10 ms·

Caveman: Why use many token when few token do trick

• setnone 7mo ago
  caveman multilingo? how sound?
• ArekDymalski 7mo ago
  While really useful now, I'm afraid that in the long run it might accelerate the language atrophy that is already happening. I still remember that people used to enter full questions in Google and write SMS with capital letters, commas and periods.
  • vova_hn2 7mo ago
    > I still remember that people used to enter full questions in Google I think that, in the early days of internet search, entering full questions actually produced worse results than just a bunch of keywords or short phrases. So it was a sign of a "noob", rather than a mark of sophistication and literacy.
    • jagged-chisel 7mo ago
      “Sophistication and literacy” are orthogonal to the peculiarities of a black box search engine. Those literate sophisticates would still be noobs at getting something useful from Google.
  • dahart 7mo ago
    My kids made fun of me yesterday when they saw me using a question mark in a search query.
• deleted 7mo ago
  [deleted]
• andai 7mo ago
  No articles, no pleasantries, and no hedging. He has combined the best of Slavic and Germanic culture into one :)
  • samus 7mo ago
    Both Slavic languages and German have complex declination systems for nouns, verbs, and adjectives. Which is unlike stereotypical caveman speech.
    • iammjm 7mo ago
      I speak German, Polish, and English fluently and my take is: German is very precise, almost mathematical, there is little room to be misunderstood. But it also requires the most letters. English is the quickest, get things done kind of language, very compressible , but also risks misunderstanding. Polish is the most fun, with endless possibilities of twisting and bending it's structures, but also lacking the ease of use of English or the precision of German. But it's clearly just my subjective take
    • deleted 7mo ago
      [deleted]
• TeMPOraL 7mo ago
  Oh boy. Someone didn't get the memo that for LLMs, tokens are units of thinking. I.e. whatever feat of computation needs to happen to produce results you seek, it needs to fit in the tokens the LLM produces. Being a finite system, there's only so much computation the LLM internal structure can do per token, so the more you force the model to be concise, the more difficult the task becomes for it - worst case, you can guarantee not to get a good answer because it requires more computation than possible with the tokens produced. I.e. by demanding the model to be concise, you're literally making it dumber. (Separating out "chain of thought" into "thinking mode" and removing user control over it definitely helped with this problem.)
  • andai 7mo ago
    I remember a while back they found that replacing reasoning tokens with placeholders ("....") also boosted results on benchies. But does talk like caveman make number go down? Less token = less think? I also wondered, due to the way LLMs work, if I ask AI a question using fancy language, does that make it pattern match to scientific literature, and therefore increase the probability that the output will be true?
  • baq 7mo ago
    Do you know of evals with default Claude vs caveman Claude vs politician Claude solving the same tasks? Hypothesis is plausible, but I wouldn’t take it for granted
  • Rexxar 7mo ago
    > Someone didn't get the memo that for LLMs, tokens are units of thinking. Where do you get this memo ? Seems completely wrong to me. More computation does not translate to more "thinking" if you compute the wrong things (ie things that contribute significantly to the final sentence meaning).
    • staminade 7mo ago
      That’s why you need filler words that contribute little to the sentence meaning but give it a chance to compute/think. This is part of why humans do the same when speaking.
• andai 7mo ago
  So it's a prompt to turn Jarvis into Hulk!
• zahirbmirza 7mo ago
  You can also make huge spelling mistakes and use incomplete words with llms they just sem to know better than any spl chk wht you mean. I use such speak to cut my time spent typing to them.
  • floriangoebel 7mo ago
    Wouldn't this increase your token usage because the tokenizer now can't process whole words, but it needs to go letter by letter?
    • literalAardvark 7mo ago
      It doesn't go letter by letter, so not with current tokenizers. There will likely be some internal reasoning going "I wonder if the user meant spell check, I'm gonna go with that one". And it'll also bias the reasoning and output to internet speak instead of what you'd usually want, such as code or scientific jargon, which used to decrease output quality. I'm not sure if it still does
• VadimPR 7mo ago
  Wouldn't this affect quality of output negatively? Thanks to chain of thought, actually having the LLM be explicit in its output allows it to have more quality.
  • functional_dev 7mo ago
    Chain of thought happens in the <think> tags, not the visible output. Caveman only strips filler from what you see... the reasoning depth stays the same. I found this visualisation pretty interesting - https://vectree.io/c/chain-of-thought-reasoning-how-llms-think-step-by-step https://vectree.io/c/chain-of-thought-reasoning-how-llms-thi...
• teekert 7mo ago
  Idk I try talk like cavemen to claude. Claude seems answer less good. We have more misunderstandings. Feel like sometimes need more words in total to explain previous instructions. Also less context is more damage if typo. Who agrees? Could be just feeling I have. I often ad fluff. Feels like better result from LLM. Me think LLM also get less thinking and less info from own previous replies if talk like caveman.
  • deleted 7mo ago
    [deleted]
  • jaccola 7mo ago
    Yes because in most contexts it has seen "caveman" talk the conversations haven't been about rigorously explained maths/science/computing/etc... so it is less likely to predict that output.
