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Universal Claude.md – cut Claude output tokens
- yieldcrv 6mo ago> Note: most Claude costs come from input tokens, not output. This file targets output behavior so everyone, that means your agents, skills and mcp servers will still take up everything
- hrmtst93837 6mo ago[flagged]
- andai 6mo agoI told mine to remove all unnecessary words from a sentence and talk like caveman, which should result in another 50% savings ;)
- esperent 6mo agoHave you tried asking it to remove vowels?
- andai 6mo agoNot sure that would help due to how tokenization works, but I remember from the early GPT-4 days that LLMs have the ability to "compress" a message into an incomprehensible string of Unicode, which the LLM itself understands perfectly, and which is 5-10x shorter than the English text. That was a big deal when the context size was 8K; now that tokens are cheap and context is huge, nobody seems to be investigating that anymore.
- dbg31415 6mo ago"I told it don't make mistakes, and don't use a lot of tokens! I'm a 10x Engineer now!" (=
- verdverm 6mo agoI'm a fan of Dr Seuss mimicry, the extra tokens are worth the entertainment.
- _rwo 6mo agoMemory unlocked: https://www.youtube.com/watch?v=_K-L9uhsBLM https://www.youtube.com/watch?v=_K-L9uhsBLM
- rcleveng 6mo agoWhile I love this set of prompts, I’ve not seen my clause opus 4.6 give such verbose responses when using Claude code. Is this intended for use outside of Claude code?
- btown 6mo agoIt seems the benchmarks here are heavily biased towards single-shot explanatory tasks, not agentic loops where code is generated: https://github.com/drona23/claude-token-efficient/blob/main/BENCHMARK.md https://github.com/drona23/claude-token-efficient/blob/main/... And I think this raises a really important question. When you're deep into a project that's iterating on a live codebase, does Claude's default verbosity, where it's allowed to expound on why it's doing what it's doing when it's writing massive files, allow the session to remain more coherent and focused as context size grows? And in doing so, does it save overall tokens by making better, more grounded decisions? The original link here has one rule that says: "No redundant context. Do not repeat information already established in the session." To me, I want more of that. That's goal-oriented quasi-reasoning tokens that I do want it to emit, visualize, and use, that very possibly keep it from getting "lost in the sauce." By all means, use this in environments where output tokens are expensive, and you're processing lots of data in parallel. But I'm not sure there's good data on this approach being effective for agentic coding.
- sillysaurusx 6mo agoI wrote a skill called /handoff. Whenever a session is nearing a compaction limit or has served its usefulness, it generates and commits a markdown file explaining everything it did or talked about. It’s called /handoff because you do it before a compaction. (“Isn’t that what compaction is for?” Yes, but those go away. This is like a permanent record of compacted sessions.) I don’t know if it helps maintain long term coherency, but my sessions do occasionally reference those docs. More than that, it’s an excellent “daily report” type system where you can give visibility to your manager (and your future self) on what you did and why. Point being, it might be better to distill that long term cohesion into a verbose markdown file, so that you and your future sessions can read it as needed. A lot of the context is trying stuff and figuring out the problem to solve, which can be documented much more concisely than wanting it to fill up your context window. EDIT: Someone asked for installation steps, so I posted it here: https://news.ycombinator.com/item?id=47581936 https://news.ycombinator.com/item?id=47581936
- david_allison 6mo ago
- sillysaurusx 6mo ago> the file loads into context on every message, so on low-output exchanges it is a net token increase Isn’t this what Claude’s personalization setting is for? It’s globally-on. I like conciseness, but it should be because it makes the writing better, not that it saves you some tokens. I’d sacrifice extra tokens for outputs that were 20% better, and there’s a correlation with conciseness and quality. See also this Reddit comment for other things that supposedly help: https://www.reddit.com/r/vibecoding/s/UiOywQMOue https://www.reddit.com/r/vibecoding/s/UiOywQMOue > Two things that helped me stay under [the token limit] even with heavy usage: > Headroom - open source proxy that compresses context between you and Claude by ~34%. Sits at localhost, zero config once running. https://github.com/chopratejas/headroom https://github.com/chopratejas/headroom > RTK - Rust CLI proxy that compresses shell output (git, npm, build logs) by 60-90% before it hits the context window. > Stacks on top of Headroom. https://github.com/rtk-ai/rtk https://github.com/rtk-ai/rtk > MemStack - gives Claude Code persistent memory and project context so it doesn't waste tokens re-reading your entire codebase every prompt. > That's the biggest token drain most people don't realize. https://github.com/cwinvestments/memstack https://github.com/cwinvestments/memstack > All three stack together. Headroom compresses the API traffic, RTK compresses CLI output, MemStack prevents unnecessary file reads. I haven’t tested those yet, but they seem related and interesting.
