#llm
- Two Cents a Task Changes What You Bother Automating
The number that stopped me wasn't 89.0% on ARC-AGI-1. It was $0.02 per task. I've spent the last year writing prompts as if every call were…
- The bottleneck is me, not the model
The uncomfortable part of Goedecke's argument is that it explains my own logs. When I'm working on the Astro site or a Cloudflare Worker…
- Cheap tokens only matter if the model shuts up
Luna at $0.20 in / $1.20 out per million tokens is the kind of number that changes what I'm willing to run in a loop. The jobs I've been…
- Half a token per second, and nothing leaves the machine
Half a token per second sounds like a punchline until you see what it buys: a frontier-scale model that answers with no network, no…
- Your cellar count is the hero, not a stat
The redesigned Cellar card in A Wine App makes one quiet bet: the size of your collection is the design. The bottle count is set as a…
- The Pulse: one tile that tells you what the app knows about you
The new fourth card in A Wine App, the Pulse, folds the learning loop into one adaptive tile. It shows one read of your palate: a…
- Free models first, paid model last — as a pipeline law
Decision. Catalog enrichment is a strict cascade, and each phase may only fill the NULLs the previous one left: inherit from the producer's…
- Ask the model twice; ship only what it agrees with itself on
I batch-enrich a 100k+ bottling wine catalog with Claude Sonnet — style scores, identity backfill, storage potential. The safety mechanism…
- NULL beats a plausible guess
What I learned. When an AI-extracted field can't be grounded in the source text, NULL beats a plausible guess. Where it bit me: the batch…