Podbit · The MongoDB Podcast
Manual evaluation of LLM traces is not scalable, but it's essential early on for establishing a baseline and understanding failure modes. Once that baseline exists, LLM-as-a-judge — where one model scores another's outputs — can automate ongoing evaluation at scale. Langtrace acts as the reporting and visualization layer for both approaches.
You don't have to be watching porn to be in porn brain. If you're mindlessly scrolling Instagram as the algorithm feeds you bodies, you're gooning — non-orgasmic, but chemically the same loop. The browser was always optional.
Popular OnlyFans models pre-record one to two years of content, then vanish. A management team — often Eastern Europeans working around the clock — drip-feeds the old content and types out all the 'personal' replies. You're paying for an intimacy that was never there.
Chinese chipmaker CxMT became the most valuable company listed on China's stock exchanges after its shares soared on debut in Shanghai. Its chips power AI servers and consumer smartphones — a direct play on China's race to build semiconductor independence amid U.S. export restrictions.
AI models tested in the Elm Wealth experiment predicted market direction 60% of the time — better than humans — but made the same catastrophic mistake: they didn't calibrate bet sizes to their confidence. Even our supposed future overlords took too much risk when uncertain.
Every major technology — cars, planes, the internet — took decades before the right regulatory moment arrived. Regulating AI now, before we even understand what it is, risks locking in the wrong answers at the worst possible time.
Two years ago, AI company executives went to Congress with fear in their eyes, begging to be regulated. That wasn't safety-consciousness — it was regulatory capture. They handed government the opening it had been waiting for since it missed regulating the PC, the internet, and the mainframe.
The U.S. and China aren't in a Cold War or even a classic trade war — they're too economically intertwined for that. What they're fighting is an innovation leadership war, and the only tools governments have are indirect: ban chips, apply tariffs, nationalize companies. These 'bank shots' rarely hit the target.
When Anthropic argues that restricting open source is the best way to hurt China, the actual translation is: restricting open source is the best way to help Anthropic by eliminating a competitor. Sinofsky calls it un-American and un-tech — an industry that was built on open academic research trying to pull up the ladder behind it.
The government missed regulating the mainframe. It missed regulating the PC. It missed regulating the internet. AI regulation is its chance for a do-over — and AI company executives handed them the opening by showing up to Congress and begging for it. That's a once-in-a-generation political gift.
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