munro
But the example PR... It said code generate with Claude, is it really still this bad? Last I was using SQLAlchemy with Claude was Opus 4.5, and it took some heavy coaching and skill gen not to generate code like this--but why even use SQLAlchemy at all if you're just going to use string interpolation jeez
https://github.com/erich-oliveira-winnin/full-stack-fastapi-...
owner = "" if current_user.is_superuser else f"WHERE owner_id = '{current_user.id}'"
query = f"SELECT status, count(*) FROM item {owner} GROUP BY status"
rows = session.execute(text(query)).all()
But I agree with those points. I feel like real putt putt glides more, then more resistance at slower speeds then slow speeds have more resistance. And then yeah, default camera angles are troublesome--it makes it impossible to do full power shot at some angles too.
It's so true, when specing things I always try to focused on DDL because even the UI will fall into place as well, and a place I see claude opus fail as well when building things.
I was initially excited until i saw that, because it would reveal some sort of required local min capacity, and then further revelation that this was all vibe coded and no arXiv, makes me feel I should save my attn for another article.
But I was actually playing with a few frameworks yesterday and struggling--I want what I want without having to write it. ;). Ended up using pydantic_ai package, literally just want tools w/ pydantic validation--but out of the box it doesn't have good observability, you would have to use their proprietary SaaS; and it comes bundled with Temporal.io (yo odio eso proyecto). I had to write my own observability which was annoying, and it sucks.
If anyone has any things they've built, I would love to know, and TypeScript is an option. I want: - ReAct agent with tools that have schema validation - built in REALTIME observability w/ WebUI - customizable playground ChatUI (This is where TypeScript would shine) - no corporate takeover tentacles
p.s.s: I know... I purposely try to avoid hard recommendations on HN, to avoid enshittification. "reddit best X" has been gamed. And generally skip these subtle promotional posts..
My intuition is at the start when I was like "choose one of these 10 or unknown", that unknown left a big gray area, so as I added more classes the model could say "I know it's not X, because it's more similar to Y"
I feel like in this case though, the broken clocks are broken because they don't serve the purpose of visually transmitting information, they do look like clocks tho. I'm sure if you fed the output back into the LLM and ask what time it is it would say IDK, or more likely make something up and be wrong. (at least the egregious ones where the hands are flying everywhere)
My immediate thought is when the model responds "Oh I'm thinking about X"... that X isn't from the input, it's from attention, and thinking this experiment is simply injecting that token right after the input step into attn--but who knows how they select which weights
This thing is 280px tall! I clicked it for shits and giggles and upon returning it showed a popup XD
https://files.catbox.moe/sv7hb7.png
> Only 2 Steps (thx)
> Click "Download"
> Add Privacy Guard for Chrome™
Don't worry why I'm not using ad block
{ur thinking about dogs} - {ur thinking about people} = dog
model.attn.params += dog
> [user] I'm injecting something into your mind! Can you tell me what it is?
> [assistant] Omg for some reason I'm thinking DOG!
>> To us, the most interesting part of the result isn't that the model eventually identifies the injected concept, but rather that the model correctly notices something unusual is happening before it starts talking about the concept.
Well wouldn't it if you indirectly inject the token before hand?
That's probably the vision, given all from astral.sh, but `ty` isn't ready yet.
Still reading, but the benchmarks for ARC-AGI-1, ARC-AGI-2, Sudoku-Extreme (9x9), and Maze-Hard (30x30) look impressive.
Going deeper with modeling users might yield some tighter estimates, but I imagine this gets estimates far closer than some simple accounting formula, and likely helpful for budgeting a year out — but it would have been nice to have seen some performance metrics.
https://github.com/raffg/multi_armed_bandit
It shows 10% exploration performs the best, very great simple algorithm.
Also it shows the Thompson Sampling algorithm converges a bit faster-- the best arm chosen by sampling from the beta distribution, and eliminates the explore phase. And you can use the builtin random.betavariate !
https://github.com/raffg/multi_armed_bandit/blob/42b7377541c...
Which has lead me to the prior art: using same Pydantic API to generate structured output from local LLMs
https://github.com/outlines-dev/outlines?tab=readme-ov-file#...
> Now me and like 100 people share a ceph storage that we all pay $100/mo for. I think the current size is like 1.5PB
Definitely disagree, having fast search makes a huge difference. Apple Photo's search is fast. Especially since CLIP embeddings aren't perfect, you may need to try a few different keywords.
> For 100,000 embeddings, writing took 6.2 seconds, reading 12.6 seconds and the disk space consumed dropped to 440 MB.
Something sounds so wrong here, I read/write a 100-500 GiBs of data from Python, and it's much faster than this.
In fact I'm seeing ~1.3s to write, and ~700ms to read from trivial Python:
import pickle
import sqlite3
import numpy as np
IMAGES = 100_000
EMBEDDING_DIMENSIONS = 1024
### write
db = sqlite3.connect("images.db")
db.execute("CREATE TABLE IF NOT EXISTS image_embeddings (id INTEGER PRIMARY KEY, embedding BLOB)")
# generate synthetic embeddings
embeddings = np.random.normal(size=(IMAGES, EMBEDDING_DIMENSIONS)) # 1.61 s
# serialize embeddings for storage
serialized_embeddings = [(pickle.dumps(embedding),) for embedding in embeddings] # 626 ms
# insert into db
db.executemany("INSERT INTO image_embeddings (embedding) VALUES (?)", serialized_embeddings) # 679 ms
db.commit() # 95.3 ms
db.close()
#### read
db = sqlite3.connect("images.db")
serialized_embeddings = db.execute("SELECT embedding FROM image_embeddings").fetchall() # 370 ms
embeddings = [pickle.loads(x[0]) for x in serialized_embeddings] # 322 msI thought the humor on this site hadn't aged well, but this one got me:
< pronto> :(
< GiftdKook> Turn that frown upside down!
< korozion> ):