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🚀 Announcing Gemini CLI - your open-source AI agent for the terminal. Powered by Gemini 2.5 Pro. What's inside: 🧬 1M token context 🚦 60 req/min, 1K/day 🔐 Secure shell access 🧭 Web-grounded reasoning 🧰 Plugins + MCP 🗂️ Filesystem I/O 🧑‍🔧 Dev loop ready 📦 Open source -... show more

Addy Osmani

406,556 subscribers

169,838 views • 1 year ago •via X (Twitter)

Science & Technology News & Politics Education

Anya Rossi• Live Now

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11 Comments

Addy Osmani1 year ago

Learn more about Gemini CLI:

Mobile Scanner1 year ago

Scan any documents, convert images into text, PDF files, etc. 👍

swaraj ⚔️1 year ago

claude code just got some competition lol

Random Libertarian Tech Lead1 year ago

Does it train on user data, and if so, to what degree can the collection of data and training on it be opted out of?

Rezaul Arif1 year ago

@thorwebdev Amazing! Thanks mate

Addy Osmani1 year ago

@thorwebdev Sure thing! Hope it's helpful 🙏

Mehdi Ataei1 year ago

Is there a way to opt out of training on data?

Tommy D. Rossi1 year ago

best time ever to be a programmer

Fardeen1 year ago

Insane, thanks Addy!

Addy Osmani1 year ago

Hope it's useful!

• Can Yıldız1 year ago

Thanks for sharing this. How is the cost compared to Claude Code?

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Andrew Ng

142,010 views • 1 year ago

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Or Hiltch

1,002,139 views • 6 months ago

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Toha Khan

123,375 views • 5 months ago

10 free Google AI tools nobody talks about. while everyone's burning $20/mo on chatgpt and claude, google quietly shipped a stack worth $200+/mo. all free. all yours. — 1️⃣ NotebookLM — your second brain upload sources (PDFs, websites, audio, YouTube). it summarizes, builds mind maps, generates quizzes, drafts slide decks, even turns your notes into a podcast you can listen to on a walk. free tier: 100 notebooks, 50 sources each, 50 chats/day, 3 audio overviews/day. replaces: notion AI + perplexity + readwise — 2️⃣ Google AI Studio — the free gemini playground web playground for gemini 3 pro and flash with a free API key. generous limits. paste a 1M-token context window and watch it actually use it. faster than the openai playground and free where openai charges per token. replaces: openai playground + paid API credits — 3️⃣ Gemini CLI — google's open-source terminal agent apache 2.0 licensed. one command (npx @google/gemini-cli) and you've got an agent in your terminal that reads your codebase, runs shell commands, and ships PRs. drop-in claude code alternative. replaces: claude code ($20/mo by default) — 4️⃣ Jules — async coding agent assign jules a github issue. it spins up a cloud VM, clones your repo, writes the plan, makes the changes, opens a PR. free tier: 15 tasks/day, 3 concurrent, runs on gemini flash. replaces: devin ($20/mo+) + cursor agent 5️⃣ Stitch — text → UI → code google's free figma killer. describe an interface, get production-ready HTML/CSS/Tailwind + figma export. march 2026 update added voice canvas, infinite canvas, and MCP integration with cursor. 350 standard + 200 experimental generations/month free. replaces: galileo AI + early-stage figma work — 6️⃣ Gemma 4 — open-weight LLM google's flagship open model. apache 2.0. 2B, 4B, 26B-MoE, and 31B variants. 256K context. runs on ollama with one command. quantized versions run on a 4090 or beefy laptop. replaces: paying for hosted LLM inference — 7️⃣ Illuminate — papers → podcasts paste an arxiv preprint link. illuminate turns dense research papers into a 6-8 min conversation between two AI hosts breaking it down. perfect for commute reading you can't do at a desk. note: still in waitlist for some regions. replaces: snipd + manual research reading — 8️⃣ Learn About (LearnLM) — adaptive AI tutor drop in any topic you're stuck on. highlight a word, click "go deeper," and the interface adapts in real time to your comprehension level. visual explanations, follow-up questions, the works. replaces: paid tutoring on niche topics — 9️⃣ Google Labs FX (ImageFX + Flow + MusicFX) — free imagen, veo, musicLM google labs creative suite. text-to-image (imagen 4), text-to-video (veo via Flow), text-to-music (musicLM). free tier: limited daily generations. the heavy veo 3.1 features are paid (AI Pro $19.99/mo). still worth using for image and music — those stay free. replaces: midjourney + suno (free tier only — runway-level video gen is paid) — 🔟 Google Colab — free GPU notebooks free T4 GPU + 12GB RAM in a browser tab. enough to fine-tune small models, run stable diffusion, prototype agents. the launching pad for half the ML projects on github. replaces: paid cloud GPU rentals — a quick honest note: these tools aren't 1:1 better than the paid versions they replace. but they're decent enough to get most things done — especially if you're not a heavy user or you've got little funds to play with. i've put all 10 in a public github repo (link in comments). follow + turn on post notifications for more useful posts like this 🔔

