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The W&B MCP server is officially LIVE! Coding agents could always read your code. Now they can read your experiments, monitor training, and drive their own research loops. 20 tools, hosted on every W&B deployment, plugs into Claude Code, Cursor, Codex, Gemini-CLI, and LeChat.

Weights & Biases

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16,305 次观看 • 2 个月前 •via X (Twitter)

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Introducing Generative UI for MCP Apps One server. Infinite interfaces. Instead of building views, define a component catalog. The AI assembles the right UI based on your API, CLI or MCP server tools. Works in Claude, ChatGPT, VS Code, Cursor and more

Introducing Generative UI for MCP Apps One server. Infinite interfaces. Instead of building views, define a component catalog. The AI assembles the right UI based on your API, CLI or MCP server tools. Works in Claude, ChatGPT, VS Code, Cursor and more

Chris Tate

165,297 次观看 • 5 个月前

Excited to announce AI Research Skills - an open-source library of 82 specialized skills for AI coding tools. One command gives your agent expert knowledge in: → Model training & fine-tuning (TRL, Unsloth ...) → Distributed systems (DeepSpeed, FSDP ...) → Inference optimization (vLLM, TensorRT ...) → Agent building ? (Langchain, AutoGPT ...) Works with Claude Code, Cursor, Gemini CLI, Windsurf, and Codex with one click interactive installation @ npx orchestra-research/ai-research-skills If you found the ML paper writing skill useful, check out the comprehensive collection

Excited to announce AI Research Skills - an open-source library of 82 specialized skills for AI coding tools. One command gives your agent expert knowledge in: → Model training & fine-tuning (TRL, Unsloth ...) → Distributed systems (DeepSpeed, FSDP ...) → Inference optimization (vLLM, TensorRT ...) → Agent building ? (Langchain, AutoGPT ...) Works with Claude Code, Cursor, Gemini CLI, Windsurf, and Codex with one click interactive installation @ npx orchestra-research/ai-research-skills If you found the ML paper writing skill useful, check out the comprehensive collection

Zechen Zhang

75,583 次观看 • 6 个月前

Available in the MCP Marketplace: Apple Native Tools Watch Cline use the new Apple MCP server to read project details from Mail, automatically create tasks in Reminders, make a related code edit, and then send an update email -- all without leaving VS Code. Stop juggling apps and losing focus. Automate the admin work that pulls you away from coding. See how Cline integrates your personal productivity tools into your development workflow.

Available in the MCP Marketplace: Apple Native Tools Watch Cline use the new Apple MCP server to read project details from Mail, automatically create tasks in Reminders, make a related code edit, and then send an update email -- all without leaving VS Code. Stop juggling apps and losing focus. Automate the admin work that pulls you away from coding. See how Cline integrates your personal productivity tools into your development workflow.

Cline

27,192 次观看 • 1 年前

MOONSHOT JUST MADE A FREE REPLICA OF CLAUDE CODE. It’s called Kimi Code CLI. It’s open source, free to use, and built by the team behind Kimi K3. It comes with features you won’t find in Claude Code: → upload a screen recording as input → built-in coding, planning, and research agents that work in separate contexts → plan your changes before editing any files → set up MCP servers directly from the CLI with /mcp-config → works with VS Code, JetBrains, and Zed → lightweight - no Node.js setup, starts in seconds The CLI is completely free. The Kimi K3 model behind it starts at $3 per million input tokens. Grab it below 👇

MOONSHOT JUST MADE A FREE REPLICA OF CLAUDE CODE. It’s called Kimi Code CLI. It’s open source, free to use, and built by the team behind Kimi K3. It comes with features you won’t find in Claude Code: → upload a screen recording as input → built-in coding, planning, and research agents that work in separate contexts → plan your changes before editing any files → set up MCP servers directly from the CLI with /mcp-config → works with VS Code, JetBrains, and Zed → lightweight - no Node.js setup, starts in seconds The CLI is completely free. The Kimi K3 model behind it starts at $3 per million input tokens. Grab it below 👇

MIKE

45,132 次观看 • 17 天前

You can now understand any codebase instantly with this free tool. It's called Understand Anything. It scans your entire project and builds an interactive visual map of every file, function, class, and dependency. Click anything. Search anything. Ask questions about it directly. Works with Claude Code, Codex, Cursor, Copilot, and Gemini CLI. One command to install. 100% Free. Open Source.

