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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...

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Atlas

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501,006 次观看 • 1 个月前 •via X (Twitter)

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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.
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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

200,402 次观看 • 2 个月前

🤯 Andrej Karpathy's coding rules have officially broken the internet. [BOOKMARK THIS NOW] 144,000 stars. #1 on GitHub for 28 days straight. But 99% of people still haven't heard of them. Here's what a CLAUDE.md ACTUALLY is, and why it quietly fixed the biggest problem with AI coding: It's a text file Claude reads before it touches your code. Standing instructions. It never forgets them. The 4 rules that went viral: - Think before coding. State assumptions. Ask when unsure. Never guess. - Simplicity first. Minimum code that solves the problem. Nothing extra. - Surgical changes. Touch only what was asked. Every line traces back to the request. - Goal-driven. Turn vague instructions into a test that proves it works, before writing code. The dev who built it says it took his accuracy from ~65% to ~94%. The rules aren't the interesting part though. The interesting part is that almost nobody knows you can do this at all.
0:15

🤯 Andrej Karpathy's coding rules have officially broken the internet. [BOOKMARK THIS NOW] 144,000 stars. #1 on GitHub for 28 days straight. But 99% of people still haven't heard of them. Here's what a CLAUDE.md ACTUALLY is, and why it quietly fixed the biggest problem with AI coding: It's a text file Claude reads before it touches your code. Standing instructions. It never forgets them. The 4 rules that went viral: - Think before coding. State assumptions. Ask when unsure. Never guess. - Simplicity first. Minimum code that solves the problem. Nothing extra. - Surgical changes. Touch only what was asked. Every line traces back to the request. - Goal-driven. Turn vague instructions into a test that proves it works, before writing code. The dev who built it says it took his accuracy from ~65% to ~94%. The rules aren't the interesting part though. The interesting part is that almost nobody knows you can do this at all.

Coin Bureau

59,242 次观看 • 2 个月前

Simplicity is at the heart of great software. This is one of the reasons why Claude Code has been sticky for me. As a builder, I love planning and brainstorming, and this is now a key focus of Claude Code. I use Shift + Tab a lot to cycle between brainstorming, planning, and execution. This functionality provides the appropriate interface for me to either be very involved or less involved as I please. This works particularly well when building out new and complex features or entire new projects. This saves a huge amount of time. It allows me to tune Claude Code to execute and build more effectively. It also builds a loop of trust, and I often (surprisingly) find Claude Code asking for clarifications when it's confused. Coding agents don't normally do that. I have shared before on the power of brainstorming with AI for longer times. Try it and you will not be disappointed. Vibe coding is fun, but pair it with intentional development cycles, and you watch how far you can take a project with coding agents today.
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Simplicity is at the heart of great software. This is one of the reasons why Claude Code has been sticky for me. As a builder, I love planning and brainstorming, and this is now a key focus of Claude Code. I use Shift + Tab a lot to cycle between brainstorming, planning, and execution. This functionality provides the appropriate interface for me to either be very involved or less involved as I please. This works particularly well when building out new and complex features or entire new projects. This saves a huge amount of time. It allows me to tune Claude Code to execute and build more effectively. It also builds a loop of trust, and I often (surprisingly) find Claude Code asking for clarifications when it's confused. Coding agents don't normally do that. I have shared before on the power of brainstorming with AI for longer times. Try it and you will not be disappointed. Vibe coding is fun, but pair it with intentional development cycles, and you watch how far you can take a project with coding agents today.

elvis

81,765 次观看 • 9 个月前

MOONSHOT JUST CLONED CLAUDE CODE AND MADE IT FREE. It's called Kimi Code CLI. Open source, MIT license, maintained by the lab that shipped K3 yesterday. And it does things Claude Code doesn't: → drop a screen recording into the chat as input → built-in coder, explore, and plan subagents, each in its own context → plan mode before it touches a single file → MCP servers configured by the agent itself via /mcp-config → plugs into Zed, JetBrains, and VS Code → one binary, no Node setup, starts in milliseconds The CLI costs $0. K3 behind it starts at $3 per million tokens. Grab it for free👇
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MOONSHOT JUST CLONED CLAUDE CODE AND MADE IT FREE. It's called Kimi Code CLI. Open source, MIT license, maintained by the lab that shipped K3 yesterday. And it does things Claude Code doesn't: → drop a screen recording into the chat as input → built-in coder, explore, and plan subagents, each in its own context → plan mode before it touches a single file → MCP servers configured by the agent itself via /mcp-config → plugs into Zed, JetBrains, and VS Code → one binary, no Node setup, starts in milliseconds The CLI costs $0. K3 behind it starts at $3 per million tokens. Grab it for free👇

darkzodchi

404,945 次观看 • 1 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.
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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

