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🚀 New on ModelScope: MiniMax M2.1 is open-source! ✅ SOTA in 8+ languages (Rust, Go, Java, C++, TS, Kotlin, Obj-C, JS) ✅ Full-stack Web & mobile dev: Android/iOS, 3D visuals, vibe coding that actually ships ✅ Smarter, faster, 30% fewer tokens — with lightning mode (M2.1-lightning) for high-TPS workflows... show more

ModelScope

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16,939 次观看 • 6 个月前 •via X (Twitter)

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Still relying on AI tools that can’t turn your ideas into real, working apps? Most can write code snippets but fall apart when it’s time to actually build something complete. That’s where Replit ⠕ changes everything. I simply asked it: “Build a responsive calculator app with a clean UI in light/dark mode.” Within minutes, it built the entire app with a beautiful UI, logic, light/dark mode, animations, and full responsiveness. Literally no setup. No debugging headache. Just vibe coding. Here’s why Replit stands out 👇 ✅ Build in Plain English: Just describe what you want and Replit turns it into a production-ready app. ✅ All-in-One Platform: Code, AI help, hosting, auth, and database, everything you need, already built in. ✅ Vibe Code Anywhere: Build on the go with the Replit mobile app for iOS & Android. ✅ Connected Ecosystem: Integrate with tools you already use like Notion, Slack, Google Drive, Dropbox, and more. ✅ Design to Live App: Import directly from Figma and bring your ideas to life in minutes. No micromanaging. No chaos. Just you, your idea, and Replit doing the heavy lifting. 👉 Try it now: 🎁 Get $10 credit when you purchase Replit Core via my referral link. → #Replit #VibeCoding #AIApps #NoCode #BuildWithReplit #AItools #ReplitCore

Still relying on AI tools that can’t turn your ideas into real, working apps? Most can write code snippets but fall apart when it’s time to actually build something complete. That’s where Replit ⠕ changes everything. I simply asked it: “Build a responsive calculator app with a clean UI in light/dark mode.” Within minutes, it built the entire app with a beautiful UI, logic, light/dark mode, animations, and full responsiveness. Literally no setup. No debugging headache. Just vibe coding. Here’s why Replit stands out 👇 ✅ Build in Plain English: Just describe what you want and Replit turns it into a production-ready app. ✅ All-in-One Platform: Code, AI help, hosting, auth, and database, everything you need, already built in. ✅ Vibe Code Anywhere: Build on the go with the Replit mobile app for iOS & Android. ✅ Connected Ecosystem: Integrate with tools you already use like Notion, Slack, Google Drive, Dropbox, and more. ✅ Design to Live App: Import directly from Figma and bring your ideas to life in minutes. No micromanaging. No chaos. Just you, your idea, and Replit doing the heavy lifting. 👉 Try it now: 🎁 Get $10 credit when you purchase Replit Core via my referral link. → #Replit #VibeCoding #AIApps #NoCode #BuildWithReplit #AItools #ReplitCore

Manish Kumar Shah

52,314 次观看 • 8 个月前

While your team is just getting started vibe coding prototypes, the team at Stripe is vibe coding a full vibe coding platform. Owen Williams ⚡ is a design manager at Stripe who built Protodash - an internal AI prototyping tool that lets designers and PMs spin up real, clickable prototypes in minutes. In this ep he walks through: - why generic AI tools produce "blurple slop" that doesn't match your design system - how he built a full prototyping studio on top of dev boxes with cursor + cc - why more PMs than designers now use Protodash - the design review mode that lets teams ship feedback direct in the app Plus, we talk about getting to the ultimate dream: demos, not memos. ty to our sponsors! 🧠 CESAR - Intelligent automation built for AI 💻 Cursor - The best way to code with AI Full episode on yt:

While your team is just getting started vibe coding prototypes, the team at Stripe is vibe coding a full vibe coding platform. Owen Williams ⚡ is a design manager at Stripe who built Protodash - an internal AI prototyping tool that lets designers and PMs spin up real, clickable prototypes in minutes. In this ep he walks through: - why generic AI tools produce "blurple slop" that doesn't match your design system - how he built a full prototyping studio on top of dev boxes with cursor + cc - why more PMs than designers now use Protodash - the design review mode that lets teams ship feedback direct in the app Plus, we talk about getting to the ultimate dream: demos, not memos. ty to our sponsors! 🧠 CESAR - Intelligent automation built for AI 💻 Cursor - The best way to code with AI Full episode on yt:

claire vo 🖤

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☕ Launch Your Own Café App – No Code Needed! Cafés, coffee roasters, and baristas—it’s time to serve customers online with your own custom app! With our dApp Builder, you can create a cross-platform café app in minutes—no coding required. ✅ Sell Coffee, Beverages, Snacks & More – Directly to Customers ✅ Cross-Platform by Default – Android, iOS, Web, Windows, Mac, Linux ✅ Decentralized & Secure – Built on #ION for direct, fee-free transactions No more high commission fees—own your ordering system, offer subscriptions, and let customers grab their coffee anytime. ☕ The future of coffee ordering is here. Ready to build?

