Feb 26 · 6 min read · Two years ago I was flitting between tools like a restless editor switching brushes: one day a fast tiny model for quick drafts, the next a heavyweight for deep dives. It felt smart-pick the specialist for each job-but the noise cost me time and conf...
Join discussionFeb 24 · 6 min read · A common trap for engineers and founders is paralysed choice: dozens of model names, comparable-sounding benchmarks, and blog posts that advertise "best" without the use-case that matters to you. As a senior architect and technology consultant, the j...
Join discussionFeb 23 · 6 min read · Before: the rough way teams picked models Teams often start from the obvious trap: chase a brand name, run a single prompt, and pick the answer that “feels” best. That quick judgment ignores latency budgets, prompt stability, cost-per-cal...
Join discussionFeb 22 · 5 min read · Before: When picking a model felt like throwing darts Teams often begin with a spreadsheet of model names, a few benchmark scores, and a hope that the one with the highest score will behave in production. The result is predictable: unexpe...
Join discussionFeb 18 · 5 min read · Before you reach for the latest benchmark chart or copy-paste another prompt, pause and picture the usual scramble: multiple model accounts, inconsistent outputs between experiments, and an app that behaves one way in staging and another way in produ...
Join discussionFeb 17 · 5 min read · The post-mortem began with a single sentence nobody wanted to hear: the demo looked great, but users stopped using the feature a month after launch. The team had chosen the newest, loudest model because it scored highest on a handful of benchmarks an...
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