old code failed seven cases. The fix published each key once, and the stronger oracle went on to find another invalid insert when a pending delete crossed an optimistic insert. The new observation point made an entire path of previously hidden failures visible. +A review exposed another gap. While reviewing [PR #2004](https://github.com/TanStack/db/pull/2004), CodeRabbit found that replacing the server's source rows during an optimistic edit could send **two inserts for the same key**. The oracle already knew that was wrong, but the subscriber used by the test filtered out the second insert before the oracle saw it. -The original report was fixed in #1902. The stronger oracle still found invalid updates and duplicate inserts. Each round forced me to spell out more of what collection change messages should guarantee, then teach the oracle to enforce it. +For [PR #2005](https://github.com/TanStack/db/pull/2005), an agent added a subscriber initialized with the current visible rows that validated each raw message, then compared its reconstructed rows with the independent model after every batch. The old code failed seven cases. The fix published each key once, and the stronger oracle went on to find another invalid insert when a pending delete overlapped an optimistic insert. Changing what the oracle could observe exposed failures that had previously been invisible. -This is how I pursue a whole class of bugs. I turn the report into a law, generate related histories, and check the public behavior at the point where a user or subscriber would notice the difference. I also test the instrument against the old implementation or a plausible wrong change. When another path reveals a blind spot, I expand what the oracle drives or observes. That stronger instrument stays in the repository for the next agent. +The original report was fixed in #1902. The later investigations required making the design more precise—and making the oracle capable of enforcing distinctions it had previously missed. -## Look for cranes +## A recipe for designing an oracle -I've been reading Daniel Dennett's [_Darwin's Dangerous Idea_](https://www.simonandschuster.com/books/Darwins-Dangerous-Idea/Daniel-C-Dennett/9780684824710). Darwin's startling claim, in Dennett's account, was that natural selection, with no foresight, could produce eyes, brains, and other astonishingly intricate adaptations. Many people doubted that a blind process could do this and looked for an outside intelligence to supply the design. Dennett calls that explanatory move a **skyhook**: it lifts the finished complexity into place from above, like the _deus ex machina_ of Greek theater, where stage machinery lowers a god to resolve a plot. To explain how complexity actually develops, we need mechanisms that work within the process. +Here's how I build one with my agents: -Dennett calls these evolutionary mechanisms **cranes**—construction cranes. Both skyhooks and cranes lift; the difference is that a crane works from solid support. It helps you reach upwards to build a new layer. Once that layer is built, it can support new cranes that reach higher and build further layers. In evolution, a crane makes more possibilities reachable for the next round of variation and selection. Sexual reproduction, for example, shuffles inherited variants into new combinations, opening paths that mutation in a single lineage would reach much more slowly. _The path to complexity runs through accumulating mechanisms to speedily explore and test the design space_. +1. **Choose one precise promise.** State what someone using the software should observe, and when. This is the oracle's law. Resolve ambiguous cases: our task-list model needs a rule for ordering tasks with equal priority. -The skyhook story in agentic programming is that a sufficiently intelligent model, given a repository, will infer every unstated rule and deliver the right system in one leap. One-shot coding makes that easy to believe for familiar tasks, but those successes stand on compilers, types, libraries, conventions, examples, tests, and a vast history of solved problems in the model's training. Every substantial software project runs into questions that history hasn't settled. Features interact in unexpected ways; new uses reveal missing guarantees; performance work opens up alternative implementations. There is no finished answer for an agent to retrieve. The work is to search the design space, decide what should hold, and test candidate answers. Agents need cranes that make that search productive and preserve what each investigation learns. +2. **Calculate the answer independently.** Derive it from the promise. Reusing production's algorithms or helpers can reproduce the same mistake on both sides and leave the oracle happily agreeing with broken code. -For agents working on DB itself, an oracle is a crane because it turns a reported failure into a repeatable search over a law. [Fast-check](https://fast-check.dev/) generates legal sequences of operations and shrinks a failure into a short counterexample. An agent can run proposed fixes and simpler implementations through the same instrument and see where their behavior diverges. The oracle makes many neighboring cases cheap to explore and bad candidates easy to reject. A more precise oracle gives me more freedom to try a simpler algorithm or a faster implementation; benchmarks tell me whether a performance experiment actually improved things. Those experiments can expose further gaps in what we've specified or verified. +3. **Choose where and when to observe production.** Compare the behavior at the moment the promise applies. For the collection oracle, that meant observing raw messages as subscribers received them, alongside the final rows. -I've set up the repository so a fresh agent can use these instruments without reconstructing months of design decisions. [Project guidelines](https://github.com/TanStack/db/blob/main/AGENTS.md) point it to the glossary, architecture documents, and [coverage map](https://github.com/TanStack/db/blob/main/docs/contributing/oracle-coverage.md), which identifies the oracle responsible for each behavior and its limits. The oracle files put the law beside the small model, generated histories, production path, and comparison, so an agent can read the promise and run an experiment in the same place. The [authoring](https://github.com/TanStack/db/blob/main/.agents/skills/oracle-authoring/SKILL.md) and [review](https://github.com/TanStack/db/blob/main/.agents/skills/oracle-review/SKILL.md) guides teach agents to strengthen that experiment; hostile reviewers follow the same paths to find plausible wrong implementations. A new session starts with a map of the system, runnable experiments, and ways to challenge the experiments themselves. +4. **Generate meaningful sequences.** Vary the data, operation order, and relevant timing. This draws on property-based testing and fuzzing (we use [fast-check](https://fast-check.dev/) extensively). Fast-check also shrinks failures into short counterexamples, making an otherwise bewildering failure practical to investigate. -Framework conformance tests and benchmarks add feedback for shared behavior and speed. I often use [TLA+](https://lamport.org/tla/tla.html) to explore potential changes to complex designs. +5. **Try to fool the oracle.** Deliberately break a rule in the implementation and verify that the oracle rejects it for the intended reason. Ask a hostile reviewer—an agent tasked with finding holes—which wrong designs could still pass. -I shape the _fitness landscape_ generally by editing the [AGENTS.md](https://github.com/TanStack/db/blob/main/AGENTS.md). For example I say "all PRs must be net negative for production code". This pushes agents hard to avoid bloated and duplicative implementations. +6. **Diagnose disagreements before changing expectations.** The implementation might violate the promise, the model might misstate it, or the promise itself might need redesign. Work out which account failed before deciding what to repair. Keep the shortened failing sequence as a regression case beside the model. -That extensive machinery I've developed matters because DB's API opened a huge design space. On-demand queries, pagination, persistence, optimistic writes, and synchronization meet in combinations we never enumerated when designing it. Together, these instruments help us build a richer theory of how clients should load, update, reconcile, and present data. +I've written and heavily iterated on [oracle-authoring](https://github.com/TanStack/db/blob/main/.agents/skills/oracle-authoring/SKILL.md) and [oracle-review](https://github.com/TanStack/db/blob/main/.agents/skills/oracle-review/SKILL.md) SKILLs, plus a rigorous [14-part rubric](https://github.com/TanStack/db/blob/main/docs/contributing/oracle-rubric.md) that agents use to evaluate and improve their oracles. -Paradoxically, the bug list keeps growing because our ability to find bugs keeps improving. Three months and many billions of tokens have produced more than a pile of fixes: agents now investigate DB with instruments that get sharper as they use them. That change in their working environment has mattered far more than any model upgrade I've seen. +### How this relates to formal verification -## Superhistory +For difficult designs, I often start in [TLA+](https://lamport.org/tla/tla.html), explore the consequences of proposed rules, then translate the model into a TypeScript oracle that runs against DB. -An afternoon with coding agents can compress what used to be weeks of software history. Venkatesh Rao calls this agent-mediated compression of experience [“artificial time”](https://contraptions.venkateshrao.com/p/superhistory-not-superintelligence). I spend much of the day in what feels like a succession of intense design meetings, with fresh evidence arriving 100x faster than I could have gathered it myself. +That model is an **executable specification**: a precise account of the intended behavior. This shares a foundation with [formal verification](https://www.anthropic.com/research/formal-verification). A formal proof, using a system such as [Lean](https://lean-lang.org/), establishes a specified property for all cases covered by its assumptions. Our generated tests exercise the actual implementation and compare its behavior with the specification. -Building software starts as an act of invention, but much of its maturation is discovery. DB's API created a world with rules whose consequences we didn't fully know. As users try it and agents probe its combinations, unexpected interactions reveal ambiguities, missing guarantees, and layers of architecture we hadn't anticipated. Some consequences follow from existing promises; others call for new rules. +## Look for cranes -That's what artificial time feels like from inside: questions that normally accumulate across years are arriving in months. Implementation work clears quickly, leaving me in a steady stream of design decisions. I spend much more of my day deciding what DB should guarantee and whether a fix simplifies the system or adds unnecessary machinery. Programming has become more intellectually demanding for me even as writing the code has become cheaper. +In Daniel Dennett's [_Darwin's Dangerous Idea_](https://www.simonandschuster.com/books/Darwins-Dangerous-Idea/Daniel-C-Dennett/9780684824710), Dennett chronicles Darwin's startling claim that natural selection, with no foresight, could produce eyes, brains, and other astonishingly intricate adaptations. Many people doubted that a blind process could do this and looked for an outside intelligence to supply the design, an explanatory move Dennett calls a **skyhook**: it lowers the finished complexity into place from above like the _deus ex machina_ of Greek theater, where stage machinery lowers a god to resolve the plot. -Artificial time changes the economics of rigor. Oracles add lots of rigor but are demanding to write and very verbose. Since this push began, DB has gained roughly 330,000 lines of tests—probably more code than I wrote in my entire life before AI. Agents make that work practical. +Dennett contrasts this with **cranes**—construction cranes. Both skyhooks and cranes lift, but a crane works from solid support. It helps you reach upwards to build a new layer. Once that layer is built, it can support new cranes that reach higher and build further layers. In evolution, cranes make more possibilities accessible to variation and selection. Sexual reproduction, for example, recombines inherited variants, letting selection work on combinations that mutation in a single lineage would reach much more slowly. -Expensive engineering doesn't always come with a business model that can fund it. Client-side infrastructure like DB can be useful to huge numbers of applications without offering an obvious way to charge for that value. Bringing the cost of implementation and design discovery down makes a thoroughly engineered framework possible on a much smaller budget. +The skyhook story in agentic programming is that a sufficiently intelligent model, given a repository, will infer every unstated rule and deliver the right system in one leap. One-shot coding makes that easy to believe for familiar tasks. Those successes stand on compilers, libraries, conventions, and a vast history of solved problems in the model's training. Much of the design work has already been done. -## What source code can't tell an agent +Every substantial software project runs into questions that history hasn't settled. Features interact in unexpected ways; new uses reveal missing guarantees. More intelligence cannot reconstruct a decision we haven't made or captured. The work is to search the design space, decide what should hold, and test candidate answers. -A codebase is an incomplete account of the software we want to build. It records what the implementation does today, but doesn't reliably tell an agent which behaviors are promises, which are