Google investing $40bn in a company that competes directly with Gemini is one of those moves that only makes sense if you think of it as buying compute customers, not backing a competitor. Anthropic pays Google for TPUs and Cloud services, a big chunk of this investment surely has to flow right back to Google.
The acting_vs_clarifying change is the one I notice most as a heavy user. Older Claude would ask 3 clarifying questions before doing anything. Now it just picks the most reasonable interpretation and goes. Way less friction in practice.
Fair point on open models + EU hosting, that's a much better option than I gave it credit for. I was thinking more about the "just plug in an API key and go" experience where OpenAI/Anthropic are still way ahead, but yeah if you're willing to do the work the gap is closing fast.
Good list but the biggest missing piece for most new SaaS products right now is AI/LLM APIs. If you're building anything with AI features you're calling OpenAI, Anthropic, or similar - all US. Mistral exists but the ecosystem around it is much thinner. That's probably the hardest US dependency to drop in 2026 that I can think of.
Plausible is a great pick though, been using it and it covers most of what you'd want from analytics unless you need GA/GTM tied to ad campaigns.
The fact that they deliberately manufacture the satellite clocks to tick at the wrong frequency on the ground (10.22999999543 MHz instead of 10.23 MHz) so that relativity makes them tick correctly in orbit is one of my favorite engineering details in any system.
The headline is dramatic but this is literally how bitcoin is designed to work. Miners leave, difficulty drops, costs go down, mining becomes profitable again. The interesting part isn’t the loss per coin, it’s how long the lag between unprofitable mining and difficulty adjustment keeps forced selling pressure on the market.
The quote from the CMU guy about modern Agile and DevOps approaches challenging architectural discipline is a nice way of saying most of us have completely forgotten how to build deterministic systems. Time-triggered Ethernet with strict frame scheduling feels like it's from a parallel universe compared to how we ship software now.
Sure, but the study is saying something slightly different, it's not that people write bad prompts for artifacts, they actually write better ones (more specific, more examples, clearer goals,...). They just stop evaluating the result. So the input quality goes up but the quality control goes down.
So I guess the key takeaway is basically that the better Claude gets at producing polished output, the less users bother questioning it. They found that artifact conversations have lower rates of fact-checking and reasoning challenges across the board. That's kind of an uncomfortable loop for a company selling increasingly capable models.
The real insight buried in here is "build what programmers love and everyone will follow." If every user has an agent that can write code against your product, your API docs become your actual product. That's a massive shift.
The "start over in an hour" philosophy is underrated. I've been running my own infrastructure for years and the single most empowering thing isn't the setup, it's the peace of mind that you can just nuke it and spin up somewhere else.
Knowing that, I started looking at every SaaS subscription very differently.
The benchmarks are cool and all but 1M context on an Opus-class model is the real headline here imo. Has anyone actually pushed it to the limit yet? Long context has historically been one of those "works great in the demo" situations.
Living in the EU, I'm skeptical any of this happens. Our leaders have been pretty reluctant to push back on anything so far and most of these assets are private anyway.
Hi Troy, just wanted to let you know that I just sent you an email! :)
Also, just to be sure, I sent it to on-board.ai domain as well, as that seemed like the correct website (onboard.ai just showed "for sale" page). Might help some others too.