Any one in New York City that wants to build robot let me know you have to be a super nerd and a hobbyist Thank you submitted by /u/BirdAccording7249 [link] [Kommentare]
I've been teaching myself how LLMs actually work, not at the API level, but down to the matrix multiplications. To force myself to really understand the forward pass, I first built a complete transformer by hand in a spreadsheet from embeddings through to the loss. Then I turned the forward pass into a web page so it's easier to share. It's a full transformer (single attention head, single block) shrunk to the smallest size where every single number still fits on screen: a 6-word vocabulary, 3-dimensional embeddings. It reads four words and predicts the next one, and it walks through the whole thing top to bottom: word vectors, Q/K/V, attention scores, the causal mask, softmax, the feed-forward network, logits, and the final probabilities. The part I found most useful for my own understanding: the weights and word vectors are editable, and everything downstream recomputes live. There's also a Randomize button that scrambles all the weights, and the prediction immediately turns to nonsense. That's the honest point of the whole thing: with random (untrained) weights the guess is meaningless, and training is the entire story this page deliberately leaves out. It's a single self-contained HTML file, no libraries, no build step. Backward propagation (how the weights actually get good) is the next one I want to build. Link: https://dgochin.github.io/transformer/ I'm not an ML researcher, I'm a software engineer learning this from the ground up, so if anything's wrong or could be explained better, I'd genuinely like to hear it. This was just my attempt of trying to understand the transformer in the most basic way. submitted by /u/DanielMoGo [link] [Kommentare]
Many would have seen this many times over the years, but this is for those who haven't. If this relates to you just drop a comment lol submitted by /u/gh0stprotoco1x [link] [Kommentare]
> Article except. Throughout much of 2026, Strategy has relied heavily on the issuance of perpetual preferred shares like STRC to raise billions of dollars to fund the majority of its bitcoin purchases. But that doesn't come without cost. Those securities carry roughly $1.2 billion in annual dividend obligations, while the company's cash reserves have dipped to around $1.4 billion, according to CryptoQuant. This has created a negative feedback loop that has put pressure on STRC, which itself hit a fresh low of around $71.40 on Friday before recovering to close at $74.72. That's nearly 26% below its intended $100 par value. submitted by /u/zesushv [link] [Kommentare]
I’m starting a new build-in-public project: oomwoo, an open-source robot vacuum you build yourself. Raspberry Pi, ROS 2, 2D LiDAR, Home Assistant, 3D printed, local-first — and open from the first commit.
Building a custom octocopter from scratch with an RL controller that sustains flight through motor failures. Follow the build log.
I’ve been talking to people building robots and keep hearing the same things: sim-to-real issues, hardware availability, debugging deployment failures, and testing taking way longer than expected. I’m doing a Cornell Master’s project to understand where robotics teams actually spend their time and what slows them down. The survey covers things like: - simulation tools (Isaac, Gazebo, MuJoCo, etc.) - ROS/ROS2 and middleware - RL, VLA, and classical stacks - testing and validation - deployment failures - world modeling and sim-to-real It takes about 4 minutes. If you’re working on real robots, your responses would be especially helpful. There’s also an optional follow-up interview with a $25 Amazon gift card :) submitted by /u/Realistic-Ganache446 [link] [Kommentare]
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