My lecture at MIT!✨ From Physics to Linear Algebra & Machine learning, I have learned a lot from MIT! Yesterday, I had the honour of delivering a guest lecture on The state of AI Engineering, exploring: - Prompt Engineering - Retrieval Augmented Generation. - Fine-Tuning

I recommend these 6 open-source tools for AI Engineers. You can use them to: - Build an enterprise-grade RAG solution - Build and deploy multi-agent workflows - Fine-tune 100+ LLMs - And more... All of these are 100% open-source:

That's a wrap! If you found it insightful, reshare with your network. Find me → @akshay_pachaar ✔️ For more insights and tutorials on LLMs, AI Agents, and M https://t.co/lEGLyJpOlz

These 6 open-source tools handle it all: RAG pipelines, agentic workflows, even fine-tuning LLMs without a single line of code. Here's a graphic summing them up:

6️⃣ Anything LLM The all-in-one AI app you were looking for. Chat with your docs, use AI Agents, hyper-configurable, multi-user, & no frustrating setup required. Can run locally on your computer! 100% open-source with 48k+ stars! 🔗

5️⃣ Llama Factory LLaMA-Factory lets you train and fine-tune open-source LLMs and VLMs without writing any code. Supports 100+ models, multimodal fine-tuning, PPO, DPO, experiment tracking, and much more! 100% open-source with 57k+ stars! 🔗

4️⃣ AutoAgent AutoAgent is a zero-code framework that lets you build and deploy Agents using natural language. - Universal LLM support - Native self-managing Vector DB - Function-calling and ReAct interaction modes. 100% open-source with 5k stars! 🔗

3️⃣ RAGFlow RAGFlow is a RAG engine for deep document understanding! It lets you build enterprise-grade RAG workflows on complex docs with well-founded citations. Supports multimodal data, deep research, etc. 100% open-source with 63k+ stars! 🔗

2️⃣ Transformer Lab Transformer Lab is an app to experiment with LLMs: - Train, fine-tune, or chat. - One-click LLM download - Drag-n-drop UI for RAG. - Built-in logging, and more. A 100% open-source and local! 🔗

1️⃣ Sim AI A drag-and-drop UI to build AI agent workflows! Sim AI is a lightweight, user-friendly platform that makes creating AI agent workflows accessible to everyone. Supports all major LLMs, MCP servers, vectorDBs, etc. 100% open-source. 🔗

You can use these 6 open-source repos/tools for: - building an enterprise-grade RAG solution - build and deploy multi-agent workflows - finetune 100+ LLMs - and more... Let's learn more about them one by one:

6 GitHub repositories that will give you superpowers as an AI Engineer:

Build agents that can actually do real-world tasks! Agent Reinforcement Trainer (ART) is a framework to train multi-step LLM agents for real-world tasks using GRPO. You just need a few lines of code. No manual rewards needed! ✨ 100% open-source.

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!

Link to the GitHub repo: https://t.co/AOWVc4YmOC (don't forget to star 🌟)

Build agents that can actually do real-world tasks! Agent Reinforcement Trainer (ART) is a framework to train multi-step LLM agents for real-world tasks using GRPO. You just need a few lines of code. No manual rewards needed! ✨ 100% open-source.

Who is a Full-stack AI Engineer? Production-grade AI systems demand a deep understanding of how LLMs are engineered, deployed, and optimized. Here are the 8 pillars that define serious LLM development:

That's a wrap! 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!

These 8 skills separate hobby projects from production-ready AI systems. Master them, and you'll build LLM applications that actually work in the real world! Over to you: What other LLM development skills would you add?

8️⃣ Context Engineering Context engineering is rapidly becoming a crucial skill for AI engineers. It's no longer just about clever prompting; it's about the systematic orchestration of context. This post tells you more about what it actually means:

7️⃣ LLM Observability No matter how simple or complex your LLM app is, you must learn how to implement tracing, logging, and dashboards to monitor prompts, responses, and failure cases. @Cometml's Opik is 100% open-source solution for this. Check this👇 https://t.co/Lw9jifK9Hk