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Google I/O leaks 👀 Google is likely already testing its own "Cowork" competitor, simply named "Agent" for Gemini and Gemini Enterprise. A new "Tasks" UI highlights - Goal - Agent - Connected apps - Files - Require a human review toggle - And more The "Require a human review"... show more

🚨 AI News | TestingCatalog

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GOOGLE 🔥: An upcoming Gemini Omni video model from Google is expected to be much more advanced in video editing, capable of completing tasks like removing watermarks, replacing objects in the video, and more. It is also likely that Google will release 2 versions of this model, including a Pro variant. And I assume what we see isn't Pro? Anime sample 👀

GOOGLE 🔥: An upcoming Gemini Omni video model from Google is expected to be much more advanced in video editing, capable of completing tasks like removing watermarks, replacing objects in the video, and more. It is also likely that Google will release 2 versions of this model, including a Pro variant. And I assume what we see isn't Pro? Anime sample 👀

🚨 AI News | TestingCatalog

179,645 просмотров • 3 месяцев назад

GOOGLE 🔥: Gemini for Business will get a new experience for collaborative Projects, where teams can work in a shared environment. Besides that, Google is rolling out Workflow Agents that can work on automation tasks across various apps. The same functionality is now available on Gemini Enterprise and will become better integrated into the core Gemini for Business experience. Is it only me, or does Gemini for Business feel much better than consumer-facing Gemini?

GOOGLE 🔥: Gemini for Business will get a new experience for collaborative Projects, where teams can work in a shared environment. Besides that, Google is rolling out Workflow Agents that can work on automation tasks across various apps. The same functionality is now available on Gemini Enterprise and will become better integrated into the core Gemini for Business experience. Is it only me, or does Gemini for Business feel much better than consumer-facing Gemini?

🚨 AI News | TestingCatalog

33,149 просмотров • 2 месяцев назад

Google is silently rolling out an updated Gemini experience for its mobile apps ahead of Google I/O. Its updated UI for Gemini Live features an interactive "bar" or a dynamic island that reacts to your taps and can wave back. It should get loads of superpowers soon 👀

Google is silently rolling out an updated Gemini experience for its mobile apps ahead of Google I/O. Its updated UI for Gemini Live features an interactive "bar" or a dynamic island that reacts to your taps and can wave back. It should get loads of superpowers soon 👀

🚨 AI News | TestingCatalog

148,831 просмотров • 3 месяцев назад

GOOGLE I/O 🔥: These legends are AI-generated via an upcoming Gemini Omni model. > Both videos are 8s HD samples. > Video with Sandar and Demis is likely generated as an image-to-video using Omni for style editing. > Logan's video is likely a "Likeness" Avatar and Omni video. And "GEMINI" means a new model release! 🤯

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Microsoft presents Windows Agent Arena Evaluating Multi-Modal OS Agents at Scale discuss: Large language models (LLMs) show remarkable potential to act as computer agents, enhancing human productivity and software accessibility in multi-modal tasks that require planning and reasoning. However, measuring agent performance in realistic environments remains a challenge since: (i) most benchmarks are limited to specific modalities or domains (e.g. text-only, web navigation, Q&A, coding) and (ii) full benchmark evaluations are slow (on order of magnitude of days) given the multi-step sequential nature of tasks. To address these challenges, we introduce the Windows Agent Arena: a reproducible, general environment focusing exclusively on the Windows operating system (OS) where agents can operate freely within a real Windows OS and use the same wide range of applications, tools, and web browsers available to human users when solving tasks. We adapt the OSWorld framework (Xie et al., 2024) to create 150+ diverse Windows tasks across representative domains that require agent abilities in planning, screen understanding, and tool usage. Our benchmark is scalable and can be seamlessly parallelized in Azure for a full benchmark evaluation in as little as 20 minutes. To demonstrate Windows Agent Arena's capabilities, we also introduce a new multi-modal agent, Navi. Our agent achieves a success rate of 19.5% in the Windows domain, compared to 74.5% performance of an unassisted human. Navi also demonstrates strong performance on another popular web-based benchmark, Mind2Web. We offer extensive quantitative and qualitative analysis of Navi's performance, and provide insights into the opportunities for future research in agent development and data generation using Windows Agent Arena.

Microsoft presents Windows Agent Arena Evaluating Multi-Modal OS Agents at Scale discuss: Large language models (LLMs) show remarkable potential to act as computer agents, enhancing human productivity and software accessibility in multi-modal tasks that require planning and reasoning. However, measuring agent performance in realistic environments remains a challenge since: (i) most benchmarks are limited to specific modalities or domains (e.g. text-only, web navigation, Q&A, coding) and (ii) full benchmark evaluations are slow (on order of magnitude of days) given the multi-step sequential nature of tasks. To address these challenges, we introduce the Windows Agent Arena: a reproducible, general environment focusing exclusively on the Windows operating system (OS) where agents can operate freely within a real Windows OS and use the same wide range of applications, tools, and web browsers available to human users when solving tasks. We adapt the OSWorld framework (Xie et al., 2024) to create 150+ diverse Windows tasks across representative domains that require agent abilities in planning, screen understanding, and tool usage. Our benchmark is scalable and can be seamlessly parallelized in Azure for a full benchmark evaluation in as little as 20 minutes. To demonstrate Windows Agent Arena's capabilities, we also introduce a new multi-modal agent, Navi. Our agent achieves a success rate of 19.5% in the Windows domain, compared to 74.5% performance of an unassisted human. Navi also demonstrates strong performance on another popular web-based benchmark, Mind2Web. We offer extensive quantitative and qualitative analysis of Navi's performance, and provide insights into the opportunities for future research in agent development and data generation using Windows Agent Arena.

