cipherself
cipherself on Hacker News
Posted by
cipherself on Monday July 20, 2026 @02:35PM
from the mohamed.computer dept.
For anyone else who's suffering, paste this in the console in devtools:
document.getElementsByTagName('main')[0].style.margin = '0 auto';
It's quite exciting how far we've come from the modern exposition of world models by David Ha and Jürgen Schmidhuber in 2018
https://worldmodels.github.io/
I don't think this is bad, if you know Python then most of the code will be fine for you. I think you're probably referring to pm_reduce_collapse, but while it looks daunting at first, it really isn't when you consider the alternatives, I'd be curious on how you'd improve it short of creating a DSL.
I have written parsers using parser combinators in Haskell and Clojure. I find that ML-like (Haskell, OCaml, StandardML) languages generally are great at writing parsers, even hand-written ones in it is a superior experience.
In this case, this was a project at $EMPLOYER in an existing codebase with colleagues who have never seen Haskell code, using Haskell would've been a major error in judgement.
One anecdote in the same vein, a couple of months ago, I wanted to parse
systemd-networkd INI files in Python and the python built-in ConfigParser [0] and pytest's
iniconfig parser [1] couldn't handle multiple sections with the same name so I ended up writing 2 parsers, one using a ParserCombinator library and one by hand and ended up using the latter given it was much simpler to understand and I didn't have to introduce an extra dependency.
Admittedly, INI is quite a simple format, hence I mention this as an anecdote.
[0] https://docs.python.org/3/library/configparser.html
[1] https://github.com/pytest-dev/iniconfig
That was a great book, I wonder what the 2025 equivalent of it is...
12 (13?) years ago I had also written a Naïve Bayes classifier in Perl
https://github.com/cipherself/NaiveBayes_perlIIRC, next thing on my TODO list was to add vectorization. Also (like OP) it uses log probabilities to avoid floating-point underflow.
Assuming that's what he meant, why would it be considered baseline versus anything else? I am genuinely curious because I'd like to know more about issues people face with this or that vector store in general.
Got it, I think this might make sense for a "conversation" type of search not for an instant search feature because lowest latency is gonna be too high IMO.
S3 Vectors is hands down the SOTA hereSOTA for what? Isn't it just a vector store?
I am working on search but rather for text-to-image retrieval, nevertheless, I am curious if by
that's all baked into Azure AI search you also meant
synthetic query generation from the grandparent comment. If so, what's your latency for this? And do you extract structured data from the query? If so, do you use LLMs for that?
Moreover I am curious why you guys use bm25 over SPLADE?
While I haven’t tried --pull=missing, I have tried --pull=never, which I assume is a stricter version and it was still attempting the HEAD request.
I’ll admit I haven’t checked before posting, perhaps an admin can merge both submissions and change the URL on the one you linked to the one in this submission.
In the case where you still have an image locally, trying to build will fail with an error complaining about not being able to load metadata for the image because a HEAD request failed. So, the real question is, why isn't there a way to disable the HEAD request for loading metadata for images? Perhaps there's a way and I don't know it.
Posted by
cipherself on Wednesday September 24, 2025 @07:15PM
from the dockerstatus.com dept.
There was a way to experience this demo interactively
https://dougengelbart.org/content/view/374/Unfortunately, it seems like it's not working properly anymore. I just messaged Bret Victor and maybe he can get it back in working order or can reach out to someone.
In the list of features, it mentions:
> vision-based search for comprehensive document understanding
but it's not clear to me what this means, is it just vector embeddings for each image in every document via a CLIP-like model?
In addition, I'd be curious what's the rationale behind using the plethora of databases, given the docs on running it in production spins them all up, I assume they're all required, for instance I'd be curious on the trade-offs between using postgres with something like pg_search (for bm25 support, which vanilla postgres FTS doesn't have) vs using both postgres and ElasticSearch.
The docs are also very minimal, I'd have loved to see at least 1 example of usage.
Posted by
cipherself on Monday June 16, 2025 @05:51PM
from the docs.github.com dept.
Pygfx uses webgpu while VisPy uses OpenGL.
This is ~albeit not being very deep~ very cool.
I'm curious, why would that be worth mentioning?