✨ Exciting news! #WiNLP is joining hands with AACL 2025 2025 🙌 Together with D&I Chairs - Tirthankar Ghosal, Dr. Sriparna Saha & Sudeshna Sarkar — we’ll launch initiatives to broaden diversity & inclusion at #AACL2025. 📢 More updates coming soon — stay tuned!

🇮🇳 #AACL2025 is coming to India — and I’m firing on all cylinders! 🚀 As SRW Chair and with #WiNLP teaming up with the D&I Chairs, I’m thrilled to help make the conference more inclusive & unforgettable. Big thanks to Tirthankar Ghosal for leading this collab — WiNLP is all in! 💜

Universal Deep Research NVIDIA recently published another banger tech report! The idea is simple: allow users to build their own custom, model-agnostic deep research agents with little effort. Here is what you need to know:

This is probably one of THE most important paper of the last few months. Small language models are sufficiently powerful, operationally suitable, and economical Agentic tasks. - Phi-2 matches 30 billion models running 15x faster. - Serving a 7 billion parameter small language

Unveiling our cool logo for ACM/IEEE JCDL 2025. The ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL) is a premier international forum focused on research and practice in digital libraries, bridging technical, practical, and social dimensions. #JCDL2025 #digitallibraries

I’ve been sharing NotebookLM tips here for a while. Just compiled them all into one article so it’s easier to follow. All these ideas come from my personal experiments.

I just read this new paper from Google and I’m absolutely buzzing 🤯 The core idea is almost offensively simple: ditch recurrence and convolutions, and use only attention. That’s it. And somehow…it unlocks a whole new regime of performance, scale, and simplicity. Here’s what

Cool paper from Microsoft. And it's on the very important topic of in-context learning. So what's new? Let's find out:

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

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!

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 🧵 ↓

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.

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

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

🏆📚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 ↓ 》

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.

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