Ice Open Network

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💥 A 450M model just beat bigger VLAs on real robot tasks, and it’s 100% open source [📍 bookmark for later] Came across SmolVLA, a new vision-language-action model for robotics that’s compact, fast, and trained entirely on open community datasets from LeRobot via Hugging Face. What stood out to me is how it matches or outperforms much larger models like ACT using noisy, real-world community data instead of giant private datasets. Why it’s worth a look ✅ 26% performance boost from pretraining on open-source data ✅ Runs on consumer hardware, even a MacBook ✅ 30% faster responses with async inference and smart architecture tweaks ✅ Strong results across Meta-World, LIBERO, SO100, and SO101 ✅ Fully open source: weights, code, training pipeline, eval stack They also introduced smart efficiency tricks like using fewer visual tokens, pulling outputs from mid-layer, and separating perception from action to make it all run fast. SmolVLA is a strong case for what can happen when the robotics community shares data and builds in the open. Definitely worth keeping an eye on.

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Ilir Aliu - eu/acc

17,353 次观看 • 10 个月前

Today, we’re introducing new tools and model updates to help you build, deploy, and scale Voice AI applications.🎙️ 🆕 Speech Understanding: Turn transcripts into actionable data with speaker identification, custom formatting & translation 🆕 LLM Gateway: One API for your voice-to-intelligence pipeline, with GPT, Claude, Gemini & more 🆕 Voice AI Guardrails: End-to-end protection for safe, compliant content Model Upgrades: ✅ 99 languages with auto code-switching ✅ 64% fewer speaker counting errors ✅ 57% better accuracy on critical terms with 1,000-word context Real results: 🔥 Calabrio: 80% boost in customer satisfaction, 22% revenue increase 🔥 Siro: "10/10 customers say 'wow, that insight was crisp'" Build & scale Voice AI in minutes! Try it in our Playground or check the docs. 🚀

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AssemblyAI

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Sabine VanderLinden

87,169 次观看 • 1 年前

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David Ch

28,065 次观看 • 3 个月前

Alibaba just released a coding model that hits 82 percent on SWE-Bench Verified. That is the highest score ever published for an open-source model. The weights are free. The license is Apache 2.0. You can run it today. The model is Qwen 4 Coder 32B. Here is what 82 percent on SWE-Bench Verified actually means. SWE-Bench Verified tests whether an AI can autonomously resolve real bugs pulled from real production GitHub repositories. Not synthetic exercises. Real open-source projects that real teams depend on. A model gets a bug report, reads the code, writes a fix, and either passes the test suite or it does not. At 82 percent, Qwen 4 Coder 32B resolves 82 out of every 100 real production bugs it is given. Without a human guiding it. On code it has never seen before. For comparison: Qwen 4 Coder 32B: 82 percent SWE-Bench Verified. Open source. Apache 2.0. Claude Fable 5: 80.3 percent SWE-Bench Pro. $10 input / $50 output per million tokens. Currently suspended. GPT-5.6 Sol: Competitive on Terminal-Bench. $5 input / $30 output per million tokens. An open-weight model that you can download and run for free just beat both of them on the benchmark designed to measure real software engineering capability. Here is the architecture. Qwen 4 Coder 32B is a 32 billion parameter dense model. Not a Mixture-of-Experts. Every parameter is active on every request. This matters for inference: a dense 32B model runs on 22 gigabytes of VRAM, which fits on a single high-end consumer GPU or a MacBook Pro with 64GB of unified memory. The smaller variant, Qwen 4 Coder 4B, runs at approximately 135 tokens per second on an M5 Max and fits inside 8 gigabytes of RAM. For a model with usable coding capability, that is a new bar for what fits in a single laptop. The training methodology continued Alibaba's approach of reinforcement learning on verifiable coding tasks. The model gets rewarded when its code passes tests. It gets penalized when it fails. Over millions of training steps, the model learns to write code that actually runs rather than code that looks plausible. License: Apache 2.0. Full commercial use. No attribution requirement. No revenue threshold. No monthly active user ceiling. Weights: Hugging Face, available today. Runs on: vLLM, Ollama, SGLang, and any standard GGUF-compatible inference engine. Qwen 4 32B also runs at approximately 135 tokens per second on an M5 Max chip, setting a new bar for what a sub-8GB model can do on Apple Silicon. The open-source coding model just beat the best closed-source model in the world on the benchmark designed to test whether AI can actually do software engineering. The weights are free. The subscription is optional. Source: Autom8Labs AI Insight July 2026, State of Open Source LLMs June 2026, Kunal Ganglani blog June 2026.

