Claude Code: Best practices for agentic coding
anthropic.com
anthropic.com
> We recommend using the word "think" to trigger extended thinking mode, which gives Claude additional computation time to evaluate alternatives more thoroughly. These specific phrases are mapped directly to increasing levels of thinking budget in the system: "think" < "think hard" < "think harder" < "ultrathink." Each level allocates progressively more thinking budget for Claude to use.
I had a poke around and it's not a feature of the Claude model, it's specific to Claude Code. There's a "megathink" option too - it uses code that looks like this:
let B = W.message.content.toLowerCase();
if (
B.includes("think harder") ||
B.includes("think intensely") ||
B.includes("think longer") ||
B.includes("think really hard") ||
B.includes("think super hard") ||
B.includes("think very hard") ||
B.includes("ultrathink")
)
return (
l1("tengu_thinking", { tokenCount: 31999, messageId: Z, provider: G }),
31999
);
if (
B.includes("think about it") ||
B.includes("think a lot") ||
B.includes("think deeply") ||
B.includes("think hard") ||
B.includes("think more") ||
B.includes("megathink")
)
return (
l1("tengu_thinking", { tokenCount: 1e4, messageId: Z, provider: G }), 1e4
);
..as if we're operating this machine as analog synth
The consumer-facing chatbot interfaces just hide all that because they're aiming for a non-technical audience.
Also 14 string scans seems a little inefficient!
/thinking-tokens 32k
/think 32kSurely Anthropic could do a better job implementing dynamic thinking token budgets.
This fits my memory of it:
https://www.voicy.network/sounds/o_2r357a-kKEe5PaiWyYnA-quak...
Was it quake 2?
Similarly, I do miss an —add command line flag to manual specify the context (files) during the session. Right now I pretty much end up copy pasting the relative paths from VSCode and supply to Claude. Aider has much better semantics for such stuff.
You can use English or —add if you want to tell Claude to reference them.
BTW, as I was using it today, I was quite surprised to see `@` working now. Turns out in 0.27.5 they added this feature: https://github.com/anthropics/claude-code/blob/main/CHANGELO... :)
https://www.paritybits.me/think-toggles-are-dumb/
https://nilock.github.io/autothink/
LLMs with broad contextual capabilities shouldn't need to be guided in this manor. Claude can tell a trivial task from a complex one just as easily as I can, and should self-adjust, up to thresholds of compute spending, etc.
I mean finding your way around a manor can be hard, it's easier in an apartment.
---
If you get a hang of controlling costs, it's much cheaper. If you're exhausting the context window, I would not be surprised if you're seeing high cost.
Be aware of the "cache".
Tell it to read specific files (and only those!), if you don't, it'll read unnecessary files, or repeatedly read sections of files or even search through files.
Avoid letting it search - even halt it. Find / rg can have a thousands of tokens of output depending on the search.
Never edit files manually during a session (that'll bust cache). THIS INCLUDES LINT.
The cache also goes away after 5-15 minutes or so (not sure) - so avoid leaving sessions open and coming back later.
Never use /compact (that'll bust cache, if you need to, you're going back and forth too much or using too many files at once).
Don't let files get too big (it's good hygiene too) to keep the context window sizes smaller.
Have a clear goal in mind and keep sessions to as few messages as possible.
Write / generate markdown files with needed documentation using claude.ai, and save those as files in the repo and tell it to read that file as part of a question. I'm at about ~$0.5-0.75 for most "tasks" I give it. I'm not a super heavy user, but it definitely helps me (it's like having a super focused smart intern that makes dumb mistakes).
If i need to feed it a ton of docs etc. for some task, it'll be more in the few $, rather than < $1. But I really only do this to try some prototype with a library claude doesn't know about (or is outdated). For hobby stuff, it adds up - totally.
For a company, massively worth it. Insanely cheap productivity boost (if developers are responsible / don't get lazy / don't misuse it).
I definitely get value out of it- more than any other tool like it that I've tried.
