Practice · 2024–now · AI Present
Reasoning models for grep tasks
o1-class deliberation for tickets that needed a filter. Resume-driven inference.
Reasoning-model overkill failed as a default because expensive deliberation is not a substitute for clear specs and cheap tools. Frontier reasoning sticks on hard planning and math; it fades when every autocomplete call escalates to a thinking model. The fad was status; the stuck layer is model routing.
Cost of the fad
Thinking tokens burned on problems a SQL query and a unit test would settle. Latency and invoices grew; correctness did not.
Patterns

Context
Demos consolidate; evals remain
AI pair programming changed how code is typed faster than how it is reviewed. Agent frameworks and standalone vector stores sorted into demos versus durable plumbing; mid-market RAG folded back into Postgres. The permanent layer is familiar: evals that gate deploys, model routing for cost, tool protocols instead of plugin snowflakes, and humans who own production. Autopilot rewrites and vibe-shipped auth middleware are still big-bang migrations with better slides — and a longer on-call.
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Related
Architecture · 2024–now
Model routing / tiered inference
Cheap model drafts; frontier model escalates. The boring cost-control layer that survived the agent hype.
Framework · 2023–now
LLM app frameworks
LangChain-class glue gave way to vendor SDKs and thin wrappers. The durable pieces are boring: evals, retrieval, and product UX.
Practice · 2021–now
AI pair programming
Autocomplete that understands the file — now table stakes. Generation outran review capacity; ownership is the bottleneck.
Practice · 2023–now
RAG as default architecture
Retrieval-augmented generation as the answer to every knowledge problem — then "just stuff the window" as the counter-fad. Often right; often Postgres with pgvector would suffice.
$ Teams stood up vector pipelines, chunking strategies, and rerankers before asking if fine-tuning or a SQL query would answer the question. Retrieval infra became the product.