Articles
273 posts tagged "insights"
The 4-Part Loop That Eliminates AI Slop (in Your Apps and Your Content)
If your AI output keeps hitting a ceiling, the fix isn't better prompting, it's a scoring loop with an independent judge. Here's the 4-part pattern, with the prompt I use.
Stop Debating Which LLM to Use, That's Not Where the Lock-in Happens
Swapping LLMs is easy. Swapping your organizational memory isn't. The real AI lock-in isn't the model — it's where your expertise, instructions, and institutional knowledge live. Keep it portable or lose your freedom.
How to Use Process Mining to Find What AI Should Automate
Your ticket system already records every escalation, every reopen, every bottleneck — with timestamps. Process mining turns that data into a map of exactly where AI automation will have the most impact. No interviews. No opinions. Just the data.
You Don't Need to Replace Your SaaS. You Need to Automate Your Operations.
Companies are being told to ditch SaaS and build AI replacements. That's the wrong move. The real opportunity is automating operations, and tools like Twin.so make it possible without writing code.
Why I Never Let AI Grade Its Own Work
Most people accept their first AI draft because it exceeded their expectations. But your expectations of AI aren't your standards. Here's what happened when I made Claude Code and Codex evaluate each other using my own voice profile and audience segments as the rubric.
The Extraction Pattern: How Companies Actually Start Using AI
Every company follows the same AI adoption pattern. Seven phases from scattered experiments through knowledge extraction to compound results where skills, artifacts, and outputs compound.
The Hardest Thing in AI Right Now Isn't AI
AI feedback loops can't improve without rubrics and quality criteria extracted from human expertise. The real bottleneck isn't technical.
Why I Build Before I Write Requirements (And Why AI Makes That Smart)
After 20 drafts of our core product over more than a year, I've learned the most valuable requirements come from building — not planning. Here's what negative specification taught me about product development with AI.
Prompt Engineering Isn't Dead. Your Requirements Just Got Promoted.
Everyone's declaring prompt engineering dead. They're wrong. The part that died was managing the LLM. The part that matters, articulating your unique requirements, just got promoted.
How to Adopt AI Coding Without Creating Technical Debt
Five steps to AI code quality that compounds over time — from repeatable process and reviewable decisions to encoded engineering judgment.
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