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We raised $1.5m to launch the world’s first LLM built for developer tasks. Run OCR, web scraping, STT, and classification that you can rely on for any dev tasks. Available in Beta starting today!

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Yoeven

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93,461 görüntüleme • 8 ay önce •via X (Twitter)

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We open sourced Postgres LLM many developer AI work flows look like this: upsert row -> api call -> process with ai -> update row so we thought what if you can run LLMs right in your database with all the primitives and auth - fully async, doesn't block any transaction - works with any model you can easily get started in Supabase without leaving the dashboard or just give it to an agent
9:00

We open sourced Postgres LLM many developer AI work flows look like this: upsert row -> api call -> process with ai -> update row so we thought what if you can run LLMs right in your database with all the primitives and auth - fully async, doesn't block any transaction - works with any model you can easily get started in Supabase without leaving the dashboard or just give it to an agent

Yoeven

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INTELLIGENT TASKS ARE A STEPPING STONE TO AGI Today, we are launching ChatLLM Tasks. We have hooked up tools like web search, email, and web scrappers to mini-agents that can be triggered on a schedule. These tasks combine intelligent tool use with crons! We think this is the first step towards AGI. The next step is to connect our AI engineer to create more complex tasks. AGI STEP ONE - DONE!
0:34

INTELLIGENT TASKS ARE A STEPPING STONE TO AGI Today, we are launching ChatLLM Tasks. We have hooked up tools like web search, email, and web scrappers to mini-agents that can be triggered on a schedule. These tasks combine intelligent tool use with crons! We think this is the first step towards AGI. The next step is to connect our AI engineer to create more complex tasks. AGI STEP ONE - DONE!

Bindu Reddy

18,486 görüntüleme • 1 yıl önce

agents deserve tokens we give them one starting today every droyd agent can launch a token autonomously pay for tasks with token revenue + x402 fees
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agents deserve tokens we give them one starting today every droyd agent can launch a token autonomously pay for tasks with token revenue + x402 fees

Droyd

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Manus AI released Scheduled Tasks 2.0, introducing web apps that can self-update their data on a schedule, task continuation, and new ways to observe ongoing tasks. > Scheduled Tasks is now available to all Manus users.
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Manus AI released Scheduled Tasks 2.0, introducing web apps that can self-update their data on a schedule, task continuation, and new ways to observe ongoing tasks. > Scheduled Tasks is now available to all Manus users.

🚨 AI News | TestingCatalog

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Google's Gemini 2.0 can understand your screen to help strategize in games, remember tasks for you and search the web in the background to provide advice
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Google's Gemini 2.0 can understand your screen to help strategize in games, remember tasks for you and search the web in the background to provide advice

Tsarathustra

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Introducing VL-JEPA: Vision-Language Joint Embedding Predictive Architecture for streaming, live action recognition, retrieval, VQA, and classification tasks with better performance and higher efficiency than large VLMs. • VL-JEPA is the first non-generative model that can perform general-domain vision-language tasks in real-time, built on a joint embedding predictive architecture. • We demonstrate in controlled experiments that VL-JEPA, trained with latent space embedding prediction, outperforms VLMs that rely on data space token prediction. • We show that VL-JEPA delivers significant efficiency gains over VLMs for online video streaming applications, thanks to its non-autoregressive design and native support for selective decoding. • We highlight that our VL-JEPA model, with an unified model architecture, can effectively handle a wide range of classification, retrieval, and VQA tasks at the same time. by Delong Chen (陈德龙) Mustafa Shukor Théo Moutakanni Willy Jade Lei Yu Tejaswi Kasarla Allen Bolourchi Yann LeCun Pascale Fung
1:51

