Thoughts on AI, the tech stack, and the indie hacker journey.
The Flash tier, not the flagship, decides whether an agent product makes money. Google just repriced it again. Here's the cost math, where Flash-Lite is actually good enough, and why the winning move is to re-measure, not re-architect.
The deal everyone read as 'Microsoft hedging against OpenAI' has a quieter consequence: European enterprises can now run credible frontier AI on local infrastructure. For a solo operator, where the data physically sits just became a billable problem.
Turbopack is now the default and stable for both dev and prod builds. 'Faster builds' is the least interesting reason to touch it. As the entire engineering org, you need an upgrade decision, not a hype post: so here's the checklist.
The July 2026 funding numbers say the quiet part: capital is concentrating into fewer, larger, strategically-led rounds, and nearly 88% of AI money goes to U.S. companies. If your product sits on a frontier API, your base layer is owned by a shrinking group. Here's what that means for a solo operator.
The creator economy passed $250 billion in 2026 with 200 million-plus self-described creators. But more than half make under $15,000 a year, and 57% of full-timers fall below a $44,000 living wage. The gap between the headline and the income distribution is the number that should shape your plan.
CuspAI raised $450M and launched a materials-discovery consortium with Nvidia, Meta, Samsung, and 45+ founding partners. It screened 300 trillion molecular structures to find 20 candidates in six months. The lesson for a solo operator isn't the lab — it's the shape of the bet.
An LLM closed a convex-optimization gap open since 1996, and the proof compiled in Lean 4 with no unproven steps. Mathematicians are arguing about whether it's novel. The argument, and the checker that made it possible, is the part builders should copy.
Strix, an open-source AI penetration-testing agent, hit #1 on GitHub Trending and is adding roughly 10,000 stars a week. It runs your code, chains exploits, proves each finding with a working PoC, and writes patches. Here's the sane way to use it — and where pointing it costs you.
Two of the labs you might depend on have committed gigawatts of capacity to Amazon silicon. Cheaper inference is good for you. Concentration behind your API is a tail risk — and the hedge costs one config value.