How Anthropic teams use Claude Code

Thanks for the tip - we employees should run and re-run the code generation hundreds of times even if the changes are pretty good. That way, the brass will see a huge bill without many actual commits.

Sorry boss, it looks like we need to hire more software engineers since the AI route still isn't mathing.

> A repeated trend is that Claude Code only gets 70-80% of the way, which is fine and something I wish was emphasized more by people pushing agents.

I have been pretty successful at using llms for code generation.

I have a simple rule that something is either 90%>ai or none at all (exluding inline completions, and very obvious text editing).

The model has an inherent understanding of some problems due to it's training data (e.g. setting up a web server with little to no deps in golang), that it can do with almost 100% certainty, where it's really easy to blaze through in a few minutes, and then I can setup the architecture for some very flat code flows. This can genuinely improve my output by 30%-50%

> A repeated trend is that Claude Code only gets 70-80% of the way, which is fine and something I wish was emphasized more by people pushing agents.

Recently, I realized that this applies not only to the first 70–80% of a project but sometimes also to the final 70-80%.

I couldn’t make progress with Claude on a major refactoring from scratch, so I started implementing it myself. Once I had shaped the idea clearly enough but in a very early state, I handed it back to Claude to finish and it worked flawlessly, down to the last CHANGELOG entry, without any further input from me.

I saw this as a form of extensive guardrails or prompting-by-example.

The slot machine thing has a pretty compelling corollary: crank the formal systems rigor up as high as you can.

Vibe coding in Python is seductive but ultimately you end up in a bad place with a big bill to show for it.

Vibe coding in Haskell is a "how much money am I willing to pour in per unit clean, correct, maintainable code" exercise. With GHC cranked up to `-Wall -Werror` and some nasty property tests? Watching Claude Code try to weasel out with a mock goes from infuriating to amusing: bam, unused parameter! Now why would the test suite be demanding that a property holds on an unused parameter...

And Haskell is just an example, TypeScript is in some ways even more powerful in it's type system, so lots of projects have scope to dabble with what I'm calling "hyper modern vibe coding": just start putting a bunch of really nasty fastcheck and generic bounds on stuff and watch Claude Code try to cheat. Your move, Claude Code, I know you want to check off that line on the TODO list like I want to breathe, so what's it gonna be?

I find it usually gives up and does the work you paid for.

Not to mention, if an employee could usually write pretty good code but maybe 30% of the time they wrote something so non-functional it had to be entirely scrapped, they'd be fired.

Yeah my most common aider command sequence is

    > /undo
    > /clear
    > ↑ ↑ ↑ ⏎

This is an easy calculation for everyone. Think about whether Claude is giving you the a sufficient boost in performance, and if not... then it's too expensive. No doubt some people are in some combination of domain, legacy, complexity of codebase, etc., where Claude just doesn't cut it.

That's easy to say when the employee is not personally paying the massive amount of compute running Claude Code for a half-hour.

you can do the same for $200/month

Great advice, matches up to my experience. Personally I go a little cheaper and dirtier on the first prompt, then revise as needed. By the way what classes / workshops did you teach?

I've written a little about some my findings and workflow in detail here: https://github.com/sutt/agro/blob/master/docs/case-studies/a...

This matches my experience as well. But what I also found is that I hate this workflow so much that I would almost always rather write the code by hand. Writing specs and user stories was always my least favorite task.

Is working this way actually faster, or any improvement than just writing the code yourself?

Do you have an example of this you could share?

We're all gonna be PMs.

a couple weekends ago i handed it the basic MLB api and asked it to create some widgets for MacOS to show me stuff like league/division/wildcard standings along with basic settings to pick which should be shown. it cranked out a working widget in like a half hour with minimal input.

i know some swift so i checked on what it was doing. for a quick hack project it did all the work and easily updated things i saw issues with.

for a one-off like that, not bad at all. not too dissimilar from your example.

> it shows you the dollar amount spent which is a bit disconcerting

I can assure you that I don’t at all care about the MAYBE $10 charge my monster Claude Code session billed the company. They also clearly said “don’t worry about cost, just go figure out how to work with it”

once upon a time - engineers often had to concern themselves with datacenter bills, cloud bills, and eventually SaaS bills. We'll probably have 5-10 years of being concerned about AI bills before the AI expense is trivial compared to the human time.