You can now understand any codebase instantly with this free tool. It's called Understand Anything. It scans your entire project and builds an interactive visual map of every file, function, class, and dependency. Click anything. Search anything. Ask questions about it directly. Works with Claude Code, Codex, Cursor, Copilot, and Gemini CLI. One command to install. 100% Free. Open Source.

Simplifying AI

22,410 次观看 • 20 天前

Alrighty, everything is ready 😎 here’s an unofficial “2x Codex limits” promo from my side for you all. meet DevSpace — an MCP connector app that turns ChatGPT into Codex. npm install -g @waishnav/devspace After installing, tunnel the MCP server over the internet and enjoy 2x limits. You can use GPT-5.5 Pro, xHigh, or High for planning, then hand off the task to your local Codex/pi/opencode/cursor/claude code instance. Or you can just use it for reviewing code written by other local coding agents Go ahead, experiment with different workflows, and keep the feedback coming on GitHub Issues or in my DMs And let’s thank OpenAI for being so generous by giving us separate ChatGPT and Codex limits and by being so chill around this MCP :) Please use it sparingly, only when you run out of limits. Don’t overuse it — in the end, they do have a button to stop it 🙂

Alrighty, everything is ready 😎 here’s an unofficial “2x Codex limits” promo from my side for you all. meet DevSpace — an MCP connector app that turns ChatGPT into Codex. npm install -g @waishnav/devspace After installing, tunnel the MCP server over the internet and enjoy 2x limits. You can use GPT-5.5 Pro, xHigh, or High for planning, then hand off the task to your local Codex/pi/opencode/cursor/claude code instance. Or you can just use it for reviewing code written by other local coding agents Go ahead, experiment with different workflows, and keep the feedback coming on GitHub Issues or in my DMs And let’s thank OpenAI for being so generous by giving us separate ChatGPT and Codex limits and by being so chill around this MCP :) Please use it sparingly, only when you run out of limits. Don’t overuse it — in the end, they do have a button to stop it 🙂

waishnav

530,666 次观看 • 1 个月前

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.

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

200,319 次观看 • 2 个月前

if you use claude code, this will save you real money. the problem: every time you make an edit, your ai rereads the whole codebase to figure out what changed. tens of thousands of tokens, every turn, for context it already had. this repo fixes it. it’s called code review graph, and it just maps your entire codebase: every file, every function, every connection laid out so you can see the actual shape of your project how to set up (2 min): 1. pip install code-review-graph 2. code-review-graph install - auto-configures Claude Code, Cursor, Codex, Gemini CLI + more 3. code-review-graph build change one function, and it traces exactly what that touches... every caller, every dependent file, every test. your ai only reads what's affected, not the whole repo. and the savings are wild. a task that used to burn ~100,000 tokens (about a dollar) now runs closer to a penny.

if you use claude code, this will save you real money. the problem: every time you make an edit, your ai rereads the whole codebase to figure out what changed. tens of thousands of tokens, every turn, for context it already had. this repo fixes it. it’s called code review graph, and it just maps your entire codebase: every file, every function, every connection laid out so you can see the actual shape of your project how to set up (2 min): 1. pip install code-review-graph 2. code-review-graph install - auto-configures Claude Code, Cursor, Codex, Gemini CLI + more 3. code-review-graph build change one function, and it traces exactly what that touches... every caller, every dependent file, every test. your ai only reads what's affected, not the whole repo. and the savings are wild. a task that used to burn ~100,000 tokens (about a dollar) now runs closer to a penny.