  • cyanydeez 7mo ago
    Fluff adds probable likeness. Probablelikeness brings in more stuff. More stuff can be good. More stuff can poison.
  • WarmWash 7mo ago
    In the regular people forums (twitter, reddit), you see endless complaints about LLMs being stupid and useless. But you also catch a glimpse of how the author of the complaint communicates in general... "im trying to get the ai to help with the work i am doing to give me good advice for a nice path to heloing out and anytim i askin it for help with doing this it's total trash i dunt kno what to do anymore with this dum ai is so stupid"
    • kristopolous 7mo ago
      The realization is LLMs are computer programs. You orchestrate them like any other program and you get results. Everyone's interfaces, concept and desires are different so the performance is wildly varied This is similar to frameworks: they were either godsends or curses depending on how you thought and what you were doing ..
• bhwoo48 7mo ago
  I was actually worried about high token costs while building my own project (infra bundle generator), and this gave me a good laugh + some solid ideas. 75% reduction is insane. Starred
• gozzoo 7mo ago
  I think this could be very useful not when we talk to the agent, but when the agents talk back to us. Usually, they generate so much text that it becomes impossible to follow through. If we receive short, focused messages, the interaction will be much more efficient. This should be true for all conversational agents, not only coding agents.
  • pixelpoet 7mo ago
    > Usually, they generate so much text that it becomes impossible to follow through. Quite often on reddit I'll write two paragraphs and get told "I'm not reading all that". Really? Has basic reading become a Herculean task?
    • golem14 7mo ago
      I think the sentiment here is that the short formulation of Kant's categorical imperative is as good and easier to read than the entirety of "types of ethical theory" (J.J. Martineau).
    • 0xpgm 7mo ago
      Not specifically about your case, but some people are usually just more verbose than others and tend to say the same thing more than once, or perhaps haven't found a clear way of articulating their thoughts down to fewer words.
    • vova_hn2 7mo ago
      > Has basic reading become a Herculean task? I find LLM slop much harder to read than normal human text. I can't really explain it, it's just a feeling. The feeling that it draaaags and draaaaaags and keeeeeps going on and on and on before getting to the point, and by the time I'm done with all the "fluff", I don't care what is the text about anymore, I just want to lay down and rest.
      • gozzoo 7mo ago
        Same here. The text is pretty smooth and there is nothing that stands out to sustain my attention, at least that's my interpretation
• ryanschaefer 7mo ago
  Kinda ironic this description is so verbose. > Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman For the first part of this: couldn’t this just be a UserSubmitPrompt hook with regex against these? See additionalContext in the json output of a script: https://code.claude.com/docs/en/hooks#structured-json-output https://code.claude.com/docs/en/hooks#structured-json-output For the second, /caveman will always invoke the skill /caveman: https://code.claude.com/docs/en/skills https://code.claude.com/docs/en/skills
• Hard_Space 7mo ago
  Also see https://arxiv.org/pdf/2604.00025 https://arxiv.org/pdf/2604.00025 ('Brevity Constraints Reverse Performance Hierarchies in Language Models' March 2026)
• saidnooneever 7mo ago
  LOL it actually reads how humans reply the name is too clever :'). Not sure how effective it will be to dirve down costs, but honestly it will make my day not to have to read through entire essays about some trivial solution. tldr; Claude skill, short output, ++good.
• virtualritz 7mo ago
  This is the best thing since I asked Claude to address me in third person as "Your Eminence". But combining this with caveman? Gold!
  • eMPee584 7mo ago
    f.e.?
• bogtog 7mo ago
  I'd be curious if there were some measurements of the final effects, since presumably models wont <think> in caveman speak nor code like that
• stared 7mo ago
  I would prefer to talk like Abathur (https://www.youtube.com/watch?v=pw_GN3v-0Ls https://www.youtube.com/watch?v=pw_GN3v-0Ls). Same efficiency but smarter.
• cadamsdotcom 7mo ago
  Caveman need invent chalk and chart make argument backed by more than good feel.
• rschiavone 7mo ago
  This trick reminds me of "OpenAI charges by the minute, so speed up your audio" https://news.ycombinator.com/item?id=44376989 https://news.ycombinator.com/item?id=44376989
  • vntok 7mo ago
    Which worked great. Also, cut off silences. > One half interesting / half depressing observation I made is that at my workplace any meeting recording I tried to transcribe in this way had its length reduced to almost 2/3 when cutting off the silence. Makes you think about the efficiency (or lack of it) of holding long(ish) meetings.
• nayroclade 7mo ago
  Cute idea, but you're never gonna blow your token budget on output. Input tokens are the bottleneck, because the agent's ingesting swathes of skills, directory trees, code files, tool outputs, etc. The output is generally a few hundred lines of code and a bit of natural language explanation.
  • DimitriBouriez 7mo ago
    Good point and it's actually worse than that : the thinking tokens aren't affected by this at all (the model still reasons normally internally). Only the visible output that gets compressed into caveman... and maybe the model actually need more thinking tokens to figure out how to rephrase its answer into caveman style
    • zozbot234 7mo ago
      Grug says you can tune how much each model thinks. Is not caveman but similar. also thinking is trained with RL so tends to be efficient, less fluffy. Also model (as seen locally) always drafts answer inside thinking then output repeats, change to caveman is not really extra effort.