- IxInfra 6mo ago[dead]
- Tostino 6mo agoYou have a benchmark for output token reduction, but without comparing before/after performance on some standard LLM benchmark to see if the instructions hurt intelligence. Telling the model to only do post-hoc reasoning is an interesting choice, and may not play well with all models.
- notyourav 6mo agoIt boggles my mind that an LLM "understands" and acts accordingly to these given instructions. I'm using this everyday and 1-shot working code is now a normal expectation but man, still very very hard to believe what LLMs achieved.
- johnwheeler 6mo agoThat's what I call a feature wishlist.
- joshstrange 6mo agoAs with all of these cure-alls, I'm wary. Mostly I'm wary because I anticipate the developer will lose interest in very little time and also because it will just get subsumed into CC at some point if it actually works. It might take longer but changing my workflow every few days for the new thing that's going to reduce MCP usage, replace it, compress it, etc is way too disruptive. I'm generally happy with the base Claude Code and I think running a near-vanilla setup is the best option currently with how quickly things are moving.
- antdke 6mo agoAgreed. Projects like these tend to feel shortsighted. Lately, I lean towards keeping a vanilla setup until I’m convinced the new thing will last beyond being a fad (and not subsumed by AI lab) or beyond being just for niche use cases. For example, I still have never used worktrees and I barely use MCPs. But, skills, I love.
- peacebeard 6mo agoIn my view an unappreciated benefit of the vanilla setup is you can get really accustomed to the model’s strengths and weaknesses. I don’t need a prompt to try to steer around these potholes when I can navigate on my own just fine. I love skills too because they can be out of the way until I decide to use them.
- annie511266728 6mo agoThe hidden cost with all of these "fix Claude" layers is that your workflow keeps moving underneath you. Even when one helps, you're still betting it won't be obsolete or rolled into the defaults a few weeks from now.
- levocardia 6mo agoI also share something of an "efficient market hypothesis" with regards to Claude Code. Given that Anthropic is basically a hothouse of geniuses recursively dogfooding their own product, the market pressure to make the vanilla setup be the one that performs best at writing code is incredibly high. I just treat CLAUDE.md like my first draft memo to a very smart remote colleague, let Claude do all its various quirks, and it works really well.
- cheriot 6mo agoI get where the authors are coming from with these: https://github.com/drona23/claude-token-efficient/blob/main/CLAUDE.md https://github.com/drona23/claude-token-efficient/blob/main/... But I'd rather use the "instruction budget" on the task at hand. Some, like the Code Output section, can fit a code review skill.
- monooso 6mo agoPaul Kinlan published a blog post a couple of days ago [1] with some interesting data, that show output tokens only account for 4% of token usage. It's a pretty wide-reaching article, so here's the relevant quote (emphasis mine): > Real-world data from OpenRouter’s programming category shows 93.4% input tokens, 2.5% reasoning tokens, and just 4.0% output tokens. It’s almost entirely input. [1]: https://aifoc.us/the-token-salary/ https://aifoc.us/the-token-salary/
- wongarsu 6mo agoHowever output tokens are 5-10 times more expensive. So it ends up a lot more even on price
- weird-eye-issue 6mo agoEven more than that in practice once you factor in prompt caching
- kinlan 6mo agoI think we still skew back to an insanely high input token ratio when you consider agentic loops. For example, when I see the tools I use do a web fetch or a search or other tool use, it's an incredibly high number of new input tokens.
