Readme.io

Readme.io AI

Readme.io AI

Readme.io AI

Readme.io AI

Readme.io AI

Readme.io AI

Readme.io AI

Readme.io AI

Readme.io AI

Readme.io AI

I built ContextAtlas: A new take on context carry over and helps claude pick up new sessions where it left off in scope of your previous design decisions while saving your tokens avoiding rediscovery

When the "Build with Opus 4.7" hackathon was announced, I had been obsessing over the tokenomics of agents and how to make sessions go further without burning context on rediscovery work. We all have probably hit a session limit and wondered how it went so fast. I applied with that thesis, didn't get in, but I built it anyway over the last four weeks. I am proud to share that v1.0 ships today. Note up front: this is specifically a tool for development users. If you're using claude.ai web or Projects, ContextAtlas won't plug in directly. But if Claude Code is your main work flow or you utilize the Anthropic API, this tool was made for you. The pain: Claude Code learns your codebase fresh every session. "Where is OrderProcessor?" triggers a flurry of greps. "What depends on AuthMiddleware?" is another round of file reads. On a mid-sized codebase, an architectural question can burn 40+ tool calls and a lot of tokens before Claude has enough context to reason well. And the architectural rules in your ADRs and design docs? Claude has no path to those, so it confidently suggests changes that break constraints you may have documented elsewhere in your repo. What I built: ContextAtlas is an MCP server that pre-computes a curated atlas of your codebase (symbols, ADR-extracted architectural intent, git history, test coverage) and serves it to Claude Code in one call at query time in a smaller, token saving compact shape via a few lightweight mcp tools. Initial indexing happens once; querying is local and free. Example of what comes back when Claude calls get_symbol_context("OrderProcessor"): SYM OrderProcessor@src/orders/processor.ts:42 class SIG class OrderProcessor extends BaseProcessor INTENT ADR-07 hard "must be idempotent" RATIONALE "All order processing must be safely retryable." REFS 23 [billing:14 admin:9] GIT hot last=2026-03-14 TESTS src/orders/processor.test.ts (+11) Claude sees the idempotency constraint before proposing changes, not after a review catches the violation. https://i.redd.it/0ons3o28t32h1.gif Numbers: 45-72% token reduction on architectural prompts across three benchmark repos (TypeScript, Python, Go), with zero quality regression on measured axes. Full methodology and paired-t confidence intervals in the linked write-up. I wanted measurements, not vibes. Honest limits: single-judge model at v1.0 (cross-vendor panel is post-launch work). Quantitative claims bounded to three benchmark repos. Tie-bucket and trick-bucket prompts routinely show ContextAtlas net-negative; that's reported inline rather than buried. Install (two ways): In Claude Code: /index-atlas and /generate-adrs skills. No API key needed; runs under your subscription. Via CLI: uses Anthropic API for indexing. ​ npm install -g contextatlas contextatlas init && contextatlas index # then add the MCP server entry to your Claude Code config (snippet in the README) Both produce structurally identical atlases. Supported languages at v1.0: TypeScript (tsserver), Python (Pyright), Go (gopls), Ruby (ruby-lsp). Rust, Java, and C# are next on the roadmap; the adapter interface is small enough that they're realistic community contributions. What's next: v1.1 thesis is shaping up around developer onboarding flows and quality-validation work that was deferred from v0.8. And integrating external documentation of your code base into pre-indexing workflow. Full write-up: https://www.contextatlas.io/blog/v1.0.0 Repo: https://github.com/traviswye/ContextAtlas Also launching on DevHunt today: https://devhunt.org/tool/contextatlas; votes are very appreciated if you find ContextAtlas useful or an interesting approach. Built solo, hackathon-shaped scope, not pretending it's a full blown research paper, but did attempt to treat methodology as seriously. Happy to answer anything in the comments. Star the repo if you want to follow along, file an issue if it breaks for you on your codebase, and please be honest; this only gets better with feedback from people running it on real repos. submitted by /u/Kitchen-Leg8500 [link] [comments]

