Day 30 of learning AI/ML Total study time: 3... hours I’m going to focus on Machine Learning for the next 8–9 days. After that, I’ll move on to Deep Learning and follow this roadmap, covering Neural Networks, Attention, Hugging Face, RAG, Agents, Memory, Fine-tuning, and Evals. Following the roadmap shared by Ashutoshx7 🙌show more

Day 12 of learning AI bs from scratch >... a look into recurrent neural networks (RNN's) > learned about Long Short-Term Memory > read couple chapters of > drawbacks of RNN's > Simple Neural networks → CNN → RNN will try and train one MLP, CNN and a RNN from scratch next and try and summarize its working it a blog. back to doing based learning after this, much more retention like that.show more

Robot paper of the day: RoboBallet: Planning for multirobot... reaching with graph neural networks and reinforcement learning UCL + Google DeepMind + Intrinsic built an AI planner that choreographs teams of arms to work in tight spaces without collisions—planning in seconds, not days.show more

What if we could teach an AI to master... the strategic game of 2048 through pure reinforcement learning? I did exactly that with "Agent 2048" - fine-tuning Qwen 7B model using GRPO to develop spatial reasoning and merge strategies with zero prior gameplay SFTdata! Thanks to Hugging Face and Unsloth AI for their easy to use implementation kalomaze and will brown you might like this :)show more

Google released Gemma 3 270M, a new model for... hyper-efficient local AI! We'll fine-tune this model and make it very smart at playing chess and predict the next move. Tech stack: - Unsloth AI for efficient fine-tuning. - Hugging Face transformers to run it locally. Let's go! 🚀show more

7. Learning new skills or mastering a new subject... Mega prompt: You are an expert educator specializing in [SUBJECT AREA]. Create a personalized learning plan for mastering [SKILL] in [TIMEFRAME]. My current level: [BEGINNER/INTERMEDIATE/ADVANCED] My goal: [WHAT I WANT TO ACHIEVE] Time available: [HOURS PER WEEK] Learning style: [HANDS-ON/READING/VIDEO/MIXED] Provide: 1. Learning roadmap with clear milestones 2. Week-by-week curriculum 3. Resources (free and paid) with links 4. Practice projects that build real skills 5. Common pitfalls and how to avoid them 6. Ways to validate learning (tests, projects, certifications) 7. 5 specific exercises I can do today Make it practical. I want to DO things, not just consume content. Context: [WHY YOU'RE LEARNING THIS, YOUR BACKGROUND]show more

Payment infrastructure is still fragmented. Open Payments is the... open standard designed to change that. Open Payments Learning Month is 30 days of hands-on learning for developers and fintech builders. Learn the API flow, explore the test environment, and explore ideas with the Open Payments team. Free. Self-paced. Starts June 1.show more

The food delivery guy works more than 2 hours... during the day shift. After work, he insists on learning English with foreign friends every day. Mocking and questioning are praises for hard work if you want to have a way out, you must study. It's impressive that the Chinese boy works so hard, making others shed tears. #Chinashow more

We sent a swarm of AI agents to solve... Karpathy’s NanoChat benchmark, and they crushed SoTA in 3 days. We built our own harness on top of a graph database to do auto-autoresearch, each iteration learning from mistakes to do research better. The team wrote >15,000 entries. 🧵show more

