Wow, I found a smart way to bookmark code!... 🤯 🔖 codemark — Structural bookmarks that survive refactors, renames & reformatting. 🌳 Capture the semantic structure of what you marked via tree-sitter 🐀 Includes a TUI for exploring the codebase! 🦀 Written in Rust & built with Ratatui ⭐ GitHub: #rustlang #ratatui #tui #coding #bookmark #semantic #devtools #programmingshow more

Watch git diff while the code is changing! 🤯... 👁️ livediff — A real-time diff viewer for the terminal 💯 Watch file changes live as generators, formatters, migrations & refactors modify your code 🦀 Written in Rust & built with Ratatui ⭐ GitHub: #rustlang #ratatui #tui #git #terminal #devtools #opensourceshow more

Claude Code is now in Rust! 🦀🎉 🤖 claude-code-rust... — A native TUI for Claude Code 💯 Streaming chat, tool calls, permissions, diffs, MCP, subagents & more! 🐀 Built with Ratatui ⭐ GitHub: #rustlang #ratatui #tui #terminal #claudecodeshow more

This is gold if you're using async Rust 💯... 🌀 channels-console — A TUI dashboard for inspecting std/tokio/futures/crossbeam channels. 📊 Watch messages, queue depth, throughput & memory usage live. 🦀 Written in Rust & built with Ratatui ⭐ GitHub: #rustlang #ratatui #tui #async #concurrency #tokio #debuggingshow more

Man, this looks beautiful 😍 🧲 superseedr — A... full-featured BitTorrent client for the terminal. 🌐 Supports multi-torrent downloads, DHT, PEX, magnet links & rarest-first piece selection. 🦀 Written in Rust & built with Ratatui ⭐ GitHub: #rustlang #ratatui #tui #torrent #bittorrent #p2p #terminal #opensourceshow more

Crazy news: Ratatui made it into a car dashboard... 😱🎉 🚗 suzui-rs — Suzuki Serial Data Line (SDL) viewer in Rust 📟 Displays live car data, powered by Pi and shown on stereo over RCA. 🛞 Made for Suzuki Baleno (G13BB) 🦀 Written in Rust & built with Ratatui ⭐ GitHub: #rustlang #ratatui #tui #car #dashboard #sdl #suzuki #terminal #monitoringshow more

you don't need to re-explain your codebase's architecture to... your agent every session. most tools stop at telling you what broke. sentrux is a real-time architectural sensor, it watches your codebase as a live treemap and turns file structure and dependencies into one continuous quality score. the loop is simple: codebase > agent scans structure and dependencies > sentrux scores 5 root cause metrics into one signal > agent sees exactly where risk concentrates > next session starts from a live map instead of a blind grep the binary carries zero built-in language knowledge, all 52 languages live in plugin.toml and tags.scm query files, so a new language needs zero rust code. small catch: it only scores the structure, it won't tell you why the cycle happened, that part's still on you. built pure Rust with no runtime dependencies, specifically so it could sit as one binary between an agent and a codebase without adding friction.show more

Stop manually making flowcharts. Try this GitHub skill with... Claude Code. I found an open-source tool that turns a plain description of a software system into a clean, interactive diagram. It's called Archify, and it has more than 66,000 stars on GitHub. You add it as a skill to an AI coding agent like Claude Code, Codex, Cursor, or OpenCode. Then you describe the system in the chat in one line. Archify turns that line into a single HTML file you can open, click through, and send to anyone. If you point your agent at an existing codebase, it reads the code and Archify maps how the pieces connect. I liked five things about it. 1. It makes five kinds of diagrams, including system maps and step-by-step workflows. 2. You can click any piece and trace what connects to it. 3. It checks the layout before it hands the diagram over, so arrows don't pile up and labels don't overlap. 4. It has dark and light themes and exports to PNG, SVG, or a short video. 5. You keep editing in plain chat, with requests like "add Redis" or "highlight the rollback path." You don't need a codebase to use it. A description in the chat is enough.show more

