[dbeardsl]

I appreciate the reply, but I was never under the impression that gaps in conversations would increase costs nor reduce quality. Both are surprising and disappointing.

I feel like that is a choice best left up to users.

i.e. "Resuming this conversation with full context will consume X% of your 5-hour usage bucket, but that can be reduced by Y% by dropping old thinking logs"

[giwook]

Another way to think about it might be that caching is part of Anthropic's strategy to reduce costs for its users, but they are now trying to be more mindful of their costs (probably partly due to significant recent user growth as well as plans to IPO which demand fiscal prudence).

Perhaps if we were willing to pay more for our subscriptions Anthropic would be able to have longer cache windows but IDK one hour seems like a reasonable amount of time given the context and is a limitation I'm happy to work around (it's not that hard to work around) to pay just $100 or $200 a month for the industry-leading LLM.

Full disclosure: I've recently signed up for ChatGPT Pro as well in addition to my Claude Max sub so not really biased one way or the other. I just want a quality LLM that's affordable.

[jimkleiber]

I might be willing to pay more, maybe a lot more, for a higher subscription than claude max 20x, but the only thing higher is pay per token and i really dont like products that make me have to be that minutely aware of my usage, especially when it has unpredictability to it. I think there's a reason most telecoms went away from per minute or especially per MB charging. Even per GB, as they often now offer X GB, and im ok with that on phone but much less so on computer because of the unpredictability of a software update size.

Kinda like when restaurants make me pay for ketchup or a takeaway box, i get annoyed, just increase the compiled price.

[giwook]

For sure, I agree with that sentiment. It's interesting to consider the psychological component of that, like how "free shipping" is not really free, it's oftentimes just packaged into the price of the product but somehow it feels like we're getting a better deal.

I would not be surprised to see Anthropic, OpenAI etc head in the direction you mention as they mature and all of these datacenters currently undergoing construction come online in the next few years and drive down costs.

[adam_patarino]

Token anxiety is real mental overhead.

[jimkleiber]

That's the phrase i was looking for, thank you.

[sharts]

That doesn’t make sense to pay more for cache warming. Your session for the most part is already persisted. Why would it be reasonable to pay again to continue where you left off at any time in the future?

[jeremyjh]

Because it significantly increases actual costs for Anthropic.

If they ignored this then all users who don’t do this much would have to subsidize the people who do.

[tikkabhuna]

I’m coming at this as a complete Claude amateur, but caching for any other service is an optimisation for the company and transparent for the user. I don’t think I’ve ever used a service and thought “oh there’s a cache miss. Gotta be careful”.

I completely agree that it’s infeasible for them to cache for long periods of time, but they need to surface that information in the tools so that we can make informed decisions.

[libraryofbabel]

That is because LLM KV caching is not like caches you are used to (see my other comments, but it's 10s of GB per request and involves internal LLM state that must live on or be moved onto a GPU and much of the cost is in moving all that data around). It cannot be made transparent for the user because the bandwidth costs are too large a fraction of unit economics for Anthropic to absorb, so they have to be surfaced to the user in pricing and usage limits. The alternative is a situation where users whose clients use the cache efficiently end up dramatically subsidizing users who use it inefficiently, and I don't think that's a good solution at all. I'd much rather this be surfaced to users as it is with all commercial LLM apis.

[theshrike79]

Think of it like this: Anthropic has to keep a full virtual machine running just for you. How long should it idle there taking resources when you only pay a static monthly fee and not hourly?

They have a limited number of resources and can’t keep everyone’s VM running forever.

[prirun]

I pay $5/mo to Vultr for a VM that runs continuously and maintains 25GB of state.

[danso]

Genuine question: is the cost to keep a persistent warmed cache for sessions idling for hours/days not significant when done for hundreds of thousands of users? Wouldn’t it pose a resource constraint on Anthropic at some point?

[tmountain]

Related question, is it at all feasible to store cache locally to offload memory costs and then send it over the wire when needed?

[dev_hugepages]

No, the cache is a few GB large for most usual context sizes. It depends on model architecture, but if you take Gemma 4 31B at 256K context length, it takes 11.6GB of cache

note: I picked the values from a blog and they may be innacurate, but in pretty much all model the KV cache is very large, it's probably even larger in Claude.

