CraigJPerry

Same, but the cost of maintaining a fork fell through the floor in recent years. If a decent fork gains momentum, i can probably even stop spending tokens on mine.

I'm a fairly heavy user of LLMs [1] but i'm probably in the "AI is just a tool" camp.

Are you of the view that LLMs have more potential than the internet? The internet has been around for so long that it's had time to have actual impact and yet it barely shows up in GDP. It still has so much untapped potential even yet. LLMs are too new to have amassed much impact yet, certainly not on the scale of the internet. But they have lots of potential still untapped. I just don't see how we get to even 5% GDP impact with them (either positive or negative). But i could be in a blind spot, genuinely curious to learn another view point.

[1] I've built all sorts with them. I've built various ML models but the only LLM i've built was effectively following a tutorial, nanochat

The UK has stated that the current mileage check within the annual roadworthiness test will supply this data.

A clear weakness is that mileage readings can be altered. This used to matter on a smaller scale: dodgy second-hand sales, but now more or less anyone could be considered as potentially incentivised to interfere.

The mileage must remain a changeable field since the recording component may need replacement, for example, after accident damage or to correct a fault in the assembly that recording memory is part of.

Designers republic and memphis were the only designs i couldn't immediately see were AI generated.

The only reason i couldn't tell memphis is because it's aping a known design language already, it's just a perfect copy so me not being able to tell it wasn't AI isn't a free pass in this case.

I think if there was a little less "grid" feeling to the layout details then i wouldn't be able to tell on a few of the other designs either.

Edit: the designers republic version is quite a lot different than the others, it's not just that it's the only design to avoid pairing every feature with an icon, it's the whole layout variety - there's different font sizes, the positioning of elements is very well done to my eye. I'm gonna say this has to be another case like memphis, there's some pre-existing design language that i don't know of and it's just aping that. It's much more human than the others which i can believe are straight ai.

Why couldn't you express all those as test cases rather than instructions?

In test cases i can do anything, a test framework is just a way of discovering and then scheduling functions to run. I can emit useful instructions to the agent from the failed test case: "After walking the AST of all use of state machine X, a branch was found at Y which reused stale state. Ensure stale references are dropped..."

I can force the agent to pass the test suite before it considers itself done. I can reject edits of such test cases to partially mitigate reward hacking. etc etc

This isn't ai slop. It's fascinating and well written.

But I learned something really basic - i didn't know that the ANE (and the data pipeline around it) was designed for CNN rather than transformers. It's always been an open loop in my head, wondering why the ANE was less impactful than i understood it should be.

Yank on that thread harder, developers and engineers are not safe just because they rely on different patterns.

But once you've toppled the house of knowledge work, you'll find you can rebuild it easily because the foundation still exists, and it's the thing missing in this analysis; judgement.

Describing NPM as boring is a stretch. Given its security model, i think spicy is a far more apt label.

I have switched to pnpm already.

>> They still import a lot

I don't think it's common knowledge how much they dropped imports since the Iran / straight of hormuz crisis.

Back at the start of that conflict there were a lot of breathless takes about what catastrophe was in store in a few weeks time, and what materialised was bad but not in anything like the same league as what was predicted.

The thing that wasn't accounted for in the analysis at the time was just how rapidly and by how much China could suppress its appetite for oil imports.

System 1: a system for recording an image of a location at a given place over time.

System 1 can refer to the dashcam or the mk1 eyeball + notepad in your example.

System 2: a system for tracking the presence of a person across both time and location

An example of system 2 would be the facial id system being trialled on the london underground currently.

These are a difference in kind not in degree. It doesn't matter how many system 1's you deploy, you cannot unlock the capability of querying where any given face was observed across time and location.

>> Currency interventions never work

Where did you learn that? It doesn't reflect the structural volumes present. Central banks make interventions all the time in line with little stabilisation programs. Those are almost always deemed success.

Maybe you deduced it by yourself? If so, fx is weird despite traditionally being seen as the simplest area in finance. E.g. It's counter-intuitive but we tend to think trade make up most FX volume globally. It's in the area of less than 3%. The majority by far is speculative and hedging.

The other trap is fx volume, people assume the know what volume is but then they learn expressions of fx volume is almost always actually tick volume.

I think the mental model is the right thing to focus on. I'm not denying there are cases where async at the edge of an app can make sense but there's no free lunch and i that doesn't appear to be well understood.

Maybe i will write a blog post, if nothing else than to get my own thoughts in order.

I'm not sure i see it as a hack, but i do feel unduly burdened as a developer by async in python. I feel like there's a lot that i have to think about and i'd appreciate more help from the language.

I don't want to be overly critical, there's languages that people complain about and then there are languages that no one uses... If i compare it to js/ts, some stuff is genuinely better in Python - e.g. if you missed an await. While both ecosystems have lint tools available for this, but the behaviour is just friendlier in python.

Structured concurrency is better in python, but even if TaskGroup is nicer to use than AbortController, it still has its own foibles which means i'll usually advocate for AnyIO.

But the js/ts ecosystem just generally benefits from being async from the get-go where python you're just a time.sleep() away from a bug that will slip through dev envs and ci pipelines undetected and only rear its head under load in prod.

If i have a tip to share its the debug flag for asyncio run:

    asyncio.run(f(), debug=True)  # find some more issues before prod

There are so many footguns[1] in async python but it really has stolen the zeitgeist of modern python web. Synchronous django has a lot to commend it. If you deploy it reasonably well (workers, behind a caching reverse proxy etc) it's easy to operate in production even under duress.

It's not going to be the right stack for long lived websocket connections or whatever but for a CRUD-ish or enterprise app, often very productive.

[1] buffer bloat is too easy to sleep walk into

    queue = asyncio.Queue()  # oops

unbounded concurrency

    await asyncio.gather(*(fetch(item) for item in items))  # look mum! no outbound sockets left or maybe even no file descriptors

accidental blocking

    data = requests.get(url).json()  # oops! we're blocking the event loop

and sure you can setup a separate task to measure the event loop latency and alert on that but this is the kinda thing you'll never find in a tutorial, you just need to experience this stuff and figure out a solution you like

I can go on and on about async python (cancellation bugs, leaking a task - that has a cataclysmic failure mode where if you forget to hold a reference to your asyncio.create_task() then it's all weak refs in that machinery so your task can get garbage collected before it ran or completed in production! super tricky forensics. Then there's all the obvious stuff like race conditions, new ways of creating deadlocks, blah blah i really can go on for days.