Universal Claude.md – cut Claude output tokens(github.com) |
Universal Claude.md – cut Claude output tokens(github.com) |
Sent from my iPhone
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.
There doesn't seem to be any adults left in the room.
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.
The entire hypothesis for doing this is somewhat dubious.
“Planning mode” in my estimation should just turn off writing to files.
lol, closed
Sounds like coming directly out of Umberto Eco's simple rules for writing.
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.
Meanwhile, their products:
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.)
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.
use up ur monthly quota at your pace, call it quits til' the 1st, relax with a drink, and read a book
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
> 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.
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.
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
[0] https://github.com/search?q=repo%3Aadam-s%2Fintercept%20hand...
* update our architecture.md and other key md files in folders affected by updates and learnings in this session. * update claude.md with changes in workflows/tooling/conventions (not project summaries) * commit
It's been pretty good so far. Nothing fancy. Recently I also asked to keep memories within the repo itself instead of in ~/.claude.
Only downside is it is slow but keeps enough to pass the baton. May be "handoff" would have been a better name!
I've got a separate script which parses the jsonl files that claude creates for sessions and indexes them in a local database for longer term searchability. A number of times I've found myself needing some detail I knew existed in some conversation history, but CC is pretty bad and slow at searching through the flat files for relevant content. This makes that process much faster and more consistent. Again, this is due to my lack of discipline with contexts. I'll be working with my recipe planner context and have a random idea that I just iterate with right there. Later I'll never remember that idea started from the recipe context. With this setup I don't have to.
Your system is great and I do similar, my problem is I have a bunch of sessions and forget to 'handoff'.
The clawbots handle this automatically with journals to save knowledge/memory.
Came here for the same reason.
I can't calculate how many times this exact section of Claude output let me know that it was doing the wrong thing so I could abort and refine my prompt.
As far as redundancy...it's quite useful according to recent research. Pulled from Gemini 3.1 "two main paradigms: generating redundant reasoning paths (self-consistency) and aggregating outputs from redundant models (ensembling)." Both have fresh papers written about their benefits.
Claude is already pretty light on flourishes in its answers, at least compared to most other SotA models. And for everything else it's not at all obvious to me which parts are useless. And benchmarking it is hard (as evidenced by this thread). I'd rather spend my time on something else
Not all extra tokens help, but optimizing for minimal length when the model was RL'd on task performance seems detrimental.
That was how I realized why the chat interfaces like to start with all that seemingly unnecessary/redundant text.
It basically seeds a document/dialogue for it to complete, so if you make it start out terse, then it will be less likely to get the right nuance for the rest of the inference.
Distilled mini/nano models need regular reminders about their objectives.
As documented by Manus https://manus.im/blog/Context-Engineering-for-AI-Agents-Less...
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.
There are a few papers actually that describe how to get faster results and more economic sessions by instructing the LLM how to compress its thinking (“CCoT” is a paper that I remember, compressed chain of thought). It basically tells the model to think like “a -> b”. There’s loss in quality, though, but not too much.
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".
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.
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
> 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
> 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
> 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
> 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.
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.
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.
> 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.
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.
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.
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.
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.
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.. >_<
Why do they have to have that "I'm special" syndrome and do everything weirdly
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.
Telling the model to only do post-hoc reasoning is an interesting choice, and may not play well with all models.
"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.
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.
so everyone, that means your agents, skills and mcp servers will still take up everything
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.
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.
That said, most of this repo is solving the wrong problem. "Answer before reasoning" actively hurts quality, and the benchmark is basically meaningless. But the anti-sycophancy rules should just be default. "Great Question!" has never really helped anyone debug anything.
> "because it will just get subsumed into CC at some point if it actually works."
Anthropic's survival depends on Claude Code performing as well as it can, by all metrics.
If the Very Smart People working on CC haven't integrated a feature or put text into the System Prompt, it's probably because it doesn't improve performance.
Put another way: The product is probably as optimized as it can get when it comes out the box, and I'm skeptical about claims otherwise without substantial proof.
So you could run these 'cure-alls' that maybe relevant today, as long as you are constantly updating your md files, you should be ahead of the curve [lack of better term]
Even when one helps, you're still betting it won't be obsolete or rolled into the defaults a few weeks from now.
For complex tasks this is not a useful prompt.
This doesn't stop it from reasoning before answering. This only affects the user-facing output, not the reasoning tokens. It has already reasoned by the time it shows the answer, and it just shows the answer above any explanation.
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.
That’s why I’m only interested in first party tools over things like OpenCode right now.
So the market kind of works in this instance.
