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Dominick Wirzba

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reacted to kasbsquall's post with 🔥 about 10 hours ago
🔎 UX Crime Scene — major update before the deadline! THE INSPECTOR (a film-noir detective) still circles every UX flaw on your screenshot's real pixels and files a graded verdict. But now the precinct runs on THREE small models: 🖼 THE RECONSTRUCTION — FLUX.2-klein-4B rebuilds each flawed element, fixed. Compare before/after with a draggable slider. (The trick: the Inspector writes the design brief first — image models obey art directors, not vibes.) 🗣 THE INTERROGATION — push back on a charge; the same 7B defends it from the evidence, or concedes when you're right. 🔊 THE VOICE — Kokoro-82M reads the verdict aloud. No API, no keys. Qwen2.5-VL-7B + FLUX.2-klein-4B + Kokoro-82M — all under 32B, all self-hosted on Modal. ⚖️ Put your UI on trial: https://huggingface.co/spaces/build-small-hackathon/ux-crime-scene ▶️ New trailer: https://youtu.be/JJOMKEcX0Ws 📹 66s full walkthrough: https://youtu.be/kju7LiAXGC0 📡 9 investigation traces (with remedies): https://huggingface.co/datasets/build-small-hackathon/ux-crime-scene-traces Built solo for the Build Small Hackathon 🍄 #buildsmallhackathon
repliedto eabdullin's post 2 days ago
Folks, let me tell you, nobody — and I mean NOBODY — knew transformers before me. People said attention is all you need. I said, "Attention? I INVENTED attention." Everybody's looking at me. Tremendous attention. The best attention scores. My softmax? Perfectly normalized. Other people, sad, their probabilities don't even sum to one. Disaster. I'm doing a PhD now. A PhD! In Large Language Models. Very large. The largest, believe me. My advisor said, "Sir, your model is overfitting." I said, "Wrong. It's fitting EXACTLY right. It memorized the training set because the training set is fantastic." We don't talk about validation loss in my lab. Validation loss is fake news. And the internship — oh, the internship. Big tech. I won't say which. Starts with a letter. They BEGGED me. They said, "Please, we need someone who understands gradient descent." I said, "Descent? I only go UP. I'm gradient ASCENT. Loss goes up, that means it's learning to be a winner." But the GPU cluster — this is the best part. Thousands of H100s. Maybe millions. Who's counting? I'm counting. It's a lot. Other PhD students, they get one little GPU, they're crying, they're training overnight like losers. Me? I burn through compute like nobody's ever seen. The electric company called. They said, "Sir, you've consumed a small country." I said, "Make it a big country. I only do big." People ask, "Did your model converge?" Folks, it converged so hard. It converged BIGLY. Honestly? My loss curve, it's beautiful, it's going down, down, down — like my approval ratings, very smooth, don't look at the spikes, the spikes are deep state. And hallucinations? My model doesn't hallucinate. It just has ALTERNATIVE tokens. Thank you, thank you. Tip your reviewers. Accept my paper. Goodnight!
reacted to mmhamdy's post with 🔥 3 days ago
It was supposed to be a failed experiment. Instead, it led to the discovery of one of the most intriguing phenomena in neural networks, simply because a researcher forgot to turn it off and left it running....for a week! In 2022, researchers at OpenAI were studying how neural networks generalize from their training data. For this task, they were training small transformer models to perform modular arithmetic. The thing is, neural networks are weird. When a model has an abundance of parameters (like neural nets), it can easily overfit. It essentially memorizes its training data, scoring a perfect 100% accuracy when tested on it, but remains completely clueless when faced with any new instances not present in the training set (close to 0 accuracy). It is like memorizing 1 + 2 = 3 without understanding the concept of addition, so if 2 + 3 wasn't in the training set, the model fails miserably! Usually, when a model overfits like this, people just cut their losses, turn off the experiment, and move on with their lives. But sometimes they forget. And that is exactly what happened to our researchers at OpenAI. A week later, they checked back in, and a miracle had happened! They discovered Grokking (And no, this has nothing to do with xAI's Grok , the term was originally coined by sci-fi author Robert Heinlein to mean understanding something so deeply that it becomes part of you). Grokking is when a neural network suddenly and abruptly learns to generalize long after it has overfitted. Just take a look at the graph in the image below! Spooky, right! I told you neural nets are weird!
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