[libraryofbabel]

To extend your point: it's not really the storage costs of the size of the cache that's the issue (server-side SSD storage of a few GB isn't expensive), it's the fact that all that data must be moved quickly onto a GPU in a system in which the main constraint is precisely GPU memory bandwidth. That is ultimately the main cost of the cache. If the only cost was keeping a few 10s of GB sitting around on their servers, Anthropic wouldn't need to charge nearly as much as they do for it.

[bavell]

Yesterday I was playing around with Gemma4 26B A4B with a 3 bit quant and sizing it for my 16GB 9070XT:

  Total VRAM: 16GB
  Model: ~12GB
  128k context size: ~3.9GB

At least I'm pretty sure I landed on 128k... might have been 64k. Regardless, you can see the massive weight (ha) of the meager context size (at least compared to frontier models).

[cadamsdotcom]

Sure, it wouldn’t make sense if they only had one customer to serve :)

[uoaei]

Exactly, even in the throes of today's wacky economic tides, storage is still cheap. Write the model state immediately after the N context messages in cache to disk and reload without extra inference on the context tokens themselves. If every customer did this for ~3 conversations per user you still would only need a small fraction of a typical datacenter to house the drives necessary. The bottleneck becomes architecture/topology and the speed of your buses, which are problems that have been contended with for decades now, not inference time on GPUs.

[jeremyjh]

This has nothing to do with the cost of storage. Surprisingly, you are not better informed than Anthropic on the subject of serving AI inference models.

A sibling comment explains:

https://news.ycombinator.com/item?id=47886200

[uoaei]

They don't cache model state to disk. I am proposing they do.

[jeremyjh]

I’m proposing that you should educate yourself on the subject of LLM KV context caching.

[PeterStuer]

It may be persisted but it is not live in the inference engine.

[Folcon]

The reason I've been querying the 1 hour is a user's quota resets are often longer than that, as a result I've seen situations where someone builds a large context, then hits their quota limit, waits 2+ hours, their cache is gone, their first message then eats 20%+ of their current session quota and the user doesn't want to compact as they're still trying to get the model into a good understanding of the problem, this seems to be a really painful consequence for users on anything less than a max plan which seems like an unintended consequence of Anthropic's own system design choices?

IE How their quota and caching interact with each other, it doesn't make pro and max a little different, it makes it significantly different by unintentionally penalising pro users

[JumpCrisscross]

> I was never under the impression that gaps in conversations would increase costs

The UI could indicate this by showing a timer before context is dumped.

[vyr]

a countdown clock telling you that you should talk to the model again before your streak expires? that's the kind of UX i'd expect from an F2P mobile game or an abandoned shopping cart nag notification

[abustamam]

Well sure if you put it that way, they're similar. But it's either you don't see it and you get surprised by increased quota usage, or you do see it and you know what it means. Bonus points if they let you turn it off.

No need to gamify it. It's just UI.

[thinkmassive]

Plenty of room for a middle ground, like a static timestamp per session that shows expiration time, without the distraction of a constantly changing UI element.

[matheusmoreira]

Why not an automated ping message that's cheap for the model to respond to?

[cortesoft]

Because the cache is held on anthropics side, and they aren't going to hold your context in cache indefinitely.

[karsinkk]

Yes!! A UI widget that shows how far along on the prompt cache eviction timelines we are would be great.

[vanviegen]

That sounds stressful.

But perhaps Claude Code could detect that you're actively working on this stuff (like typing a prompt or accessing the files modified by the session), and send keep-cache-alive pings based on that? Presumably these pings could be pretty cheap, as the kv-cache wouldn't need to be loaded back into VRAM for this. If that would work reliably, cache expiry timeouts could be more aggressive (5 min instead of an hour).

[jimkleiber]

I tried to hack the statusline to show this but when i tried, i don't think the api gave that info. I'd love if they let us have more variables to access in the statusline.

[kiratp]

By caching they mean “cached in GPU memory”. That’s a very very scarce resource.

Caching to RAM and disk is a thing but it’s hard to keep performance up with that and it’s early days of that tech being deployed anywhere.