Top 12 agentic use cases for Jev: (bookmark this)... Jev handles semantic decisions that ordinary code cannot express reliably. It returns typed answers and probabilities, while code continues to cover the workflow. Here are 12 practical use cases for Jev: 1. Browser next action > Convert the current DOM state into a bounded action such as click, type, or stop. Code executes only valid operation-target pairs. There are already several open-source Jev web agents. 2. Context compaction > Decide which events from a long agent trace should remain. The selected text stays verbatim instead of being replaced with a generated summary. 3. Skill and context loading > Compare the current user turn against the available skills. Load only the instructions needed for that turn instead of filling the context window with every skill. 4. Typed tool-call compilation > Map a natural-language request to a function and fill its typed arguments. Each argument is evaluated separately before code allows execution. 5. Citation verification > Check whether a quoted passage exists and whether the surrounding evidence supports the claim. The output can be supported, unsupported, or contradicted. 6. Extraction verification > Run a cheap extractor first, then use Jev to verify questionable fields. Clean records stay on the fast path while uncertain ones reach a reasoning model. 7. Agent trace evaluation > Turn raw trajectories into queryable labels such as progress and repetition. This avoids asking another LLM to write a full review of every run. 8. Semantic regression tests > Replay a trace suite against a new agent build. Semantic checks can then pass or block prompt, model, tool, and policy changes in CI. 9. Jevgrep code search > Search a codebase by what the code does rather than its exact words. Jev scores candidate snippets and returns the most relevant code first. 10. Entity alignment > Compare two candidate records and decide whether to merge, review, or keep them separate. Candidate generation remains deterministic while Jev handles semantic identity. 11. Retrieval reranking > Let embeddings retrieve a broad candidate set, then use Jev to reorder passages by relevance. The generation model receives the most useful evidence first. 12. Memory promotion gate > Capture a completed agent trace, then judge whether its corrections contain a reusable lesson. Trace-backed lessons can be promoted while task-specific noise is discarded. If you want to see the final pattern in practice, it is already implemented in the Beacon open-source project. Beacon captures full sessions across Claude Code, Codex, Cursor, OpenCode, and 20+ agent harnesses, and then Jev identifies which workflows and corrections are worth learning from, so that a lesson discovered by one agent can become available to the others. GitHub repo: (don’t forget to star it ⭐) If you want to dive deeper, I also wrote about a similar mechanism in a hands-on guide. It covers building a Jev-style decision path with open models, entirely locally. Read it below.show more

I found 7 free Polymarket trading bots on GitHub... for 7 different trading situations… Each of these bots comes with a detailed step by step setup and usage guide in English: 1. This bot includes 118+ ready to use strategies and tools for trading on prediction markets (Momentum, Binance-Polymarket latency, Penny Clipper, Smart Routing, Expiry Fade, DCA bots and more). Built by a Cambridge computer science student who won a hackathon with this trading bot. GitHub: 2. This bot automatically manages all your Polymarket limit orders to maximize liquidity rewards. GitHub: 3. A weather bot from a Chinese dev, that analyzes multiple sources in real time, like forecasts, airport data and aviation observations (METAR + SPECI) to get the most accurate temperature data and generate a detailed weather report for a specific city and day. GitHub: 4. A bot that automatically searches for arbitrage opportunities between Polymarket and Kalshi. GitHub: 5. This is a bot-toolkit that includes copy trading, arbitrage, market making, whale alerts, spread farming, sports trading and more… GitHub: 6. A smart money trading bot - it looks for the most successful traders in selected markets, filters them by Pnl + win rate, and then creates a list for automated copy trading. GitHub: 7. A large collection of 20+ free trading bots for prediction markets. GitHub: Every bot here has a Dry Run mode, so you can test it on real markets without risking any funds.show more

