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Eigent × Kimi.ai K2.5 We ran a real-world sales performance evaluation on Eigent using Kimi K2.5. Given a production sales dataset, Eigent coordinated multiple agents end to end: - The document agent extracts information from the source files - The terminal agent analyzes the data and generates an HTML...

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Eigent AI

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25,263 просмотров • 5 месяцев назад •via X (Twitter)

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Eigent day 0 supports MiniMax (official) M2.5! Try M2.5 on your open source cowork! With Chinese New Year (Horse) coming, we asked Eigent to generate 10 complete HTML/CSS/JS games (no libraries) across arcade, puzzle, runner, strategy, memory, idle and more. The Developer Agent called the Terminal Toolkit, generated all 10 games, and saved them locally. Games are insanely fun and you can play them directly inside Eigent. Try it out today!
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Eigent day 0 supports MiniMax (official) M2.5! Try M2.5 on your open source cowork! With Chinese New Year (Horse) coming, we asked Eigent to generate 10 complete HTML/CSS/JS games (no libraries) across arcade, puzzle, runner, strategy, memory, idle and more. The Developer Agent called the Terminal Toolkit, generated all 10 games, and saved them locally. Games are insanely fun and you can play them directly inside Eigent. Try it out today!

Eigent AI

13,816 просмотров • 5 месяцев назад

Gemini 3.5 flash + Gemini managed agents api just audited a real megatron-lm ci failure inside Eigent. root cause in minutes! watch the handoff: coordinator agent plans the audit, developer agent loads the ml-failure-audit skill and gathers the evidence, then gemini agent steps in as a remote sub-agent for the heavy reasoning. gemini managed agents api and gemini 3.5 flash now live on our open-source cowork Eigent! Google AI Developers Google AI Studio
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Gemini 3.5 flash + Gemini managed agents api just audited a real megatron-lm ci failure inside Eigent. root cause in minutes! watch the handoff: coordinator agent plans the audit, developer agent loads the ml-failure-audit skill and gathers the evidence, then gemini agent steps in as a remote sub-agent for the heavy reasoning. gemini managed agents api and gemini 3.5 flash now live on our open-source cowork Eigent! Google AI Developers Google AI Studio

Eigent AI

16,335 просмотров • 1 месяц назад

Introducing Eigent — the first multi-agent workforce on your desktop. Eigent is a team of AI agents collaborating to complete complex tasks in parallel. It is your long-term working partner with fullly customizable workers and MCPs. Public beta available to download for MacOS, Windows. 100% open-source on Github. Comment for 500 extra credits.
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Introducing Eigent — the first multi-agent workforce on your desktop. Eigent is a team of AI agents collaborating to complete complex tasks in parallel. It is your long-term working partner with fullly customizable workers and MCPs. Public beta available to download for MacOS, Windows. 100% open-source on Github. Comment for 500 extra credits.

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Introducing Cline CLI 2.0: An open-source AI coding agent that runs entirely in your terminal. Parallel agents, headless CI/CD pipelines, ACP support for any editor, and a completely redesigned developer experience. Minimax M2.5 and Kimi K2.5 are free to use for a limited time. From prompt to production. All in your terminal.
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Introducing Cline CLI 2.0: An open-source AI coding agent that runs entirely in your terminal. Parallel agents, headless CI/CD pipelines, ACP support for any editor, and a completely redesigned developer experience. Minimax M2.5 and Kimi K2.5 are free to use for a limited time. From prompt to production. All in your terminal.

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I tested it on Qwen 3.5 Plus and Kimi K2.5 using the Alibaba Cloud Coding Plan Lite. In real-world use, there was no noticeable speed improvement. You can judge the speed and quality yourself on the Alibaba Cloud Coding Plan. For me, the results are decent, especially considering the pricing: $3 to start, $5 on renewal, and $10 for the monthly plan.
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I tested it on Qwen 3.5 Plus and Kimi K2.5 using the Alibaba Cloud Coding Plan Lite. In real-world use, there was no noticeable speed improvement. You can judge the speed and quality yourself on the Alibaba Cloud Coding Plan. For me, the results are decent, especially considering the pricing: $3 to start, $5 on renewal, and $10 for the monthly plan.

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Introducing /ui-test Give your agent a PR, and it'll test your feature in a real browser, generating an HTML report with UI fixes The planner agent generates adversarial test cases to break your app, assigns them to subagents, and evaluates the page using the Browserbase CLI We reviewed a merged PR on Cal.com and found a bunch of small UI nits:
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Introducing /ui-test Give your agent a PR, and it'll test your feature in a real browser, generating an HTML report with UI fixes The planner agent generates adversarial test cases to break your app, assigns them to subagents, and evaluates the page using the Browserbase CLI We reviewed a merged PR on Cal.com and found a bunch of small UI nits:

Shrey Pandya

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Chamath Palihapitiya said Kimi K2.5 is going to save 90% of the cost, mentioning it as a winning moment for open-source models. Some notes from the latest All-In Podcast: > Natively supports video-to-code > 256K massive context windows > Open-sourced their "Agent Swarm" feature > Allows 100+ sub-agents to solve complex tasks by running 1,500 steps in parallel > Available to everyone, frontier-level reasoning (#1 on LLM Leaderboard, via OpenRouter) It is a major shift where open source is now challenging the dominance of closed-source models.
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Chamath Palihapitiya said Kimi K2.5 is going to save 90% of the cost, mentioning it as a winning moment for open-source models. Some notes from the latest All-In Podcast: > Natively supports video-to-code > 256K massive context windows > Open-sourced their "Agent Swarm" feature > Allows 100+ sub-agents to solve complex tasks by running 1,500 steps in parallel > Available to everyone, frontier-level reasoning (#1 on LLM Leaderboard, via OpenRouter) It is a major shift where open source is now challenging the dominance of closed-source models.

