In the latest interview with Claude Code's author: https://podcasts.apple.com/us/podcast/lennys-podcast-product..., Boris said that writing code is a solved problem. This brings me to a hypothetical question: what if engineers stop contributing to open source, in which case would AI still be powerful enough to learn the knowledge of software development in the future? Or is the field of computer science plateaued to the point that most of what we do is linear combination of well established patterns?
finnjohnsen2 21 minutes ago [-]
I like this. This is an accurate state of AI at this very moment for me. The LLM is (just) a tool which is making me "amplified" for coding and certain tasks.
I will worry about developers being completely replaced when I see something resembling it. Enough people worry about that (or say it to amp stock prices) -- and they like to tell everyone about this future too. I just don't see it.
DrewADesign 19 minutes ago [-]
Amplified means more work done by fewer people. It doesn’t need to replace a single entire functional human being to do things like kill the demand for labor in dev, which in turn, will kill salaries.
finnjohnsen2 14 minutes ago [-]
I would disagree. Amplified meens me and you get more s** done.
Unless there a limited amount of software we need to produce per year globally to keep everyone happy, then nobody wants more -- and we happen to be at that point right NOW this second.
I think not. We can make more (in less time) and people will get more. This is the mental "glass half full" approach I think. Why not take this mental route instead? We don't know the future anyway.
kiba 10 minutes ago [-]
Jevon's paradox means this is untrue because it means more work not less.
cogman10 18 minutes ago [-]
The more likely outcome is that fewer devs will be hired as fewer devs will be needed to accomplish the same amount of output.
HPsquared 4 minutes ago [-]
The old shrinking markets aka lump of labour fallacy. It's a bit like dreaming of that mythical day, when all of the work will be done.
m_ke 60 minutes ago [-]
It's the new underpaid employee that you're training to replace you.
People need to understand that we have the technology to train models to do anything that you can do on a computer, only thing that's missing is the data.
If you can record a human doing anything on a computer, we'll soon have a way to automate it
xyzzy123 30 minutes ago [-]
Sure, but do you want abundance of software, or scarcity?
Thr price of having "star trek computers" is that people who work with computers have to adapt to the changes. Seems worth it?
worldsayshi 18 minutes ago [-]
My only objection here is that technology wont save us unless we also have a voice in how it is used. I don't think personal adaptation is enough for that. We need to adapt our ways to engage with power.
agumonkey 9 minutes ago [-]
It's a strange economical morbid dependency. AI companies promises incredible things but AI agents cannot produce it themselves, they need to eat you slowly first.
Gigachad 20 minutes ago [-]
Data clearly isn't the only issue. LLMs have been trained on orders of magnitude more data than any person has ever seen.
xnx 56 minutes ago [-]
Exactly. If there's any opportunity around AI it goes to those who have big troves of custom data (Google Workspace, Office 365, Adobe, Salesforce, etc.) or consultants adding data capture/surveillance of workers (especially high paid ones like engineers, doctors, lawyers).
badgersnake 17 minutes ago [-]
I think we’re past the “if only we had more training data” myth now. There are pretty obviously far more fundamental issues with LLMs than that.
polotics 42 minutes ago [-]
How much practice have you got on software development with agentic assistance. Which rough edges, surprising failure modes, unexpected strengths and weaknesses, have you already identified?
How much do you wish someone else had done your favorite SOTA LLM's RLHF?
cesarvarela 56 minutes ago [-]
LLMs have a large quantity of chess data and still can't play for shit.
This benchmark doesn't have the latest models from the last two months, but Gemini 3 (with no tools) is already at 1750 - 1800 FIDE, which is approximately probably around 1900 - 2000 USCF (about USCF expert level). This is enough to beat almost everyone at your local chess club.
cesarvarela 17 minutes ago [-]
Yeah, but 1800 FIDE players don't make illegal moves, and Gemini does.
runarberg 17 minutes ago [-]
Wait, I may be missing something here. These benchmarks are gathered by having models play each other, and the second illegal move forfeits the game. This seems like a flawed method as the models who are more prone to illegal moves are going to bump the ratings of the models who are less likely.
Additionally, how do we know the model isn’t benchmaxxed to eliminate illegal moves.
For example, here is the list of games by Gemini-3-pro-preview. In 44 games it preformed 3 illegal moves (if I counted correctly) but won 5 because opponent forfeits due to illegal moves.
I suspect the ratings here may be significantly inflated due to a flaw in the methodology.
EDIT: I want to suggest a better methodology here (I am not gonna do it; I really really really don’t care about this technology). Have the LLMs play rated engines and rated humans, the first illegal move forfeits the game (same rules apply to humans).
deadbabe 22 minutes ago [-]
Why do we care about this? Chess AI have long been solved problems and LLMs are just an overly brute forced approach. They will never become very efficient chess players.
