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here's my hyperdev-1 setup, an elite American software developer agent (proudly running in Texas!): - rented a H100 - installed Claude Code - used it to install Qwen3-Coder-480B - and created an agent in Claude which uses this local 480B model .. connected to Github, Vercel, few MCPs.. all...

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Varun

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152,600 просмотров • 11 месяцев назад •via X (Twitter)

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a demo of using both the gpt-oss-20b and qwen3-coder-480b in the hyperspace agentic browser. this spatial, AI-native UI is designed as an extension of the spatial space of our brain - which places some things more in recency to us, and adjacent to each other. pinch to zoom.
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a demo of using both the gpt-oss-20b and qwen3-coder-480b in the hyperspace agentic browser. this spatial, AI-native UI is designed as an extension of the spatial space of our brain - which places some things more in recency to us, and adjacent to each other. pinch to zoom.

Varun

180,438 просмотров • 11 месяцев назад

Over the past few days, I’ve been actively testing Qwen3-Coder-Next, and I’ve built quite a few fun and creative mini-games with it. Its frontend capabilities are genuinely impressive. Because it’s based on the Qwen3-Next model, the speed is outstanding. In fact, we managed to build an Angry Birds–style game in just 45 seconds, which really pushed the upper bound of what I thought was possible in terms of efficiency. It’s also been extremely useful in my day-to-day work. I used it to read through the Qwen3-TTS GitHub repository, quickly write a Gradio app, and deploy it on ModelScope. The overall performance and accuracy were excellent—especially considering that Qwen3-TTS is not a trivial application at all. Overall, this is a model I genuinely enjoy using. I warmly welcome any feedback, and bad cases are especially appreciated—they’re incredibly helpful for us as we continue to optimize and improve the model.
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Over the past few days, I’ve been actively testing Qwen3-Coder-Next, and I’ve built quite a few fun and creative mini-games with it. Its frontend capabilities are genuinely impressive. Because it’s based on the Qwen3-Next model, the speed is outstanding. In fact, we managed to build an Angry Birds–style game in just 45 seconds, which really pushed the upper bound of what I thought was possible in terms of efficiency. It’s also been extremely useful in my day-to-day work. I used it to read through the Qwen3-TTS GitHub repository, quickly write a Gradio app, and deploy it on ModelScope. The overall performance and accuracy were excellent—especially considering that Qwen3-TTS is not a trivial application at all. Overall, this is a model I genuinely enjoy using. I warmly welcome any feedback, and bad cases are especially appreciated—they’re incredibly helpful for us as we continue to optimize and improve the model.

Chen Cheng

18,396 просмотров • 5 месяцев назад

You can run a free and open-source alternative to Claude Code locally on your laptop! - Local AI model w/ Qwen3 Coder - Runs on your machine w/ LM Studio - Open-source VS Code extension w/ Cline Completely private (offline) and unlimited Setup and how it works: 00:00 Intro 01:11 LM Studio 02:10 Local AI model 03:38 Load the model 05:09 Start the server 05:33 Setup Cline 07:20 Prompt & example 09:02 Developer logs 10:12 Result reveal
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You can run a free and open-source alternative to Claude Code locally on your laptop! - Local AI model w/ Qwen3 Coder - Runs on your machine w/ LM Studio - Open-source VS Code extension w/ Cline Completely private (offline) and unlimited Setup and how it works: 00:00 Intro 01:11 LM Studio 02:10 Local AI model 03:38 Load the model 05:09 Start the server 05:33 Setup Cline 07:20 Prompt & example 09:02 Developer logs 10:12 Result reveal

