That NotebookLM podcast was like the most unpleasant way I can imagine to consume content. Reading transcripts of live talks is already pretty annoying because it's less concise than the written word. Having it re-expanded by robot-voice back to audio to be read to me just makes it even more unpleasant.
Also sort of perverse we are going audio->transcript->fake audio. "YC has said the official video will take a few weeks to release," - I mean shouldn't one of the 100 AI startups solve this for them?
Anyway, maybe it's just me.. I'm the kind of guy that got a cynical chuckle at the airport the other week when I saw a "magazine of audio books".
The voices sounded REALLY good the first time I used it. But then sounded exactly the same every time after that and became underwhelmed.
https://vocaroo.com/1nZBz5hdjwEh
As a bonus its hilarious in its own right.
I don’t think it’s the 4th wave of pioneering a new dawn of civilization but it’s clear LLMs will remain useful when applied correctly.
It felt like that was the direction for a while, but in the last year or so, the gap seems to have widened. I'm curious whether this is my perception or validated by some metric.
Another way to put it, is that over time you see this, it usually takes a little while for open source projects to catch up, but once they do they gain traction quite quickly over the closed source counter parts.
The time horizons will be different as they always are, but I believe it will happen eventually.
I’d also argue that browsers got complicated pretty fast, long cry from libhtml in a few short years.
[0]: of which I contend most useful applications of this technology will not be the generalized ChatGPT interface but specialized highly tuned models that don’t need the scope of a generalized querying
One crazy thing is that since I keep all my PIM data in git in flat text I now have essentially "siri for Linux" too if I want it. It's a great example of what Karpathy was talking about where improvements in the ML model have consumed the older decision trees and coded integrations.
I'd highly recommend /nothink in the system prompt. Qwen3 is not good at reasoning and tends to get stuck in loops until it fills up its context window.
My current config is qwen2.5-coder-0.5b for my editor plugin and qwen3-8b for interactive chat and aider. I use nibble quants for everything. 0.5b is not enough for something like aider, 8b is too much for interactive editing. I'd also recommend shrinking the ring context in the neovim plugin if you use that since the default is 32k tokens which takes forever and generates a ton of heat.
I stick by my general thesis that OSS will eventually catch up or the gap will be so small only frontier applications will benefit from using the most advanced models
If you read the talk you can find out this and more :)
One bundles "AGI" with broken promises and bullshit claims of "benefits to humanity" and "abundance for all" when at the same time it takes jobs away with the goal of achieving 10% global unemployment in the next 5 years.
The other is an overpromised scam wrapped up in worthless minted "tokens" on a slow blockchain (Ethereum).
Terms like "Software 3.0", "Web 3.0" and even "AGI" are all bullshit.
Reminds me of work where I spend more time figuring out how to run repos than actually modifying code. A lot of my work is focused on figuring out the development environment and deployment process - all with very locked down permissions.
I do think LLMs are likely to change industry considerably, as LLM-guided rewrites are sometimes easier than adding a new feature or fixing a bug - especially if the rewrite is something into more LLM-friendly (i.e., a popular framework). Each rewrite makes the code further Claude-codeable or Cursor-codeable; ready to iterate even faster.
> imagine that the inputs for the car are on the bottom, and they're going through the software stack to produce the steering and acceleration
> imagine inspecting them, and it's got an autonomy slider
> imagine works as like this binary array of a different situation, of like what works and doesn't work
--
Software 3.0 is imaginary. All in your head.
I'm kidding, of course. He's hyping because he needs to.
Let's imagine together:
Imagine it can be proven to be safe.
Imagine it being reliable.
Imagine I can pre-train on my own cheap commodity hardware.
Imagine no one using it for war.
The danger I see is related to psychological effects caused by humans using LLMs on other humans. And I don't think that's a scenario anyone is giving much attention to, and it's not that bad (it's bad, but not world end bad).
I totally think we should all build it. To be trained from scratch on cheap commodity hardware, so that a lot of people can _really_ learn it and quickly be literate on it. The only true way of democratizing it. If it's not that way, it's a scam.
"Q: What does your name (badmephisto) mean?
A: I've had this name for a really long time. I used to be a big fan of Diablo2, so when I had to create my email address username on hotmail, i decided to use Mephisto as my username. But of course Mephisto was already taken, so I tried Mephisto1, Mephisto2, all the way up to about 9, and all was taken. So then I thought... "hmmm, what kind of chracteristic does Mephisto posess?" Now keep in mind that this was about 10 years ago, and my English language dictionary composed of about 20 words. One of them was the word 'bad'. Since Mephisto (the brother of Diablo) was certainly pretty bad, I punched in badmephisto and that worked. Had I known more words it probably would have ended up being evilmephisto or something :p"
Unbelievable. Perhaps some techies should read Goethe's Faust instead of Lord of the Rings.
