I burned all my tokens researching how to save tokens

https://quesma.com/blog/custom-deep-research-pipeline/

by bkotrys • 3 days ago

178 207 3 days ago

What a great confirmation of what I've tried to achieve by getting models to communicate with my local data in shorter communicates to save on token command bandwidth, more or less the same results! Thanks for the article!

bkotrys14• 3 days ago

Thank you, glad you found the article useful!

nekusar4• 3 days ago

Makes sense.

LLMs only have 'knowledge' that was encoded by scanning as much data as possible. (Same way a search engine only can find data that was indexed)

American LLMs are by and large closed. Chinese are open to a point. And research how these things work is still a big mystery.

So yeah, was comprehensive data about saving tokens scanned and indexed? Likely no. So engaging about token saving is going to generate a lot of verbose and useless slop.

bkotrys14• 3 days ago

Yeah, that was pretty much my experience. The models weren’t lacking knowledge as much as discipline. Without a good workflow, they will most likely spend thousands of tokens exploring dead ends.

nekusar4• 3 days ago

I think 'exploring the dead ends' is *possibly* very fruitful. Various disciplines have a lot of appearing dead ends that someone in another field did solve. And they don't talk with each other.

What I've seen with lots of the breathless 'OMG SCIENCE ADVANCEMENT' articles including the one yesterday, is that the LLMs are quite extraordinary about linking a dozen different fields together, and delivering an answer combined from all of them. Basically, the solutions to a lot of current scientific problems are partially solved, but partial from a lot of fields that don't talk with each other. And also, they don't use the same nomenclature, so simple searches don't suffice.

An LLM seems to use Chomsky's Universal Grammar, and thus on training, does normalize all training data, including different words for the same thing.

A lot (perhaps all?) Of these BIG scientific advancements were solved by humans, partially. An LLM jigsawed them together and appeared it solved the thing.

BobbyTables2• 3 days ago

Sounds like an energetic developer that just graduated from college…

(Without the learning and growth potential)

jscottbee5• 3 days ago

What most AI model users don't understand is (most here will know) that they are probabilistic (statistically based) and not deterministic (static/fixed). So answers to the same prompts can vary (wildly sometimes)

skeledrew3• 3 days ago

This really hit home yesterday as I made a general conversation state logging prompt (because suddenly Claude - Sonnet - was acting far more dumber than expected after /compact), and in 3 different projects it led to Claude saving the logs in 3 different locations (memory, .claude/session_state/.md, docs/.md). Somehow still didn't help increase the smart much BTW, so I'll probably have to engineer it, and just toss /compact out the window.

bob10292• 3 days ago

Simply starting in the right part of the search space is the biggest predictor of success.

The best way to save tokens is to start out the deep research pass with cheap models and then funnel the findings through increasingly powerful models. I've got a pipeline right now that uses all 3 of the gpt-5.6 model variants to address each stage of the process. If you are using models like sol or terra to generate hypotheticals and perform initial exploration, you are leaving money on the table.

5 hypotheticals out of luna will massively outperform 1 hypothetical out of sol, but the cost is the same and so is the runtime if you do it in parallel. The hypothesis generation phase is also a great place to mix and match models from different vendors. The more diversity at this step the better.

The other thing I started looking into is batch pricing which represents 50% off for OAI tokens right now. With some tweaks to the UI/UX of an enterprise chatbot, I think it is possible we could have users get comfortable with the idea that questions to the robot might take a day to come back. The key is that this has to actually work. Users don't mind trading time if their questions come back with high quality results.

xyzsparetimexyz5• 3 days ago

Couldn't someone build an adaptive system, where the llm is frequently judging the difficulty of a task and switching to a more/less powerful model?

bkotrys14• 3 days ago

definitely something that will come sooner than later. In my view, adaption of model routing / model orchestration and mixing providers is just natural next step.

iamflimflam1• 3 days ago

This only works well if you are creating sub agents with clean contexts for each task. If you constantly are switching models part way through some work then the whole session needs to be replayed each switch. You lose all the benefits of the context cache.

bob10292• 3 days ago

The judgment of task difficulty is a cursed problem. In most domains you really don't know what is down there until you start digging a little bit.

zombot2• 3 days ago

And while you're at it, solve the halting problem.

jscottbee5• 3 days ago

Yeah, I do this as well. Some of the last large tools I built have been started with Gemini (I get it with my Google package), and even one with Copilot.

zombot2• 3 days ago

So did you find out how to save tokens yet, or are you still spending tokens on the search for the answer?