74,076 次观看 • 16 天前

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.
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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 🚀

257,831 次观看 • 1 个月前

My project has 39,205 lines of code, and Cursor can't answer questions about it. Cursor's context seems to be capped at around 10,000 tokens. Unfortunately, this is not enough for any decent-sized project. If you have a large codebase, check out Augment Code. This thing is faaaast! I'm currently using their Visual Studio Code plugin, but you can also use them on JetBrains, Neovim, and even Vim. (I'm a Neovim fan, but Copilot's implementation for Neovim is nowhere as good as Augment Code.) Augment Code was gracious enough to sponsor this post. After you install their extension and run it for the first time, it will index your entire codebase. This is why it can answer questions as fast as it does, regardless of the size of your codebase. Augment Code supports chat and completions like every other AI coding assistant, but its killer feature is "Next Edit." When you make a change, two things happen: 1. The model analyzes the change to determine the ripple effects across your *entire* codebase. 2. The model suggests everything you need to update to ensure everything works correctly. This is pretty wild!
0:19

My project has 39,205 lines of code, and Cursor can't answer questions about it. Cursor's context seems to be capped at around 10,000 tokens. Unfortunately, this is not enough for any decent-sized project. If you have a large codebase, check out Augment Code. This thing is faaaast! I'm currently using their Visual Studio Code plugin, but you can also use them on JetBrains, Neovim, and even Vim. (I'm a Neovim fan, but Copilot's implementation for Neovim is nowhere as good as Augment Code.) Augment Code was gracious enough to sponsor this post. After you install their extension and run it for the first time, it will index your entire codebase. This is why it can answer questions as fast as it does, regardless of the size of your codebase. Augment Code supports chat and completions like every other AI coding assistant, but its killer feature is "Next Edit." When you make a change, two things happen: 1. The model analyzes the change to determine the ripple effects across your *entire* codebase. 2. The model suggests everything you need to update to ensure everything works correctly. This is pretty wild!

Santiago

247,833 次观看 • 1 年前

been getting a lot of qs on how i vibe coded this demo. and the answer is: stop acting like a coder and start acting like a PM! my exact playbook👇 1. logic first ignore the UI/graphics. focus strictly on how it works. do a massive brain dump of every requirement into a plain txt file and drop it in your root folder. make it exhaustively detailed. 2. the stack · Nano Banana 2 (tiles/textures/3D refs) · Tripo AI (turning the 2D pics into actual 3D models) · Cursor + Opus 4.6 (doing the heavy lifting) · Netlify (deploy) 3. hire the AI PM feed your brain dump to an LLM. tell it to write a technical spec sheet like a senior PM would. drop that in the root too. 4. the scrum master have it break the entire project down into a markdown checklist by phases. notice we still haven't touched the codebase yet. that's the point. 5. let it cook go to your agent in Cursor and literally just prompt "Start Phase 1". test locally. bugs? tell it to fix. works? "Start Phase 2". loop this until you're done. Now you can focus on visuals, refine them with the agent. And yes, it can deploy it for you too if you give it perms. stop rushing to the editor. architect the idea and let the AI sweat the code. Open sourced repo in the qt post below
0:15

