i'd keep the first two passes fully separate, then do one explicit reconcile pass into a single diffable plan. the artifact matters more than which agent wins; it lets you see what changed and why.

how aeon actually fills every box: model → diff providers, works with openai / anthropic api system use your existing subscription. loop → not a babysitter loop. heartbeat checks every run, scores it, & when a skill breaks 3x, skill-repair diagnoses it and ships the fix PR itself. health files the issue, repair closes it. tools → 193 skills, each one a markdown prompt file. add your own in a sentence can self generate new tools. sandbox → every skill runs in an ephemeral github actions runner. isolated, disposable, free. connectors → mcp both ways (skills call servers get exposed as tools) a2a memory → committed to the repo. goals, topic notes, run metrics - plain files in git, versioned & diffable. context → skills chain into each other, outputs pass downstream via .outputs. no separate context store to host. persistence → git is the db. crash mid-run? state's already committed, it resumes. fully replayable. secrets → github secrets, scoped per repo. observability → haiku scores every run 1-5, 30-run rolling health per skill, token cost logged per run. guardrails → off by default - autonomy is the point. flip on fleet watcher for allow/block, spend caps, fail-closed. no db to wire. no vector store to host. no control plane to babysit. it's a repo that runs itself.

Markdown as the storage format is smart because it stays readable and diffable without a database dependency. The failure mode is search quality when your corpus hits a few thousand files and fuzzy matching starts confidently returning the wrong document.

Storing knowledge as markdown is deceptively smart because it stays readable and diffable without a database. The failure mode is search quality at scale when your markdown corpus hits a few thousand files and fuzzy matching starts returning garbage.

Capsules are just JSON. git add capsule.json git commit -m 'context' git push Your context is now versioned, diffable, reviewable. Welcome to 2026.

KNOWDLEDGE Has to be diffable (in version control)

That’s exactly why I’m building the language. Most animation tools today serialize editor state into JSON or binary formats. I’m interested in animations as source code: diffable, reviewable, composable, deterministic, and compilable across platforms.

vs liteparse, beyond raw speed: - OCR is pay-per-page, proven by signals - not on by default (that's the 110×) - 5.5ms cold start vs 56ms - matters when agents parse per tool call - uncertainty flags debug-page explain every routing decision - deterministic output: cacheable, diffable in CI and the speed claims are literally CI tests - if they stop being true, the build goes red.

Knowledge bases are starting to look like code again! Markdown in git beats a beautiful wiki when the reader is an agent: grepable, diffable, reviewable, branchable, owned. If a doc controls what the agent does, it needs the mechanics of code.

Others: - Memory: sheal, to mine past sessions for learnings - Tasks: a git-tracked tasks folder; the good ones graduate to ADRs - EDA/dataviz: marimo (diffable .py, not notebook JSON)

Honored and humbled that our paper GRaD-Nav won the RA-L best paper award for 2025. Qianzhong figured out how to train an RL policy for language-commanded drone navigation that works on real drones. The trick is in using a photo-realistic diffable 3DGS simulator for training.

GET the same endpoint and you get plain, diffable JSON back: panels, layout, and the ES|QL queries behind each chart. Commit it next to your code. Now dashboards move through PRs, code review, and CI/CD like everything else you ship. API docs: go.es.io/49RQE36

same setup here — plain markdown, one folder per team, agents read and write directly. once agents are the primary writers the format has to be diffable and greppable, which kills anything proprietary

Most AI-generated 3D tools create meshes you can look at. Forgent3D is trying to create something much more useful: Editable CAD models generated through AI coding agents. Instead of one-shot text-to-3D generation, Forgent3D gives Codex, Claude Code and Cursor a local feedback loop where agents generate model code, rebuild geometry, inspect previews, verify dimensions and iterate automatically. The key idea is powerful: Code becomes the model. Which means generated parts stay editable, parameterized, diffable and reusable instead of becoming static meshes. The system is already being used for mechanical parts, enclosures, assemblies and product prototypes. The bigger shift? AI isn't just generating content anymore. It's starting to generate manufacturable engineering artifacts through feedback-driven design loops. That's where AI CAD is heading. GitHub: github.com/forgent3d/forgent… Follow @Alacritic_Super for more AI infrastructure, robotics & open-source breakthroughs 🚀

Careful, keep going like this and Windows devs may accidentally rediscover Unix. Text output. Automated testing. Scripts. Diffable snapshots in VCS. Tools reading tools. (whispering) Claude Code.

Every useful AI chat just vanishes when you close the tab. Kept captures conversations from ChatGPT, Claude, Gemini and more and writes them to your local filesystem as plain Markdown — searchable, diffable, yours. No cloud, no account. Worth a closer look. #DevTools

Diffable feels like a wrong intuition here. Unless we are discussing ForEach, SwiftUI does diff via structural identity. You basically get it for free – okay we paid for all the complexity around some Type and result builder. Breaking apart big views does make view update more granular though.