10 free Google AI tools nobody talks about. while everyone's burning $20/mo on chatgpt and claude, google quietly shipped a stack worth $200+/mo. all free. all yours. — 1️⃣ NotebookLM — your second brain upload sources (PDFs, websites, audio, YouTube). it summarizes, builds mind maps, generates quizzes, drafts slide decks, even turns your notes into a podcast you can listen to on a walk. free tier: 100 notebooks, 50 sources each, 50 chats/day, 3 audio overviews/day. replaces: notion AI + perplexity + readwise — 2️⃣ Google AI Studio — the free gemini playground web playground for gemini 3 pro and flash with a free API key. generous limits. paste a 1M-token context window and watch it actually use it. faster than the openai playground and free where openai charges per token. replaces: openai playground + paid API credits — 3️⃣ Gemini CLI — google's open-source terminal agent apache 2.0 licensed. one command (npx @google/gemini-cli) and you've got an agent in your terminal that reads your codebase, runs shell commands, and ships PRs. drop-in claude code alternative. replaces: claude code ($20/mo by default) — 4️⃣ Jules — async coding agent assign jules a github issue. it spins up a cloud VM, clones your repo, writes the plan, makes the changes, opens a PR. free tier: 15 tasks/day, 3 concurrent, runs on gemini flash. replaces: devin ($20/mo+) + cursor agent 5️⃣ Stitch — text → UI → code google's free figma killer. describe an interface, get production-ready HTML/CSS/Tailwind + figma export. march 2026 update added voice canvas, infinite canvas, and MCP integration with cursor. 350 standard + 200 experimental generations/month free. replaces: galileo AI + early-stage figma work — 6️⃣ Gemma 4 — open-weight LLM google's flagship open model. apache 2.0. 2B, 4B, 26B-MoE, and 31B variants. 256K context. runs on ollama with one command. quantized versions run on a 4090 or beefy laptop. replaces: paying for hosted LLM inference — 7️⃣ Illuminate — papers → podcasts paste an arxiv preprint link. illuminate turns dense research papers into a 6-8 min conversation between two AI hosts breaking it down. perfect for commute reading you can't do at a desk. note: still in waitlist for some regions. replaces: snipd + manual research reading — 8️⃣ Learn About (LearnLM) — adaptive AI tutor drop in any topic you're stuck on. highlight a word, click "go deeper," and the interface adapts in real time to your comprehension level. visual explanations, follow-up questions, the works. replaces: paid tutoring on niche topics — 9️⃣ Google Labs FX (ImageFX + Flow + MusicFX) — free imagen, veo, musicLM google labs creative suite. text-to-image (imagen 4), text-to-video (veo via Flow), text-to-music (musicLM). free tier: limited daily generations. the heavy veo 3.1 features are paid (AI Pro $19.99/mo). still worth using for image and music — those stay free. replaces: midjourney + suno (free tier only — runway-level video gen is paid) — 🔟 Google Colab — free GPU notebooks free T4 GPU + 12GB RAM in a browser tab. enough to fine-tune small models, run stable diffusion, prototype agents. the launching pad for half the ML projects on github. replaces: paid cloud GPU rentals — a quick honest note: these tools aren't 1:1 better than the paid versions they replace. but they're decent enough to get most things done — especially if you're not a heavy user or you've got little funds to play with. i've put all 10 in a public github repo (link in comments). follow + turn on post notifications for more useful posts like this 🔔

m0h

11,673 views • 2 months ago

Hermes Agent just made API keys feel outdated. And the feature nobody is watching might be the biggest shift. The Big Upgrade: → Run `hermes proxy` → Create a local OpenAI-compatible endpoint → Connect tools like Aider, Continue, and Codex CLI → Use Claude Pro, ChatGPT Pro, or SuperGrok through your existing subscriptions Why This Matters: ✓ One setup instead of separate API keys ✓ Your AI subscriptions become real developer infrastructure ✓ Coding tools can route through Hermes instead of separate accounts ✓ Setup takes around 30 seconds once Hermes is installed The Wild Part: → Grok now supports a 1M token context window inside Hermes → You can load huge codebases, long research docs, or multi-hour sessions → The model can connect details across files instead of forgetting half the workflow And It Gets Better: ✓ GPT, Gemini, and Grok Vision can now use computer control ✓ Models get real image pixels instead of text summaries ✓ Browser automation is now 180x faster with persistent Chrome connections Hermes went from “cool open-source agent” to a real command center for AI workflows. The practical lesson: Stop thinking of AI subscriptions as chat apps. Start thinking of them as infrastructure.

Hermes Agent just made API keys feel outdated. And the feature nobody is watching might be the biggest shift. The Big Upgrade: → Run `hermes proxy` → Create a local OpenAI-compatible endpoint → Connect tools like Aider, Continue, and Codex CLI → Use Claude Pro, ChatGPT Pro, or SuperGrok through your existing subscriptions Why This Matters: ✓ One setup instead of separate API keys ✓ Your AI subscriptions become real developer infrastructure ✓ Coding tools can route through Hermes instead of separate accounts ✓ Setup takes around 30 seconds once Hermes is installed The Wild Part: → Grok now supports a 1M token context window inside Hermes → You can load huge codebases, long research docs, or multi-hour sessions → The model can connect details across files instead of forgetting half the workflow And It Gets Better: ✓ GPT, Gemini, and Grok Vision can now use computer control ✓ Models get real image pixels instead of text summaries ✓ Browser automation is now 180x faster with persistent Chrome connections Hermes went from “cool open-source agent” to a real command center for AI workflows. The practical lesson: Stop thinking of AI subscriptions as chat apps. Start thinking of them as infrastructure.