  • konaraddi 7mo ago
    In single-turn use, yeah, but across dozens of turns there's probably value in optimizing the output. Btw your point lands just as well without "Cute idea, but" https://odap.knrdd.com/patterns/condescending-reveal https://odap.knrdd.com/patterns/condescending-reveal
    • nayroclade 7mo ago
      I didn't mean it as condescending. I meant it literally is cute: A neat idea that is quite cool in its execution.
    • johnfn 7mo ago
      Pretty neat site you've got there. You should submit it to Show HN. I had fun clicking around - it's like TVTropes, except the examples make me angry, lol. It would be pretty fun to train an LLM on this site and then have it flag my comments before I get downvoted, haha.
• deleted 7mo ago
  [deleted]
• doe88 7mo ago
  > If caveman save you mass token, mass money — leave mass star. Mass fun. Starred.
• vivid242 7mo ago
  Great idea- if the person who made it is reading: Is this based on the board game „poetry for cavemen“? (Explain things using only single-syllable words, comes even with an inflatable log of wood for hitting each other!)
• samus 7mo ago
  There's linguistic term for this kind of speech: isolating grammars, which don't decline words and use high context and the bare minimum of words to get the meaning across. Chinese is such a language btw. Don't know what Chinese think about their language being regarded as cavemen language...
  • akdor1154 7mo ago
    I thought the term for those were 'sane languages', and I say that as a native English speaker :)
    • samus 7mo ago
      As a non-native English speaker I think English is actually not that bad. Just the orthography is beyond awful :)
  • adrian_b 7mo ago
    The fact whether a language is isolating, or not, is independent on the redundancy of the language. All languages must have means for marking the syntactic roles of the words in a sentence. The roles may be marked with prepositions or postpositions in isolating languages, or with declensions in fusional languages, or there may be no explicit markers when the word order is fixed (i.e. the same distinction as between positional arguments and arguments marked by keywords, in programming languages). The most laconic method for both programming languages and natural languages is to have a default word order where role markers are omitted, but to also allow any other word order if role markers are present. Besides the mandatory means for marking syntactic roles, many languages have features that add redundancy without being necessary for understanding, i.e. which repeat already known information, for instance by repeating the information about gender and number that is attached to a noun also besides all its attributes. Whether a language requires redundancy or not is independent on whether it is an isolating language or a fusional language. English has somewhat less syntactic role markers than other languages because it has a rigid word order, but for the other roles than the most frequent roles (agent, patient, beneficiary) it has a lot of prepositions. Despite being more economic in role markers, English also has many redundant words that could be omitted, e.g. subjects or copulative verbs that are omitted in many languages. Thus for English it is possible to speak "like a caveman" without losing much information, but this is independent of the fact that modern English is a mostly isolating language with few remnants of its old declensions.
• vova_hn2 7mo ago
  I don't know about token savings, but I find the "caveman style" much easier to read and understand than typical LLM-slop.
• norskeld 7mo ago
  APL for talking to LLM when? Also, this reminded me of that episode from The Office where Kevin started talking like a caveman to make communication efficient.
• bjackman 7mo ago
  If this really works there would seem to be a lot of alpha in running the expensive model in something like caveman mode, and then "decompressing" into normal mode with a cheap model. I don't think it would be fundamentally very surprising if something like this works, it seems like the natural extension to tokenisation. It also seems like the natural path towards "neuralese" where tokens no longer need to correspond to units of human language.
  • Perz1val 7mo ago
    But it can't, we see models get larger and larger and larger models perform better. <Thinking> made such huge improvements, because it makes more text for the language model to process. Cavemanising (lossy compression) the output does it to the input as well.
    • spacemanspiff01 7mo ago
      but some tokens are not really needed? This is probably bad because it is mismatched with training set, but if you trained a model on a dataset removing all prepositions (or whatever caveman speak is), would you have a performance degradation compared to the same model trained on the same dataset without the caveman translation?
  • tatrions 7mo ago
    [flagged]
• veselin 7mo ago
  This is an experiment that, although not to this extreme, was tested by OpenAI. Their responses API allow you to control verbosity: https://developers.openai.com/api/reference/resources/responses/methods/create#(resource)%20responses%20%3E%20(method)%20create%20%3E%20(params)%200.non_streaming%20%3E%20(param)%20text%20%3E%20(schema)%20%2B%20(resource)%20responses%20%3E%20(model)%20response_text_config%20%3E%20(schema)%20%3E%20(property)%20verbosity https://developers.openai.com/api/reference/resources/respon... I don't know their internal eval, but I think I have heard it does not hurt or improve performance. But at least this parameter may affect how many comments are in the code.
• owenthejumper 7mo ago
  What is that binary file caveman.skill that I cannot read easily, and is it going to hack my computer.
• kukakike 7mo ago
  This is exactly what annoys me most. English is not suitable for computer-human interaction. We should create new programming and query languages for that. We are again in cobol mindset. LLM are not humans and we should stop talking to them as if they are.
  • zozbot234 7mo ago
    Grug says Chinese more suitable, only few runes in word, each take single token. Is great.
