YHK - yet another hacker news clone

Worse, everything after the /grill-me is injected a second time into the prompt as a <command-args> so your prompt is literally added to the context TWICE. (first time in the original user prompt.)

I swear Anthropic is intentionally bloating your context so you burn tokens on useless bullshit like this.

their claude code changelog 'fixed' % over time.

https://i.imgur.com/KJcem6c.png

I believe their peak month was >13k issues opened, but worth bearing in mind: "This issue has been automatically locked since it was closed and has not had any activity for 7 days.".

Human contributions to issues, comments or submissions, were drowning in a sea of AI vomit. Tons of the issues had those AI issue submissions with the classic confidence of having identified the problem, but that smell I know a lot of us recognize of it not actually knowing what the problem is at all because it hadn't failed to fix it 5 times yet.

Of course, just like "line of code" is a worthless metric, "change merge per day" is;

  for i in {1..10000000}
  do
    echo "I am working !" >> README.md
    git add README.md
    git commit -s -m "feat: working with AI"
  done

Choose something you want to do but don't really care that much about. Be more ambitious than you think you could be, have a high bar on the outcome but purposefully choose to completely ignore the actual code. Make it write tests, dashboards, monitoring - whatever you need other than code to feel confident.

Now is the cheapest time it will ever be to experiment.

If an engineer paid $150,000 a year reviews some code manually for about 30 minutes, the cost of their time is about $36.

So dbs, seed files, etc, and then just a review prompt and some skills and a few scripts.

It triggers on a new published PR or if tagged @bot-name. It's way better than the other services we've tried (which was admittedly ages ago) because you can get it to work exactly how you want it and it's dead simple.

With Codex, for the time being at least, you can use a subscription, so it's basically free if you're already paying for one. But on API pricing it's still not too bad as it doesn't produce that much output.

I set this all up in about half a day. Codex does 99% of the heavy lifting.

It's pretty effective and lets you do nice stuff like compare the code to the tickets, reply to comments and stuff like that.

Now we’re just going to outsource our capacity to design and review because it’s faster that way, as if the thing that was wrong with the software industry was that releases weren’t being pushed out often enough.

Although, following Anthropic’s ethos and taking this to its conclusion, I’m not even sure why they need human reviewers at all. What, they bring in a human to look at a diff of an “important” section of code that was written by agents and has changed a dozen times since last week through 12 PRs of churn? Obviously they don’t know what’s going on. So what’s a human to do? Do what the other approvers (if any) did: fire up a Claude to command it “verify this change has no regression; check carefully for edge cases”, then come back after a half hour of High Thinking to see “Change is clean (with some non-blocking findings below, in order of significance)” and click Approve. Not sure what the purpose of the human is in such a process.

New features have actually been remarkable. For me they are almost always in one of two categories: things I've always wanted (but never actually bothered communicating to the devs), and things I didn't know I needed but did. The product team is clearly very tuned in.

All in all, I'd say their approach is working.

Compare that to the cost of human code review which can take up a third of an engineer’s time. You’re looking at $50-100K a year per engineer fully loaded in code review costs.

If AI assists in code review (not even replaces – we still do human review ourselves), you could spend thousands a month per engineer and still come out ahead.

Ironic that this comment talks about cost without weighing the numbers.

I'm glad they're doing this, because it lets me see an extreme of LLM development.

I don't think we've gotten to that level of reliable review with code, but I don't see any reason we can't continue moving in that direction.

Its like telling other ppl about your dreams. Not interesting.

I know some people will groan hearing the phrase again, but: "Reverse centaur." A centaur is a synergistic combination of two things, but now those things are in the wrong position: The human is the computer's tool.

Naturally, how "wrong" it is tends to depend on whether you ask the people who do all the work or the people who own all the computer-capital.

From the article, the 30,000 changes a month are in the monorepo, and Claude Code is in a separate repo. So it’s not the TUI that has 1,000 PRs a day.

I could easily see 1,000 PRs a day on the backend. Big tech was doing more than that pre-AI.