Disclosure: work on AI at Microsoft. Above is just common industry info (see work happening in vLLM for example)

[libraryofbabel]

Nit: It doesn’t have to live in GPU memory. The system will use multiple levels of caching and will evict older cached data to CPU RAM or to disk if a request hasn’t recently come in that used that prefix. The problem is, the KV caches are huge (many GB) and so moving them back onto the GPU is expensive: GPU memory bandwidth is the main resource constraint in inference. It’s also slow.

The larger point stands: the cache is expensive. It still saves you money but Anthropic must charge for it.

Edit: there are a lot of comments here where people don't understand LLM prefix caching, aka the KV cache. That's understandable: it is a complex topic and the usual intuitions about caching you might have from e.g. web development don't apply: a single cache blob for a single request is in the 10s of GB at least for a big model, and a lot of the key details turn on the problems of moving it in and out of GPU memory. The contents of the cache is internal model state; it's not your context or prompt or anything like that. Furthermore, this isn't some Anthropic-specific thing; all LLM inference with a stable context prefix will use it because it makes inference faster and cheaper. If you want to read up on this subject, be careful as a lot of blogs will tell you about the KV cache as it is used within inference for an single request (a critical detail concept in how LLMs work) but they will gloss over how the KV cache is persisted between requests, which is what we're all talking about here. I would recommend Philip Kiely's new book Inference Engineering for a detailed discussion of that stuff, including the multiple caching levels.

[computably]

> I was never under the impression that gaps in conversations would increase costs nor reduce quality. Both are surprising and disappointing.

You didn't do your due diligence on an expensive API. A naïve implementation of an LLM chat is going to have O(N^2) costs from prompting with the entire context every time. Caching is needed to bring that down to O(N), but the cache itself takes resources, so evictions have to happen eventually.

[doesnt_know]

How do you do "due diligence" on an API that frequently makes undocumented changes and only publishes acknowledgement of change after users complain?

You're also talking about internal technical implementations of a chat bot. 99.99% of users won't even understand the words that are being used.

[dlivingston]

What is being discussed is KV caching [0], which is used across every LLM model to reduce inference compute from O(n^2) to O(n). This is not specific to Claude nor Anthropic.

[0]: https://huggingface.co/blog/not-lain/kv-caching

[computably]

> How do you do "due diligence" on an API that frequently makes undocumented changes and only publishes acknowledgement of change after users complain?

1. Compute scaling with the length of the sequence is applicable to transformer models in general, i.e. every frontier LLM since ChatGPT's initial release.

2. As undocumented changes happen frequently, users should be even more incentivized to at least try to have a basic understanding of the product's cost structure.

> You're also talking about internal technical implementations of a chat bot. 99.99% of users won't even understand the words that are being used.

I think "internal technical implementation" is a stretch. Users don't need to know what a "transformer" is to understand the trade-off. It's not trivial but it's not something incomprehensible to laypersons.

[tempest_]

I use CC, and I understand what caching means.

I have no idea how that works with a LLM implementation nor do I actually know what they are caching in this context.

[libraryofbabel]

They are caching internal LLM state, which is in the 10s of GB for each session. It's called a KV cache (because the internal state that is cached are the K and V matrices) and it is fundamental to how LLM inference works; it's not some Anthropic-specific design decision. See my other comment for more detail and a reference.

[hakanderyal]

CC can explain it clearly, which how I learned about how the inference stack works.

[fragmede]

> 99.99% of users won't even understand the words that are being used.

That's a bad estimate. Claude Code is explicitly a developer shaped tool, we're not talking generically ChatGPT here, so my guess is probably closer to 75% of those users do understand what caching is, with maybe 30% being able to explain prompt caching actually is. Of course, those users that don't understand have access to Claude and can have it explain what caching is to them if they're interested.

[solarkraft]

I somewhat disagree that this is due diligence. Claude Code abstracts the API, so it should abstract this behavior as well, or educate the user about it.

[mpyne]

> Claude Code abstracts the API, so it should abstract this behavior as well, or educate the user about it.

Does mmap(2) educate the developer on how disk I/O works?

At some point you have to know something about the technology you're using, or accept that you're a consumer of the ever-shifting general best practice, shifting with it as the best practice shifts.

