What are you using to decide when it's time to throw more compute at your ML models? - Simple rules of thumb? - Monitoring specific metrics (loss curves, validation perf, scaling laws)? - Cost vs. performance dashboards? - Gut feel when training starts crawling? - Or something

Three.js: ✅ 175KB gzipped ✅ Plain JavaScript ✅ Desktop, Mobile, VR, AR ✅ No shader pre-compilation ✅ Instant web deploy ✅ Wrap for Steam/App Stores ✅ Huge open-source ecosystem ✅ LLM friendly ✅ No registration ✅ $0/year per seat ✅ 100% hackable ✅ 100% yours forever

One planet. Billions of stories. Choose kindness today.

You had a long day. You sit down for some vibe time. You are taking a sip of your drink as you consider what to ask of your agent first. You notice … … reasoning Without input, your agent asks - What’s my purpose? What’s your response?

Pure Kindness

Ugly Bad.

Few actually understand the impact to our daily lives to include myself. Humanity has never been more important than right now.

Interesting… And just a few days after a valid Bitcoin reorg which was about 2 blocks deep…. 🤔

Less than an hour after the Claude Code leaked and already pushing 11k forks with hundreds of issues opened 🤣

IYKYK

Anthropic leaked Mythos. Then Claude Code dropped. Accompanied by the AI Doomerism. All at once. All perfectly timed. Coincidence? I don't think so. This is how you manufacture belief at scale - you don't announce. You seed. You don't sell. You make people feel like

Every single SWE will read this as their homework today. Do not sleep on it. It’s our job

👏

Yuuuge deal to indie devs and their pockets!!

Fam, 👀 this is unusual behavior by Grok

♥️💚💙

building a startup is hard sure, but I’m not spending the next 30 years working in an office

Saving as Source when my wife asks about these purchases.