I guess "context length" in AIs is what I intuitively tracked with people already. It can be a struggle to connect the Zabbix alert, the ticket and the situation on the system already, even if you don't track down all the zabbix code and scripts. And then we throw in Ansible configuring the thing, and then the business requriements by more, or less controlled dev-teams. And then you realize dev is controlled by impossible sales-terms.
These are scope -- or I guess context -- expansions that cause people to struggle.
"please fix the authorization bug in /api/users/:id".
You'd start by grepping the code base and trying to understand it.
Compare that to, "fix the permission in src/controllers/users.ts in the function `getById`. We need to check the user in the JWT is the same user that is being requested"
This may be a quick quip or a rant. But the things we say have a way of reinforcing how we think. So I suggest refining until what we say cuts to the core of the matter. The claim above is a false dichotomy. Let's put aside advertisements and hype. Trying to map between AI capabilities and human ones is complicated. There is high quality writing on this to be found. I recommend reading literature reviews on evals.
If you give a junior an overly broad prompt, they are going to have to do a ton of searching and reading to find out what they need to do. If you give them specific instructions, including files, they are more likely to get it right.
I never said they were replacements. At best, they're tools that are incredibly effective when used on the correct type of problem with the right type of prompt.
> they're tools that are incredibly effective when used on the correct type of problem with the right type of prompt.
So, a junior developer who has to be told exactly what to do.
As for the "correct type of problem with the right type of prompt", what exactly are those?
You'll be prompting and evaluating and iterating entirely finished pieces of software and be able to see multiple attempts at each solve at once, none of this deep in the weeds fixing a bug stuff.
We're rapidly approaching a world where a lot of software will be being made without an engineer hire at all, maybe not the hardest most complex or novel software but a lot of software that previously required a team of 3-15 wont have a single dev.
My current estimate is mid 2026
our current popular stack is quicksand.
unless we're talking about .net core, java, Django and more of these stable platforms.
VIM, Emacs and Excel are obvious power tools which may require you to think but often produce unrivalled productivity for power users
So I don't think the verdict that the product has a bad UI is fair. Natural language interfaces is such a step up from old school APIs with countless flags and parameters
I use Aider. It's awesome. You explicitly specify the files. You don't have to do work to limit context.
I do find Claude Code to be really good at exploration though - like checking out a repository I'm unfamiliar with and then asking questions about it.
What do you use for the model? Claude? Gemini? o3?
The output of /cost looks like this:
> /cost
⎿ Total cost: $0.1331
Total duration (API): 1m 13.1s
Total duration (wall): 1m 21.3sJust to make sure we're on the same page. There are two things in play. First, a language model's ability to know what file you are referring to. Second, an assistant's ability to make sure the right file is in the context window. In your experience, how does Claude Code compare to Copilot w.r.t (1) and (2)?
Developers tend to seriously underestimate the opportunity cost of their own time.
Hint - it’s many multiples of your total compensation broken down to 40 hour work weeks.
I myself didn’t think about not running linters however it makes obvious sense now and gives me the insight about how Claude Code works allowing me to use this insight in related engineering work.
Substitute “cost” with “time” in the above post and all of the same tips are still valuable.
I don’t do much agentic LLM coding but the speed (or lack thereof) was one of my least favorite parts. Using any tricks that narrow scope, prevent reprocessing files over and over again, or searching through the codebase are all helpful even if you don’t care about the dollar amount.
Mostly I just use the 4o model instead of the newer better models because it is faster. It's good enough mostly and I prefer getting a good enough answer quickly than the perfect answer after a few minutes. Mostly what I ask is not rocket science so perfect is the enemy of good here. I rarely have to escalate to better models. The reasoning models are annoyingly slow. Especially when they go down the wrong track, which happens a lot.
And my cost is a predictable 20$/month. The downside is that the scope of what I can ask is more limited. I'd like it to be able to "see" my whole code base instead of just 1 file and for me to not have to micro manage what the model looks at. Claude can do that if you don't care about money. But if you do, you are basically micro managing context. That sounds like monkey work that somebody should automate. And it shouldn't require an Einstein sized artificial brain to do that.