The correct solution is to have a conventional chess AI as a tool and use the LLM as a front end for humanized output. A software engineer who proposes just doing it all via raw LLM should be fired.
rodiger 18 minutes ago [-]
It's a proxy for generalized reasoning.
The point isn't that LLMs are the best AI architecture for chess.
runarberg 14 minutes ago [-]
> It's a proxy for generalized reasoning.
And so for I am only convinced that they have only succeeded on appearing to have generalized reasoning. That is, when an LLM plays chess they are performing Searle’s Chinese room thought experiment while claiming to pass the Turing test
iugtmkbdfil834 48 minutes ago [-]
Hm.. but do they need it.. at this point, we do have custom tools that beat humans. In a sense, all LLM need is a way to connect to that tool ( and the same is true is for counting and many other aspects ).
Windchaser 33 minutes ago [-]
Yeah, but you know that manually telling the LLM to operate other custom tools is not going to be a long-term solution. And if an LLM could design, create, and operate a separate model, and then return/translate its results to you, that would be huge, but it also seems far away.
But I'm ignorant here. Can anyone with a better background of SOTA ML tell me if this is being pursued, and if so, how far away it is? (And if not, what are the arguments against it, or what other approaches might deliver similar capacities?)
BeetleB 17 minutes ago [-]
Are you saying an LLM can't produce a chess engine that will easily beat you?
menaerus 14 minutes ago [-]
Did you already forget about the AlphaZero?
acjohnson55 11 minutes ago [-]
> Autonomous agents fail because they don't have the context that humans carry around implicitly.
Yet.
This is mostly a matter of data capture and organization. It sounds like Kasava is already doing a lot of this. They just need more sources.
bwestergard 4 minutes ago [-]
Self-conscious efforts to formalize and concentrate information in systems controlled by firm management, known as "scientific management" by its proponents and "Taylorism" by many of its detractors, are a century old[1]. It has proven to be a constantly receding horizon.
Humans don’t have an internal notion of “fact” or “truth.” They generate statistically plausible text.
Reliability comes from scaffolding: retrieval, tools, validation layers. Without that, fluency can masquerade as authority.
The interesting question isn’t whether they’re coworkers or exoskeletons. It’s whether we’re mistaking rhetoric for epistemology.
whyenot 15 minutes ago [-]
> LLMs aren’t built around truth as a first-class primitive.
neither are humans
> They optimize for next-token probability and human approval, not factual verification.
while there are outliers, most humans also tend to tell people what they want to hear and to fit in.
> factuality is emergent and contingent, not enforced by architecture.
like humans; as far as we know, there is no "factuality" gene, and we lie to ourselves, to others, in politics, scientific papers, to our partners, etc.
> If we’re going to treat them as coworkers or exoskeletons, we should be clear about that distinction.
I don't see the distinction. Humans exhibit many of the same behaviours.
xlerb 9 minutes ago [-]
have fun using the engagement metrics tool, whether it is yourself or an augment of yourself. There are FACTS when you are programming assembly code -- there are facts in MATH -- LLMs are not built for this. FACTS.
13415 13 minutes ago [-]
Strangely, the GP replaced the ChatGPT-generated text you're commenting on by an even worse and more misleading ChatGPT-generated one. Perhaps in order to make a point.
kiba 16 minutes ago [-]
A much more useful tool is a technology that check for our blind spots and bugs.
For example fact checking a news article and making sure what's get reported line up with base reality.
I once fact check a virology lecture and found out that the professor confused two brothers as one individual.
I am sure about the professor having a super solid grasp of how viruses work, but errors like these probably creeps in all the time.
oxag3n 25 minutes ago [-]
> We're thinking about AI wrong.
And this write up is not an exception.
Why even bother thinking about AI, when Anthropic and OpenAI CEOs openly tell us what they want (quote from recent Dwarkesh interview) - "Then further down the spectrum, there’s 90% less demand for SWEs, which I think will happen but this is a spectrum."
So save thinking and listen to intent - replace 90% of SWEs in near future (6-12 months according to Amodei).
Galanwe 16 minutes ago [-]
I don't think anyone serious believes this. Replacing developers with a less costly alternative is obviously a very market bullish dream, it has existed since as long as I've worked in the field. First it was supposed to be UML generated code by "architects", then it was supposed to be developers from developing countries, then no-code frameworks, etc.
AI will be a tool, no more no less. Most likely a good one, but there will still need to be people driving it, guiding it, fixing for it, etc.