Alibaba just released a coding model that hits 82 percent on SWE-Bench Verified. That is the highest score ever published for an open-source model. The weights are free. The license is Apache 2.0. You can run it today. The model is Qwen 4 Coder 32B. Here is what 82 percent on SWE-Bench Verified actually means. SWE-Bench Verified tests whether an AI can autonomously resolve real bugs pulled from real production GitHub repositories. Not synthetic exercises. Real open-source projects that real teams depend on. A model gets a bug report, reads the code, writes a fix, and either passes the test suite or it does not. At 82 percent, Qwen 4 Coder 32B resolves 82 out of every 100 real production bugs it is given. Without a human guiding it. On code it has never seen before. For comparison: Qwen 4 Coder 32B: 82 percent SWE-Bench Verified. Open source. Apache 2.0. Claude Fable 5: 80.3 percent SWE-Bench Pro. $10 input / $50 output per million tokens. Currently suspended. GPT-5.6 Sol: Competitive on Terminal-Bench. $5 input / $30 output per million tokens. An open-weight model that you can download and run for free just beat both of them on the benchmark designed to measure real software engineering capability. Here is the architecture. Qwen 4 Coder 32B is a 32 billion parameter dense model. Not a Mixture-of-Experts. Every parameter is active on every request. This matters for inference: a dense 32B model runs on 22 gigabytes of VRAM, which fits on a single high-end consumer GPU or a MacBook Pro with 64GB of unified memory. The smaller variant, Qwen 4 Coder 4B, runs at approximately 135 tokens per second on an M5 Max and fits inside 8 gigabytes of RAM. For a model with usable coding capability, that is a new bar for what fits in a single laptop. The training methodology continued Alibaba's approach of reinforcement learning on verifiable coding tasks. The model gets rewarded when its code passes tests. It gets penalized when it fails. Over millions of training steps, the model learns to write code that actually runs rather than code that looks plausible. License: Apache 2.0. Full commercial use. No attribution requirement. No revenue threshold. No monthly active user ceiling. Weights: Hugging Face, available today. Runs on: vLLM, Ollama, SGLang, and any standard GGUF-compatible inference engine. Qwen 4 32B also runs at approximately 135 tokens per second on an M5 Max chip, setting a new bar for what a sub-8GB model can do on Apple Silicon. The open-source coding model just beat the best closed-source model in the world on the benchmark designed to test whether AI can actually do software engineering. The weights are free. The subscription is optional. Source: Autom8Labs AI Insight July 2026, State of Open Source LLMs June 2026, Kunal Ganglani blog June 2026.

Harman

41,100 次观看 • 14 天前

🚨 This tool just made every AI coding plugin look outdated. Cline CLI 2.0 is a completely rebuilt, open-source AI coding agent that runs entirely in the terminal. Model-agnostic, editor-agnostic, Apache 2.0 licensed, and trusted by over 5 million developers. Same core engine as the VS Code extension rebuilt for how serious development actually happens. The new interactive UI feels genuinely native. Redesigned navigation, real-time agent reasoning, model switching mid-session, all without leaving the terminal. Parallel agents let multiple Cline instances run across tmux panes simultaneously, each with fully isolated state, working different tasks on different branches without conflicts. Headless mode with the `-y` flag plugs Cline directly into CI/CD pipelines, pipe a git diff in, get a code review out. And the `--acp` flag connects to Zed, Neovim, Emacs, and any ACP-compatible editor, so there's no forced editor choice. At launch, Minimax M2.5 and Kimi K2.5 are completely free. No API key. No credit card. Zero barrier to try it. `npm install -g cline` Docs → GitHub → The future of AI coding isn't a plugin. It's infrastructure.

🚨 This tool just made every AI coding plugin look outdated. Cline CLI 2.0 is a completely rebuilt, open-source AI coding agent that runs entirely in the terminal. Model-agnostic, editor-agnostic, Apache 2.0 licensed, and trusted by over 5 million developers. Same core engine as the VS Code extension rebuilt for how serious development actually happens. The new interactive UI feels genuinely native. Redesigned navigation, real-time agent reasoning, model switching mid-session, all without leaving the terminal. Parallel agents let multiple Cline instances run across tmux panes simultaneously, each with fully isolated state, working different tasks on different branches without conflicts. Headless mode with the `-y` flag plugs Cline directly into CI/CD pipelines, pipe a git diff in, get a code review out. And the `--acp` flag connects to Zed, Neovim, Emacs, and any ACP-compatible editor, so there's no forced editor choice. At launch, Minimax M2.5 and Kimi K2.5 are completely free. No API key. No credit card. Zero barrier to try it. `npm install -g cline` Docs → GitHub → The future of AI coding isn't a plugin. It's infrastructure.

AI Frontliner

60,126 次观看 • 5 个月前

Make your CLI 10x more powerful with custom subagents! Mistral Vibe is an open-source coding agent that runs in your terminal. Most terminal agents give you one assistant that does everything. With Vibe, you can build custom subagents for targeted tasks like deploy scripts, PR reviews, and test generation, then invoke them on demand. Here's how it works: Each subagent runs independently and handles its job without cluttering your main workflow. You might have one for PR reviews, another for testing, and another for deployment. The agent also has full access to your codebase. It scans your project structure and Git status automatically, so it understands context across multiple files, not just the one you're editing. Key capabilities: • Web search and fetching - query and fetch content from the web mid-session for documentation, or API references. • Multi-choice clarifications - when your instruction is ambiguous, Vibe prompts with options instead of guessing. You pick the right path before it acts. • Full skills support - slash commands for preconfigured workflows, custom subagents for specialized tasks, and extensibility through the Agent Skills standard. Vibe works in your terminal or connects to VS Code, JetBrains, and Zed through the Agent Client Protocol. Mistral Vibe is available through Le Chat Pro and Team plans. It's 100% open source Link to Mistral Vibe in the comments!

Make your CLI 10x more powerful with custom subagents! Mistral Vibe is an open-source coding agent that runs in your terminal. Most terminal agents give you one assistant that does everything. With Vibe, you can build custom subagents for targeted tasks like deploy scripts, PR reviews, and test generation, then invoke them on demand. Here's how it works: Each subagent runs independently and handles its job without cluttering your main workflow. You might have one for PR reviews, another for testing, and another for deployment. The agent also has full access to your codebase. It scans your project structure and Git status automatically, so it understands context across multiple files, not just the one you're editing. Key capabilities: • Web search and fetching - query and fetch content from the web mid-session for documentation, or API references. • Multi-choice clarifications - when your instruction is ambiguous, Vibe prompts with options instead of guessing. You pick the right path before it acts. • Full skills support - slash commands for preconfigured workflows, custom subagents for specialized tasks, and extensibility through the Agent Skills standard. Vibe works in your terminal or connects to VS Code, JetBrains, and Zed through the Agent Client Protocol. Mistral Vibe is available through Le Chat Pro and Team plans. It's 100% open source Link to Mistral Vibe in the comments!

