AI Weekly Malaysia

In this How I AI episode, John Lindquist (creator of egghead.io, now running mega.dev) demos eight uses of "Jev" — a real-time voice assistant, data deduplication, app routing, chess analysis, multi-agent coordination, a live presentation coach and more — arguing it should be treated as a fast, cheap decision engine rather than a chatbot, since it returns scores, classifications, probabilities and function calls instead of prose. Concrete cost figures: 73 cents across 23 development runs, and a separate run where Claire processed 5 GB of JSON for 40 cents. In a chess benchmark, Jev analysed a full game in under a second — 10x faster and 4x cheaper than a low-reasoning LLM with no accuracy loss. The excerpt also teases an OpenAI ChatGPT Sites segment and a DevDay recap, but gives no details on either.

Why: If your agents spend tokens on classification, routing or branch-selection calls, this is a concrete cost argument for re-pricing those paths: 73 cents over 23 dev runs and 5 GB of JSON for 40 cents is a different order of magnitude from per-token LLM calls, and the chess benchmark (10x faster, 4x cheaper, same accuracy) is the one directly comparable number. The recommended mental model — put Jev wherever a traditional program would have an if/else, switch or branch, and layer multiple cheap classifications instead of chasing one perfect prompt — is something you can apply this week. Caveat worth stating on air: the excerpt never says who makes Jev, what it costs in production, or how to access it, so treat this as a pattern to test, not a product to adopt. There is no Malaysia or Southeast Asia angle in the text.