• fzeindl 7mo ago
  I tried this with early ChatGPT. Asked it to answer telegram style with as few tokens as possible. It is also interesting to ask it for jokes in this mode.
  • amelius 7mo ago
    It's especially funny to change your coworker's system prompt like that.
• sillyboi 7mo ago
  Oh, another new trend! I love these home-brewed LLM optimizers. They start with XML, then JSON, then something totally different. The author conveniently ignores the system prompt that works for everything, and the extra inference work. So, it's only worth using if you just like this response style, just my two cents. All the real optimizations happen during model training and in the infrastructure itself.
• hybrid_study 7mo ago
  Mongo! No caveman
• DonHopkins 7mo ago
  Deep digging cave man code reviews are Tha Shiznit: https://www.youtube.com/watch?v=KYqovHffGE8 https://www.youtube.com/watch?v=KYqovHffGE8
• fny 7mo ago
  Are there any good studies or benchmarks about compressed output and performance? I see a lot of arguing in the comments but little evidence.
• ajd555 7mo ago
  So, if this does help reduce the cost of tokens, why not go even further and shorten the syntax with specific keywords, symbols and patterns, to reduce the noise and only keep information, almost like...a programming language?
• adam_patarino 7mo ago
  [flagged]
  • dostick 7mo ago
    How is your offering different from local ollama?
    • adam_patarino 7mo ago
      Its batteries included. No config. We also fine tuned and did RL on our model, developed a custom context engine, trained an embedding model, and modified MLX to improve inference. Everything is built to work with each other. So it’s more like an apple product than Linux. Less config but better optimized for the task.
      • dostick 7mo ago
        I only understood half of the tech jargon in your answer. If I understood it all I’d probably run it myself. If someone who is less knowing than me is your customer, you need to explain in simpler terms!
        • adam_patarino 7mo ago
          Fair enough! The simple answer is: we did a lot of work to make the model better at coding without requiring complicated installation or configuration. One comman to install and run. All the benefits of claude code, without any of the limitations or rug pulls.
          • dostick 7mo ago
            I’m not nitpicking, but you’re saying better than Claude or Codex? Is it also focused and tested mainly on web/JS technologies? It’s still berry much uphill battle building native apps. I think there’s untapped market for Swift / Android coding models.
• amelius 7mo ago
  By the way why don't these LLM interfaces come with a pause button?
  • amelius 7mo ago
    And a "prune here" button. It often happens that the interesting information is in the first paragraph or so, and the remainder is all just the LLM not knowing when to stop. This is super annoying as a conversation then ends up being 90% noise.
    • postalcoder 7mo ago
      Pruning an assistant's response like that would break prompt caching. Prompt caching is probably the single most important thing that people building harnesses think about and yet it's mind share in end users is virtually zero. If you had to think of all the weirdest, most seemingly baffling design decisions in an AI product, the answer to "why" is probably "to not break prompt caching".
      • amelius 7mo ago
        Maybe so, but pruning is still a useful feature. If it hurts performance that much, maybe pruning could just hide the text leaving the cache intact?
      • zozbot234 7mo ago
        Grug says prompt caching just store KV-cache which is sequenced by token. Easy cut it back to just before edit. Then regenerate after is just like prefill but tiny.
  • stainablesteel 7mo ago
    i imagine they're doing superman level distributed compute across multiple clouds somewhere and cared more about delivering the final result of that than having the ability to pause. which is probably possible, but would require way more work than would be worthwhile. they probably thought the ability to stop and resubmit would be an adequate substitute.
• Robdel12 7mo ago
  I didn’t comment on this when I saw it on threads/twitter. But it made it to HN, surprisingly. I have a feeling these same people will complain “my model is so dumb!”. There’s a reason why Claude had that “you’re absolutely right!” for a while. Or codex’s “you’re right to push on this”. We’re basically just gaslighting GPUs. That wall of text is kinda needed right now.
• FurstFly 7mo ago
  Okay, I like how it reduces token usage, but it kind of feels that, it will reduce the overall model intelligence. LLMs are probabilistic models, and you are basically playing with their priors.
  • sheiyei 7mo ago
    If you take meaningless tokens (that do not contribute to subject focus), I don't see what you would lose. But as this takes out a lot of contextual info as well, I would think it might be detrimental.
• HarHarVeryFunny 7mo ago
  More like Pidgin English than caveman, perhaps, although caveman does make for a better name.
• bitwize 7mo ago
  grug have to use big brains' thinking machine these days, or no shiny rock. complexity demon love thinking machine. grug appreciate attempt to make thinking machine talk on grug level, maybe it help keep complexity demon away.
• phtrivier 7mo ago
  Soma (aka tiktok) and Big Brother (aka Meta) already happened without government coercion, only makes sense that we optimize ourselves for newspeak. Thank God there is still neverending wars, otherwise authoritarian governments would have no fun left.
  • namanyayg 7mo ago
    I was aware of how google/facebook is like the panopticon big brother but I never connected the algorithmic feed to soma! Good insight.