accidents, or why I chose one rule over another. More intelligence cannot reconstruct a decision we haven't made or captured. +For my agent swarms, the cranes make that search productive. Oracles make candidate implementations cheap to try and incorrect behavior easier to reject. Hostile reviews strengthen the instruments doing the rejecting. Benchmarks tell me whether an implementation that preserves the behavior also improves performance. Together, these mechanisms let agents explore more ambitious changes and get useful feedback about which ones to pursue. -Design develops through many successive adaptations. Each decision has to fit what's already there, and its consequences reveal further questions to work through. Programmers have always drawn on accumulated design knowledge to make those decisions—recognizing parallels, adapting ideas, and discovering where they stop fitting. LLMs give coding agents a super fast search interface into that history. I use them to explore how those ideas apply to DB's particular problems and gradually develop a more complete and coherent design. This has become the bulk of programming for me. +I also shape the _fitness landscape_—the constraints and feedback that favor some candidates over others—by editing [AGENTS.md](https://github.com/TanStack/db/blob/main/AGENTS.md). For example, I tell agents to treat every line of production code as a continuing cost and prefer fixes that remove code. This pushes them hard to avoid bloated and duplicative implementations. -Design is immensely complicated. Establishing or improving a single oracle can take hours of investigation between me and my agents: figuring out which distinctions matter, what the system should promise, and whether our model actually expresses that promise. A small executable model can embody a great deal of intellectual work. Those 176 oracle files represent a huge accumulated investment in understanding how this framework should behave. +The layering matters. A stronger oracle lets an agent attempt a change that would previously have been difficult to verify. That investigation can expose another missing distinction, which becomes part of the model and opens further possibilities. The cranes help build the next layer, which supports the next cranes. -Each oracle distills a small part of DB's behavior into an explicit model, with the reasoning behind its rules written beside it. Verifying implementations is one powerful use of that account; helping future programmers and agents understand and extend the system is another. +The work has taken three months and many billions of tokens. Each investigation leaves later agents with sharper instruments. That change in their working environment has mattered far more than any model upgrade I've seen. -An agent working on a change will often run afoul of an oracle and get told its design is wrong. It then reads the oracle carefully to understand the design it needs to respect and revises its approach. Hours of earlier design work guide a later agent at the moment it needs that understanding. +## Superhistory -Design that can talk back. +Coding agents can compress what used to be weeks of work into an afternoon. Venkatesh Rao calls this agent-mediated compression of experience [“artificial time”](https://contraptions.venkateshrao.com/p/superhistory-not-superintelligence). I spend much of the day in what feels like a succession of intense design meetings, with fresh evidence arriving 100x faster than I could have gathered it myself. -## The world DB creates for your agents +Questions that normally accumulate across years are arriving in months. Implementation work clears quickly, leaving me in a steady stream of design decisions. I spend much more of my day deciding what DB should guarantee and whether a fix simplifies the system or adds unnecessary machinery. Programming has become more intellectually demanding for me even as writing the code has become cheaper. + +When an idea would take weeks to investigate, I need a strong reason to try it. With agents and an oracle, I can afford to take a much less certain idea far enough to find out whether it works. That changes which designs I can explore. + +Rigorous engineering is much cheaper now. Oracles work incredibly well, but they're demanding to write and very verbose. In the months spent preparing DB for 1.0, the repository has gained roughly 330,000 lines of tests—probably more code than I wrote in my entire life before AI. Agents make that work practical. -Good programming abstractions create smaller, more consistent worlds for the code above them. A filesystem gives programs files and paths instead of disk sectors. TCP gives them an ordered stream even when the network drops or reorders packets. A programming language gives them functions and types instead of registers and jump instructions. Each settles a mass of lower-level questions so the next layer can make more ambitious moves. +## Drawing out the design -DB creates a simpler world for client data for application developers and their agents. An agent building an application can work with typed collections backed by TanStack Query over an existing API, a sync engine, or local persistence. It can join issues with projects, show one team's open work, and edit optimistically without building a separate endpoint and cache path for every screen. DB keeps the live results current while handling on-demand fetching, pagination, and reconciliation with the server. +Programmers have always drawn on accumulated design knowledge—recognizing parallels, adapting ideas, and discovering where they stop fitting. LLMs give coding agents a super fast search interface into that history. I use them to search a vast repertoire of algorithms and design ideas for things that fit DB's particular problems, then work through the adaptations those ideas need. -For that world to exist, DB must deliver on its guarantees. A joined, filtered view stays current as its underlying records change. An optimistic edit appears immediately, then remains when persistence succeeds or rolls back when it fails. With on-demand sync, the query can shape backend requests so a screen loads only the rows it needs. Those promises still have to hold when pagination, offline persistence, and server updates interact—and at the speed a demanding application needs. The last three months of work have been about making this simplified world reliable enough to build on. +Design develops through many successive adaptations. Each decision has to fit what's already there, and its consequences reveal further questions to work through. An idea that solves one problem can expose a distinction we hadn't previously needed, or a conflict between two otherwise useful rules. Working through those decisions and distilling them into oracle models has become the bulk of programming for me. -When you start a new application, you can experiment more freely by building on DB's mature data mechanics. You and your agents can focus on behaviors you want to create instead of rebuilding query, cache, and sync behavior for each screen. +Design is immensely complicated. Establishing or improving a single oracle can take hours of investigation between me and my agents: figuring out which distinctions matter, what the system should promise, and whether our model actually expresses that promise. A small executable model can embody a great deal of intellectual work. Together, those models embody an increasingly detailed theory of how clients should load, update, reconcile, and present data. -Frameworks, infrastructure, and developer tools embody detailed theories of their domains. Their APIs shape applications; that knowledge can also guide the agents writing them. [SKILLs](https://agentskills.io/home) are a first step. The oracles I've described verify DB itself. DB should also provide oracles for custom collections, so developers and their agents can verify that they sync data correctly and get a short failing history when they don't. +## The world DB creates for your agents + +All this design work gives an agent building your application a simpler world to work in. It can join issues with projects, show one team's open work, and edit optimistically through DB's collections and live queries. DB keeps the results current and handles data loading, persistence, and synchronization, leaving you and your agents to focus on the behaviors you want to create. -The framework can also supply instruments for application-specific requirements. DB could expose how much data a query loads or how often it refreshes; an application could set the budgets that matter for its users. The framework supplies the instruments, and the application supplies the requirements. Agents then have both explicit expectations and evidence to guide their changes. +Frameworks, infrastructure, and developer tools all do this: they embody detailed theories of their domains and make those domains easier to work in. Their APIs already shape applications. With coding agents, that knowledge can also guide how application code gets written. [SKILLs](https://agentskills.io/home) are a first step: they let a framework explain which abstractions to use, what those abstractions guarantee, and where the application has a decision to make. -These are cranes for userland: mechanisms that work inside the application-building process, helping agents explore and construct the next layer on top of a framework. The next generation of frameworks, infrastructure, and developer tools will create rich, lawful, playable worlds for applications and equip coding agents to build within them. +For DB, I want to put that accumulated design knowledge in your agents' hands: oracles and other cranes that help them turn ambitious ideas into applications that hold up in practice. An agent building a custom collection should be able to run an oracle to verify that it syncs correctly. An agent tuning a screen should have instruments to optimize query loading speed and minimize data on the wire. You set the requirements; DB should work with your agents to translate your intent into optimal code. -Here's another idea we're exploring for where to take TanStack DB next: write ordinary server functions to query and mutate Postgres, and DB compiles them into the collections and actions your client uses. +### From server functions to live collections + +The instruments built while hardening DB also make more ambitious abstractions practical to pursue. One idea I'm exploring is to let you write ordinary server functions to query and mutate Postgres, then have DB compile them into the collections and actions your client uses. ```tsx // Server: define a query @@ -152,12 +205,14 @@ addTodo({ id: crypto.randomUUID(), text: 'Hello world' }) _Simplified sketch of an experimental API._ -DB also generates the data-loading and mutation wiring. When a mutation runs, the client tells the server which query collections are live. The server determines which need updating and returns fresh results with the mutation response. +In this design, DB also generates the data-loading and mutation wiring. When a mutation runs, the client tells the server which query collections are live. The server determines which need updating and returns fresh results with the mutation response. + +Making this work broadly means understanding enough of Postgres's vast language to preserve query meaning, determine which reads a write affects, and reconcile client state correctly. A few years ago, I wouldn't have considered attempting the larger project. The work of the last three months has made it tractable enough to pursue. -Making this work broadly means understanding enough of Postgres's vast language to preserve query meaning, determine which reads a write affects, and reconcile client state correctly. A few years ago, I wouldn't have considered attempting the larger project. The last three months have changed the economics: agents can explore that space, find where the design breaks, and carry stronger instruments into the next investigation. That makes an extraordinarily demanding abstraction tractable enough to pursue. +### Building the next layer There's a useful name for what these cranes improve: _evolvability_, a system's capacity to support further useful development. Autonomy asks how much work an agent can do without me. Evolvability asks what new work becomes possible because of what we've learned. An agent arriving tomorrow can attempt something more ambitious using the same LLM because it inherits better cranes. My days are full of decisions about what matters, which promises to make, and when an investigation has gone far enough. Each answer can become part of the machinery that makes further development possible. That's what makes me excited about where programming is going: the possibility of much more robust, delightful, and varied software, including ideas that used to cost too much to pursue. -I began this push with a finite bug list. TanStack DB 1.0 RC1 is the result so far. [Try it](https://tanstack.com/db/latest/docs/quick-start) in a demanding application and tell us [what breaks](https://github.com/TanStack/db/issues) or [what DB makes needlessly hard to build](https://github.com/TanStack/db/discussions). Those reports are how the next layer of design begins. +I began by trying to fix every known DB bug. TanStack DB 1.0 RC1 is the result so far. [Try it](https://tanstack.com/db/latest/docs/quick-start) in a demanding application and tell us [what breaks](https://github.com/TanStack/db/issues) or [what DB makes needlessly hard to build](https://github.com/TanStack/db/discussions). From 4d2230f260e6c27f790e7f081b63aace7ee1a672 Mon Sep 17 00:00:00 2001 From: Kyle Mathews Date: Tue, 27 Oct 2026 07:41:58 -0600 Subject: [PATCH 04/10] docs: sync latest TanStack DB RC1 essay revisions --- ...tack-db-1.0-rc1-going-all-in-on-oracles.md | 96 +++++++++++-------- 1 file changed, 57 insertions(+), 39 deletions(-) diff --git a/src/blog/tanstack-db-1.0-rc1-going-all-in-on-oracles.md b/src/blog/tanstack-db-1.0-rc1-going-all-in-on-oracles.md index cf8d6872b..3b0cde0bc 100644 --- a/src/blog/tanstack-db-1.0-rc1-going-all-in-on-oracles.md +++ b/src/blog/tanstack-db-1.0-rc1-going-all-in-on-oracles.md @@ -10,26 +10,26 @@ authors: I'm delighted to announce on behalf of the core DB maintainers, myself, Sam Willis, Kevin De Porre, and the entire DB community, that TanStack DB 1.0 RC1 is here! -After 1.5 years of development and betas and many discussions with users, the API is powerful and stable. We expect to ship 1.0 in a few short weeks. +After 1.5 years of development and betas and many discussions with users, the API is powerful, production-ready, and stable. Since our [0.6 release](https://tanstack.com/blog/tanstack-db-0.6-app-ready-with-persistence-and-includes), we have shipped [server rendering and deep integration with TanStack Start](https://tanstack.com/db/latest/docs/guides/ssr), worked through a long run of correctness problems closing every known bug, added new collection adapters for Neon Realtime and Convex, and [made DB much faster for applications with many simultaneous live queries](https://github.com/TanStack/db/pull/1987). -After a quick introduction to DB and this release, the post has eight sections: +The post has eight sections covering my work to prepare DB for 1.0: -1. [From unit tests to oracles](#from-unit-tests-to-oracles): what an oracle is and how it helps explore whole categories of bugs. +1. [From unit tests to oracles](#from-unit-tests-to-oracles): what an oracle is and why I'm so excited about them. 2. [Crafting a playable environment for agents](#crafting-a-playable-environment-for-agents): making DB's design knowledge available to coding agents. -3. [The bug list kept growing](#the-bug-list-kept-growing): what I did and learned while making DB epically production ready. +3. [Hardening DB for 1.0](#hardening-db-for-10): what I did and learned while making DB epically production ready. 