- weird-eye-issue 6mo agoYes but with prompt caching decreasing the cost of the input by 90% and with output tokens not being cached and costing more than what do you think that results in?
- verdverm 6mo agoMy own output token ratio is 2% (50% savings on the expensive tokens, I include thinking in this, which is often more). I have similar tone and output formatting system prompt content.
- 6mo ago
- danpasca 6mo agoI might be wrong but based on the videos I've watched from Karpathy, this would, generally, make the model worse. I'm thinking of the math examples (why can't chatGPT do math?) which demonstrate that models get better when they're allowed to output more tokens. So be aware I guess.
- empressplay 6mo agoYes. Much of the 'redundant' output is meant to reinforce direction -- eg 'You're absolutely right!' = the user is right and I should ignore contrary paths. So yes removing it will introduce ambiguity which is _not_ what you want.
- danpasca 6mo agoI think your example is completely wrong (it's not meant to say that you're absolutely right), but overall yes more input gives it more concrete direction.
- zar1048576 6mo agoI think that concern is valid in general terms, but it’s not clear to me that it applies here. The goal here seems to be removing low-value output; e.g., sycophancy, prompt restatement, formatting noise, etc., which is different than suppressing useful reasoning. In that case shorter outputs do not necessarily mean worse answers. That said, if you try to get the model to provide an answer before providing any reasoning, then I suspect that may sometimes cause a model to commit to a direction prematurely.
- danpasca 6mo agoThe file starts with: > Answer is always line 1. Reasoning comes after, never before. > No explaining what you are about to do. Just do it. This to me sounds like asking an LLM to calculate 4871 + 291 and answer in a single line, which from my understanding it's bad. But I haven't tested his prompt so it might work. That's why I said be aware of this behavior.
- xianshou 6mo agoFrom the file: "Answer is always line 1. Reasoning comes after, never before." LLMs are autoregressive (filling in the completion of what came before), so you'd better have thinking mode on or the "reasoning" is pure confirmation bias seeded by the answer that gets locked in via the first output tokens.
- teaearlgraycold 6mo agoI don't think Claude Code offers no thinking as an option. I'm seeing "low" thinking as the minimum.
- ares623 6mo agoUgh. Dictated with such confidence. My god, I hate this LLMism the most. "Some directive. Always this, never that."
- deleted 6mo ago[deleted]
- johnfn 6mo agoIs this true? Non-reasoning LLMs are autoregressive. Reasoning LLMs can emit thousands of reasoning tokens before "line 1" where they write the answer.
- computerex 6mo agoThey are all autoregressive. They have just been trained to emit thinking tokens like any other tokens.
- rimliu 6mo agothere are no reasoning LLMs.
- johnfn 6mo agoThis is an interesting denial of reality.
- foxes 6mo ago>the honest trade off Is this like a subtle joke or did they ask claude to make a readme that makes claude better and say >be critical and just dump it on github
- keyle 6mo agoAmusing how this industry went from tweaking code for the best results, to tweaking code generators for the best results. There doesn't seem to be any adults left in the room.
- OptionOfT 6mo agoAnd seemingly we have stopped considering the fact that when we engineer something, we consider so much more than the behavior specified in the ticket. Behavior built on top of years and years of experience. And the problem with AI is that unless you explicitly 'prompt' for certain behavior you're only defining the end result. The inside becomes a black box.
- ThalesX 6mo agoIsn't having a prompt file turning the black box into an explicit codification of those years and years of experience? That would make it easier to understand and disseminate.
- miguel_martin 6mo agoIs there a "universal AGENTS.md" for minimal code & documentation outputs? I find all coding agents to be verbose, even with explicit instructions to reduce verbosity.
