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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 #opensource

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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 #claudecode
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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 #claudecode

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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 #opensource

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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 #programming

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New version of Ratty is out! 🎉🧀 🐀 Ratty — A GPU-rendered terminal emulator with inline 3D graphics. 💯 Now supports SSH-friendly object payloads, transform control and keyboard scrollback navigation! 🦀 Written in Rust & built with Ratatui ⭐ GitHub: #rustlang #terminal #ratty #ratatui #opensource
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New version of Ratty is out! 🎉🧀 🐀 Ratty — A GPU-rendered terminal emulator with inline 3D graphics. 💯 Now supports SSH-friendly object payloads, transform control and keyboard scrollback navigation! 🦀 Written in Rust & built with Ratatui ⭐ GitHub: #rustlang #terminal #ratty #ratatui #opensource

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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 #debugging

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cursor can now read ur terminal in real time! we built a subagent to watch logs, training runs, security alerts, and step in to patch code with the cursor-cli
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cursor can now read ur terminal in real time! we built a subagent to watch logs, training runs, security alerts, and step in to patch code with the cursor-cli

rajan agarwal

119,565 görüntüleme • 1 yıl önce

ANTHROPIC JUST TURNED AI AGENTS INTO GIT REPOS Anthropic shipped "ant" - a CLI that runs every Claude API endpoint straight from your terminal. The headline isn't the terminal access. It's that you can now version-control an AI agent as YAML in Git and have CI sync it to the Claude Platform, the same way you ship code. - Every API resource is a subcommand: messages, models, files, agents, sessions - Define an agent in a YAML file, check it into your repo, and keep it in sync with one update command - Spin up a session, send it an event, then pull every event and tool call back from the same CLI - Claude Code knows how to drive ant out of the box - it shells out and reads the results with no glue code Agents just stopped being prompts you babysit and became infrastructure you deploy.
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ANTHROPIC JUST TURNED AI AGENTS INTO GIT REPOS Anthropic shipped "ant" - a CLI that runs every Claude API endpoint straight from your terminal. The headline isn't the terminal access. It's that you can now version-control an AI agent as YAML in Git and have CI sync it to the Claude Platform, the same way you ship code. - Every API resource is a subcommand: messages, models, files, agents, sessions - Define an agent in a YAML file, check it into your repo, and keep it in sync with one update command - Spin up a session, send it an event, then pull every event and tool call back from the same CLI - Claude Code knows how to drive ant out of the box - it shells out and reads the results with no glue code Agents just stopped being prompts you babysit and became infrastructure you deploy.

BuBBliK

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You can now watch your agents work live as Hermes Desktop streams the screen of any bot's screen or session in real time. Watch it drive a browser, type into a terminal, or open a window. Take over anytime to interact with the Bot Screen or type credentials and seamlessly hand back off when you're done.
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You can now watch your agents work live as Hermes Desktop streams the screen of any bot's screen or session in real time. Watch it drive a browser, type into a terminal, or open a window. Take over anytime to interact with the Bot Screen or type credentials and seamlessly hand back off when you're done.

Nous Research

486,066 görüntüleme • 20 gün önce

Claude Code Desktop now opens a new window for each session This makes it much easier to visualize multiple Claude Code agents running in parallel My current stack depends on the task: - Ghostty: when starting a project. Bash commands, git, env variables, provider connections. All manual through the terminal with a Claude panel running alongside. - Claude Code Desktop: once everything is configured. GitHub connected, CLAUDE.md, Skills, subagents and Hooks ready. Claude Code runs on its own, no more terminal setup, just panels running and outputs to review. - VSCode: when I need to review code by hand. I use it less and less, but there are moments where I have to confirm Claude got it right. I usually open the Claude extension inside VSCode, but it lacks most of the CLI features so it's limited Solid update. Worth trying once your workflows are already set up 👇
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Claude Code Desktop now opens a new window for each session This makes it much easier to visualize multiple Claude Code agents running in parallel My current stack depends on the task: - Ghostty: when starting a project. Bash commands, git, env variables, provider connections. All manual through the terminal with a Claude panel running alongside. - Claude Code Desktop: once everything is configured. GitHub connected, CLAUDE.md, Skills, subagents and Hooks ready. Claude Code runs on its own, no more terminal setup, just panels running and outputs to review. - VSCode: when I need to review code by hand. I use it less and less, but there are moments where I have to confirm Claude got it right. I usually open the Claude extension inside VSCode, but it lacks most of the CLI features so it's limited Solid update. Worth trying once your workflows are already set up 👇