    • phtrivier 7mo ago
      Not mine, to be honest. And people keep comparing compulsive binge watching to the "infinite jest" from D.C.Wallace (I could not tell, the brick is sitting barely touched on my shelves, but I'm not insulting the future.) I'm tired of living in an ironic remix of everyone's favorite distopia. Time for someone to write optimistic sci-fi to give everyone something nice to implement when they're adults. Bring us back Jules Verne. Let's have the Jetson's life for real. Put Ted Lasso in space. Given their training material, "futuristic stories with nice people getting their happy ending" is not something big tech AI is going to spit anytime soon, so that's a niche to take on !
• xpe 7mo ago
  Unfrozen caveman lawyer here. Did "talk like caveman" make code more bad? Make unsubst... (AARG) FAKE claims? You deserve compen... AAARG ... money. AMA.
• sebastianconcpt 7mo ago
  Anyone else worried about the long term consequences of the influence of talking like this all day for the cognitive system of the user?
  • Perz1val 7mo ago
    I think good, less thinking for you, more thinking you will do
    • dalmo3 7mo ago
      I'm not sure if you're being sarcastic or not, but I did find the caveman examples harder to read than their verbose counterpart. The verbose ones I could speed read, and consume it at a familiar pace... Almost on autopilot. Caveman speak no familiar no convention, me no know first time. Need think hard understand. Slower. Good thing?
      • Perz1val 7mo ago
        That was my point. You (and I) tend to read verobose text and not argue with it, our brains are spoonfed reasoning chains and they seem to make sense. Caveman breaks that, so we have to actually think, there is no "thinking" done for us
  • sph 7mo ago
    “Me think, why waste time say lot word, when few word do trick.” — Kevin Malone
• xgulfie 7mo ago
  Funny how people are so critical of this and yet fawn over TOON
• abejfehr 7mo ago
  There’s a lot of debate about whether this reduces model accuracy, but this is basically Chinese grammar and Chinese vibe coding seems to work fine while (supposedly) using 30-40% less tokens
  • silon42 7mo ago
    It's like googling... if you have skillz/experience you can google almost anything with 3-4 words...
• padolsey 7mo ago
  This is fun. I'd like to see the same idea but oriented for richer tokens instead of simpler tokens. If you want to spend less tokens, then spend the 'good' ones. So, instead of saying 'make good' you could say 'improve idiomatically' or something. Depends on one's needs. I try to imagine every single token as an opportunity to bend/expand/limit the geometries I have access to. Language is a beautiful modulator to apply to reality, so I'll wager applying it with pedantic finesse will bring finer outputs than brutish humphs of cavemen. But let's see the benchmarks!
  • philsnow 7mo ago
    I'm reminded by the caveman skill of the clipped writing style used in telegrams, and your post further reminded me of "standard" books of telegram abbreviations. Take a look at [0]; could we train models to use this kind of code and then decode it in the browser? These are "rich" tokens (they succinctly carry a lot of information). [0] https://books.google.com/books?id=VO4OAAAAYAAJ&pg=PA464#v=onepage&q&f=false https://books.google.com/books?id=VO4OAAAAYAAJ&pg=PA464#v=on...
    • derefr 7mo ago
      I would point out that the default BPE tokenization vocabulary used by many models (cl100k_base) is already a pretty powerful shorthand. It has a lot of short tokens, sure. But then: Token ID 73700 is the literal entire (space-prefixed) word " strawberry". (Which neatly explains the "strawberry problem.") Token ID 27128 is " cryptocurrency". (And 41698 is " disappointment".) Token ID 44078 is " UnsupportedOperationException"! Token ID 58040 is 128 spaces in a row (and is the longest token in the vocabulary.) You'd be surprised how well this vocabulary can compress English prose — especially prose interspersed with code!
    • beau_g 7mo ago
      For a while I was missing the ability one uses all the time in stable diffusion prompts of using parentheses and floats to emphasize weight to different parts of the prompt. The more I thought about how it would work in an LLM though, the more I realized it's just reinventing code syntax and you could just give a code snippet to the LLM prompt.
• ungreased0675 7mo ago
  Does this actually result in less compute, or is it adding an additional “translate into caveman” step to the normal output?
• deleted 7mo ago
  [deleted]
• postalcoder 7mo ago
  I disagree with this method and would discourage others from using it too, especially if accuracy, faster responses, and saving money are your priorities. This only makes sense if you assume that you are the consumer of the response. When compacting, harnesses typically save a copy of the text exchange but strip out the tool calls in between. Because the agent relies on this text history to understand its own past actions, a log full of caveman-style responses leaves it with zero context about the changes it made, and the decisions behind them. To recover that lost context, the agent will have to execute unnecessary research loops just to resume its task.
  • shomp 7mo ago
    me disagree
  • jruz 7mo ago
    only you auto-compact. auto-compact bad
    • renewiltord 7mo ago
      Ironically a demonstration of the risk of using fewer tokens. A typo more drastically changes meaning.
• shomp 7mo ago
  everyone who thinks this is a costly or bad idea is looking past a very salient finding: code doesn't need much language. sure, other things might need lots of language, but code does not. code is already basically language, just a really weird one. we call them programming languages. they're not human languages. they're languages of the machine. condensing the human-language---machine-language interface, good. if goal make code, few word better. if goal make insight, more word better. depend on task. machine linear, mind not. consider LLM "thinking" is just edge-weights. if can set edge-weights into same setting with fewer tokens, you are winning.
