New @huggingface cookbook: Optimizing LLMs with @DSPyOSS GEPA! ✨ 11% accuracy boost with <$0.50 total cost 🧠 Dual-model magic: cheap inference + smart reflection 📊 NuminaMath-1.5 dataset 🎯 Reflective prompt optimization @lateinteraction https://t.co/Njh9ELaFZW #GEPA #DSPy

👨‍🔧 Github: RAG-Anything: All-in-One RAG Framework 7.6k Stars ⭐️ All-in-One Multimodal Document Processing RAG system built on LightRAG. You can query documents containing interleaved text, visual diagrams, structured tables, and mathematical formulations through one interface.

Large Language Models like ChatGPT can help you get a lot done as a dev. For example, creating dynamic user interfaces, navigating through tons of textual data, and more. In this course, you'll learn the basics & how to use LLMs in your coding projects.

Text-to-dialogue model, clones voices

Beautiful! You can now do: > uv pip install mcp2py dspy And in just 6 lines of Python code, you have an AI agent that can retrieve information through Google Chrome MCP DevTools. With the added bonus that you're only 1 or 2 steps away from doing prompt optimization for that

RL really sucks. It takes 10 hours just to learn breakout. ... a few years ago. It's <30 seconds on 1 GPU now in PufferLib and still dropping. Write faster code.

GEPA appears at rank #4 as emerging repositories as per clickpy by @ClickHouseDB! If you haven't yet, check it out to optimize all your AI pipelines and agents!

If you've got 308GB to spare and want a high-quality document dataset to add to your VLM tasks, CommonForms is now hosted on @huggingface! It only took 24 hours to upload from my home network. 😭 https://t.co/mV2EpozJDP

@jsuarez5341 I very much hope you continue working on RL! I think it's a misunderstanding that I am suggesting we need some kind of a replacement for RL. That's not accurate and I tried to clear it but did so poorly - they layer. Layer 1 was base model autocomplete. Layer 2 was instruct

@Hesamation Found it absolutely helpful. Watch full episode here:

Karpathy spilled the formula of truly learning something: > don’t write blog posts > don’t do slides > write the code > arrange it > get it to work if you can truly build it you can say you know it. build from scratch guys.

Open-source on-device TTS model

Claude Code is so freaking good now Here it is asking me exactly what test cases I want in an interactive UI More of this, please

follow along: https://t.co/H4Hy38ghfv

Yesterday I realized that we could turn MCP on its head. Since it's a self-documenting protocol of tools (functions) with structured, typed outputs, any MCP server can be easily mapped into a module/library in Python (or most other languages). That means you can make a

Data Structures and Algorithms Ultimate Tier List S-Tier $$ Ready >Arrays – The Only Thing You Actually Know >Strings – Regex PTSD >Hash Maps – O(1) Flex >Binary Search – Classic Brag A-Tier Makes You Look Smart >Trees – Trie, BST, Segment… choose your weapon >Graphs –

Git branching strategies clearly explained. A well-planned Git branching strategy helps coordinate the development team’s work and keeps the development process consistent. Let's take a look at some common approaches to branching: 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗯𝗿𝗮𝗻𝗰𝗵𝗶𝗻𝗴 is a popular

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

I love lazygit so much. Such a nice way to deal with git, partial commits, catching up on history, creating new branches, seeing what's there. Incredible power up for any developer.

This guy literally shared a step-by-step roadmap to build your first AI agent, and it's absolute 🔥

Remember Golden Gate Claude? I brought it back to life thanks to Skills. Skills are actually incredible for steering model behaviors and eliciting different personality. Much stronger than tools and MCP. 👇🔗