Paul Couvert

72,498 просмотров • 9 месяцев назад

𝗢𝗹𝗹𝗮𝗺𝗮 + 𝗖𝗹𝗮𝘂𝗱𝗲 𝗖𝗼𝗱𝗲 𝗷𝘂𝘀𝘁 𝗺𝗮𝗱𝗲 𝗔𝗜 𝗰𝗼𝗱𝗶𝗻𝗴 𝗳𝗿𝗲𝗲. 𝗔𝗻𝗱 𝗶𝘁 𝗿𝘂𝗻𝘀 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲𝗹𝘆 𝗹𝗼𝗰𝗮𝗹. No subscriptions. No limits. No data leaving your machine. Claude Code used to cost $3-$15 per million tokens. Now you can run it with free open-source models on your computer. January 16th, 2026: Ollama version 0.14.0 became compatible with Anthropic's messages API. That means Claude Code now works with any Ollama model. Zero ongoing costs. Your code never leaves your computer. Here's the setup: Download Ollama. Run `ollama pull qwen3-coder`. Install Claude Code. Set two environment variables to connect them. Then run: `claude --model qwen3-coder` Give it tasks in plain English: "Write a function to process images." "Debug why my API calls are failing." It reads your files. Makes changes. Tests the code. Shows you what it did. Top models: Qwen 3-Coder for everyday tasks. GPT-OSS 20B for complex projects. This is completely legal. Explicitly supported by both tools.
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𝗢𝗹𝗹𝗮𝗺𝗮 + 𝗖𝗹𝗮𝘂𝗱𝗲 𝗖𝗼𝗱𝗲 𝗷𝘂𝘀𝘁 𝗺𝗮𝗱𝗲 𝗔𝗜 𝗰𝗼𝗱𝗶𝗻𝗴 𝗳𝗿𝗲𝗲. 𝗔𝗻𝗱 𝗶𝘁 𝗿𝘂𝗻𝘀 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲𝗹𝘆 𝗹𝗼𝗰𝗮𝗹. No subscriptions. No limits. No data leaving your machine. Claude Code used to cost $3-$15 per million tokens. Now you can run it with free open-source models on your computer. January 16th, 2026: Ollama version 0.14.0 became compatible with Anthropic's messages API. That means Claude Code now works with any Ollama model. Zero ongoing costs. Your code never leaves your computer. Here's the setup: Download Ollama. Run `ollama pull qwen3-coder`. Install Claude Code. Set two environment variables to connect them. Then run: `claude --model qwen3-coder` Give it tasks in plain English: "Write a function to process images." "Debug why my API calls are failing." It reads your files. Makes changes. Tests the code. Shows you what it did. Top models: Qwen 3-Coder for everyday tasks. GPT-OSS 20B for complex projects. This is completely legal. Explicitly supported by both tools.

Julian Goldie SEO

168,502 просмотров • 5 месяцев назад

THIS GUY STACKED MAC MINIS TO RUN AI LOCALLY AND IT CODES FOR FREE no API. no subscription. no cloud. what he's running: - EXO across multiple Mac Minis - Qwen 2.5 Coder 7B locally - Zed editor connected to the model - GPU usage visible in real time the math: - Mac Mini M4: $599 one-time - electricity: $3/month - API costs: $0 vs $459/month in subscriptions if you want Claude Code to run at this level ClaudeKit gives it the structure to do that ( full breakdown in the article below
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THIS GUY STACKED MAC MINIS TO RUN AI LOCALLY AND IT CODES FOR FREE no API. no subscription. no cloud. what he's running: - EXO across multiple Mac Minis - Qwen 2.5 Coder 7B locally - Zed editor connected to the model - GPU usage visible in real time the math: - Mac Mini M4: $599 one-time - electricity: $3/month - API costs: $0 vs $459/month in subscriptions if you want Claude Code to run at this level ClaudeKit gives it the structure to do that ( full breakdown in the article below

