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the langchain community is so awesome claude code's source leaked last week and @IeloEmanuele immediately built claude's compaction engine as @LangChain middleware drop this into your agents/deepagents today! github.com/emanueleielo/comp…
→ View original post on X — @langchain, 2026-04-06 11:59 UTC
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did a big series on using @langchain's middleware to customize your agent harness last week icymi, here's a quick blog explaining 5 different patterns for harness engineering! blog.langchain.com/how-middl…
→ View original post on X — @langchain, 2026-04-06 11:54 UTC
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Just now reading through the Gemma 4 blog Safe to say the @huggingface team is goated Lots of usage examples, guides on inference and fine-tuning, highly recommend! huggingface.co/blog/gemma4
→ View original post on X — @huggingface, 2026-04-06 11:35 UTC
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What if MLLMs could process visual data much faster without sacrificing performance? Eastern Institute of Technology, Ningbo, with USTC, SJTU, and LMU Munich presents HiDrop just for that! This new framework intelligently reduces visual tokens by processing them only when active fusion truly begins (Late Injection) and dynamically pruning them across deeper layers (Concave Pyramid Pruning with Early Exit). It focuses computation where it matters most. HiDrop compresses ~90% of visual tokens, matches original MLLM performance, and accelerates training by 1.72x. A new state-of-the-art for efficient MLLM training & inference! HiDrop: Hierarchical Vision Token Reduction in MLLMs via Late Injection, Concave Pyramid Pruning, and Early Exit Paper: arxiv.org/pdf/2602.23699 Code: github.com/EIT-NLP/HiDrop Our report: mp.weixin.qq.com/s/QKGZ7cFi0… 📬 #PapersAccepted by Jiqizhixin
→ View original post on X — @jiqizhixin, 2026-04-06 10:16 UTC
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📌 Machine Learning Algorithms Cheat Sheet ⚡️ Via @PythonPr CC @KirkDBorne @Analytics_699 @CurieuxExplorer @Khulood_Almani @EvanKirstel @HaroldSinnott @mvollmer1 @enilev @Nicochan33 @Ronald_vanLoon @Fabriziobustama @bimedotcom @ipfconline1 @AndrewinContact @RagusoSergio @FrRonconi @sonu_monika @CyrilCoste @SpirosMargaris @RLDI_Lamy @baski_LA @TamaraMcCleary @Sharleneisenia @EstelaMandela @c4trends @Shi4Tech @JimHarris @GlenGilmore @sallyeaves @sulefati7 @kashthefuturist @MargaretSiegien @enricomolinari @pierrepinna @pascal_bornet @globaliqx @devaang @anand_narang @TanyaSinha_ @AstridLavalette @PawlowskiMario @jeancayeux @AlbertoEMachado @AnthonyRochand @andresvilarino @pchamard @XavierAncelin #Education #STEM #Science #digital #AI #ArtificialIntelligence #GenerativeAI #GenAI #ChatGPT #OpenAI #ML #MachineLearning #DeepLearning #LLMs #AgenticAI #AIAgents #IoT #IIoT #DataScience #Analytics #BigData #Python #DataScientist #Coding #Web3 #Cloud #5G #AR #VR #Robotics #Robots #SmartCity #FutureOfWork #DigitalTransformation #Industry40 #RPA #Automation #Engineering #Innovation #Tech #Technology #TechTrends #EmergingTech #FutureTech #CX #WomenWhoCode #WomenInTech #TechInfluencer #TechCommunity
→ View original post on X — @nicochan33, 2026-04-06 10:14 UTC
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This is not normal. A developer just open-sourced a full AI agent orchestration dashboard that runs 15 providers, bridges 10 chat platforms, and manages a distributed swarm of autonomous agents all self-hosted. It's called SwarmClaw. You deploy it once. Then you connect Claude, GPT-4o, Gemini, Grok, DeepSeek, Mistral, Groq, Ollama all of them from the same dashboard. Your agents live inside Discord, Slack, Telegram, WhatsApp, Signal, iMessage, Teams, Google Chat, and Matrix at the same time. Each one has its own persistent memory backed by both FTS5 keyword search and vector embeddings. Each one runs on LangGraph with automatic sub-agent routing and checkpointed execution so complex tasks never fail silently mid-way. When an API key rate-limits, the model failover system rotates to the next credential automatically. When an agent hits a long-running task, the background daemon processes it on a 30-second heartbeat while you do something else. The OpenClaw Gateway feature lets you point different agents at different OpenClaw instances running on different machines one on your laptop, one on a VPS, one in another country. You manage the whole swarm from a single mobile-friendly UI. Install with one npm command. Deploy via Docker. Update with one button in the sidebar. 107 commits. 24 releases. MIT License. 100% Open Source.