4. [A recipe for designing an oracle](#a-recipe-for-designing-an-oracle): how to build one, and how it relates to formal verification. 5. [Look for cranes](#look-for-cranes): why the tools around a coding agent matter so much. -6. [Superhistory](#superhistory): how agents change the pace and economics of programming. -7. [Drawing out the design](#drawing-out-the-design): how agents help develop and refine DB's design. +6. [Superhistory](#superhistory): how agents change the pace and economics of programming and software maturity. +7. [Accelerating design](#accelerating-design): how agents help develop and refine DB's design. 8. [The world DB creates for your agents](#the-world-db-creates-for-your-agents): what this means for frameworks and application developers. If you haven't used it yet, TanStack DB is a reactive client data framework that makes backend data feel local. Records from your API or sync engine become typed collections. Components query them for joined, filtered live results. Optimistic edits appear immediately and reconcile with the server later. -Perhaps the biggest difference between TanStack DB and from earlier client-side databases is that you can adopt TanStack DB incrementally. Historically, getting reactive queries, optimistic mutations, and offline support often meant adopting a new database and sync architecture. With TanStack DB, you can start with your existing REST APIs and [TanStack Query](https://tanstack.com/query/latest) setup, immediately gain fast incremental relational queries, and progressively add [local persistence](https://tanstack.com/db/latest/docs/guides/sqlite-persistence), [durable offline mutations](https://tanstack.com/db/latest/docs/guides/offline-transactions), or real-time sync as your needs evolve. Because the application-facing query model stays the same, you can go from a conventional web application to a fully offline-capable, local-first app without rewriting everything along the way. +Perhaps the biggest difference between TanStack DB from earlier client-side databases is that you can adopt TanStack DB incrementally. Historically, getting reactive queries, optimistic mutations, and offline support often meant adopting a new database and sync architecture. With TanStack DB, you can start with your existing REST APIs and [TanStack Query](https://tanstack.com/query/latest) setup, immediately gain fast incremental relational queries, and progressively add [local persistence](https://tanstack.com/db/latest/docs/guides/sqlite-persistence), [durable offline mutations](https://tanstack.com/db/latest/docs/guides/offline-transactions), or real-time sync as your needs evolve. Because the application-facing query model stays the same, you can go from a conventional web application to a fully offline-capable, local-first app without rewriting everything along the way. -We began building DB in early 2025 at [ElectricSQL](https://electric-sql.com/), which incubated the project for its first year and a half. We [shipped the first beta that July](https://tanstack.com/blog/tanstack-db-0.1-the-embedded-client-database-for-tanstack-query). Since [Neon acquired us in August](https://neon.com/blog/electrifying-neon-building-the-realtime-backend), it has continued to back DB and we recently released [Neon Realtime](https://neon.com/docs/realtime/overview) with [TanStack DB support](https://neon.com/docs/realtime/sdks/typescript#tanstack-db). +We began building DB in early 2025 at [ElectricSQL](https://electric-sql.com/), which incubated the project for its first year and a half. Since [Neon acquired us in August](https://neon.com/blog/electrifying-neon-building-the-realtime-backend), it has continued to back DB and we recently released [Neon Realtime](https://neon.com/docs/realtime/overview) with [TanStack DB support](https://neon.com/docs/realtime/sdks/typescript#tanstack-db). Adoption is accelerating, with teams using TanStack DB to build everything from developer tools to consumer mobile apps. Prisma uses DB in [Prisma Studio](https://github.com/prisma/studio), its open-source database editor. Unkey uses it extensively across its [developer dashboard](https://github.com/unkeyed/unkey/tree/main/web/apps/dashboard), and Skylight uses DB and its offline capabilities in its [React Native family calendar app](https://www.youtube.com/watch?v=HSUKoa4JoRw). @@ -61,13 +61,15 @@ flowchart TD The answer key can come from a small model I write or an existing tool that's already been thoroughly tested. A built-in JavaScript `Map` can track which keyed rows should exist after a sequence of inserts, updates, and deletes. SQLite can tell us what a query should return—I've used it to validate DB's query behavior. In each case, we have an independent way to calculate the expected answer and compare it with production. -Now agents can automatically try thousands of sequences—adding tasks, changing priorities, completing and reopening them—while the model calculates the answer after each change. A reported bug becomes the starting point for exploring a whole category of failures: different starting data, different changes, different orders. +I combine the model with **property-based testing and fuzzing**. Agents write tests that automatically generate and run thousands of varied sequences—adding tasks, changing priorities, completing and reopening them—while the model supplies the expected answer after each change. A reported bug becomes the starting point for exploring a whole category of failures: different starting data, different changes, different orders. **The model gives us an answer key for cases we never wrote out by hand.** +Oracle models optimize for understanding; production code optimizes for speed and code size. Developing the model is scientific work: making a theory of how this part of DB should behave precise enough to investigate and challenge. Production code is the engineering that puts that understanding to work. + ## Crafting a playable environment for agents -An agent reading DB's implementation can see what it does today. To make good changes, it also needs to understand which behaviors are promises, which are accidents, and why I chose one rule over another. +An agent reading DB's implementation can see what it does today. To make good changes, it also needs to understand which behaviors are promises, which are accidents, and why we chose one rule over another. I've been reshaping DB's repository into a "playable" environment where fresh agents can find those design decisions, experiment, and get clear feedback. Each oracle captures a small part of DB's intended behavior, with the reasoning behind its rules written beside the model. @@ -87,35 +89,49 @@ An agent working on a change will often run afoul of an oracle and get told its Design that can talk back. -## The bug list kept growing +## Hardening DB for 1.0 + +A mature library has answers to questions its authors didn’t know to ask. Users combine features in unexpected ways; maintainers fix the bugs and resolve the ambiguities those combinations expose. Years of that work turn a promising API into something applications can depend on. + +DB makes that maturation particularly demanding by promising that backend data can feel local. A screen asks for the ten highest-priority open tasks; completing one can mean fetching the next task from the server. Now make that edit offline, preserve it across an app restart, and reconnect after the server’s data has changed. What should the screen show along the way? What must DB retain, fetch, and reconcile? The application still uses the same query, while the number of situations DB must handle has exploded. + +Oracles let me explore that design space dramatically faster, without waiting for users to discover each failure. The history of these collection oracles shows how that exploration builds on itself. -No software survives contact with reality. Software becomes battle-tested when many people use it in different ways and maintainers fix what those uses reveal. That maturation normally takes years. +### How the oracles got stronger -For DB, becoming battle-tested means making the promise that backend data feels local hold up under all those different uses. Components query collections for live results, and edits appear immediately. Behind that simple experience, DB coordinates records arriving from the server, changes made on the client, and updates to what's on screen. +Collections notify other code—their subscribers—about changes with insert, update, and delete messages. A user [reported a bug](https://github.com/TanStack/db/issues/1901) where they deleted a row and reinserted a replacement with the same key in one turn. DB sent the subscriber an insert for the replacement followed by a delete for the same key, with no later message to put it back. A direct read returned the replacement, but replaying those messages added it to the subscriber's cache and then removed it. -Loading data a page at a time, saving it across restarts, and supporting offline edits make that coordination much harder. An application can restart with an edit still waiting to reach the server. If a screen has loaded only the ten highest-priority open tasks, completing one means fetching the next task from the server. Each feature introduces more interactions that need to work correctly together. The application still uses the same simple API, but making that abstraction reliable becomes an immense testing problem. +[PR #1902](https://github.com/TanStack/db/pull/1902) fixed the immediate failure and added an oracle for the broader promise: **a subscriber applying every change message should end up with the same rows as a direct read.** -### How one oracle got stronger +The test constructs histories of writes, exploring that promise from several directions: -Collections notify other code—their subscribers—about changes with insert, update, and delete messages. A user [reported a bug](https://github.com/TanStack/db/issues/1901) where they deleted a row and reinserted a replacement with the same key in one turn. A direct read returned the replacement, but a subscriber replaying the messages could end with no row at all. An application maintaining a cache from those messages could lose data. +- Start empty, with one row already present, or with two rows present. +- Insert, update, delete, and reinsert the same key; interleave writes to different keys. Give each inserted or updated row a distinct value so stale data is visible. +- Issue writes back-to-back before waiting for them to finish, or let each write finish before starting the next. +- Enumerate every legal short sequence: up to four writes on one initially absent key, or three writes on two keys from every starting combination. +- Generate longer histories—up to twenty writes across four keys—with both a fixed seed for repeatable coverage and fresh random seeds for further exploration. -[PR #1902](https://github.com/TanStack/db/pull/1902) fixed the immediate failure and added an oracle for the broader promise: **a subscriber applying every change message should end up with the same rows as a direct read.