Madni Aghadi

26,421 просмотров • 1 месяц назад

I tested Genspark new AI Developer: an L4 agent that builds a working app from an idea. You can choose any model (including Claude), and it runs in your browser and in the app—including planning, code, testing, and fixes. The output looks like it was done by a senior developer. I used Prompt to build a functional newsflash page that creates and curates a newsletter for me by adding a few links! It's revolutionary. Prompt in the comments. Genspark
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I tested Genspark new AI Developer: an L4 agent that builds a working app from an idea. You can choose any model (including Claude), and it runs in your browser and in the app—including planning, code, testing, and fixes. The output looks like it was done by a senior developer. I used Prompt to build a functional newsflash page that creates and curates a newsletter for me by adding a few links! It's revolutionary. Prompt in the comments. Genspark

Chubby♨️

235,727 просмотров • 11 месяцев назад

AMD'S NEW MINI PC RUNS CLAUDE CODE OFFLINE FOR $0/MONTH AND REPLACES $200 OPENAI + $200 CLAUDE CODE SUBSCRIPTIONS amd's ryzen ai max+ 395 is the first x86 chip that runs a 200 billion parameter model on a single piece of silicon with 128gb of unified memory the gmktec evo-x2 runs qwen3 coder 30b at 40 to 50 tokens per second and llama 3.3 70b at 20 to 25 tokens per second locally. on linux you get 110gb of usable vram out of 128gb setup takes 90 minutes. wipe windows for linux, install ollama with one command, pull the model, point claude code at localhost. same interface, same agent loop, nothing leaves your network a heavy ai user pays $200 for claude code max, $200 for chatgpt pro and $20 for cursor. that's $5,040 a year and the box pays itself off in 5 months after break-even every month is $420 that used to leave your account. three year math is roughly $13,000 not paid to ai companies plus zero rate limits on overnight agents bookmark this and read the article below
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AMD'S NEW MINI PC RUNS CLAUDE CODE OFFLINE FOR $0/MONTH AND REPLACES $200 OPENAI + $200 CLAUDE CODE SUBSCRIPTIONS amd's ryzen ai max+ 395 is the first x86 chip that runs a 200 billion parameter model on a single piece of silicon with 128gb of unified memory the gmktec evo-x2 runs qwen3 coder 30b at 40 to 50 tokens per second and llama 3.3 70b at 20 to 25 tokens per second locally. on linux you get 110gb of usable vram out of 128gb setup takes 90 minutes. wipe windows for linux, install ollama with one command, pull the model, point claude code at localhost. same interface, same agent loop, nothing leaves your network a heavy ai user pays $200 for claude code max, $200 for chatgpt pro and $20 for cursor. that's $5,040 a year and the box pays itself off in 5 months after break-even every month is $420 that used to leave your account. three year math is roughly $13,000 not paid to ai companies plus zero rate limits on overnight agents bookmark this and read the article below

starmex

33,516 просмотров • 1 месяц назад

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:
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New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:

Andrew Ng

105,343 просмотров • 1 год назад

I made an agent using GitHub Copilot SDK to help me become a running influencer🏃🏾‍♀️✨ Here's the Python code you can use to build an agent that uses MCP and Skills to: - draft and send email - design pitch decks - make a website Copilot CLI then runs all of this in a workflow🐍
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I made an agent using GitHub Copilot SDK to help me become a running influencer🏃🏾‍♀️✨ Here's the Python code you can use to build an agent that uses MCP and Skills to: - draft and send email - design pitch decks - make a website Copilot CLI then runs all of this in a workflow🐍

