API price for gemini-3.5-flash is 3x gemini-3-flash-preview so they might be throttling it 3x sooner. They should either drop API prices or not advertise AI Pro as supporting Antigravity.
This is cool. I'm observing a trend of "build a tiny version from the ground-up to understand it" a la Karpathy's micrograd/minGPT. Seems like one of the best ways to learn.
I switched to a new x86 machine. Running Linux on Mac just made things unnecessarily complicated and hurt performance. Im still open to using docker on Mac to run Linux containers but once you want a GUI life was simpler when I switched off.
I don't have an x86 laptop at the moment so sticking with Macbook for now. My assumption is Mac laptops still are far superior given M-series chips and OS that are tuned for battery efficiency. Would love to find out this is no longer the case.
I switched my desktop from macOS (10+ years) to Ubuntu 25 last year and I'm not going back. The latest release includes a Gnome update which fixed some remaining annoyances with high res monitors.
I'd say it pretty much "just works" except less popular apps are a bit more work to install. On occasion you have to compile apps from source, but it's usually relatively straightforward and on the upside you get the latest version :)
For anyone who is a developer professionally I'd say the pros outweigh the cons at this point for your work machine.
I don't know if you can generally say that "LLM training is faster on TPUs vs GPUs". There is variance among LLM architectures, TPU cluster sizes, GPU cluster sizes...
They are both designed to do massively parallel operations. TPUs are just a bit more specific to matrix multiply+adds while GPUs are more generic.
What Raspberry Pi is to Broadcom (developer-friendly SBCs), Beagleboard is to TI.
It's a slightly different approach -- Beagleboard is a non-profit and emphasizes openly purchasable components. But similar in that it is the cheapest way to tinker with SoCs from that vendor.
I would expect nearly every active AI engineer who trained models in the pre-LLM era to be up to speed on the transformer-based papers and techniques. Most people don't study AI and then decide "I don't like learning" when the biggest AI breakthroughs and ridiculous pay packages all start happening.
I don't expect the majority of tech companies to want to run their own physical data centers. I do expect them to shift to more bare-metal offerings.
If I'm a mid to large size company built on DynamoDB, I'd be questioning if it's really worth the risk given this 12+ hour outage.
I'd rather build upon open source tooling on bare metal instances and control my own destiny, than hope that Amazon doesn't break things as they scale to serve a database to host the entire internet.
For big companies, it's probably a cost savings too.
I'm trying to make a DIY security camera that can run local models, and stream video over wifi.
The TI SDK makes it easy to run demos but making any custom apps quickly gets complicated unless you are familiar with embedded Linux dev, Yocto, etc. Certainly much more complex than iOS/Android.
Hopefully over time the tools for embedded can catch up to mobile.
There's still relevance in making it stupidly easy to make an LED blink and make basic apps on circuit boards. Education + weekend hardware hackers might look for something different in a framework than a professional.
But certainly for pro use cases the hardware specific frameworks are way more powerful (but also complex).
You're right there is no way to specifically target the neural engine. You have to use it via CoreML which abstracts away the execution.
If you use Metal / GPU compute shaders it's going to run exclusively on GPU. Some inference libraries like TensorFlow/LiteRT with backend = .gpu use this.
I've always been a bit confused about when to run models on the GPU vs the neural engine. The best I can tell, GPU is simpler to use as a developer especially when shipping a cross platform app. But an optimized neural engine model can run lower power.
With the addition of NPUs to the GPU, this story gets even more confusing...
In 2024, 21% of all bachelor's degrees awarded were Computer Science from University of Maryland College Park.
It was 3% in 2011.
I don't agree with the article that AI is wrecking job prospects. I see it is as companies are just now trending towards running leaner vs hiring every good engineer available during ZIRP.
Nonetheless, it's gotta be tough out there for new grads.
API price for gemini-3.5-flash is 3x gemini-3-flash-preview so they might be throttling it 3x sooner. They should either drop API prices or not advertise AI Pro as supporting Antigravity.
https://ai.google.dev/gemini-api/docs/pricing#gemini-3.5-fla...