Practice · 2023–now · AI Present
Local inference
Run the model on your laptop or rack. Privacy, cost control, and the refusal to send every token to a vendor.
Local inference stuck for privacy-sensitive and offline workflows as open weights and tooling matured. It failed as a default for teams that needed frontier quality without GPU ops. The fad was "replace the API tomorrow"; the stuck practice is hybrid — local for routine, cloud for peak.
Context
The stack gets a co-pilot
AI pair programming is already changing how code is written. Agent frameworks and vector stores are still sorting winners from demos. The durable layer will look familiar: evals, retrieval quality, product UX, and ownership. Autopilot rewrites without tests are just big-bang migrations with better slides.
Compare with
Related
Framework · 2023–now
LLM app frameworks
Chains, agents, and prompt wrappers shipping weekly. The durable pieces will be boring: evals, retrieval, and product UX.
Platform · 2022–now
Vector DB gold rush
Specialized stores for embeddings. Many workloads are folding back into Postgres extensions — after the vendor tour.
Practice · 2021–now
AI pair programming
Autocomplete that actually understands the file. Changing how code is written faster than how it's reviewed.