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nyudatascience.medium.com/yann-lecuns-de… Taught by @ylecun @alfcnz atcold.github.io/pytorch-Deep-L… github.com/atcold/pytorch… reddit.com/r/NYU_DeepLear… youtube.com/playlist?list=…

Andrej Karpathy on the shift to agentic engineering

Here's a prompt that you can use to generate AI prototypes and apps that don't look like slop. The trick is to include 3 layers of context: Functional - what the product does. Visual - a wireframe of the layout. Data - A JSON of the product's synthetic data. Below is an example 3 layer prompt for a music discovery product. The best thing about this system is that you can swap the data layer (JSON) at any time to have your app highlight completely different content (e.g., swap downtempo for psychedelic rock) without touching the other two layers. 📌 Get the full breakdown with a live demo in my tutorial with @ravi_mehta here: youtu.be/wUWljYoQN8g

Hermes Agent now has multi-agent via the Kanban, new in v0.12.0. Agents claim tasks from a board, work in parallel, and hand off when blocked. You watch progress and unblock from one easy view instead of juggling terminals. We asked it to plan and make this video about itself:

Our first dive into Multi-Agent Coordination and Cooperation is here, with Hermes Agent Kanban Orchestrate tasks across multiple agent profiles and dependencies easily and visually. Achieve more. See the docs here: hermes-agent.nousresearch.com/docs/user-guid…

More things it handles so your agent doesn’t have to: › Presenter mode with speaker notes + timer › Export to static HTML & PDF › Built-in assets manager with svgl logo search › One-click deploy to Vercel / Cloudflare / Netlify open-slide.dev

Here is a full guide on how to scaffold, build, and deploy your next presentation using open-slide and @vercel. From CLI init to a live URL!

The nux Morse Code Card Pro combines classic design with interactive technology! nux-modellbau.de/g41731.2/nux-m…

Here's a concise version for X (under ~280 chars): "Why @BlackboxNode? 🤖 Local AI (llama.cpp) 📡 LoRa mesh + Meshtastic 🗺️ TAK/CoT off-grid ⚡ Bitcoin + Cashu ecash 💾 ./data/ - no cloud, no accounts" That's ~230 characters. Need shorter?

STOP judging local coding agents only by model benchmarks. A LocalLLaMA reddit post showed Pi Coding Agent + local Qwen3.6 35B working well on real projects. But the unlock was not just the model. It was a plan-first skill: repo analysis → questions → TODO md → approval → task-by-task execution Agents need workflow, not just tokens.

This dock turns a Mac mini into a classic Macintosh. wow

Day 46 🥳Introducing Recordly v1.2.0! • New Sequoia cursor type • Project auto-save (no more manual saves!) • Custom recording position • Renameable Headers • Refreshed background options • + Many stability fixes to previous breaking changes

It's safe to say there is enough demand to share a complete research agent setup guide. Until then I'll share more about what mine does exactly: • watches my own X posts/replies • watches priority X lists • scans GitHub releases + repo activity • checks arXiv • scans Hugging Face • reads blogs/RSS • runs targeted domain/news searches • uses browser enrichment on selected items • tracks package momentum • checks community sources • keeps local Hermes build evidence • routes useful signals into content, research, build, and monetization queues Taste, source verification, memory, and information routing is a whole other beast that the setup guide will cover in full. Coming soon, follow for more.

Can an open-weight coding agent + harness match Claude Code at training a domain-specific model? Same one-line prompt. ~13 min e2e. Pushed to @huggingface. Pi + @moonshotai Kimi K2.6 vs Claude Code + Opus 4.7. Task: classify NC session laws (1866-1967) as Jim Crow or not

Everybody is adding a feature where you can manage your agents from your phone. Don't use it. You'll just get even more addicted, and will burn out even quicker.