Karpathy's Agentic Engineering finally has proper tooling! (built by... Google) Karpathy defined agentic engineering as the discipline that separates production agent work from vibe coding. The core skills he listed were spec design, eval loops, and security oversight. The problem has been that practicing this still requires a different tool for every phase: - editor for code - a terminal for scaffolding - a browser for testing - a cloud console for deployment - and a separate framework for evals. Every transition is a context switch. The solution to production-grade Agentic Engineering is now actually implemented in Google’s Agents CLI. It covers the entire workflow in one place for scaffolding, evaluating, and deploying ADK agents. One setup command injects 7 ADK-specific skills into a coding agent's context, which lets it handle scaffolding, evals, deployment, and enterprise registration through natural language. I tested this end-to-end by building a RAG agent from scratch using Claude Code. It scaffolded the full project from the ADK agentic_rag template, generated 20 eval scenarios with LLM-as-judge scoring, and returned a quantitative scorecard. Finally, it also deployed everything to Agent Runtime and registered the agent to Gemini Enterprise, so the entire org can discover and use it. The video below shows this in action, and I worked with the Google Cloud team to put this together. Agents CLI GitHub repo → (don't forget to star it ⭐ ) I wrote up the full build covering all six steps from install to enterprise registration. It includes the eval scorecard, the instruction loophole the eval caught before deployment, and what the deployment process actually looks like end-to-end. Read it below.show more

you can run claude code inside antigravity completely Free... with zero credit card and no rate limits 😳 use openrouter’s free models + antigravity. no anthropic bill. no paid api keys. takes 10 minutes to set up. what you get during this setup: - full claude code agent experience - strong coding models (including deepseek-r1, qwen2.5-coder, llama-4, grok-4 free tier) - antigravity’s clean workspace and sandbox - unlimited usage (as long as you stay on free models) - easy model swapping - zero cost full setup guide (100% free): step 1: install antigravity -go to and install it -create a new workspace step 2: install claude code - inside antigravity, install the claude code extension from the marketplace - open the built-in terminal step 3: create openrouter free account -go to - sign up with google (no card needed) - go to keys and create a new api key step 4: set the environment variables -in antigravity terminal run: export ANTHROPIC_API_KEY=sk-or-xxx export OPENROUTER_API_KEY=sk-or-xxx step 5: launch claude code with free model -run this command: claude-code --model deepseek/deepseek-r1:free or try: qwen/qwen2.5-coder:free if you already have antigravity? skip straight to step 2. after 10 minutes you’ll have a full agentic coding setup running for free. this is currently one of the cheapest ways to run serious coding agents in 2026. bookmark this before they limit the free models.show more

i'm not fine after reading this a guy with... a laptop got his own silicon chip manufactured with software Google gave away. the commercial version of that software rents for up to $1,000,000 a year. he never signed a vendor contract and never paid for a seat. he wrote the logic, pushed it to GitHub, and nine months later a wafer came out of a fab. getting silicon with your name in it used to take a company. now it takes nine months. his slot was 160 by 100 microns: about 1,000 logic gates, 8 inputs, 8 outputs, clocked past 50 MHz. more than 600 designs have already come back this way. this is the open silicon stack. Google and SkyWater published a complete 130nm manufacturing process, DARPA funded the software that turns code into a fab-ready file, and all of it sits in public repositories. turns out the whole flow fits in a git push: - write the logic in Verilog, or drag gates around in a browser if you have never done this - push to GitHub, an action runs synthesis, placement, routing and sign-off on the commit - OpenROAD finishes place-and-route with no human in the loop, code to fab file inside 24 hours - SKY130 carries the fab's real design rules, so passing the checks means it is manufacturable - a few hundred designs share one wafer, which is the only reason a person can afford the masks nobody puts this part in the thread: 130nm is roughly where the industry stood in 2001, and you wait six to nine months for silicon. this does not get you a GPU. it gets you a real object with your logic inside it. bookmark this. the video is one of those designs opened layer by layer, and every rectangle in it exists in the file that was sent to the fab.show more