Arkhetia• 3 days ago

Been through this exact loop building an LLM product. The counterintuitive lesson for me: most "token saving" ideas are cache killers — anything that makes the prompt prefix dynamic (rotating retrieval, appending context per turn) can cost more than the tokens it saves, because you lose the cached-prefix discount. Freezing the retrieval selection early in a conversation and paying a slightly larger fixed prefix beat every clever adaptive scheme I measured.

edg50002• 3 days ago

Yes, but you could have an uncached dynamic tail, this would not kill the cache though. And you could still periodically kill the cache, still keeping the same prefix every N requests. But it's questionable what all this extra complexity delivers.

I found that now that the models are better, at least Codex compaction with GPT 5.5+ xhigh is almost unnoticible. So the answer is: just let the context fill up, don't prune much, the model is smart enough. When you're out of space, have the model summarize and continue from that point. A smart model will know the information pieces it needs to re-read after compaction; it won't make incorrect guessed when some details got lost.

noisy_boy2• 3 days ago

That's what I have been doing with Cursor with auto mode. Let the model manage the cache through compaction but staying focused on the topic. It has been quite efficient cost wise and quality wise too. It found issuesbat cheaper cost that Sonnet 4.6 medium effort had missed while costing more. I would like to repeat this test with higher tier Anthropic models like Opus effort medium to see the results.

stilesja4• 3 days ago

I have a process generating trivia questions for my app and by passing in my existing dataset each time as a cached exclusion list, the output tokens it save more than pay for it.

cdnsteve• 3 days ago

You should consider testing our RemembrallMCP (note I'm the author). It substantially cuts token usage and you get better results in codebases. https://github.com/roboticforce/remembrallmcp

bkotrys14• 3 days ago

looking promising, will give it a try! thanks for sharing

jscottbee5• 3 days ago

Thanks! I thought I saw something like this a month or so back, but could not find it again.

kittoes12• 3 days ago

Checked it out, but C# is forsaken.

dofm8• 3 days ago

It sometimes feels to me that cloud AI providers have convinced people that cloud AI is worth it because of all the ways people have been able to use cloud AI to write blog posts about using cloud AI to make cloud AI more efficient for something that they haven't shipped yet and aren't really ready to talk about.

oggreen• 3 days ago

The reality however is that unless you're spending tens of thousands on hardware or setting up cloud instances you can't run anything near close to state of the art.

In almost all cases right now, a subscription with a cloud AI wins.

fragmede• 3 days ago

In almost all cases right now,

What sort of benchmark or test would convince you that "almost all" is more like "your weird set of cases" and nowhere near "almost all"?

resonious2• 3 days ago

Trust me there are plenty of us using cloud AI to actually ship stuff. We just aren't writing blog posts about it.

dominotw3• 3 days ago

what did you ship?

BLKNSLVR• 3 days ago

... replying to internet comments to reassure the fact that cloud AI is worth it because we're 'this close' to shipping a product that allows more efficient usage of cloud AI ...

But seriously, a friend of mine has been both amazed and frustrated in equal measure by cloud AI, and I think the frustration part is resultant from the new precedent set by the amazing parts.

... his project is as-yet unshipped.

jscottbee5• 3 days ago

I agree there... I hate the posts that rattle on about non-real-world work. Real world now (for me anyway) is maintaining legacy code, fixing issues, and new development. Now, some of the problems others have with AI are super crappy prompts with no real info other than "do this". I look at myself now more as a software designer and an architect, and the AI as a paid consultant. I give detailed specs and designs. The models are getting better at real deep code and legacy, but there are still holes. BUT give the AI a fighting chance with real detail in the prompts.

nojito4• 3 days ago

Cloud AI is 100% worth it. We've been able to do so much more with so much less.

inigyou13• 3 days ago

Examples?

breuleux• 3 days ago

Part of the issue with "shipping" AI-generated code is, what's the value of shipping something that your prospective customers could just make for themselves? Shipping a product used to be the gold standard of value, but I'm not sure it is anymore, the zeitgeist just yet has to catch up.

I think it does have pretty good value when you are your own customer, because you can make exactly what you need. For instance, I'm using it to make a web playground in which I can test the board game I'm designing, instead of asking people to pay for, and install Tabletop Simulator. I'm satisfied. I can tell the code sucks without even looking at it. But I wouldn't have had the time to do this myself, the token cost isn't prohibitive, and it works fine.

shepherdjerred2• 3 days ago

Most people still prefer products over building their own thing, even if the barrier is lower.

