Last week, I wrote about what could go wrong with my AI Teaching Assistant - API failures, cost explosions, bad outputs, and data privacy concerns. I focused on building resilience: error handling, fallbacks, and safety checks. But here's what I didn't address: How would I even
MAJOR STORIES 1. OpenAI's "Code Red" & The Great Model Race Imagine... Your VP of Product asks whether to wait for GPT-5.2 or commit to Gemini 3. But the real question isn't which model is better—it's whether this panic-driven release
I spent last week trying to think of ways to break (aka improve) my AI Teaching Assistant. That's probably my QA experience talking. And no, that's not because I enjoy chaos, but because building a working demo is the easy part. The hard part is figuring
My daughter was struggling with tests. She'd ace assignments and labs, but when quiz day came, her grades dropped. Being an active AI user myself, I suggested she try something: drop her class materials into Gemini and ask it to generate practice quizzes. It worked - her next
Imagine... You're a CTO at a biotech startup racing to develop a new therapy. Your R&D timeline is 3-5 years, but your funding runway is 18 months. The government just announced it's building an AI platform using DOE supercomputers that could "turn years
MAJOR STORIES 1. Anthropic Locks Into Google's Chip Ecosystem: $10B+ TPU Deal Imagine... You're the CTO planning your AI infrastructure for the next 18 months. Your vendor just announced they're spending tens of billions on chips from a company that also competes with you