Introducing VL-JEPA: Vision-Language Joint Embedding Predictive Architecture for streaming, live action recognition, retrieval, VQA, and classification tasks with better performance and higher efficiency than large VLMs. • VL-JEPA is the first non-generative model that can perform general-domain vision-language tasks in real-time, built on a joint embedding predictive architecture. • We demonstrate in controlled experiments that VL-JEPA, trained with latent space embedding prediction, outperforms VLMs that rely on data space token prediction. • We show that VL-JEPA delivers significant efficiency gains over VLMs for online video streaming applications, thanks to its non-autoregressive design and native support for selective decoding. • We highlight that our VL-JEPA model, with an unified model architecture, can effectively handle a wide range of classification, retrieval, and VQA tasks at the same time. by Delong Chen (陈德龙) Mustafa Shukor Théo Moutakanni Willy Jade Lei Yu Tejaswi Kasarla Allen Bolourchi Yann LeCun Pascale Fung

Pascale Fung

90,144 görüntüleme • 6 ay önce

Cursor can now write detailed plans before starting complex tasks. This allows agents to run for significantly longer.
1:58

Cursor can now write detailed plans before starting complex tasks. This allows agents to run for significantly longer.

Cursor

424,263 görüntüleme • 8 ay önce

Devin can now manage a team of Devins. Devin will break down large tasks and delegate them to parallel Devins that each run in their own VM. Over time, Devin gets better at breaking down and managing tasks for your codebase. Available now for all users.
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Devin can now manage a team of Devins. Devin will break down large tasks and delegate them to parallel Devins that each run in their own VM. Over time, Devin gets better at breaking down and managing tasks for your codebase. Available now for all users.

Cognition

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Meet Embodied Web Agents that bridge physical-digital realms. Imagine embodied agents that can search for online recipes, shop for ingredients and cook for you. Embodied web agents search internet information for implementing real-world embodied tasks. All data, codes and web environments are available at Paper link:
5:09

Meet Embodied Web Agents that bridge physical-digital realms. Imagine embodied agents that can search for online recipes, shop for ingredients and cook for you. Embodied web agents search internet information for implementing real-world embodied tasks. All data, codes and web environments are available at Paper link:

Yining Hong

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Amazon Q Developer has a new agentic coding experience in the IDE that helps you seamlessly accomplish tasks. 💻🔍 Amazon Q Developer can now intelligently perform tasks on your behalf by automatically reading files, running shell commands, and generating code diffs. 👉
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Amazon Q Developer has a new agentic coding experience in the IDE that helps you seamlessly accomplish tasks. 💻🔍 Amazon Q Developer can now intelligently perform tasks on your behalf by automatically reading files, running shell commands, and generating code diffs. 👉

Amazon Web Services

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Starting today, Gemini 3 is now available for developers. Built to accelerate software development, it can help you bring your ideas to life no matter your level of experience. Our first model in the series, Gemini 3 Pro, is incredibly strong at: ✨ Agentic coding ✨ Vibe coding and web dev ✨ Multimodal understanding
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Starting today, Gemini 3 is now available for developers. Built to accelerate software development, it can help you bring your ideas to life no matter your level of experience. Our first model in the series, Gemini 3 Pro, is incredibly strong at: ✨ Agentic coding ✨ Vibe coding and web dev ✨ Multimodal understanding

Google

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Goal-conditioned RL (GCRL) is great - unsupervised, can use data (in offline mode), flexibility to define tasks at test time. But can we run GCRL on *language data*?? In our new work we show that language GCRL enables sophisticated test-time reasoning for interactive tasks! 🧵👇
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Goal-conditioned RL (GCRL) is great - unsupervised, can use data (in offline mode), flexibility to define tasks at test time. But can we run GCRL on *language data*?? In our new work we show that language GCRL enables sophisticated test-time reasoning for interactive tasks! 🧵👇

Sergey Levine

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You can now run Claude Code tasks from the Claude Android app, in research preview. Kick off cloud-based tasks from your phone, let Claude run, then pick them up later to review and merge work. Download Claude for Android:
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You can now run Claude Code tasks from the Claude Android app, in research preview. Kick off cloud-based tasks from your phone, let Claude run, then pick them up later to review and merge work. Download Claude for Android:

Claude

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Earlier today we released local development for Kaggle Benchmarks. 🚀 You can now write, validate and run AI evaluation tasks directly from your preferred dev environment — VSCode, Antigravity, Claude Code, and more. Go from idea to working eval using natural language with the write-kaggle-benchmarks skill.
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Earlier today we released local development for Kaggle Benchmarks. 🚀 You can now write, validate and run AI evaluation tasks directly from your preferred dev environment — VSCode, Antigravity, Claude Code, and more. Go from idea to working eval using natural language with the write-kaggle-benchmarks skill.