All these discourses from CEO are just that, stock market pumping, because tech is the most profitable sector, and software engineers are costly, so having investors dream about scale + less costs is good for the stock price.
jacquesm 20 minutes ago [-]
Not without some major breakthrough. What's hilarious is that all these developers building the tools are going to be the first to be without jobs. Their kids will be ecstatic: "Tell me again, dad, so, you had this awesome and well paying easy job and you wrecked it? Shut up kid, and tuck in that flap, there is too much wind in our cardboard box."
metaltyphoon 18 minutes ago [-]
I have a feeling they internally say "not me, I won't be replaced" and just keep moving...
oxag3n 17 minutes ago [-]
Or they get FY money and fatFIRE.
pavlov 58 minutes ago [-]
> “The AI handles the scale. The human interprets the meaning.”
Claude is that you? Why haven’t you called me?
ares623 53 minutes ago [-]
But the meaning has been scaled massively. So the human still kinda needs to handle the scale.
delichon 2 hours ago [-]
If we find an AI that is truly operating as an independent agent in the economy without a human responsible for it, we should kill it. I wonder if I'll live long enough to see an AI terminator profession emerge. We could call them blade runners.
orphea 56 minutes ago [-]
> an AI that is truly operating as an independent agent in the economy without a human responsible for it
Sounds like the "customer support" in any large company (think Google, for example), to be honest.
Was it ever verified that this was an independent AI?
bGl2YW5j 50 minutes ago [-]
I like the analogy and will ponder it more. But it didn't take long before the article started spruiking Kasava's amazing solution to the problem they just presented.
givemeethekeys 23 minutes ago [-]
Closer to a really capable intern. Lots of potential for good and bad; needs to be watched closely.
badgersnake 14 minutes ago [-]
I’ve been playing with qwen3-coder recently and that intern is definitely not getting hired, despite the rave reviews elsewhere.
yifanl 32 minutes ago [-]
AI is not an exoskeleton, it's a pretzel.
rishabhaiover 21 minutes ago [-]
it's a dry scone
dwheeler 33 minutes ago [-]
I prefer the term "assistant". It can do some tasks, but today's AI often needs human guidance for good results.
hintymad 28 minutes ago [-]
Or software engineers are not coachmen while AI is diesel engine to horses. Instead, software engineers are mistrels -- they disappear if all they do is moving knowledge from one place to another.
sibeliuss 12 minutes ago [-]
This utterly boring AI writing. Go, please go away...
ge96 50 minutes ago [-]
It's funny developing AI stuff eg. RAG tools and being against AI at the same time, not drinking the kool aid I mean.
But it's fun, I say "Henceforth you shall be known as Jaundice" and it's like "Alright my lord, I am now referred to as Jaundice"
xnx 56 minutes ago [-]
An electric bicycle for the mind.
clickety_clack 46 minutes ago [-]
Maybe more of a mobility scooter for the mind.
xnx 25 minutes ago [-]
Indeed that may be more apt.
I like the ebike analogy because [on many ebikes] you can press the button to go or pedal to amplify your output.
nancyminusone 48 minutes ago [-]
An electric chair for the mind?
ares623 52 minutes ago [-]
I prefer mind vibe-rator.
cindyllm 49 minutes ago [-]
[dead]
mikkupikku 46 minutes ago [-]
Exoskeletons sound cool but somebody please put an LLM into a spider tank.
functionmouse 46 minutes ago [-]
blogger who fancies themselves an ai vibe code guru with 12 arms and a 3rd eye yet can't make a homepage that's not totally broken
How typical!
lukev 35 minutes ago [-]
Frankly I'm tired of metaphor-based attempts to explain LLMs.
Stochastic Parrots. Interns. Junior Devs. Thought partners. Bicycles for the mind. Spicy autocomplete. A blurry jpeg of the web. Calculators but for words. Copilot. The term "artificial intelligence" itself.
These may correspond to a greater or lesser degree with what LLMs are capable of, but if we stick to metaphors as our primary tool for reasoning about these machines, we're hamstringing ourselves and making it impossible to reason about the frontier of capabilities, or resolve disagreements about them.
A understanding-without-metaphors isn't easy -- it requires a grasp of math, computer science, linguistics and philosophy.
But if we're going to move forward instead of just finding slightly more useful tropes, we have to do it. Or at least to try.
gf263 31 minutes ago [-]
“The day you teach the child the name of the bird, the child will never see that bird again.”
blibble 53 minutes ago [-]
an exoskeleten made of cheese
filipeisho 24 minutes ago [-]
By reading the title, I already know you did not try OpenClaw. AI employees are here.
BeetleB 18 minutes ago [-]
Looking into OpenClaw, I really do want to believe all the hype. However, it's frustrating that I can find very few, concrete examples of people showcasing their work with it.
Can you highlight what you've managed to do with it?
Rendered at 22:19:25 GMT+0000 (Coordinated Universal Time) with Vercel.
I will worry about developers being completely replaced when I see something resembling it. Enough people worry about that (or say it to amp stock prices) -- and they like to tell everyone about this future too. I just don't see it.
