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Just curious, about how long did this project take you? I don't see that mentioned in the article.

We had our third kid in late November, and I worked sporadically on it over the following two months of paternity leave and holiday... If I had to bet, I'd say I put in well over 200 hours of work on it, the majority of that being manual auditing/driving of the generation process. If any AI model were reliable at checking the generated pixels, I could have automated this process, but they simply aren't there yet, so I had to do a lot more manual work than I'd anticipated.

All told I probably put in less than 20 hours of actual software engineering work, though, which consisted entirely of writing specs and iterating with various coding agents.


> If any AI model were reliable at checking the generated pixels, I could have automated this process, but they simply aren't there yet, so I had to do a lot more manual work than I'd anticipated.

Since the output is so cool and generally interesting, there might be an opportunity for those forking this to do other cities to deploy a web app to crowd source identifying broken tiles and maybe classifying the error or even providing manual hinting for the next run. It takes a village to make a (sim) city! :-)


Yeah I'll get the code out there soon - it's just very vibe-y right now, the repo is a bit of a mess since I never bothered to organize things. The secret sauce is really in the fine-tuning, can definitely get those datasets/models public on oxen.ai too

144, using css variables, with fallback and p instead of li

https://codepen.io/dsmmcken/pen/WbwYOEQ?editors=0100

p{counter-increment:n;--n:counter(n)}p:nth-child(3n){--f:"Fizz"}p:nth-child(5n){--b:"Buzz";--n:''}p::after{content:var(--f,var(--n))var(--b,'')}


Level 48 is the last level, and you get a pdf certificate proving you are human.


Two of those wishlists css features already exist as specs:

> n-th child variable

See sibiling-index() and sibling-count() https://developer.mozilla.org/en-US/docs/Web/CSS/sibling-ind...

> Reusable blocks

See @function and @mixin draft spec, https://drafts.csswg.org/css-mixins-1/ and https://css-tricks.com/functions-in-css/

Both are available in chrome already.


The tool we use for our docs AI answers lets you mine that data for feature requests. It generates a report of what it didn't have answers for and summarizes them as potential feature gaps. (Or at least what it is aware it didn't have answers for).

People seem more willing to ask an AI about certain things then be judged by asking the same question of a human, so in that regard it does seem to surface slightly different feature requests then we hear when talking to customers directly.

We use inkeep.com (not affiliated, just a customer).


> We use inkeep.com (not affiliated, just a customer).

And what do you pay? It's crazy that none of these AI CSRs have public pricing. There should just be monthly subscription tiers, which include some number of queries, and a cost per query beyond that.


FYI the "watch video" button in the hero of https://codevideo.io/ doesn't work, missing the video ID.


Great catch - real link shipping to prod as we speak. Thanks for checking out the site!



scrollbar-gutter: stable; to those unfamiliar. https://developer.mozilla.org/en-US/docs/Web/CSS/scrollbar-g...


Man, this makes me feel old.


I feel like I should be writing with the goal that the end reader is actually an LLM. The LLM will be the one spitting out the answers to the actual users via things like co-pilot, but I am not sure how that should change my approach to structure or level of detail in docs. Heavier on the number of code examples?


Well, look at the process of training a chatbot:

- first you make a "raw" corpus, with all the information needed to produce an answer

- then you generate sample question-answer pairs

- then you use AI to make better questions and better answers (look at e.g. WizardLM https://arxiv.org/pdf/2304.12244)

- can also finetune with RLHF or modify the Q-A pairs directly

- then you have a final model finetune once the Q-A pairs look good

- then you use RAG over the corpus and the Q-A pairs because the model doesn't remember all the facts

- then you have a bullshit detector to avoid hallucinations

So the corpus is very important, and the Q-A pairs are also important. I would say you've got to make the corpus by hand, or by very specific LLM prompts. And meanwhile you should be developing the Q-A pairs with LLMs as the project develops - this gives a good indication of what the LLM knows, what needs work, etc. When you have a good set of Q-A pairs you could probably publish it as a static website, save money on LLM generation costs if people don't need super-specific answers.

To add to the current top-scoring comment, though (https://news.ycombinator.com/item?id=42326324), one advantage of an LLM-based workflow is that the corpus is the single source of truth. It is true that good documentation repeats itself, but from a maintenance standpoint, changing all the occurrences of a fact, idea, etc. is hard work, whereas changing it once in the corpus and then regenerating all the QA pairs is straightforward.


Ask an LLM to write it?


Link is gated behind a facebook/meta account.


You can "Decline optional cookies" and browse without being logged in, at least in Europe. (Just tested in a private window, FB/Meta is only allowed in a separate container on my computer.)


As is the Meta Quest 3S.


Sure, but I need an Apple account to use an iPad or an iPhone, but I don't need to log into my Apple account to browse hardware on my computer.

I hate Facebook in general, but I like that Meta is investing in a hardware platform. I can live with needing an account to use a piece of hardware, if the hardware is good. I have a quest3, I like it. But they care so much about tracking you that it undermines their hardware and the adoption of it.


They could have the best VR hardware in the industry by 100x and I would still avoid purchasing their headsets.


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