Daniel San

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i don't f*cking understand why this isn't popular yet someone has created a memory system that uses 90% fewer tokens while still finding all the expected symbols it builds a local graph of source symbols and their relationships. architecture, decisions and handoffs live as Git-tracked Markdown. a note can point to the code behind it, so a code change can flag that knowledge for review the loop: scan the repo → map the code → write the why → anchor notes to symbols → retrieve a task-sized slice → review drift → commit to Git → continue in the next session in the author's small, one-repo test, graph retrieval returned 10.74× less context than grep top-3 while finding every expected symbol across six tasks the useful part is the connection between memory and evidence. the repo carries what the agent learned, and the code gives you a way to check whether that knowledge still holds save this, then build a second brain for your company⭣
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i don't f*cking understand why this isn't popular yet someone has created a memory system that uses 90% fewer tokens while still finding all the expected symbols it builds a local graph of source symbols and their relationships. architecture, decisions and handoffs live as Git-tracked Markdown. a note can point to the code behind it, so a code change can flag that knowledge for review the loop: scan the repo → map the code → write the why → anchor notes to symbols → retrieve a task-sized slice → review drift → commit to Git → continue in the next session in the author's small, one-repo test, graph retrieval returned 10.74× less context than grep top-3 while finding every expected symbol across six tasks the useful part is the connection between memory and evidence. the repo carries what the agent learned, and the code gives you a way to check whether that knowledge still holds save this, then build a second brain for your company⭣

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Built a native macOS viewer for ledger plain-text accounting file format. Most expense tracking apps are built around lock-in. Proprietary formats, mandatory accounts, servers you don't control. When their VC funding dries up, they shut down or pivot, and you start over. Plain-text accounting takes the opposite approach: your finances live in a .ledger file. Open it in vim. Query it with awk. Put it in git and get a full audit trail of every change you've ever made. It's just a plain text file. Ledge is just a lens over that file. Fast, native, no cloud. Following kepano 's file over app philosophy
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Built a native macOS viewer for ledger plain-text accounting file format. Most expense tracking apps are built around lock-in. Proprietary formats, mandatory accounts, servers you don't control. When their VC funding dries up, they shut down or pivot, and you start over. Plain-text accounting takes the opposite approach: your finances live in a .ledger file. Open it in vim. Query it with awk. Put it in git and get a full audit trail of every change you've ever made. It's just a plain text file. Ledge is just a lens over that file. Fast, native, no cloud. Following kepano 's file over app philosophy

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This isn’t a 3D model file. It’s 100% code Our world-famous Mars Cat avatar has been reconstructed entirely as Three.js source code No GLB at runtime. No model download. No textures or UV atlas. Just code, rendered directly in the browser It was created with IMG2THREEJS by Nick. An incredible open-source project with over 15,000 stars on GitHub And our Mars Cat is now featured on the home page gallery See it live below ↓. Grab the code. Add it to your game New to Three.js? It’s one of the most popular open-source JavaScript libraries for building interactive 3D experiences and games on the web
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This isn’t a 3D model file. It’s 100% code Our world-famous Mars Cat avatar has been reconstructed entirely as Three.js source code No GLB at runtime. No model download. No textures or UV atlas. Just code, rendered directly in the browser It was created with IMG2THREEJS by Nick. An incredible open-source project with over 15,000 stars on GitHub And our Mars Cat is now featured on the home page gallery See it live below ↓. Grab the code. Add it to your game New to Three.js? It’s one of the most popular open-source JavaScript libraries for building interactive 3D experiences and games on the web

