@WiNLPWorkshop is partnering with the @aaclmeeting D&I Committee to launch a mentorship program for students, early-career researchers, and first-time attendees! 🤝 https://t.co/u2pBN5wqjl No one should navigate NLP alone — let’s build a community where everyone belongs 💙🌍

Hugging Face just dropped a 200 page playbook on training LLMs. It covers everything from pre-training to post-training and infrastructure with real examples of what worked and what didn't. 100% free and Opensource.

Seriously... why did no one tell me about this?! BrowserOS is a 100% open-source agentic browser alternative to ChatGPT Atlas and Perplexity Comet 🤯 Gave it a spin, it’s surprisingly smooth and stable. Repo in 🧵 ↓

Agentic RAG Tech Stack

𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 and what you need to know about it as an AI Engineer? Simple naive RAG systems are rarely used in real world applications. We are usually adding some agency to the RAG system - ideally a minimal amount. There is 𝗻𝗼 𝘀𝗶𝗻𝗴𝗹𝗲 𝗯𝗹𝘂𝗲𝗽𝗿𝗶𝗻𝘁 on how

I finally understand the fundamentals of building real AI agents. This new paper “Fundamentals of Building Autonomous LLM Agents” breaks it down so clearly it feels like a blueprint for digital minds. Turns out, true autonomy isn’t about bigger models. It’s about giving an LLM

What is RAG? What is Agentic RAG? 𝐑𝐀𝐆 (𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥-𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧) RAG connects a generation model to external knowledge through retrieval. Here’s how it works - 1./ A user submits a query. 2./ The system searches a pre-indexed set of

Combining the benefits of RL and SFT with on-policy distillation, a promising approach for training small models for domain performance and continual learning.

Writing a lit review is easy if you have a strategy. Modern AI tools can also make it fast and efficient. Here is how:

Build MCP AI Agents with reasoning, system prompts, and tool orchestration. Nanobot wraps existing MCP servers into intelligent agents and renders React components directly in chat via MCP-UI. 100% open-source.

Google open-sourced LangExtract Python library! It uses LLMs to extract entities, attributes, and relations with exact source grounding from unstructured documents. Flexible LLM support (Gemini, OpenAI, Ollama) 100% open-source.

Build an MCP server that extract structured data from complex documents!

Toolkit for fine-tuning and training large language and vision models

Stanford CS229: Building Large Language Models This brilliant 1.5h lecture unpacks how ChatGPT-like models are built: From tokenization & scaling laws to training hurdles, benchmarks, SFT/RLHF, and efficiency Lecture link in 🧵 ↓

🏆📚This 200-Page LLM Paper Is a 𝗚𝗼𝗹𝗱𝗺𝗶𝗻𝗲 — and it’ll save you months 𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴, 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴, 𝗮𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 — finally crystal clear. If you don’t have time to read all 200+ pages, here are the most valuable 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀 ↓ 》

Open-ended reasoning is one of the hardest problems in reasoning LLMs rn. So in this paper, they aim to solve this by reverse-engineering plausible thought chains from good answers via a gradient-free search With DeepWriter-8B trained on this data outperforming top OS models!

If you want to learn Deep Learning from the ground up to advanced techniques, this open resource is a gem. Full notebook suite -> Link in comments

Still one of the best roadmaps and resource dumps of AI Engineering in 2025. 50 papers, models, blogs across 10 fields in AI Eng: LLMs, Benchmarks, Prompting, RAG, Agents, Vision, Diffusion, Finetuning.

Finally, an open-source, enterprise-grade RAG solution! If you're building an enterprise-grade RAG system, you’ll run into 2 big challenges: - Data scattered across 100s of sources - Need for real-time sync Knowledge bases by MindsDB is an open-source solution that tackles

Our team's AI-Researcher has been accepted by NeurIPS 2025 and selected as a Spotlight! 🌟 The project has also garnered 2.4K stars on GitHub and made it to the GitHub Trending list. Congratulations to our core team members: Jiabin, Lianghao, and Zhonghang! 👏 Over the past six