Sumanth

10,483 次观看 • 4 个月前

🚀New Amazon Q Developer agent for software development is available to customers: This agent is based on a new agent architecture that has exciting results coming from the SWE-bench scores (on the full and verified benchmarks) representing AI models’ ability to resolve real-world coding problems. Interesting aspect of Q Agent is that with these newest updates, Q drove nearly 50% more successful coding tasks completed. What makes Q Dev Agent remarkable? The agent architecture is not just about using the best LLMs (which we do), but also giving the agent the ability to constantly explore multiple paths to find the best way to resolve a particular problem (and back tracking when it has reached dead end like a developer would do). Needless to say, we are just getting started on the developer agent and we are constantly pushing to advance our AI capabilities while maintaining quality, security, privacy, and reliability to keep Amazon Q Developer an innovative and trusted option available to our customers using agents for software development. We highlighted the results of our first SWE-bench submission of Amazon Q Developer back in June blog post; with these updates, our new agent resolves 51% more coding tasks than its previous iteration on the SWE-bench verified dataset, and 43% more on the full dataset. That’s the difference a few months make, and I can’t wait to share what our teams will deliver at re:Invent this December. Here's a quick demo showcasing our new Agent in action:

🚀New Amazon Q Developer agent for software development is available to customers: This agent is based on a new agent architecture that has exciting results coming from the SWE-bench scores (on the full and verified benchmarks) representing AI models’ ability to resolve real-world coding problems. Interesting aspect of Q Agent is that with these newest updates, Q drove nearly 50% more successful coding tasks completed. What makes Q Dev Agent remarkable? The agent architecture is not just about using the best LLMs (which we do), but also giving the agent the ability to constantly explore multiple paths to find the best way to resolve a particular problem (and back tracking when it has reached dead end like a developer would do). Needless to say, we are just getting started on the developer agent and we are constantly pushing to advance our AI capabilities while maintaining quality, security, privacy, and reliability to keep Amazon Q Developer an innovative and trusted option available to our customers using agents for software development. We highlighted the results of our first SWE-bench submission of Amazon Q Developer back in June blog post; with these updates, our new agent resolves 51% more coding tasks than its previous iteration on the SWE-bench verified dataset, and 43% more on the full dataset. That’s the difference a few months make, and I can’t wait to share what our teams will deliver at re:Invent this December. Here's a quick demo showcasing our new Agent in action:

Swami Sivasubramanian

28,946 次观看 • 1 年前

🔥 Pi Network Has Officially Entered Beast Mode – Global Payment Giants Now Support It! While others doubted, we built. While the world watched, Pi Network quietly integrated with the biggest financial platforms on Earth. And now… we’re ready. 💪🌎 💥 The Walls Are Down. Pi Is Borderless. The Open Mainnet is live, the infrastructure is in place, and now the final pieces are falling into place — Pi Network is supported by a massive alliance of global payment powerhouses, including: 🛡️ Exchange Titans & Fiat Gateways: •✅ Binance P2P •✅ Binance Connect •✅ Transak •✅ Sardine •✅ Topper •✅ UTORG •✅ Paybis •✅ Onmeta •✅ Onramp Money •✅ •✅ TransFi •✅ DFX •✅ Alchemy Pay •✅ Banxa •✅ BTC Direct •✅ Coinify •✅ MoonPay •✅ Fonbnk •✅ GateConnect •✅ Unlimit •✅ Guardarian •✅ Koywe •✅ LocalRamp •✅ Yellow Card 💳 World-Class Fintechs Now Support Pi: •✅ Stripe •✅ Skrill Crypto •✅ Revolut 🌐 This Is Bigger Than Just Crypto Stripe and Skrill aren’t just crypto services — they’re global payment kings. And now they’re helping Pi bridge traditional finance with the new digital economy. Revolut is a top fintech unicorn, and it’s already supporting Pi. Binance is the biggest crypto exchange in the world — and it’s ready. Let that sink in. 🔥 🚀 Why This Changes Everything •🌍 Anyone can access Pi, anywhere in the world •🏦 Buy and sell Pi directly with bank cards, local fiat, Apple Pay, and more •💼 Businesses can now prepare to integrate Pi into payments •🧠 Investors now see the foundation for real-world use and massive growth This is what true mass adoption looks like — infrastructure before hype. Utility before listing. Power before price. 🔮 The World’s Not Ready, But We Are. While other coins chased listings, Pi Network built alliances. While others pumped and dumped, Pi built its own economy. And now? The rocket is fully fueled. We’re not waiting for the future — we’re building it. “You don’t have to be first. You just have to be the one who finishes the race prepared. And Pi Network is ready to dominate.” 📢 Pioneers, Stand Tall Your patience, your faith, your mining, your contribution — it’s all paying off. With 40+ global financial platforms now supporting Pi, we’ve gone from an idea… to an unstoppable force. 🎯 Pi Network is not just another crypto project. 🚀 Pi is the people’s currency — and now the world is opening its gates to it. 📣 Share this. Shout it. Post it. Let every Pioneer know — Pi is ready. Let every skeptic watch — we told you so. Let every investor understand — the game has changed. Pi is no longer coming. Pi is here. 🔥 And the world is about to feel it. Pi Network Nicolas Kokkalis Chengdiao Fan ✅✅✅✅🚀🚀🚀🚀🚀🚀🚀🚀

