Because humans have basic needs like food, shelter and they can be put it jail. And they have loyalty, patriotism, feel part of the society. Those threats & incentives have made it for a good society to function so far.
I think that line is getting blurrier by the day with the current administration. The companies very quickly fell in line before even any consequence of note. Donations to the ballroom, calling it super intelligence, renaming the gulf, etc.
Would you really trust that OpenAI wouldn't skew the results to tend to a certain way or that Grok will always be neutral in how it operates?
From a third world, there was a time when US products & companies were more trustworthy, I agree. But, there has been enough display from these Cos to show that they have lost that badge.
This is like one of Google’s infrastructure blog. Google scale things are only relevant and appropriate for them. We had lots of bitter lessons from smaller engineering teams following the process with the justification that “this is how Google does it”.
Similarly, Anthropic can afford to do this because they have virtually unlimited tokens and labs to train models for their specific usecase. The rest of us need code comprehension lest we become dependent on continuous supply of tokens (which is a state these labs badly want to achieve)
So, unless you work at a lab, or have unlimited tokens guaranteed through the life of your company, it would do both the company and you good to continue to read the code and not let these 30k PRs per engineer be the norm.
An excerpt from the article makes my point
> Nobody on the team keeps a complete mental model of Claude Code in their head, and we don't think anyone needs to. When someone wants to understand a part of the codebase, they ask Claude to write an explainer for exactly that slice. It takes minutes and reflects the code as it is today.
Even then do we really have evidence that what they are doing is reasonable or practical for them at all? They are edging on like 3 9s uptime, what leaked of Claude code looked like soup, tons of security issues, they can't build a sandbox to save their company, and last I heard they aren't even profitable without adjusting their numbers for arbitrary things.
Think I'm good on taking advice from companies that aren't making money.
Exactly. What they’re doing, repeatedly, is setting a narrative that this is how software engineering should be. Always be reliant on tokens. Don’t worry about uptime or errors - agents can address it, don’t worry about security - agents can address it, don’t worry about code comprehension - agents will always be there for you.
If they're cash-flow positive with 75% of compute going to research and training, what do you think will happen to cash flow when they shift to 50-50 training-inference?
Because "pacing the frontier" is more about flipping on the money printer switch than it is about safety.
> they ask Claude to write an explainer for exactly that slice
If their Claude is the same as our Claude, then this is a joke. Claude’s writing style is insidiously mind numbing. It takes constant pushing to get it to explain things in digestible English.
It also mixes important with unimportant information. It tends to think that a commit/bugfix is important and will happily list it together with important properties of the feature.
At least Opus 5.5 does that. Its writing is better than that if other models, though. I had other models explain stuff in an undigestable language, technically correct, but practically not what anything is about.
I hate it when it realizes something that was contextually obvious to me, and then treats it like a precious nugget of wisdom, including it in the README, the comments, and the commit message. Even worse when it’s a hypothetical situation. “Even if the raw chicken is bagged and refrigerated, we want to avoid a leaky bag.”
I believe this is a variant of it: I dislike how often it mentions stuff that's not there or relevant (anymore), just because it was referenced somewhere:
"We use a double instead of a float there so that the addition will not overflow". Yeah, thanks, that was a bug 2 years ago, nobody cares that the line now does what it's supposed to do.
Today I had AI do a design doc cleanup and conciseness sweep on a vibe code project I have.
It removed ~50% totaling 30k words! So annoying how everything I discuss with AI gets documented in 10 the length, and then every subsequent decision is documented in 5 places.
Try having it explain something in “ISO Plain Language standard”. It made an enormous difference for me yesterday. The language will change from dense large paragraphs to simply-worded 2-3 sentence paragraphs. I was using gpt though.
Even getting it to write less obtuse English isn't enough IMO. It seems to have real issues with structuring documentation in a way that makes it easy to follow. Also it's a real demon for leaking its context as implicit assumptions into documentation.
They pretty much fixed that. Plus you can influence the writing style to some extent. I tell it to use full sentences and avoid assuming I already understand the code.
Another thing is that it's very good at creating diagrams (I think someone wrote a skill for it so maybe not by default). I usually find the diagrams are even more valuable than the text.
Honestly I was skeptical but you really can just point Claude at some code and ask it to make a markdown + SVG explainer and it will do a shockingly good job.