** A plain map of keyed rows supplied the expected state. Across generated changes, applied individually and in batches, the oracle compared that state with both DB's direct reads and the rows a subscriber reconstructed from its messages. +For each history, a plain JavaScript `Map` calculates which rows those requested writes should leave behind. Real DB receives the same writes, while a subscriber maintains a **separate map using only the change messages DB sends it**. After each completed write or group of writes, the oracle compares three things: the expected rows, DB’s direct reads, and the subscriber’s reconstructed rows. It also checks subscriber/read agreement whenever DB delivers a batch of messages. In the original failure, the model and DB contained the replacement row, while the subscriber’s map was empty. -Later, an agent tried removing branches from the collection code to see which ones the tests actually needed. One removal produced a better change message, yet both the old and new code passed the oracle. Its simulated subscriber treated an **insert** and an **update** as the same map operation. An update could recreate a row the subscriber had already deleted. The final rows looked right, even though a real subscriber that updates only existing rows would reject the message or lose the replacement. +Later, an agent challenged the oracle by deliberately changing how DB chose between insert and update messages. The oracle accepted both versions, even though they sent different messages for the same history. Investigating that disagreement revealed a bug in production—and a blind spot in the oracle. After a subscriber had removed a deleted row, a sync commit made the same key visible again. DB remembered the earlier optimistic operation and sent an **update**. But the subscriber no longer had that row. It needed an **insert**. -I strengthened the rule: **insert requires an absent key; update and delete require a present key.** The old code then failed 26 of 378 histories already reaching that path. [PR #1995](https://github.com/TanStack/db/pull/1995) made DB send an insert when the key became visible again. Agreement on final rows was no longer enough; every message had to be valid when received. +The oracle hid the mistake. Its simulated subscriber used JavaScript’s `Map.set` for both inserts and updates, so the invalid update quietly recreated the missing row. The expected rows, direct reads, and subscriber’s reconstructed rows all agreed. I made the oracle enforce what each message means: **insert requires an absent key; update and delete require a present key.** Without adding any histories, 26 of the existing 378 cases failed. [PR #1995](https://github.com/TanStack/db/pull/1995) removed the branch that chose an update for a previously absent key. The code became smaller, and all 378 cases passed. -A review exposed another gap. While reviewing [PR #2004](https://github.com/TanStack/db/pull/2004), CodeRabbit found that replacing the server's source rows during an optimistic edit could send **two inserts for the same key**. The oracle already knew that was wrong, but the subscriber used by the test filtered out the second insert before the oracle saw it. +Another gap surfaced during CodeRabbit’s review of [PR #2004](https://github.com/TanStack/db/pull/2004). While a local optimistic edit was active, a server refresh replaced the collection’s source rows. DB sent **delete → insert → insert** for the same key. Restoring the optimistic edit announced the row once; processing the server’s replacement announced it again. The code intended to remove that second announcement, but searched for it before it had been added. -For [PR #2005](https://github.com/TanStack/db/pull/2005), an agent added a subscriber initialized with the current visible rows that validated each raw message, then compared its reconstructed rows with the independent model after every batch. The old code failed seven cases. The fix published each key once, and the stronger oracle went on to find another invalid insert when a pending delete overlapped an optimistic insert. Changing what the oracle could observe exposed failures that had previously been invisible. +A separate oracle already explored these interactions. Its model started with server rows and overlaid local edits according to whether they were still pending, had succeeded, or had failed. Histories interleaved those edits and outcomes with server updates, including complete replacements. But its subscriber used a mode that filtered out repeated inserts. The oracle never saw the second insert. Meanwhile, the strict subscriber from #1995 never exercised server replacements. One oracle reached the failure but hid it; the other could recognize it but never reached it. -The original report was fixed in #1902. The later investigations required making the design more precise—and making the oracle capable of enforcing distinctions it had previously missed. +For [PR #2005](https://github.com/TanStack/db/pull/2005), I added a second subscriber that received messages without that filtering. It started with the independent model’s expected rows, checked every message against the rows it already held, and compared its reconstructed rows with the model after each history step. The old implementation failed seven cases. The fix stopped announcing rows that the optimistic restoration had already published. + +The stronger oracle then found another duplicate insert during a random run, without any server replacement. A delete succeeded, a new optimistic insert restored the same key, and a later sync commit finished clearing away the old delete. That cleanup forgot the subscriber had already seen the new row and announced it again. The same message rule caught a different route to the failure. + +The original report was fixed in #1902. Each subsequent investigation sharpened what the oracles could distinguish: matching final rows, valid individual messages, and valid messages through more complicated interactions. That made the next investigation more capable. ## A recipe for designing an oracle Here's how I build one with my agents: -1. **Choose one precise promise.** State what someone using the software should observe, and when. This is the oracle's law. Resolve ambiguous cases: our task-list model needs a rule for ordering tasks with equal priority. +1. **Choose one precise promise.** State what someone using the software should observe, and when. This is the oracle's law. Resolve ambiguous cases e.g. our task-list model needs a rule for ordering tasks with equal priority. 2. **Calculate the answer independently.** Derive it from the promise. Reusing production's algorithms or helpers can reproduce the same mistake on both sides and leave the oracle happily agreeing with broken code. @@ -139,17 +155,17 @@ That model is an **executable specification**: a precise account of the intended In Daniel Dennett's [_Darwin's Dangerous Idea_](https://www.simonandschuster.com/books/Darwins-Dangerous-Idea/Daniel-C-Dennett/9780684824710), Dennett chronicles Darwin's startling claim that natural selection, with no foresight, could produce eyes, brains, and other astonishingly intricate adaptations. Many people doubted that a blind process could do this and looked for an outside intelligence to supply the design, an explanatory move Dennett calls a **skyhook**: it lowers the finished complexity into place from above like the _deus ex machina_ of Greek theater, where stage machinery lowers a god to resolve the plot. -Dennett contrasts this with **cranes**—construction cranes. Both skyhooks and cranes lift, but a crane works from solid support. It helps you reach upwards to build a new layer. Once that layer is built, it can support new cranes that reach higher and build further layers. In evolution, cranes make more possibilities accessible to variation and selection. Sexual reproduction, for example, recombines inherited variants, letting selection work on combinations that mutation in a single lineage would reach much more slowly. +Dennett contrasts this with **cranes**—construction cranes. Both skyhooks and cranes lift, but a crane works from existing designs. It helps you reach upwards to build a new layer. Once that layer is built, it can support new cranes that reach higher and build further layers. In evolution, cranes make more possibilities accessible to variation and selection. Sexual reproduction, for example, recombines inherited variants, letting selection work on combinations that mutation in a single lineage would reach much more slowly. The skyhook story in agentic programming is that a sufficiently intelligent model, given a repository, will infer every unstated rule and deliver the right system in one leap. One-shot coding makes that easy to believe for familiar tasks. Those successes stand on compilers, libraries, conventions, and a vast history of solved problems in the model's training. Much of the design work has already been done. Every substantial software project runs into questions that history hasn't settled. Features interact in unexpected ways; new uses reveal missing guarantees. More intelligence cannot reconstruct a decision we haven't made or captured. The work is to search the design space, decide what should hold, and test candidate answers. -For my agent swarms, the cranes make that search productive. Oracles make candidate implementations cheap to try and incorrect behavior easier to reject. Hostile reviews strengthen the instruments doing the rejecting. Benchmarks tell me whether an implementation that preserves the behavior also improves performance. Together, these mechanisms let agents explore more ambitious changes and get useful feedback about which ones to pursue. +For my agent swarms, the cranes I've added to the DB repo make that search productive and token efficient. Oracles make candidate implementations cheap to try and incorrect behavior easier to reject. Hostile reviews strengthen the instruments doing the rejecting. Benchmarks tell me whether an implementation that preserves the behavior also improves performance. Together, these mechanisms let agents explore more ambitious changes and get useful feedback about which ones to pursue. I also shape the _fitness landscape_—the constraints and feedback that favor some candidates over others—by editing [AGENTS.md](https://github.com/TanStack/db/blob/main/AGENTS.md). For example, I tell agents to treat every line of production code as a continuing cost and prefer fixes that remove code. This pushes them hard to avoid bloated and duplicative implementations. -The layering matters. A stronger oracle lets an agent attempt a change that would previously have been difficult to verify. That investigation can expose another missing distinction, which becomes part of the model and opens further possibilities. The cranes help build the next layer, which supports the next cranes. +The cranes help agents reach further, and the oracles give that progress a ratchet. Once a design decision is captured and enforced by an oracle, it stays locked in. The regressions I still find expose assumptions the oracles weren’t testing. Agents can radically rework the implementation without losing the design knowledge we’ve already established. Each investigation starts from a firmer platform, reaches another ambiguity, and locks in another piece of the design. The work has taken three months and many billions of tokens. Each investigation leaves later agents with sharper instruments. That change in their working environment has mattered far more than any model upgrade I've seen. @@ -157,15 +173,17 @@ The work has taken three months and many billions of tokens. Each investigation Coding agents can compress what used to be weeks of work into an afternoon. Venkatesh Rao calls this agent-mediated compression of experience [“artificial time”](https://contraptions.venkateshrao.com/p/superhistory-not-superintelligence). I spend much of the day in what feels like a succession of intense design meetings, with fresh evidence arriving 100x faster than I could have gathered it myself. -Questions that normally accumulate across years are arriving in months. Implementation work clears quickly, leaving me in a steady stream of design decisions. I spend much more of my day deciding what DB should guarantee and whether a fix simplifies the system or adds unnecessary machinery. Programming has become more intellectually demanding for me even as writing the code has become cheaper. +I’m speedrunning DB through the software maturation process. Oracles let agents explore huge stretches of design space, uncovering failures and unresolved design questions that would normally emerge over years of use. Interactions between synchronization, optimistic edits, persistence, and queries get investigated long before a user happens upon that particular combination. Working through those discoveries is giving DB years of maturation in months. + +That acceleration changes how programming feels. Implementation work clears quickly, leaving me in a steady stream of design decisions. I spend much more of my day deciding what DB should guarantee and whether a fix simplifies the system or adds unnecessary machinery. Programming has become more intellectually demanding for me even as writing the code has become cheaper. When an idea would take weeks to investigate, I need a strong reason to try it. With agents and an oracle, I can afford to take a much less certain idea far enough to find out whether it works. That changes which designs I can explore. -Rigorous engineering is much cheaper now. Oracles work incredibly well, but they're demanding to write and very verbose. In the months spent preparing DB for 1.0, the repository has gained roughly 330,000 lines of tests—probably more code than I wrote in my entire life before AI. Agents make that work practical. +Rigorous engineering is much cheaper now. Oracles have always worked incredibly well, but they're demanding to write and very verbose. In the months spent preparing DB for 1.0, the repository has gained roughly 330,000 lines of tests—probably more code than I wrote in my entire life before AI. Agents make that work practical. -## Drawing out the design +## Accelerating design -Programmers have always drawn on accumulated design knowledge—recognizing parallels, adapting ideas, and discovering where they stop fitting. LLMs give coding agents a super fast search interface into that history. I use them to search a vast repertoire of algorithms and design ideas for things that fit DB's particular problems, then work through the adaptations those ideas need. +Programmers have always drawn on accumulated design knowledge—recognizing parallels, adapting ideas, and discovering where they stop fitting. LLMs give programmers and coding agents a super fast search interface into that