There must be people that are experimenting with using locally running more limited AI models to do all the micromanaging that then escalate to remote models as needed. That's more or less what Apple pitched for Apple AI at some point. Sounds like a good path forward. I'd be curious to learn about coding tools that do something like that.
In terms of cost, I don't actually think it's unreasonable to spend a few hundred dollars per month on this stuff. But I question the added value over the 20$ I'm spending. I don't think the improvement is 20x better. more like 1.5x. And I don't like the unpredictability of this and having to think about how expensive a question is going to be.
I think a lot of the short term improvement is going to be a mix of UX and predictable cost. Currently the tools are still very clunky and a bit dumb. The competition is going to be about predictable speed, cost and quality. There's a lot of room for improvement here.
If I spend $2 instead of $0.50 on a session but I had to spend 6 minutes thinking about context, I haven't gained any money.
4 hours coding project X or 3 hours and a short hike with your partner / friends etc
Correlation or causation aside, the same people I see complain about cost, complain about quality.
It might indicate more tightly controlled sessions may also produce better results.
Or maybe it's just people that tend to complain about one thing, complain about another.
And most of all Claude Code is overeager to start messing with your code and run unnecessary $$ instead of making sensible plan.
This isn't problem with Claude Sonnet - it is fundamnetal problem with Claude Code.
Though their own AI Assistant and Junie might be equally good choices there too.
Yesterday I gave up and disabled my format-on-save config within VSCode. It was burning way too many tokens with unnecessary file reads after failed diffs. The LLMs still have a decent number of failed diffs, but it helps a lot.
So instead of Write Hit Hit Hit
It's Write Write Hit Hit Hit
In most cases, it is because I am asking the model to do too much at once. Which is fine, I am learning the right level of abstraction/instruction where the model is effective consistently.
But when I read these best practices, I can't help but think of the cost. The multiple CLAUDE.md files, the files of context, the urls to documentation, the planning steps, the tests. And then the iteration on the code until it passes the test, then fixing up linter errors, then running an adversarial model as a code review, then generating the PR.
It makes me want to find a way to work at Anthropic so I can learn to do all of that without spending $100 per PR. Each of the steps in that last paragraph is an expensive API call for us ISV and each requires experimentation to get the right level of abstraction/instruction.
I want to advocate to Anthropic for a scholarship program for devs (I'd volunteer, lol) where they give credits to Claude in exchange for public usage. This would be structured similar to creator programs for image/audio/video gen-ai companies (e.g. runway, kling, midjourney) where they bring on heavy users that also post to social media (e.g. X, TikTok, Twitch) and they get heavily discounted (or even free) usage in exchange for promoting the product.
There are ways to use LLMs cheaply, but it will always be expensive to get the most out of them. In fact, the top end will only get more and more costly as the lengths of tasks AIs can successfully complete grows.
It would be no different than me saying "it sucks university is so expensive, I wish I could afford to go to an expensive college but I don't have a scholarship" and someone then answers: why should it be cheap.
So, allow me the space to express my feelings and propose alternatives, of which scholarships are one example and creative programs are another. Another one I didn't mention would be the same route as universities force now: I could take out a loan. And I could consider it an investment loan with the idea it will pay back either in employment prospects or through the development of an application that earns me money. Other alternatives would be finding employment at a company willing to invest that $100/day through me, the limit of that alternative being working at an actual foundational model company for presumably unlimited usage.
And of course, I could focus my personal education on squeezing the most value for the least cost. But I believe the balance point between slightly useful and completely transformative usages levels is probably at a higher cost level than I can reasonably afford as an independent.
Not when you need an SWE in order for it to work successfully.
There's an ocean of B2B SaaS services that would save customers money compared to building poor imitations in-house. Despite the Joel Test (almost 25 years old! craxy...) asking whether you buy your developers the best tools that money can buy, because they're almost invariably cheaper than developer salaries, the fact remains that most companies treat salaries as a fixed cost and everything else threatens the limited budget they have.