I built a self-hosted memory layer for Claude that runs free on Cloudflare — open source

https://preview.redd.it/touwnxi2z80h1.png?width=1774&format=png&auto=webp&s=b4bf6c2e1f096f692562a2b8b27e72dc2f9cb1c0 Claude forgetting everything between sessions was driving me crazy, so I built a fix. It's a Cloudflare Worker that acts as an MCP server — four tools: remember, recall, list_recent, forget. Claude calls them automatically based on instructions in your system prompt. You set it up once and stop thinking about it. The part I'm most happy with is how recall works. Every note gets vector-embedded using Workers AI (bge-small-en-v1.5) and stored in Cloudflare Vectorize. So when Claude searches your memory, it's matching by meaning, not keywords. Store "users drop off at checkout" and recall it later with "onboarding problems" — it finds it. What I used Claude for building this: Wrote most of the MCP server implementation in TypeScript Helped me work through the Vectorize + D1 architecture Generated the iOS Shortcuts templates and bookmarklet Wrote the README (Claude writing docs for a Claude memory tool felt appropriate) Stack: Cloudflare Workers + D1 (SQLite) + Vectorize + Workers AI. The whole thing runs on Cloudflare's free tier for personal use. One-click deploy button in the repo. Works with Claude Desktop, Claude Code, and claude.ai (via custom connectors). Repo: https://github.com/rahilp/second-brain-cloudflare Happy to answer questions about the implementation — the semantic search piece especially has some interesting tradeoffs worth discussing. submitted by /u/rahilpirani5 [link] [comments]

RT @gkoberger: New @readme homepage! readme [.] com https://t.co/7xQUUY0jdy

RT @gkoberger: New @readme homepage! readme [.] com https://t.co/7xQUUY0jdy

ReadMe CLI — Write and lint docs from your terminal. Catches broken links, duplicate slugs, invalid frontmatter, and broken MDX. Auto-fixes most issues, and works with Claude or Codex for the rest. Wr

ReadMe CLI — Write and lint docs from your terminal. Catches broken links, duplicate slugs, invalid frontmatter, and broken MDX. Auto-fixes most issues, and works with Claude or Codex for the rest. Write from ReadMe's editor, your IDE, or the CLI. Your customers just see great https://t.co/dlArhYmxdW

AI Writer — When your team pushes code changes to GitHub, ReadMe creates a branch with suggested doc updates for you to review and merge. Docs stay current with product development without the manual

AI Writer — When your team pushes code changes to GitHub, ReadMe creates a branch with suggested doc updates for you to review and merge. Docs stay current with product development without the manual chase. Connect your repo to get started: https://t.co/eKfi3O373V https://t.co/bEvbKDNJit

Learn more about why and how we built MDXish: https://t.co/Rt2UMHyRk3

Learn more about why and how we built MDXish: https://t.co/Rt2UMHyRk3

New editor — We rebuilt it from the ground up. It handles Markdown and MDX, and brings AI right into the editing flow. Highlight text, get suggestions, apply changes without leaving the page. Plus: br

New editor — We rebuilt it from the ground up. It handles Markdown and MDX, and brings AI right into the editing flow. Highlight text, get suggestions, apply changes without leaving the page. Plus: branching is built in, so you can draft rewrites without touching your live docs. https://t.co/UAcIEdR8Ds

We redesigned ReadMe from the ground up. Here's what's new: 1. Simpler admin interface that's easier to navigate 2. AI-native editor with Markdown + MDX that keeps you in the loop 3. AI Writer that

We redesigned ReadMe from the ground up. Here's what's new: 1. Simpler admin interface that's easier to navigate 2. AI-native editor with Markdown + MDX that keeps you in the loop 3. AI Writer that watches your GitHub commits and creates docs from them 4. CLI tool for working https://t.co/uai4nQ5kTU

We’re shipping something big tomorrow. Here’s a little hint 👀 https://t.co/kCkEoCT8I9

We’re shipping something big tomorrow. Here’s a little hint 👀 https://t.co/kCkEoCT8I9

You're all still repping last year's swag and we love it 💙 Great seeing everyone at @writethedocs. This year's batch is coming off the printer hot! https://t.co/jABnXZRmtx

You're all still repping last year's swag and we love it 💙 Great seeing everyone at @writethedocs. This year's batch is coming off the printer hot! https://t.co/jABnXZRmtx

We’re at @writethedocs in Portland this week so swing by to say hi, grab a custom engraved notebook, collect a few pins and a tote bag! 🦉 https://t.co/ighRavhgqD

