You give your AI coding agent a task. It gets to work. Thirty tool calls later you have code — and it’s not what you needed. The agent understood the words but missed the intent. It made a dozen small decisions that individually seemed reasonable, and collectively built the wrong thing. This isn’t a capability […]
Read More →You’ve seen the numbers. 7B. 70B. 405B. Everyone talks about parameter counts. But what are they? Why does size matter? And what actually happens when you hit “Generate”? This post covers the mechanics: what parameters are, where they live in the model architecture, how scaling affects them, and what that means if you’re running or choosing […]
Read More →TL;DR: The Kano Model isn’t just a PM’s tool — it’s a critical framework for engineers to evaluate feature value, manage technical debt, and define MVPs. By categorizing requirements into Must-be, One-dimensional, and Attractive qualities, teams can align technical execution with user satisfaction dynamics. Nowhere is this more visible right now than in AI agentic coding tools, where yesterday’s delighters […]
Read More →You’ve heard the pitch: run AI privately, offline, on your own hardware — no API keys, no usage limits, no data leaving your machine. You open Hugging Face, find a model called Qwen3-30B-A3B-GGUF, download 20GB, try to run it, and your laptop grinds to a halt or produces nothing at all. The problem isn’t that local […]
Read More →If you’ve been using AI coding tools long enough, you’ve probably noticed that they all start to feel the same. Cursor, Claude Code, Codex, Amp — they’re converging on a set of common features: plan modes, permission popups, sub-agents, MCP integration, to-do tracking. More surface area, more abstraction, more decisions made for you. Pi bets against […]
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