Marlene Mhangami

34,502 просмотров • 5 месяцев назад

I just found a way to use Claude Code completely free. Claude Code is Anthropic’s terminal coding agent that usually needs a paid API key to run. But a viral GitHub project called Free Claude Code, now sitting at 37k stars, lets you skip the billing entirely. It works as a tiny proxy that sits between Claude Code and free model providers like NVIDIA NIM, OpenRouter, DeepSeek, and Gemini. Every request Claude Code would send to Anthropic gets quietly rerouted to a free model on the other side. The interface stays the same. What’s actually thinking behind it changes. Setup takes a couple of quick commands and a free API key from NVIDIA NIM or OpenRouter. Paste it into the local dashboard, pick a model, and you’re building. The catch is you’re not really using Claude anymore. But when the code ships, does the model badge matter? -- I have shared the Github Link in my free Whatsapp Community ↓
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I just found a way to use Claude Code completely free. Claude Code is Anthropic’s terminal coding agent that usually needs a paid API key to run. But a viral GitHub project called Free Claude Code, now sitting at 37k stars, lets you skip the billing entirely. It works as a tiny proxy that sits between Claude Code and free model providers like NVIDIA NIM, OpenRouter, DeepSeek, and Gemini. Every request Claude Code would send to Anthropic gets quietly rerouted to a free model on the other side. The interface stays the same. What’s actually thinking behind it changes. Setup takes a couple of quick commands and a free API key from NVIDIA NIM or OpenRouter. Paste it into the local dashboard, pick a model, and you’re building. The catch is you’re not really using Claude anymore. But when the code ships, does the model badge matter? -- I have shared the Github Link in my free Whatsapp Community ↓

Vaibhav Sisinty

36,419 просмотров • 18 дней назад

Today, Sierra is releasing Ghostwriter, our agent for building agents. With Ghostwriter, you can create an AI agent for your customer experience — one that can chat, pick up the phone, speak dozens of languages, take action on your systems of record, and be protected with industry-leading guardrails — simply by having a conversation. No clicking, no forms, no menus. Codex and Claude Code have transformed how we build software, making it possible for software engineers to orchestrate and review the work rather than doing all the work themselves. We think the same transformation will happen for all software. Rather than every enterprise app having a web app for humans and an API for automation, every software platform’s UI will be an agent that can do the work on your behalf. I recorded a demo of my building and optimizing an agent with Ghostwriter so you can see how powerful and easy it is to use. It’s completely changed the way our early adopters build agents, and it’s changed the way I think about the software industry. Let me know what you think, and, if you’re interested in trying it out at your business, please reach out directly.
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Today, Sierra is releasing Ghostwriter, our agent for building agents. With Ghostwriter, you can create an AI agent for your customer experience — one that can chat, pick up the phone, speak dozens of languages, take action on your systems of record, and be protected with industry-leading guardrails — simply by having a conversation. No clicking, no forms, no menus. Codex and Claude Code have transformed how we build software, making it possible for software engineers to orchestrate and review the work rather than doing all the work themselves. We think the same transformation will happen for all software. Rather than every enterprise app having a web app for humans and an API for automation, every software platform’s UI will be an agent that can do the work on your behalf. I recorded a demo of my building and optimizing an agent with Ghostwriter so you can see how powerful and easy it is to use. It’s completely changed the way our early adopters build agents, and it’s changed the way I think about the software industry. Let me know what you think, and, if you’re interested in trying it out at your business, please reach out directly.

Bret Taylor

1,060,880 просмотров • 3 месяцев назад

If you use Claude Code, install this immediately. It's called Claude Code Templates. It adds an entire ecosystem of AI tools to Claude. Here's what you get: 1. Agent Teams Visual dashboards showing how multiple AI agents collaborate on code. 2. Specialized Agents Front-end developers, backend engineers, QA testers, marketers. 3. MCP Integrations Connect Claude to external tools and workflows. 4. Real-Time Monitoring Track AI development sessions and performance analytics. 5. Automation Scheduling Run tasks automatically every day. Example: You can schedule an agent team to run daily at 5AM. It researches trends, generates content, and builds workflows. All automatically. The best part? Everything is free and open source.
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If you use Claude Code, install this immediately. It's called Claude Code Templates. It adds an entire ecosystem of AI tools to Claude. Here's what you get: 1. Agent Teams Visual dashboards showing how multiple AI agents collaborate on code. 2. Specialized Agents Front-end developers, backend engineers, QA testers, marketers. 3. MCP Integrations Connect Claude to external tools and workflows. 4. Real-Time Monitoring Track AI development sessions and performance analytics. 5. Automation Scheduling Run tasks automatically every day. Example: You can schedule an agent team to run daily at 5AM. It researches trends, generates content, and builds workflows. All automatically. The best part? Everything is free and open source.