history. I use them to search a vast repertoire of algorithms and design ideas for things that fit DB's particular problems, then work through the adaptations those ideas need. Design develops through many successive adaptations. Each decision has to fit what's already there, and its consequences reveal further questions to work through. An idea that solves one problem can expose a distinction we hadn't previously needed, or a conflict between two otherwise useful rules. Working through those decisions and distilling them into oracle models has become the bulk of programming for me. @@ -173,15 +191,15 @@ Design is immensely complicated. Establishing or improving a single oracle can t ## The world DB creates for your agents -All this design work gives an agent building your application a simpler world to work in. It can join issues with projects, show one team's open work, and edit optimistically through DB's collections and live queries. DB keeps the results current and handles data loading, persistence, and synchronization, leaving you and your agents to focus on the behaviors you want to create. +Frameworks give application code a simpler world to operate in. DB lets you query backend data and edit it as though it were local, while handling the complicated coordination underneath. The design decisions I’ve described become problems your application doesn’t have to solve again. -Frameworks, infrastructure, and developer tools all do this: they embody detailed theories of their domains and make those domains easier to work in. Their APIs already shape applications. With coding agents, that knowledge can also guide how application code gets written. [SKILLs](https://agentskills.io/home) are a first step: they let a framework explain which abstractions to use, what those abstractions guarantee, and where the application has a decision to make. +With coding agents, that accumulated understanding can do more. Frameworks, infrastructure, and developer tools can help guide how applications get written. [SKILLs](https://agentskills.io/home) are a first step: DB can explain its abstractions and guarantees to an agent as it works. I want to go further and give your agents executable models and instruments they can use to develop and verify their designs. -For DB, I want to put that accumulated design knowledge in your agents' hands: oracles and other cranes that help them turn ambitious ideas into applications that hold up in practice. An agent building a custom collection should be able to run an oracle to verify that it syncs correctly. An agent tuning a screen should have instruments to optimize query loading speed and minimize data on the wire. You set the requirements; DB should work with your agents to translate your intent into optimal code. +An agent building a custom collection could use a DB oracle to discover where its sync behavior breaks down. An agent making a screen faster could experiment with loading strategies, measuring query latency and data transferred while verifying that the results stay correct. The design knowledge accumulated while hardening DB would become something your agents can actively work with—helping translate your intent into optimal application code. ### From server functions to live collections -The instruments built while hardening DB also make more ambitious abstractions practical to pursue. One idea I'm exploring is to let you write ordinary server functions to query and mutate Postgres, then have DB compile them into the collections and actions your client uses. +The instruments built while hardening DB also make more ambitious abstractions practical to pursue. One idea I'm exploring is to let you write ordinary server functions to query and mutate Postgres, then have DB compile them into the collections and actions for your clients and server code for your API. ```tsx // Server: define a query @@ -207,12 +225,12 @@ _Simplified sketch of an experimental API._ In this design, DB also generates the data-loading and mutation wiring. When a mutation runs, the client tells the server which query collections are live. The server determines which need updating and returns fresh results with the mutation response. -Making this work broadly means understanding enough of Postgres's vast language to preserve query meaning, determine which reads a write affects, and reconcile client state correctly. A few years ago, I wouldn't have considered attempting the larger project. The work of the last three months has made it tractable enough to pursue. +Making this work broadly means understanding enough of Postgres's vast language to preserve query meaning, determine which reads a write affects, and reconcile client state correctly. A few years ago, I wouldn't have considered attempting this hugely ambitious API. But the learnings of the last few months makes it feel tractable enough to pursue. -### Building the next layer +### Software that gets better at getting better -There's a useful name for what these cranes improve: _evolvability_, a system's capacity to support further useful development. Autonomy asks how much work an agent can do without me. Evolvability asks what new work becomes possible because of what we've learned. An agent arriving tomorrow can attempt something more ambitious using the same LLM because it inherits better cranes. +There's a useful name for what these cranes I've added to the codebase improve: _evolvability_, a system's capacity to support further useful development. Autonomy asks how much work an agent can do without me. Evolvability asks what new work becomes possible because of what we've learned. An agent arriving tomorrow can attempt something more ambitious using the same LLM because it inherits better cranes. -My days are full of decisions about what matters, which promises to make, and when an investigation has gone far enough. Each answer can become part of the machinery that makes further development possible. That's what makes me excited about where programming is going: the possibility of much more robust, delightful, and varied software, including ideas that used to cost too much to pursue. +That's what makes me excited about where programming is going: the possibility of much more robust, delightful, and varied software, including ideas that used to cost too much to pursue. -I began by trying to fix every known DB bug. TanStack DB 1.0 RC1 is the result so far. [Try it](https://tanstack.com/db/latest/docs/quick-start) in a demanding application and tell us [what breaks](https://github.com/TanStack/db/issues) or [what DB makes needlessly hard to build](https://github.com/TanStack/db/discussions). +[Try out our 1.0 RC](https://tanstack.com/db/latest/docs/quick-start) in a demanding application and let us know how it goes. From 83a63a5e409e37b987219b8bb5f20cb00d2450d4 Mon Sep 17 00:00:00 2001 From: Kyle Mathews Date: Tue, 27 Oct 2026 15:33:50 -0600 Subject: [PATCH 05/10] docs: update DB RC1 essay with revised oracle explanations --- ...tack-db-1.0-rc1-going-all-in-on-oracles.md | 116 ++++++++++-------- 1 file changed, 66 insertions(+), 50 deletions(-) diff --git a/src/blog/tanstack-db-1.0-rc1-going-all-in-on-oracles.md b/src/blog/tanstack-db-1.0-rc1-going-all-in-on-oracles.md index 3b0cde0bc..d59d8fe0c 100644 --- a/src/blog/tanstack-db-1.0-rc1-going-all-in-on-oracles.md +++ b/src/blog/tanstack-db-1.0-rc1-going-all-in-on-oracles.md @@ -8,11 +8,11 @@ authors: - Kyle Mathews --- -I'm delighted to announce on behalf of the core DB maintainers, myself, Sam Willis, Kevin De Porre, and the entire DB community, that TanStack DB 1.0 RC1 is here! +I'm delighted to announce on behalf of the core DB maintainers, myself, Sam Willis, Kevin De Porre, and the entire TanStack community, that TanStack DB 1.0 RC1 is here! After 1.5 years of development and betas and many discussions with users, the API is powerful, production-ready, and stable. -Since our [0.6 release](https://tanstack.com/blog/tanstack-db-0.6-app-ready-with-persistence-and-includes), we have shipped [server rendering and deep integration with TanStack Start](https://tanstack.com/db/latest/docs/guides/ssr), worked through a long run of correctness problems closing every known bug, added new collection adapters for Neon Realtime and Convex, and [made DB much faster for applications with many simultaneous live queries](https://github.com/TanStack/db/pull/1987). +Since our [0.6 release](https://tanstack.com/blog/tanstack-db-0.6-app-ready-with-persistence-and-includes), we’ve shipped [server rendering and deep integration with TanStack Start](https://tanstack.com/db/latest/docs/guides/ssr), added collection adapters for [Neon Realtime](https://neon.com/docs/realtime/overview) and [Convex](https://github.com/TanStack/db/pull/2230), and expanded framework support to include [Preact](https://github.com/TanStack/db/pull/2235), our sixth framework adapter. We’ve fixed an enormous number of bugs while hardening DB for 1.0—and made it much faster and smaller along the way. The post has eight sections covering my work to prepare DB for 1.0: @@ -27,7 +27,7 @@ The post has eight sections covering my work to prepare DB for 1.0: If you haven't used it yet, TanStack DB is a reactive client data framework that makes backend data feel local. Records from your API or sync engine become typed collections. Components query them for joined, filtered live results. Optimistic edits appear immediately and reconcile with the server later. -Perhaps the biggest difference between TanStack DB from earlier client-side databases is that you can adopt TanStack DB incrementally. Historically, getting reactive queries, optimistic mutations, and offline support often meant adopting a new database and sync architecture. With TanStack DB, you can start with your existing REST APIs and [TanStack Query](https://tanstack.com/query/latest) setup, immediately gain fast incremental relational queries, and progressively add [local persistence](https://tanstack.com/db/latest/docs/guides/sqlite-persistence), [durable offline mutations](https://tanstack.com/db/latest/docs/guides/offline-transactions), or real-time sync as your needs evolve. Because the application-facing query model stays the same, you can go from a conventional web application to a fully offline-capable, local-first app without rewriting everything along the way. +Perhaps the biggest difference between TanStack DB from earlier client-side databases is that you can adopt TanStack DB features incrementally. Historically, getting reactive queries, optimistic mutations, and offline support often meant adopting a new database and sync architecture. With TanStack DB, you can start with your existing REST APIs and [TanStack Query](https://tanstack.com/query/latest) setup, immediately gain fast incremental relational queries, and progressively add [local persistence](https://tanstack.com/db/latest/docs/guides/sqlite-persistence), [durable offline mutations](https://tanstack.com/db/latest/docs/guides/offline-transactions), or real-time sync as needed. Because the application-facing query model stays the same, you can go from a conventional web application to a fully offline-capable, local-first app without rewriting everything along the way. We began building DB in early 2025 at [ElectricSQL](https://electric-sql.com/), which incubated the project for its first year and a half. Since [Neon acquired us in August](https://neon.com/blog/electrifying-neon-building-the-realtime-backend), it has continued to back DB and we recently released [Neon Realtime](https://neon.com/docs/realtime/overview) with [TanStack DB support](https://neon.com/docs/realtime/sdks/typescript#tanstack-db). @@ -41,51 +41,63 @@ I've worked on open-source libraries for a long time and previously found it har ## From unit tests to oracles -Every software test needs something to try and a way to tell whether the result is right. That second part—the answer key—is called an **oracle**. +I’m using oracles to make DB’s design executable: to express what it should do in a form I can run, challenge, and verify the production code against. I’ll build up to that from an ordinary unit test. -In a typical unit test, I write down a specific example and its expected result: start with three open tasks, mark one complete, and check that the list contains the other two. That expected list is the oracle for that test. I supply the answer by hand. +A conventional unit test records the expected result for a particular example: give a sorting function `[3, 1, 2]` and verify that it returns `[1, 2, 3]`. I choose the input and supply the answer. -The model-based oracles I'm using calculate expected answers for many different examples. Suppose a screen shows the ten highest-priority open tasks. DB's production code uses complex machinery to keep that list current efficiently as tasks change. The model trades efficiency for simplicity: take all the tasks, remove the completed ones, sort by priority, and take the first ten. It rebuilds the whole answer from scratch after every change—a few straightforward lines of code can describe what a much more complex implementation should produce. +**Property-based testing** generalizes from examples to rules. A sorting function should return exactly the original elements in ascending order. A generator produces thousands of inputs—empty lists, repeated values, negative numbers, long lists—and the test verifies that rule for each one. -Give the model and DB the same changes, then compare their results. The simple model becomes an independent answer key for the optimized production code. +The generator supplies examples, but I still have to supply the rules. For a library like DB, that means spelling out which actions are allowed, how they change the data, and what its users should observe. Building a useful generated test becomes a way of working out and expressing the subsystem’s design. -```mermaid -flowchart TD - A["Same sequence of changes"] --> M["Simple model"] - A --> D["Real DB"] - M --> E["Expected result"] - D --> R["Actual result"] - E --> C["Compare"] - R --> C +Consider this live query over a collection of tasks: + +```ts +const topTasks = createLiveQueryCollection((q) => + q + .from({ task: tasks }) + .where(({ task }) => eq(task.completed, false)) + .orderBy(({ task }) => task.priority, 'desc') + .orderBy(({ task }) => task.id, 'asc') + .limit(10), +) ``` -The answer key can come from a small model I write or an existing tool that's already been thoroughly tested. A built-in JavaScript `Map` can track which keyed rows should exist after a sequence of inserts, updates, and deletes. SQLite can tell us what a query should return—I've used it to validate DB's query behavior. In each case, we have an independent way to calculate the expected answer and compare it with production. +The query selects up to ten open tasks, highest priority first, breaking ties by ID. DB must maintain the result defined by those clauses as the source records change. Completing a selected task removes it and brings the next eligible task into the window. Raising another task’s priority can move it into the window and displace one already there. Once DB has processed a batch of changes, the published rows and their order must match that query. -I combine the model with **property-based testing and fuzzing**. Agents write tests that automatically generate and run thousands of varied sequences—adding tasks, changing priorities, completing and reopening them—while the model supplies the expected answer after each change. A reported bug becomes the starting point for exploring a whole category of failures: different starting data, different changes, different orders. +A small model can express this design directly. It keeps the task records in a plain JavaScript `Map`, applies the requested changes, then filters out completed tasks, sorts by priority and ID, and takes the first ten. It uses ordinary collection and array operations, independently of DB’s query machinery. -**The model gives us an answer key for cases we never wrote out by hand.