Alvaro Cintas

55,795 次观看 • 2 天前

Robinhood just dropped a full guide on exactly how to use AI agents for autonomous trading. It breaks down how to use their new agentic MCP for financial research, live trading & more. Feed this webpage into Claude, and it will become your personal trading assistant.

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Miles Deutscher

64,246 次观看 • 14 天前

Your Claude Code setup is probably bloated and you don't even know it. Every MCP server you add loads its full tool list into your context window the second your session starts. Stack too many and you're burning context before you type a word. So the move was never "add more." It's "add the right ones." Anthropic dropped an official plugin that does exactly that. It scans your actual codebase and tells you what's worth adding across all five layers: → hooks → skills → MCP servers → subagents → slash commands React project? It suggests Playwright. Auth code? It flags a security reviewer. It's read-only. Touches nothing. Just hands you the blueprint. The plugin isn't the edge. Knowing it exists is. /plugin install claude-code-setup@claude-plugins-official Steal this before everyone does.

Your Claude Code setup is probably bloated and you don't even know it. Every MCP server you add loads its full tool list into your context window the second your session starts. Stack too many and you're burning context before you type a word. So the move was never "add more." It's "add the right ones." Anthropic dropped an official plugin that does exactly that. It scans your actual codebase and tells you what's worth adding across all five layers: → hooks → skills → MCP servers → subagents → slash commands React project? It suggests Playwright. Auth code? It flags a security reviewer. It's read-only. Touches nothing. Just hands you the blueprint. The plugin isn't the edge. Knowing it exists is. /plugin install claude-code-setup@claude-plugins-official Steal this before everyone does.

Evan Luthra

36,455 次观看 • 1 个月前

GITHUB JUST KILLED THE WORST PART OF VIBE CODING they shipped a free tool called Spec Kit and it already crossed 120,000 stars the fix is stupidly simple instead of tossing vague prompts at an agent and praying it doesn't wreck your project Spec Kit makes the AI write a full structured spec before it touches a single line of code it works through the problem first figures out what you want to build asks about the gaps lays out the project then it starts coding you get fewer insane bugs, cleaner output and results you can predict the flow looks like this: /constitution for your rules and standards /specify for what you want to build /clarify for the open questions before you start /plan for architecture and stack /tasks for the ordered work /implement to run it it plugs into Claude Code, Cursor, Copilot, Codex, Gemini CLI and 25+ other agents 120,000 stars, 10,000 forks, open source, shipped by GitHub itself learning to drive agents like this is most of what separates people getting hired as AI engineers from everyone still fighting their prompts

GITHUB JUST KILLED THE WORST PART OF VIBE CODING they shipped a free tool called Spec Kit and it already crossed 120,000 stars the fix is stupidly simple instead of tossing vague prompts at an agent and praying it doesn't wreck your project Spec Kit makes the AI write a full structured spec before it touches a single line of code it works through the problem first figures out what you want to build asks about the gaps lays out the project then it starts coding you get fewer insane bugs, cleaner output and results you can predict the flow looks like this: /constitution for your rules and standards /specify for what you want to build /clarify for the open questions before you start /plan for architecture and stack /tasks for the ordered work /implement to run it it plugs into Claude Code, Cursor, Copilot, Codex, Gemini CLI and 25+ other agents 120,000 stars, 10,000 forks, open source, shipped by GitHub itself learning to drive agents like this is most of what separates people getting hired as AI engineers from everyone still fighting their prompts

Atlas

499,540 次观看 • 22 天前

Your enterprise content should power every AI tool and agent you use. With the Box MCP server, Box acts as a secure, governed bridge, so teams can search, retrieve, analyze, and act on Box content directly inside the tools they already use. No one-off integrations. Use it to: 🔹Ask questions over files in Anthropic Claude + Mistral AI Le Chat 🔹Ground designs in Figma or @ mention Box agents in Atlassian Jira 🔹Pull content into GitHub Copilot, Cursor + Claude Code 🔹Build agents with LangChain LangSmith Agent Builder + OpenAI Agent Builder 🔹Automate work in Claude Cowork + Amazon Web Services Quick Suite 🔹Enforce access + audit trails with Runlayer Secure. Standardized. Built for real work →