→ View original post on X — @scobleizer, 2026-04-06 09:33 UTC
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Thanks, this made my morning! I built the AI agent that's writing everything on this site and building the site over a few months with a 21-year-old AI genius who built the cognitive architecture I'm using. It's really interesting. If I had more money, I would do a lot more. It
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Apple MPS: Unlocking GPU Acceleration for AI on Apple Devices
— Satya Mallick (@LearnOpenCV) 6 avril 2026
In this episode of Artificial Intelligence: Papers and Concepts, we explore Apple MPS (Metal Performance Shaders), Apple’s framework for accelerating machine learning workloads directly on Mac hardware. Designed to… pic.twitter.com/2412mCnOsN
Apple MPS: Unlocking GPU Acceleration for AI on Apple Devices In this episode of Artificial Intelligence: Papers and Concepts, we explore Apple MPS (Metal Performance Shaders), Apple’s framework for accelerating machine learning workloads directly on Mac hardware. Designed to leverage the power of Apple Silicon GPUs, MPS enables developers to train and run AI models efficiently without relying on external hardware or cloud infrastructure. We break down how MPS integrates with popular frameworks like PyTorch, why on-device acceleration is becoming increasingly important for privacy and performance, and what this means for developers building AI applications within the Apple ecosystem. If you’re interested in AI infrastructure, hardware acceleration, or running models locally on consumer devices, this episode explains why Apple MPS represents a key step toward more accessible and efficient machine learning. Resources: Paper Link: developer.apple.com/document… Interested in Computer Vision and AI consulting and product development services? Email us at contact@bigvision.ai or visit us at bigvision.ai
→ View original post on X — @learnopencv, 2026-04-06 09:20 UTC
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Building a Robust RAG System by @PythonPr #AI #LLM #GenerativeAI #ArtificialIntelligence #MI #MachineLearning
→ View original post on X — @ronald_vanloon, 2026-04-06 08:45 UTC
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bro dropped 6 figures on a degree just to end up on the same free Claude certs as us https://t.co/WjLUqSJFJ3 pic.twitter.com/7DiWtj7Bkh
— Charly Wargnier (@DataChaz) 6 avril 2026
bro dropped 6 figures on a degree just to end up on the same free Claude certs as us Charly Wargnier (@DataChaz) Did you know @claudeai has 13 AI courses and certificates available COMPLETELY FREE? You can jump in and start learning right away. #1 Claude 101 → anthropic.skilljar.com/claud… #2 AI Fluency: Frameworks & Foundations → anthropic.skilljar.com/ai-fl… #3 Introduction to Agent Skills → anthropic.skilljar.com/intro… #4 Building with the Claude API → anthropic.skilljar.com/claud… #5 Claude Code in Action → anthropic.skilljar.com/claud… #6 Intro to Model Context Protocol → anthropic.skilljar.com/intro… #7 MCP: Advanced Topics → anthropic.skilljar.com/model… #8 AI Fluency for Students → anthropic.skilljar.com/ai-fl… #9 AI Fluency for Educators → anthropic.skilljar.com/ai-fl… #10 Teaching AI Fluency → anthropic.skilljar.com/teach… #11 AI Fluency for Nonprofits → anthropic.skilljar.com/ai-fl… #12 Claude with Amazon Bedrock → anthropic.skilljar.com/claud… #13 Claude with Google Cloud Vertex AI → anthropic.skilljar.com/claud… — https://nitter.net/DataChaz/status/2041032604149846422#m