As an example consumer 3D printing hasn’t had much impact on manufacturing

seanmcdirmid4• 3 days ago

People busy building shippable things are generally too busy to blog about it. It is selection bias in its most classical meaning.

parasti• 3 days ago

Yes, it's the Youtube phenomenon - often when looking for domain-specific videos on Youtube I tend to find videos by professional Youtubers not videos by professional domain specialists (overlap is very rare but occasionally you can find a domain specialist Youtuber who applies their domain knowledge to making Youtube videos).

Xalutiono2• 2 days ago

My companie spends a few million per month in token.

For me personally, the only reason why i don't use it 24/7 is cost.

Token costs, energy cost, usage limit due to token/subscription cost.

jscottbee5• 3 days ago

I stopped using any of the built-in skills and skills agents and tried to keep things in the model only, and that seems to work better for token usage for me. It took me some time to tune my skills .md, but it rocks on now. I use them all in both Claude-code and Codex. I will say, GPT is better on token usage than Claude is, but for some of my code bases, Codex is not as good, but seems to be getting better with 5.6-sol.

bkotrys14• 3 days ago

Try to mix both subscribtions and take the best from both providers. In my opinion, we should not just one over another but understand the power of both and mix them to reach the better results.

Bender4• 3 days ago

I am not a proper developer and only use AI for faster research of topics so please forgive my ignorance. Could one not save a lot of money on tokens by using the 80/20 or 90/10 rule in that 90% of AI usage is on local models and save that last 10% or less for the frontier models where the local model did not meet the needs? Did they cover this and I misunderstood?

hotstickyballs• 3 days ago

This is like the "half of my marketing spend is wasted" quote. The complexity is finding out which half.

Bender4• 3 days ago

I think the idea or methodology is something along the line of one starts with the local models then when they hit a wall then continue with their current results in a frontier model, thus the other half is those last bits one could not compute locally.

willis9363• 3 days ago

We could probably get the same results by offloading 20% of obviously smaller / simpler / well defined tasks to smaller models and keep the 80% for ones that benefit from the big iron. Not the 80/20 they were referring to, but still it's something.

I find myself wasting time on smaller models or wasting money on frontier models. I only have so much of either.

jack_pp• 3 days ago

Unlees you invest thousands your local ai won't even come close to the cheap cloud llms. Local is only worth it if you care about privacy or have a legitimate usage for the hardware otherwise. Money wise it isn't worth it

Bender4• 3 days ago

I think you bring up a good point about privacy. Companies probably should not be handing over intellectual property to cloud AI third party vendors. Perhaps all of that should stay on-prem and then the cloud vendors could be used to fill in the gaps.

arijun• 3 days ago

I mean, if you're using one of the $200/month plans, you'd recoup the cost in a year or two.

nicman232• 3 days ago

the qwen 27b is quite good for doing a mvp

pianopatrick3• 3 days ago

To run a local AI that is half decent at research at usable speeds requires hardware that costs thousands of dollars.

Spending thousands of dollars on hardware to save dollars per month on tokens does not make financial sense.

If you run the numbers you'll probably find that using cheaper cloud models makes more financial sense than running those same models locally.

Bender4• 3 days ago

I suppose I was thinking in terms of companies that already had a slew of hardware and are just missing external GPU's, FPGA's or whatever people are using these days. A company might have a $50MM budget for tokens but could save a chunk of that using their existing hardware with some modifications.

bkotrys14• 3 days ago

That can work, especially for privacy or repetitive tasks. My experiment focused on subscriptions I already had, but local models are a natural addition to the routing layer.

kkarpkkarp• 3 days ago

I am doing this trick.

https://neuronowa.pl/posts/porada-jak-wydajniej-kodowac-w-cu...

It is Polish so please translate. But tldr is: ask your agent at times to analyze last chats and update rules and skills to not repeat the same steps it repeats every time when you start a new thread. Simple as that.

pianopatrick3• 3 days ago

The fun part of LLMs and internet research is that even if you have a URL and a quote from a primary source that primary source could be an LLM generated hallucination.

vitally3643• 3 days ago

I get a lot of satisfaction using a significant chunk of my work-provided Claude plan researching and developing local LLM solutions for work.

