Ghostty has crashed nightly for me with ~10 terminals open across a few windows. So, I haven't been able to run it nor would I want to
embed it inside anything I daily drive.
With AI it feels writing software that is open is less attractive. It's hard to trust OSS made recently b/c you can tell if someone knows what they're doing and even spent any time on quality. Also, often times people don't reach for software others make (unless it's boring and old stuff, in which case this advice doesn't apply.)
IMO types are the main lever you can use other than procedural abstraction. I feel that Haskell gives you both in a way that marries them for maximum constraint-building. Constraints that prevent illogical or illegal programs are the bread and butter of reliable software.
I've been poking at running LLMs in the browser. It feels like we're definitely close (<1 year) to seeing real use cases there.
Ubiquity and coverage of devices is what will take longest. Largely dependent on how well we can shrink models with similar performance and how much we can accelerate mobile devices. This feels like it's but further (<3 years?)
Reading only the abstract: LLMs prefer output of their own generation over humans or even other models.
This is a very good reason to avoid using model-generated data to train future models. We'd be deepening this bias by continuing to do that, essentially forcing society to reshape their output using LLMs to increase engagement. This feels like a form of enshittification that doesn't just touch one product but all of society.
I'm surprised Anthropic didn't also say this on the issue. Weird that they wouldn't. It seems to have made for unnecessary bad PR.
It feels to me that Anthropic is less focused on quality, and more focused on PR stunts/flash. My experience with Claude is always "it's pretty and feels cool", where-as codex feels like "solid and boring". I realize I'm probably biased. Am I alone in this thinking?
I built a small tool that makes one cleanup commit at a time, keeps it only if tests pass, and moves on. The article is about how it grew from that basic loop into taste files, staged migrations, and a way to keep repos getting a little cleaner in the background.
Been tinkering on my personal site and wanted to add some AI features without being the one paying for tokens on every visit. So I went looking at how close browser-side inference actually is to practical. Closer than I expected.
The post is less a tutorial and more me walking through what it felt like. Kicking tires on random models, getting one to actually run, then doing the small unglamorous work to turn "demo" into something I'd put in front of a reader.
I added live translation to this site, wrote about the weird edges, then made this little playground to mess with them directly. Proper nouns, tags and markdown syntax were always coming through weird, so this helped me investigate.
TranslateGemma was one of the few models I tried. Something interesting I noticed is that each model has a different expectation of interface (fields passed in, naming of those fields and such.)
Been tinkering on my personal site and wanted to add some AI features without being the one paying for tokens on every visit. So I went looking at how close browser-side inference actually is to practical. Closer than I expected.
The post is less a tutorial and more me walking through what it felt like. Kicking tires on random models, getting one to actually run, then doing the small unglamorous work to turn "demo" into something I'd put in front of a reader.
I am only trying it out for now. But what I'd love is for things to be quite automatic. I join a zoom, get a prompt "what do you want? record, record+transcribe or record+transcribe+summarize (or use some custom prompt)"