HN user

Difwif

313 karma
Posts3
Comments115
View on HN

I think it's pretty obvious what category you see yourself in.

I don't think you're a hacker. I think you enjoy writing code (good for you). Some of us just enjoy making the computer execute our ideas - like a digital magician. I've also gotten very good at the code writing and debugging part. I've even enjoyed it for long periods of time but there's times where I can't execute my ideas because they're bigger than what I can reasonably do by myself. Then my job becomes pitching, hiring, and managing humans. Now I write code to write code and no project seems too big.

But I'm looking forward to collapsing the many layers of abstraction we've created to move bits and control devices. It was always about what we could do with the computers for me.

This statement feels like a farmer making a case for using their hands to tend the land instead of a tractor because it produces too many crops. Modern farming requires you to have an ecosystem of supporting tools to handle the scale and you need to learn new skills like being a diesel mechanic.

How we work changes and the extra complexity buys us productivity. The vast majority of software will be AI generated, tools will exist to continuously test/refine it, and hand written code will be for artists, hobbyists, and an ever shrinking set of hard problems where a human still wins.

This is temporary. AI models have their own Moore's law. Yes the mega corps will have the best models but soon enough what is currently SOTA will be open source and run on your own local machine if you want.

the mega corps are getting all of us and the investors to fund the RnD.

This just seems like an engineered pipeline of existing GenAI to get a 3d procedurally generated world that doesn't even look SOTA. I'm really sorry to dunk on this for those that worked on it, but this doesn't look like progress to me. The current approach looks like a dead end.

An end-to-end _trained_ model that spits out a textured mesh of the same result would have been an innovation. The fact that they didn't do that suggests they're missing something fundamental for world model training.

The best thing I can say is that maybe they can use this to bootstrap a dataset for a future model.

I used to be in this camp until I tried and bought an M1 Macbook as my daily driver. I thought I was going to be Thinkpad/XPS w/ Linux until I die. I don't love MacOS but POSIX is mostly good enough for me and the hardware is so good that I'm willing to look past the shortfalls.

Seriously I would love to switch back to a full-time Linux distro but I'm more interested in getting work done and having a stable & performant platform. Loosing a day of productivity fixing drivers and patching kernels gets old. The M-series laptops have been the perfect balance for me so far.

(2) Seems like a media narrative rather than truth. I don't think that would be anywhere remotely high on a CEO's priority list unless they were a commercial real estate company.

It's far more likely a mixture of (1) and actual results - in-person/hybrid teams produce better outcomes (even if why that's true hasn't been deeply evaluated or ultimately falls on management)

It would be interesting to see two versions of a model. A primary model tuned for precision that's focused on correctness that works with or orchestrates a creative model that's tuned for generating new (and potentially incorrect) ideas. The primary model is responsible for evaluating and reasoning about the ideas/hallucinations. Feels like a left/right brain architecture (even though that's an antiquated model of human brain hemispheres).

I took a quick informal poll of my coworkers and the majority of us have found workflows where CC is producing 70-99% of the code on average in PRs. We're getting more done faster. Most of these people tend to be anywhere from 5-12 yrs professional experience. There are some concerns that maybe more bugs are slipping through (but also there's more code being produced).

We agree most problems stem from: 1. Getting lazy and auto-accepting edits. Always review changes and make sure you understand everything. 2. Clearly written specification documents before starting complex work items 3. Breaking down tasks into a managable chunk of scope 4. Clean digestible code architecture. If it's hard for a human to understand (e.g: poor separation of concerns) it will be hard for the LLM too.

But yeah I would never waste my time making that video. Having too much fun turning ideas into products to care about proving a point.

My parents could have said your first paragraph when I tried to teach them they could Google their questions and find answers.

Technology moves forward and productivity improves for those that move with it.

Seems short sighted to me. LLMs could have any data in their training set encoded as tokens. Either new specialized tokens are explicitly included (e.g: Vision models) or the language encoded version of everything that usually exists (e.g: the research paper and the csv with the data).

To improve next token prediction performance on these datasets and generalize requires a much richer latent space. I think it could theoretically lead to better results from cross-domain connections (ex: being fluent in a specific area of advanced mathematics, quantum mechanics, and materials engineering is key to a particular breakthrough)

GPT-5 12 months ago

My mental model is a bit different:

Context -> Attention Span

Model weights/Inference -> System 1 thinking (intuition)

Computer memory (files) -> Long term memory

Chain of thought/Reasoning -> System 2 thinking

Prompts/Tool Output -> Sensing

Tool Use -> Actuation

The system 2 thinking performance is heavily dependent on the system 1 having the right intuitive models for effective problem solving via tool use. Tools are also what load long term memories into attention.

GPT-5 12 months ago

And LLM memories are stored in an electrical charge trapped in a floating gate transistor (or as magnetization of a ferromagnetic region on an alloy platter).

Or they write CLAUDE.md files. Whatever you want to call it.

I believe it's quite easy to look at any humans actions and cherry pick a narrative of malfeasance or malice if that's what you're looking for.

Musk does a lot of things at a very high level publicly so I think it's an even easier task. I'm sure you'll disagree but I believe it's this false narrative and who's creating it that you should be doubting.

Many people don't have a problem with a lot of what Musk has done. He's not perfect and does make mistakes which he openly admits like any sane rational person should. I do believe his good intent is there and he generally tries to right wrongs.

I'm watching closely what he does and sometimes I have my doubts. If I ever see him actually cross a line I'll change my mind. For now, most of the narrative has been pretty typical fake news and timeless partisan disagreement on methods of governance.

Why actually travel when you can easily generate photos and videos of yourself doing all sorts of things in all sorts of places?

This is such a depressing but accurate take on travel.

At least if it's true I can finally enjoy everything without the crowds of selfie sticks.