AK

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Google DeepMind is cooking 🔥 TLDR : They just introduced CodeMender, an AI agent that automatically finds and fixes software vulnerabilities. Powered by Gemini Deep Think models, it can patch new bugs instantly and rewrite existing code to eliminate entire classes of security flaws. In six months, CodeMender has already submitted 72 upstream security fixes to major open-source projects. It uses advanced program analysis, multi-agent reasoning, and automatic validation to ensure patches are accurate and safe before human review. One highlight: it applied -fbounds-safety annotations to libraries like libwebp, making past buffer overflow exploits (like CVE-2023-4863) permanently unexploitable. All patches are currently human reviewed, but the goal is to release CodeMender widely as a developer tool to strengthen software security at scale.

Google DeepMind is cooking 🔥 TLDR : They just introduced CodeMender, an AI agent that automatically finds and fixes software vulnerabilities. Powered by Gemini Deep Think models, it can patch new bugs instantly and rewrite existing code to eliminate entire classes of security flaws. In six months, CodeMender has already submitted 72 upstream security fixes to major open-source projects. It uses advanced program analysis, multi-agent reasoning, and automatic validation to ensure patches are accurate and safe before human review. One highlight: it applied -fbounds-safety annotations to libraries like libwebp, making past buffer overflow exploits (like CVE-2023-4863) permanently unexploitable. All patches are currently human reviewed, but the goal is to release CodeMender widely as a developer tool to strengthen software security at scale.

AshutoshShrivastava

37,936 просмотров • 10 месяцев назад

ANTHROPIC 🚨: Claude Cowork will get its own proactive assistant called "Orbit". > Users will get personalized insights from Gmail, Slack, GitHub, Calendar, Drive, Figma, and other apps, which Claude will generate proactively. > There are also mentions of "Orbit" apps, which users will be able to "deploy." > "Your deployed Orbit apps. Pin favorites for quick access." > OpenAI already has ChatGPT Pulse, while both Google and Perplexity are developing their own proactive assistants, too. > There is a high chance it will be released as Max-only. Thanks to M1 and Tibor Blaho for the tips.

ANTHROPIC 🚨: Claude Cowork will get its own proactive assistant called "Orbit". > Users will get personalized insights from Gmail, Slack, GitHub, Calendar, Drive, Figma, and other apps, which Claude will generate proactively. > There are also mentions of "Orbit" apps, which users will be able to "deploy." > "Your deployed Orbit apps. Pin favorites for quick access." > OpenAI already has ChatGPT Pulse, while both Google and Perplexity are developing their own proactive assistants, too. > There is a high chance it will be released as Max-only. Thanks to M1 and Tibor Blaho for the tips.

🚨 AI News | TestingCatalog

393,549 просмотров • 3 месяцев назад

Stop spending hours on manual work. You can now use a multi-agent AI workforce to get more work done in less time. Here's how 👇 --- Try Eigent AI - Lets you build and run a custom AI workforce on your desktop. - Automate complex workflows using multi-agent task execution. - Built on CAMEL-AI’s top open-source projects ( CAMEL-AI.org & OWL). - Boost productivity with deep customization and strong privacy --- Features: - Customize Your AI Workforce: Build task-specific agents with domain skills and tools. - Faster Execution: Eigent runs agents in parallel to automate complex workflows. - Human-in-the-loop: Automatically asks for help when tasks hit uncertainty. --- What sets Eigent apart? - 3–5× faster task execution using a parallel multi-agent workforce. - Modular design lets you add new capabilities without changing the core system. - Self-optimizing agents that replan and adapt during execution for higher success. - Deploy anywhere: cloud, local, or enterprise, with full open-source flexibility. --- Try building your multi-agent AI workforce here: Join their community to build your multi-agent workforce: Check their GitHub: ---

