#llms

Pol Alvarez Vecino (Answer.AI) applies Peter Naur's 'Programming as Theory Building' to LLM coding: the real program is the Theory held in engineers' minds, not the code, so LLMs can't reduce complexity that isn't measurable in the code itself. Illustrates with Answer.AI's own Stripe billing redesign for Solveit, where they chose a simpler 'Theory' (charge a saved card manually or automatically) over Stripe's more capable but more complex managed-subscription model, even when an LLM later suggested reverting to subscriptions to fix an edge case in India.

Investigates the Chinese gray-market 'relay' ecosystem that resells cheap, bulk-sourced access to OpenAI, Anthropic, and Google APIs, tracing the supply chain from stolen/bulk accounts through account pools to consumer-facing relays used for cheap inference and model distillation.

Explains model distillation as both a legitimate technique for building smaller, cheaper models and a contested way to clone a rival lab's model via its API, plus why proving distillation happened is forensically hard. Covers the 2015 Hinton/Vinyals/Dean paper's soft-target vs. hard-label distinction.

Argues AI progress is real and driven by Moore's law and general computing progress, not by frontier labs, whose anti-open-source arguments are read as fear of commodification rather than safety concerns. Pushes back on "singularity" hype as a distraction, while affirming coding agents give a genuine, if overstated, productivity boost.

ThePrimeagen argues that AI-driven FOMO — the fear that not "token maxing" today means falling permanently behind — is structurally similar to past hype cycles (React server components, early ChatGPT 3.5) where the specific skills people rushed to learn turned out not to matter a year later. He shares his own history of anxiety-driven overwork while starting a company, and reads from George Hotz's "I love LLMs, I hate hype" on companies profiting from users' fear of being left behind.

Tim Ferriss shares his own book sales data showing a roughly 80% collapse in print copies sold between 2022 and 2026 — a drop that tracks almost perfectly with LLM adoption. He argues prescriptive nonfiction is the canary in the coal mine for AI disruption of information-based businesses, and that the only durable moats left are voice, taste, and transformation — not information transfer.

I recently asked library staff at my local library if they have seen lending similarly go down, and they indicated they have not yet seen a noticeable difference, which might indicate that libraries are not yet being impacted. Maybe because of the lack of cost associated with lending compared to purchasing books.

Computerphile's Mike walks through why agentic coding sessions burn so many tokens: the entire context window re-enters the model on every forward pass, and each file read by a coding agent adds thousands of tokens that compound across every subsequent step. A two-file bug-fix demo accumulates ~55–60k tokens; a six-prompt starfield screensaver hit 2 million input tokens.

The GitHub Copilot flat-fee-to-per-token switch is the clearest case study here — the old model was just a subsidy. The tire-wear analogy for measuring productivity in tokens is apt. Most agentic use cases are still hard to justify on pure cost grounds outside of very targeted, short-context tasks.

via An Inside Look at the Relay Market Powering Token Resellers and Fraud

via y code when ai?