Most teams use separate tools for analytics, observability, and Web Vitals. Raah combines all of it together with user-side logs. What you get: → Real user traffic Web Vitals → API & page latency → ISP-level diagnostics → Session data → User- Journey → AI chat that answers based on your data Plug with your Coding Agents One simple script for setup.🔥 Try now → raah.dev

Meet Signals! A Web Intelligence Agent to do source-aware research and prepare case-study you can review, edit, audit, and export. ✅ Nextjs dashboard with: → Live web evidence collection → reasoning verification → Editable case-study documents → comments, presence, and audit trail → SQLite run history and document storage Built using: → @olostep for web search, scrape, map, crawl, and answers → Nemotron-3-Ultra-550b from @nebiustf → multi-agent workflow via @mastra → collaboration, comments, presence, and immutable audit logs via @veltjs Open source and easy to customize for your own research workflow 🔥

Watch me set up Google Ads for @raahdotdev using Codex 🔥 Just tried @hellyeah_ai CLI, super smooth experience. As a founder, planning, creating, and launching ads is usually a time-consuming process. Now, it's just a prompt away. Best part? I got it done in under 3 minutes and can iterate instantly with the agent. I can even plan and manage ads using WhatsApp via AIMA.

Everyone is talking about AI harnesses right now. But what is a harness actually? Simple way to think about it: The model is the brain. The harness is everything around the model that helps it do useful work. A raw LLM can write, reason, explain, and code. But an AI agent needs more than that. It needs context. It needs tools. It needs memory. It needs permissions. It needs workflows. It needs evals and feedback loops. That surrounding layer is the harness. And this is why two agent products using the same model can feel completely different. One agent feels confused. It loads too much context, calls the wrong tool, burns tokens, and loses the task halfway. Another agent feels sharp. It finds the right context, uses the right tools, remembers what matters, asks for approval when needed, and finishes reliably. Same model. Different harness. Big difference. It is the execution layer that turns model intelligence into actual work.

The US government, citing national security authorities, has issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees. The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance. Access to all other Claude models is not affected. We apologize for this disruption to our customers. We believe this is a misunderstanding and are working to restore access as soon as possible. Read our full statement: anthropic.com/news/fable-myt…

Someone tested @MiniMax_AI M3 and @Kimi_Moonshot K2.6

Intelligence should be open, accessible, and ready to build with, empowering every developer, everywhere. GLM-5.2 is now available to all GLM Coding Plan users, including Lite, Pro, Max, and Team plans. docs.z.ai/devpack/latest-mod… As our new flagship model, GLM-5.2 delivers powerful coding capabilities, usable 1M-context support, and continued strengths in long-horizon tasks. API and Chatbot services will launch next week. The model will also be officially open-sourced next week under the MIT License. The future of AI is open, and it belongs to the people.

Build your own local coding agents using Cursor harness. VoxCode is a voice-powered coding workspace that runs entirely on your machine. No need to share with the world. Run it locally for your own use case and API keys. ✅ What it has: → Voice-based codebase summaries → Architecture Q&A sessions → Voice based File Edits → Live activity logs for Agent events in UI Built using: → @DeepgramAI for Voice Agent orchestration → @nebiustf for reasoning, tool routing and other complex tasks → @cursor_ai SDK for codebase inspection and file edits Code is open source, customize it based on your need🔥

Introducing Hellyeah — the first AI marketing agent that runs growth campaign within seconds. Building a product is easy now. Getting it discovered isn't. That changes today. The product you're building deserves to be discovered. Beta's open🚀

🌘 Kimi-K2.7-Code, our latest coding model, is now released and open-sourced! 🔷 Improved coding & agent performance over K2.6: 21.8% on Kimi Code Bench v2, 11.0% on Program Bench, and 31.5% on MLS Bench Lite. 🔷 Reasoning efficiency: Less overthinking, with 30% lower reasoning-token usage compared to K2.6. 🔷 Long-horizon coding: Improved instruction following, higher end-to-end coding task success rates. ⚡️ 6x High-Speed Mode coming soon! 🔌 Available today via Kimi API and Kimi Code. 🔗 Kimi Code: kimi.com/code 🔗 API: platform.moonshot.ai

It's open source now!

