Choosing and upgrading LLM models without the hype
A vendor-neutral framework for picking and switching models, define your eval tasks, weigh cost/latency/quality, run your own evals, and keep models swappable.
On-device or cloud? Choosing where your models run
A decision framework for splitting AI workloads between local and cloud models, privacy, latency, cost, and capability, plus how sensitivity should route the data.
Self-hosting open models without a GPU farm
When local inference makes sense, and how quantization and right-sizing let capable open-weight models run on modest hardware, with the tradeoffs spelled out.