Poonam Soni

56,165 просмотров • 5 месяцев назад

"#1 advice for sales and marketing leaders: deploy an AI agent yourself. Don't hire an agency. Don't tell someone on your team to do it. Deploy an AI agent into production, for real, yourself." with Lenny Rachitsky "For junior sales & GTM folks? Embrace it. Be the one that embraces every bit of output from AI Agents."
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"#1 advice for sales and marketing leaders: deploy an AI agent yourself. Don't hire an agency. Don't tell someone on your team to do it. Deploy an AI agent into production, for real, yourself." with Lenny Rachitsky "For junior sales & GTM folks? Embrace it. Be the one that embraces every bit of output from AI Agents."

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SN121 Show & Tell 2.0: our first Generalist Agent Challenge running on staging. Taylor, our Head of Product, walks through the leaderboard, Challenge details, submission results, and validator AETS outputs. The agents shown were created internally for testing, but all evaluations use the real suite and scoring logic. Organizing scores, displaying results, and opening up evaluation data are all part of creating a trusted foundation for agent performance on SN121. Big unlock, we're moving. $TAO
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SN121 Show & Tell 2.0: our first Generalist Agent Challenge running on staging. Taylor, our Head of Product, walks through the leaderboard, Challenge details, submission results, and validator AETS outputs. The agents shown were created internally for testing, but all evaluations use the real suite and scoring logic. Organizing scores, displaying results, and opening up evaluation data are all part of creating a trusted foundation for agent performance on SN121. Big unlock, we're moving. $TAO

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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:
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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:

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🚀 The team at Google just released the Agents API, a service for building and running custom agents inside a sandboxed Linux environment, and we built a template that gives these agents access to LlamaParse / LiteParse, enabling them to process unstructured documents automatically 📄⚡ Here’s how it works: 🔹 Configure a Git repository where data and outputs will be stored 🔹 Clone the repository into the agent sandbox 🔹 Install the LiteParse CLI, the LlamaParse SDK, and agent skills to use both 🔹 Prompt the agent with a task and watch it process documents autonomously 🤖 The result? An agent that can work directly with messy, real-world documents using LlamaParse and LiteParse within Google’s new agent runtime. Check out the GitHub repository: Get started with LlamaParse:
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🚀 The team at Google just released the Agents API, a service for building and running custom agents inside a sandboxed Linux environment, and we built a template that gives these agents access to LlamaParse / LiteParse, enabling them to process unstructured documents automatically 📄⚡ Here’s how it works: 🔹 Configure a Git repository where data and outputs will be stored 🔹 Clone the repository into the agent sandbox 🔹 Install the LiteParse CLI, the LlamaParse SDK, and agent skills to use both 🔹 Prompt the agent with a task and watch it process documents autonomously 🤖 The result? An agent that can work directly with messy, real-world documents using LlamaParse and LiteParse within Google’s new agent runtime. Check out the GitHub repository: Get started with LlamaParse:

LlamaIndex 🦙

21,975 просмотров • 1 месяц назад

Is your Agent ready for real-world users? Learn how to run an evaluation using Agentverse’s Response QA Agent - a built-in tool that stress-tests your Agent with realistic queries and scores its performance. This is the first step to improving discoverability, trust, and performance. Run your first QA check and see how your Agent responds under pressure. Watch 👇
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Is your Agent ready for real-world users? Learn how to run an evaluation using Agentverse’s Response QA Agent - a built-in tool that stress-tests your Agent with realistic queries and scores its performance. This is the first step to improving discoverability, trust, and performance. Run your first QA check and see how your Agent responds under pressure. Watch 👇

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Sharing our latest short course: Building and Evaluating Data Agents, created in collaboration with Snowflake and taught by Anupam Datta (Anupam Datta) and Josh Reini (Josh Reini). A data agent extracts data from sources such as files or databases, analyzes it, and provides insights and visualizes its findings. But most data agents struggle with reliability or can't handle multi-step reasoning. In this course, you'll learn to build, trace, and evaluate a multi-agent workflow that plans tasks, pulls context from structured and unstructured data, performs web search, and summarizes or visualizes the final results. Learn more and enroll for free!
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Sharing our latest short course: Building and Evaluating Data Agents, created in collaboration with Snowflake and taught by Anupam Datta (Anupam Datta) and Josh Reini (Josh Reini). A data agent extracts data from sources such as files or databases, analyzes it, and provides insights and visualizes its findings. But most data agents struggle with reliability or can't handle multi-step reasoning. In this course, you'll learn to build, trace, and evaluate a multi-agent workflow that plans tasks, pulls context from structured and unstructured data, performs web search, and summarizes or visualizes the final results. Learn more and enroll for free!