Ok, here you go: https://gist.github.com/shawwn/56d9f2e3f8f662825c977e6e5d0bf...
Installation steps:
- In your project, download https://gist.github.com/shawwn/56d9f2e3f8f662825c977e6e5d0bf... into .claude/commands/handoff.md
- In your project's CLAUDE.md file, put "Read `docs/agents/handoff/*.md` for context."
Usage:
- Whenever you've finished a feature, done a coherent "thing", or otherwise want to document all the stuff that's in your current session, type /handoff. It'll generate a file named e.g. docs/agents/handoff/2026-03-30-001-whatever-you-did.md. It'll ask you if you like the name, and you can say "yes" or "yes, and make sure you go into detail about X" or whatever else you want the handoff to specifically include info about.
- Optionally, type "/rename 2026-03-23-001-whatever-you-did" into claude, followed by "/exit" and then "claude" to re-open a fresh session. (You can resume the previous session with "claude 2026-03-23-001-whatever-you-did". On the other hand, I've never actually needed to resume a previous session, so you could just ignore this step entirely; just /exit then type claude.)
Here's an example so you can see why I like the system. I was working on a little blockchain visualizer. At the end of the session I typed /handoff, and this was the result:
- docs/agents/handoff/2026-03-24-001-brownie-viz-graph-interactivity.md: https://gist.github.com/shawwn/29ed856d020a0131830aec6b3bc29...
The filename convention stuff was just personal preference. You can tell it to store the docs however you want to. I just like date-prefixed names because it gives a nice history of what I've done. https://github.com/user-attachments/assets/5a79b929-49ee-461...
Try to do a /handoff before your conversation gets compacted, not after. The whole point is to be a permanent record of key decisions from your session. Claude's compaction theoretically preserves all of these details, so /handoff will still work after a compaction, but it might not be as detailed as it otherwise would have been.
"write a summary handoff md in ./planning for a fresh convo"
and it's generally good enough), but maybe a skill like you've done would save some typing, hmm
My ./planning directory is getting pretty big, though!
It wasn’t anything important. I appreciate you pointing that out though.
I just keep old sessions for keepsake. No reason really. I thought maybe I’d want them for some reason but never did.
The docs are the important part. It helps me (and future sessions) understand old decisions.
That sounded like a nice idea, so I made it effortless beyond typing /handoff.
The generated docs turned out to be really handy for me personally, so I kept using it, and committed them into my project as they're generated.
I see. So this isn't as scary. Claude is helping me understand how to use it properly.
It definitely adds a layer of cognitive load, in wrangling/shepherding/accomodating/accepting the unpredictable personalities and stochastic behaviors of the agents. It has strong default behaviors for certain small tasks, and where humans would eventually habituate prescribed procedures/requirements, the LLM's never really internalize my preferences. In that way, they are more like contractors than employees.
Unless you're a true and invested believer of souls, free will, and other spiritualistic nonsense (or have a vested political affiliation to pretend so), it should be tautological that everything you read and experience biases you. LLM output then is no different.
If you are a believer, then either nothing ever did, or LLMs are special in some way, or everything else is. Which just doesn't make sense to me.
[0] It's jarring to observe the boundaries of one's agency, sure, but LLMs are really nothing special in this way. For example, I somewhat frequently catch myself using words and phrases I saw earlier during the day elsewhere, even if I did not process them consciously.
For example, "let's gate the new logic behind a feature flag".
Reasoning tokens are just regular output tokens the model generates before answering. The UI just doesn't show the reasoning. Conceptually, the output is something like:
<reasoning>
Lots of text here
</reasoning>
<answer>
Part you see here. Usually much shorter.
</answer>If you steer it in strange (for it, as in not seen before in training) text, you are now in out-of-distribution, very weak generalization capabilities territory.
Exactly. And this instruction isn't telling it to skip the reasoning. That part is unaffected. The instruction is only for the user-visible output.
By the time the reasoning models get to writing the output you see, they've already decided what they are going to say. The answer is based on whatever it decided while reasoning. It doesn't matter whether you tell it to put the answer first or the explanation first. It already knows both by the time it starts outputting either.
You're basically hoping that adding more CoT in the output after reasoning will improve the answer quality. It won't. It's already done way more CoT while reasoning, and its answer is already decided by then.
To understand my point, think about a prompt to tell the model “here is a very difficult code problem, answer in a single word.”
It thinks a lot and answer. You send the next prompt. At this moment, you are completely in out of distribution territory.
E.g., a hammer doesn't do anything, and neither does a lawnmower. It would be silly to argue (just because these tools are static objects doing nothing in the absence of direct human involvement) that those tools don't have a very clear value.