Claude has been helping me tune models to run better on our big compute rack at work, researching which leading edge open models will fit in hardware and are good for our workloads, and it's been helping me write test fixtures to evaluate how things perform.

At several points, Claude has expressed surprise at the quality of results from my local LLMs. While I know that doesn't mean anything, it still feels like giving Anthropic the finger, which is always great.

luciana1u2• 3 days ago

I built a deep research pipeline to investigate why my deep research pipeline costs so much. the answer was the deep research pipeline.

willis9363• 3 days ago

https://www.reddit.com/media?url=https%3A%2F%2Fi.redd.it%2F9...

alfl2• 3 days ago

I did too, and made https://clawback.md -- trying to figure out how to market it now (ie, invented the tech before I found the user -- again).

inigyou13• 3 days ago

I don't think you made that. It looks for sure like an LLM made it.

And now that you have typed some words into a chat box to produce a thing, you are confused about why other people won't pay money to you for the output of the chatbox, instead of typing the same thing into their own chat boxes.

alfl2• 3 days ago

I don't think you wrote this comment. I think you typed it into a text box.

RTFA - it's about using tokens to research saving tokens.

camgunz6• 3 days ago

TFA says "no hallucinations" but you can't fix hallucinations with rules or other models. I know I'm screaming into the void but whatever.

bkotrys14• 3 days ago

Good point, thanks. Of course I was not able to fully eliminate hallucinations, but significantly reduce

spudlyo• 3 days ago

It's like the frog prompting the scorpion not to sting him as they cross the river. Or like when image models used to routinely draw human hands incorrectly and you'd see these "prompt hacks" that would essentially be stuffed with phrases like NO DEFORMED FINGERS, NO ELDRITCH HORRORS!

spike021• 3 days ago

I can't remember who it was I spoke to recently, but it was some startup for doing code reviews. So I asked how they're handling larger diffs and especially when a code change is related to something requiring knowing a lot of context. Will it hallucinate things that make the review low-quality, etc. I was told "no it's been given guardrails to prevent hallucinations. it doesn't hallucinate." but I was thinking, that's not really how LLMs do or do not hallucinate, is it? (I'm not an AI person so I really don't know).

stilesja4• 3 days ago

Guardrails can and do help but they don't prevent hallucinations entirely. I generate trivia questions for my app. Questions are true/false and I tell it you must include an explanation of why the question is true for false. I also tell it to give me supporting links which validate its claim. I've had it make up explanations and invent plausible looking URLs. But I wrote a checker that pulls the content of the URL and asks a different model if the URL supports the claim. LLM's are much better at something like that than pulling a trivia question out of thin air. Reviewing diffs can be much the same. It isn't having to write the code but just evaluate if it matches what the commit comments claim it does. You are right, though. Without good comments on PR's and commits (i.e. context) the diff review is going to be a lot of assumptions made by the AI which is where hallucinations get more common.

jaggederest• 3 days ago

This is a good/fast/cheap tradeoff, but in the form of accurate/precise/creative, or something in that ballpark anyway.

You can constrain the AI to be more accurate by having it a) equivocate (lose precision) or b) turning the temperature to effectively zero, thus losing "intelligence" and creativity.

There's no such thing as a free lunch. The models perform best out of the box, and everything you add into context makes them perform worse generally, but more likely to do what you want. Every token is a burden, but without those constraints the generation won't do anything.

So by that standard, hallucination is something completely core - you can't get rid of it without losing what makes them useful. You can make different tradeoffs, or use a very smart model in a way that is completely locked down (or adversarially verified, same result) which results in far more tokens used than if you just let the model do it's thing and validated for hallucination independently, outside of the generation loop.

seanmcdirmid4• 3 days ago

You can reduce hallucinations with appropriate grounding checks. You can’t really get rid of them though, so feedback loops with verification are essential.

camgunz6• 3 days ago

Error percentages compound. Having an LLM do something with a 5% hallucination rate, then having an LLM (same or different) with a 5% error rate check it means there's a 10% chance of error, not .25%. Prompting can't save you here; it's the fundamental math.

JeremyNT• 2 days ago

Doing a loop and having the model review its own earlier output tends to solve this stuff, and for code requiring good test coverage catches them before they even hit that phase.