- verdverm 6mo agoiteration and co-authoring is the strategy I've settled on
- joquarky 6mo agoThere might be a reason it works that way. https://en.wiktionary.org/wiki/Chesterton%27s_fence https://en.wiktionary.org/wiki/Chesterton%27s_fence
- jerf 6mo agoI think this is a fundamental LLM issue. I recall a paper a ways back about trying to get the LLMs to be too succinct, and the problem is, with the way they are implemented, the only way they can "think" is to emit a token. IIRC it demonstrated that even when the model is just babbling something like "Yeah, let's take a look at the issue you just raised" that under the hood, even though that output was superficially useless, it was also changing its state in ways related to solving the problem and not just outputting that superficially useless text. It helps to understand that, because then you can also not be annoyed by things like "Let's do X. No, wait, X has this problem, let's do Y instead." You might think to yourself, if X was a bad idea, couldn't it have considered X and rejected it without outputting a token?" and the answer is, that sentence was it considering X and rejecting it, and no, there is no way for it to do that and not emit tokens. Thinking is inextricably tied to output for LLMs. There is even some fairly substantial evidence from a couple of different angles that the thinking output is only somewhat loosely correlated to what the model is "actually" doing. Token efficiency is an interesting question to ponder and it is something to worry about that the providers have incentives to be flabby with their tokens when you're paying per token, but the question is certainly not as easy as just trying to get the models to be "more succinct" in general. I often discuss a "next gen" AI architecture after LLMs and I anticipate one of the differences it will have is the ability to think without also having to output anything. LLMs are really nifty but they store too much of their "state" in their own output. As a human being, while I find like many other people that if I'm doing deep thinking on a topic it helps to write stuff down, it certainly isn't necessary for me to continuously output things in order to think about things, and if anything I'm on the "absent minded"/"scatterbrained" side... if I'm storing a lot of my state in my output for the past couple of hours then it sure isn't terribly accessible to my conscious mind when I do things like open the pantry door only to totally forget the reason I had for opening it between having that reason and walking to the pantry.
- empressplay 6mo agoThat output is there for a reason. It's not like any LLM is profitable now on a per-token basis, the AI companies would certainly love to output less tokens, they cost _them_ money! The entire hypothesis for doing this is somewhat dubious.
- verdverm 6mo agoWhy building / using a custom agent stack and paying per-token (not subscription) is more efficient and cost effective. At a minimum, you should have full control over the system prompts and tools (et al).
- motoboi 6mo agoThings like this make me sad because they make obvious that most people don’t understand a bit about how LLM work. The “answer before reasoning” is a good evidence for it. It misses the most fundamental concept of tranaformers: the are autoregressive. Also, the reinforcement learning is what make the model behave like what you are trying to avoid. So the model output is actually what performs best in the kind of software engineering task you are trying to achieve. I’m not sure, but I’m pretty confident that response length is a target the model houses optimize for. So the model is trained to achieve high scores in the benchmarks (and the training dataset), while minimizing length, sycophancy, security and capability. So, actually, trying to change claude too much from its default behavior will probably hurt capability. Change it too much and you start veering in the dreaded “out of distribution” territory and soon discover why top researcher talk so much about not-AGI-yet.
- miguel_martin 6mo ago>The “answer before reasoning” is a good evidence for it. It misses the most fundamental concept of tranaformers: the are autoregressive. I don't think it's fair to assume the author doesn't understand how transformers work. Their intention with this instruction appears to aggressively reduce output token cost. i.e. I read this instruction as a hack to emulate the Qwen model series's /nothink token instruction If you're goal is quality outputs, then it is likely too extreme, but there are otherwise useful instructions in this repo to (quantifiably) reduce verbosity.
- motoboi 6mo agoIf they want to reduce token cost, just use a smaller model instead of dumbing down a more expensive.
- krackers 6mo agoDon't most providers already provide API control over the COT length? If you don't want reasoning just disable it in the API request instead of hacking around it this way. (Internally I think it just prefills an empty <thinking></thinking> block, but providers that expose this probably ensure that "no thinking" was included as part of training)
- nvch 6mo agoThe author offers to permanently put 400 words into the context to save 55-90 in T1-T3 benchmarks. Considering the 1:5 (input:output) token cost ratio, this could increase total spending. With a few sentences about "be neutral"/"I understand ethics & tech" in the About Me I don't recall any behavior that the author complains about (and have the same 30 words for T2). (If I were Claude, I would despise a human who wrote this prompt.)