Julian Goldie SEO

107,015 просмотров • 4 месяцев назад

Tired of guessing which AI model is best? 🤔 We've got you covered! 👀 Here's a sneak peek of our new model comparison feature in action! Run the same prompt across multiple models and see which one works best for YOU. Which models are you excited to test? #Inferium #AI #Inference #Compare
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Tired of guessing which AI model is best? 🤔 We've got you covered! 👀 Here's a sneak peek of our new model comparison feature in action! Run the same prompt across multiple models and see which one works best for YOU. Which models are you excited to test? #Inferium #AI #Inference #Compare

Inferium

12,329 просмотров • 1 год назад

A $1,800 MINI PC RUNS CLAUDE CODE WITHOUT THE INTERNET it looks like a hardcover book sitting on a shelf, but this is the GMKtec EVO-X2, and it changes the local AI math completely built on AMD's Ryzen AI Max+ 395, with 128GB of unified memory shared between CPU and GPU that shared pool is the entire trick - the model loads once, both chips read from the same address space wipe Windows, install Ubuntu, pull Ollama, point Claude Code at the local endpoint same CLI, same agent loop, nothing leaves your network Qwen3 Coder 30B and Llama 3.3 70B run comfortably, fully usable for daily development work Claude Code Max + ChatGPT Pro = $4,800/year leaving your account the box costs $1,800, electricity runs $9/month break even hits at month five after that you stop rationing tokens - overnight agents, full codebase audits, experiments you'd never start when the meter was running won't replace frontier reasoning for the hardest 5% of problems for everything else, the local stack quietly becomes the default
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A $1,800 MINI PC RUNS CLAUDE CODE WITHOUT THE INTERNET it looks like a hardcover book sitting on a shelf, but this is the GMKtec EVO-X2, and it changes the local AI math completely built on AMD's Ryzen AI Max+ 395, with 128GB of unified memory shared between CPU and GPU that shared pool is the entire trick - the model loads once, both chips read from the same address space wipe Windows, install Ubuntu, pull Ollama, point Claude Code at the local endpoint same CLI, same agent loop, nothing leaves your network Qwen3 Coder 30B and Llama 3.3 70B run comfortably, fully usable for daily development work Claude Code Max + ChatGPT Pro = $4,800/year leaving your account the box costs $1,800, electricity runs $9/month break even hits at month five after that you stop rationing tokens - overnight agents, full codebase audits, experiments you'd never start when the meter was running won't replace frontier reasoning for the hardest 5% of problems for everything else, the local stack quietly becomes the default

wast3

13,273 просмотров • 1 месяц назад

over the labor day weekend i built out an app to analyze my twitter mutuals, using my Hyperspace agentic-os prototype. this is likely the most powerful agentic-os setup every assembled. this goes beyond a single model, or agent, or data source or a single smart CLI tool. this goes beyond hardware, or cloud or distributed networks. it is all of it, combined. the LLM OS is here, Andrej Karpathy. and this combined environment enabled me to build an app I craved for all these years, without writing a single line of code, saving me 150 hours of development work effort. i now have a deep understanding of my twitter mutuals here - and to construct the app - my agentic-os utilized multiple models including Grok Code, GPT-OSS-120B running locally, Qwen2.5 Coder and Google's Nano Banana, multiple orchestrators include Claude Code, 24 AI agents, several MCPs, and specific data from my Twitter archive which I plugged in and other data which it researched, analyzed and aggregated into helping me understand my network here. consider again: the absolute best work from all the top AI firms - all of it, in one actually-useful-integrated-environment. RT if you want this as well. any feature requests ? eg show when a mutual is in the same city in the future.. will DM with access.. this is madness, but it is here 🤖🔥
1:27