Nebius just launched Data Lab inside @nebiustf! And I think this is the missing piece in most LLM improvement workflows. Fine-tuning itself is not the hard part anymore. The hard part is the loop: → find useful production logs → isolate failure cases → clean and reshape the data → create a training dataset → run post-training → deploy the improved model → repeat without rebuilding everything That’s exactly what Data Lab is trying to fix. It turns inference logs and existing datasets into reusable training data inside Token Factory. So instead of treating model improvement like a one-time cleanup project, you can run it like a loop: Logs → curated dataset → post-training → better model → redeploy → repeat. I also tested this in my own workflow for: → Dataset preparation → Teacher-student distillation → Data Lab batch inference → LoRA fine-tuning → Serverless adapter deployment → Model comparison with a Gradio app The interesting part is that the workflow stays connected. You are not jumping between random scripts, notebooks, storage exports, training tools, and deployment infra. Data Lab makes the model improvement cycle much faster for production apps.

Do you remember when you joined X? I do! #MyXAnniversary

Codex Down?

Slack 🤝 AG-UI Protocol @SlackHQ is now a first-class AG-UI frontend with: → Streaming replies → Tool calls → Human in the Loop approvals → Full thread context Same agent you built for the web, now living where your users actually work.

𝟳 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝗱𝗲𝗺𝗼𝘀. From AI memory to fraud detection - all powered by Weaviate. 𝗢𝘂𝗿 𝗻𝗲𝘄 𝗽𝗹𝗮𝘆𝗴𝗿𝗼𝘂𝗻𝗱 𝗶𝘀 𝗻𝗼𝘄 𝗹𝗶𝘃𝗲, with example projects showcasing everything from search, RAG, agents, memory, and more. Every demo comes with copy-paste prompts so you can start building right out of the box! 𝟭. 𝗘𝗻𝗴𝗿𝗮𝗺 - Persistent memory for AI agents. Watch how facts and summaries are extracted in the background and recalled across conversations. 𝟮. 𝗙𝗿𝗮𝘂𝗱 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗤𝘂𝗲𝗿𝘆 𝗔𝗴𝗲𝗻𝘁 - Ask questions about transactions and products. The Query Agent translates them into structured queries across multiple collections to surface fraud patterns. 𝟯. 𝗪𝗲𝗖𝗼𝗺𝗺𝗲𝗿𝗰𝗲 - Browse, search, and discover products powered by keyword, vector, and hybrid search with real-time faceted filtering and recommendations. 𝟰. 𝗪𝗲𝗮𝘃𝗶𝗲𝘄 𝗖𝗵𝗿𝗼𝗻𝗶𝗰𝗹𝗲𝘀 - A newspaper-themed clustering demo. Reports are embedded and clustered using hybrid semantic search, character n-gram similarity, and Leiden community detection. 𝟱. 𝗩𝗲𝗰𝘁𝗼𝗿 𝗦𝗲𝗮𝗿𝗰𝗵 𝘃𝘀 𝗞𝗲𝘆𝘄𝗼𝗿𝗱 𝗦𝗲𝗮𝗿𝗰𝗵 - Learn the differences between vector and keyword search, and how they compare to hybrid search. 𝟲. 𝗚𝗹𝗼𝘄𝗲 - Skincare recommendation app demonstrating how domain knowledge agents, custom embedding strategies, and vector search create intelligent product recommendations. 𝟳. 𝗘𝗹𝘆𝘀𝗶𝗮 - Decision-tree agent that picks which tools to run on the fly to query and reason over your Weaviate data. Explore it here: playground.weaviate.io?utm_s… Plus, our new tier means you can starting building FREE forever on Weaviate Cloud. Sign up here: weaviate.io/go/console?utm_s…

Most teams use separate tools for analytics, observability, and Web Vitals. Raah combines all of it together with user-side logs. What you get: → Real user traffic Web Vitals → API & page latency → ISP-level diagnostics → Session data → User- Journey → AI chat that answers based on your data Plug with your Coding Agents One simple script for setup.🔥 Try now → raah.dev