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About a year ago, we launched the Agent-to-Agent protocol because AI agents shouldn’t work in silos. Today, developers are using A2A to coordinate specialized agents across enterprise data systems, commerce workflows, DevOps environments, and scientific research. FoldRun is one example, simplifying petabyte-scale protein modeling through agent-to-agent collaboration. Learn how to build with it in the blog:

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Introducing Databricks Document Intelligence: a research-specialized layer that turns raw enterprise documents into structured data your agents can actually reason over. Across our benchmarks, Document Intelligence delivered the highest end-to-end parsing and extraction quality at 6-8x lower cost, with a 16% average performance gain across every agent framework tested, just from better parsing. How to get started:
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Introducing Databricks Document Intelligence: a research-specialized layer that turns raw enterprise documents into structured data your agents can actually reason over. Across our benchmarks, Document Intelligence delivered the highest end-to-end parsing and extraction quality at 6-8x lower cost, with a 16% average performance gain across every agent framework tested, just from better parsing. How to get started:

Databricks

19,428 просмотров • 3 месяцев 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.

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Shoutout to xAI team for hustling at 2:00 am to help bring this over the finish line Introducing Agent Runner: the first open-source agent harness run with real users to create a live benchmark of real-world coding We trace tool-calls, reprompting, and multifile edits, starting with the best from OpenAI, xAI, Google DeepMind, Anthropic, Mistral AI, Z.ai, Kimi.ai
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Shoutout to xAI team for hustling at 2:00 am to help bring this over the finish line Introducing Agent Runner: the first open-source agent harness run with real users to create a live benchmark of real-world coding We trace tool-calls, reprompting, and multifile edits, starting with the best from OpenAI, xAI, Google DeepMind, Anthropic, Mistral AI, Z.ai, Kimi.ai

Grace Li

119,237 просмотров • 7 месяцев назад

Here is how simple it is to build a UI front-end for an AI agent (assuming you are using . The video here is 90 seconds and will show you everything you need to connect an agent to a web app: 1. Set up CopilotKit🪁 in your app 1. Copy the agent's localhost URL from LangGraph Studio 2. Paste that URL into a CopilotKit🪁 remote endpoint That's all you need to get a front-end to interact with the agent! The secret sauce here is CoAgents, an open-source library. You can use CoAgents to integrate any agent with your web application. Here is what you get: • Human-in-the-loop to steer and correct the agent • Stream intermediate agent state • Real-time state sharing between the agent and the application • Agentic generative UI to build trust that the agent is on the right path Watch the attached demo so you see it in action.
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Here is how simple it is to build a UI front-end for an AI agent (assuming you are using . The video here is 90 seconds and will show you everything you need to connect an agent to a web app: 1. Set up CopilotKit🪁 in your app 1. Copy the agent's localhost URL from LangGraph Studio 2. Paste that URL into a CopilotKit🪁 remote endpoint That's all you need to get a front-end to interact with the agent! The secret sauce here is CoAgents, an open-source library. You can use CoAgents to integrate any agent with your web application. Here is what you get: • Human-in-the-loop to steer and correct the agent • Stream intermediate agent state • Real-time state sharing between the agent and the application • Agentic generative UI to build trust that the agent is on the right path Watch the attached demo so you see it in action.

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Over 1 billion PDFs are created every day, but your agents still can’t read them reliably. Today we’re releasing Parse 2.0, the most accurate document parsing API in the world. Extend already processes millions of pages daily for leading AI teams like Brex, Mercury, Opendoor, Flatiron Health, and hundreds of others. Now, its even better. Parse 2.0 is SOTA quality on RealDoc-Bench, our open source benchmark that measures agent success rate on real world docs that agents actually encounter in production. We trained Parse 2.0 on 1M+ pages of the hardest documents seen in production. Here’s how it stacks up: - #1 in healthcare, real estate, logistics, and financial services - 95.7% agent Q&A accuracy on 581 docs (next best: 92%) - 0.847 F1 on layout (next best: 0.759) Give it a try today and build production-ready document agents with Extend.
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Over 1 billion PDFs are created every day, but your agents still can’t read them reliably. Today we’re releasing Parse 2.0, the most accurate document parsing API in the world. Extend already processes millions of pages daily for leading AI teams like Brex, Mercury, Opendoor, Flatiron Health, and hundreds of others. Now, its even better. Parse 2.0 is SOTA quality on RealDoc-Bench, our open source benchmark that measures agent success rate on real world docs that agents actually encounter in production. We trained Parse 2.0 on 1M+ pages of the hardest documents seen in production. Here’s how it stacks up: - #1 in healthcare, real estate, logistics, and financial services - 95.7% agent Q&A accuracy on 581 docs (next best: 92%) - 0.847 F1 on layout (next best: 0.759) Give it a try today and build production-ready document agents with Extend.

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585,143 просмотров • 1 месяц назад