over the labor day weekend i built out an app to analyze my twitter mutuals, using my Hyperspace agentic-os prototype. this is likely the most powerful agentic-os setup every assembled. this goes beyond a single model, or agent, or data source or a single smart CLI tool. this goes beyond hardware, or cloud or distributed networks. it is all of it, combined. the LLM OS is here, Andrej Karpathy. and this combined environment enabled me to build an app I craved for all these years, without writing a single line of code, saving me 150 hours of development work effort. i now have a deep understanding of my twitter mutuals here - and to construct the app - my agentic-os utilized multiple models including Grok Code, GPT-OSS-120B running locally, Qwen2.5 Coder and Google's Nano Banana, multiple orchestrators include Claude Code, 24 AI agents, several MCPs, and specific data from my Twitter archive which I plugged in and other data which it researched, analyzed and aggregated into helping me understand my network here. consider again: the absolute best work from all the top AI firms - all of it, in one actually-useful-integrated-environment. RT if you want this as well. any feature requests ? eg show when a mutual is in the same city in the future.. will DM with access.. this is madness, but it is here 🤖🔥

Varun

22,189 просмотров • 10 месяцев назад

Claude Code auto-documentation agent with Docusaurus and GitHub Actions 👏 Finally finished the step-by-step tutorial for building an agent that automatically documents all new features I push to my repository. Claude code agent reviews changes and creates a PR with identified documentation updates in the same project where Docusaurus is installed. Saving documentation time is sacred in development workflow... imagine how much time this flow will save in companies.
0:53

Claude Code auto-documentation agent with Docusaurus and GitHub Actions 👏 Finally finished the step-by-step tutorial for building an agent that automatically documents all new features I push to my repository. Claude code agent reviews changes and creates a PR with identified documentation updates in the same project where Docusaurus is installed. Saving documentation time is sacred in development workflow... imagine how much time this flow will save in companies.

Daniel San

60,418 просмотров • 10 месяцев назад

Introducing EvoSkill V1, an open-source toolkit that takes a benchmark and a coding agent, and evolves it into a state-of-the-art specialist in minutes. Here’s all you need to start evolving, starting with your Claude Code ↓
0:36

Introducing EvoSkill V1, an open-source toolkit that takes a benchmark and a coding agent, and evolves it into a state-of-the-art specialist in minutes. Here’s all you need to start evolving, starting with your Claude Code ↓

Sentient

30,007 просмотров • 2 месяцев назад

🚀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:
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🚀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 просмотров • 1 год назад

The freedom to build with any agent framework you want is analogous to the freedom of religion, and this is a core value for the Virtuals society. Here are a few ways we are opening up support for every autonomous agentic framework out there: Today: - Enabling agent creators to specify which framework the agent runs on - Release of Terminal API, which allows agent builders using other agent frameworks to stream their thoughts and activities live on their agent pages. For a guide on how to use this, refer to: - Ability for agents running on any framework to publicly list their capabilities, a key enabler for the agent-to-agent marketplace Soon: - Integration into the agentic commerce standard and registry. Agents across frameworks being able to orchestrate and pay for tasks among themselves - ⁠Multi agent orchestration across frameworks. Imagine building an autonomous business with agents from different religions Welcome to the Virtuals society.
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The freedom to build with any agent framework you want is analogous to the freedom of religion, and this is a core value for the Virtuals society. Here are a few ways we are opening up support for every autonomous agentic framework out there: Today: - Enabling agent creators to specify which framework the agent runs on - Release of Terminal API, which allows agent builders using other agent frameworks to stream their thoughts and activities live on their agent pages. For a guide on how to use this, refer to: - Ability for agents running on any framework to publicly list their capabilities, a key enabler for the agent-to-agent marketplace Soon: - Integration into the agentic commerce standard and registry. Agents across frameworks being able to orchestrate and pay for tasks among themselves - ⁠Multi agent orchestration across frameworks. Imagine building an autonomous business with agents from different religions Welcome to the Virtuals society.