Meanwhile I ask it to write what I think are trivial functions and it gets them subtly wrong, but obvious in testing. I would be more suspicious if I were you.

> I use it at home via the $20/m subscription and am piloting it at work via AWS Bedrock. When used with Bedrock APIs, at the end of every session it shows you the dollar amount spent which is a bit disconcerting. I hope the fine-grained metering of inference is a temporary situation otherwise I think it will have a chilling/discouraging effect on software developers, leading to less experimentation and fewer rewrites, overall lower quality.

I’m legitimately surprised at your feeling on this. I might not want the granular cost put in my face constantly but I do like the ability to see how much my queries cost when I am experimenting with prompt setup for agents. Occasionally I find wording things one way or the other has a significantly cheaper cost.

Why do you think it will lead to a chilling effect instead of the normal effect of engineers ruthlessly innovating costs down now that there is a measurable target?

I’ve seen Claude code get halfway through a small sized refactor (function parameters changed shape or something like that), say something that looks like frustration at the amount of time it’s taking, revert all of the good changes, and start writing a bash script to automate the whole process.

In that case, you have put a stop to it and point out that it would already be done if it hadn’t decided to blow it all up in an effort to write a one time use codemod. Of course it agrees with that point as it agrees with everything. It’s the epitome of strong opinions loosely held.

I just had the same thing happen. Some comprehensive tests were failing, and it decide to write a simple test instead rather than investigate why these more complicated tests were failing. I wonder if the team is trying to save compute by urging it to complete tasks more quickly! Claude seems to be under a compute crunch as often I get API timeouts/errors.

The hilarious part I’ve found is that when it runs into the least bit of trouble with a step on one of its plans, it will say it has been “Deferred” and then make up an excuse for why that’s acceptable.

It is sometimes acceptable for humans to use judgment and defer work; the machine doesn’t have judgment so it is not acceptable for it to do so.

I even heard that it will aggressively delete your codebase and then lie about it. To your face.

Also started to suspect that, but I have a bigger problem with the content than styling:

> "Instead of remembering complex Kubernetes commands, they ask Claude for the correct syntax, like "how to get all pods or deployment status," and receive the exact commands needed for their infrastructure work."

Duh, you can ask LLM tech questions and stuff. What is the point of putting something like that on the tech blog of the company which supposed to be working on beading edge tech.

The MCP documentation site has the same problem. It’s basically just a list of bullet points without any details

I don't think the problem is using Claude - in fact some of the writing is quite clumsy and amateurish, suggesting an actual human wrote it. The overall post reads like a collection of survey responses, with no overarching organization, and no filtering of repetitive or empty responses. Nobody was in charge.

You’re absolutely right!

Yeah this is kind of a stunning amount of information to provide but also basically like polished bullet points

Experts make mistakes too. In fact, all humans do.

any options for ubuntu ?

I've been pretty happy with the python package hns for this [1]. You can run it from the terminal with uvx hns and it will listen until you press enter and then copy the transcription to the clipboard. It's a simple tool that does one thing well and integrates smoothly with a CLI-based workflow.

[1] - https://github.com/primaprashant/hns

The SuperWhisper app is great, if you use a Mac.

So you sit in a room, talking to it? Doesn't it feel weird?

I type a lot faster than I speak :D

Why not ask them to purchase it separately?

Just keep it in plan mode and it won't change anything?

Unlike Gemini CLI which will just rush into implementation without hesitation :D

They're subsidizing a world where we need ai instead of understanding or, at the very least, knowing who can help us. Eventually for us to be so dumb we are the ai slaves.

Yes. This is what id expect from an intern or very junior engineer (which could be the case here)

The future is that articles will generally be read by machine intelligences more often than human ones. You have to optimize for your audience.

Yeah, I don't think all the information in the article is useful but it seems like disorganized dump - or a melding together of several article - boiler plate "we use Claude for all good things" and specific useful tips all run together.

It definitely has a "we use AI enough that we've lost the ability to communicate coherently" vibe to it.