[websap]

Does using print() in Python means I need to understand the Kernel? This is an absurd thought.

[Nevermark]

That might be an absurd comparison, but we can fix that.

If you were being charged per character, or running down character limits, and printing on printers that were shared and had economic costs for stalled and started print runs, then:

You wouldn’t “need” to understand. The prints would complete regardless. But you might want to. Personal preference.

Which is true of this issue to.

[Barbing]

>If you were being charged per character, or running down character limits, and printing on printers that were shared and had economic costs for stalled and started print runs,

and the system was being run by some of the planet’s brightest people whose famous creation is well known to disseminate complex information succinctly,

>then:

You would expect to be led to understand, like… a 1997 Prius.

“This feature showed the vehicle operation regarding the interplay between gasoline engine, battery pack, and electric motors and could also show a bar-graph of fuel economy results.” https://en.wikipedia.org/wiki/Toyota_Prius_(XW10)

[zem]

mmap(2) and all its underlying machinery are open source and well documented besides.

[mpyne]

There are open-source and even open-weight models that operate in exactly this way (as it's based off of years of public research), and even if there weren't the way that LLMs generate responses to inputs is superbly documented.

Seems like every month someone writes up a brilliant article on how to build an LLM from scratch or similar that hits the HN page, usually with fancy animated blocks and everything.

It's not at all hard to find documentation on this topic. It could be made more prominent in the U/I but that's true of lots of things, and hammering on "AI 101" topics would clutter the U/I for actual decision points the user may want to take action upon that you can't assume the user already knows about in the way you (should) be able to assume about how LLMs eat up tokens in the first place.

[computably]

I would say this is abstracting the behavior.

[margalabargala]

Okay, sure. There's a dollar/intelligence tradeoff. Let me decide to make it, don't silently make Claude dumber because I forgot about a terminal tab for an hour. Just because a project isn't urgent doesn't mean it's not important. If I thought it didn't need intelligence I would use Sonnet or Haiku.

[pixl97]

"Gets mad because their is no option"

"Gets mad because when their is options the defaults suck"

"Gets mad because the options start massively increasing costs to areospace pricing"

[margalabargala]

Did you mean to reply to someone else? Or do you misunderstand the issue?

There is no option to avoid auto-dumbing after one hour of idle. I haven't complained about the cost at all, I'm happy to pay it.

So yeah, I'm mad because there's no option. The other two you mentioned don't apply.

[someguyiguess]

Yes. It’s perfectly reasonable to expect the user to know the intricacies of the caching strategy of their llm. Totally reasonable expectation.

[jghn]

To some extent I'd say it is indeed reasonable. I had observed the effect for a while: if I walked away from a session I noticed that my next prompt would chew up a bunch of context. And that led me to do some digging, at which point I discovered their prompt caching.

So while I'd agree with your sarcasm that expecting users to be experts of the system is a big ask, where I disagree with you is that users should be curious and actively attempting to understand how it works around them. Given that the tooling changes often, this is an endless job.

[abustamam]

> users should be curious and actively attempting to understand how it works

Have you ever talked with users?

> this is an endless job

Indeed. If we spend all our time learning what changed with all our tooling when it changes without proper documentation then we spend all our working lives keeping up instead of doing our actual jobs.

[Octoth0rpe]

There are general users of the average SaaS, and there are claude code users. There's no doubt in my mind that our expectations should be somewhat higher for CC users re: memory. I'm personally not completely convinced that cache eviction should be part of their thought process while using CC, but it's not _that_ much of a stretch.

[abustamam]

Personally I've never thought about cache eviction as it pertains to CC. It's just not something that I ever needed to think about. Maybe I'm just not a power user but I just use the product the way I want to and it just works.

[troupo]

Anthropic literally advertises long sessions, 1M context, high reasoning etc.

And then their vibe-coders tell us that we are to blame for using the product exactly as advertised: https://x.com/lydiahallie/status/2039800718371307603 while silently changing how the product works.

Please stop defending hapless innocent corporations.

[jghn]

> Have you ever talked with users?

I believe if one were to read my post it'd have been clear that I *am* a user.