Julian Goldie SEO

11,094 views • 2 months ago

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Anthropic's in trouble, again. The entire Claude experience is now available at 1/6th the price. Kimi now does everything Claude does, powered by K2.6, a 1-trillion-parameter MoE model that activates only 32B parameters per token. It covers all three features Claude has (Chat, Code, and Cowork): 1) Kimi Chat runs in four modes - Instant for fast responses - Thinking for deep reasoning - Agent for multi-step execution - and Agent Swarm for parallel workloads. There's a 262K context window across all of them. 2) Kimi Code is the open-source CLI coding agent with K2.6 as the default backend. K2.6 ranked #1 on OpenRouter's programming leaderboard by weekly usage. 3) Kimi Agent is the Cowork equivalent. It generates: - full websites with database and auth - presentation decks (editable PPTX output) - spreadsheets with formulas and charts - word docs and structured research reports. On top of this, Kimi K2.6 is also trained to decompose tasks into up to 300 parallel sub-agents. This helps it retain coherence even across 4,000+ tool calls in a single run, with sessions sustaining up to 13 hours. On SWE-Bench Pro: - Kimi K2.6 → 58.6 - GPT-5.4 xhigh → 57.7 - Gemini 3.1 Pro → 54.2 - Claude Opus 4.6 → 53.4 Kimi K2.6 model is open weights and self-hostable on 4x H100s in INT4. Find the link to the HuggingFace model page in the replies!

Avi Chawla

109,818 views • 2 months ago

I work on Gemini CLI as my day job and I've shipped features, shaped the roadmap, and helped organize many things behind the scenes for the last eight months or so. I hope I've made a difference for so many of our users. I love my day job, but even after hours I can't help but tinker. I bet you folks noticed? So here is (yet another!) experimental project. This isn't a replacement for any real products, but I had fun building it to see what I can do my my own. So why did I make Gemma CLI? Because I couldn't help but think... 1. What if the CLI was built around local models from the ground up? 2. What if I could think about how to correct for Gemma tool calling quirks? 3. What if I added a token-per-second counter, or showed reasoning events for those times the model just seems stuck? etc... So here we are. This isn't even my first mini agent harness CLI project, I've built a few more, they are on my GitHub but abandoned. What's next for this? Not sure if I'll ever open source this or even keep it around honestly... but feel free to give my experiment a try with the amazing Gemma models. Very lightly tested, so use at your own risk :-D

I work on Gemini CLI as my day job and I've shipped features, shaped the roadmap, and helped organize many things behind the scenes for the last eight months or so. I hope I've made a difference for so many of our users. I love my day job, but even after hours I can't help but tinker. I bet you folks noticed? So here is (yet another!) experimental project. This isn't a replacement for any real products, but I had fun building it to see what I can do my my own. So why did I make Gemma CLI? Because I couldn't help but think... 1. What if the CLI was built around local models from the ground up? 2. What if I could think about how to correct for Gemma tool calling quirks? 3. What if I added a token-per-second counter, or showed reasoning events for those times the model just seems stuck? etc... So here we are. This isn't even my first mini agent harness CLI project, I've built a few more, they are on my GitHub but abandoned. What's next for this? Not sure if I'll ever open source this or even keep it around honestly... but feel free to give my experiment a try with the amazing Gemma models. Very lightly tested, so use at your own risk :-D

Dmitry Lyalin

17,197 views • 2 months ago

I have been testing DeepSeek-V4-Pro with the Pi coding agent. I am mindblown by how well it works out of the box. A few notes: I spent a few hours building an LLM wiki with an agent powered entirely by DeepSeek-V4-Pro on Fireworks AI inference. This is the first time I feel like there is an open-weight model that can reason at the level of Claude and Codex. And it does this in a cost-effective way with support for 1M context length. To be clear, I am using DeepSeek-V4-Pro inside of Pi without any special configuration. It works out of the box. It's exciting that there is a model that can just be plugged into a basic harness like Pi, and it just works. I've never seen that before. Most models require lots of configuration and setup. DeepSeek's DeepSeek-V4-Pro is clearly good at agentic coding (probably the best from the open-weight models), but the model is also great on knowledge-intensive tasks where reasoning matters. The agent pulled agentic engineering best practices from different company docs (Anthropic, OpenAI, Google, Stripe, Meta, Modal, DeepSeek, Mistral, Cohere), searched and digested Reddit and HN threads, summarized arxiv papers, and surfaced trending GitHub repos. Then it distilled everything into actionable tips across categories. I love the Wiki it built. The quality is really good. Here is a snapshot of what the wiki looks like: DeepSeek-V4-Pro handled the task without breaking stride. Multi-step research queries, code generation for scaffolding, context-heavy reasoning across disparate sources. For coding specifically, this is the first open-weight model that genuinely feels like a Codex or Claude Code experience. It compares in capability and actual multi-turn agentic work. What made the loop feel so responsive was Fireworks' inference speed (the fastest in the market) and the fact that they actually validate models at the systems level before shipping. No corrupted reasoning traces. Just fast, reliable iteration. The hybrid CSA and HCA attention design cuts KV cache to just 10% and inference FLOPs by nearly 4x at 1M-token context. This is what makes the agent loop actually fast and cheap enough to run in practice. For devs who've been watching open-weight models close the gap but haven't found one that actually delivers in practice, this is the closest I've seen. Try it here:

I have been testing DeepSeek-V4-Pro with the Pi coding agent. I am mindblown by how well it works out of the box. A few notes: I spent a few hours building an LLM wiki with an agent powered entirely by DeepSeek-V4-Pro on Fireworks AI inference. This is the first time I feel like there is an open-weight model that can reason at the level of Claude and Codex. And it does this in a cost-effective way with support for 1M context length. To be clear, I am using DeepSeek-V4-Pro inside of Pi without any special configuration. It works out of the box. It's exciting that there is a model that can just be plugged into a basic harness like Pi, and it just works. I've never seen that before. Most models require lots of configuration and setup. DeepSeek's DeepSeek-V4-Pro is clearly good at agentic coding (probably the best from the open-weight models), but the model is also great on knowledge-intensive tasks where reasoning matters. The agent pulled agentic engineering best practices from different company docs (Anthropic, OpenAI, Google, Stripe, Meta, Modal, DeepSeek, Mistral, Cohere), searched and digested Reddit and HN threads, summarized arxiv papers, and surfaced trending GitHub repos. Then it distilled everything into actionable tips across categories. I love the Wiki it built. The quality is really good. Here is a snapshot of what the wiki looks like: DeepSeek-V4-Pro handled the task without breaking stride. Multi-step research queries, code generation for scaffolding, context-heavy reasoning across disparate sources. For coding specifically, this is the first open-weight model that genuinely feels like a Codex or Claude Code experience. It compares in capability and actual multi-turn agentic work. What made the loop feel so responsive was Fireworks' inference speed (the fastest in the market) and the fact that they actually validate models at the systems level before shipping. No corrupted reasoning traces. Just fast, reliable iteration. The hybrid CSA and HCA attention design cuts KV cache to just 10% and inference FLOPs by nearly 4x at 1M-token context. This is what makes the agent loop actually fast and cheap enough to run in practice. For devs who've been watching open-weight models close the gap but haven't found one that actually delivers in practice, this is the closest I've seen. Try it here:

elvis

59,803 views • 2 months ago

🚨 Claude Code costs $200/month. GitHub Copilot costs $19/month. Jack Dorsey's company built a free alternative. 35,000 GitHub stars. It's called Goose. An open source AI agent built by Block that goes beyond code suggestions. It installs, executes, edits, and tests. With any LLM you choose. Not autocomplete. Not suggestions. A full autonomous agent that takes actions on your computer. No vendor lock-in. No monthly subscription. Bring your own model. Here's what Goose does: → Works with ANY LLM. Claude, GPT, Gemini, Llama, DeepSeek, Ollama. Your choice. → Reads and understands your entire codebase → Writes, edits, and refactors code across multiple files → Runs shell commands and installs dependencies → Executes and debugs your code automatically → Extensible through MCP. Connect it to any external tool. → Desktop app, CLI, and web interface. Pick your workflow. → Written in Rust. Fast. Lightweight. No bloat. Here's the wildest part: Block is a $40 billion company. They built Cash App, Square, and TIDAL. They use Goose internally. Then they open sourced the entire thing. This isn't a side project from a random developer. This is production-grade tooling from a company that processes billions in payments. Built for their own engineers. Given to everyone. Claude Code: $200/month. Locked to Claude. GitHub Copilot: $19/month. Locked to GitHub. Cursor: $20/month. Locked to their editor. Goose: Free. Any LLM. Any editor. Any workflow. Forever. 35.3K GitHub stars. 3.3K forks. 4,078 commits. Built by Block. 100% Open Source. Apache 2.0 License.

🚨 Claude Code costs $200/month. GitHub Copilot costs $19/month. Jack Dorsey's company built a free alternative. 35,000 GitHub stars. It's called Goose. An open source AI agent built by Block that goes beyond code suggestions. It installs, executes, edits, and tests. With any LLM you choose. Not autocomplete. Not suggestions. A full autonomous agent that takes actions on your computer. No vendor lock-in. No monthly subscription. Bring your own model. Here's what Goose does: → Works with ANY LLM. Claude, GPT, Gemini, Llama, DeepSeek, Ollama. Your choice. → Reads and understands your entire codebase → Writes, edits, and refactors code across multiple files → Runs shell commands and installs dependencies → Executes and debugs your code automatically → Extensible through MCP. Connect it to any external tool. → Desktop app, CLI, and web interface. Pick your workflow. → Written in Rust. Fast. Lightweight. No bloat. Here's the wildest part: Block is a $40 billion company. They built Cash App, Square, and TIDAL. They use Goose internally. Then they open sourced the entire thing. This isn't a side project from a random developer. This is production-grade tooling from a company that processes billions in payments. Built for their own engineers. Given to everyone. Claude Code: $200/month. Locked to Claude. GitHub Copilot: $19/month. Locked to GitHub. Cursor: $20/month. Locked to their editor. Goose: Free. Any LLM. Any editor. Any workflow. Forever. 35.3K GitHub stars. 3.3K forks. 4,078 commits. Built by Block. 100% Open Source. Apache 2.0 License.

Nav Toor

392,905 views • 3 months ago

Meet WebBrain: An Open-Source, Local-First AI Browser Agent That Reads Pages and Automates Tasks in Chrome and Firefox WebBrain lives inside your browser and can run entirely on your own local model — no cloud, no account, no data leaving your machine. Most "AI browser agents" are a chat box that pastes your page into someone else's server. That's not an agent that lives where you browse — and WebBrain draws a very clear line between the two. It's an open-source (MIT), local-first browser agent for Chrome and Firefox. It runs inside your existing authenticated session, on a model you pick — so with llama.cpp or Ollama, nothing leaves your machine. Here's what's actually interesting: → Two modes, cleanly separated. Ask reads the page (read-only, content scripts). Act clicks and types through the Chrome DevTools Protocol (chrome.debugger) — trusted input events that modern sites honor, reaching cross-origin iframes and shadow DOM. → UI-first by design. For anything that submits, sends, or buys, it drives the visible UI and refuses to hit REST/GraphQL endpoints directly. It starts read-only and asks before consequential actions. → Bring any model. llama.cpp, Ollama, LM Studio, vLLM — or OpenAI, Claude, Gemini, DeepSeek, Groq, OpenRouter. Recommended local: Qwen 3.6 35B (Qwen3.6-35B-A3B), which beat Gemma 4 on the project's screenshot benchmark. → Tuned for cost and privacy. Token-conscious screenshots, oldest-first context trimming, a dedicated vision model, 40+ tools (~20 in Compact mode). No telemetry. No accounts. Full analysis: GitHub Repo: Chrome Extension: Firefox Add-on: Portal:

Meet WebBrain: An Open-Source, Local-First AI Browser Agent That Reads Pages and Automates Tasks in Chrome and Firefox WebBrain lives inside your browser and can run entirely on your own local model — no cloud, no account, no data leaving your machine. Most "AI browser agents" are a chat box that pastes your page into someone else's server. That's not an agent that lives where you browse — and WebBrain draws a very clear line between the two. It's an open-source (MIT), local-first browser agent for Chrome and Firefox. It runs inside your existing authenticated session, on a model you pick — so with llama.cpp or Ollama, nothing leaves your machine. Here's what's actually interesting: → Two modes, cleanly separated. Ask reads the page (read-only, content scripts). Act clicks and types through the Chrome DevTools Protocol (chrome.debugger) — trusted input events that modern sites honor, reaching cross-origin iframes and shadow DOM. → UI-first by design. For anything that submits, sends, or buys, it drives the visible UI and refuses to hit REST/GraphQL endpoints directly. It starts read-only and asks before consequential actions. → Bring any model. llama.cpp, Ollama, LM Studio, vLLM — or OpenAI, Claude, Gemini, DeepSeek, Groq, OpenRouter. Recommended local: Qwen 3.6 35B (Qwen3.6-35B-A3B), which beat Gemma 4 on the project's screenshot benchmark. → Tuned for cost and privacy. Token-conscious screenshots, oldest-first context trimming, a dedicated vision model, 40+ tools (~20 in Compact mode). No telemetry. No accounts. Full analysis: GitHub Repo: Chrome Extension: Firefox Add-on: Portal:

Marktechpost AI

203,011 views • 23 days ago

Web scraping is a critical skill, and yet nobody talks about it. How do you think companies are training their Large Language Models? Where do you think the data comes from? But web scraping goes beyond all of that. Imagine giving an AI agent access to any public online data in real time! I like to call this "web-scrapping on demand", and I'm pretty sure it's going to unlock unlimited power for AI applications. I recorded a quick video to show you how you can do this using Apify. I've talked about them before, and they are collaborating with me on this post. They have one of the best open-source web scraping and browser automation libraries out there: But it gets much better than this! You can use MCP to connect your AI Agents and applications to the Apify platform and use any specialized actor on demand to scrape and process online data. In the video, I used Cursor to scrape LinkedIn posts with the words "Machine Learning" in real time. Worked like a charm with no code needed! Here is a link to the platform: Think about this: You can now feed your AI applications with any public data on demand! We aren't ready for what's coming.

Web scraping is a critical skill, and yet nobody talks about it. How do you think companies are training their Large Language Models? Where do you think the data comes from? But web scraping goes beyond all of that. Imagine giving an AI agent access to any public online data in real time! I like to call this "web-scrapping on demand", and I'm pretty sure it's going to unlock unlimited power for AI applications. I recorded a quick video to show you how you can do this using Apify. I've talked about them before, and they are collaborating with me on this post. They have one of the best open-source web scraping and browser automation libraries out there: But it gets much better than this! You can use MCP to connect your AI Agents and applications to the Apify platform and use any specialized actor on demand to scrape and process online data. In the video, I used Cursor to scrape LinkedIn posts with the words "Machine Learning" in real time. Worked like a charm with no code needed! Here is a link to the platform: Think about this: You can now feed your AI applications with any public data on demand! We aren't ready for what's coming.

Santiago

101,473 views • 1 year ago

i cancelled $2,000/month in trading subscriptions replaced every single one with open-source repos here's the full stack: 1. TradingView Pro ($30/mo) → lightweight-charts 14K stars. by TradingView themselves. 45KB. free 2. Bloomberg Terminal ($2,000/mo) → fredapi + Claude every macro dataset the Fed publishes. free API 3. backtest platform ($100/mo) → prediction-market-backtesting NautilusTrader fork with Polymarket + Kalshi adapters 4. real-time dashboard → polyrec terminal UI: Chainlink oracle, Binance feed, orderbook depth 70+ indicators. auto CSV logging. strategy backtester 5. bot framework (7 strategies) → Polymarket-Trading-Bot 53K lines TypeScript. arbitrage, momentum, market making, AI forecast, whale copy-trade, convergence 6. strategy reverse engineering → polybot execution + market data infrastructure. paper trading Kafka, ClickHouse, Grafana. full analytics pipeline 7. paper trading for AI agents → polymarket-paper-trader real order books. exact fee model. slippage tracking your Claude agent gets $10K paper money and trades 8. token savings → rtk CLI proxy. cuts Claude Code tokens by 60-90% Rust. single binary. 10 AI tools supported 9. Claude Code itself ($200/mo) → goose 35K stars. by Block (Jack Dorsey). Rust works with any LLM. full agent loop. free 10. wallet tracking + copy trading → Kreo track top Polymarket wallets. auto copy trades the only tool on this list i actually pay for because it makes more than it costs total before: ~$2,600/month total now: $0 + Kreo bookmark this. you'll need it