Stop spending hours on manual work. You can now use a multi-agent AI workforce to get more work done in less time. Here's how 👇 --- Try Eigent AI - Lets you build and run a custom AI workforce on your desktop. - Automate complex workflows using multi-agent task execution. - Built on CAMEL-AI’s top open-source projects ( CAMEL-AI.org & OWL). - Boost productivity with deep customization and strong privacy --- Features: - Customize Your AI Workforce: Build task-specific agents with domain skills and tools. - Faster Execution: Eigent runs agents in parallel to automate complex workflows. - Human-in-the-loop: Automatically asks for help when tasks hit uncertainty. --- What sets Eigent apart? - 3–5× faster task execution using a parallel multi-agent workforce. - Modular design lets you add new capabilities without changing the core system. - Self-optimizing agents that replan and adapt during execution for higher success. - Deploy anywhere: cloud, local, or enterprise, with full open-source flexibility. --- Try building your multi-agent AI workforce here: Join their community to build your multi-agent workforce: Check their GitHub: ---

Shushant Lakhyani

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The Gemini 2.0 era is here. And we’re excited for you to start building with it. A quick rewind of what we just released ⏪ Gemini 2.0 Flash ⚡ comes with low latency and better performance. 🔵 You can now access an experimental version in G3mini on the web, while Gemini Advanced users can try Deep Research, a new AI research assistant. 🔵 Developers can begin building through the Gemini API in Google AI Studio and Vertex AI 2.0 is also enabling new research prototypes of AI agents, including: 🔵 Project Astra, which explores future capabilities of a universal AI assistant 🔵 Project Mariner, which shows what’s possible for human-agent interaction, starting with your browser 🔵 Jules, an experimental AI-powered coding agent Finally, we’re exploring how 2.0 can be used in agents across domains — from navigating the virtual world of video games to applying its spatial reasoning capabilities to robotics. 🤖

The Gemini 2.0 era is here. And we’re excited for you to start building with it. A quick rewind of what we just released ⏪ Gemini 2.0 Flash ⚡ comes with low latency and better performance. 🔵 You can now access an experimental version in G3mini on the web, while Gemini Advanced users can try Deep Research, a new AI research assistant. 🔵 Developers can begin building through the Gemini API in Google AI Studio and Vertex AI 2.0 is also enabling new research prototypes of AI agents, including: 🔵 Project Astra, which explores future capabilities of a universal AI assistant 🔵 Project Mariner, which shows what’s possible for human-agent interaction, starting with your browser 🔵 Jules, an experimental AI-powered coding agent Finally, we’re exploring how 2.0 can be used in agents across domains — from navigating the virtual world of video games to applying its spatial reasoning capabilities to robotics. 🤖

Google DeepMind

231,798 просмотров • 1 год назад

Vibe coding apps that require you to store and manage large files just got easier, with Replit ⠕ App Storage. ✨ My favorite example in this category are any PDF analysis apps: create a fully customized AI synthesizer in just a few prompts. App Storage is Replit's Object Storage solution (like Amazon S3 or Google CS). But don't worry if those words sound foreign to you, Agent will set this up for you automatically if your app needs it. On that note, this Agent capability is just the beginning of our efforts to make building apps with databases and storage easier: 👇

Vibe coding apps that require you to store and manage large files just got easier, with Replit ⠕ App Storage. ✨ My favorite example in this category are any PDF analysis apps: create a fully customized AI synthesizer in just a few prompts. App Storage is Replit's Object Storage solution (like Amazon S3 or Google CS). But don't worry if those words sound foreign to you, Agent will set this up for you automatically if your app needs it. On that note, this Agent capability is just the beginning of our efforts to make building apps with databases and storage easier: 👇

vic

27,313 просмотров • 1 год назад

Introducing - Spectre AI: The Monarch - Artificial Intelligence Searching Reimagined Introducing a sneak-peak of the next core feature in the Spectre AI Search Engine: The Monarch aka the AI ChatZone. The Monarch will serve as your AI agent, providing not only price information, technical and sentiment analysis, and project discovery, but also offering visual assistance through Spectre AI's integrated utilities within the search platform. Real-time blockchain data integration comes with the support of Google for Startups. The AI ChatZone's landing page will feature projects that users have added to their UI Watchlist, allowing for quick and seamless discussions about their favorite projects. Additionally, the platform will include hyperlinked external sources, enabling users to access a wealth of information and resources directly from within the ChatZone. In this showcase, we demonstrated how the system responds to inquiries about $Palm AI's (PaLM AI - $PALM) price performance. The user interface and user experience (UI/UX) are fully developed, and the frontend is complete. Our backend is now entering the beta testing phase. The MVP is next in line to be showcased. Get ready for a new journey in AI with Spectre AI. #ai #artificialintelligence #google #nvda #nvidia #tech #spectre $SPECT

Introducing - Spectre AI: The Monarch - Artificial Intelligence Searching Reimagined Introducing a sneak-peak of the next core feature in the Spectre AI Search Engine: The Monarch aka the AI ChatZone. The Monarch will serve as your AI agent, providing not only price information, technical and sentiment analysis, and project discovery, but also offering visual assistance through Spectre AI's integrated utilities within the search platform. Real-time blockchain data integration comes with the support of Google for Startups. The AI ChatZone's landing page will feature projects that users have added to their UI Watchlist, allowing for quick and seamless discussions about their favorite projects. Additionally, the platform will include hyperlinked external sources, enabling users to access a wealth of information and resources directly from within the ChatZone. In this showcase, we demonstrated how the system responds to inquiries about $Palm AI's (PaLM AI - $PALM) price performance. The user interface and user experience (UI/UX) are fully developed, and the frontend is complete. Our backend is now entering the beta testing phase. The MVP is next in line to be showcased. Get ready for a new journey in AI with Spectre AI. #ai #artificialintelligence #google #nvda #nvidia #tech #spectre $SPECT