This is the crux. Take pause, because hidden here is a sea-change in our fundamental trust model of software...

There is no longer anyone to turn to if this system breaks by, eg, having a critical bug that no one can figure out. No causal model in anyone's head that can be appealed to, to slowly and methodically debug. By design, the system is too large for that, and everything hinges on Claude itself being able to find and understand the right slice of the system in any situation. As long as that very new invariant holds, everything will be all right. If it fails catastrophically, there is no recovery. You are the owner of a recovered alien spacecraft with tech you have no hope of understanding.

The new trust model is: We trust Claude to always understand the system enough to debug anything or make any reasonable change we could want. The risk might be worth the reward. But it's worth explicitly laying out the deal we're making.

Tbf, it's worth pointing out that many large legacy systems already work like that (sort of), with "Claude" replaced by "whoever happens to be maintaining it this year".

There's clearly enormous (human) work that goes in to building and tuning their models.

The software they develop around their model is, relatively, trivial. It's not clear that enormous amounts of (human) energy need to go in to that code.

It seems largely a function of applying human energy towards the things that are marginally most important to generating you money and putting you in front of competitors.

Thank you! This is a huge sticking point for me. There is no knowing the code without experiencing the code. If nothing else, you have to read the tests to know they verify what you think they do. Even then, you might know it does what you want, but can’t know it doesn’t do anything it shouldn’t.

There are no shortcuts to true understanding.

I guess it wouldn't be surprising for people prone to AI psychosis to be attracted to working at the frontier labs, resulting companies that are heavily in the throes of mass AI psychosis.

Being tongue-in-cheek here.

I think when you repeatedly expose and immerse yourself to such a degree in reading and writing language, it brushes off on oneself. So I could also absolutely believe someone from Anthropic wrote this.

> This is not a cryptographic break, new attack, or weakness in the ECDSA curves.

I used to do it but now I only review code organization and structure.

Mostly I define the product behavior I want and discuss the technical implementation beforehand.

Once that's in a spec I let it build in my durable implementation engine. Automated verification, review rounds from different agent types across different models, issues triaged into meaningful clusters and flagged for false positives before finally being fixed.

That loops until the code is up to spec.

There is no special secret, LLMs need repetition and multiple passes.

They'll one shot bigger and bigger things in their default harnesses but they'll be filled with issues no matter what.

What are llm freaks in such a hurry for? Its like everyone is manically racing towards a finish line without realizing the finish line is you out of a job. Its like everyone is excited to be completely obsolete and poor.

Regressions are usually the absolute worst.

Up to now we've just been eating shit on the DX side as far as regressions in Codex and Claude Code because everything is all "go-go-go OMG I don't want to fall behind" and also.. Access to new models is tied to harness updates.

But a ton of industries have users that are not so willing to eat the regressions in the name of more features and changes than they can keep up with.

Edit: Also moving too fast.. You may have been less likely to "get it right" than ever before leading to design churn. Again, depending on industry and users, they may not be very tolerant to releasing the 5th complete overhaul of some feature in so many weeks..

* They can afford frequent CC incidents since their value is having the best models, and 95%+ uptime is still OK-ish.

* The core functionality of CC (the harness "agentic loop") is simple, an if that works then for most purposes CC works.

* CC just stores a bunch of config and history files, there isn't any financial or irreversible safety loss if they mess it up once in a while.

Once you work on something where a minor slipup can result in a huge real world mess, then I don't see how their approach can work.

I disagree. Many of us have an emotional attachment to the way we do things and have difficulty letting go. Anthropic is showing a different way of doing things. And I am not going to judge them too harshly if the final output is good. I use Claude Code every day. And it is good. Ahead of the pack.

Think I'm good on taking advice from companies that aren't making money.

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.

The less you understand code, the more you're impressed by coding chatbots - in part because you're less likely to read the bot's code, and if you do, less likely to realise how poor it is. Code gets judged good simply because it exists and costed little to create.