** +DB uses complex machinery to maintain the result efficiently. The model recomputes everything from scratch. It trades efficiency for simplicity, making the expected behavior straightforward to understand and inspect. The model serves as an **oracle**: an independent reference for what DB should produce. -Oracle models optimize for understanding; production code optimizes for speed and code size. Developing the model is scientific work: making a theory of how this part of DB should behave precise enough to investigate and challenge. Production code is the engineering that puts that understanding to work. +The design now guides the generated test. The generator varies the starting records and sequences of supported inserts, updates, and deletes, applied individually or in batches. The test gives the same changes to the model and DB. After DB processes each batch, it compares the live query’s public result with the model’s answer. -## Crafting a playable environment for agents +```mermaid +flowchart TD + P["Explicit design"] --> G["Generate permitted changes"] + P --> M["Independent model"] + P --> C["Compare promised public behavior"] + G --> M + G --> D["Real DB"] + M -->|Expected result| C + D -->|Actual result| C +``` + +An oracle can also draw on an established system’s understanding. For server-to-client synchronization, Postgres can supply the reference: make changes in Postgres, let them synchronize, and require the client’s corresponding rows and values to match Postgres exactly. We can build on existing abstractions instead of writing every reference computation ourselves. -An agent reading DB's implementation can see what it does today. To make good changes, it also needs to understand which behaviors are promises, which are accidents, and why we chose one rule over another. +A reported bug becomes the starting point for exploring a category of failures: different starting data, different changes, different orders. **The model turns the design into an answer key for cases we never wrote out by hand.** -I've been reshaping DB's repository into a "playable" environment where fresh agents can find those design decisions, experiment, and get clear feedback. Each oracle captures a small part of DB's intended behavior, with the reasoning behind its rules written beside the model. +Oracle models optimize for understanding; production code optimizes for speed and code size. Developing the model is scientific work: making a theory of how this part of DB should behave precise enough to investigate and challenge. Production code is the engineering that puts that understanding to work. With an independent model of the behavior to preserve, agents can experiment with faster algorithms or much simpler implementations. -I use one recurring loop to develop that understanding: start with a bug report or design question and work out the rules for the whole class of cases: +## Crafting a playable environment for agents -- Which actions are allowed, and which should be rejected? -- What should a user see after each action, and when? -- What should happen if actions overlap, fail, or arrive in a different order? +An agent reading DB's implementation can see what it does today. To make good changes, it needs to understand which behaviors are promises, which are accidents, and where we still need to decide what DB should do. -I encode that reasoning in the oracle's model, turning the design decisions into precise, executable rules the codebase must satisfy. Then agents search for counterexamples and verify their fixes against those rules across generated cases. The goal is to fix the entire class of bugs, with the model continuing to enforce the design as the codebase changes. +I've been reshaping DB's repository into a "playable" environment where fresh agents can find those design decisions, experiment, and get clear feedback. Each oracle controls a small part of DB's intended behavior, with the reasoning behind its rules written beside the model. -I make the product decisions; agents do much of the searching. +I use one recurring loop: start with a bug report or design question, investigate what DB should guarantee across the whole class of cases, and encode that understanding in an oracle. Agents search for counterexamples and verify proposed fixes. I make the product decisions; agents do much of the searching. What we learn stays in the repository, enforced by the oracle, so the next investigation starts from a stronger understanding. DB now has **176 oracle test files across 15 packages**, covering [the messages sent when data changes](https://github.com/TanStack/db/blob/main/packages/db/tests/change-event-history-oracle.test.ts), [how queries assemble related records](https://github.com/TanStack/db/blob/main/packages/db/tests/query/includes-cross-formulation-oracle.property.test.ts), and [which rows pagination returns](https://github.com/TanStack/db/blob/main/packages/query-db-collection/tests/cursor-pagination.oracle.test.ts), among other behaviors. The repository's [coverage map](https://github.com/TanStack/db/blob/main/docs/contributing/oracle-coverage.md) helps agents find the relevant oracle. -An agent working on a change will often run afoul of an oracle and get told its design is wrong. It then reads the oracle carefully to understand the design it needs to respect and revises its approach. Hours of earlier design work guide a later agent at the moment it needs that understanding. +An agent working on a change will often run afoul of an oracle and get told its design is wrong. It then reads the oracle carefully to understand the design it needs to respect and revises its approach. The failure directs it to the relevant model, a concrete counterexample, and the reasoning behind the rule. Hours of earlier design work become available when needed, without requiring every fresh agent to absorb the entire design up front. Design that can talk back. @@ -93,17 +105,19 @@ Design that can talk back. A mature library has answers to questions its authors didn’t know to ask. Users combine features in unexpected ways; maintainers fix the bugs and resolve the ambiguities those combinations expose. Years of that work turn a promising API into something applications can depend on. -DB makes that maturation particularly demanding by promising that backend data can feel local. A screen asks for the ten highest-priority open tasks; completing one can mean fetching the next task from the server. Now make that edit offline, preserve it across an app restart, and reconnect after the server’s data has changed. What should the screen show along the way? What must DB retain, fetch, and reconcile? The application still uses the same query, while the number of situations DB must handle has exploded. +The task query above shows why DB’s maturation is particularly demanding. Completing one task can mean fetching the next task from the server. Now make that edit offline, preserve it across an app restart, and reconnect after the server’s data has changed. What should the screen show along the way? What must DB retain, fetch, and reconcile? -Oracles let me explore that design space dramatically faster, without waiting for users to discover each failure. The history of these collection oracles shows how that exploration builds on itself. +Oracles let me explore that design space dramatically faster, without waiting for users to discover each failure. Let's look at the history of a few of these oracles that cover collection data updates to see how that exploration builds on itself. ### How the oracles got stronger -Collections notify other code—their subscribers—about changes with insert, update, and delete messages. A user [reported a bug](https://github.com/TanStack/db/issues/1901) where they deleted a row and reinserted a replacement with the same key in one turn. DB sent the subscriber an insert for the replacement followed by a delete for the same key, with no later message to put it back. A direct read returned the replacement, but replaying those messages added it to the subscriber's cache and then removed it. +Collections notify other code—their subscribers—about changes with insert, update, and delete messages. A user [reported a bug](https://github.com/TanStack/db/issues/1901) where they deleted a row and reinserted a replacement with the same key in one turn. DB sent the subscriber a delete for the row but no later insert message to put it back. A direct read returned the replacement, but replaying the collection messages left the subscriber's cache without the reinserted row. [PR #1902](https://github.com/TanStack/db/pull/1902) fixed the immediate failure and added an oracle for the broader promise: **a subscriber applying every change message should end up with the same rows as a direct read.** -The test constructs histories of writes, exploring that promise from several directions: +The oracle has three parts: a small reference model, a real DB collection, and a subscriber listening to that collection. The model applies the requested writes to a plain JavaScript `Map`, calculating which rows should exist. DB receives the same writes. The subscriber maintains its own map, updated only through the insert, update, and delete messages DB sends it. This lets us verify both that DB stores the right rows and that its messages give subscribers enough information to reconstruct them. + +The oracle constructs histories of writes, exploring that promise from several directions: - Start empty, with one row already present, or with two rows present. - Insert, update, delete, and reinsert the same key; interleave writes to different keys. Give each inserted or updated row a distinct value so stale data is visible. @@ -111,27 +125,29 @@ The test constructs histories of writes, exploring that promise from several dir - Enumerate every legal short sequence: up to four writes on one initially absent key, or three writes on two keys from every starting combination. - Generate longer histories—up to twenty writes across four keys—with both a fixed seed for repeatable coverage and fresh random seeds for further exploration. -For each history, a plain JavaScript `Map` calculates which rows those requested writes should leave behind. Real DB receives the same writes, while a subscriber maintains a **separate map using only the change messages DB sends it**. After each completed write or group of writes, the oracle compares three things: the expected rows, DB’s direct reads, and the subscriber’s reconstructed rows. It also checks subscriber/read agreement whenever DB delivers a batch of messages. In the original failure, the model and DB contained the replacement row, while the subscriber’s map was empty. +After each completed write or group of writes, the oracle compares the model’s expected rows, a direct read from DB, and the subscriber’s reconstructed rows. All three should agree. Whenever DB delivers a batch of messages, it also verifies that applying those messages leaves the subscriber with the same rows DB currently exposes. In the original failure, the model and DB held the replacement row, but DB’s messages left the subscriber’s map empty. Later, an agent challenged the oracle by deliberately changing how DB chose between insert and update messages. The oracle accepted both versions, even though they sent different messages for the same history. Investigating that disagreement revealed a bug in production—and a blind spot in the oracle. After a subscriber had removed a deleted row, a sync commit made the same key visible again. DB remembered the earlier optimistic operation and sent an **update**. But the subscriber no longer had that row. It needed an **insert**. The oracle hid the mistake. Its simulated subscriber used JavaScript’s `Map.set` for both inserts and updates, so the invalid update quietly recreated the missing row. The expected rows, direct reads, and subscriber’s reconstructed rows all agreed. I made the oracle enforce what each message means: **insert requires an absent key; update and delete require a present key.