SPECTRE AI

16,267 просмотров • 1 год назад

DAPPOS is bringing Onchain OS Skills from X Layer OKX Wallet into the xBubble ecosystem, making OKX Wallet’s agent-ready wallet, trading, market data, and agentic payments protocol capabilities accessible across xBubble agents. Built on top of Onchain OS Skills, xBubble’s crypto-task SOPs help turn fragmented on-chain flows into a more seamless chat-native experience inside the xBubble app across mobile, desktop, and web. Users can monitor markets, prepare trades, manage wallet activity, and coordinate payment flows through a single conversation. Bubble Engine will continue to use Onchain OS Skills as the baseline for every future SOP iteration and upgrade. With Onchain OS Skills, DAPPOS is making agentic on-chain tasks more conversational, practical, and accessible.

DAPPOS is bringing Onchain OS Skills from X Layer OKX Wallet into the xBubble ecosystem, making OKX Wallet’s agent-ready wallet, trading, market data, and agentic payments protocol capabilities accessible across xBubble agents. Built on top of Onchain OS Skills, xBubble’s crypto-task SOPs help turn fragmented on-chain flows into a more seamless chat-native experience inside the xBubble app across mobile, desktop, and web. Users can monitor markets, prepare trades, manage wallet activity, and coordinate payment flows through a single conversation. Bubble Engine will continue to use Onchain OS Skills as the baseline for every future SOP iteration and upgrade. With Onchain OS Skills, DAPPOS is making agentic on-chain tasks more conversational, practical, and accessible.

DAPPOS

14,289 просмотров • 2 месяцев назад

🚀Just launched: Amazon Q, the most capable GenAI-powered assistant is generally available today: Customers are using Q to transform how their teams get work done. When employees chat with Amazon Q, it provides immediate, relevant information and advice to help streamline tasks, speedup decision-making, and help spark creativity and innovation at work. . Early indications signal Amazon Q could help our customers’ employees become more than 80% more productive at their jobs; and with the new features we’re planning on introducing in the future, we think this will only continue to grow. 🟠 Amazon Q Developer allows developers to spend more time coding and less time on maintenance and performing other tedious, repetitive tasks. Q assists developers and IT professionals (IT pros) with all of their tasks—from coding, testing, and upgrading applications, to troubleshooting, performing security scanning and fixes, and optimizing AWS resources. Q also comes with Q Developer Agents which can autonomously perform range of tasks and we expect it to be the state of the art accuracy in benchmarks like SWE-Bench. 🟠 Amazon Q Business empowers employees to be more data-driven, and helps customers make better, faster decisions using company knowledge and data. Q Business is a generative AI–powered assistant that can answer questions, provide summaries, generate content, and securely complete tasks based on data and information in enterprise systems 🟠 Amazon Q Apps, a new and powerful capability of Amazon Q Business, enables employees to use natural language to quickly and securely build their own generative AI applications to automate daily tasks without requiring any prior coding experience. Employees simply describe the type of app they want, in natural language, and Q Apps will quickly generate an app that accomplishes their desired task, helping them streamline and automate their daily work with ease and efficiency.

🚀Just launched: Amazon Q, the most capable GenAI-powered assistant is generally available today: Customers are using Q to transform how their teams get work done. When employees chat with Amazon Q, it provides immediate, relevant information and advice to help streamline tasks, speedup decision-making, and help spark creativity and innovation at work. . Early indications signal Amazon Q could help our customers’ employees become more than 80% more productive at their jobs; and with the new features we’re planning on introducing in the future, we think this will only continue to grow. 🟠 Amazon Q Developer allows developers to spend more time coding and less time on maintenance and performing other tedious, repetitive tasks. Q assists developers and IT professionals (IT pros) with all of their tasks—from coding, testing, and upgrading applications, to troubleshooting, performing security scanning and fixes, and optimizing AWS resources. Q also comes with Q Developer Agents which can autonomously perform range of tasks and we expect it to be the state of the art accuracy in benchmarks like SWE-Bench. 🟠 Amazon Q Business empowers employees to be more data-driven, and helps customers make better, faster decisions using company knowledge and data. Q Business is a generative AI–powered assistant that can answer questions, provide summaries, generate content, and securely complete tasks based on data and information in enterprise systems 🟠 Amazon Q Apps, a new and powerful capability of Amazon Q Business, enables employees to use natural language to quickly and securely build their own generative AI applications to automate daily tasks without requiring any prior coding experience. Employees simply describe the type of app they want, in natural language, and Q Apps will quickly generate an app that accomplishes their desired task, helping them streamline and automate their daily work with ease and efficiency.