We’re at @writethedocs in Portland this week so swing by to say hi, grab a custom engraved notebook, collect a few pins and a tote bag! 🦉 https://t.co/ighRavhgqD

LLM proxy that lets Claude Code talk to any model

I built rosetta-llm — an open-source multi-format LLM proxy that acts as a drop-in Claude Code gateway. Works as a Claude Code LLM gateway — set `ANTHROPIC_BASE_URL` and all configured models appear in `/model` picker Translates between formats — Anthropic Messages ↔ OpenAI Chat ↔ OpenAI Responses at the wire level Thinking blocks round-trip correctly — this is the hard part and why I built this Provider routing — `openai/gpt-5.4`, `anthropic/claude-opus-4-7`, `groq/llama-4` all through one endpoint Streaming on everything — passthrough fast path + cross-format translation with proper SSE handling The thinking-block problem Most proxies lose reasoning continuity. LiteLLM has had open PRs for thinking block handling for a long time — some dating back months — and they're still not merged. Without proper round-tripping, prompt caching breaks across turns and Claude Code loses context. Rosetta encodes encrypted reasoning into Anthropic's `signature` field and decodes it back — so multi-turn agentic workflows keep their prompt-cache hits. Zero-setup Hugging Face Space Literally a two-line Dockerfile: FROM ghcr.io/lokesh-chimakurthi/rosetta-llm:latest COPY --chown=app:app config.json /app/config.json Add config.json file and above Dockerfile into a HF Space (Docker SDK) and it's running. No clone, no build, no venv. The GHCR image has everything baked in. Make your HF space private and add api keys in hf space secrets. Check readme in github Also works with # No install — ephemeral uvx rosetta-llm # Persistent install uv tool install rosetta-llm rosetta-llm --config ~/.rosetta-llm/config.json # Docker docker run -p 7860:7860 \ -v ~/.rosetta-llm/config.json:/app/config.json \ ghcr.io/lokesh-chimakurthi/rosetta-llm:main Why another proxy? I looked at existing solutions: LiteLLM — thinking block round-trip PRs going nowhere, too many abstractions OpenRouter — great but closed-source, no self-hosting Direct passthrough proxies — don't translate between formats Nothing gave me lossless cross-format translation with proper reasoning fidelity. Links GitHub: https://github.com/Lokesh-Chimakurthi/rosetta-llm PyPI: https://pypi.org/project/rosetta-llm/ Contributions welcome I built this for myself and it works for my use cases. But there's a lot more it could do — better multimodal handling, embeddings support, rate limiting, an admin UI. If any of this sounds interesting, PRs are absolutely welcome. Happy to answer questions in the comments. submitted by /u/DataNebula [link] [comments]

What do guys think of this ? Anyone has tried it ?

I stumbled upon this post today and wanted to have you advices. Is it hype ? useful ? Should be adapted ? If anyone has already tried it I would be happy to have your feedback on it. Thanks everyone The post : « This is the most complete Claude Code setup that exists right now. 27 agents. 64 skills. 33 commands. All open source. The Anthropic hackathon winner open-sourced his entire system, refined over 10 months of building real products. What's inside: → 27 agents (plan, review, fix builds, security audits) → 64 skills (TDD, token optimization, memory persistence) → 33 commands (/plan, /tdd, /security-scan, /refactor-clean) → AgentShield: 1,282 security tests, 98% coverage 60% documented cost reduction. Works on Claude Code, Cursor, OpenCode, Codex CLI. 100% open source. Link: https://github.com/affaan-m/everything-claude-code » submitted by /u/La-terre-du-pticreux [link] [comments]

Learn more about it in our docs: https://t.co/Kqkg87SuFS

Learn more about it in our docs: https://t.co/Kqkg87SuFS

Your AI Dropdown now shows MCP connection options. If your project has MCP enabled, developers browsing your docs can connect their AI tools right from the page. No hunting for URLs or setup instruct

Your AI Dropdown now shows MCP connection options. If your project has MCP enabled, developers browsing your docs can connect their AI tools right from the page. No hunting for URLs or setup instructions. Enable it in your AI settings to try it out! https://t.co/x6O9qYi1UX