Anybody who has ever tried to sell developer tooling knows, you're competing with free/open-source solutions, and it aint a fair fight.
You can protect your files in a non-AI way: by simply not giving write access to Aider.
Also, apparently Aider is a bit more economic with tokens than other tools.
I am hesitant because I am paying for Cursor now and I get a lot of model usage included within that monthly cost. I'm cheap, perhaps to a fault even when I could afford it, and I hate the idea of spending twice when spending once is usually enough. So while Aider is potentially cheaper than Claude Code, it is still more than what I am already paying.
I would appreciate any comments on people who have made the switch from Cursor to Aider. Are you paying more/less? If you are paying more, do you feel the added value is worth the additional cost? If you are paying less, do you feel you are getting less, the same or even more?
I don't find this particularly problematic because I can quickly see the unnecessary changes in git and revert them.
Like, I guess it would be nice if I didn't have to do that, but compared to the value I'm getting it's not a big deal.
I have become obsessive about doing git commits in the way I used to obsess over Ctrl-S before the days of source control. As soon as I get to a point I am happy, I get the LLM to do a check-point check in so I can minimize the cost of doing a full directory revert.
But from a time and cost perspective, I could be doing much better. I've internalized the idea that when the LLM goes off the rails it was my fault. I should have prompted it better. So I am now consider: how do I get better faster? And the answer is I do it as much as I can to learn.
I don't just want to whine about the process. I want to use that frustration to help me improve, while avoiding going bankrupt.
Tweaking claude.md files until the desired result is achieved is similar to a back and forth email chain with the contractor. The difference being that the contractor can be held accountable in our human legal system and can be made to follow their "prompt" very strictly. The LLM has its own advantages, but they seem to be a subset since the human contractor can also utilize an LLM.
Those who get a lot of uplift out of the models are almost certainly using them in a cybernetic manner wherein the model is an integral part of an expert's thinking loop regarding the program/problem. Defining a pile of policies and having the LLM apply them to a codebase automatically is a significantly less impactful use of the technology than having a skilled human developer leverage it for immediate questions and code snippets as part of their normal iterative development flow.
If you've got so much code that you need to automate eyeballs over it, you are probably in a death spiral already. The LLM doesn't care about the terrain warnings. It can't "pull up".
But in my experience, the user has to be better than an Infosys employee to know how to convey the task to the LLM and then verify iteratively.
So more like an experienced engg outsourcing work to a service company engg.
Faced with us as a client, the LLM has infinite patience at linear but marginal cost (relative to your thinking/design time cost, and the value of instant iteration as you realize what you meant to picture and say).
With offshoring, telling them they're getting it wrong is not just horrifically slow thanks to comms and comprehension latency, it makes you a problem client, until soon you'll find the do-over cost becomes neither linear nor marginal.
Don't sleep on the power of small fast iterations (not vibes, concrete iterations), with an LLM tool that commits as you go and can roll back both code and mental model when you're down a garden path.
> We humans undervisualize until we see concrete results.
> Could you elaborate on this with another paragraph or two?
Volunteer as a client-facing PdM at a digital agency for a week*, you'll be able to elaborate with a book.
* Well, long enough to try to iterate a client instruction based deliverable.
`aider --model gemini --architect --editor-model claude-3.7` and aider will take care of all the fiddly bits including git commits for you.
right now `aider --model o3 --architect` has the highest rating on the Aider leaderboards, but it costs wayyy more than just --model gemini.
Reading the thread, somehow people are paying. It is mindblowing how in place of getting cheaper, development just got more expensive for businesses.
I find LLM-based tools helpful, and use them quite regularly but not 20 bucks+, let alone 100+ per month that claude code would require to be used effectively.
I find this argument very bizarre. $100 is pay for 1-2 hours of developer time. Doesn't it save at least that much time in a whole month?
After 2 years of GPT4 release, we can safely say that LLMs don't make finding PMF that much easier nor improve general quality/UX of products, as we still see a general enshittification trend.
If this spending was really game-changing, ChatGPT frontend/apps wouldn't be so bad after so long.