I'd still rather read human documentation, but even before AI almost nobody writes any - especially for internal code. And they certainly don't make great diagrams.
I’ve been using diagrams a lot lately. My team currently owns a wide surface area, so there is a lot of mental context churn as we bounce between parts of the system. HTML diagrams, or artifacts in the Claude desktop client, are super for helpful for bootstrapping/refreshing my mental model when starting a new project.
You don't still feel that mental friction when 5.5 responds with a wall of text and you drop into that first tedious sentence? I even had it generate a syntactically invalid English sentence the other day. It's still pretty bad.
If we as engineers didn't use similar terminology to describe any code not written by ourselves for the last few decades perhaps people would take it seriously.
I would love to ask them why this scale is even necessary for a conceptually simple CLI app like Claude Code, but they wouldn't be able to answer because they don't even understand the scope of it apparently.
It's absolutely insane that they can't keep track of a CLI application that makes shell calls. This is so far from rocket science even mentioning the term is misleading.
Yea not at all. There are dozens of oss Claude code clones written by 1 person with a few contributors that work as well if not better. So it's really not some massive challenge. A decent cs student with enough coffee could get 40% the way there in a week without any llms. The same can't be said for rocket building or even design...
What people believe are the core features has never been difficult to implement. Building "something like" Twitter or facebook or reddit has always been possible within a few days.
It's an iceberg, most of the complexity lies underneath the obvious stuff.
But in Twitter or Facebook, the complexity is not in the app but with the scale. How do you support the network, graph, querying, fetching, etc. That is not the case for a self-contained binary like Claude code CLI that talks to the same API irrespective of 100 people are using or 1M people are using around the world.
No like there are dozens of open source Claude code alternatives that are as good or better than Claude code. This isn't a theoretical thing. It's not new either.
It needs to gather a bunch of inputs and feed into the remote GPUs correctly and as efficiently as possible. Lots of room to optimize under the constraints that users love cheap tokens, demand a fair billing structure, and want responsiveness and low latency.
They do have quite a few products now, the desktop apps, websites, cowork, cloud session sandboxes, API, enterprise, etc. have their own gotchas, but after a core development, the 30000 commits a day does not feel justified, or is that an online training dataset prep, that's hard to tell. That's why I feel with their endless tokens, it feels just petty marketing and that's sad because it does not bring much to our engineering table.
It's also a desktop app, integrated into the mobile app, and it has cloud hosted session capabilities too. Plugs into Slack and Github, maybe others. So definitely there's backend stuff as well, beyond the inference.
I don’t think you can infer the scale for Claude Code alone. They said it doubled but not what the starting point was. The 30k figure is for the entire Anthropic monorepo.
Well I personally didn't say anything about 30k PRs. I still stand by my belief the "known" scope of Claude Code doesn't seem to warrant the scale of "vibe coding" and lack of comprehensive understanding they are communicating to us.
I have heard from someone who works at Anthropic that they will have postmortems that sometimes devolve into "my claude says X" vs "well my claude says Y". Maybe this is the future of software engineering, but it seems pretty fucking sad.
This is basically just being a manager in the before times. "My engineer said X!" "Well, mine said Y!"
Which is fine, whatever, I guess this is how I pay the mortgage off instead of having any clue what the hell Claude is actually writing. Hopefully I can semi-retire soon and go study dunnarts or take up welding or something else that's more rewarding than this nonsense.
> Nobody on the team keeps a complete mental model of Claude Code in their head.
For sufficiently large codebases in large organization that is how it has been with humans, who come and go. In many respects, What Anthropic is doing is a massive upgrade from the status quo.
Really? Hundreds of billions of dollars in debt is an improvement? The product that this code is supporting can't even pay for its debt.
Jesus. Give me 1 billion dollars and I'll keep your entire codebase in my head + personal notes for you until I die. Hell, I'll even force my descendants to do the same. Now _that's_ an improvement.
Another challenge is getting developers to stop assuming they know what the code does without reading it! Having AI “fact check” various documents against the source will help immensely.
Google's infra genuinely pushed the industry forward though, things like kubernetes and SRE methodology are generally useful even if you aren't Google. Reading this I'm not even sure this sounds like the best way to run an organisation with infinite free LLM tokens let alone an actual engineering org where it just an impact on development cost. The people who are getting maximum leverage from LLM reasoning and routing queries intelligently who've made big strides in reducing inference while increasing throughout are the ones who impress me the most.