been getting a lot of qs on how i vibe coded this demo. and the answer is: stop acting like a coder and start acting like a PM! my exact playbook👇 1. logic first ignore the UI/graphics. focus strictly on how it works. do a massive brain dump of every requirement into a plain txt file and drop it in your root folder. make it exhaustively detailed. 2. the stack · Nano Banana 2 (tiles/textures/3D refs) · Tripo AI (turning the 2D pics into actual 3D models) · Cursor + Opus 4.6 (doing the heavy lifting) · Netlify (deploy) 3. hire the AI PM feed your brain dump to an LLM. tell it to write a technical spec sheet like a senior PM would. drop that in the root too. 4. the scrum master have it break the entire project down into a markdown checklist by phases. notice we still haven't touched the codebase yet. that's the point. 5. let it cook go to your agent in Cursor and literally just prompt "Start Phase 1". test locally. bugs? tell it to fix. works? "Start Phase 2". loop this until you're done. Now you can focus on visuals, refine them with the agent. And yes, it can deploy it for you too if you give it perms. stop rushing to the editor. architect the idea and let the AI sweat the code. Open sourced repo in the qt post below

TechHalla

70,286 次观看 • 5 个月前

🚨 JUST IN: CHINA just released an AI EMPLOYEE that works 24X7 on its own. 100% OPEN SOURCE. It researches, codes, builds websites, creates slide decks, and generates videos. All by itself. All on your computer. It's called DeerFlow. You give it a task. It makes a plan, spins up its own team of sub-agents, and gets to work. You come back and there's a finished deliverable waiting. Not a draft. Not a summary. The actual thing. Not a chatbot. Not a research assistant. An AI with its own computer that works while you sleep. Here's what it does on its own: → Spawns multiple sub-agents in parallel, each tackling a different piece of your task, then combines everything into one finished output → Writes real code, runs it, reads the results, and fixes its own mistakes without asking you once → Builds slide decks, websites, full research reports, and data dashboards from scratch → Remembers you across sessions. Your writing style. Your tech stack. Your preferences. Gets better every time. → Reads files you upload, works with them inside its own filesystem, hands you clean finished outputs → Searches the web, runs commands, calls any tool you plug in Here's how it thinks: You give one instruction. The lead agent makes a plan. Sub-agents fan out and work in parallel. Results come back. Everything gets synthesized. You get a deliverable. A single research task might split into a dozen sub-agents, each exploring a different angle, then converge into one finished website with generated visuals. Here's the wildest part: DeerFlow 2.0 launched on February 28th 2026 and hit number 1 on all of GitHub Trending the same day. Version 2.0 was a complete rewrite. Zero shared code with version 1. Because users kept using it for things the team never intended. Data pipelines. Dashboards. Entire content workflows. The community told them what it needed to become. So they burned it down and rebuilt it. 22.7K GitHub stars. 2.7K forks. Built by ByteDance 100% Open Source. MIT License.
0:17

🚨 JUST IN: CHINA just released an AI EMPLOYEE that works 24X7 on its own. 100% OPEN SOURCE. It researches, codes, builds websites, creates slide decks, and generates videos. All by itself. All on your computer. It's called DeerFlow. You give it a task. It makes a plan, spins up its own team of sub-agents, and gets to work. You come back and there's a finished deliverable waiting. Not a draft. Not a summary. The actual thing. Not a chatbot. Not a research assistant. An AI with its own computer that works while you sleep. Here's what it does on its own: → Spawns multiple sub-agents in parallel, each tackling a different piece of your task, then combines everything into one finished output → Writes real code, runs it, reads the results, and fixes its own mistakes without asking you once → Builds slide decks, websites, full research reports, and data dashboards from scratch → Remembers you across sessions. Your writing style. Your tech stack. Your preferences. Gets better every time. → Reads files you upload, works with them inside its own filesystem, hands you clean finished outputs → Searches the web, runs commands, calls any tool you plug in Here's how it thinks: You give one instruction. The lead agent makes a plan. Sub-agents fan out and work in parallel. Results come back. Everything gets synthesized. You get a deliverable. A single research task might split into a dozen sub-agents, each exploring a different angle, then converge into one finished website with generated visuals. Here's the wildest part: DeerFlow 2.0 launched on February 28th 2026 and hit number 1 on all of GitHub Trending the same day. Version 2.0 was a complete rewrite. Zero shared code with version 1. Because users kept using it for things the team never intended. Data pipelines. Dashboards. Entire content workflows. The community told them what it needed to become. So they burned it down and rebuilt it. 22.7K GitHub stars. 2.7K forks. Built by ByteDance 100% Open Source. MIT License.

