The renter problem: why cloud LLMs feel inevitable (until they don't) If you work with AI in any serious capacity, you're probably sending requests to an API. Claude, GPT, Gemini. You paste in
The Per-User Product: How LLMs Are Forcing a New SaaS Architecture When Every User Can Get a Different Product I've been thinking about where software architecture is headed in the context of LLMs,
When code stops being the source of truth A paradigm shift is emerging in software engineering: Requirements, not Code, are becoming the Source of Truth. For decades, engineers have treated code as the
This week I kept circling back to the same idea: the tools are getting smarter, but the real advantage is still how fast you and your team can learn.One thread is where LLMs
Technical TL;DR (for busy engineers) Static weights are the bottleneck. Most LLMs can infer in-session, but they don't durably update from experience unless you retrain or fine-tune. Context windows, RAG, and "memory" features help,
TL;DR Time invested: ~4 weeks of focused preparation Resources used: Frank Kane's Udemy course, Stephane Maarek's AI Practitioner tests, Tutorials Dojo practice exams, AWS documentation, hands-on Bedrock projects Difficulty level: Hardest AWS exam
You’re trying to ship AI features fast—without creating a security, cost, or reliability mess.This week’s three insights connect into one theme: move quickly, but design for enterprise constraints from day one. That means
You don’t need “more AI.” You need AI that survives enterprise constraints: security reviews, platform standards, and teams that still have to ship.This week’s three insights connect the dots: pick the right enterprise
I’ve been playing with a bunch of “AI + web” setups lately, and I keep running into the same vibe: the model is smart, but the search layer feels… constrained. You ask for
1. The enterprise AI bet: what AWS is actually optimizing for Here’s the uncomfortable truth about AWS in AI: they’re not trying to “win the model leaderboard.” They’re trying to win regulated, enterprise