I'm on my third Surface Pro (an 11 this time) and basically love it. Tablet form factor, lightweight, multitouch, with a real desktop OS (I mean not ipad's iOS). Decent battery life. True, it's not perfect: slow to wake, the touchpad stops working once in a while & needs to be reset. Missing a GPS chip. But it runs Adobe, Resolve, Chrome of course, msys2, and Linux (WSL2) quite well. I love the absolutely gorgeous HiDPI screen.
The software emulator system is a little weird (arm64/arm64EC/arm64X, with no true universal x64/arm64 binary) for software developers, but it basically works fine from a user perspective.
I say all this as someone whose daily driver is an Arm M1 Macbook Pro, also nice but not a tablet and quite heavier. I don't use a dock, just a simple USBC hub with a magnetic USBC connector.
What happens to humanity when AIs are better at being human than most humans? (More patient, more empathetic even if it's simulated empathy, more knowledgeable)
I spend a lot of time doing this too -- cutting up band rehearsal tapes into songs and exporting all. As weird as Audacity's UI is, I haven't found anything better than it at this.
I'm newly mostly-retired as a VFX software developer & CTO. I'm writing about AI, climate change and more in my blog, https://oberbrunner.com, running Long Now Boston (https://longnowboston.org) to promote long-term thinking, and working through my lifetime backlog of "wouldn't it be great if somebody wrote this" ideas using Claude, at https://github.com/garyo.
You should check out my new open source software build tool, https://pcons.org.
As a heavy AI-assisted open source code creator (and someone with 40+ years of dev experience), this seems wrong-headed to me. I think it is an excellent policy, as they say, to "value contributors over their contributions," but this policy excludes all potential contributors who use the latest tools. It will eventually doom zig to a smaller "artisanal" pool of contributors, rather than welcoming newbies and helping them become better open-source developers.
I hate CMake as much as anyone, but I actually did something about it: https://github.com/DarkStarSystems/pcons. As one of the original developers of SCons, I tried to get the best parts of SCons and CMake into a simple, modern, python-based software build tool. I take toolsmithing seriously, so I plan to build pcons into a strong, useful open-source project.
I find correction is rarely necessary with Opus 4.6. Definitely not so much that "it's not really the LLM writing it anymore." More like it's the author and I'm the editor (in this limited case -- of course architecturally the ideas are all mine.)
But I totally respect that my prompt style, the type of app I'm writing, and other factors could be influencing my success vs. others' lack of success.
I can't agree here. https://pelorus-nav.com/ (one of my side projects) is 95-98% written by Claude Opus 4.6, all in very nice typescript which I carefully review and correct, and use good prompting and context hygiene to ensure it doesn't take shortcuts. It's taken a month or so but so worth it. And my packing list app packzen.org is also pretty decent typescript all through.
This post is excellent, and accurately describes my experience writing pcons (pcons.org) as a side project. I was one of the original developers of SCons and have wanted to rebuild it better for more than a decade. All the same roadblocks Maganti describes kept me from starting it, and Claude Opus 4.6 suddenly opened the door, and now it's live and people are starting to use it as a cmake or scons replacement. My experience over the last few months mirror Maganti's in many ways: ease of refactoring, investigating many more design ideas, getting frustrated with blind alleys and its not understanding the big picture, and ultimately getting a product I'm proud of.
Vision, taste and good judgment are going to be the key skills for software developers from now on.
I agree with this article, and I think it goes further than astrophysics or even physics. As agentic LLMs are starting to prove long-standing open mathematical conjectures and even invent new ones, I fear we may reach a point in mathematical research (which, as Hogg's article describes astrophysics, has no "right edge") where the machines are just better than us. At that point do we just mostly lose interest? You can see it already in the Go-playing community. Why study for years to be a "pretty good" 9P when machines will always play better? What PhD student will spend years on a hard, interesting, deep math problem? Sure, a few will. Some people become monks too. But not many.
Yes, and this is an incredibly powerful idea. Running fluid flow simulations inside an optimization loop (monte carlo + gradient descent) revolutionized aircraft design, nuclear simulations and geophysics.
When the tool being called updates the LLM's training data, or runs experiments that the LLM can learn from, then that potentially becomes a self-improvement loop.
I've been fascinated by elevator algorithms since visiting NYC as a kid. The interesting stuff starts to happen when you account for popular floors, people going to work, coming home at the end of the day, dog-walking times, subway arrivals, all the semi-deterministic behavior we see in real life.
All of this, the news articles, the social media discussion, this very discussion, will be part of the training set for future AIs. What will they learn from this?