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!
@akshay_pachaar
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Excellent Projects on AI Engineering Hub GitHub Repository
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Some really good projects here. + We have 90 more on our AI Engineering Hub, GitHub repo.
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Agent Lightning: Easy Starting Point for Getting Started
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You're not wrong. That said, Agent Lightning is a really good and easy way to get started in this direction.
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Essential LLM Fine-Tuning Techniques to Master
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LLM fine-tuning techniques I'd learn if I were to customize them:
— Akshay 🚀 (@akshay_pachaar) 11 janvier 2026
Bookmark this.
1. LoRA
2. QLoRA
3. Prefix Tuning
4. Adapter Tuning
5. Instruction Tuning
6. P-Tuning
7. BitFit
8. Soft Prompts
9. RLHF
10. RLAIF
11. DPO (Direct Preference Optimization)
12. GRPO (Group Relative… pic.twitter.com/PQ5VrHyr34LLM fine-tuning techniques I'd learn if I were to customize them: Bookmark this. 1. LoRA
2. QLoRA
3. Prefix Tuning
4. Adapter Tuning
5. Instruction Tuning
6. P-Tuning
7. BitFit
8. Soft Prompts
9. RLHF
10. RLAIF
11. DPO (Direct Preference Optimization)
12. GRPO (Group Relative -
Knowledge Graphs Enable Impressive Cross-Video Understanding
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The use of knowledge graphs for cross-video understanding is indeed very impressive.
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Share AI and Machine Learning 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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VideoRAG: Local AI Framework for Chat with Long Videos
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RAG over videos (100+ hrs) running fully local! VideoRAG is the first framework enabling chat with extremely long videos, all done locally – Runs on a single RTX 3090 (24GB)
– Comes with drag-n-drop desktop app
– Cross-video understanding via knowledge graphs 100% open-source. -
AG-UI Protocol Stack for Agent-to-User Communication and State Streaming
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Great visual! Would like to add AG-UI to this mental model. MCP handles agent-to-tool. A2A handles agent-to-agent. AG-UI handles agent-to-user and standardizes how agent state and actions stream to the frontend. All three together give you the full protocol stack for agentic
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Testing AI Agent Outputs Over Process Steps
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Engineering at Anthropic dropped another banger. Their internal playbook for evaluating AI agents. Here's the most counterintuitive lesson I learned from it: Don't test the steps your agent took. Test what it actually produced. This goes against every instinct. You'd think