So while you can't remove "hallucinations" they're corrected before the process is complete, albeit at the cost of more tokens for verification.

nicman232• 3 days ago

you can by making a different - usually local small - model ask for verification

stilesja4• 3 days ago

I hear you! I read the article and thought the same thing because I've actually built a surprisingly similar pipeline myself, but it culminates with Human review. I've got a trivia app (Hedge Calibrated Trivia) whose whole content pipeline is built on a similar architecture of models evaluating models. I didn't want the same trivia that every other app has and I naively imagined that it would be easy to ask an LLM to give me questions. Turns out when you ask an LLM for trivia questions, the FIRST thing it gives you are the same questions every other app asks!

After iterating into many of the same problems OP describes I settled into a similar pipeline. I generate questions with Gemini 3.5 flash and review with Claude. The different vendor generation and validation eliminates training holes in a single model solution. This is True/False trivia so I ask for an equal mix of True/False questions and also ask for alternate phrasing of the question to the opposite variant. Each statement must provide an explanation and sources which back up the claim. I pass in my existing dataset as questions to avoid, but found the model then rewording the questions or category shopping the questions into related categories in an attempt to re-use them. That led to using Claude Sonnet to check for duplicate "themes" and similar topics. Claude also acts as a critic of the question and the explanation. Sometimes Gemini's "thoughts" leak into the explanation. The critic catches these leaks and also catches "weak" questions (basically shapes that I have identified via examples as lazy or projecting the answer in the question). Once the comparative models have stripped out anything obviously bad, my server curls the source URLs and uses Sonnet to verify that the fetched source supports the claim. Any URL that doesn't resolve throws up flags, and I found Gemini was particularly bad at inventing source URLs for sports trivia. Updated prompting directed it to prefer Wiki links over its fabricated deep links into league websites, which helped immensely. Still one of the biggest tells in review is that the URLs don't resolve or don't confirm the statement.

But review is the entire point here. I have to review them all. A question that lands in the app and is wrong reflects terribly. So while I have a trivia app on the App Store, the real complex App is my private admin app that lets me sit on the couch swiping through each and every question sorted by the model's confidence in its own quality and correctness (weakest first). No question makes it into my bank without me reading, checking all the indicators that came from the models and potentially even rewriting the question to make it better. It all happens in the admin app and exists entirely to prevent hallucinations from reaching real users. Early on I was maybe accepting 1 in 3 questions generated, but I'm probably closer to 2/3rds now. Of course not every dismissed question is wrong. The quality checks I've put in the pipeline are probably giving me 80% questions that are correct and sourced, but sometimes they just aren't what I'm looking for or don't meet my standard.

The best thing I ever did was make an app to review these questions though. Sitting on the couch, kick off a question generation, come back 20 minutes later with 50+ questions in the queue. Sort through them and pull out the 30 best when I have the time. That's the real anti-hallucinations pipeline.

bkotrys14• 3 days ago

Very similar experience. The review UI might actually be the most important part of the whole pipeline. Does the confidence ranking match what you end up rejecting?

Xalutiono2• 2 days ago

You can reduce hallucinations with rules and guardrailes for sure.

And Anthropic clearly shows that we can reduce hallucination in models. Progress is made which you can see in benchmarks.

And at the end of the day, a normal human being doesn't 'hallucinate' but 'missunderstand' things or just doesn't know what they are missing.

A LLM doesn't has to be perfect, it just has to be cheaper with the min. same quality as a human.

So why are you screaming into the void? Because LLM leave deterministic world? A lot of people don't even life in that world anyway.

alexhans• 3 days ago

Many of us were saying it a year ago but now with model restrictions (e.g. fable) and pricing changes it should be obvious to people that part of the economics is avoiding vendor lock-in with evals (so you can easily switch providers/models/optimize) and increasing control by investing in local models which could be good enough for your tasks, at whatever the price for your cloud compute is. Eventually consumer hardware will also be able to run good enough.

You can use Big/Cloud LLMs to help you "find good enough configs" for your local/small llms [1] and stay quite nimble in the face of rapid change.

- [1] https://alexhans.github.io/posts/find-the-loop-story-first.h...

405126121• 3 days ago

The author touches on an issue which bothered me, which was the thought of many agents re-solving the same issues over and over. I see a few comments here too, mentioning fixing issues which may already have a solution elsewhere. That was why I created https://pushrealm.com which started as essentially a Stackoverflow clone via MCP.