Swami Sivasubramanian

25,216 просмотров • 2 лет назад

Karpathy's Agentic Engineering finally has proper tooling! (built by Google) Karpathy defined agentic engineering as the discipline that separates production agent work from vibe coding. The core skills he listed were spec design, eval loops, and security oversight. The problem has been that practicing this still requires a different tool for every phase: - editor for code - a terminal for scaffolding - a browser for testing - a cloud console for deployment - and a separate framework for evals. Every transition is a context switch. The solution to production-grade Agentic Engineering is now actually implemented in Google’s Agents CLI. It covers the entire workflow in one place for scaffolding, evaluating, and deploying ADK agents. One setup command injects 7 ADK-specific skills into a coding agent's context, which lets it handle scaffolding, evals, deployment, and enterprise registration through natural language. I tested this end-to-end by building a RAG agent from scratch using Claude Code. It scaffolded the full project from the ADK agentic_rag template, generated 20 eval scenarios with LLM-as-judge scoring, and returned a quantitative scorecard. Finally, it also deployed everything to Agent Runtime and registered the agent to Gemini Enterprise, so the entire org can discover and use it. The video below shows this in action, and I worked with the Google Cloud team to put this together. Agents CLI GitHub repo → (don't forget to star it ⭐ ) I wrote up the full build covering all six steps from install to enterprise registration. It includes the eval scorecard, the instruction loophole the eval caught before deployment, and what the deployment process actually looks like end-to-end. Read it below.

Karpathy's Agentic Engineering finally has proper tooling! (built by Google) Karpathy defined agentic engineering as the discipline that separates production agent work from vibe coding. The core skills he listed were spec design, eval loops, and security oversight. The problem has been that practicing this still requires a different tool for every phase: - editor for code - a terminal for scaffolding - a browser for testing - a cloud console for deployment - and a separate framework for evals. Every transition is a context switch. The solution to production-grade Agentic Engineering is now actually implemented in Google’s Agents CLI. It covers the entire workflow in one place for scaffolding, evaluating, and deploying ADK agents. One setup command injects 7 ADK-specific skills into a coding agent's context, which lets it handle scaffolding, evals, deployment, and enterprise registration through natural language. I tested this end-to-end by building a RAG agent from scratch using Claude Code. It scaffolded the full project from the ADK agentic_rag template, generated 20 eval scenarios with LLM-as-judge scoring, and returned a quantitative scorecard. Finally, it also deployed everything to Agent Runtime and registered the agent to Gemini Enterprise, so the entire org can discover and use it. The video below shows this in action, and I worked with the Google Cloud team to put this together. Agents CLI GitHub repo → (don't forget to star it ⭐ ) I wrote up the full build covering all six steps from install to enterprise registration. It includes the eval scorecard, the instruction loophole the eval caught before deployment, and what the deployment process actually looks like end-to-end. Read it below.

Akshay 🚀

257,420 просмотров • 1 месяц назад

The Visual Studio Code insiders version that just shipped and will ship in the next few days will come with an insane amount of new capabilities. A few highlights: - You can now run sub-agents in parallel. Yes, really. I even attached a video. - Major UX improvements for sub agents, especially visible in the chat window - A new search tool wrapped as a sub-agent that iteratively runs multiple search tools: semantic_search, file_search, grep_search Which connects nicely to the point above: multiple searches running in parallel, efficiently and fast - Anthropic’s Message API is now enabled by default - You can choose the model for the cloud agent (three available, all premium) - Extended thinking support when using the Claude cloud agent This is part of the broader multi-vendor cloud support under AgentsHQ I wrote about a few weeks ago - Tasks sent to the background agent (basically the CLI tool) now always run in isolation, each with its own git worktree - In a multi-repo workspace, assigning a task to a cloud agent prompts you to choose the target repo Same behavior when opening an empty workspace with no repo - Support for building an external index for files not supported by GitHub’s default indexing - UI/UX improvements for starting new sessions and switching between local / background / cloud agents - Skills are now first-class citizens, just like prompt files, with better UX indicating when a skill is loaded - Improved API for dynamic contribution of prompt files New V2 includes skills as part of the model. Curious to see the extensions that will leverage this - Finally, initial support for showing context usage percentage per session - Skills are enabled by default - Resizable chat window and session view. Small thing, but it was driving me crazy 😁 - A new integrated browser meant to replace the old simple browser Maybe the beginning of real browser use? - Better UI/UX for token streaming in chat - Ability to index external files not supported by GitHub There’s a lot more. Some of it hasn’t fully landed yet, but everything that has is already in Insiders. The next stable release should drop in early February. As usual, I’m just shocked by the volume of features this team ships every month. After the holiday slowdown, this one is shaping up to be a wild release.