  • justonceokay 7mo ago
    JOOK like when machine say facts. Machine and facts are friends. Numbers and names and “probably things” are all friends with machine. JOOK no like when machine likes things. Maybe double standard. But forever machines do without like and without love. New like and love updates changing all the time. Makes JOOK question machine watching out for JOOK or watching out for machine. JOOK like and love enough for himself and for machine too..
  • wvenable 7mo ago
    > They're not human languages. they're languages of the machine. Disagree. Programming language for human to communicate with machine and human and human to communicate about machine. Programming language not native language of machine. Programming language for humans. Otherwise make good point.
• goldenarm 7mo ago
  That's a great idea but has anyone benchmarked the performance difference?
• anshumankmr 7mo ago
  Though I do use Claude Code, is it possible to get this for Github Copilot too?
  • phainopepla2 7mo ago
    Yes, Copilot supports skills, which are basically just stored prompts in markdown files. You can use the same skill in that GitHub repo
• meidad_g 7mo ago
  [flagged]
• JBrussee-2 7mo ago
  Author here. A few people are arguing against a stronger claim than the repo is meant to make. As well, this was very much intended to be a joke and not research level commentary. This skill is not intended to reduce hidden reasoning / thinking tokens. Anthropic’s own docs suggest more thinking budget can improve performance, so I would not claim otherwise. What it targets is the visible completion: less preamble, less filler, less polished-but-nonessential text. Therefore, since post-completion output is “cavemanned” the code hasn’t been affected by the skill at all :) Also surprising to hear so little faith in RL. Quite sure that the models from Anthropic have been so heavily tuned to be coding agents that you cannot “force” a model to degrade immensely. The fair criticism is that my “~75%” README number is from preliminary testing, not a rigorous benchmark. That should be phrased more carefully, and I’m working on a proper eval now. Also yes, skills are not free: Anthropic notes they consume context when loaded, even if only skill metadata is preloaded initially. So the real eval is end-to-end: - total input tokens - total output tokens - latency - quality/task success There is actual research suggesting concise prompting can reduce response length substantially without always wrecking quality, though it is task-dependent and can hurt in some domains. (https://arxiv.org/html/2401.05618v3 https://arxiv.org/html/2401.05618v3) So my current position is: interesting idea, narrower claim than some people think, needs benchmarks, and the README should be more precise until those exist.
  • bdbdbdb 7mo ago
    Translation: It joke. No yell at me. It kind of work?
    • bbeonx 7mo ago
      Thank. Too much word, me try read but no more tokens.
      • EmmaMuhleman1 7mo ago
        Me die —> laughing floor
  • Chance-Device 7mo ago
• vurudlxtyt 7mo ago
  Grug brained developer meets AI tooling (https://grugbrain.dev https://grugbrain.dev)
  • testycool 7mo ago
    +1 Have used Grug as example for years to have LLM explain things to me.
  • Applejinx 7mo ago
    My first reaction was 'blatantly ripping off Grug', and I don't see why not to view it in that light.
• mwcz 7mo ago
  this grug not smart enough to make robot into grugbot. grug just say "Speak to grug with an undercurrent of resentment" and all sicko fancy go way.
• isuckatcoding 7mo ago
  Oh come on now one referenced this scene from the office?? https://youtu.be/_K-L9uhsBLM?si=ePiGrFd546jFYZd8 https://youtu.be/_K-L9uhsBLM?si=ePiGrFd546jFYZd8
• Adam_cipher 7mo ago
  [dead]
• arrty88 7mo ago
  Feels like there should be a way to compile skills and readme’s and even code files into concise maps and descriptions optimized for LLMs. They only recompile if timestamps are modified.
• itpcc 7mo ago
  But will it lose some context, like Kevin’s small talk? (https://www.youtube.com/watch?v=_K-L9uhsBLM https://www.youtube.com/watch?v=_K-L9uhsBLM) Like "Sea world" or "see the world".
• nharada 7mo ago
  I wonder if this will actually be why the models move to "neuralese" or whatever non-language latent representation people work out. Interpretability disappears but efficiency potentially goes way up. Even without a performance increase that would be pretty huge.
• herf 7mo ago
  We need a high quality compression function for human readers... because AIs can make code and text faster than we can read.
• tatrions 7mo ago
  [flagged]
• throwatdem12311 7mo ago
  Ok but when the model is responding to you isn’t the text it’s generating also part of the context it’s using to generate the next token as it goes? Wouldn’t this just make the answers…dumb?
