If you bounce off the wall at the start, and RNG gods look down on you, you're lined up to go through castle on first shot. Then knock it through castle, and lined up for 3rd final putt into hole.
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.
Lately I just have Claude build most things in Rust, it's really amazing. I tried Go, but I found it wasn't as good--Rust really does to
me feel like Python. That said, it still struggles with the same class of errors of building complex systems. I've tried using TLA+, Alloy, and other things but haven't found the trick yet. The best I've found is reimplementing all external systems in memory and e2e testing everything extensively, without reimplementing the tests become unusably slow, and Claude can rewrite huge surface areas with ease--it's somewhere between mocking and literally just reimplementing the external systems.
> Rule 5. Data dominates. If you've chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming.
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.
ChatGPT needs to catch up hard, for me their model is unusable and I cancelled my subscription 5-6 months ago, so to me this post is hot air. I need to see results, going back to try Codex 5.2, then 5.3, downloading their new desktop client, their VS code extension, their weird browser, is time I wish I had back.
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.
I was looking at using this on an LTO tape library, it seems the only resiliency is through replication, but this was my main concern with this project, what happens with HW goes bad
I had built an agent with LangGraph a 9 months ago--now seems React agents are in LangChain. Over all pretty happy with that, I just don't use any of the dumb stuff like embedding/search layer: just tools&state
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..
I built an ML classifier for product categories way back, as I added more classes/product types, individual class PR metrics improved--I kept adding more and more until I ended up with ~2,000 classes.
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)
Amazing, some people are so enamored with LLMs who use them for soft outcomes, and disagree with me when I say be careful they're not perfect -- this is such a great non technical way to explain the reality I'm seeing when using on hard outcome coding/logic tasks. "Hey this test is failing", LLM deletes test, "FIXED!"
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
I wish they dug into how they generated the vector, my first thought is: they're injecting the token in a convoluted way.
{ur thinking about dogs} - {ur thinking about people} = dog
model.attn.params += dog
> [user] whispers dogs
> [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?
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.