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Last year, Image Line introduced Gopher for FL Studio, an AI chatbot that was basically a glorified instruction manual. You asked it how to do something, and it would serve up the relevant instructions. It's the kind of thing I actually use AI for on a semi-regular basis. But in the new release, Gopher can […]

One of the things I do when approaching a new codebase is to find the entry points and start exploring down the call paths. This gives a good overview of the different components in the code and how they're connected. I wanted to translate that to a visual experience, similar to how you would use call graphs, but there's a couple of problems with classical call graphs. One, call graphs represent flow at the function level, so the architectural context is lost. And call graphs tend to get very la

SpaceX is currently ahead of last year's record-setting pace for Starlink satellite deployments. SpaceX launched 1,589 Starlink satellites into low-Earth orbit in the first half of 2026, according to launch data compiled by Jonathan McDowell's satellite tracker, compared to 1,489 satellites deployed at the same point in 2025. 2025 was already a record year for […]

There is a mobile game called DragonBox. It sort of tricks you into learning algebra by starting with very abstract manipulations of a puzzle that must follow rules... gradually the game teaches you more and more rules and also strips out the more abstract elements until on the last levels you are finally solving real equations. I loved it, it taught my kids algebra.... and it was just fun. Over the years I often thought that there should be a calculator for Algebra that works this way... someth

NHTSA administrator Jonathan Morris called reports that self-driving cars had driven into emergency scenes and blocked ambulances and firefighters “unacceptable.”

The BBC wanted to launch a UK-wide streaming service with ITV and Channel 4 in 2007... and again in 2017... will the third time be the charm?

If you could pick only one counterintuitive number from the YC 2026 batches, make it this one: out of 477 real-ish company records, 366 list San Francisco as their location — roughly 77%. For comparison: New York City has 24. London 10. Boston 7. Los Angeles 4. Fully remote? 3 companies. Even if you add the 11 tagged "San Francisco + Remote", the conclusion doesn't budge: AI startups aren't spreading across the map. They're re-concentrating in one city. This isn't Bay Area nostalgia. It's industry structure casting a vote. Remote won work. It didn't win startup density. One of the most popular takes of the past few years: software teams can start anywhere, so companies no longer need the Bay Area. That take wasn't entirely wrong — tooling, cloud services, open models, and online fundraising genuinely lowered the barrier to starting a company. But the YC 2026 location data is a reminder that a lower barrier is not the same as a vanished advantage. Building an AI startup isn't just writing code. It runs on model gossip, talent flow, customer pilots, investor feedback, peer pressure, and extremely fast narrative iteration. Much of that works online. But the densest informal information still travels fastest offline. San Francisco's edge was never the office space — it's collision frequency. AI made same-city learning matter again In the classic SaaS era, most domain knowledge came from customers and product cycles were relatively stable. You could build a vertical software company in any city and grind toward PMF at your own pace. The AI era doesn't work like that. Model capabilities turn over every few months. Agent architectures keep getting rewritten. Inference costs, context windows, voice, tool calling, and eval infrastructure are all on rolling release. A seemingly minor technical shift can redraw your product's boundaries overnight. In that environment, whoever hears real feedback earlier, learns earlier what others tripped over, and understands earlier what inv