🔥 Pi Network Has Officially Entered Beast Mode – Global Payment Giants Now Support It! While others doubted, we built. While the world watched, Pi Network quietly integrated with the biggest financial platforms on Earth. And now… we’re ready. 💪🌎 💥 The Walls Are Down. Pi Is Borderless. The Open Mainnet is live, the infrastructure is in place, and now the final pieces are falling into place — Pi Network is supported by a massive alliance of global payment powerhouses, including: 🛡️ Exchange Titans & Fiat Gateways: •✅ Binance P2P •✅ Binance Connect •✅ Transak •✅ Sardine •✅ Topper •✅ UTORG •✅ Paybis •✅ Onmeta •✅ Onramp Money •✅ •✅ TransFi •✅ DFX •✅ Alchemy Pay •✅ Banxa •✅ BTC Direct •✅ Coinify •✅ MoonPay •✅ Fonbnk •✅ GateConnect •✅ Unlimit •✅ Guardarian •✅ Koywe •✅ LocalRamp •✅ Yellow Card 💳 World-Class Fintechs Now Support Pi: •✅ Stripe •✅ Skrill Crypto •✅ Revolut 🌐 This Is Bigger Than Just Crypto Stripe and Skrill aren’t just crypto services — they’re global payment kings. And now they’re helping Pi bridge traditional finance with the new digital economy. Revolut is a top fintech unicorn, and it’s already supporting Pi. Binance is the biggest crypto exchange in the world — and it’s ready. Let that sink in. 🔥 🚀 Why This Changes Everything •🌍 Anyone can access Pi, anywhere in the world •🏦 Buy and sell Pi directly with bank cards, local fiat, Apple Pay, and more •💼 Businesses can now prepare to integrate Pi into payments •🧠 Investors now see the foundation for real-world use and massive growth This is what true mass adoption looks like — infrastructure before hype. Utility before listing. Power before price. 🔮 The World’s Not Ready, But We Are. While other coins chased listings, Pi Network built alliances. While others pumped and dumped, Pi built its own economy. And now? The rocket is fully fueled. We’re not waiting for the future — we’re building it. “You don’t have to be first. You just have to be the one who finishes the race prepared. And Pi Network is ready to dominate.” 📢 Pioneers, Stand Tall Your patience, your faith, your mining, your contribution — it’s all paying off. With 40+ global financial platforms now supporting Pi, we’ve gone from an idea… to an unstoppable force. 🎯 Pi Network is not just another crypto project. 🚀 Pi is the people’s currency — and now the world is opening its gates to it. 📣 Share this. Shout it. Post it. Let every Pioneer know — Pi is ready. Let every skeptic watch — we told you so. Let every investor understand — the game has changed. Pi is no longer coming. Pi is here. 🔥 And the world is about to feel it. Pi Network Nicolas Kokkalis Chengdiao Fan ✅✅✅✅🚀🚀🚀🚀🚀🚀🚀🚀

Mr Spock 𝛑

42,315 次观看 • 1 年前

🚨 ALIBABA JUST OPEN-SOURCED THE MOST POWERFUL MODEL IN THE WORLD - Qwen3.5 plus And it changes the AI race forever. This is not another “bigger model” release. This is a complete architectural reset. 397B parameters → only 17B active at inference, Qwen3.5 plus delivers performance on par with GPT-5.2 and Gemini-3-Pro. A new Hybrid Sparse MoE + Linear Attention architecture + Multi-Token Prediction = near-instant long-context responses and massive speed gains. Built as a true native multimodal model, it doesn’t just chat it builds. With a single prompt: “Use the React framework to create a visually impressive Mapbox demo featuring locations in Beijing and Shanghai” it generates a live interactive 3D map UI, structured components, real layouts, and production-ready frontend logic. This is real agentic, multimodal coding not a static code block. MMLU-Pro: 87.8 GPQA: 88.4 IFBench: 76.5 ⚡ up to 8× faster inference And it’s fully open-sourced under Apache-2.0. No locked APIs. No restricted tiers. 201 languages supported bringing frontier AI to developers, startups, and researchers worldwide. The race is no longer about the biggest model. It’s about the most usable intelligence. Open source just entered the top tier.

🚨 ALIBABA JUST OPEN-SOURCED THE MOST POWERFUL MODEL IN THE WORLD - Qwen3.5 plus And it changes the AI race forever. This is not another “bigger model” release. This is a complete architectural reset. 397B parameters → only 17B active at inference, Qwen3.5 plus delivers performance on par with GPT-5.2 and Gemini-3-Pro. A new Hybrid Sparse MoE + Linear Attention architecture + Multi-Token Prediction = near-instant long-context responses and massive speed gains. Built as a true native multimodal model, it doesn’t just chat it builds. With a single prompt: “Use the React framework to create a visually impressive Mapbox demo featuring locations in Beijing and Shanghai” it generates a live interactive 3D map UI, structured components, real layouts, and production-ready frontend logic. This is real agentic, multimodal coding not a static code block. MMLU-Pro: 87.8 GPQA: 88.4 IFBench: 76.5 ⚡ up to 8× faster inference And it’s fully open-sourced under Apache-2.0. No locked APIs. No restricted tiers. 201 languages supported bringing frontier AI to developers, startups, and researchers worldwide. The race is no longer about the biggest model. It’s about the most usable intelligence. Open source just entered the top tier.

