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The underlying problem is that it's difficult to make a principled decision. So, it's ripe for SEO mining and such fuzzy takes happen.

The meta point of the article, I agree with though. The line is moving and AI makes having multiple platforms with code specific to them faster. Getting them right is still difficult.

Agree on this point. Recent anecdotal testing I did found Whisper is still better than Parakeet.

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.

You're right in my neighborhood. Signed up! Fantastic!

Building a team to operate based on your own personal preferences is selfish leadership... or even dictatorship.

There's a very strong "focus culture" which relies on the idea that work is not done in meetings. This is wrong. Progress comes in many forms.

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 have been pondering this for a while. Cat's out of the bag.

Maybe the better way to author your work is to:

1. Write what you want

2. Loop through a random set of "tumbler" skills that preserve meaning

3. Finally pass the output through a "my style" skill that applies what you about

In order for this to work the "my style" would have to be a very common-place style.

Legit first post I've wanted to upvote in a long time.

This type of thing requires an economic driver to monetize the service.

I'd have a strong inclination to run such software if I knew that I was both helping host repos and getting paid.

The timing makes me wonder if this is a direct response to Deepseek V4 having performance comparable to SOTA models.