I’ve stopped using Claude on the desktop, just because of how slow the app is to start up and interact with. It’s an absolute clunker; I’m mystified why they can’t ship something that works well given their rhetoric about ai.
It’s still dedicated ram on a separate chip, which is affected by supply shortages.
The ram is soldered onto the SoC in close proximity to the main arm chip. What’s different is that it is simultaneously addressable by cpu and gpu cores, not part of the same die as the apple silicon unit.
I’m not clear on the difference between atom and RSS. Atom seemed to be the better spec, but for my Astro blog I ended up sticking to the built in `rss` helper it ships with.
There’s a lot to be said about his approach with go for simplicity. Python needs virtual environments, package managers, dependencies on disk, a wsgi/asgi server to run forked copies of the server, and all of that uses 4x-20x the ram usage of go. Docker usually gets involved around here and before you know it you’re neck deep in helm charts and cursing CNI configs in an EKS cluster.
The go equivalent of just coping one file across to a server a restarting its process has a lot of appeal and clearly works well for him.
Their working is exceptionally thorough, all the concerns about additional maths and transformations is well considered. I really have to wonder if introducing any semantic-changing transformations for what I’m guessing are minuscule savings of bytes is worth it though. Safe things like stripping white space and shortening identifiers would be the major contributors to minification surely. How many CSS colours need to be defined for oklab rounding to even affect how many tcp packets are sent for instance?
I don’t lose bags either, but airlines do. The AirTag let me tell United which building in Houston in ended up in (after getting lost at SFO), and refute their gaslighting multiple times that it was heading my way. Worth its weight in gold, literally.
While it’s hardly insightful that SQL is useful, I would have liked to read more about what the actual workload involving duckdb on a local machine looked like. I’m fully on board that local or single vm workloads can do an awful lot, but I’ve never been particularly satisfied with the pipelines I’ve seen (including my own). Usually they’re piles of scripts and intermediate data files sitting around and are hard to make idempotent and understand if you aren’t the author.
Also fwiw there’s no such thing as an M4 Ultra chip. That detail was either a mistake or hallucinated.
I’ve spent a lot of time with both, and hands down the wired one is far more flakey. Granted I think that’s more a Mazda software issue, but a solid 10% of the time I get “CarPlay failed” and the only way to fix it is to turn the car on and off. Never once had an issue with wireless in a Hyundai.
Looks like Django 6 is getting an offical task system.
There’s no real world brokers or workers supported (at least built in), but still centralising around a standard interface might make things nicer than the celery tentacle monsters Django apps eventually tend to mutate into.
Introducing JIT features has a lot of opportunities beyond numerical numpy/numba vectorisation. There’s endless amounts of hot loops, data shuffling, garbage collection, and monomorphisation that could be done in real world python that would benefit a lot, much like V8 has done for JS.
It’s true that phones cameras are miracles of technology, especially considering their size. But I take a modern Fuji traveling because the modern phone camera look is so over-processed and distinct. There’s no faking the real optics a large aperture and sensor gives, the portrait mode on phones is still a poor imitation of the real thing.
Fuji then has the whole film simulation system with all their colour science from the last century. It’s a ton of fun, and the jpgs it produces are distinct and beautiful, and I believe better than 99% of people could achieve from post processing the raws, myself included.
The middle-age guy part is accurate though, I got it as a thirtieth present.
Yeah it really was a social phenomena. Ten years ago conferences were swarmed with docker employees, swag, plenty of talks and excitement.
The effort to introduce the concepts to the mainstream can’t be understated. It seems mundane now but it took a lot of grassroots effort and marketing to hit that critical mass.