If you want to scoff at anyone, scoff at 1990s Blizzard Entertainment for using those names in that way
I think it's a bit early to change your mind here. We love your 2.0, let's wait for some more time till th e dust settles so we can see clearly and up the revision number.
In fact I'm a bit confused about the number AK has in mind. Anyone else knows how he arrived at software 2.0?
I remember a talk by professor Sussman where he suggest we don't know how to compute, yet[1].
I was thinking he meant this,
Software 0.1 - Machine Code/Assembly Code Software 1.0 - HLLs with Compilers/Interpreters/Libraries Software 2.0 - Language comprehension with LLMs
If we are calling weights 2.0 and NN with libraries as 3.0, then shouldn't we account for functional and oo programming in the numbering scheme?
Nerds are good at the sort of reassuring arithmetic that can make people confident in an idea or investment. But oftentimes that math misses the forest for the trees, and we're left betting the farm on a profoundly bad idea like Theranos or DogTV. Hey, I guess that's why it's called Venture Capital and not Recreation Investing.
If anything it seemed like the middle ground between AI boosters and doomers.
Maybe they didn't, and it's just your perception.
Software 3.0 isn't about using AI to write code. It's about using AI instead of code.
So not Human -> AI -> Create Code -> Compile Code -> Code Runs -> The Magic Happens. Instead, it's Human -> AI -> The Magic Happens.
Started learning metal guitar seriously to forget about industry as a whole. Highly recommended!
This is why I think the AI industry is mostly smoke and mirrors. If these tools are really as revolutionary as they claim they are, then they should be able to build better versions of themselves, and we should be seeing exponential improvements of their capabilities. Yet in the last year or so we've seen marginal improvements based mainly on increasing the scale and quality of the data they're trained on, and the scale of deployments, with some clever engineering work thrown in.
3 to 5 companies iso of the hundreds of thousands who sell software now
Recursive self-improvement is literally the endgame scenario - hard takeoff, singularity, the works. Are you really saying you're dissatisfied with the progress of those tools because they didn't manage to end the world as we know it just yet?
If the former then yes singularity. The only hope is it's "good will" (wouldn't bet on that) or turning off switches.
If the latter you still need more workers (programmers or whatever they'll be called) due to increased demand for compute solutions.
That's too coarse of a choice. It's better than people at increasingly large number of distinct tasks. But it's not good enough to recursively self-improve just yet - though it is doing it indirectly: it's useful enough to aid researchers and businesses in creating next generation of models. So in a way, the recursion and resulting exponent are already there, we're just in such early stages that it looks like linear progress.
I think there will be a wall hit eventually with this, much like there was with visual recognition in the mid 2010s[0]. It will continue to improve but not exponentially
To be fair I am bullish it will make white collar work fundamentally different but smart companies will use it to accelerate their workforce productivity, reliability and delivery, not simply cut labor to the bone, despite that seemingly being every CEOs wet dream right now
[0]: remember when everyone was making demos and apps that would identify objects and such, and all the facial augmentation stuff? My general understanding is that the tech is now in the incremental improvement stage. I think LLMs will hit the same stage in the near term and likely hover there for quite awhile
I'm personally 50/50 on this prediction at this point. It doesn't feel like we have enough ingredients for end-to-end recursive self-improvement in the next 5 years, but the overall pace is such that I'm hesitant to say it's not likely either.
Still, my reply was to the person who seemed to say they won't be impressed until they see AIs "able to build better versions of themselves" and "exponential improvements of their capabilities" - to this I'm saying, if/when it happens, it'll be the last thing that they'll ever be impressed with.
> remember when everyone was making demos and apps that would identify objects and such, and all the facial augmentation stuff? My general understanding is that the tech is now in the incremental improvement stage.
I thought that this got a) boring, and b) all those advancements got completely blown away by multimodal LLMs and other related models.
My perspective is that we had a breakthrough across the board in this a couple years ago, after the stuff you mentioned happened, and that isn't showing signs of slowing down.
The progress has been adequate and expected, save for very few cases such as generative image and video, which has exceeded my expectations.
Before we reach the point where AI is self-improving on its own, we should go through stages where AI is being improved by humans using AI. That is, if these tools are capable of reasoning and are able to solve advanced logic, math, and programming challenges as shown in benchmarks, then surely they must be more capable of understanding and improving their own codebases with assistance from humans than humans could do alone.
My point is that if this was being done, we should be seeing much greater progress than we've seen so far.
Either these tools are intelligent, or they're highly overrated. Which wouldn't mean that they can't be useful, just not to the extent that they're being marketed as.
Yes and we've actually been able to witness in public the dubious contributions that Copilot has made on public Microsoft repositories.