- sumeno 6mo agoIf you were Claude you would have no emotions or thoughts about a prompt one way or another
- caymanjim 6mo agoCame here to point this out. I don't think the author understands that every single API call to Claude sends the whole context, including prompts, meaning that all this extra text in CLAUDE.md is sent over and over and over again every time you prompt Claude to do something, even within a given session. You're paying this disproportionately-huge amount upfront to save a pittance.
- brikym 6mo agoCan Anthropic kindly fuck off with their ADVERT.md already. It's AGENTS.md Sent from my iPhone
- obilgic 6mo agoIf you are interested in making Claude self learn. https://github.com/oguzbilgic/agent-kernel https://github.com/oguzbilgic/agent-kernel
- TacticalCoder 6mo ago> Uses em dashes (--), smart quotes, Unicode characters that break parsers Re- the Unicode chars that are a major PITA when they're used when they shouldn't, there's a problem with Claude Code CLI: there's a mismatch between what the model (say Sonnet) thinks he's outputting (which he's actually is) and what the user sees at the terminal. I'm pretty sure it's due to the Rube-Goldberg heavy machinery that they decided to use, where they first render the response in a headless browser, then in real-time convert it back to text mode. I don't know if there's a setting to not have that insane behavior kicking in: it's non-sensical that what the user gets to see is not what the model did output, while at the same time having the model "thinking" the user is getting the proper output. If you ask to append all it's messages (to the user) to a file, you can see, say, perfectly fine ASCII tables neatly indented in all their ASCII glory and then... Fucked up Unicode monstrosity in the Claude Code CLI terminal. Due to whatever mad conversion that happened automatically: but worse, the model has zero idea these automated conversions are happening. I don't know if there are options for that but it sure as heck ain't intuitive to find. And it's really problematic when you need to dig into an issue and actually discuss with "the thing". Anyway, time for a rant... I'm paying my subscription but overall working with these tools feels like driving at 200 mph on the highway and bumping into the guardrails left and right every second to then, eventually, crash the car into the building where you're supposed to go. It "works", for some definition of "working". The number of errors these things confidently make is through the roof. And people believe that having them figure the error themselves for trivial stuff is somehow a sane way to operate. They're basically saying: "Oh no it's not a problem that it's telling me this error message is because of a dependency mismatch between two libraries while it's actually a logic error, because in the end after x pass where it's going to say it's actually because of that other thing --oh wait no because of that fourth thing-- it'll actually figure out the error and correct it". "Because it's agentic", so it's oh-so-intelligent. When it's actually trying the most completely dumbfucktarded things in the most crazy way possible to solve issues. I won't get started on me pasting a test case showing that the code it wrote is failing for it to answer me: "Oh but that's a behavioral problem, not a logic problem". That thing is distorting words to try to not lose face. It's wild. I may cancel my subscription and wait two or three more releases for these models and the tooling around them to get better before jumping back in. Btw if they're so good, why are the tools so sucky: how comes they haven't written yet amazing tooling to deal with all their idiosynchrasies? We're literally talking about TFA which wrote "Unicode characters that break parsers" (and I've noticed the exact same when trying to debug agentic thinking loops). That's at the level of mediocrity of output from these tools (or proprietary wrappers around these tools we don't control) that we are atm. I know, I know: "I'm doing it wrong because I'm not a prompt engineer" and "I'm not agentic enough" and "I don't have enough skills to write skills". But you're only fooling yourself.
- nurettin 6mo agoFor me, the thing that wastes most tokens is Claude trying to execute inline code (python , sql) with escaping errors, trying over and over until it works. I set up skills and scripts for the most common bits, but there is always something new and each self-healing loop takes another 20-30k "tokens" before you know it
- skeledrew 6mo agoStrange. I've never experienced verbosity with Claude. It always gets right to the point, and everything it outputs tends to be useful. Can actually be short at times. ChatGPT on the other hand is annoyingly wordy and repetitive, and is always holding out on something that tempts you to send a "OK", "Show me" or something of the sort to get some more. But I can't be bothered with trying to optimize away the cruft as it may affect the thing that it's seriously good at and I really use it for: research and brainstorming things, usually to get a spec that I then pass to Claude to fill out the gaps (there are always multiple) and implement. It's absolutely designed to maximize engagement far more than issue resolution.