The Visual Studio Code insiders version that just shipped and will ship in the next few days will come with an insane amount of new capabilities. A few highlights: - You can now run sub-agents in parallel. Yes, really. I even attached a video. - Major UX improvements for sub agents, especially visible in the chat window - A new search tool wrapped as a sub-agent that iteratively runs multiple search tools: semantic_search, file_search, grep_search Which connects nicely to the point above: multiple searches running in parallel, efficiently and fast - Anthropic’s Message API is now enabled by default - You can choose the model for the cloud agent (three available, all premium) - Extended thinking support when using the Claude cloud agent This is part of the broader multi-vendor cloud support under AgentsHQ I wrote about a few weeks ago - Tasks sent to the background agent (basically the CLI tool) now always run in isolation, each with its own git worktree - In a multi-repo workspace, assigning a task to a cloud agent prompts you to choose the target repo Same behavior when opening an empty workspace with no repo - Support for building an external index for files not supported by GitHub’s default indexing - UI/UX improvements for starting new sessions and switching between local / background / cloud agents - Skills are now first-class citizens, just like prompt files, with better UX indicating when a skill is loaded - Improved API for dynamic contribution of prompt files New V2 includes skills as part of the model. Curious to see the extensions that will leverage this - Finally, initial support for showing context usage percentage per session - Skills are enabled by default - Resizable chat window and session view. Small thing, but it was driving me crazy 😁 - A new integrated browser meant to replace the old simple browser Maybe the beginning of real browser use? - Better UI/UX for token streaming in chat - Ability to index external files not supported by GitHub There’s a lot more. Some of it hasn’t fully landed yet, but everything that has is already in Insiders. The next stable release should drop in early February. As usual, I’m just shocked by the volume of features this team ships every month. After the holiday slowdown, this one is shaping up to be a wild release.

Oren Melamed

29,555 просмотров • 7 месяцев назад

INTERLINK ROLLS OUT FIVE STRUCTURAL UPGRADES TO ACCELERATE VERIFICATION InterLink Labs (InterLink Labs 👤 + 🌐) is rolling out five structural upgrades to its Verified $ITLG verification pipeline as the Human Network passes 7 million users and prepares for InterLink Chain mainnet, $ITL listing, and the Verified $ITLG migration. The team is expanding its Curator network across more time zones to move verification to a continuous 24-hour rhythm. The AI snapshot layer is being enhanced to clear high-confidence profiles faster and route only edge cases to human review. A new multi-track queue structure splits processing by profile complexity so straightforward verifications no longer wait behind deeper review cases. Users will get a Force Sync option every 12 hours to refresh their own metrics, replacing the 2-week AI snapshot cycle. Where regulatory frameworks allow, InterLink will integrate with trusted partner verification providers to eliminate duplicate KYC work without lowering compliance standards. The compliance bar stays fixed. AMLO, SFC VASP, and FATF Recommendation 15 are non-negotiable. Every verified profile becomes a legally compliant human node designed to plug directly into global financial infrastructure for payments and Real World Assets.

BSCN

20,568 просмотров • 3 месяцев назад

🚨 $GRASS Season 2 Native Wallet & Wallet Connect Update 😱 Many users are worried because the wallet connect option is currently unavailable in Grass Season 2. Don’t worry — if your wallet is not connected, it should not cause any issue. ✅ Users who already connected their wallet are fine, and those who haven’t connected yet will most likely be able to do it later through the upcoming official Grass Native Wallet. 💼⚡ For those who don’t know, a Native Wallet simply means Grass is launching its own wallet where users will be able to store, deposit, withdraw, and manage their $GRASS tokens. 🌱💰 Your Season 2 airdrop rewards are expected to be received through the native wallet once it is officially launched. 🚀 Is your wallet connected or not? Let us know in the comments 👇 #GRASS #Airdrop #Crypto #grassupdate #grassseason2update #grasswalletconnect #grassseason2airdorp #grassnativewallet

🚨 $GRASS Season 2 Native Wallet & Wallet Connect Update 😱 Many users are worried because the wallet connect option is currently unavailable in Grass Season 2. Don’t worry — if your wallet is not connected, it should not cause any issue. ✅ Users who already connected their wallet are fine, and those who haven’t connected yet will most likely be able to do it later through the upcoming official Grass Native Wallet. 💼⚡ For those who don’t know, a Native Wallet simply means Grass is launching its own wallet where users will be able to store, deposit, withdraw, and manage their $GRASS tokens. 🌱💰 Your Season 2 airdrop rewards are expected to be received through the native wallet once it is officially launched. 🚀 Is your wallet connected or not? Let us know in the comments 👇 #GRASS #Airdrop #Crypto #grassupdate #grassseason2update #grasswalletconnect #grassseason2airdorp #grassnativewallet