RAVI KUMAR SAHU

380,093 次观看 • 5 个月前

The wait is over 365 days ago, we launched our closed Alpha Since then, we have: ✅ Developed a custom ReAct agent with improved prompting, skill handling, and Nillion data storage ✅ Revealed some of our partnerships (big ones have yet to be announced 👀) ✅ Migrated to a new Uniswap Base (V3) pool for better price stability ✅ Allowed users to create their own Characters ✅ Added new Skills (so many more to go live) ✅ Overhauled the agent UI/UX ✅ Integrated with Telegram We are not building one single agent that does XYZ. We are building: ➡️ An Agent Framework ➡️ Secure Credential Storage ➡️ Enterprise-grade Access Control ➡️ Request tracking, and a lot of other stuff.. that allows thousands of people to create AI agents under minutes. The greatest leverage is not in using tools It’s in creating the tools that create tools The closed Alpha was our proof of concept What comes next is our proof of vision Full platform launch on February 25th 🌐

The wait is over 365 days ago, we launched our closed Alpha Since then, we have: ✅ Developed a custom ReAct agent with improved prompting, skill handling, and Nillion data storage ✅ Revealed some of our partnerships (big ones have yet to be announced 👀) ✅ Migrated to a new Uniswap Base (V3) pool for better price stability ✅ Allowed users to create their own Characters ✅ Added new Skills (so many more to go live) ✅ Overhauled the agent UI/UX ✅ Integrated with Telegram We are not building one single agent that does XYZ. We are building: ➡️ An Agent Framework ➡️ Secure Credential Storage ➡️ Enterprise-grade Access Control ➡️ Request tracking, and a lot of other stuff.. that allows thousands of people to create AI agents under minutes. The greatest leverage is not in using tools It’s in creating the tools that create tools The closed Alpha was our proof of concept What comes next is our proof of vision Full platform launch on February 25th 🌐

Skillful AI

25,927 次观看 • 1 年前

Autonomy for All: My Experience with Replit ⠕ Agent 3 🚀 If you’ve ever wished building software felt less like coding with a computer and more like collaborating with a teammate, you need to try Agent 3. I’ve been using it recently, and honestly it’s a whole new way to build. Try now: 💡 Here’s what stood out for me: ✅ It’s 10X more autonomous — I’ve had it run for up to 200 minutes straight without me touching a thing. ✅ It tests and fixes its own code in a live browser — and it’s 3X faster and 10X more cost-effective than the usual testing models. ✅ It can build other Agents & Automations — I’ve spun up Slackbots to query data, Telegram bots to track habits, and even time-triggered workflows that email me daily stock news. How it feels to use: → You describe what you want in plain language. → Agent 3 just… gets to work — building, testing, improving, and delivering. → You can even track progress live from your phone while it’s still working. This isn’t just an upgrade, it’s a leap. From idea → app → automation, Agent 3 makes it feel like you’ve got another person on your team, not just another tool. 🔗 Check out Replit Agent 3 here: If you build anything cool, I’d love to see it.

Autonomy for All: My Experience with Replit ⠕ Agent 3 🚀 If you’ve ever wished building software felt less like coding with a computer and more like collaborating with a teammate, you need to try Agent 3. I’ve been using it recently, and honestly it’s a whole new way to build. Try now: 💡 Here’s what stood out for me: ✅ It’s 10X more autonomous — I’ve had it run for up to 200 minutes straight without me touching a thing. ✅ It tests and fixes its own code in a live browser — and it’s 3X faster and 10X more cost-effective than the usual testing models. ✅ It can build other Agents & Automations — I’ve spun up Slackbots to query data, Telegram bots to track habits, and even time-triggered workflows that email me daily stock news. How it feels to use: → You describe what you want in plain language. → Agent 3 just… gets to work — building, testing, improving, and delivering. → You can even track progress live from your phone while it’s still working. This isn’t just an upgrade, it’s a leap. From idea → app → automation, Agent 3 makes it feel like you’ve got another person on your team, not just another tool. 🔗 Check out Replit Agent 3 here: If you build anything cool, I’d love to see it.

Nayeem Sheikh

28,223 次观看 • 10 个月前

"Confession: I've abandoned more AI-generated apps than I’ve deployed. Until last week." Let’s be real e’ve all chased the "wow" of AI dev tools that generate code in seconds. But what happens when real users break everything? You’re left debugging blindfolded, drowning in spaghetti code, and praying it doesn’t collapse. I’ve been there. Then I saw Pythagora 2.0 launch today. Try it free → Guys, it’s not just another "talk-to-build" tool. It’s the first platform that treats AI apps like actual software, not disposable demos. Here’s why it’s my new core stack: ✅Full-stack architecture with real frontend/backend/database relationships (no more Frankenstein UI demos). ✅Debug like a human: Breakpoints, logs, and live DB inspection when things break (because they will). ✅Deploy anywhere. heir cloud (AWS-backed) or your own infra. No lock-in. ✅Production armor: Security, scalability, and maintainability baked in. 💡 The "Aha" Moment While other tools ghost you after code gen, Pythagora obsesses over the messy middle: debugging, iterating, and deploying real apps. If you’re building anything beyond a weekend prototype, this is your exit ramp from "AI toy" to "actual software." Try it free → (No, this isn’t a drill. Yes, it’s that good.) Also show them some support on Product Hunt : #AIdevelopment #WebDev #StartupTech #Debugging #FullStack