When people use other people like tools, i.e. use them to enable themselves to accomplish something, do those people cease to do things as well? Or is that not a terminology you recognize as sensible maybe?
I appreciate that for some people the verb "do" is evidently human(?) exclusive, I just struggle to wrap my head around why. Or is this an animate vs. inanimate thing, so animals operating tools also do things in your view?
How do you phrase things like "this API consumes that kind of data" in your day to day?
To put my comment in context again: I replied to a comment that said "By that logic, nothing computers do is scary." as a response to "Why would it be scary? Claude is just parroting other human knowledge. It has no goal or agency.". I was following the train of "logic" of this chain of comments.
As long as this hypothetical Claude doesn't have control over objects, it cannot do anything. It completely depends on how its output is processed and received. An LLM spits out words, a computer ultimately spits out bits; what makes them "scary" is not what they produce, but fully depends how that product is translated into action by its environment. It cannot be determined in isolation by only looking at the computer/LLM ("mind") part.
The mind part does not do. A computer may be attributed to have "agency", but the "objects" around it can have agency too. A computer cannot force anything on its own; a toddler (or a president) may have agency, but they require cooperation by their environment to exercise that agency. If you break a leg, you can want to move it all you want, it won't do.
It's not merely a "linguistic semantics thing". Think Nuremberg trials. Who is responsible, in a network of 'autonomous agents'?
To be clear, I am not the person you were originally replying to. I personally don't care much for the terminology semantics of whether we should say "hammers do things" (with the opponents claiming it to be incorrect, since hammers cannot do anything on their own). I am more than happy to use whichever of the two terms the majority agrees upon to be the most sensible, as long as everyone agrees on the actual meaning of it.
> I appreciate that for some people the verb "do" is evidently human(?) exclusive, I just struggle to wrap my head around why. Or is this an animate vs. inanimate thing, so animals operating tools also do things in your view?
To me, it isn't human-exclusive. I just thought that in the context of this specific comment thread, the user you originally replied to used it as a human-exclusive term, so I tried explaining in my reply how they (most likely) used it. For me, I just use whichever term that I feel makes the most sense to use in the context, and then clarify the exact details (in case I suspect the audience to have a number of people who might use the term differently).
> How do you phrase things like "this API consumes that kind of data" in your day to day?
I would use it the exact way you phrased it, "this API consumes that kind of data", because I don't think anyone in the audience would be confused or unclear about what that actually means (depends on the context ofc). Imo it wouldn't be wrong to say "this API receives that kind of data as input" either, but it feels too verbose and awkward to actually use.
I am not disagreeing with you in the slightest, I feel like this is just a linguistic semantics thing. And I, personally, don't care how people use those words, as long as we are on the same page about the actual meaning of what was said. And, in this case, I feel like we are fully on the same page.
To me, what's essential for any "doing" to happen is an entity, a causative relationship, and an occurrence. So a lawnmower can absolutely mow the lawn, but also the wind can shape a canyon.
In a reference frame where a lawnmower cannot mow independently because humans designed it or operate it, humans cannot do anything independently either. Which is something I absolutely do agree with by the way, but then either everything is one big entity, or this is not a salient approach to segmenting entities. Which is then something I also agree with.
And so I consider the lawnmower its own entity, the person operating or designing it their own entity, and just evaluate the process accordingly. The person operating the lawnmower has a lot of control on where the lawnmower goes and whether it is on, the lawnmower has a lot of control over the shape of the grass, and the designer of the lawnmower has a lot of control over what shapes can the lawnmower hope to create.
Clearly they then have more logic applied, where they segment humans (or tools) in this a more special way. I wanted to probe into that further, because the only such labeling I can think of is spiritualistic and anthropocentric. I don't find such a model reasonable or interesting, but maybe they have some other rationale that I might. Especially so, because to me claiming that a given entity "does things" is not assigning it a soul, a free will, or some other spiritualistic quality, since I don't even recognize those as existing (and thus take great issue with the unspoken assumption that I do, or that people like me do).
The next best thing I can maybe think of is to consider the size of the given entity's internal state, and its entropy with relation to the occurred causative action and its environment. This is because that's quite literally how one entity would be independent of another, while being very selective about a given action. But then LLMs, just like humans, got plenty of this, much unlike a hammer or a lawnmower. So that doesn't really fit their segmentation either. LLMs have a lot less of it, but still hopelessly more than any virtual or physical tool ever conceived prior. The closest anything comes (very non-coincidentally) are vector and graph databases, but then those only respond to very specific, grammar-abiding queries, not arbitrary series of symbols.