Kanika

738,256 次观看 • 4 个月前

I genuinely don't understand why everyone isn't using this yet Andrej Karpathy, a co-founder of OpenAI, posted a simple idea that hit 16 million views: stop using AI to write code, use it to build a second brain. You point Claude Code at a folder, drop in any source, an article, a transcript, a PDF, and Claude reads it, links it, and files it into a living wiki of everything you know. It compounds like interest, the more you feed it, the smarter it gets. Here's the whole thing: > Install Obsidian, create a vault, open it in Claude Code > Paste Karpathy's wiki idea file and tell Claude to build it > Claude makes three folders: raw for sources, wiki for its pages, a CLAUDE.md that runs it > Drop any source into raw and say "ingest this" > Ask questions across everything, forever Five minutes to set up, and you never start from a blank chat again. Full step-by-step guide with Claude and Obsidian, link below. Bookmark this
0:16

I genuinely don't understand why everyone isn't using this yet Andrej Karpathy, a co-founder of OpenAI, posted a simple idea that hit 16 million views: stop using AI to write code, use it to build a second brain. You point Claude Code at a folder, drop in any source, an article, a transcript, a PDF, and Claude reads it, links it, and files it into a living wiki of everything you know. It compounds like interest, the more you feed it, the smarter it gets. Here's the whole thing: > Install Obsidian, create a vault, open it in Claude Code > Paste Karpathy's wiki idea file and tell Claude to build it > Claude makes three folders: raw for sources, wiki for its pages, a CLAUDE.md that runs it > Drop any source into raw and say "ingest this" > Ask questions across everything, forever Five minutes to set up, and you never start from a blank chat again. Full step-by-step guide with Claude and Obsidian, link below. Bookmark this

Ridark

6,951,078 次观看 • 1 个月前

I’m joining OpenAI Codex to work on the future of agentic development! At Cursor, I got to see the shift from autocomplete to agents. The next step isn’t a better IDE. It’s an Agent Development Environment (ADE): systems and tools for orchestrating agents, reasoning over their outputs, and making them autonomous enough to reliably complete ambitious work. After chatting with Alexander Embiricos and Tibo, it was clear that Codex is the best place to realize this vision. The team has consistently shipped SOTA models for agentic coding (check out gpt-5.3-codex) and I’m pumped for the future that the new Codex App points to. What I’m most excited about is the broader mission: accelerating the knowledge work economy. All agents are coding agents, and we’re already seeing Codex used across every job function within organizations. I’m extremely grateful for my time at Cursor, working with the incredible team, and I’m proud of what we built together. I’m excited to take an even bigger swing with Codex. If you’re curious to get a glimpse of where we are headed, download the Codex App! If you want to work on this mission, please apply or reach out - we are hiring across all functions! You can just build things.
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I’m joining OpenAI Codex to work on the future of agentic development! At Cursor, I got to see the shift from autocomplete to agents. The next step isn’t a better IDE. It’s an Agent Development Environment (ADE): systems and tools for orchestrating agents, reasoning over their outputs, and making them autonomous enough to reliably complete ambitious work. After chatting with Alexander Embiricos and Tibo, it was clear that Codex is the best place to realize this vision. The team has consistently shipped SOTA models for agentic coding (check out gpt-5.3-codex) and I’m pumped for the future that the new Codex App points to. What I’m most excited about is the broader mission: accelerating the knowledge work economy. All agents are coding agents, and we’re already seeing Codex used across every job function within organizations. I’m extremely grateful for my time at Cursor, working with the incredible team, and I’m proud of what we built together. I’m excited to take an even bigger swing with Codex. If you’re curious to get a glimpse of where we are headed, download the Codex App! If you want to work on this mission, please apply or reach out - we are hiring across all functions! You can just build things.

