LangSmith Fleet template spotlight: @TavilyAI Competitor Research Researches companies and summarizes findings in a concise report. A research agent that takes a list of company names, digs deep across the web, and drops findings straight into Slack threads. Try it today:
TOOLS
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Most AI Agents Fail in Production Because They’re Built Backward
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Most AI Agents Fail in Production Because They’re Built Backward! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming
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NVIDIA AI for Media: Synthetic Video Detector and RTX Upscaling
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NVIDIA AI for Media is bringing real-time AI performance into live and on-demand media workflows. Synthetic Detector delivers up to 92% accuracy with latency as low as 22 ms for AI video authenticity checks. RTX Super Resolution and Frame Generation upscale
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GitHub repo for AI engineering toolkit, contribute new tools
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Github Repo: https://
github.com/Sumanth077/ai-
engineering-toolkit
… Feel free to add new tools and contribute! -
AI Engineering Toolkit: 100+ Libraries for LLMs, RAG, AI Agents
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AI Engineering Toolkit! I have curated a list of 100+ libraries and frameworks for training, fine-tuning, building, evaluating and deploying LLMs, RAG, and AI Agents. Categories of LLM Libraries include: • Vector Databases – Store and retrieve embeddings efficiently.
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GLM 5.1 outperforms Kimi 2.6, Composer 2.5 smarter but with RL-fried issues
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A note about why GLM 5.1 over Kimi 2.6. I found the former to be more capable on domains I'm working on, significantly so. Composer 2.5 seems smarter than Kimi, but exhibits RL-fried behavior, which causes much friction in interactive coding…
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AI simulations: test business choices risk-free, boost confidence
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AI simulations help leaders test business choices before real money is at risk. By modeling customers, competitors, and market reactions, teams can compare options, find weak points, and decide with more confidence. Microblog @antgrasso
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LangSmith automates agent development lifecycle continuously
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The agent development lifecycle has been manual for too long.
— LangChain (@LangChain) 2 juin 2026
We’re building a future where it runs continuously, without manual triggers.
Where well-understood issue types resolve without human review.
Where your harnesses get smarter about your agents over time.
LangSmith… pic.twitter.com/10OwQHAciTThe agent development lifecycle has been manual for too long. We’re building a future where it runs continuously, without manual triggers. Where well-understood issue types resolve without human review. Where your harnesses get smarter about your agents over time. LangSmith
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Learn AI Engineering: 435 Lessons, 320h, Python, Rust, MCP, Free
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Learn AI engineering from scratch with this repository: → 435 lessons
→ 320 hours of content
→ Python, TypeScript, Rust…
→ Prompts, skills, agents, and MCP servers Practical exercises in each lesson
100% open source
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Google I/O Conversation on Gemma, Open Models, and AI Future
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At @Google I/O, I sat down with @osanseviero and @DynamicWebPaige from Google DeepMind to talk about Gemma, open models, AI Studio, on-device AI, sovereign AI and the future of AI development.
— Chubby♨️ (@kimmonismus) 2 juin 2026
A great conversation on how building with AI is becoming more open, local and… pic.twitter.com/Wk0rU5ZUMjAt @Google I/O, I sat down with @osanseviero and @DynamicWebPaige from Google DeepMind to talk about Gemma, open models, AI Studio, on-device AI, sovereign AI and the future of AI development. A great conversation on how building with AI is becoming more open, local and