It’s the opposite, ML is most dependent on excess compute.
2,436 karma · joined December 19, 2022
They are spending 1T to ruin the quality of work we do, also probably ruining our chances of securing a livelihood and then they want us to for some reason welcome it with open arms and openly say look how great this is and marvel at human obsolescence. Yeah no thanks.
The retard AI boosters, tell us to be AI first, learn this harness bro, loops are yesterday, now it’s graphs, use 100 agents a minimum or be left behind, actually world will require 100X more software engineers.
If this was a thousand piece puzzle, I would still venture recursive backtracker with good heuristics will beat CP-SAT, even in the sudoku case some good heuristics with backtracking beats CP-SAT. Not sure why Claude immediately jumped to using CP-SAT.
A week later, same prompt, really low poly blender model. Either they reduced token usage per person, or they quantized the model, idk, but it really doesn’t work as good as day of release anymore
1. Boston dynamics pioneered hydraulic actuators (for their humanoid Atlas) and some really complex motors for their quadrupeds (I don’t think they ever explained their design)
2. There was a whole research line on Series Elastic Actuators that was pursued in the quadrupeds from Europe, famously Anymal from ETH Zurich.
The main problem they were trying to solve was high torque density with back drivability. If you wanted large torque, you could use a high gear ratio but then you lose back drivability and the walking becomes really stiff. MIT Cheetah basically custom built a motor with a really large radius that just output a really large torque. So to summarize, we had hydraulics, a really complex motors + spring mechanism, a really complex large motor to solve this problem and they all required huge amounts of money and complexity ( arguably MIT cheetah was simple but I heard thermal management was a big headache, and the entire robot cost millions)
Ben Katz, then at MIT, showed that you could use regular BLDC Motors, heavily used in drones with a small gear ratio, like say 1:10 or 1:20 and get a really good torque density, backdrivability tradeoff. It started around the above blogpost, in the coming year as a part of his masters thesis, he build the MIT Mini Cheetah, that was also the first quadruped I saw that performed a backflip, (maybe Boston dynamics was earlier and so it was the second then), and that basically started the goldrush into his actuator tech that we call Quasi Direct Drives.
Looking now 10 years later, in some ways he was vindicated, all humanoids and quadrupeds are made with this actuator now, Unitree is basically QDDs through and through. But the same faults we saw then, still hobble it now. It just does not generate enough torque, its position control isn’t milli degree precise, and so it never took off in industrial robot arms, though many tried (Berkeley Open Arms for one).
The bigger question to me, is the original implementation, are agents going to do that, or will humans hand code it. My two cents, I’ve been using agents heavily now, I review thousands of lines of code a day, I don’t think this works unless I have a clear idea of the spec I want to build in my mind already.
I get a better understanding of what the architecture should be and how I should build it by actually doing the thing, which means typing code at human speeds. I don’t reason my way into a good architecture, I don’t think I ever did that much in the past and I definitely don’t get it by asking the agent to check its plan and explain it to me the hundredth time. And then once I know the architecture, I don’t think the agent even speeds me up that much. It changes 7 days of coding to 1, but those 7 days weren’t worth much and I generate even more ideas and mental models in those 7 days that pay dividends for years to come.
Would you have told a tailor that using the industrial sewing machine improves his skill at making delicate, beautiful clothes? Of course not, actually even in 2026, large swathes of high quality delicate clothes are hand sewn. There will be an equivalent for coding, even DHH admits it, when he hedges with “most”.
I think, for me I’ll navigate my career to focus more on extremely high quality, delicate custom code and leave this agentic coding to the sweatshop workers. I wanted to do this anyway before agents, now I need to accelerate it. yes FAANG is sweatshop work.
HN just selects for the latter who care. Yes it’s probably much harder for those who care to have a meaningful life in the presence of LLM’s unfortunately.
- Human collaboration with agents leads to significant discoveries
- The prompt given to Claude was just a high level overview and Claude figured out everything else on its own.
If I’d guess, it’s the second future that Ant wants to create, especially the way they described they Reimann Zeta Function results, “I just prompted it to be confident, and try harder and it proved something”. They should own this future, if they really think it’s desirable and worth trying to create (I don’t think it’s worth creating, but we can disagree on that)
Agents complicate something that should be much smaller and simpler and then agents speed it up adding more complexity. I suppose functionally you may say this is fine but aesthetically it is hideous!
I think a reasonable conclusion now that AI is getting better than us humans at this, is that humans should stop doing this. I think there are strong reasons that humans should never have been doing this in the past but now it’s far more clear, AI will out complicate whatever complexity you can build and then beat at you at its handling of intricate complexity.
Humans should focus on finding simple solutions that lead to a 10X improvement. AI rarely does this, though I have noticed it some times doing this in maths, but this was the case when the solution was already present in some distant literature and no human had the expertise in 2 distant areas to make the connection. When AI does this, it is a welcome advancement, and hopefully the bridge it shows leads to exciting new developments.
But LLMs rarely do this, and I think humans should exclusively focus on this, because it seems like we are far better than LLMs at keeping things simple and finding new insights that leads to 10X, 100X improvements
Anytime a piece of art really moves me, I proceed to study the inspiration behind it, what the creative process of the artist was like. Just imagining that in the future some “artist” when asked his creative process says “well I prompted the AI”, fills me with disgust.
Your personal opinion is shortsighted. You do need to support a certain amount of bottom tier art jobs to keep a healthy art community. Yes a restaurant poster is not all that consequential “art”, but aesthetically I would much rather that money go to a living breathing aspiring artist than OpenAI. AI really just has no place whatsoever in any of the arts, the sooner society realizes that the better.
Then somewhere along the way, they decided they wanted to be a real browser for some reason even though we have Chrome, Safari and Mozilla which is open source. This is IMO the worst use of your time as talented engineers, your product is completely undifferentiated and can never be differentiated since it’s a browser. You’re competing against very well funded adversaries, who are giving away everything for free.
Inevitably to compete, you will lean heavily into AI coding, at which point the “learning” goal is completely left at the wayside.
I’d analogize, there are sometimes good reasons to build a game engine, maybe it’s for learning, keeping the skill alive, something unique you want to demonstrate or you just want to reinvent the wheel for your game but using AI, a bajillion tokens to build an Unreal clone is a waste of your time, which is exactly what Ladybirds doing.