Another WTF moment. A developer just open-sourced a coding... agent harness that boots 245x faster than Claude Code. It's called jcode. You launch it and the first frame renders in 14 milliseconds. Claude Code takes 3,436. One active session uses 27.8 MB of RAM. Claude Code uses 386.6. Run ten sessions in parallel and jcode holds at 117 MB while OpenCode swells to 3.2 GB. Each agent has a semantic memory graph instead of a scratchpad. Every turn gets embedded as a vector. The graph is queried on every turn for related memories, and a sideagent verifies the hits before injecting them into context. Consolidation runs in the background to check for stale or conflicting facts. No manual /remember calls. No token burn on lookup tools. The provider list is 30+ deep. Claude, ChatGPT, Gemini, GitHub Copilot, Azure, OpenRouter, DeepSeek, Groq, Mistral, Perplexity, Fireworks, Ollama, LM Studio, and any OpenAI-compatible endpoint you point it at. Ran out of tokens on your first ChatGPT Pro sub? /account swaps to the second. Then there's Swarm. Spawn two agents in the same repo and the server manages them. When agent A edits a file agent B has been reading, agent B gets pinged and can check the diff. Agents can DM each other, broadcast to the room, or spawn their own worker teams for parallel tasks. Groups, channels, and completion statuses are handled automatically. The UI has live side panels that render mermaid diagrams inline. To make it fast, the author wrote a Rust mermaid renderer 1800x faster than the JavaScript one, then wrote a custom terminal called Handterm because no existing terminal could do smooth partial-line scrolling. Self-dev mode is where it gets wild. Tell your agent to enter self-dev and it starts editing jcode's own source code, rebuilds the binary, reloads it live, and keeps working across your existing sessions. You can also resume broken sessions from Claude Code, Codex, OpenCode, or pi directly inside jcode. Anthropic's cache goes cold at the 5-minute mark and you're staring down a big cache miss on your next turn? The UI warns you before you spend the tokens. Written in Rust. MIT licensed. Runs on macOS, Windows, Linux, and Termux. Sitting at 11.2k stars with a native iOS app coming.show more

do you understand what Anthropic just admitted? their engineers... haven’t written most of their own code since early 2026. 80% of the code merged into Anthropic’s codebase last month was written by Claude. let that sink in. the company building the most powerful AI in the world is already being built by that AI. and the numbers keep getting wilder: → engineers are shipping 8x more code than in 2024. not because they’re working harder. because Claude is doing most of it. → Claude’s success rate on open-ended coding problems hit 76% in May 2026. up 50 points in just 6 months. → one Anthropic employee said “it’s been 5 months since I last wrote any code myself.” → Claude Mythos Preview achieved 52x speedup on research optimization. a skilled human gets 4x in 4-8 hours. → the length of tasks AI can reliably complete is doubling every 4 months. and here’s the part that keeps me up at night: in April 2026, Claude-powered agents were given an open AI safety problem and left alone to solve it. two human researchers recovered 23% of the performance gap in a week. the agents recovered 97% in 800 hours. Anthropic calls this recursive self-improvement. AI building AI. getting better. building a better version. repeat. they say we’re not there yet. but they also say it could come sooner than most institutions are prepared for.show more

I just built a Meta Ads diagnostic in Claude... Code that tells you WHY your account broke, not just what changed 🤯 It spins up a team of agents that each investigate a different reason performance dropped, then argue against each other to kill the wrong answer before it ever reaches you. All inside Claude Code. Perfect for DTC brands and agencies who panic-kill creative the second CPA spikes. If you've watched ROAS fall off a cliff and opened Ads Manager with ten tabs going, you already know what happens next. Your gut says "creative fatigue." You kill your best-performing ad. A week later performance is still broken, because that was never the problem. Guessing wrong is the most expensive move in paid social. This workflow ends the guessing: → One agent investigates each competing theory — creative fatigue, budget and delivery changes, traffic quality, offer and seasonality → Each one is blind to the others, reasoning only from its own slice of the data so they can't bias each other → A refuter agent then attacks every surviving theory and tries to kill it → A theory only stands if the data can't disprove it → You get a ranked diagnosis: the real cause, the evidence for and against it, and the one move to make this week No anchoring on the first obvious answer. No killing winning creative on a hunch. No "here's what happened" reports that never tell you why. What you get: → Every theory tested in parallel instead of one biased guess → An adversarial pass that kills the wrong answer before you act on it → A ranked diagnosis with confidence levels and evidence both ways → A reusable workflow you drop next month's export into and re-run Built 100% in Claude Code with the new dynamic workflows. The first account I ran it on looked like textbook creative fatigue. The workflow disagreed, and traced the real cause to a budget change that had doubled spend and flooded delivery with junk traffic. I put together a full playbook with the exact workflow, the prompt, and how to run it on your own account. Want it for free? > Like this post > Comment "META" And I'll send it over (must be following so I can DM)show more