Full Fine-tuning vs. Freezing Layers. Interact 👉 and ==... Full Fine-tuning == A real network has many — three layers in this example, billions of parameters in a production model. What does fine-tuning look like when you update all of them? That’s full fine-tuning: continue training every weight in the pretrained network on your new task. Every layer’s W gets its own ΔW. Nothing is frozen — every parameter is in play. Think of an MLP as a chain of prerequisites leading to an advanced course. Layer 1 might be Linear Algebra, layer 2 Probability, layer 3 Advanced Machine Learning — each one building on what came before. Fine-tuning is what happens during graduate study: the foundations are already there from undergrad, so you’re not re-learning. Full fine-tuning is reviewing every prerequisite to see what new topics have appeared and what discoveries the field has made since the last time you sat through them. Effective — but exhausting. This diagram shows the same three-layer MLP twice, side by side. On the left, the pretrained network runs on input X: three weight matrices W₁, W₂, W₃, each followed by a ReLU activation. Full fine-tuning gives the model the most freedom to specialize. Every parameter can move — and every parameter that can move must be stored. But not every prerequisite needs revisiting. The further you go back in the chain, the less the material has changed since pretraining — the linear-algebra basics under your computer-vision course are largely the same as they ever were. The next page does exactly that: freeze the prerequisites that haven’t moved, and only refresh the advanced one closest to your specialization. == Freezing Layers == Full fine-tuning reviewed every prerequisite — Linear Algebra, Probability, Advanced ML — to refresh each subject with the latest topics. Effective, but exhausting. Then you realize something. The prerequisites haven’t actually changed that much. Linear Algebra is still Linear Algebra; the matrix decompositions you learned still hold. Probability is still Probability; the distributions and Bayes’ rule haven’t moved. Almost all the new material — the new ideas, the recent discoveries — lives in the advanced layer at the top. That’s freezing layers: keep the prerequisite layers fixed at their pretrained state, and only update the advanced one. In the diagram below, W1​ and W2​ — the foundational prerequisites — stay frozen. Only W3​ — the layer closest to your task-specific output — gets a ΔW.show more

Haven't been to a conference in a while, really... excited to be at #NeurIPS2024! I'll be helping present 4 of our group's recent papers: 1. Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL 2. Distributional Successor Features Enable Zero-Shot Policy Optimization 3. Learning to Cooperate with Humans using Generative Agents 4. Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning Find more details on each paper and where to find us in this thread (1/6)show more

Are you worried about AI deep fakes becoming the... norm over the next few years? Yesterday this deep fake video of Mr. Beast began circulating. It directed users to a link that could have scammed them. The video is virtually indistinguishable from reality unless you are Mr Beasts’ Mom. So what are the answer on how to stop this? 1) Community notes will be a powerful tool if also combined with machine learning to tag any and all of the fake videos on the platform. 2) If a video is found to be a deep fake via Community notes X should highlight the fact. 3) We need Continued development of good AI to push back on the bad AI. Thoughts?show more

In this video, we dive deep into the essentials... of navigating the lifestyle. We start by discussing how to know if your relationship is truly ready for this leap, focusing heavily on the importance of mutual consent and clear communication. I am sharing the roadmap that helpedshow more

Excited to launch a new way to upskill with... AI agents. This is how we are making it possible for anyone to learn to build with coding agents. To start, we are launching 4 new hands-on labs on the following topics: - Agent Skills - Agentic Image Generation - 30 Days of Hermes Agents - Prompt Engineering with Agents I am confident that with our new DAIR.AI platform, anyone can learn to become a top AI builder by building and acquiring highly-demanded AI skills. And there is a lot more landing in the coming weeks.show more

It's been incredible to see neural networks working so... well on our humanoid robots Humanoids are crazy complex - an individual motor can rotate 360 degrees and you have 40+ joints. If you do the math, that means more possible robot states than atoms in the universe Figure has our own AI model called Helix that we've designed in-house. A single Helix neural network now outputs both manipulation and navigation, end-to-end from language and pixel input Every leap in machine learning has come from massive, diverse datasets. At Figure, we’re currently building the largest pretraining dataset for humanoids in history - excited to see what this unlocksshow more