Jay B 😻🚀

15,044 görüntüleme • 1 ay önce

A FLY IS NOW WRITING PYTHON CODE WITH ITS OWN NERVOUS SYSTEM I genuinely didn't expect a fly to end up behind a code editor. The setup is strangely simple. A fly sits on a keyboard while its neural activity is captured in real time. On the other side of the screen, Python code starts appearing line by line inside wordle_solver_v1.py, with the neural visualization changing as the input comes through. The model contains 100,010 flyemg neurons, with 88,172 represented in the current neural model. As activity changes, the system translates those signals into keyboard input and the cursor keeps moving through the code. Then you start looking at what is actually being typed. import random import unicodedata def quitar_acentos(palabra): It's not just random characters filling the editor. The video shows an actual Python function being constructed while the neural activity and keyboard input remain synchronized beside it. Brain activity → keyboard → code. A tiny biological nervous system is being used as the interface between a fly and a programming environment. The fly isn't sitting there watching someone code. It's sitting on the keyboard while the system turns its neural activity into the next input. And somehow the first thing it ended up building was a Wordle solver
0:18

A FLY IS NOW WRITING PYTHON CODE WITH ITS OWN NERVOUS SYSTEM I genuinely didn't expect a fly to end up behind a code editor. The setup is strangely simple. A fly sits on a keyboard while its neural activity is captured in real time. On the other side of the screen, Python code starts appearing line by line inside wordle_solver_v1.py, with the neural visualization changing as the input comes through. The model contains 100,010 flyemg neurons, with 88,172 represented in the current neural model. As activity changes, the system translates those signals into keyboard input and the cursor keeps moving through the code. Then you start looking at what is actually being typed. import random import unicodedata def quitar_acentos(palabra): It's not just random characters filling the editor. The video shows an actual Python function being constructed while the neural activity and keyboard input remain synchronized beside it. Brain activity → keyboard → code. A tiny biological nervous system is being used as the interface between a fly and a programming environment. The fly isn't sitting there watching someone code. It's sitting on the keyboard while the system turns its neural activity into the next input. And somehow the first thing it ended up building was a Wordle solver

Insomnia

12,054 görüntüleme • 29 gün önce

> be Boris Cherny > principal engineer at Meta > wrote the O'Reilly book on TypeScript > joins Anthropic > builds a little terminal tool as a side project > it becomes Claude Code > half of Silicon Valley now codes through it > leaves for Cursor > comes back to Anthropic two weeks later > new Opus drops > everyone writes 2-page prompts > Boris cuts most of the Claude Code system prompt > says your old detailed prompts now work against the model > suggests deleting your CLAUDE.md every few months just to see what it can do without it > the guy who built the tool is prompting less than you in 12 minutes he explains why. watch it below ↓
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> be Boris Cherny > principal engineer at Meta > wrote the O'Reilly book on TypeScript > joins Anthropic > builds a little terminal tool as a side project > it becomes Claude Code > half of Silicon Valley now codes through it > leaves for Cursor > comes back to Anthropic two weeks later > new Opus drops > everyone writes 2-page prompts > Boris cuts most of the Claude Code system prompt > says your old detailed prompts now work against the model > suggests deleting your CLAUDE.md every few months just to see what it can do without it > the guy who built the tool is prompting less than you in 12 minutes he explains why. watch it below ↓

Kirill

335,874 görüntüleme • 2 gün önce

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.
0:17

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.

Brady Long

209,050 görüntüleme • 2 ay önce

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.
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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.

Simplifying AI

18,308 görüntüleme • 2 ay önce

I thought my AI agent needed a bigger context window wrong the leak was my shell every time it ran git diff HEAD~5 the model ate 92,000 tokens after ztk 18,000 ls -la src 2,000 tokens became 53 cargo test passing 397 tokens became 21 the fix is a 260KB Zig binary that sits between your agent and the shell it compresses command output before it hits the model not by summarizing everything by knowing what each command actually needs to preserve > diffs keep changed lines > tests keep failures > ls keeps structure > logs dedupe repeats > JSON and errors pass through untouched 5.8M tokens saved in one 256 command session stop buying bigger context windows while feeding your agent raw terminal sewage
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I thought my AI agent needed a bigger context window wrong the leak was my shell every time it ran git diff HEAD~5 the model ate 92,000 tokens after ztk 18,000 ls -la src 2,000 tokens became 53 cargo test passing 397 tokens became 21 the fix is a 260KB Zig binary that sits between your agent and the shell it compresses command output before it hits the model not by summarizing everything by knowing what each command actually needs to preserve > diffs keep changed lines > tests keep failures > ls keeps structure > logs dedupe repeats > JSON and errors pass through untouched 5.8M tokens saved in one 256 command session stop buying bigger context windows while feeding your agent raw terminal sewage

