Shadow testing is underrated. Though even shadow testing has a blind spot. If your candidate model has side effects (like triggering downstream API calls or writes), you can't truly "shadow" it without extra isolation. Learned this the hard way.
@akshay_pachaar
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Meta’s REFRAG: An AI Optimization Layer for RAG Architectures
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Meta's REFRAG is quite an interesting optimization layer that works on top of any RAG architecture. Essentially, instead of tokenizing all retrieved chunks, it compresses most into embeddings and feeds them directly to the decoder. An RL policy selectively expands only the
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Promoting Insights and Tutorials on LLMs, AI Agents, and Machine Learning
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If you found it insightful, reshare with your network.
— Akshay 🚀 (@akshay_pachaar) 26 février 2026
Find me → @akshay_pachaar ✔️
For more insights and tutorials on LLMs, AI Agents, and Machine Learning!https://t.co/946wCX6bPVIf you found it insightful, reshare with your network. Find me → @akshay_pachaar For more insights and tutorials on LLMs, AI Agents, and Machine Learning!
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Sim: Open-Source Platform for No-Code AI Agent Workflows
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OpenClaw, but built for normal people.
— Akshay 🚀 (@akshay_pachaar) 26 février 2026
Sim is an open-source platform that lets you build AI agent workflows on a drag-and-drop canvas. Connect them to channels like Telegram and WhatsApp and deploy without writing a single line of code.
They also have a built-in Copilot that… pic.twitter.com/gFchHDH4gKOpenClaw, but built for normal people. Sim is an open-source platform that lets you build AI agent workflows on a drag-and-drop canvas. Connect them to channels like Telegram and WhatsApp and deploy without writing a single line of code. They also have a built-in Copilot that
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New RAG Approach Achieves 98.7% Accuracy Without Vector Database
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Researchers built a new RAG approach that:
— Akshay 🚀 (@akshay_pachaar) 22 février 2026
– does not need a vector DB.
– does not embed data.
– involves no chunking.
– performs no similarity search.
And it hit 98.7% accuracy on a financial benchmark (SOTA).
Here's the core problem with RAG that this new approach solves:… pic.twitter.com/g8vf7HT3WKResearchers built a new RAG approach that: – does not need a vector DB.
– does not embed data.
– involves no chunking.
– performs no similarity search. And it hit 98.7% accuracy on a financial benchmark (SOTA). Here's the core problem with RAG that this new approach solves: -
ZeroClaw: Lightweight AI Assistant Running on $10 Hardware
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ZeroClaw Your personal AI assistant that runs on $10 hardware with <5MB RAM. That's 99% less memory than OpenClaw and 98% cheaper than a Mac Mini. 100% Rust. GitHub (16k stars ): https://
github.com/zeroclaw-labs/
zeroclaw
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Share LLM and AI Agent Insights with Your Network
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If you found it insightful, reshare with your network. Find me → @akshay_pachaar For more insights and tutorials on LLMs, AI Agents, and Machine Learning!
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Structuring Skills for AI Agents with Skill Graphs
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Skill Graphs > SKILL .md Everyone's talking about skills for AI agents. But almost nobody is talking about how to structure them. Right now, the default approach is simple. You write one skill file that captures one capability. A skill for summarizing. A skill for code review.
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Train LLMs in VS Code for Free with Unsloth and Colab
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Now you can train LLMs in VS Code for free!
— Akshay 🚀 (@akshay_pachaar) 18 février 2026
This guide by Unsloth shows you how to connect any fine-tuning notebook in VS Code to a Colab runtime.
Train locally or on a free Google Colab GPU.
Guide: https://t.co/ChBeoyw6Mh
GitHub: https://t.co/smDHEA1tI8 pic.twitter.com/ma2qgblO8eNow you can train LLMs in VS Code for free! This guide by Unsloth shows you how to connect any fine-tuning notebook in VS Code to a Colab runtime. Train locally or on a free Google Colab GPU. Guide: https://
unsloth.ai/docs/get-start
ed/install/vs-code
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GitHub: https://
github.com/unslothai/unsl
oth
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