Why would the volume that any of this be increasing exponentially? And what are all the engineers doing when (besides the model quality) their products don't seem to be changing very much?
As a user of Astra, it writes code that appears to work. It does not necessarily write sensible code that is architected in a way that makes sense based on what it does. I just watched it do a major rework of a couple thousand lines of code by keeping the original organization and kludging up an adapter to make old code have new semantics.
I would expect that this approach, coupled with lots of fun asking for changes and none of the grunt work of actually thinking about the result, could result in exponentially increasing amounts of mess.
I haven’t used a recent Anthropic model - maybe it’s better at this.
just exercise. it will be ok, really. somehow we managed for millions of years, kiddos, and both gravity and the human body are unchanged in any relevant way since which suddenly requires "an app". I can get out of bed without an app, or an AI. I can prepare a meal and eat without any kind of app, or any kind of AI or LLM. its a true and real thing, in the real world, promise!
I would have loved to have all that in an app over the last 25-30 years so I could see my progress.
There are tons of different conditions people are in, with different histories of injuries, etc. and even people without any issues benefit massively from having a sensible, well structured workout plan that ensures their weaknesses are addressed and they work towards their personal goals. You don't need AI, an app or a personal trainer for that but while many people do enjoy learning a lot about exercising in their (often limited) free time, many don't and it's great if they have an affordable/free option. Why do you think physiotherapy exists?
Additionally, studies have proven that progressive overload is a really important aspect for progressing, so you should be tracking your workouts in some way, in an app or on paper, to ensure you really are challenging yourself.
I hope this practice doesn't die out, or complexity will skyrocket for no reason.
It writes like it works at big tech and is angling for a promotion to L{n}+1 with this "project".
If you are wondering what these changes are, you can take a peek at https://github.com/openclaw/openclaw/commits/main/
This isn't to say that such pace of iteration is good or bad. That's a separate discussion.
https://git.kernel.org/pub/scm/linux/kernel/git/torvalds/lin...
I think it’s simply a matter of dogfooding and new feature development, they do have much higher velocity than companies of equivalent size and an unlimited token budget helps as well.
I do believe this will force the AI labs to eventually solve the bloat problem with AI, but we’re definitely not there yet.
Give a verifiable benchmark for the AI agent to hit such as reduce token processing time by 5% without affecting output.
Kernels, performance-required stacks etc. is different, so you optimise them for that.
It feels like OpenAI and Anthropic have a ton of room to optimizing that regard, but they choose to rather focus on moving the frontier forward than doing things that are already possible in a more efficient way.
It's better than the ductape and glue pipelines that we had previous to this.
The software is the definition of plumbing no one cares about and testing it is trivial.
many yaks to shave. many bikesheds to paint.
if you're paid by the line, why not go to town...
Can’t roll out fusion powered data centers in 2030 without a simulation engine.
Because the limiting factor for software has always been the supply, not the demand. When the average human engineer would ship 100 lines of production code / week, you could only afford to do the most critical things. But if code gets cheaper to produce, the projects that were marginal become worth doing, and the projects that were obviously insane become marginal. And that's exactly what is happening as models get cheaper and better, leading to the frontier of worthwhile projects expanding rapidly.
> And what are all the engineers doing when (besides the model quality) their products don't seem to be changing very much?
That's got some nice But other than that, Mrs. Lincoln, how was the play? energy to it!
So you think that the move from 4o to Astra required something like 1000x the code to pull off? I find it hard to believe as an MLE, but since I don't work with LLMs I might be wrong about how complicated the implementations have become.
I do know people who work at OAI and it seems like none of them work directly on the models, but that's n=1.
Surely it's obvious?
Slop cannons firing at a rate so high no human can hope to restrain them.
Such a colossal volume of slop that even the most well engineered, efficient technology on the planet, written by one of the greatest coders of all time, is no longer sufficient to contain it.
And git is enough to version control some of the most complex software like the Linux Kernel, the FreeBSD operating system, QEMU, browsers like Firefox,... Git was insufficient for Facebook and Google because they wanted to put the whole org inside a single repo, which broke the assumption where decentralization in git would happen (repo level, not subtree level). Now it seems that another assumption is broken, that you wanted a version control system, not a snapshot or a rolling log.
In doing so, they obviously need to keep track of the myriad of changes, hence the requirement to ensure Git can scale up.
(I'd like to think /s, but you know what, maybe not)
Of course, then the question is if it's even worth committing them - you might as well just commit the design spec and let the model generate ephemeral code to run this whenever you need it. For now, I almost consider committing generated code a caching step in some sense, so you don't have to spend the tokens and time to generate it again.
Once super fast and super cheap tokens become more ubiquitous, however, I expect this to change.
What about user testing though?
Or it's yet another iteration to get another test with overbuilt providence catalogling, boated code, overbuilt nonsense. Suddenly you're 2.5 Billion tokens deep and you just wonder "What the actual Feck did they do, how did they burn that many tokens? It's just a web app with a plugin system, my god."
[0] https://martinfowler.com/bliki/ArchitectureDecisionRecord.ht...
I've seen it on other workplaces, but from what I see from open ai and anthropic, the code shouldn't be as extended.
But maybe I'm crazy lol