i cancelled $2,000/month in trading subscriptions replaced every single one with open-source repos here's the full stack: 1. TradingView Pro ($30/mo) → lightweight-charts 14K stars. by TradingView themselves. 45KB. free 2. Bloomberg Terminal ($2,000/mo) → fredapi + Claude every macro dataset the Fed publishes. free API 3. backtest platform ($100/mo) → prediction-market-backtesting NautilusTrader fork with Polymarket + Kalshi adapters 4. real-time dashboard → polyrec terminal UI: Chainlink oracle, Binance feed, orderbook depth 70+ indicators. auto CSV logging. strategy backtester 5. bot framework (7 strategies) → Polymarket-Trading-Bot 53K lines TypeScript. arbitrage, momentum, market making, AI forecast, whale copy-trade, convergence 6. strategy reverse engineering → polybot execution + market data infrastructure. paper trading Kafka, ClickHouse, Grafana. full analytics pipeline 7. paper trading for AI agents → polymarket-paper-trader real order books. exact fee model. slippage tracking your Claude agent gets $10K paper money and trades 8. token savings → rtk CLI proxy. cuts Claude Code tokens by 60-90% Rust. single binary. 10 AI tools supported 9. Claude Code itself ($200/mo) → goose 35K stars. by Block (Jack Dorsey). Rust works with any LLM. full agent loop. free 10. wallet tracking + copy trading → Kreo track top Polymarket wallets. auto copy trades the only tool on this list i actually pay for because it makes more than it costs total before: ~$2,600/month total now: $0 + Kreo bookmark this. you'll need it

self.dll

803,358 views • 3 months ago

Introducing Workshop: cloud + on-device agentic AI. And to celebrate, we're giving away $250k in Google Gemini AI credits. (details below). The future of AI work is neither cloud-based nor local. It's both. In Workshop Cloud, you can use agents powered by frontier models like Claude and/or open source models like Z.ai's GLM-5 to build internal tools, dashboards, and AI web apps. Or, breeze through tasks like managing your Google and Meta Ads. In Workshop Desktop, you can do all the same right on your computer, plus make desktop apps, mobile apps, and 3D creations. Our favorite part? You can power the full agent experience with local models like Qwen 3.5 family on your computer. Fully offline. 2026 is the year in which local models for agentic tasks will become viable for mainstream use. But the setup for tools like OpenClaw is like setting up Linux from scratch on your computer. Workshop Desktop is one-click to install on Windows, Mac, and Linux. It recommends which open source model you should use for your hardware and lets you download and run it right in the app. And its agent harness allows you to chat, create websites, build personal utilities, and analyze data. 100% offline. Or multitask with AI models in the cloud while running other agent threads locally. Start in Workshop Cloud when you want flexibility and speed. Download your project and continue in Workshop Desktop when you want local files, privacy, and/or better performance on large code bases. Publish from either. The agent tooling space is maturing and discerning users have come to expect a lot from their tools. We've packed Workshop with features to help you 10x your productivity. - Native support for skills - Autocompaction for seamless context management - Built-in AI for your apps - Dozens of connectors, like Google Drive, Big Query, and Supabase - dbt integration to ground your dashboards in your semantic layer - Native Github integration - Private app deployment - ... and more (+ we're shipping super fast) To access the free credit offer, RT this post and reply with "Workshop". Make sure you are following us so we can DM you the instructions to redeem. - First 100 to RT + comment get $500 in credits. - Everyone else gets up to $250 And thanks to our partners Modal, Google Gemini, and Z.ai!

Introducing Workshop: cloud + on-device agentic AI. And to celebrate, we're giving away $250k in Google Gemini AI credits. (details below). The future of AI work is neither cloud-based nor local. It's both. In Workshop Cloud, you can use agents powered by frontier models like Claude and/or open source models like Z.ai's GLM-5 to build internal tools, dashboards, and AI web apps. Or, breeze through tasks like managing your Google and Meta Ads. In Workshop Desktop, you can do all the same right on your computer, plus make desktop apps, mobile apps, and 3D creations. Our favorite part? You can power the full agent experience with local models like Qwen 3.5 family on your computer. Fully offline. 2026 is the year in which local models for agentic tasks will become viable for mainstream use. But the setup for tools like OpenClaw is like setting up Linux from scratch on your computer. Workshop Desktop is one-click to install on Windows, Mac, and Linux. It recommends which open source model you should use for your hardware and lets you download and run it right in the app. And its agent harness allows you to chat, create websites, build personal utilities, and analyze data. 100% offline. Or multitask with AI models in the cloud while running other agent threads locally. Start in Workshop Cloud when you want flexibility and speed. Download your project and continue in Workshop Desktop when you want local files, privacy, and/or better performance on large code bases. Publish from either. The agent tooling space is maturing and discerning users have come to expect a lot from their tools. We've packed Workshop with features to help you 10x your productivity. - Native support for skills - Autocompaction for seamless context management - Built-in AI for your apps - Dozens of connectors, like Google Drive, Big Query, and Supabase - dbt integration to ground your dashboards in your semantic layer - Native Github integration - Private app deployment - ... and more (+ we're shipping super fast) To access the free credit offer, RT this post and reply with "Workshop". Make sure you are following us so we can DM you the instructions to redeem. - First 100 to RT + comment get $500 in credits. - Everyone else gets up to $250 And thanks to our partners Modal, Google Gemini, and Z.ai!