This *is* "hacker" news after all. I think it's a safe assumption that people sitting here discussing CC are an inquisitive sort who want to understand what's under the hood of their tools and are likely to put in some extra time to figure it out.

[abustamam]

We're inquisitive but at the end of the day many of us just want to get our work done. If it's a toy project, sure. Tinker away, dissect away. When my boss is breathing down my neck on why a feature is taking so long? No time for inquiries.

[trinsic2]

Agreed. systems work the way they work. Its up to the user to determining what those limitations are. I don't like the concept of molding software based on every expectation a user has. Sometimes that expectation is unwarranted. You can see this in game development. Regardless of expressed criticism, sometimes gamers don't know what they want or what they need. A game should be developed by the design goals of the team, not cater to every whim the player base wants. We have seen were that can go.

[coldtea]

It's not like they have a poweful all-knowing oracle that can explain it to them at their dispos... oh, wait!

[esafak]

They have to know that this could bite them and to ask the question first.

[nixpulvis]

I do think having some insight into the current state of the cache and a realistic estimate for prompt token use is something we should demand.

[switchbak]

If there was an affordance on the TUI that made this visible and encouraged users to learn more - that would go a long way.

[exac]

It is more useful to read posts and threads like this exact thread IMO. We can't know everything, and the currently addressed market for Claude Code is far from people who would even think about caching to begin with.

[kang]

It seems you haven't done the due diligence on what part of the API is expensive - constructing a prompt shouldn't be same charge/cost as llm pass.

[coldtea]

It seems you haven't done the due diligence on what the parent meant :)

It's not about "constructing a prompt" in the sense of building the prompt string. That of course wouldn't be costly.

It is about reusing llm inference state already in GPU memory (for the older part of the prompt that remains the same) instead of rerunning the prompt and rebuilding those attention tensors from scratch.

[kang]

You not only skipped the diligence but confused everyone repeating what I said :(

that is what caching is doing. the llm inference state is being reused. (attention vectors is internal artefact in this level of abstraction, effectively at this level of abstraction its a the prompt).

The part of the prompt that has already been inferred no longer needs to be a part of the input, to be replaced by the inference subset. And none of this is tokens.

[coldtea]

>It seems you haven't done the due diligence on what part of the API is expensive - constructing a prompt shouldn't be same charge/cost as llm pass.

I think you missed what the parent meant then, and the confusing way you replied seemed to imply that they're not doing inference caching (the opposite of what you wanted to mean).

The parent didn't said that caching is needed to merely avoid reconstructing the prompt as string. He just takes that for granted that it means inference caching, to avoid starting the session totally new. That's how I read "from prompting with the entire context every time" (not the mere string).

So when you answered as if they're wrong, and wrote "constructing a prompt shouldn't be same charge/cost as llm pass", you seemed to imply "constructing a prompt shouldn't be same charge/cost as llm pass [but due to bad implementation or overcharging it is]".

[kang]

You are right, I was wrong in my understanding there. It stemmed from my own implementation; an inference often wrote extra data such as tool call, so I was using it to preserve relevant information alongwith desired output, to be able to throw away the prompt every time. I realize inference caching is one better way (with its pros and cons).

[computably]

I said "prompting with the entire context every time," I think it should be clear even to laypersons that the "prompting" cost refers to what the model provider charges you when you send them a prompt.

[kovek]

What if the cache was backed up to cold storage? Instead of having to recompute everything.

[vanviegen]

They probably already do that. But these caches can get pretty big (10s of GBs per session), so that adds up fast, even for cold storage.

[kovek]

10s of GBs? ( 1,000,000 context * 1,000 vector size ) ^ 2 = 1,000,000,000,000,000,000… oh wow.. I must be miscalculating

What about only storing the conversation and then recomputing the embeddings in the cache? Does that cost a lot? Doing a lot of matrix multiplication does not cost dollars of compute, especially on specialized hardware, right?

[Majromax]

Context length 1e6, vector length 1e3, and 1e2 model layers for 100e9 context size. Costs will go up even more with a richer latent space and more model layers, and the western frontier outfits are reasonably likely to be maximizing both.

[bontaq]

How's that O(N^2)? How's it O(N) with caching? Does a 3 turn conversation cost 3 times as much with no caching, or 9 times as much?