** Without adding any histories, 26 of the existing 378 cases failed. [PR #1995](https://github.com/TanStack/db/pull/1995) removed the branch that chose an update for a previously absent key. The code became smaller, and all 378 cases passed. -Another gap surfaced during CodeRabbit’s review of [PR #2004](https://github.com/TanStack/db/pull/2004). While a local optimistic edit was active, a server refresh replaced the collection’s source rows. DB sent **delete → insert → insert** for the same key. Restoring the optimistic edit announced the row once; processing the server’s replacement announced it again. The code intended to remove that second announcement, but searched for it before it had been added. +Another gap surfaced during a review of [PR #2004](https://github.com/TanStack/db/pull/2004). While a local optimistic edit was active, a server refresh replaced the collection’s source rows. DB sent **delete → insert → insert** for the same key. DB assembled the outgoing messages in two stages. First, it restored the row with its pending optimistic edit and added an insert message. Later, while processing the server’s replacement rows, it added another insert for the same row. The duplicate-removal step ran between those stages, before the second insert existed, so it removed nothing. -A separate oracle already explored these interactions. Its model started with server rows and overlaid local edits according to whether they were still pending, had succeeded, or had failed. Histories interleaved those edits and outcomes with server updates, including complete replacements. But its subscriber used a mode that filtered out repeated inserts. The oracle never saw the second insert. Meanwhile, the strict subscriber from #1995 never exercised server replacements. One oracle reached the failure but hid it; the other could recognize it but never reached it. +A separate oracle already explored server refreshes during optimistic edits. But its subscriber requested both the initial rows and later changes. In that mode, DB tracks which rows it has already delivered and filters repeated inserts. The duplicate disappeared before the oracle could inspect it. Meanwhile, the strict subscriber from #1995 could recognize the invalid message but never exercised server refreshes. -For [PR #2005](https://github.com/TanStack/db/pull/2005), I added a second subscriber that received messages without that filtering. It started with the independent model’s expected rows, checked every message against the rows it already held, and compared its reconstructed rows with the model after each history step. The old implementation failed seven cases. The fix stopped announcing rows that the optimistic restoration had already published. +In [PR #2005](https://github.com/TanStack/db/pull/2005), I extended the oracle to exercise DB’s other subscription mode: receive only future changes, without that filtering. I initialized this subscriber’s map from the independent model, then verified every incoming message and compared its reconstructed rows with the model after each history step. The old implementation failed seven cases. The production fix stopped generating the duplicate insert; the filtering remained unchanged. The stronger oracle then found another duplicate insert during a random run, without any server replacement. A delete succeeded, a new optimistic insert restored the same key, and a later sync commit finished clearing away the old delete. That cleanup forgot the subscriber had already seen the new row and announced it again. The same message rule caught a different route to the failure. -The original report was fixed in #1902. Each subsequent investigation sharpened what the oracles could distinguish: matching final rows, valid individual messages, and valid messages through more complicated interactions. That made the next investigation more capable. +The original report was fixed in #1902. Each subsequent investigation clarified what a subscriber should receive or exposed a weakness in how we verified it. + +The same iterative design process is happening across DB’s oracles: finding production bugs, closing gaps in coverage, discovering missing laws, and correcting mistaken assumptions in the models themselves. Each oracle develops a more precise account of its small part of DB. The design and the instruments for verifying it mature together, with each discovery preserved for every later change. ## A recipe for designing an oracle Here's how I build one with my agents: -1. **Choose one precise promise.** State what someone using the software should observe, and when. This is the oracle's law. Resolve ambiguous cases e.g. our task-list model needs a rule for ordering tasks with equal priority. +1. **Choose one precise promise.** State what someone using the software should observe, and when. This is the oracle's law. Resolve ambiguous cases explicitly: the task query above breaks ties between equal-priority tasks by ID. Record why you chose the rule and what assumptions it depends on, so later agents can recognize when the decision needs revisiting. 2. **Calculate the answer independently.** Derive it from the promise. Reusing production's algorithms or helpers can reproduce the same mistake on both sides and leave the oracle happily agreeing with broken code. @@ -147,9 +163,9 @@ I've written and heavily iterated on [oracle-authoring](https://github.com/TanSt ### How this relates to formal verification -For difficult designs, I often start in [TLA+](https://lamport.org/tla/tla.html), explore the consequences of proposed rules, then translate the model into a TypeScript oracle that runs against DB. +The oracle’s model is an **executable specification**. For difficult designs, I often develop that specification first in [TLA+](https://lamport.org/tla/tla.html), a language used in formal verification. Its model checker can explore every allowed ordering of operations within a bounded model. Can an obsolete request overwrite a newer result? Can releasing one subscription cancel work another still needs? Can a sequence of valid operations leave the system stuck? A counterexample gives me the exact sequence where a proposed rule fails, so I can investigate what the design needs to change. -That model is an **executable specification**: a precise account of the intended behavior. This shares a foundation with [formal verification](https://www.anthropic.com/research/formal-verification). A formal proof, using a system such as [Lean](https://lean-lang.org/), establishes a specified property for all cases covered by its assumptions. Our generated tests exercise the actual implementation and compare its behavior with the specification. +I then translate the model into a TypeScript oracle that runs against DB. TLA+ tests whether the modeled design preserves its stated rules; the oracle tests whether the actual implementation follows that design across generated histories. ## Look for cranes @@ -159,23 +175,23 @@ Dennett contrasts this with **cranes**—construction cranes. Both skyhooks and The skyhook story in agentic programming is that a sufficiently intelligent model, given a repository, will infer every unstated rule and deliver the right system in one leap. One-shot coding makes that easy to believe for familiar tasks. Those successes stand on compilers, libraries, conventions, and a vast history of solved problems in the model's training. Much of the design work has already been done. -Every substantial software project runs into questions that history hasn't settled. Features interact in unexpected ways; new uses reveal missing guarantees. More intelligence cannot reconstruct a decision we haven't made or captured. The work is to search the design space, decide what should hold, and test candidate answers. +Every substantial software project runs into questions that software history hasn't settled. Features interact in unexpected ways; new uses reveal missing guarantees. More intelligence cannot reconstruct a decision we haven't made or captured. The work is to search the design space, decide what should hold, and test candidate answers. For my agent swarms, the cranes I've added to the DB repo make that search productive and token efficient. Oracles make candidate implementations cheap to try and incorrect behavior easier to reject. Hostile reviews strengthen the instruments doing the rejecting. Benchmarks tell me whether an implementation that preserves the behavior also improves performance. Together, these mechanisms let agents explore more ambitious changes and get useful feedback about which ones to pursue. -I also shape the _fitness landscape_—the constraints and feedback that favor some candidates over others—by editing [AGENTS.md](https://github.com/TanStack/db/blob/main/AGENTS.md). For example, I tell agents to treat every line of production code as a continuing cost and prefer fixes that remove code. This pushes them hard to avoid bloated and duplicative implementations. +I also shape the _fitness landscape_—the constraints and feedback that favor some candidates over others—by editing [AGENTS.md](https://github.com/TanStack/db/blob/main/AGENTS.md). For example, I tell agents to prefer fixes that remove production code. The cranes help agents reach further, and the oracles give that progress a ratchet. Once a design decision is captured and enforced by an oracle, it stays locked in. The regressions I still find expose assumptions the oracles weren’t testing. Agents can radically rework the implementation without losing the design knowledge we’ve already established. Each investigation starts from a firmer platform, reaches another ambiguity, and locks in another piece of the design. -The work has taken three months and many billions of tokens. Each investigation leaves later agents with sharper instruments. That change in their working environment has mattered far more than any model upgrade I've seen. +The work has taken three months and many billions of tokens. Each investigation leaves later agents with sharper instruments. These changes in their working environment has mattered far more than any model upgrade I've seen. ## Superhistory -Coding agents can compress what used to be weeks of work into an afternoon. Venkatesh Rao calls this agent-mediated compression of experience [“artificial time”](https://contraptions.venkateshrao.com/p/superhistory-not-superintelligence). I spend much of the day in what feels like a succession of intense design meetings, with fresh evidence arriving 100x faster than I could have gathered it myself. +Coding agents equipped with those instruments can compress what used to be weeks of work into an afternoon. Venkatesh Rao calls this agent-mediated compression of experience [“artificial time”](https://contraptions.venkateshrao.com/p/superhistory-not-superintelligence). I spend much of the day in what feels like a succession of intense design meetings, with fresh evidence arriving 100x faster than I could have gathered it myself. I’m speedrunning DB through the software maturation process. Oracles let agents explore huge stretches of design space, uncovering failures and unresolved design questions that would normally emerge over years of use. Interactions between synchronization, optimistic edits, persistence, and queries get investigated long before a user happens upon that particular combination. Working through those discoveries is giving DB years of maturation in months. -That acceleration changes how programming feels. Implementation work clears quickly, leaving me in a steady stream of design decisions. I spend much more of my day deciding what DB should guarantee and whether a fix simplifies the system or adds unnecessary machinery. Programming has become more intellectually demanding for me even as writing the code has become cheaper. +Programming has become more intellectually demanding for me even as writing the code has become cheaper. When an idea would take weeks to investigate, I need a strong reason to try it. With agents and an oracle, I can afford to take a much less certain idea far enough to find out whether it works. That changes which designs I can explore. @@ -183,17 +199,17 @@ Rigorous engineering is much cheaper now. Oracles have always worked incredibly ## Accelerating design -Programmers have always drawn on accumulated design knowledge—recognizing parallels, adapting ideas, and discovering where they stop fitting. LLMs give programmers and coding agents a super fast search interface into that history. I use them to search a vast repertoire of algorithms and design ideas for things that fit DB's particular problems, then work through the adaptations those ideas need. +Programmers have always drawn on accumulated design knowledge—recognizing parallels, adapting ideas, and discovering where they stop fitting. LLMs give programmers and coding agents a super fast search interface into that history. I use them to search a vast repertoire of algorithms and design ideas for things that fit DB's particular problems, then work out how to implement. Design develops through many successive adaptations. Each decision has to fit what's already there, and its consequences reveal further questions to work through. An idea that solves one problem can expose a distinction we hadn't previously needed, or a conflict between two otherwise useful rules. Working through those decisions and distilling them into oracle models has become the bulk of programming for me. -Design is immensely complicated. Establishing or improving a single oracle can take hours of investigation between me and my agents: figuring out which distinctions matter, what the system should promise, and whether our model actually expresses that promise. A small executable model can embody a great deal of intellectual work. Together, those models embody an increasingly detailed theory