It would benefit many people (especially Anthropic employees) if, after an LLM gives you a response like this, to go into a different session and ask another LLM if the explainer is correct. Not just so that you hopefully get the correct information, but also to see how easily fooled you can be by a plausible-looking, confident response about some code that you have no reference for. Use 5.5 on any thinking level, you will encounter this. The spaghetti mess that is CC likely makes this more of an issue.
Mark my words, these thrusters and flying car stunts will eventually be used as a justification to merge Tesla into SpaceX, the same way how they justified space datacenter to merge X.ai onto SpaceX.
There was optimus robots already on Tesla side and inference / r&d funded by x.ai (now spacex).
I don't think it does either, but it's surely overvalued and have to sustain it's ridiculously high forward P/E. Hence the semi-regular news about FSD, TeslaCab, Optimus Bots, and now Flying cars. Seems it's working. It's always priced in with the "potential future".
One thing I've observed Gemini being the best is in the context of non-western stuff. Like, when I ask about obscure srilankan food, or chat about Indian/Tamil movies or songs, Religious customs & beliefs, Gemini almost always answers accurately and very close to how helpful they are in Anglosphere related topics. ChatGPT also does do a bit better recently, but Claude/Grok are absolute trash on those fronts.
Google's years of research project into Indic scripts, image trainings, translation efforts & transliteration efforts is literally paying off. The AI adoption from non-English, non-Chinese population will only if you have these contextual localisations, which I think Gemini is poised to do well.
For example, you can ask for a shopping list for a marriage function in a town from Tamil nadu (the marriage custom varies by districts), and Gemini provides contextual and helpful answer (like what is the custom followed there, what is gifted traditionally, etc), whereas if you ask Claude/ChatGPT, it talks about marriage registeries or pretty generalised answers which is not relevant or useful.
100% agreed. When I ask to generate an image or a poster with an indic language, chatgpt and claude always make gibberish words for something that is not at the center but gemini nails it almost every time in first attempt.
That has always been my experience with Google and how they work their datasets. I think it's already well known how their picture post-processing is close to the best for people with darker skin tones as their post-processing models are trained with more varied datasets in an intentional way. Which is somewhat close to what you're mentioning here.
The quickest sign I have right now to find if the developer/team don't read their code is looking at their recently AI-generated test cases. Often, coding agents go too verbose in their tests and they are most often overlooked even during normal reviews. If there are verbose tests, like declaring a constant and asserting it's value, you know that the code isn't being read much.
Is it only me or is the changing background disturbing for others as well? Maybe it's my ADD brain finding the hook, but it takes the focus away from the content whenever it shifted.
I moved the opposite direction. Few months back, I was absolutely sure that the days of me being a software engineer are numbered. The programming I used to love and fun was gone. But, as I got to work more with Agents, factory & the infrastructure surrounding it, I'm more optimistic about the kinds of problems we could tackle with this, that wasn't possible/cost-effective before. We've only barely scratched the surface with what AI could be leveraged in the real world.
> Agents, factory & the infrastructure surrounding it
Can you be more specific about what isn't just a growing pain/maturity problem that will eventually be baked into the default products once the unit costs come down?
I can one-shot full video production that takes almost an hour on claude right now and it'll run ffmpeg for a good chunk of that. I don't see how the delegation + infra isn't a solved problem a couple years from now.
Why do you think there will be a human in the loop on any of these tasks, and why do you think that you will be that human?
I've watched the robots become better/faster software developers than I ever was over the last year. I've got a lifetime of esoteric knowledge that's completely obsolete now. I can ask robots to do things, but so can any other random person.
What, exactly, do you think we are going to be prompting these things to do? And why would anybody pay us to do that, rather than just prompt them directly?
The difficulty with building software has mostly never been the code itself, but how to tease out what to actually build and how to connect system components together in a sane way that meets business requirements.
It is indeed neat that LLMs with fancy agent harnesses can write code very well at this point, but that doesn’t mean the average person can think through problems in the way that is required to build software that operates at meaningful scale with complex trade-offs and architecture decisions to be made. In my experience with others in other fields, that is not a common skill, and I really don’t see how an LLM is going to solve that.
For this reason, I do not see software engineering going anywhere, but I do believe it is possible/likely there will be negative changes in hiring and wages.
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