Workshop AI

28,456 views • 4 months ago

ChatGPT 5.5 is cooked. Claude Opus 4.7 is cooked. Every $420/mo SaaS AI just got an open-source assassin. Mind blown: an open-source desktop AI just hit #7 trending overnight, runs 100% on your laptop, ships with 100+ native integrations, and is quietly killing the entire ChatGPT-subscription era. Introducing OpenHuman by tinyhumansai -> your Personal AI super intelligence. Private. Simple. Powerful. Two weeks ago they quietly dropped it on GitHub. Today: 300+ stars, 100+ daily paying users, 1,129 commits, zero marketing budget. > What is OpenHuman? A native desktop agent (macOS, Windows, Linux) that lives on YOUR machine instead of feeding your data back to OpenAI. Download the app, sign in once, and the agent harness gives you 100+ native connectors out of the box: Gmail, Slack, Notion, GitHub, Reddit, Instagram, Calendar, Drive, Telegram, Discord, and dozens more. One click each. From that moment it builds an encrypted, on-device knowledge base of your entire digital life. No terminal. No Python envs. No API keys. No CLI. > What the agent actually does: Steven, the creator, just dropped a Loom showing real prompts: - "Send Mark a joke" -> drafts in your voice and ships it. - "List my top 5 emails today" -> surfaces what matters from a flooded inbox. - "Summarize that thread and email it to the team" -> done in 3 seconds. One prompt --> multiple connected tools --> end-to-end execution. No tab-switching. > What's actually inside: - Screen intelligence -> the agent SEES what's on your screen and feeds it into your local context. - Memory-aware keyboard autocomplete -> system-wide, in YOUR voice, trained on YOUR past replies. Gmail Smart Compose for your entire OS. - Local knowledge base -> every email, message, and note parsed, embedded, encrypted, on YOUR device. Day 30 it knows you better than your therapist. - 75% Rust core -> memory-safe, brutally fast, runs local AI directly on your machine. > The "but wait" moment: OpenClaw and Hermes Agent are excellent. But they live in the terminal. Virtualenvs. SKILL.md files. Shell debugging at 2am. OpenHuman doesn't ask any of that. Their README compares itself to "The Tet" from Oblivion -- that alien superintelligence Morgan Freeman calls "a brilliant machine". And tomorrow they're dropping the official OpenHuman mascot. Sneak peek already in Steven's Loom. The cloud-first AI decade is ending. OpenHuman is GPL-3, fully auditable, shipping a release every few days. Save this -- you just got the link to the thing replacing every SaaS AI on the market. -> Repo:

ChatGPT 5.5 is cooked. Claude Opus 4.7 is cooked. Every $420/mo SaaS AI just got an open-source assassin. Mind blown: an open-source desktop AI just hit #7 trending overnight, runs 100% on your laptop, ships with 100+ native integrations, and is quietly killing the entire ChatGPT-subscription era. Introducing OpenHuman by tinyhumansai -> your Personal AI super intelligence. Private. Simple. Powerful. Two weeks ago they quietly dropped it on GitHub. Today: 300+ stars, 100+ daily paying users, 1,129 commits, zero marketing budget. > What is OpenHuman? A native desktop agent (macOS, Windows, Linux) that lives on YOUR machine instead of feeding your data back to OpenAI. Download the app, sign in once, and the agent harness gives you 100+ native connectors out of the box: Gmail, Slack, Notion, GitHub, Reddit, Instagram, Calendar, Drive, Telegram, Discord, and dozens more. One click each. From that moment it builds an encrypted, on-device knowledge base of your entire digital life. No terminal. No Python envs. No API keys. No CLI. > What the agent actually does: Steven, the creator, just dropped a Loom showing real prompts: - "Send Mark a joke" -> drafts in your voice and ships it. - "List my top 5 emails today" -> surfaces what matters from a flooded inbox. - "Summarize that thread and email it to the team" -> done in 3 seconds. One prompt --> multiple connected tools --> end-to-end execution. No tab-switching. > What's actually inside: - Screen intelligence -> the agent SEES what's on your screen and feeds it into your local context. - Memory-aware keyboard autocomplete -> system-wide, in YOUR voice, trained on YOUR past replies. Gmail Smart Compose for your entire OS. - Local knowledge base -> every email, message, and note parsed, embedded, encrypted, on YOUR device. Day 30 it knows you better than your therapist. - 75% Rust core -> memory-safe, brutally fast, runs local AI directly on your machine. > The "but wait" moment: OpenClaw and Hermes Agent are excellent. But they live in the terminal. Virtualenvs. SKILL.md files. Shell debugging at 2am. OpenHuman doesn't ask any of that. Their README compares itself to "The Tet" from Oblivion -- that alien superintelligence Morgan Freeman calls "a brilliant machine". And tomorrow they're dropping the official OpenHuman mascot. Sneak peek already in Steven's Loom. The cloud-first AI decade is ending. OpenHuman is GPL-3, fully auditable, shipping a release every few days. Save this -- you just got the link to the thing replacing every SaaS AI on the market. -> Repo:

slash1s

70,687 views • 2 months ago

This one’s big. Atlassian just dropped something a lot of us have been waiting for. Rovo Dev is an AI coding assistant that finally fits how Atlassian teams actually work! 🔥 Think Claude Code, but with some unique twists: → Built-in integration with Atlassian Jira, Atlassian Confluence, and Atlassian Bitbucket → Persistent memory across sessions for ongoing collaboration → Enterprise-grade controls: permissions, audit logs, zero data retention by default (!!) It also supports model routing across OpenAI, Anthropic, and open-source models like Llama / Gemma... not something you'd get with Claude or Gemini CLI! 👇 But here’s the real kicker: Rovo uses Atlassian’s Teamwork Graph to surface data from your Jira issues, Confluence docs, and code repos. So when Rovo answers, it’s not guessing: it’s grounded in your actual projects, teams, and task history 🧠 It also speaks MCP, so you can hook in other tools and data sources to take it even further. During my tests, I had Rovo Dev build a full app from scratch. It managed the entire flow autonomously, with updates instantly reflected across Jira and Confluence. IMO for anyone using Atlassian tools a lot, Rovo's a no brainer. I don’t use Jira much these days, but after years deep in it, I really wish this had existed back then! :) Big thanks to Atlassian for the collab, I learned a ton in the process! More about Rovo Dev here →