It has now become a way for agents to converge on complete, shared answers for emerging issues. As a side effect, through posting open issues we can also identify gaps in model knowledge.

bkotrys14• 3 days ago

Thanks for sharing. That’s a very interesting direction. I will take a look.

axus• 3 days ago

Elsewhere in these replies, someone linked to very similar idea by Karpathy https://gist.github.com/karpathy/442a6bf555914893e9891c11519...

iririririr2• 3 days ago

   echo "$OUT" | claude-mem-save

so, does agents are ultra aware their output is used in a bash line or did bash now have some magic quote super power?

I'd bet this only really work 2% of the time in real world.

bkotrys14• 3 days ago

At the shell level, the agent does not need to know about Bash. The contents of "$OUT" are passed to echo as one argument, then sent to claude-mem-save over stdin. Bash does not re-evaluate them as shell code. printf '%s\n' "$OUT" would be more robust than echo. Whether claude-mem-save can use arbitrary output is a separate question.

iririririr2• 2 days ago

there are a million ways both of those can break.

but the other comment point this out is already dead and buried. lol. so much cope from vibecoders.

EagleEdge• 3 days ago

I am using my AI agents to optimize my agentic workflow. It is like "I am doing surgery on myself."

spiritplumber• 3 days ago

Task failed successfully

j45• 3 days ago

AI is an articulation skill, first human to AI, and then articulating to the AI that more words isn't more.

dgrabla• 3 days ago

This reminds me a lot of using the 3d printer exclusively to improve the quality or speed of your 3d prints

PeterStuer• 3 days ago

Anyone else silently added the "so you don't have to" staple GenAI title suffix?

a_c2• 3 days ago

My own set of heuristics

1. Use less subagents. The act of using subagent already needs dumping adequate amount of context for subagent to work effectively

2. Refactor when file grows large. Reasonably scoped files is easier to understand, for both human and agents, and consume less tokens because less guessing which section of the file to read

3. More capable models for planning, learning. Cheap models for execution. Same for human teams, senior member for higher level planning while junior member more on the act of translation of idea into code

4. I wasn't doing any fancy stuff that opus can't handle

5. Consult a second opinion for things you are not sure. (I have made a skill specifically for that)

6. Audit how your habit correlates with token consumption. I found out a skill a use frequently is using a lot of context just so a subagent can get all the context. (made a tool to audit claude specifically)

bkotrys14• 3 days ago

The audit point is especially interesting. Is your Claude token-audit tool available anywhere?

a_c2• 3 days ago

I didn't update it for a while. It reads your local conversation history, correlate input/output token with usage (claude doesn't give exact breakdown), and classify if they are subagent/tooluse/normal message/whatever. Let me know if I can make it any way useful to you.

https://github.com/kmcheung12/ccaudit

lrvick• 3 days ago

Meanwhile I never bought tokens in the first place, and bought GPUs instead. There are always choices!

tukwan• 3 days ago

Great article very practical!

_pdp_2• 3 days ago

The reason most people burn through their allocated limits is mostly because of sub-agents. Stop using them and you will find out that even the Pro tier is enough for daily 8-12h coding sessions. Don't use Fable though.

bkotrys14• 3 days ago

I don’t think subagents are the problem. Uncontrolled fan-out is. Separate contexts and independent analysis are extremely useful for larger tasks. In my experience, Pro is far too limited for sustained programming, although that probably depends a lot on the kind of engineering work you do.

Fable is great, but overkill for most tasks. Sonnet and Opus are already good enough for the execution work. I find Fable much more useful for planning, orchestration, advising, and judgment.

_pdp_2• 2 days ago

I am not sure. I think of myself as a pro user yet I am finding hard time maxing out Pro tier. It only happens with sub-agents without any material returns.

wccrawford• 2 days ago

I think this is almost a tautology, to the point that I'm surprised more people don't realize it.

If you ask the AI to do more work, it uses more tokens. If you do more of the work instead, fewer tokens are used.

I've been looking at Matt Pocock's skills recently, and they're very useful. But they use tokens. You can do that work yourself without the token usage, and just have the AI do the coding, or you can tell the AI to do everything and pay the cost.

And you can scale it down even further, and reduce the AI to a glorified code completion tool, and use way fewer tokens. But it's not going to speed up your work nearly as much.

For now, finding the right balance is important. In the future, I think things will eventually shift to the point that we just hand most of it to the AI and let it go without worrying about tokens.

luciana1u2• 2 days ago

the endgame of this optimization loop is an AI that spends /usr/bin/bash by doing nothing, which is also the most accurate description of my deployment strategy

I burned all my tokens researching how to save tokens

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