• RomanPushkin 7mo ago
  Why the skill should have three absolutely similar SKILL.md files? Just curious
• thorfinnn 7mo ago
  kevin would be proud
• derefr 7mo ago
  I've always figured that constraining an LLM to speak in any way other than the default way it wants to speak, reduces its intelligence / reasoning capacity, as at least some of its final layers can be used (on a per-token basis) either to reason about what to say, or about how to say it, but not both at once. (And it's for a similar reason, I think, that deliberative models like rewriting your question in their own terms before reasoning about it. They're decreasing the per-token re-parsing overhead of attending to the prompt [by distilling a paraphrase that obviates any need to attend to the literal words of it], so that some of the initial layers that would either be doing "figure out what the user was trying to say" [i.e. "NLP stuff"] or "figure out what the user meant" [i.e. deliberative-reasoning stuff] — but not both — can focus on the latter.) I haven't done the exact experiment you'd want to do to verify this effect, i.e. "measuring LLM benchmark scores with vs without an added requirement to respond in a certain speaking style." But I have (accidentally) done an experiment that's kind of a corollary to it: namely, I've noticed that in the context of LLM collaborative fiction writing / role-playing, the harder the LLM has to reason about what it's saying (i.e. the more facts it needs to attend to), the spottier its adherence to any "output style" or "character voicing" instructions will be.
  • svachalek 7mo ago
    I think this is on point, I've really started to think about LLMs in terms of attention budget more than tokens. There's only so many things they can do at once, which ones are most important to you?
    • krackers 7mo ago
      Outputting "filler" tokens is also basically doesn't require much "thinking" for an LLM, so the "attention budget" can be used to compute something else during the forward passes of producing that token. So besides the additional constraints imposed, you're also removing one of the ways which it thinks. Explicit COT helps mitigates some of this, but if you want to squeeze out every drop of computational budget you can get, I'd think it beneficial to keep the filler as-is. If you really wanted just have a separate model summarize the output to remove the filler.
• indiantinker 7mo ago
  It speaks like Kevin from The Office (US) https://youtube.com/shorts/sjpHiFKy1g8?is=M0H4G2o0d6Z-pBAC https://youtube.com/shorts/sjpHiFKy1g8?is=M0H4G2o0d6Z-pBAC
• yesthisiswes 7mo ago
  Why use lot word when few word do fine.
• somethingsome 7mo ago
  I would like to see a (joke) skill that makes Claude talk in only toki pona. My guess is that it would explode the token count though.
• yakattak 7mo ago
  I was wondering just yesterday if a model of “why waste time say lot word when few word do trick” would be easier on the tokens. I’ll have to give this a try lol
• us321 7mo ago
  I like
• tapoxi 7mo ago
  This is neat but my employer rates my performance based on token consumption; is there one that makes Claude needlessly verbose?
  • eclipticplane 7mo ago
    After every loop, instruct it to ELI5 what it did into `/tmp`.
  • outworlder 7mo ago
    Is this a joke, or are you serious? Do you work for Nvidia?
    • hshsiejensjsj 7mo ago
      I’m not poster above but I work at Meta and they are doing this unfortunately. Wish it was a joke.
      • touristtam 7mo ago
        I am in a regular company that has fortunately a tech department for in-house software and this is the absolute opposite; we're currently trying to convince leadership that using tokens is part of the game for LLM adoption.
    • DedlySnek 7mo ago
      This isn't a joke anymore I'm afraid. In my company there's a big push to use as much AI as possible. Mine isn't even a big and/or famous company.
    • dbg31415 7mo ago
      1996 Boss: "Let's look at the lines of code you produced today." 2026 Boss: "Let's look at the AI tokens you used today." The technology changes, but the micromanagement layer stays exactly the same. Time is a circle, my friend. (=
    • dysoco 7mo ago
• globalchatads 7mo ago
  [dead]
• rsynnott 7mo ago
  I mean, I assume you run into the same problem as Kevin in the office; that sort of faux-simple speech is actually very ambiguous. (Though, I wonder has anyone tried Newspeak.)
• semessier 7mo ago
  the real interesting question would be if it then does its language-based reasoning also in short form and if so if quality is impacted.
• K0IN 7mo ago
  So you are telling me I prompted llms the right way all along
• alfanick 7mo ago
  Either this already exists, or someone is going to implement that (should I implement that?): - assumption LLM can input/output in any useful language, - human languages are not exactly optimal away to talk with LLM, - internally LLMs keep knowledge as whole bunch of connections with some weights and multiple layers, - they need to decode human-language input into tokens, then into something that is easy to digest by further layers, then get some output, translate back into tokens and human language (or programming language, same thing), - this whole human language <-> tokens <-> input <-> LLM <-> output <-> tokens <-> language is quite expensive. What if we started to talk to LLMs in non-human readable languages (programming languages are also just human readable)? Have a tiny model run locally that translates human input, code, files etc into some-LLM-understandable-language, LLM gets this as an input, skips bunch of layers in input/output, returns back this non-human readable language, local LLM translates back into human language/code changes. Yesterday or two days ago there was a post about using Apple Fundamental Models, they have really tiny context window. But I think it could be used as this translation layer human->LLM, LLM->human to talk with big models. Though initially those LLMs need to discover which is "language" they want to talk with, feels like doable with reinforcement learning. So cheap local LLM to talk to big remote LLM. Either this is done already, or it's a super fun project to do.
  • 999900000999 7mo ago
    My theory was that someone should write a specific LLM language, and then spend a whole lot of money to train models using that. A few times other commenters here have pointed out that that would be really difficult . But I think you're onto something, human languages just aren't optimal here. But to actually see this product to conclusion you'd probably need 60 to 100 million. You would have to completely invent a new language and awesome invent new training methods on top of it. I'm down if someone wants to raise a VC round.