I kind of expect that from someone heading a company that appears to have sold-the-farm in an AI gamble. It’s interesting to see a similar viewpoint here (all biases considered)
What does this mean? An LLM is used via a software interface. I don’t understand how “take software out of the loop” makes any sense when we are using reprogrammable computers.
https://leanpub.com/patterns-of-application-development-usin...
> LLMs make mistakes that basically no human will make, like, you know, it will insist that 9.11 is greater than 9.9, or that there are two bars of strawberry. These are some famous examples.
But you answered it: It’s a stupid mistake a human makes when trying to mock the stupid mistakes that LLMs make!
I love Andrej, but come on.
Writing essentially punch cards 70 years ago, writing C 40 years ago and writing Go or Typescript or Haskell 10 years ago, these are all very different activities.
The main thing that changed about programming is the social/political/bureaucratic side.
great name already
They want to onboard as many people on their stuff and make them as dependent on it as possible, so the switching costs are more.
It's the classic scam. Look at what Meta are doing now that they reached end of the line and are trying to squeeze out people for profitability:
- Bringing Ads to WhatsApp: https://apnews.com/article/whatsapp-meta-advertising-messagi...
- Desperately trying by any illegal means possible to steal your data: https://localmess.github.io/
- Firing all the people who built their empire: https://www.thestreet.com/employment/meta-rewards-executives...
- Enabled ethnic cleansing in multiple instances: https://www.amnesty.org/en/latest/news/2022/09/myanmar-faceb...
If you can't see the total moral bankruptcy of Big Tech, you gotta be blind. Don't Be Evil my ass. To me, LLMs have only one purpose: dumb down the population, make people doubt what's real and what's not, and enrich the tech overlords while our societies drown in the garbage they create.
It takes mouse clicks, sends them to the LLM, and asks it to render static HTML+CSS of the output frame. HTML+CSS is basically a JPEG here, the original implementation WAS JPEG but diffusion models can't do accurate enough text yet.
My conclusions from doing this project and interacting with the result were: if LLMs keep scaling in performance and cost, programming languages are going to fade away. The long-term future won't be LLMs writing code, it'll be LLMs doing direct computation.
There's so much demand around this, people are just super eager to get the information. I can understand why, because it was my favorite talk as well :)
> The more reliance we have on these models, which already is, like, really dramatic
Please point me to a single critical component anywhere that is built on LLMs. There's absolutely no reliance on models, and ChatGPT being down has absolutely no impact on anything beside teenagers not being able to cheat on their homeworks and LLM wrappers not being able to wrap.
Software 2.0? 3.0? Why stop there? Why not software 1911.1337? We went through crypto, NFTs, web3.0, now LLMs are hyped as if they are frigging AGI (spoiler, LLMs are not designed to be AGI, and even if they were, you sure as hell won't be the one to use them to your advantage, so why are you so irrationally happy about it?).
Man this industry is so tiring! What is the most tiring is the dog-like enthusiasm of the people who buy it EVERY.DAMN.TYPE, as if it's gonna change the life of most of them for the better. Sure, some of these are worse and much more useless than others (NFTs), but in the core of all of it is this cult-like awe we as a society have towards figures like the Karpathy's, Musks and Altmans of this world.
How are LLMs gonna help society? How are they gonna help people work, create and connect with one another? They take away the joy of making art, the joy of writing, of learning how to play a music instrument and sing, and now they are coming for software engineering. Sure, you might be 1%/2% faster, but are you happier, are you smarter (probably not: https://www.mdpi.com/2076-3417/14/10/4115)?
pudiklubi•6h ago
https://x.com/karpathy/status/1935077692258558443
levocardia•5h ago
msgodel•4h ago
chrisweekly•4h ago
swyx•4h ago
pudiklubi•3h ago
swyx•3h ago
i exepct YC to prioritize publishing this talk so propbably the half life of any of this work is measured in days anyway.
100% of our podcast is published for free, but we still have ~1000 people who choose to support our work with a subscription (it does help pay for editors, equipment, and travel). I always feel bad that we dont have much content for them so i figured i'd put just the slide compilation up for subscribers. i'm trying to find nice ways to ramp up value for our subs over time, mostly by showing "work in progress" things like this that i had to do anyway to summarize/internalize the talk properly - which again is what we published entirely free/no subscription required
pudiklubi•2h ago
that being said, HN is a negative place, and not what I was trying to go for. thank you for your work with the slides!
dang•2h ago
(As a step towards making it a non-negative place.)
fellatio•1h ago
swyx•3h ago
theyinwhy•3h ago
Edit: the emoji at the end of the original sentence has not been quoted. How a smile makes the difference. Original tweet: https://x.com/karpathy/status/1935077692258558443
theturtletalks•3h ago
qwertox•3h ago
scottyah•3h ago
amarait•3h ago
koakuma-chan•1h ago
addaon•1h ago
pudiklubi•1h ago