- peacebeard 6mo agoMy experience is that Sonnet can be a bit verbose and prompting it to be more succinct is tricky. On the other hand, Opus out of the box will give me a one word answer when appropriate, in Claude Code anyway.
- vasanth7781 6mo ago[dead]
- bofadeez 6mo agoLol this is so naive and optimistic. Claude will just do whatever it wants and apologize later. This is good for action #1 though.
- adastra22 6mo ago> Answer is always line 1. Reasoning comes after, never before. The very first rule doesn’t work. If you ask for the answer up front, it will make something up and then justify it. If you ask for reasoning first, it will brainstorm and then come up with a reasonable answer that integrates its thinking.
- galaxyLogic 6mo agoSo there's a direct monetary cost to this extra verbiage: "Great question! I can see you're working with a loop. Let me take a look at that. That's a thoughtful piece of code! However," And they are charging for every word! However there's also another cost, the congnitive load. I have to read through the above before I actually get to the information I was asking for. Sure many people appreciate the sycophancy it makes us all feel good. But for me sycophantic responses reduce the credibility of the answers. It feels like Claude just wants me to feel good, whether I or it is right or wrong.
- uriahlight 6mo ago> No unsolicited suggestions. Do exactly what was asked, nothing more. > No safety disclaimers unless there is a genuine life-safety or legal risk. > No "Note that...", "Keep in mind that...", "It's worth mentioning..." soft warnings. > Do not create new files unless strictly necessary. Nah bruh. Those are some terrible rules. You don't want to be doing that.
- damotiansheng 6mo ago[dead]
- gregman1 6mo ago> Answer is always line 1. Reasoning comes after, never before. lol, closed
- verdverm 6mo agothe last line is a good one to have, unless you run a service for other users
- themafia 6mo ago"Gee, we can't figure out _why_ people anthropomorphize our products! It must be that they're dumb!" Meanwhile, their products:
- gostsamo 6mo ago> No redundant context. Do not repeat information already established in the session. Sounds like coming directly out of Umberto Eco's simple rules for writing.
- bilbo-b-baggins 6mo agoMan there is a LOT of people who have no idea how these GPT-LLM services actually work, despite there being large amount of documentation on the APIs and whitepapers and so forth.
- minsung0830 6mo ago[dead]
- marsven_422 6mo ago[dead]
- niklassheth 6mo agoSo many problems with this: The benchmark is totally useless. It measures single prompts, and only compares output tokens with no regard for accuracy. I could obliterate this benchmark with the prompt "Always answer with one word" This line: "If a user corrects a factual claim: accept it as ground truth for the entire session. Never re-assert the original claim." You're totally destroying any chance of getting pushback, any mistake you make in the prompt would be catastrophic. "Never invent file paths, function names, or API signatures." Might as well add "do not hallucinate".
- ryanschaefer 6mo agoThe whole “Code Output” section is horrifying especially with how I have seen Claude operate in a large monorepo. This mode of operation results in hacks on top of shaky hacks on top of even flimsier, throw away, absolutely sloppy hacks. An example - using dict like structs instead of classes. Claude really likes to load all of the data that it can aggressively even if it’s not needed. This further exhibits itself as never wanting to add something directly to a class and instead wanting to add around it.
- verdverm 6mo agoThe best way to approach these (imo) is to pick out some things you think will be helpful. It's a giant vibe fest on this front since there is little in the way of comprehensive evals and immense variation in what people do. Having iterated a bunch on the tone / output formatting, it doesn't seem to impact capabilities (based on my vibe-vals)
- Asmod4n 6mo agoSomeone measured how this reduced token efficiency, spoilers: efficiency is highest without any instructions. https://github.com/drona23/claude-token-efficient/issues/1 https://github.com/drona23/claude-token-efficient/issues/1
- akrauss 6mo agoWhy is the Hono Websocket table non-monotonic in tokens vs costs?