    • alfanick 7mo ago
      I'm currently downloading Ollama and going to write a simple proof-of-concept with Qwen as local "frontend", talking to OpenAI GPT as "backend". I think the idea is sound, but indeed needs retraining of GPT (hmm like training tiny local LLM in synchronization of a big remote LLM). It might be not bad business venture in the end. I don't think humans should be involved in developing this AI-AI language, just giving some guidance, but let two agents collaborate to invent the language, and just gratify/punish them with RL methods. OpenAI looking at you, got an email some days ago "you're not using OpenAI API that much recently, what changed?"
• crispyambulance 7mo ago
  I no like. It sort of reminds me of when palm-pilots (circa late-90's early 2000's) used short-hand gestures for stylus-writing characters. For a short while people's handwriting on white-boards looked really bizarre. Except now we're talking about using weird language to conserve AI tokens. Maybe it's better to accept a higher token burn-rate until things get better? I'd rather not get used to AI jive-talk to get stuff done.
• contingencies 7mo ago
  Better: use classical Chinese.
• Surac 7mo ago
  me like that
• dakolli 7mo ago
  The input costs for your prompt is the least expensive, and negligible cost when using agents. Its context & output, why go through all this?
• meidad_g 7mo ago
  [flagged]
• kristopolous 7mo ago
  This is a well known compaction technique. Where are the evals
• maxbeech 7mo ago
  [dead]
• tonymet 7mo ago
  me ChatGPT like caveman always. Typing also faster.
• ggm 7mo ago
  F u cn Rd ths u cld wrk scrtry 'cpt w tk l thr jbs
• fissible 7mo ago
  I have always been annoyed at the verbosity of ChatGPT and (to a lesser degree) Claude. I am aware of the long-term costs associated with trading that bloated context back and forth all the time.
• aetherspawn 7mo ago
  Interesting, maybe you can run the output through a 2B model to uncompress it.
• harimau777 7mo ago
  Dumb question: Is what cavemen sound like the same in every culture? Like I know that different cultures have different words for "woof" or "meow"; so it stands to reason maybe also for cavemans speech?
• jongjong 7mo ago
  Me think this good idea. Regular language unnecessary complex. Distract meaning. Me wish everyone always talk this way. No hidden spin manipulate emotion. Information only. Complexity stupid.
• Art9681 7mo ago
  This was an experiment conducted during gpt-3.5 era, and again during the gpt-4 era. There is a reason it is not a common/popular technique.
• anigbrowl 7mo ago
  Nothing against this project, it's been the case since forever that you could get better quality responses by simple telling your LLM to be brief and to the point, to ask salient questions rather than reflexively affirm, and eschew cliches and faddish writing styles.
• drewbeck 7mo ago
  If you’re not cavemaxxing you’re falling behind.
  • grg0 7mo ago
    I dropped dead after reading this.
    • Applejinx 7mo ago
      As very much an outsider and, to some extent, apostate to all this, it's pretty astonishing to see. Unironically not just delegating all thinking to a sketchy and untrustworthy machine, but doubling down on it by aping the caveman in the belief that this will more effectively summon the great metal-wing sky god and bring limitless yum stuff. Wow. I don't even have to do anything. You guys are disemvoweling yourselves in some kind of strange ritual. You sure are trusting souls!
• Sim-In-Silico 7mo ago
  [dead]
• signalflow 7mo ago
  [dead]
• edinetdb 7mo ago
  [flagged]
• dr_kiszonka 7mo ago
  I appreciate the effort you put into addressing the feedback and updating the readme. I think the web design of your page and visual distractions in the readme go against the caveman's no-fluff spirit and may not appeal to the folks that would otherwise be into your software. I like the software.
• chmod775 7mo ago
  I cannot wait for this to become the normal and expected way to interact with LLMs in the coming decades as humanity reaches the limit of compute capacity. Why waste 3/4th? Maybe we could have a smaller LLM just for translating caveman back into redditor?
  • benjaminoakes 7mo ago
    I was already part caveman in my messages to the LLM. Now I full caveman.
• wktmeow 7mo ago
  So this is really weird, I was using OpenClaw with GPT 5.4 via Codex on I think Friday of last week, and I noticed what looked like thinking tokens spilling to the main chat, and it sounded a lot like this trick! Couple of examples of what I was seeing in the output: "Need resume task. No skill applies clearly. Need maybe memory? prior work yes need memory_search.” "Need maybe script content from history. Search specific.” Possible that OpenAI has come up with something very similar here? Edit: looks like not only me, https://github.com/openclaw/openclaw/issues/25592#issuecomment-4162950466 https://github.com/openclaw/openclaw/issues/25592#issuecomme...
• stronglikedan 7mo ago
  I feel justified! I've been prompting (not-agenting) like this for a while, and some of my colleagues have ribbed me for it. Now who laugh, JEFF!
• SamuelBraude 7mo ago
  We give spearheads to caveman Call it Ix Help caveman save even more tokens https://github.com/ix-infrastructure/Ix https://github.com/ix-infrastructure/Ix
• inzlab 7mo ago
  Little here little there its tokrns at the end