of how clients should load, update, reconcile, and present data. +Design is immensely complicated. Establishing or improving a single oracle can take hours of investigation between me and my agents: figuring out which distinctions matter, what the system should promise, and whether our model actually expresses that promise. A small executable model can embody a great deal of intellectual work. Together, those models embody an increasingly detailed theory of how within the DB world clients should load, update, reconcile, and present data. ## The world DB creates for your agents Frameworks give application code a simpler world to operate in. DB lets you query backend data and edit it as though it were local, while handling the complicated coordination underneath. The design decisions I’ve described become problems your application doesn’t have to solve again. -With coding agents, that accumulated understanding can do more. Frameworks, infrastructure, and developer tools can help guide how applications get written. [SKILLs](https://agentskills.io/home) are a first step: DB can explain its abstractions and guarantees to an agent as it works. I want to go further and give your agents executable models and instruments they can use to develop and verify their designs. +With coding agents, that accumulated understanding can do more. Frameworks, infrastructure, and developer tools can help guide how applications get written. [SKILLs](https://agentskills.io/home) are a first step: DB can explain its abstractions and guarantees to an agent as it works. I think we can go a lot further and give your agents executable models and instruments they can use to develop and verify their designs. An agent building a custom collection could use a DB oracle to discover where its sync behavior breaks down. An agent making a screen faster could experiment with loading strategies, measuring query latency and data transferred while verifying that the results stay correct. The design knowledge accumulated while hardening DB would become something your agents can actively work with—helping translate your intent into optimal application code. @@ -225,7 +241,7 @@ _Simplified sketch of an experimental API._ In this design, DB also generates the data-loading and mutation wiring. When a mutation runs, the client tells the server which query collections are live. The server determines which need updating and returns fresh results with the mutation response. -Making this work broadly means understanding enough of Postgres's vast language to preserve query meaning, determine which reads a write affects, and reconcile client state correctly. A few years ago, I wouldn't have considered attempting this hugely ambitious API. But the learnings of the last few months makes it feel tractable enough to pursue. +Making this work broadly means understanding enough of Postgres's vast language to preserve query meaning, determine which reads a write affects, and reconcile client state correctly. A few years ago, I wouldn't have considered attempting this hugely ambitious API. But the learnings of the last few months make it feel tractable enough to pursue. ### Software that gets better at getting better From 85bf9714412e99d8ac33c8e4947f0c11840d228b Mon Sep 17 00:00:00 2001 From: Kyle Mathews Date: Wed, 28 Oct 2026 07:59:23 -0600 Subject: [PATCH 06/10] docs: address RC1 post copy review --- .../tanstack-db-1.0-rc1-going-all-in-on-oracles.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/src/blog/tanstack-db-1.0-rc1-going-all-in-on-oracles.md b/src/blog/tanstack-db-1.0-rc1-going-all-in-on-oracles.md index d59d8fe0c..dda111492 100644 --- a/src/blog/tanstack-db-1.0-rc1-going-all-in-on-oracles.md +++ b/src/blog/tanstack-db-1.0-rc1-going-all-in-on-oracles.md @@ -18,7 +18,7 @@ The post has eight sections covering my work to prepare DB for 1.0: 1. [From unit tests to oracles](#from-unit-tests-to-oracles): what an oracle is and why I'm so excited about them. 2. [Crafting a playable environment for agents](#crafting-a-playable-environment-for-agents): making DB's design knowledge available to coding agents. -3. [Hardening DB for 1.0](#hardening-db-for-10): what I did and learned while making DB epically production ready. +3. [Hardening DB for 1.0](#hardening-db-for-10): what I did and learned while making DB epically production-ready. 4. [A recipe for designing an oracle](#a-recipe-for-designing-an-oracle): how to build one, and how it relates to formal verification. 5. [Look for cranes](#look-for-cranes): why the tools around a coding agent matter so much. 6. [Superhistory](#superhistory): how agents change the pace and economics of programming and software maturity. @@ -27,7 +27,7 @@ The post has eight sections covering my work to prepare DB for 1.0: If you haven't used it yet, TanStack DB is a reactive client data framework that makes backend data feel local. Records from your API or sync engine become typed collections. Components query them for joined, filtered live results. Optimistic edits appear immediately and reconcile with the server later. -Perhaps the biggest difference between TanStack DB from earlier client-side databases is that you can adopt TanStack DB features incrementally. Historically, getting reactive queries, optimistic mutations, and offline support often meant adopting a new database and sync architecture. With TanStack DB, you can start with your existing REST APIs and [TanStack Query](https://tanstack.com/query/latest) setup, immediately gain fast incremental relational queries, and progressively add [local persistence](https://tanstack.com/db/latest/docs/guides/sqlite-persistence), [durable offline mutations](https://tanstack.com/db/latest/docs/guides/offline-transactions), or real-time sync as needed. Because the application-facing query model stays the same, you can go from a conventional web application to a fully offline-capable, local-first app without rewriting everything along the way. +Perhaps the biggest difference between TanStack DB and earlier client-side databases is that you can adopt TanStack DB features incrementally. Historically, getting reactive queries, optimistic mutations, and offline support often meant adopting a new database and sync architecture. With TanStack DB, you can start with your existing REST APIs and [TanStack Query](https://tanstack.com/query/latest) setup, immediately gain fast incremental relational queries, and progressively add [local persistence](https://tanstack.com/db/latest/docs/guides/sqlite-persistence), [durable offline mutations](https://tanstack.com/db/latest/docs/guides/offline-transactions), or real-time sync as needed. Because the application-facing query model stays the same, you can go from a conventional web application to a fully offline-capable, local-first app without rewriting everything along the way. We began building DB in early 2025 at [ElectricSQL](https://electric-sql.com/), which incubated the project for its first year and a half. Since [Neon acquired us in August](https://neon.com/blog/electrifying-neon-building-the-realtime-backend), it has continued to back DB and we recently released [Neon Realtime](https://neon.com/docs/realtime/overview) with [TanStack DB support](https://neon.com/docs/realtime/sdks/typescript#tanstack-db). @@ -37,7 +37,7 @@ Across its packages, DB now has [almost 3 million weekly NPM downloads](https:// After server rendering landed a few months ago, I thought the remaining work before 1.0 was fairly straightforward: fix every known bug. I'd fire up my agents and set them loose and be done in a few weeks. Before and during that work, though, I tried some new ideas for making the coding environment more productive for agent swarms. The new environment has proved astonishingly effective at finding new bugs and undefined behavior. My short bug-fixing expedition has now stretched to hundreds of PRs and months of intense work hardening DB's core abstractions for 1.0. Since the first PR in this push, the repository has gained roughly 330,000 lines of tests. -I've worked on open-source libraries for a long time and previously found it hard to find bugs proactively before users provide reproductions. Now essentially every new agent session stumbles on multiple new classes of bugs. Many of which are now so obscure that I suspect no one has encountered them in the wild. Yet they keep coming. Increasingly, my job is to keep agents from fixing _too much_—chasing hypothetical edge cases or adding complexity to solve problems DB never promised to solve. +I've worked on open-source libraries for a long time and previously found it hard to find bugs proactively before users provide reproductions. Now essentially every new agent session stumbles on multiple new classes of bugs. Many of these bugs are now so obscure that I suspect no one has encountered them in the wild. Yet they keep coming. Increasingly, my job is to keep agents from fixing _too much_—chasing hypothetical edge cases or adding complexity to solve problems DB never promised to solve. ## From unit tests to oracles @@ -163,7 +163,7 @@ I've written and heavily iterated on [oracle-authoring](https://github.com/TanSt ### How this relates to formal verification -The oracle’s model is an **executable specification**. For difficult designs, I often develop that specification first in [TLA+](https://lamport.org/tla/tla.html), a language used in formal verification. Its model checker can explore every allowed ordering of operations within a bounded model. Can an obsolete request overwrite a newer result? Can releasing one subscription cancel work another still needs? Can a sequence of valid operations leave the system stuck? A counterexample gives me the exact sequence where a proposed rule fails, so I can investigate what the design needs to change. +The oracle’s model is an **executable specification**. For difficult designs, I often develop that specification first in [TLA+](https://lamport.org/tla/tla.html), a language used in formal verification. A TLA+ model checker can explore every allowed ordering of operations within a bounded model. Can an obsolete request overwrite a newer result? Can releasing one subscription cancel work another still needs? Can a sequence of valid operations leave the system stuck? A counterexample gives me the exact sequence where a proposed rule fails, so I can investigate what the design needs to change. I then translate the model into a TypeScript oracle that runs against DB. TLA+ tests whether the modeled design preserves its stated rules; the oracle tests whether the actual implementation follows that design across generated histories. @@ -183,7 +183,7 @@ I also shape the _fitness landscape_—the constraints and feedback that favor s The cranes help agents reach further, and the oracles give that progress a ratchet. Once a design decision is captured and enforced by an oracle, it stays locked in. The regressions I still find expose assumptions the oracles weren’t testing. Agents can radically rework the implementation without losing the design knowledge we’ve already established. Each investigation starts from a firmer platform, reaches another ambiguity, and locks in another piece of the design. -The work has taken three months and many billions of tokens. Each investigation leaves later agents with sharper instruments. These changes in their working environment has mattered far more than any model upgrade I've seen. +The work has taken three months and many billions of tokens. Each investigation leaves later agents with sharper instruments. These changes in their working environment have mattered far more than any model upgrade I've seen. ## Superhistory @@ -199,7 +199,7 @@ Rigorous engineering is much cheaper now. Oracles have always worked incredibly ## Accelerating design -Programmers have always drawn on accumulated design knowledge—recognizing parallels, adapting ideas, and discovering where they stop fitting. LLMs give programmers and coding agents a super fast search interface into that history. I use them to search a vast repertoire of algorithms and design ideas for things that fit DB's particular problems, then work out how to implement. +Programmers have always drawn on accumulated design knowledge—recognizing parallels, adapting ideas, and discovering where they stop fitting. LLMs give programmers and coding agents a super-fast search interface into that history. I use them to search a vast repertoire of algorithms and design ideas for things that fit DB's particular problems, then work out how to implement. Design develops through many successive adaptations. Each decision has to fit what's already there, and its consequences reveal further questions to work through. An idea that solves one problem can expose a distinction we hadn't previously needed, or a conflict between two otherwise useful rules. Working through those decisions and distilling them into oracle models has become the bulk of programming for me. From 1e4832151115f5ffdb4bafb06873100853e4b71c Mon Sep 17 00:00:00 2001 From: Kyle Mathews Date: Wed, 28 Oct 2026 11:23:57 -0600 Subject: [PATCH 07/10] docs: revise DB RC1 essay and add illustrations --- .../cranes-building-new-layers.png | Bin 0 -> 1403657 bytes .../reference-tables-and-slide-rule.png | Bin 0 -> 1390299 bytes ...tack-db-1.0-rc1-going-all-in-on-oracles.md | 95 +++++++++--------- 3 files changed, 47 insertions(+), 48 deletions(-) create mode 100644 public/blog-assets/tanstack-db-1.0-rc1-going-all-in-on-oracles/cranes-building-new-layers.png create mode 100644 public/blog-assets/tanstack-db-1.0-rc1-going-all-in-on-oracles/reference-tables-and-slide-rule.png diff --git a/public/blog-assets/tanstack-db-1.0-rc1-going-all-in-on-oracles/cranes-building-new-layers.png b/public/blog-assets/tanstack-db-1.0-rc1-going-all-in-on-oracles/cranes-building-new-layers.png new file mode 100644 index 0000000000000000000000000000000000000000..5acfdf99b0b7fb6559b266b4228d96baea723bd2 GIT 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