If they just helped you to ship something valueless, you paid $75 for entertainment, like betting.
On a serious note, there is no clear evidence that any of the LLM-based code assistants will contribute to saving developer time. Depends on the phase of the project you are in and on a multitude of factors.
And that was just copy & past into ChatGPT.
I don't know about assistants or project integration. But, in my experience, LLMS are a great tool to have and worth learning how to use well, for you. And I think that's the key part. Some people like heavily integrated IDEs, some people prefer a more minimal approach with VS Code or Vim.
I think LLMs are going to be similar. Some people are going to want full integration and some are just going to want minimal interface, context, and edits. It's going to be up to the dev to figure out what works best for him or her.
https://news.ycombinator.com/item?id=43683012
Developer time "saved" indeed ;-)
(I'm not judging a specific person here, this is more of a broad commentary regarding our relationship/sense of responsibility/entitlement/lack of empathy when it comes to supporting other people's work when it helps us)
> Have multiple checkouts of your repo
I don’t know why this never occurred to me probably because it feels wrong to have multiple checkouts, but it makes sense so that you can keep each AI instance running at full speed. While LLM‘s are fast, this is one of the annoying parts of just waiting for an instance of Aider or Claude Code to finish something.
Also, I had never heard of git worktrees, that’s pretty interesting as well and seems like a good way to accomplish effectively having multiple checkouts.
How do you keep tabs on multiple agents doing multiple things in a codebase? Is the end deliverable there a bunch of MRs to review later? Or is it a more YOLO approach of trusting the agents to write the code and deploy with no human in the loop?
I like to start by describing the problem and having it do research into what it should do, writing to a markdown file, then get it to implement the changes. You can keep tabs on a few different tasks at a time and you don't need to approve Yolo mode for writes, to keep the cost down and the model going wild.
Disclaimer, I haven’t tried it personally - if you do, let us know how you go!
I did a Show HN for it a few days ago: https://news.ycombinator.com/item?id=43710576
- Aider - https://aider.chat/ | https://github.com/Aider-AI/aider
- Plandex - https://plandex.ai/ | https://github.com/plandex-ai/plandex
- Goose - https://block.github.io/goose/ | https://github.com/block/goose
Unfortunately I can only give an anecdotal answer here, but I get better results from Cursor than the alternatives. The semantic index is the main difference, so I assume that's what's giving it the edge.
Wasn't it clearly bad when facebook would get real close to buying another company... then decide naw, we got developers out the ass lets just steal the idea and put them out of business
After spending a couple of hours trying to get aider and plandex to run (and then with Google Gemini 2.5 pro), my conclusion is that these tools have a long way to go until they are usable. The breakage is all over the place. Sure, there is promise, but today I simply can't get them to work reasonably. And my time is expensive.
Claude Code just works. I run it (even in a slightly unsupported way, in a Docker container on my mac) and it works. It does stuff.
PS: what is it with all "modern" tools asking you to "curl somewhere.com/somescript.sh | bash". Seriously? Ship it in a docker container if you can't manage your dependencies.
My 2 cents on value for money and effectiveness of Claude vs Gemini for coding:
I've been using Windsurf, VS Code and the new Firebase Studio. The Windsurf subscription allowance for $15 per month seems adequate for reasonable every day use. I find Claude Sonnet 3.7 performs better for me than Gemini 2.5 pro experimental.
I still like VS Code and its way of doing things, you can do a lot with the standard free plan.
With Firebase Studio, my take is that it should good for building and deploying simple things that don't require much developer handholding.
https://kylekukshtel.com/vibecoding-claude-code-cline-sonnet...
Would definitely recommend people reading it for some insight into hands on experience with the tool.
Yep. Here are the API pricing numbers for Gemini vs Claude. All per 1M tokens.
1. Gemini 2.5: in: $0.15; out: $0.60 non-thinking or $3.50 thinking
2. Claude 3.7: in: $3.00; out: $15
[1] https://ai.google.dev/gemini-api/docs/pricing [2] https://www.anthropic.com/pricing#api
Did anyone have equal results with the „unofficial“ fork „Anon Kode“? Or with Roo Code with Gemini Pro 2.5?
