Scale AI cuts more contractors as it shifts toward more specialized AI training - Business Insider
Scale AI cuts more contractors as it shifts toward more specialized AI training Business Insider
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Fine-tuned Gemma 4 E4B for structured JSON extraction from regulatory docs - 75% to 94% accuracy, notebook + 432 examples included
Gemma 4 dropped this week so I fine-tuned E4B for a specific task: extracting structured JSON (doc type, obligations, key fields) from technical and regulatory documents. https://preview.redd.it/v7yg80prpetg1.png?width=1026 format=png auto=webp s=517fb50868405f90a94f60b54b04608bcedd2ced Results on held-out test set: - doc_type accuracy: 75% base → 94% fine-tuned - Hallucinated obligations: 1.25/doc → 0.59/doc - JSON validity: 100% - Field coverage: 100% Setup: - QLoRA 4-bit, LoRA r=16 alpha=16, Unsloth + TRL - 432 training examples across 8 doc types - 5 epochs on a single L4, ~10 min training time - Final train loss 1.04, eval loss 1.12 The whole thing is open: notebook, dataset, serve.py for FastAPI inference. https://github.com/spriyads-vault/gemma4-docparse Some things I learned the ha
Building a Decentralized Mesh Network in Rust — Lessons from the Global South
The Problem 2.6 billion people lack reliable internet access. When disasters strike, infrastructure fails, or communities are remote — traditional communication breaks down precisely when coordination is most critical. I'm a cybersecurity student in Nairobi, Kenya. I've seen what happens when communities lose connectivity: families can't check on each other after floods, rescue teams can't coordinate, and activists can't organize safely. So I built GhostWire — a decentralized, censorship-resistant mesh communication platform that works without any central servers. What Is GhostWire? GhostWire is a peer-to-peer encrypted communication platform written in Rust. Instead of connecting to a server, devices connect directly to each other. Messages hop from node to node through whatever path is a
Caveman Claude: The Token-Cutting Skill That's Changing AI Workflows
Caveman Claude: The Token-Cutting Skill That's Changing AI Workflows Meta Description: Discover the Claude Code skill that makes Claude talk like a caveman, cutting token use dramatically. Save money and speed up AI workflows with this clever technique. TL;DR: A creative Claude Code custom skill forces Claude to respond in ultra-compressed "caveman speak" — stripping out filler words, pleasantries, and verbose explanations. The result? Responses that use significantly fewer tokens while still conveying the essential information. It's quirky, it's effective, and developers are using it to slash API costs and speed up their AI pipelines. The Problem With AI That Talks Too Much If you've spent any real time working with Claude through the API or Claude Code, you've noticed something: the mode
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I wrote a fused MoE dispatch kernel in pure Triton that beats Megablocks on Mixtral and DeepSeek at inference batch sizes
Been working on custom Triton kernels for LLM inference for a while. My latest project: a fused MoE dispatch pipeline that handles the full forward pass in 5 kernel launches instead of 24+ in the naive approach. Results on Mixtral-8x7B (A100): Tokens vs PyTorch vs Megablocks 32 4.9x 131% 128 5.8x 124% 512 6.5x 89% At 32 and 128 tokens (where most inference serving actually happens), it's faster than Stanford's CUDA-optimized Megablocks. At 512+ Megablocks pulls ahead with its hand-tuned block-sparse matmul. The key trick is fusing the gate+up projection so both GEMMs share the same input tile from L2 cache, and the SiLU activation happens in registers without ever hitting global memory. Saves ~470MB of memory traffic per forward pass on Mixtral. Also tested on DeepSeek-V3 (256 experts) and
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