Wraithbytes
Making your LLM models 10X better
What it does
Common AI scrapers/crawlers in the market are redundantly inefficient and expensive. Strategy: 1. Reduce AI dependency for html parsing, by sampling multiple html docs to build custom parsers that is self healing. 2. Use AI in analyzing webpages to figure most efficient way to scrape. This leads to: 1. Cheaper, Cleaner and Faster processing. 2. No hallucinations. 3. Better token economics. Unique offering: 1. Infinite concurrency. 2. PAYG for users. 3. Result storage and API retrieval.
Does a similar job
all alternatives →- SAStop AI scrapers from hammering your self-hosted blog (using porn)Dec 2025 · github.com · ▲373
Alright so if you run a self-hosted blog, you've probably noticed AI companies scraping it for training data. And not just a little (RIP to your server bill). There isn't much you can do about it without cloudflare. These companies ignore robots.txt, and you're competing with teams with more resources than you. It's you vs the MJs of programming, you're not going to win. But there is a solution. Now I'm not going to say it's a great solution...but a solution is a solution. If your website contains content that will trigger their scraper's safeguards, it will get dropped from their data…
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- CWCrawlee – Web scraping and browser automation library for Node.js2022 · crawlee.dev · ▲282
Hey HN, This is Jan, founder of Apify, a web scraping and automation platform. Drawing on our team's years of experience, today we're launching Crawlee [1], the web scraping and browser automation library for Node.js that's designed for the fastest development and maximum reliability in production. For details, see the short video [2] or read the announcement blog post [3]. Main features: - Supports headless browsers with Playwright or Puppeteer - Supports raw HTTP crawling with Cheerio or JSDOM - Automated parallelization and scaling of crawlers for best performance - Avoids blocking using…
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com
Launched alongside, March 2026
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