Are they saying Claude needs to do the git interaction in order to work and/or will generate better code if it does?
The only problem is that this loss is permanent! As far as I can tell, there's no way to go back to the old conversation after a `/clear`.
I had one session last week where Claude Code seemed to have become amazingly capable and was implementing entire new features and fixing bugs in one-shot, and then I ran `/clear` (by accident no less) and it suddenly became very dumb.
And there's CLAUDE.md. it's like cursorrules. You can also have it modify it's own CLAUDE.md.
Then, you can edit the file at your leisure if you want to.
And when you want to load that context back in, ask it to read the file.
Works better than `/compact`, and is a lot cheaper.
Edit: It so happens I had a Claude Code session open in my Terminal, so I asked it:
Save your current context to a file.
Edit: Here's the actual file if you want to see it. https://gist.github.com/Wowfunhappy/e7e178136c47c2589cfa7e5a...
It doesn't seem to have included any points on style or workflow in the context. Most of my context documents end up including the following information:
1. I want the agent to treat git commits as checkpoints so that we can revert really silly changes it makes.
2. I want it to keep on running build/tests on the code to be sure it isn't just going completely off the rails.
3. I want it to refrain from adding low signal comments to the code. And not use emojis.
4. I want it to be honest in its dealings with me.
It goes on a bit from there. I suspect the reason that the models end up including that information in the context documents they dump in our sessions is that I give them such strong (and strongly worded) feedback on these topics.
As an alternative, I wonder what would happen if you just told it what was missing from the context and asked it to re-dump the context to file.
Whatever the internal state is of Claude Code, it's lost as soon as you /clear or close the Terminal window. You can't even experiment with a different prompt and then--if you don't like the prompt--go back to the original conversation, because pressing esc to branch the conversation looses the original branch.
I've tried Claude code this week and I really didn't like it - Claude did an okay job but was insistent on deleting some shit and hard coding a check instead of an actual conditional. It got the feature done in about $3, but I didn't really like the user experience and it didn't feel any better than using 3.7 in cursor.
With Claude Code, all bets are off there. You get a better understanding of your code in each prompt, and the bill can rack up, what, 50x faster?
If I got really stuck on a problem involving many lines of code, I could see myself spinning up Claude Code for that one issue, then quickly going back to Windsurf.
Why doesn’t Claude Code usage count against the same plan that usage of Claude.ai and Claude Desktop are billed against?
I upgraded to the $200/month plan because I really like Claude Code but then was so annoyed to find that this upgrade didn’t even apply to my usage of Claude Code.
So now I’m not using Claude Code so much.
I'm surprised at the complexity and correctness at which it infers from very simple, almost inadequate, prompts.
Claude Code uses the API interface and API pricing, and writes and edits code directly on your machine, this is a level past simply interacting with a separate chat bot. It seems a little disingenuous to say it's "hostile" to users, when the reality is yeah, you do pay a bit more for more reliable usage tier, for a task that requires it. It also shows you exactly how much it's spent at any point.
Genuinely interested: how's so?
A nice addition would be having something like /cost but to check where you are in regards to limits.
If your staff engineers are mostly doing things AI can do, then you don't need staff. Probably don't even need senior
- L3 SWE II - $193,712 USD (before overheads)
- L4 SWE III - $297,124 USD (before overheads)
- L5 Senior SWE - $377,797 USD (before overheads)
These tools and foundational models get better every day, and right now, they enable Staff+ engineers and businesses to have less need for juniors. I suspect there will be [short-to-medium-term] compression. See extended thoughts at https://ghuntley.com/screwed
Delivery via LLMs is predictable, fast, and any concerns about outcome [quality] can be programmed away to reject bad outcomes. This form of programming the LLMs has a one-time cost...
The minimum wage in Poland is around USD 1240/month. The median wage in Poland is approximately USD 1648/month. Tech salaries are considerably higher than the median.
Idk, maybe for an intern software developer it's a good salary...