This one’s big. Atlassian just dropped something a lot of us have been waiting for. Rovo Dev is an AI coding assistant that finally fits how Atlassian teams actually work! 🔥 Think Claude Code, but with some unique twists: → Built-in integration with Atlassian Jira, Atlassian Confluence, and Atlassian Bitbucket → Persistent memory across sessions for ongoing collaboration → Enterprise-grade controls: permissions, audit logs, zero data retention by default (!!) It also supports model routing across OpenAI, Anthropic, and open-source models like Llama / Gemma... not something you'd get with Claude or Gemini CLI! 👇 But here’s the real kicker: Rovo uses Atlassian’s Teamwork Graph to surface data from your Jira issues, Confluence docs, and code repos. So when Rovo answers, it’s not guessing: it’s grounded in your actual projects, teams, and task history 🧠 It also speaks MCP, so you can hook in other tools and data sources to take it even further. During my tests, I had Rovo Dev build a full app from scratch. It managed the entire flow autonomously, with updates instantly reflected across Jira and Confluence. IMO for anyone using Atlassian tools a lot, Rovo's a no brainer. I don’t use Jira much these days, but after years deep in it, I really wish this had existed back then! :) Big thanks to Atlassian for the collab, I learned a ton in the process! More about Rovo Dev here →

Charly Wargnier

58,153 views • 9 months ago

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Himanshu Kumar

227,802 views • 3 months ago

Anthropic's in trouble, again! They spent years building what's now fully open-source. What made Claude feel different from a normal app is that the agent could act inside the interface instead of only talking in a chat box. For instance, Claude Artifacts let an agent render real UI, charts, dashboards, and interactive components that assemble live inside the response. Every major AI product tried to replicate it. But the problem was that unlike reasoning, planning, tool-calling, etc., none of it shipped natively with LangGraph, CrewAI, or Google ADK. So teams started building an owned version that required engineering the entire interface layer from scratch. Most teams, however, just settled for shipping the agent as a backend API in a chat box since rendering the UI is only one piece of it. To actually make it work, the interface layer also needed real-time streaming, state kept in sync between agent and UI, conversations that persist across sessions, and reconnection when a user refreshes mid-run. CopilotKit🪁 is now the only open-source framework that actually lets you build your own full-stack Claude-like apps. It decouples the agent from the interface, talking over AG-UI (an open protocol for agent-to-user communication). Being a standard protocol, the frontend never needs to know whether it is talking to a LangGraph or a CrewAI agent. You can change the backend anytime and the UI will never notice. In practice, CopilotKit's interface layer gives several pre-implemented React building blocks that wire the agent directly into the app, like: - generative UI, so the agent renders real components instead of text - chat windows, sidebars, and popups, or a fully headless setup - shared state, so the agent and app stay in sync - human-in-the-loop approvals, where the agent waits before acting - persistent threads that store the whole session, including the agent-user interactions and generated UI, not just text And because that full history is captured, those interactions can feed a self-learning layer that also improves the agent from real usage over time. The interface layer that Anthropic spent years engineering in-house is now literally available to any developer/team. CopilotKit is open-source with 30k+ GitHub stars, and AG-UI, the protocol underneath, is already supported across every major agent framework: LangGraph, CrewAI, Mastra, Google ADK, and more. CopilotKit GitHub repo → (don't forget to star it ⭐ ) If you want to go deeper, I found a detailed breakdown by Shubham Saboo recently on the three Generative UI patterns, with implementation. Read it below.

Anthropic's in trouble, again! They spent years building what's now fully open-source. What made Claude feel different from a normal app is that the agent could act inside the interface instead of only talking in a chat box. For instance, Claude Artifacts let an agent render real UI, charts, dashboards, and interactive components that assemble live inside the response. Every major AI product tried to replicate it. But the problem was that unlike reasoning, planning, tool-calling, etc., none of it shipped natively with LangGraph, CrewAI, or Google ADK. So teams started building an owned version that required engineering the entire interface layer from scratch. Most teams, however, just settled for shipping the agent as a backend API in a chat box since rendering the UI is only one piece of it. To actually make it work, the interface layer also needed real-time streaming, state kept in sync between agent and UI, conversations that persist across sessions, and reconnection when a user refreshes mid-run. CopilotKit🪁 is now the only open-source framework that actually lets you build your own full-stack Claude-like apps. It decouples the agent from the interface, talking over AG-UI (an open protocol for agent-to-user communication). Being a standard protocol, the frontend never needs to know whether it is talking to a LangGraph or a CrewAI agent. You can change the backend anytime and the UI will never notice. In practice, CopilotKit's interface layer gives several pre-implemented React building blocks that wire the agent directly into the app, like: - generative UI, so the agent renders real components instead of text - chat windows, sidebars, and popups, or a fully headless setup - shared state, so the agent and app stay in sync - human-in-the-loop approvals, where the agent waits before acting - persistent threads that store the whole session, including the agent-user interactions and generated UI, not just text And because that full history is captured, those interactions can feed a self-learning layer that also improves the agent from real usage over time. The interface layer that Anthropic spent years engineering in-house is now literally available to any developer/team. CopilotKit is open-source with 30k+ GitHub stars, and AG-UI, the protocol underneath, is already supported across every major agent framework: LangGraph, CrewAI, Mastra, Google ADK, and more. CopilotKit GitHub repo → (don't forget to star it ⭐ ) If you want to go deeper, I found a detailed breakdown by Shubham Saboo recently on the three Generative UI patterns, with implementation. Read it below.

Avi Chawla

456,500 views • 1 month ago