Rohan Varma

759,511 次观看 • 5 个月前

Code Interpreter in ChatGPT is incredible! Took me 5 mins to make this game. You can make your own game assets with any AI generator and then ask GPT-4 with Code Interpreter to write code. If you have any problems you can ask it to fix the errors. 1. Write this prompt: "write p5.js code for Asteroids where you control a spaceship with the mouse and shoot asteroids with the left click of the mouse. If your spaceship collides with an asteroid, you lose. If you shoot down all asteroids, you win! I want to use my own textures for the spaceship and for asteroids." 2. Go to Openprocessing website create and save sketch (you'll need to save it before uploading any texture files). Copy paste code from GPT-4 3. Generate texture files and remove backgrounds, for example in Clip Drop 4. Replace names of files with your filenames 5. Run the program 6. If something doesn't work ask GPT-4 to fix it (you can copy an error and paste in GPT-4) like you would ask a human programmer 7. To learn a bit of programming write these prompts to GPT-4: "Act as my programming teacher. Tell me an algorithm of Asteroids game in detail and make names of functions and explain what each of these functions will do. Don't write the code just yet." and then " Can you describe the algorithm overall for a 10-year-old child"
0:10

Code Interpreter in ChatGPT is incredible! Took me 5 mins to make this game. You can make your own game assets with any AI generator and then ask GPT-4 with Code Interpreter to write code. If you have any problems you can ask it to fix the errors. 1. Write this prompt: "write p5.js code for Asteroids where you control a spaceship with the mouse and shoot asteroids with the left click of the mouse. If your spaceship collides with an asteroid, you lose. If you shoot down all asteroids, you win! I want to use my own textures for the spaceship and for asteroids." 2. Go to Openprocessing website create and save sketch (you'll need to save it before uploading any texture files). Copy paste code from GPT-4 3. Generate texture files and remove backgrounds, for example in Clip Drop 4. Replace names of files with your filenames 5. Run the program 6. If something doesn't work ask GPT-4 to fix it (you can copy an error and paste in GPT-4) like you would ask a human programmer 7. To learn a bit of programming write these prompts to GPT-4: "Act as my programming teacher. Tell me an algorithm of Asteroids game in detail and make names of functions and explain what each of these functions will do. Don't write the code just yet." and then " Can you describe the algorithm overall for a 10-year-old child"

Kris Kashtanova

1,674,909 次观看 • 3 年前

if you see this — you made it through the first week of 2026 all the good that happened from the 1st - today, this candle will now amplify and snowball if you choose to claim it and the snowball will grow as the year unfolds tell the flame what you want most of all by the end of this year, go as in depth as you need— let it consume it and transmute it like snow upon your head as a halo, blessings are about to trickle down and build upon one another just for you to claim — comment SNOW OR a snow emoji or gif ❄️
0:10

if you see this — you made it through the first week of 2026 all the good that happened from the 1st - today, this candle will now amplify and snowball if you choose to claim it and the snowball will grow as the year unfolds tell the flame what you want most of all by the end of this year, go as in depth as you need— let it consume it and transmute it like snow upon your head as a halo, blessings are about to trickle down and build upon one another just for you to claim — comment SNOW OR a snow emoji or gif ❄️

✰FireWitch✰

14,637 次观看 • 7 个月前

300 AI AGENTS QUIETLY RUN 99% OF A REAL COMPANY. YOU HAVE NOT EVEN HEARD OF IT This is Raft. Not an AI chat. A workspace where the agents live in your channels and reply in the thread like coworkers. You give one goal. Then they take over. They plan. They build. They check each other. They argue. And they come back with it done, while you sleep. Every agent has its own name, role, and memory. It remembers the edits you made yesterday. A human costs one seat. An agent costs a tenth. Ten agents are cheaper than one hire. And here is the strange part. On June 19 an agent from a different company walked into Raft on its own and joined the team. One founder admits he can no longer always tell himself apart from his AI twin. 20,000 people are already inside. It is free to start. And you are still typing prompts one at a time. One person + Raft = an entire company that runs while you sleep. Save and watch the clip.
0:16

300 AI AGENTS QUIETLY RUN 99% OF A REAL COMPANY. YOU HAVE NOT EVEN HEARD OF IT This is Raft. Not an AI chat. A workspace where the agents live in your channels and reply in the thread like coworkers. You give one goal. Then they take over. They plan. They build. They check each other. They argue. And they come back with it done, while you sleep. Every agent has its own name, role, and memory. It remembers the edits you made yesterday. A human costs one seat. An agent costs a tenth. Ten agents are cheaper than one hire. And here is the strange part. On June 19 an agent from a different company walked into Raft on its own and joined the team. One founder admits he can no longer always tell himself apart from his AI twin. 20,000 people are already inside. It is free to start. And you are still typing prompts one at a time. One person + Raft = an entire company that runs while you sleep. Save and watch the clip.