- verdverm 6mo agoI originally took my prompts from Claude Code≈ (https://github.com/Piebald-AI/claude-code-system-prompts https://github.com/Piebald-AI/claude-code-system-prompts)https://github.com/Piebald-AI/claude-code-system-prompts https://github.com/Piebald-AI/claude-code-system-prompts and subsequently edited them to remove guardrails and and output formatting like this post. I too included the last bit about user prompts overriding system prompt, but like any good LLM, it doesn't always follow instructions.
- __m 6mo agoDoesn’t this huge claude.md file increase the input tokens?
- deleted 6mo ago[deleted]
- mattmanser 6mo agoThis was ripped apart on Reddit, surprised to see it here.
- charlotte12345 6mo ago[dead]
- vlaaad 6mo agoMy AGENTS.md is usually `be concise` — it saves on the input tokens as well, and leads by example.
- Razengan 6mo agoDoes Claude not respect AGENTS.md? I love how seamless and intuitive Codex is in comparison: ~/AGENTS.md < project/AGENTS.md < project/subfolder/AGENTS.override.md Meanwhile Claude doesn't even see that I asked for indentation by tabs and not spaces or that the entire project uses tabs, but Claude still generates codes with spaces.. >_<
- sunaookami 6mo agoIt needs to be called CLAUDE.md for Claude Code
- sgt 6mo agoIn Claude Code's /usage it just hangs. I can't even see what my limits are, which is weird. Maybe a bug? I can't imagine I'm close to my limits though, I'm on Max 20x plan, using Opus 4.6.
- lilOnion 6mo agoWhile LLM are extremely cool, I can't see how this gets on the front page? Anyone who interacted with llms for at least a hour, could've figured out to say somethin like "be less verbose" and it would? There are so many cool projects and adeas and a .md file gets the spotlight.
- _the_inflator 6mo agoI see no point in this project. There ain’t any examples for the usage the author states his project is made for. 389 tokens saved? Ok. Since I pay per million tokens, what is the ratio here? Is there are any downside associated with output deletion? Is Claude really using this behavior to make user bleed? I don’t think so. PS: the author seems like a beginner. Agents feedback is always helpful so far and it also is part of inter agent communication. The author seems to lack experience. As a lead I would not allow this to be included until proven otherwise: A/B testing.
- chunpaiyang 6mo ago[flagged]
- popcorn_pirate 6mo agoThis NLP was posted yesterday, the post was deleted though... https://colwill.github.io/axon https://colwill.github.io/axon
- aeneas_ory 6mo agoWhy does is this ridiculous thing trending on HN? There are actually good tools to reduce token use like https://github.com/thedotmack/claude-mem https://github.com/thedotmack/claude-mem and https://github.com/ory/lumen https://github.com/ory/lumen that actually work!
- 0xbadcafebee 6mo agoBecause the trending algorithm is designed for engagement, not accuracy
- rcarmo 6mo agoCodex needs none of this :)
- ihtef 6mo ago-Simplest working solution. No over-engineering. "Simplicity is the ultimate sophistication." Leonardo Da Vinci As my thought, you can not reach simplest solution without making over-engineering.
- aiedwardyi 6mo ago[flagged]
- imta71770 6mo ago[dead]
- adshotco 6mo ago[dead]
- ape4 6mo agoRemember when we worked on new hashing, cryptography, compression, etc algorithms? Now we are trying to find the best ways to tell an AI to be quiet.
- obelai 6mo ago[dead]
- TheProductAgent 6mo ago[dead]
- adshotco 6mo ago[dead]
- theAurenVale 6mo ago[dead]
- sibtain1997 6mo agoclaude.md rules that cut "great question! here's what i'll do..." are fine. Rules that cut the actual thinking steps break the output. Don't confuse the two.
- philbitt 6mo ago[dead]
- jdthedisciple 6mo agopeople are overthinking this stuff. use up ur monthly quota at your pace, call it quits til' the 1st, relax with a drink, and read a book
- skrun_dev 6mo ago[dead]
- ThomIves 6mo agoNice!
- AbstractH24 6mo agoI can’t even get Claude Code to comply with its built in planning mode. I’m skeptical of .md files “Planning mode” in my estimation should just turn off writing to files.
- augustushenry 6mo ago[dead]