Alpha Batcher

17,516 görüntüleme • 5 ay önce

The ChainGPT AI skill for Claude Code is one of the most complete Web3-AI dev environment on the market. Let me prove it. Open Claude Code with the installed skill, and you have direct access to: • Built-in wallets across 33+ chains • DEX trading, perps, and Hyperliquid execution • Smart contract generation and auditing • NFT generation across 22 chains • Real-time crypto news API • Fine-tuned crypto LLM with live on-chain data Every part of the Web3 stack, one prompt away. Here's what that looks like in practice. I built a real-time on-chain whale tracker in a single afternoon. It's called Whale Watch. → Pulls live swap data from Ethereum DEX pools → Filters every trade over $100K → Runs each whale through the ChainGPT LLM for a trader-grade live analysis → Routes the user into 1inch with the token pair pre-loaded if they want to follow the trade Real data. Real AI. Real action layer. The skill wrote the server. It hit the right APIs. It generated the UI. It debugged itself when something broke. The only thing I supplied was the idea and the polish. Open Claude Code. Ship something with ChainGPT AI this weekend! /plugin install ChainGPT-org/chaingpt-claude-skill
0:18

The ChainGPT AI skill for Claude Code is one of the most complete Web3-AI dev environment on the market. Let me prove it. Open Claude Code with the installed skill, and you have direct access to: • Built-in wallets across 33+ chains • DEX trading, perps, and Hyperliquid execution • Smart contract generation and auditing • NFT generation across 22 chains • Real-time crypto news API • Fine-tuned crypto LLM with live on-chain data Every part of the Web3 stack, one prompt away. Here's what that looks like in practice. I built a real-time on-chain whale tracker in a single afternoon. It's called Whale Watch. → Pulls live swap data from Ethereum DEX pools → Filters every trade over $100K → Runs each whale through the ChainGPT LLM for a trader-grade live analysis → Routes the user into 1inch with the token pair pre-loaded if they want to follow the trade Real data. Real AI. Real action layer. The skill wrote the server. It hit the right APIs. It generated the UI. It debugged itself when something broke. The only thing I supplied was the idea and the polish. Open Claude Code. Ship something with ChainGPT AI this weekend! /plugin install ChainGPT-org/chaingpt-claude-skill

ChainGPT

25,853 görüntüleme • 4 ay önce

Cohort 4 is done, and the certificates are already landing in our students' inboxes. Congratulations to everyone who made it through! With that, we're officially opening applications for the Solana Rust Security Bootcamp (Cohort 5). A 6-week intensive in Rust and Solana security, built for engineers who want to move beyond writing code to identifying how real systems fail. > Supported by Solana Foundation. > Free to join. > Only 125 seats. And this round comes with a little bit of spice. We're teaming up with industry leaders you wouldn't expect to see here 🤫 C5 starts June 22. Applications close June 18. We recommend applying early. Application form below (as always) 🦀
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Cohort 4 is done, and the certificates are already landing in our students' inboxes. Congratulations to everyone who made it through! With that, we're officially opening applications for the Solana Rust Security Bootcamp (Cohort 5). A 6-week intensive in Rust and Solana security, built for engineers who want to move beyond writing code to identifying how real systems fail. > Supported by Solana Foundation. > Free to join. > Only 125 seats. And this round comes with a little bit of spice. We're teaming up with industry leaders you wouldn't expect to see here 🤫 C5 starts June 22. Applications close June 18. We recommend applying early. Application form below (as always) 🦀

Rektoff

58,104 görüntüleme • 4 ay önce