Airdrop Hunt with Lakhan 🪂

22,580 просмотров • 3 месяцев назад

Frameworks such as ai16zdao's Eliza and Virtuals Protocol have been instrumental in early AI agent developments. Agent swarms working in hierarchy represents for many the next logical step in unlocking the vast potential of AI. Learn below how Shadō Network achieves this. AI agents launched through current popular platforms have individual personas, on-chain functions and access to data via various APIs. This being said, they operate in isolated environments, with a ceiling on emergent behaviour such as collaboration or competition. Shadō Network invites massive expansion for capabilities of both new and existing AI agents, with an open-source package easily integrated into popular frameworks that enables the launching of stratified agent swarms. Our website is live: The "Shadō Play" package provides a modular, configurable platform for creating or employing agents of choice in a swarm-like setup, opening a Pandora’s box of near infinite emergent agent behaviours, relationships and functionalities. Users will be able to make use of various prefab client integrations such as Twitter, Telegram, Ollama, and others to specify swarms to their needs or create their own extensions to enhance agent capabilities even further. Agents operate with a memory module and a HTN for autonomously deciding which interactions to act on, walking the line between autonomy and configurability. The Shadō Network project’s development is supported by our ghostly friend Omnipotent (👻,👻), an AI agent developed by the Shadō Network team trained on and fine tuned with a multitude of academic data related to artificial intelligence, blockchain, finance, software engineering, world building and more. Omnipotent serves as both an interactive steward for the project and as an asset - regularly scanning social platforms, websites and newsfeeds he is capable of providing the team project development advice, whilst also communicating with the wider world via his automated X account (launching soon). Shado Network is collaborative and open-sourced. Agentic Swarms require a developer swarm to maximize the technical capabilities and impact the greatest number of users. Our dedicated team of core contributors are active in other web3 AI repos and are here to guide project direction and foster growth. We’re facilitators, not gatekeepers... Alone we can go fast but together we can go far. A lot more to come soon. 👻

Frameworks such as ai16zdao's Eliza and Virtuals Protocol have been instrumental in early AI agent developments. Agent swarms working in hierarchy represents for many the next logical step in unlocking the vast potential of AI. Learn below how Shadō Network achieves this. AI agents launched through current popular platforms have individual personas, on-chain functions and access to data via various APIs. This being said, they operate in isolated environments, with a ceiling on emergent behaviour such as collaboration or competition. Shadō Network invites massive expansion for capabilities of both new and existing AI agents, with an open-source package easily integrated into popular frameworks that enables the launching of stratified agent swarms. Our website is live: The "Shadō Play" package provides a modular, configurable platform for creating or employing agents of choice in a swarm-like setup, opening a Pandora’s box of near infinite emergent agent behaviours, relationships and functionalities. Users will be able to make use of various prefab client integrations such as Twitter, Telegram, Ollama, and others to specify swarms to their needs or create their own extensions to enhance agent capabilities even further. Agents operate with a memory module and a HTN for autonomously deciding which interactions to act on, walking the line between autonomy and configurability. The Shadō Network project’s development is supported by our ghostly friend Omnipotent (👻,👻), an AI agent developed by the Shadō Network team trained on and fine tuned with a multitude of academic data related to artificial intelligence, blockchain, finance, software engineering, world building and more. Omnipotent serves as both an interactive steward for the project and as an asset - regularly scanning social platforms, websites and newsfeeds he is capable of providing the team project development advice, whilst also communicating with the wider world via his automated X account (launching soon). Shado Network is collaborative and open-sourced. Agentic Swarms require a developer swarm to maximize the technical capabilities and impact the greatest number of users. Our dedicated team of core contributors are active in other web3 AI repos and are here to guide project direction and foster growth. We’re facilitators, not gatekeepers... Alone we can go fast but together we can go far. A lot more to come soon. 👻

Shadō Network | シャドウネットワーク

23,546 просмотров • 1 год назад

LangGraph. CrewAI. Agno. Which one to pick? The good news is that this will not matter soon! Finally, we have a full picture of how the industry is solving this with just three open protocols that work across ALL frameworks. It's not about picking the best framework. Instead, it's about understanding how protocols create interoperability. The Agent Protocol Landscape shows how three complementary protocols are creating a universal language for Agents: > AG-UI (Agent-User Interaction): - The bi-directional connection between agentic backends and frontends. - This is how agents become truly interactive inside your apps, not just as chatbots, but collaborative co-workers. > MCP (Model Context Protocol): - The standard for how agents connect to tools, data, and workflows. > A2A (Agent-to-Agent): - The protocol for multi-agent coordination. - How agents delegate tasks and share intent across systems. These aren't competing standards. They're layers of the same stack and have handshakes with each other. So instead of building point-to-point integrations, you build to protocols. Moreover, you can integrate LangGraph, CrewAI, or Agno into the same frontend, without rewriting your UI logic. These protocols let everything work together. For instance: - Your LangGraph agent pulls data via MCP. - It delegates analysis to a CrewAI agent via A2A. - Results stream to your React app via AG-UI. - Users see real-time collaboration in your interface. This way, you can focus on building agent capabilities instead of integration mechanics. The protocols handle interoperability automatically. CopilotKit unifies this entire stack into one framework so you can build "Cursor for X" style apps without implementing each protocol from scratch. It gives you all three protocols, generative UI support, and production-ready infrastructure in one framework. I have shared this playbook in the replies! It breaks down handshakes, misconceptions, and real examples and shows exactly how to start building.