~$1200 is what I'd get paid here after a few years of experience; I have never saw an internship offer in my city that paid more than minimal wage (most commonly, it's unpaid).
They do, but I’ve seen a huge slowdown in “getting better” in the last year. I wonder if it’s my perception, or reality. Each model does better on benchmarks but I’m still experiencing at least a 50% failure rate on _basic_ task completion, and that number hasn’t moved higher in many months.
I get a lot of value out of LLMs but when I see people make claims like this I know they aren’t “in the trenches” of software development, or care so little about quality that I can’t relate to their experience.
Usually they’re investors in some bullshit agentic coding tool though.
Using the proper techniques, Sonet 3.7 can generate code in the custom lexical/stdlib. So, in my eyes, the path to Stage 3 is unlocked, but it will chew lots and lots of tokens.
Well, virtually every production-grade compiler is self-compiling. Since you bring it up explicitly, I'm wondering what implications of begin self-compiling you have in mind?
> Meaning the LLM is able to program itself without training data about the programming language...
Could you clarify this sentence a bit? Does it mean the LLM will code in this new language without training in it before hand? Or is it going to enable the LLM to programm itself to gain some new capabilities?
Frankly, with the advent of coding agents, building a new compiler sounds about as relevant as introducing a new flavor of assembly language and then a new assembly may at least be justified by a new CPU architecture...
1. My company cannot justify this cost at all.
2. My company can justify this cost but I don't find it useful.
3. My company can justify this cost, and I find it useful.
4. I find it useful, and I can justify the cost for personal use.
5. I find it useful, and I cannot justify the cost for personal use.
That aside -- 200/day/dev for a "nice to have service that sometimes makes my work slightly faster" is much in the majority of the world.
A 2.5 hour session with Claude Code costs me somewhere between $15 and $20. Taking $20/2.5 hours as the estimate, $100 would buy me 12.5 hours of programming.
On average, one line of, say, JavaScript represents around 7 tokens, which means there are around 140k lines of JS per million tokens.
On Openrouter, Sonnet 3.7 costs are currently:
- $3 / one million input tokens => $100 = 33.3 million input tokens = 420k lines of JS code
- $15 / one million output tokens => $100 = 3.6 million output tokens = 4.6 million lines of JS code
For one developer? In one day? It seems that one can only reach such amounts if the whole codebase is sent again as context with each and every interaction (maybe even with every keystroke for type completion?) -- and that seems incredibly wasteful?
33.3 million input tokens / 7 tokens per loc = 4.8 million locs
3.6 million output tokens / 7 tokens per loc = 515k locs
All of the gate keeping around llm code tools are amusing. But whatever, I’m shipping 10x and making money doing it.
But if you are making money by using LLMs to write code then all power to you. I just despair at the idea of trillions of lines of LLM generated code.
The point is to get a pipeline working, cost can be optimized down after.
[0] https://www.wheresyoured.at/openai-is-a-systemic-risk-to-the...
But every Claude Code user is a 1000 requests per day user, so the economics don't work anymore.
I am not saying Claude should stop making money, I'm just advocating for giving users the value of getting some Code coverage when you migrate from the basic plan to the pro or max.
Does that make sense?
Anthropic make it seem like Claude Code is a product categorized like Claude Desktop (usage of which gets billed against your Claude.ai plan). This is how it signs off all its commits:
Generated with [Claude Code](https://claude.ai/code)
Once I had purchased the $200/month plan, I did some reading and quickly realized that I had been too quick to jump to conclusions. It still left me feeling like they had pulled a fast on one me.
I think it's just oversight on their part. They have nothing to gain by making people believe they would get Claude Code access through their regular plans, only bad word of mouth.
This is definitely not malicious on their part. Just bears pointing out.
It’s less autonomous, since it’s based on the Claude chat interface, and you need to write “continue” every so often, but it’s nice to save the $$
Some base usage included in the plan might be a good balance
It would definitely get me to use it more.
If everyone used the plan to the limit, the plan would cost the same as the API with usage equal to the limit.