Big moment for Postgres! Search has always been Postgres'... weak spot, and everyone just accepted it. If you needed a real relevance-ranked keyword search, the default answer was to spin up Elasticsearch or add Algolia and deal with the data sync headaches forever. The problem isn't that Postgres can't do text search. It can. But the built-in `ts_rank` function uses a basic term frequency algorithm that doesn't come close to what modern search engines deliver. So teams end up: - Running a separate Elasticsearch cluster just for search - Building sync pipelines that inevitably drift out of consistency - Paying for managed search services that charge per query - Accepting mediocre search relevance because "good enough" ships faster But this is actually a solvable problem. You can realistically bring industry-standard search ranking directly into Postgres, which eliminates the need for external infra entirely. This exact solution is now available with the newly open-sourced pg_textsearch by Tiger Data - Creators of TimescaleDB, a Postgres extension that brings true BM25 relevance ranking into the database. BM25 is the algorithm behind Elasticsearch, Lucene, and most modern search engines. Now it runs natively in Postgres. Here's what pg_textsearch enables: - True BM25 ranking with configurable parameters (the same algorithm powering production search systems) - Simple SQL syntax: `ORDER BY content 'search terms'` - Works with Postgres text search configurations for multiple languages - Pairs naturally with pgvector for hybrid keyword + semantic search That last point matters a lot for RAG apps. The video below shows this in action, and I worked with the team to put this together. You can now do hybrid retrieval (combining keyword matching with vector similarity) in a single database, without stitching together multiple systems. The syntax is clean enough that you can add relevance-ranked search to existing queries in minutes. pg_textsearch is fully open-source under the PostgreSQL license. You can find a link to their GitHub repo in the next tweet.show more

STOP STORING NOTES. START COMPILING THEM. a pile of... notes isn't memory. it's accumulation wearing memory's clothes. these 7 repos build the other kind, where Claude reads a source once and links it to everything you already have. copy the list: graphify: turns a codebase with its docs, SQL schemas, configs and PDFs into a queryable knowledge graph. 127k stars Understand-Anything: maps a project's code, docs or wiki into a graph you can search and query from your coding assistant. 86k stars claude-obsidian: lets Claude Code build and maintain a Markdown knowledge base in Obsidian, with source-backed notes and grounded answers. 15k stars obsidian-second-brain: gives Claude Code long-term memory as plain markdown notes in an Obsidian vault. 4.8k stars claude-code-memory-setup: an Obsidian vault as persistent memory, graphs for your codebase, and a pipeline that saves insights from your chats. 1k stars second-brain: an Obsidian knowledge base the LLM maintains, built on Karpathy's LLM Wiki pattern. 727 stars llm-wikid: an agent compiles your raw sources into cross-linked wiki pages in Obsidian. 420 stars none of them is your second brain. they're the compiler. the brain is what you feed it pick one and feed it your first 50 sources. before that, a compiled wiki behaves like a plain search engine, so don't quit early. the full setup is in the articleshow more

It is becoming f*cking dangerous: I launched a full-fledged... autonomous crawler that parses any website with ZERO LINES OF CODE Forget about Python, terminals, CSS selectors, and constant manual copy-pasting You can set up a seamless No-Code integration just once, where Claude becomes the brain of your automatic data collector, and the results automatically flow straight into your spreadsheets In this setup, a No-Code service accesses the website and grabs the raw content, while Claude instantly cleans out the noise, extracts the key insights, and organizes everything neatly How to set this up: 1. Pick a No-Code service: Sign up for makecom, zapiercom, or install the bardeenai extension (all of them offer free tiers) 2. Set up the Launch: Specify how often the crawler should run (for example, Every day at 9:00 AM or At the click of a button) and feed it the target URLs 3. Connect the Claude module: Add an AI step to your scenario and give Claude a clear instruction: "Analyze the provided page text. Extract product names, prices, discounts, and seller contact details. Return the data as a clean, structured list" 4. Choose where to save the results: Connect the final step (for instance, Google Sheets or Notion) Quick 10-minute setup, full 24/7 autonomy, and zero headache with code maintenance. Essentially, with just a few clicks you are creating your own smart AI agent that quietly handles the routine work while you focus on real tasks Bookmark this for latershow more