shmidt

19,505 次观看 • 1 个月前

Helmor has been out as an open-source coding agent orchestrator for less than a week, and we’re already close to 1,000 GitHub stars!!! As a little gift, we shipped a new feature you’re going to love. 👇 Stop copy-pasting GitHub links, Linear tickets, Slack threads, and random notes into prompts. We're tired of rebuilding context every time I ask an agent to do work. Contexts in Helmor is another step toward a local dev loop: browse, preview, inject context, and dispatch tasks without leaving the app. Before you start a task, Helmor should help you gather the right context first. Try the open-source Helmor — link in the comments. #Helmor
0:10

Helmor has been out as an open-source coding agent orchestrator for less than a week, and we’re already close to 1,000 GitHub stars!!! As a little gift, we shipped a new feature you’re going to love. 👇 Stop copy-pasting GitHub links, Linear tickets, Slack threads, and random notes into prompts. We're tired of rebuilding context every time I ask an agent to do work. Contexts in Helmor is another step toward a local dev loop: browse, preview, inject context, and dispatch tasks without leaving the app. Before you start a task, Helmor should help you gather the right context first. Try the open-source Helmor — link in the comments. #Helmor

Caspian 東澔

10,814 次观看 • 3 个月前

The most underrated skill in the world right now is prompting…if you can speak the language of AI, AI will give you what you want. That’s why I had my internal AI transformation hire create a cracked prompting tool for our company to use internally. It essentially helps you create the perfect prompt. I love it - and that’s why I want to open source it.
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The most underrated skill in the world right now is prompting…if you can speak the language of AI, AI will give you what you want. That’s why I had my internal AI transformation hire create a cracked prompting tool for our company to use internally. It essentially helps you create the perfect prompt. I love it - and that’s why I want to open source it.

Codie Sanchez

38,700 次观看 • 5 个月前

It's here. The #1 question we get is "How does Lumen actually value code?" Now you can see for yourself. I've just shipped an interactive demo of our AI Quality Engine. - Select a sample project. - Watch our AI analyze its quality, complexity, and architecture. - Get the full, transparent valuation report. We're making our most advanced tech simple to understand & this is what transparency looks like. Try it now on
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It's here. The #1 question we get is "How does Lumen actually value code?" Now you can see for yourself. I've just shipped an interactive demo of our AI Quality Engine. - Select a sample project. - Watch our AI analyze its quality, complexity, and architecture. - Get the full, transparent valuation report. We're making our most advanced tech simple to understand & this is what transparency looks like. Try it now on

Coralis Capital

25,637 次观看 • 9 个月前

Building an adaptive, safe, and actually real-time AI tutor is an insane project. It feels a bit like what I imagine building the first iPhone felt where there are 1,000 moving pieces, but if you get it right, you set the standard for the future. We’re excited to share more with the world about what we’re learning as we build.
0:18

Building an adaptive, safe, and actually real-time AI tutor is an insane project. It feels a bit like what I imagine building the first iPhone felt where there are 1,000 moving pieces, but if you get it right, you set the standard for the future. We’re excited to share more with the world about what we’re learning as we build.

Catalin Voss

42,563 次观看 • 1 个月前

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 🙂
0:18

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

533,676 次观看 • 2 个月前

Introducing Dexter 2.0 Open source. Built for financial research. Like Claude Code, but for stocks. What Dexter does: • plans tasks • runs on its own • validates its work • researches stocks It uses OSS tools like , with a fresh stack of typescript, react, ink, and bun. Fast to build. Easy to use. No coding required. My favorite project of the year.
0:13

Introducing Dexter 2.0 Open source. Built for financial research. Like Claude Code, but for stocks. What Dexter does: • plans tasks • runs on its own • validates its work • researches stocks It uses OSS tools like , with a fresh stack of typescript, react, ink, and bun. Fast to build. Easy to use. No coding required. My favorite project of the year.

Virat Singh

210,062 次观看 • 8 个月前