I am excited to introduce OpenGeoAgent, a powerful open-source multimodal AI agent for automated geospatial analysis and visualization! It supports QGIS, Jupyter notebook, and Python scripting. In this tutorial, you’ll learn how to automate GIS workflows using natural language, generate maps, analyze satellite data, and even run complex hydrological models. You can even interact with the agent using voice commands (no typing needed). Video: youtu.be/5zkXQlHUsu8 GitHub: github.com/opengeos/GeoAg… QGIS Plugin: geoagent.gishub.org/qgis-plugin #geospatial #GeoAgent #OpenSource #AI

Introducing Hermes Curator! The new system built in to Hermes Agent now helps you keep your skills that the self improvement loop creates in check, by consolidating and pruning automatically. The curator does multiple things: - keeps track of how often you use each skill, when it was last updated/created, etc - Once a week runs automatically (configurable) - Uses the analytics plus it's own scanning of your skills and consolidates or prunes them if necessary - Skips externally installed skills, built in skills, and skills you "pin" that you dont' want touched. It will only attempt curation over agent created/updated skills or user written skills. - It will then determine whether skills can be consolidated, pruned, or otherwise made more manageable. It will convert some skills that are too specific into references, templates or scripts for larger/broader skills, or integrate them directly into a consolidation of an existing skill. You can also disable it entirely in the config.yaml and/or run it manually with `hermes curator run ` Learn more on the docs here: hermes-agent.nousresearch.com/docs/user-guid…

Agents make ugly UIs because they've never seen good design. We've been fixing that, 2,000 DESIGN.md files from the world's best products, structured for a model to read and learn. Colors, type, spacing, layouts and more. Free. styles.refero.design

🤗🤗🤗introducing Hugging Science -- the home of AI for science 🤗🤗🤗 open models and datasets are the powerhouse of science (see the PDB), but finding the models and data you actually need for your breakthrough is hard af you shouldn't need to scrape arxiv, own your own wetlab, fight a custom HDF5 parser, build a fusion stellarator, and beg for compute before you've trained a single epoch so we're changing that we've put all the best science on @huggingface in one place: - 78GB of genomics data - 11TB of PDE simulations - 100M cell profiles - 9T DNA base pairs - 13M molecular trajectories - 400k medical QA pairs and much more, all open, and all ready for training (+ you can also now filter and search by domain, task, and keyword) we've put together all the biggest releases from our partners at NASA, Google, OpenAI, Meta FAIR, Arc Institute, Ginkgo, SandboxAQ, Proxima Fusion, NVIDIA, Ai2, OpenADMET, InstaDeep, Future House, Polymathic AI, LeMaterial, Earth Species Project, Merck, and Eve Bio if you're not sure where you fit in -- work on open challenges for problems that matter: including fusion stellarator design, ADMET, antibody developability, multilingual medicine, catalysis and materials, and scientific reasoning. we're already changing how science gets done: a fusion startup needed a benchmark for stellarator plasma confinement that didn't exist. @proximafusion shipped ConStellaration on Hugging Science: a leaderboard, dataset, and eval metrics, all in one place. a drug discovery team wanted to predict hPXR induction. OpenADMET put up a blind challenge: 11,000+ compounds assayed at Octant, 513 held out, two tracks (pEC50 + structure). Anyone in the world can train and submit. an antibody team at @Ginkgo released GDPa1, a developability dataset for stability, manufacturability, and immunogenicity prediction, with a live leaderboard scoring every submission. if you know a problem the ML community should be working on, let us know. make a challenge! this is about putting all the tools for solving science in one place. so we can hillclimb! → huggingscience.co

🚨BREAKING: Hugging Face just open-sourced an AI intern that reads ML papers, trains models, and ships the final model for you. It’s called ML Intern. And this is not another AI coding demo that prints a broken PyTorch script and disappears. You give it the goal. It researches. Writes code. Runs experiments. Uses Hugging Face datasets. Launches jobs. Pushes the final model. All from your terminal. `ml-intern "fine-tune llama on my dataset"` That’s the entire command. The crazy part is how deep this goes: → reads HF docs and research → searches papers and datasets → uses Hugging Face jobs → searches GitHub code → runs local and sandbox execution → streams every step back to you → asks approval before risky actions → keeps working for up to 300 iterations This is the first open-source AI intern I’ve seen that feels built for actual ML work. Not chat. Execution. 4K stars already. 100% Open Source. github.com/huggingface/ml…