251–260 of 827 posts
Earlier quoted context omitted.
We all have blind spots
Sure, but maybe suggesting that the person who literally spent countless hours educating others on how to build small models locally from scratch, is lacking knowledge about local small models is going a bit beyond "people have blind spots".
Earlier quoted context omitted.
You would definitely think so, Lean is in a great position here! I am betting though that type theory is not the right logic for this, and that Lean can be leapfrogged.
Why? By the completeness theorem, shouldn't first order logic already be sufficient? The calculus of constructions and other approaches are already available and proven. I'm not sure why we'd need a special logic for LLMs unless said logic somehow accounts for their inherently stochastic tendencies.
His dismissal of smaller and local models suggests he underestimates their improvement potential. Give phi4 a run and see what I mean.
I tried the local small models. They are slow, much less capable, and ironically much more expensive to run than the frontier cloud models.
Tight feedback loops are the key in working productively with software. I see that in codebases up to 700k lines of code (legacy 30yo 4GL ERP systems). The best part is that AI-driven systems are fine with running even more tight loops than what a sane human would tolerate. Eg. running full linting, testing and E2E/simulation suite after any minor change. Or generating 4 versions of PR for the same task so that the h…
That sounds awful. A truly terrible and demotivating way to work and produce anything of real quality. Why are we doing this to ourselves and embracing it?
A few years ago, it would have been seen as a joke to say “the future of software development will be to have a million monkey interns banging on one million keyboards and submit a million PRs, then choose one”. Today, it’s lauded as a brilliant business and cost-saving idea.
We’re beyond doomed. The first major catastrophe caused by sloppy AI code can’t come soon enough. The sooner it happens, the better chance we have to self-correct.
Software 3.0 is the code generated by the machine, not the prompts that generated it. The prompts don't even yield the same output; there is randomness. The new software world is the massive amount of code that will be burped out by these agents, and it should quickly dwarf the human output.
I think that if you give the same task to three different developers you'll get three different implementations. It's not a random result if you do get the functionality that was expected, and at that, I do think the prompt plays an important role in offering a view of how the result was achieved.
Yes, but if you want them to be compatible you need to define a protocol and conformance test suite. This is way more work than writing a single implementation.
The code is the real spec. Every piece of unintentional non-determinism can be a hazard. That’s why you want the code to be the unit of maintenance, not a prompt.
Earlier quoted context omitted.
> suggests a lack of understanding of these smaller models capabilities If anything, you're showing a lack of understanding of what he was talking about. The context is this specific time, where we're early in a ecosystem and things are expensive and likely centralized (ala mainframes) but if his analogy/prediction is correct, we'll have a "Linux" moment in the future where that equation changes (again) and local mod…
He understands the technical part, of course, I was referring to his prediction that large models will be always be necessary. There is a point where an LLM is good enough for most tasks, I don’t need a megamind AI in order to greet clients, and both large and small/medium model size are getting there, with the large models hitting a computing/energy demand barrier. The small models won’t hit that barrier anytime soo…
It’s fascinating to think about what true GUI for LLM could be like. It immediately makes me think a LLM that can generate a customized GUI for the topic at hand where you can interact with in a non-linear way.
An ever-shifting UI sounds unlearnable, and therefore unusable.
Earlier quoted context omitted.
I tried the local small models. They are slow, much less capable, and ironically much more expensive to run than the frontier cloud models.
Phi4-mini runs on a basic laptop CPU at 20T/s… how is that slow? Without optimization…
Running models locally is a privilege for the rich and those with too much disposable time.
Earlier quoted context omitted.
You can disagree with his conclusions but I don't think his understanding of small models is up for debate. This is the person who created micrograd/makemore/nanoGPT and who has produced a ton of educational materials showing how to build small and local models.
I’m going to edit, it was badly formulated, he underestimates their potential for growth is what I meant by that
As far as I understood the talk and the analogies, he's saying that local models will eventually replace the current popular "mainframe" architecture. How is that underestimating them?
Earlier quoted context omitted.
There's also those instances where Microsoft unleashed Copilot on the .NET repo, and it resulted in the most hilariously terrible PRs that required the maintainers to basically tell Copilot every single step it should take to fix the issue. They were basically writing the PRs themselves at that point, except doing it through an intermediary that was much dumber, slower and less practical than them. And don't get me s…
You're not the first, nor the last person, to have a seemingly vastly different experience than me and others. So I'm curious, what am I doing differently from what you did/do when you try them out? This is maybe a bit out there, but would you be up for sending me like a screen recording of exactly what you're doing? Or maybe even a video call sharing your screen? I'm not working in the space, have no products or ser…
I currently have an AI integrated office suite, which has attorneys, professional writers, and political activists using the system. It is office software, word processing, spreadsheets, project management and about two dozen types of AI agents that act as virtual co-workers.
No, my users are not programmers, but I do have interns; college students with anything from 3 to 10 years experience writing software.
I see the same AI use problem issues with my users, and my interns. My office system bends over backwards to address this, but people are people: they do not realize that AI does not know what they are talking about. They will frequently ask questions with no preamble, no introduction to the subject. They will change topics, not bothering to start a new session or tell the AI the topic is now different. There is a huge number of things they do, often with escalating frustration evident in their prompts, that all violate the same basic issue: the LLM was not given a context to understand the subject at hand, and the user is acting like many people and when explaining they go further, past the point of confusion, now adding new confusion.
I see this over and over. It frustrates the users to anger, yet at the same time if they acted, communicated to a human, in the same manner they'd have a verbal fight almost instantly.
The problem is one of communications. ...and for a huge number of you I just lost you. You've not been taught to understand the power of communications, so you do not respect the subject. How to communication is practically everything when it comes to human collaboration. It is how one orders their mind, how one collaborates with others, AND how one gets AI to respond in the manner they desire.
But our current software development industry, and by extension all of STEM has been short changed by never been taught how to effectively communicate, no not at all. Presentations and how to sell are not effective communications, that's persuasion, about 5% of what it takes to convey understanding in others which then unblocks resistance to changes.