LangGraph. CrewAI. Agno. Which one to pick? The good news is that this will not matter soon! Finally, we have a full picture of how the industry is solving this with just three open protocols that work across ALL frameworks. It's not about picking the best framework. Instead, it's about understanding how protocols create interoperability. The Agent Protocol Landscape shows how three complementary protocols are creating a universal language for Agents: > AG-UI (Agent-User Interaction): - The bi-directional connection between agentic backends and frontends. - This is how agents become truly interactive inside your apps, not just as chatbots, but collaborative co-workers. > MCP (Model Context Protocol): - The standard for how agents connect to tools, data, and workflows. > A2A (Agent-to-Agent): - The protocol for multi-agent coordination. - How agents delegate tasks and share intent across systems. These aren't competing standards. They're layers of the same stack and have handshakes with each other. So instead of building point-to-point integrations, you build to protocols. Moreover, you can integrate LangGraph, CrewAI, or Agno into the same frontend, without rewriting your UI logic. These protocols let everything work together. For instance: - Your LangGraph agent pulls data via MCP. - It delegates analysis to a CrewAI agent via A2A. - Results stream to your React app via AG-UI. - Users see real-time collaboration in your interface. This way, you can focus on building agent capabilities instead of integration mechanics. The protocols handle interoperability automatically. CopilotKit unifies this entire stack into one framework so you can build "Cursor for X" style apps without implementing each protocol from scratch. It gives you all three protocols, generative UI support, and production-ready infrastructure in one framework. I have shared this playbook in the replies! It breaks down handshakes, misconceptions, and real examples and shows exactly how to start building.

Avi Chawla

30,762 просмотров • 9 месяцев назад

Does LLM really need to be a helpful assistant all the time? No. If you want to simulate people, “perfectly helpful” could be the wrong objective. Meet OdysSim, a journey toward LLMs beyond assistants, as behavioral foundation models (10B tokens of real human behavior; 23 sim benchmarks, finally in one place. new open models: outperform or on par with GPT-5.5, Gemini 3.1, or Claude Opus 4.7 in many behavior-sim dimensions). Human behavior simulation is becoming essential. Agent evaluation needs realistic users before real users show up. Medical and classroom training need realistic patients and students. Social science needs synthetic participants at scale. But real people are not ideal assistants. Real patients panic or ignore good advice. Real students misunderstand. Real customers are vague, picky, impatient, or simply leave. Human behavior is messy, diverse, and often imperfect. Frontier LLMs are getting better at math, code, and long-horizon tasks. They are NOT getting better at simulating human behavior. If anything, they drift the other way: more assistant-ish, more homogeneous, fewer of the errors and quirks real humans show. This is no accident. The whole pipeline is built for helpfulness and task success, not behavioral realism. And you can't prompt your way out of that. So we rethink the recipe from scratch and release: 🧠 The OdysSim corpus: 21.4M real human interactions (~10B tokens) from 62 sources, every conversation retrofitted with social grounding (who is talking, and why) 📏 SOUL-Index: 23 human-behavior benchmarks unified into one suite across 5 axes 🤖 OSim-8B: open weights; tops more SOUL-Index benchmarks than any frontier model, acts more like a real user than any of them on τ-bench (nearly matching real humans in the reaction dimension), and writes far more human-like text along the way.

Does LLM really need to be a helpful assistant all the time? No. If you want to simulate people, “perfectly helpful” could be the wrong objective. Meet OdysSim, a journey toward LLMs beyond assistants, as behavioral foundation models (10B tokens of real human behavior; 23 sim benchmarks, finally in one place. new open models: outperform or on par with GPT-5.5, Gemini 3.1, or Claude Opus 4.7 in many behavior-sim dimensions). Human behavior simulation is becoming essential. Agent evaluation needs realistic users before real users show up. Medical and classroom training need realistic patients and students. Social science needs synthetic participants at scale. But real people are not ideal assistants. Real patients panic or ignore good advice. Real students misunderstand. Real customers are vague, picky, impatient, or simply leave. Human behavior is messy, diverse, and often imperfect. Frontier LLMs are getting better at math, code, and long-horizon tasks. They are NOT getting better at simulating human behavior. If anything, they drift the other way: more assistant-ish, more homogeneous, fewer of the errors and quirks real humans show. This is no accident. The whole pipeline is built for helpfulness and task success, not behavioral realism. And you can't prompt your way out of that. So we rethink the recipe from scratch and release: 🧠 The OdysSim corpus: 21.4M real human interactions (~10B tokens) from 62 sources, every conversation retrofitted with social grounding (who is talking, and why) 📏 SOUL-Index: 23 human-behavior benchmarks unified into one suite across 5 axes 🤖 OSim-8B: open weights; tops more SOUL-Index benchmarks than any frontier model, acts more like a real user than any of them on τ-bench (nearly matching real humans in the reaction dimension), and writes far more human-like text along the way.

Xuhui Zhou

141,404 просмотров • 2 месяцев назад