Unless there a limited amount of software we need to produce per year globally to keep everyone happy, then nobody wants more -- and we happen to be at that point right NOW this second.
I think not. We can make more (in less time) and people will get more. This is the mental "glass half full" approach I think. Why not take this mental route instead? We don't know the future anyway.
People need to understand that we have the technology to train models to do anything that you can do on a computer, only thing that's missing is the data.
If you can record a human doing anything on a computer, we'll soon have a way to automate it
Thr price of having "star trek computers" is that people who work with computers have to adapt to the changes. Seems worth it?
How much do you wish someone else had done your favorite SOTA LLM's RLHF?
This benchmark doesn't have the latest models from the last two months, but Gemini 3 (with no tools) is already at 1750 - 1800 FIDE, which is approximately probably around 1900 - 2000 USCF (about USCF expert level). This is enough to beat almost everyone at your local chess club.
Additionally, how do we know the model isn’t benchmaxxed to eliminate illegal moves.
For example, here is the list of games by Gemini-3-pro-preview. In 44 games it preformed 3 illegal moves (if I counted correctly) but won 5 because opponent forfeits due to illegal moves.
https://chessbenchllm.onrender.com/games?page=5&model=gemini...
I suspect the ratings here may be significantly inflated due to a flaw in the methodology.
EDIT: I want to suggest a better methodology here (I am not gonna do it; I really really really don’t care about this technology). Have the LLMs play rated engines and rated humans, the first illegal move forfeits the game (same rules apply to humans).
The correct solution is to have a conventional chess AI as a tool and use the LLM as a front end for humanized output. A software engineer who proposes just doing it all via raw LLM should be fired.
The point isn't that LLMs are the best AI architecture for chess.
And so for I am only convinced that they have only succeeded on appearing to have generalized reasoning. That is, when an LLM plays chess they are performing Searle’s Chinese room thought experiment while claiming to pass the Turing test
But I'm ignorant here. Can anyone with a better background of SOTA ML tell me if this is being pursued, and if so, how far away it is? (And if not, what are the arguments against it, or what other approaches might deliver similar capacities?)
Yet.
This is mostly a matter of data capture and organization. It sounds like Kasava is already doing a lot of this. They just need more sources.
[1]: https://en.wikipedia.org/wiki/Scientific_management
Reliability comes from scaffolding: retrieval, tools, validation layers. Without that, fluency can masquerade as authority.
The interesting question isn’t whether they’re coworkers or exoskeletons. It’s whether we’re mistaking rhetoric for epistemology.
neither are humans
> They optimize for next-token probability and human approval, not factual verification.
while there are outliers, most humans also tend to tell people what they want to hear and to fit in.
> factuality is emergent and contingent, not enforced by architecture.
like humans; as far as we know, there is no "factuality" gene, and we lie to ourselves, to others, in politics, scientific papers, to our partners, etc.
> If we’re going to treat them as coworkers or exoskeletons, we should be clear about that distinction.
I don't see the distinction. Humans exhibit many of the same behaviours.
For example fact checking a news article and making sure what's get reported line up with base reality.
I once fact check a virology lecture and found out that the professor confused two brothers as one individual.
I am sure about the professor having a super solid grasp of how viruses work, but errors like these probably creeps in all the time.
And this write up is not an exception.
Why even bother thinking about AI, when Anthropic and OpenAI CEOs openly tell us what they want (quote from recent Dwarkesh interview) - "Then further down the spectrum, there’s 90% less demand for SWEs, which I think will happen but this is a spectrum."
So save thinking and listen to intent - replace 90% of SWEs in near future (6-12 months according to Amodei).
AI will be a tool, no more no less. Most likely a good one, but there will still need to be people driving it, guiding it, fixing for it, etc.
All these discourses from CEO are just that, stock market pumping, because tech is the most profitable sector, and software engineers are costly, so having investors dream about scale + less costs is good for the stock price.
Claude is that you? Why haven’t you called me?
But it's fun, I say "Henceforth you shall be known as Jaundice" and it's like "Alright my lord, I am now referred to as Jaundice"
I like the ebike analogy because [on many ebikes] you can press the button to go or pedal to amplify your output.
How typical!
Stochastic Parrots. Interns. Junior Devs. Thought partners. Bicycles for the mind. Spicy autocomplete. A blurry jpeg of the web. Calculators but for words. Copilot. The term "artificial intelligence" itself.
These may correspond to a greater or lesser degree with what LLMs are capable of, but if we stick to metaphors as our primary tool for reasoning about these machines, we're hamstringing ourselves and making it impossible to reason about the frontier of capabilities, or resolve disagreements about them.
A understanding-without-metaphors isn't easy -- it requires a grasp of math, computer science, linguistics and philosophy.
But if we're going to move forward instead of just finding slightly more useful tropes, we have to do it. Or at least to try.
Can you highlight what you've managed to do with it?