"Confession: I've abandoned more AI-generated apps than I’ve deployed. Until last week." Let’s be real e’ve all chased the "wow" of AI dev tools that generate code in seconds. But what happens when real users break everything? You’re left debugging blindfolded, drowning in spaghetti code, and praying it doesn’t collapse. I’ve been there. Then I saw Pythagora 2.0 launch today. Try it free → Guys, it’s not just another "talk-to-build" tool. It’s the first platform that treats AI apps like actual software, not disposable demos. Here’s why it’s my new core stack: ✅Full-stack architecture with real frontend/backend/database relationships (no more Frankenstein UI demos). ✅Debug like a human: Breakpoints, logs, and live DB inspection when things break (because they will). ✅Deploy anywhere. heir cloud (AWS-backed) or your own infra. No lock-in. ✅Production armor: Security, scalability, and maintainability baked in. 💡 The "Aha" Moment While other tools ghost you after code gen, Pythagora obsesses over the messy middle: debugging, iterating, and deploying real apps. If you’re building anything beyond a weekend prototype, this is your exit ramp from "AI toy" to "actual software." Try it free → (No, this isn’t a drill. Yes, it’s that good.) Also show them some support on Product Hunt : #AIdevelopment #WebDev #StartupTech #Debugging #FullStack

Shruti Codes

27,005 次观看 • 1 年前

SonarQube has been catching my bugs and security issues for years. The only friction was having to leave Cursor or Windsurf to view the results. Their new MCP Server fixes that by bringing verification directly into the coding environment 🔥 This is actually perfect timing 🧵 ↓ Because we write more code than ever thanks to AI, yet productivity still doesn’t keep up. Google’s 2025 DORA Report shows the tension: → AI usage +90% → Bugs +9% → Review time +91% → PR size +154% (report here: The problem isn’t generating code. It’s verifying it quickly and reliably. And this is what SonarQube's new MCP Server brings instantly: - Live scanning → trigger SonarQube checks inside Cursor, Windsurf, Claude Code… basically any MCP-compatible IDE - Immediate surfacing → security, reliability, and maintainability issues in seconds - Smooth UI handoff → jump to the dashboard only when you need the full picture - AI-native workflow → Sonar’s long-standing rule engine integrated into your daily loop Why it’s great: • Removes constant tab-switching • Faster write → check → fix cycles • Lets the IDE handle speed while SonarQube handles structure • Feels like code quality finally meets AI-native development Setup is super simple: → Enable SonarQube's MCP Server in Cursor → Add your SonarQube instance → Open your repo → Run the scan directly inside the IDE I then pointed it to a JS component I’m building in Streamlit (psst, it’s called Streamlit ChartJS ;)) → Immediate results: security flags, reliability concerns, maintainability smells, and dependency risks ✅ Then I prompted: "Show me the full breakdown." → Cursor opens the SonarQube UI with rule details, severities, fix guidance, and project-wide quality signals! Exactly on point.

SonarQube has been catching my bugs and security issues for years. The only friction was having to leave Cursor or Windsurf to view the results. Their new MCP Server fixes that by bringing verification directly into the coding environment 🔥 This is actually perfect timing 🧵 ↓ Because we write more code than ever thanks to AI, yet productivity still doesn’t keep up. Google’s 2025 DORA Report shows the tension: → AI usage +90% → Bugs +9% → Review time +91% → PR size +154% (report here: The problem isn’t generating code. It’s verifying it quickly and reliably. And this is what SonarQube's new MCP Server brings instantly: - Live scanning → trigger SonarQube checks inside Cursor, Windsurf, Claude Code… basically any MCP-compatible IDE - Immediate surfacing → security, reliability, and maintainability issues in seconds - Smooth UI handoff → jump to the dashboard only when you need the full picture - AI-native workflow → Sonar’s long-standing rule engine integrated into your daily loop Why it’s great: • Removes constant tab-switching • Faster write → check → fix cycles • Lets the IDE handle speed while SonarQube handles structure • Feels like code quality finally meets AI-native development Setup is super simple: → Enable SonarQube's MCP Server in Cursor → Add your SonarQube instance → Open your repo → Run the scan directly inside the IDE I then pointed it to a JS component I’m building in Streamlit (psst, it’s called Streamlit ChartJS ;)) → Immediate results: security flags, reliability concerns, maintainability smells, and dependency risks ✅ Then I prompted: "Show me the full breakdown." → Cursor opens the SonarQube UI with rule details, severities, fix guidance, and project-wide quality signals! Exactly on point.