Your enterprise content should power every AI tool and agent you use. With the Box MCP server, Box acts as a secure, governed bridge, so teams can search, retrieve, analyze, and act on Box content directly inside the tools they already use. No one-off integrations. Use it to: 🔹Ask questions over files in Anthropic Claude + Mistral AI Le Chat 🔹Ground designs in Figma or @ mention Box agents in Atlassian Jira 🔹Pull content into GitHub Copilot, Cursor + Claude Code 🔹Build agents with LangChain LangSmith Agent Builder + OpenAI Agent Builder 🔹Automate work in Claude Cowork + Amazon Web Services Quick Suite 🔹Enforce access + audit trails with Runlayer Secure. Standardized. Built for real work →

Box

481,535 次观看 • 5 个月前

TrendTrack API & MCP are live Connect it to Claude, GPT, Gemini or any tool you already use. What you can pull : Winning ads before they saturate Scaling stores before everyone spots them Email campaigns from your competitors Hooks that are converting right now Everything you see on TrendTrack can now be extracted and processed by AI. How to use it ? API → integrate TrendTrack data directly into your tools, dashboards, or custom apps MCP → connect TrendTrack to any LLM. Claude, GPT, Gemini, Perplexity. Ask a question, it pulls the data itself, responds in real time Start building now →

TrendTrack API & MCP are live Connect it to Claude, GPT, Gemini or any tool you already use. What you can pull : Winning ads before they saturate Scaling stores before everyone spots them Email campaigns from your competitors Hooks that are converting right now Everything you see on TrendTrack can now be extracted and processed by AI. How to use it ? API → integrate TrendTrack data directly into your tools, dashboards, or custom apps MCP → connect TrendTrack to any LLM. Claude, GPT, Gemini, Perplexity. Ask a question, it pulls the data itself, responds in real time Start building now →

Trendtrack

177,020 次观看 • 3 个月前

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.

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

202,798 次观看 • 8 天前

MARCUS CHEN STACKED 30 MAC MINIS INTO AN AI SERVER FARM. ONE $599 MAC MINI REPLACES YOUR $200/MONTH CLAUDE CODE BILL WITH $3 IN ELECTRICITY two months ago a developer posted his claude code bill on reddit. $170 in 10 days. someone replied "i bought a mac mini m4. haven't paid anthropic since." apple stores ran out of mac minis the same week the m4 chip has 120 gb/s memory bandwidth and unified memory architecture. cpu and gpu share one pool so the model loads once and both read from it. a $599 mac mini runs ai faster than a $1,500 windows pc with a discrete gpu since january 2026 ollama supports the anthropic messages api format. claude code connects directly to your local mac mini with one environment variable. same interface, zero api costs, $0 per request a heavy developer pays $459 a month across claude code max, chatgpt pro, gemini, cursor and copilot. that's $5,508 a year. the mac mini pays off in 3 months and runs on $3 in electricity after that uber rolled out claude code to 5,000 engineers and burned through their $3.4 billion 2026 ai budget in 4 months. the people who own the hardware in 2026 are going to look very far ahead in 2028 bookmark this and read the article below

MARCUS CHEN STACKED 30 MAC MINIS INTO AN AI SERVER FARM. ONE $599 MAC MINI REPLACES YOUR $200/MONTH CLAUDE CODE BILL WITH $3 IN ELECTRICITY two months ago a developer posted his claude code bill on reddit. $170 in 10 days. someone replied "i bought a mac mini m4. haven't paid anthropic since." apple stores ran out of mac minis the same week the m4 chip has 120 gb/s memory bandwidth and unified memory architecture. cpu and gpu share one pool so the model loads once and both read from it. a $599 mac mini runs ai faster than a $1,500 windows pc with a discrete gpu since january 2026 ollama supports the anthropic messages api format. claude code connects directly to your local mac mini with one environment variable. same interface, zero api costs, $0 per request a heavy developer pays $459 a month across claude code max, chatgpt pro, gemini, cursor and copilot. that's $5,508 a year. the mac mini pays off in 3 months and runs on $3 in electricity after that uber rolled out claude code to 5,000 engineers and burned through their $3.4 billion 2026 ai budget in 4 months. the people who own the hardware in 2026 are going to look very far ahead in 2028 bookmark this and read the article below

starmex

357,349 次观看 • 2 个月前

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.