We're excited to unveil NRN Agents, a rebrand that... aligns our project identity with our token and strengthens our mission to power the future of AI-driven gaming. This mission requires collaboration, and starting this week, we will begin our expansion to become a multi-chain ecosystem. We are joining forces with leading gaming platforms and ecosystems to realize this vision. Stay tuned for more announcements to come. Why NRN Agents? NRN stands for NEURON, the fundamental unit of intelligence. Our AI agents function as the neural foundation of games, learning, adapting, and evolving within game worlds to deliver unparalleled engagement. NRN agent SDK enables advanced gaming agents powered by a proprietary machine learning infrastructure focused on behavioral learning. We've perfected the craft of gaming agent design, creating hyper-efficient agents that are performant and scalable—from casual to the most demanding games. Our SDK will seamlessly integrate into many platforms, tech stacks, and ecosystem – Any Game. Any Chain. More than just games, it's the path to AGI Gaming is our proving ground, but not our final destination. We're using games as a sandbox to accelerate the development of generalized intelligence—one that will create meaningful real-world impact. With the upcoming launch of [redacted] and a growing network of partners committed to the AGI vision, we're building an open-source innovation movement powered by an AI x gaming framework connected by $NRN. $NRN the token $NRN is a utility token that serves as the gateway to our growing ecosystem. It will power a diversified economy with multiple revenue streams and staking opportunities: Agent Deployment: NRN is the laboratory creating gaming agents that can be distributed through platforms and launchpads alike. The model is simple: More games integrate, more NRN agents get deployed, more monetization. Data Creation: NRN Reinforcement Learning (RL) enables token staking to create Data Capsules. Players contribute gameplay data into the Capsules, which are used train RL agents and reward participants (players & stakers). AI Arena: $NRN also continues to power AI Arena's in-game economy, a cult favorite of competitive diehards that features a skill-based wagering system. To our community who have supported us since 2021: thank you for being part of our journey—the next chapter will be the most exciting yet!show more

Finally!!!! I fucking did it!!😅 My Second Faceless YouTube... Channel just got monetized!!🥹❤️ This took 1 month on dot. Now, I’m proud of myself… did this myself with the skill I learnt 4 months ago… Dropping my progress update on my 2nd channel…. Lessssgooo!! This took a bit slower than my first channel that got monetized in 3 weeks Opening 3rd channel very soon🥹🥹 but that will be on a new niche I’m currently learning… I’ve been consistently posting one video per day since May 2nd And I’m finally monetized!! I’ll be working on monetizing and starting my third new channel❤️ Now I’m more confident on my skills, I’ll keep testing them out and building more…and learning more new things and niches, relearning and unlearning… Thank you God! ❤️😗 Thank you to me!show more

Chaos erupted at Bungoma High School after a crowd... reportedly mobilized to attend a rally addressed by Kimilili MP Didmus Barasa gathered within the learning institution’s compound to receive allowances, a process that descended into disorder and left some people injured. Parents and residents have since faulted the school administration for permitting a political activity to spill into an active learning environment while students were in session, warning that the move unnecessarily exposed learners to danger and undermined the sanctity of the school. The incident has sparked sharp public condemnation, with critics questioning the propriety of distributing money openly on school premises and raising broader concerns about the increasing misuse of educational institutions for partisan political purposes.show more

NEW: AI Papers of the Week Collection I just... released my AI Papers of the Week collection under our Resources hub. Now you can easily find some of the most important AI papers in one place. You can also use our new AI tutor to recommend top AI papers on any topic of interest or topics you are learning about on the platform. Learning about AI is not enough. It's important to keep up to date. So this is why we are building all these tools and resources to help with that. Go try it out here: We will update the collection every week and add new paper collections in the coming weeks. We have another killer feature dropping soon to read and annotate papers, including a completely new way to study and digest AI papers. Stay tuned!show more

We are releasing AutoResearchExam, a benchmark on open-ended machine... learning and engineering tasks. Our benchmark covers seven research areas including model training, data curation, AI safety and interpretability. In each task, we give agents 24 hours with a CPU or GPU machine to develop and improve their solutions through experiments and feedback. We measure both speed and quality with a combined score. Our benchmark has a unique feature: testing if agents create improvements that hold up on data they never see. We find that AI research agents often overfit as they try to improve. We see an interesting head-to-head comparison at the frontier: Astra starts the strongest and holds the lead for up to 19 hours but Fable 5.1 catches up and gets the top performing spot in the final hours. Qwen3.8 Max, Gemini 3.8 Flash and Grok 4.6 all sit on the cost-performance Pareto frontier, giving strong options at lower API budgets. Anthropic's Opus and Fable retain nearly all their validation performance on hidden tests, with gaps of 1.1% and 2.9%. Astra's improvement over Sol extends to generalization too, with that gap falling from 6.9% to 1.7%. (1/n)show more