Charly Wargnier

22,702 次观看 • 7 个月前

No single vendor will win the AI race, but open ecosystems might. Real velocity in AI comes from interoperability, not lock-in. And AMD just made all of its software open source. At last week’s Advancing AI 2025, we sat down with AMD’s VP of AI Software Anush Elangovan and Sharon Zhou VP of AI at AMD, to discuss their case for why an open, multi-partner ecosystem will accelerate AI innovation faster than any proprietary alternative. AMD’s announcements last week double down on this OSS focus and their commitment to AI infrastructure, including: ✅ Open Source Ecosystem: ROCm 7, AMD’s latest open-source AI software stack, introduces kernel-level improvements for GEMM operations, optimized attention mechanisms, and expanded support for distributed inference. The update brings substantial speedups for inference workloads, with average performance increases of 3.2x to 3.8x ✅ Hardware: New MI355X GPU delivers up to 40% more tokens per dollar vs competition & the MI350 Series has seen a 35x generational leap in AI inference performance ✅ Infrastructure Investments: Oracle just committed to zettascale (‼️) clusters with up to 131,072 MI355X GPUs and AMD showcased their new $10 billion partnership with Saudi Arabian AI firm HUMAIN to build AI infrastructure, including data centers, powered by AMD chips. ✅ Partnership Momentum: 7 out of 10 top AI companies now run production workloads on AMD Instinct accelerators (including Meta, OpenAI, Microsoft & xAI) By inviting interoperability and contribution at every layer, AMD is enabling developers to build faster, optimize deeper, and deploy with flexibility. Listen to Anush and Sharon’s Chain of Thought Podcast episode with host Conor Bronsdon in the next tweet to get all the details and a deep dive into AMD’s strategy 👇

No single vendor will win the AI race, but open ecosystems might. Real velocity in AI comes from interoperability, not lock-in. And AMD just made all of its software open source. At last week’s Advancing AI 2025, we sat down with AMD’s VP of AI Software Anush Elangovan and Sharon Zhou VP of AI at AMD, to discuss their case for why an open, multi-partner ecosystem will accelerate AI innovation faster than any proprietary alternative. AMD’s announcements last week double down on this OSS focus and their commitment to AI infrastructure, including: ✅ Open Source Ecosystem: ROCm 7, AMD’s latest open-source AI software stack, introduces kernel-level improvements for GEMM operations, optimized attention mechanisms, and expanded support for distributed inference. The update brings substantial speedups for inference workloads, with average performance increases of 3.2x to 3.8x ✅ Hardware: New MI355X GPU delivers up to 40% more tokens per dollar vs competition & the MI350 Series has seen a 35x generational leap in AI inference performance ✅ Infrastructure Investments: Oracle just committed to zettascale (‼️) clusters with up to 131,072 MI355X GPUs and AMD showcased their new $10 billion partnership with Saudi Arabian AI firm HUMAIN to build AI infrastructure, including data centers, powered by AMD chips. ✅ Partnership Momentum: 7 out of 10 top AI companies now run production workloads on AMD Instinct accelerators (including Meta, OpenAI, Microsoft & xAI) By inviting interoperability and contribution at every layer, AMD is enabling developers to build faster, optimize deeper, and deploy with flexibility. Listen to Anush and Sharon’s Chain of Thought Podcast episode with host Conor Bronsdon in the next tweet to get all the details and a deep dive into AMD’s strategy 👇

Galileo

78,922 次观看 • 1 年前

🚀 Less managing, more creating. Building app usually means: • Hours lost debugging • Constantly juggling tools • Non-stop AI hand-holding Replit’s new Agent 3 changes that entirely → ⁠ We just tried out Agent 3, and it’s impressive! ⁠ It’s not another ordinary AI coding assistant. It’s a full-on collaborator that: • Runs autonomously for up to 200 minutes, building your app from start to finish. • Tests its own work in a real browser and fixes bugs automatically, saving hours of manual debugging. • Lets you build other agents and automations (think Slack bots, Telegram reminders, email summaries). ⁠ Here's why Agent 3 is different: Unlike traditional AI tools where you constantly babysit and prompt, Agent 3 understands your idea and takes charge, freeing you to focus on creativity, strategy, or simply grabbing coffee while your app builds itself. And to show how easy it is, we built a complete waitlist app in less than an hour, from idea to almost finished product. No babysitting. No endless tweaking. ⁠ Replit ⠕ is calling this “Autonomy for All,” and after seeing Agent 3 in action, it’s easy to see how it can bring millions more creators online. ✅ Faster builds ✅ Less frustration ✅ More polished, reliable apps With Agent 3, app-building really does feel less like wrestling with software and more like collaborating with a teammate. ⁠ Try Replit Agent 3 yourself today…👆 Get $10 credit when you purchase Replit Core using our link above!

🚀 Less managing, more creating. Building app usually means: • Hours lost debugging • Constantly juggling tools • Non-stop AI hand-holding Replit’s new Agent 3 changes that entirely → ⁠ We just tried out Agent 3, and it’s impressive! ⁠ It’s not another ordinary AI coding assistant. It’s a full-on collaborator that: • Runs autonomously for up to 200 minutes, building your app from start to finish. • Tests its own work in a real browser and fixes bugs automatically, saving hours of manual debugging. • Lets you build other agents and automations (think Slack bots, Telegram reminders, email summaries). ⁠ Here's why Agent 3 is different: Unlike traditional AI tools where you constantly babysit and prompt, Agent 3 understands your idea and takes charge, freeing you to focus on creativity, strategy, or simply grabbing coffee while your app builds itself. And to show how easy it is, we built a complete waitlist app in less than an hour, from idea to almost finished product. No babysitting. No endless tweaking. ⁠ Replit ⠕ is calling this “Autonomy for All,” and after seeing Agent 3 in action, it’s easy to see how it can bring millions more creators online. ✅ Faster builds ✅ Less frustration ✅ More polished, reliable apps With Agent 3, app-building really does feel less like wrestling with software and more like collaborating with a teammate. ⁠ Try Replit Agent 3 yourself today…👆 Get $10 credit when you purchase Replit Core using our link above!

There's An AI For That

40,330 次观看 • 10 个月前