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.

Akshay 🚀

257,053 次观看 • 1 个月前

Your PM and Developer roll their eyes when they see interfaces like that. No more because now you can make it without code. By yourself. In Rive Developers can finally focus on development, not recreating your design again. You finished it, and it’s ready for testing and deployment on real products. Here is my Shutter speed interface for the camera.

Your PM and Developer roll their eyes when they see interfaces like that. No more because now you can make it without code. By yourself. In Rive Developers can finally focus on development, not recreating your design again. You finished it, and it’s ready for testing and deployment on real products. Here is my Shutter speed interface for the camera.

Dmitry Novikov 🇺🇦

41,785 次观看 • 1 年前

Utilizing a massive library of high quality, free UGC avatars for your three.js project is way less difficult than you think. No animation baking required, can swap out any Mixamo animations in real time. Step 1. Download an avatar on VRoid Hub Step 2. Download a skinless Mixamo animation from Step 3. Paste this into claude code / codex / cursor chat Now you have avatars, animations and a function to apply animations to your avatars whenever you desire. Source code from the video is here:

saori

14,962 次观看 • 5 个月前

Qwen 3.7-max beats Opus 4.7 and GPT-5.5 We tested three frontier models on a real agentic task: write a Tetris bot that plays the game and trains itself. Each model could read its own code, run benchmarks, and rewrite itself across 10 iterations. Then we compared the final bots head to head. Qwen 3.7-Max: training cost $1.32, bot improvement +56% Claude Opus 4.7: training cost $12.15, bot improvement +28% GPT-5.5: training cost $2.85, bot improvement +7% Qwen won on every dimension - biggest jump, 9× cheaper than Claude, 2× cheaper than GPT. Long agentic loops is where Qwen Max actually delivers.

Qwen 3.7-max beats Opus 4.7 and GPT-5.5 We tested three frontier models on a real agentic task: write a Tetris bot that plays the game and trains itself. Each model could read its own code, run benchmarks, and rewrite itself across 10 iterations. Then we compared the final bots head to head. Qwen 3.7-Max: training cost $1.32, bot improvement +56% Claude Opus 4.7: training cost $12.15, bot improvement +28% GPT-5.5: training cost $2.85, bot improvement +7% Qwen won on every dimension - biggest jump, 9× cheaper than Claude, 2× cheaper than GPT. Long agentic loops is where Qwen Max actually delivers.

atomic.chat

867,300 次观看 • 2 个月前

🚨Anthropic’s research paper proves AI coding tools are secretly making developers worse. every engineering leader bragging about AI adoption should read this twice. > 17% lower scores learning new libraries with AI. sub 40% when AI wrote everything. zero measurable. you’re measuring output. nobody is measuring comprehension loss. different problems on different timelines. one shows up on dashboards. the other shows up at 2am when prod breaks and nobody on your team can explain what the code does. “10x productivity” is technical debt with better marketing.

🚨Anthropic’s research paper proves AI coding tools are secretly making developers worse. every engineering leader bragging about AI adoption should read this twice. > 17% lower scores learning new libraries with AI. sub 40% when AI wrote everything. zero measurable. you’re measuring output. nobody is measuring comprehension loss. different problems on different timelines. one shows up on dashboards. the other shows up at 2am when prod breaks and nobody on your team can explain what the code does. “10x productivity” is technical debt with better marketing.

sui ☄️

95,564 次观看 • 4 个月前