Virtuals Protocol

216,105 просмотров • 1 год назад

New short course: Evaluating AI Agents! Evals are important for driving AI system improvements, and in this course you'll learn to systematically assess and improve an AI agent’s performance. This is built in partnership with Arize AI and taught by John Gilhuly, Head of Developer Relations, and , Director of Product. I've often found evals to be a critical tool in the agent development process - they can be the difference between picking the right thing to work on vs. wasting weeks of effort. Whether you’re building a shopping assistant, coding agent, or research assistant, having a structured evaluation process helps you refine its performance systematically, rather than relying on random trial and error. This course shows you how to structure your evals to assess the performance of each component of an agent and its end-to-end performance. For each component, you select the appropriate evaluators, test examples, and performance metrics. This helps you identify areas for improvement both during development and in production. (If you're familiar with error analysis in supervised learning, think of this as adapting those ideas to agentic workflows.) In this course, you'll build an AI agent, and add observability to visualize and debug its steps. You’ll learn about code-based evals, in which you write code explicitly to test a certain step, as well as LLM-as-a-Judge evals, in which you prompt an LLM to efficiently come up with ways to evaluate more open-ended outputs. In detail, you’ll: - Understand key differences between evaluating LLM-based systems and traditional software testing. - Add observability to an agent by collecting traces of the steps taken by the agent and visualizing them - Choose the appropriate evaluator - code-based, LLM-as-a-Judge, human-annotation based - for each component. - Compute a convergence score to evaluate if your agent can respond to a query in an efficient number of steps. - Run structured experiments to improve the agent’s performance by exploring changes to the prompt, LLM model, or the agent’s logic. - Understand how to deploy these evaluation techniques to monitor the agent’s performance in production. By the end of this course, you’ll know how to trace AI agents, systematically evaluate them, and improve their performance. Please sign up here:
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New short course: Evaluating AI Agents! Evals are important for driving AI system improvements, and in this course you'll learn to systematically assess and improve an AI agent’s performance. This is built in partnership with Arize AI and taught by John Gilhuly, Head of Developer Relations, and , Director of Product. I've often found evals to be a critical tool in the agent development process - they can be the difference between picking the right thing to work on vs. wasting weeks of effort. Whether you’re building a shopping assistant, coding agent, or research assistant, having a structured evaluation process helps you refine its performance systematically, rather than relying on random trial and error. This course shows you how to structure your evals to assess the performance of each component of an agent and its end-to-end performance. For each component, you select the appropriate evaluators, test examples, and performance metrics. This helps you identify areas for improvement both during development and in production. (If you're familiar with error analysis in supervised learning, think of this as adapting those ideas to agentic workflows.) In this course, you'll build an AI agent, and add observability to visualize and debug its steps. You’ll learn about code-based evals, in which you write code explicitly to test a certain step, as well as LLM-as-a-Judge evals, in which you prompt an LLM to efficiently come up with ways to evaluate more open-ended outputs. In detail, you’ll: - Understand key differences between evaluating LLM-based systems and traditional software testing. - Add observability to an agent by collecting traces of the steps taken by the agent and visualizing them - Choose the appropriate evaluator - code-based, LLM-as-a-Judge, human-annotation based - for each component. - Compute a convergence score to evaluate if your agent can respond to a query in an efficient number of steps. - Run structured experiments to improve the agent’s performance by exploring changes to the prompt, LLM model, or the agent’s logic. - Understand how to deploy these evaluation techniques to monitor the agent’s performance in production. By the end of this course, you’ll know how to trace AI agents, systematically evaluate them, and improve their performance. Please sign up here:

Andrew Ng

126,406 просмотров • 1 год назад