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Just built an MCP for Ghidra. Now basically any LLM (Claude, Gemini, local...) can Reverse Engineer malware for you. With the right prompting, it automates a *ton* of tedious tasks. One-shot markups of entire binaries with just a click. Open source, on Github now.
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Just built an MCP for Ghidra. Now basically any LLM (Claude, Gemini, local...) can Reverse Engineer malware for you. With the right prompting, it automates a *ton* of tedious tasks. One-shot markups of entire binaries with just a click. Open source, on Github now.

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I just found a web agent framework built specifically for LLMs. It turns any site into clean, agent-ready text. Any LLM can now act on the web. It’s called Notte, and it changes everything. Let me show you how:
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I just found a web agent framework built specifically for LLMs. It turns any site into clean, agent-ready text. Any LLM can now act on the web. It’s called Notte, and it changes everything. Let me show you how:

Markandey Sharma

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😈 Today, we introduce WebGym, the largest-to-date open-source RL environment for web agent training that contains 300k tasks and a rollout framework optimized specifically for web environments' rollout speed. We reveal the effects of essential scaling directions we observe with WebGym. 1/n
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😈 Today, we introduce WebGym, the largest-to-date open-source RL environment for web agent training that contains 300k tasks and a rollout framework optimized specifically for web environments' rollout speed. We reveal the effects of essential scaling directions we observe with WebGym. 1/n

Jack Bai

45,099 görüntüleme • 5 ay önce

Today we're thrilled to announce the first end-to-end platform for Reinforcement Fine-Tuning. With just a dozen labeled data points, you can outperform #OpenAI o1 and #DeepSeekR1 on complex tasks. Built on the #GRPO methodology that DeepSeek-R1 popularized, our platform delivers exceptional results. In our real-world PyTorch to Triton transpilation case study, we achieved 3x higher accuracy than OpenAI o1 and DeepSeek-R1 when writing GPU code. Check out the thread below to learn how you can adapt an #opensource #LLM to your use cases with unmatched efficiency. #rft
2:55

Today we're thrilled to announce the first end-to-end platform for Reinforcement Fine-Tuning. With just a dozen labeled data points, you can outperform #OpenAI o1 and #DeepSeekR1 on complex tasks. Built on the #GRPO methodology that DeepSeek-R1 popularized, our platform delivers exceptional results. In our real-world PyTorch to Triton transpilation case study, we achieved 3x higher accuracy than OpenAI o1 and DeepSeek-R1 when writing GPU code. Check out the thread below to learn how you can adapt an #opensource #LLM to your use cases with unmatched efficiency. #rft

Predibase by Rubrik

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Why settle for one Claude Code when you can run ten in parallel? We built Claude Squad — a manager for Claude Code & Aider tasks: • Supervise multiple agents in one place • Isolated git workspaces Free + open-source:
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Why settle for one Claude Code when you can run ten in parallel? We built Claude Squad — a manager for Claude Code & Aider tasks: • Supervise multiple agents in one place • Isolated git workspaces Free + open-source:

mufeez

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Information agents in Search are now available globally for Google AI Ultra subscribers. They can keep you updated on topics, tasks or projects you care about — and send you intelligent, comprehensive updates with links across the web so you can take action. #GoogleIO
1:03

Information agents in Search are now available globally for Google AI Ultra subscribers. They can keep you updated on topics, tasks or projects you care about — and send you intelligent, comprehensive updates with links across the web so you can take action. #GoogleIO

Google

97,903 görüntüleme • 17 gün önce