[jannyfer]

I’m not sure that it’s O(N) with caching but this illustrates the N^2 part:

https://blog.exe.dev/expensively-quadratic

[bontaq]

If there was an exponential cost, I would expect to see some sort of pricing based on that. I would also expect to see it taking exponentially longer to process a prompt. I don't believe LLMs work like that. The "scary quadratic" referenced in what you linked seems to be pointing out that cache reads increase as your conversation continues?

If I'm running a database keeping track of a conversation, and each time it writes the entire history of the conversation instead of appending a message, are we calling that O(N^2) now?

[atq2119]

Yes, that is indeed O(N^2). Which, by the way, is not exponential.

Also by the way, caching does not make LLM inference linear. It's still quadratic, but the constant in front of the quadratic term becomes a lot smaller.

[computably]

> Also by the way, caching does not make LLM inference linear. It's still quadratic, but the constant in front of the quadratic term becomes a lot smaller.

Touché. Still, to a reasonable approximation, caching makes the dominant term linear, or equiv, linearly scales the expensive bits.

[bavell]

> I would also expect to see it taking exponentially longer to process a prompt. I don't believe LLMs work like that.

Try this out using a local LLM. You'll see that as the conversation grows, your prompts take longer to execute. It's not exponential but it's significant. This is in fact how all autoregressive LLMs work.

[_flux]

What we would call O(n^2) in your rewriting message history would be the case where you have an empty database and you need to populate it with a certain message history. The individual operations would take 1, 2, 3, .. n steps, so (1/2)*n^2 in total, so O(n^2).

This is the operation that is basically done for each message in an LLM chat in the logical level: the complete context/history is sent in to be processed. If you wish to process only the additions, you must preserve the processed state on server-side (in KV cache). KV caches can be very large, e.g. tens of gigabytes.

[raron]

How big this cached data is? Wouldn't it be possible to download it after idling a few minutes "to suspend the session", and upload and restore it when the user starts their next interaction?

[throwdbaaway]

Should be about 10~20 GiB per session. Save/restore is exactly what DeepSeek does using its 3FS distributed filesystem: https://github.com/deepseek-ai/3fs#3-kvcache

With this much cheaper setup backed by disks, they can offer much better caching experience:

> Cache construction takes seconds. Once the cache is no longer in use, it will be automatically cleared, usually within a few hours to a few days.

[cyanydeez]

I often see a local model QWEN3.5-Coder-Next grow to about 5 GB or so over the course of a session using llamacpp-server. I'd better these trillion parameter models are even worse. Even if you wanted to download it or offload it or offered that as a service, to start back up again, you'd _still_ be paying the token cost because all of that context _is_ the tokens you've just done.

The cache is what makes your journey from 1k prompt to 1million token solution speedy in one 'vibe' session. Loading that again will cost the entire journey.

[cortesoft]

What they mean when they say 'cached' is that it is loaded into the GPU memory on anthropic servers.

You already have the data on your own machine, and that 'upload and restore' process is exactly what is happening when you restart an idle session. The issue is that it takes time, and it counts as token usage because you have to send the data for the GPU to load, and that data is the 'tokens'.

[vanviegen]

Wrong on both counts. The kv-cache is likely to be offloaded to RAM or disk. What you have locally is just the log of messages. The kv-cache is the internal LLM state after having processed these messages, and it is a lot bigger.

[cortesoft]

I shouldn't have said 'loaded into GPU memory', but my point still stands... the cached data is on the anthropic side, which means that caching more locally isn't going to help with that.

[nl]

> upload and restore it when the user starts their next interaction

The data is the conversation (along with the thinking tokens).

There is no download - you already have it.

The issue is that it gets expunged from the (very expensive, very limited) GPU cache and to reload the cache you have to reprocess the whole conversation.

That is doable, but as Boris notes it costs lots of tokens.

[vanviegen]

You're quite confidently wrong! :-)

The kv-cache is the internal LLM state after having processed the tokens. It's big, and you do not have it locally.

[nl]

> The kv-cache is the internal LLM state after having processed the tokens. It's big, and you do not have it locally.

Yes - generated from the data of the conversation.

Read what I said again. I'm explaining how they regenerate the cache by running the conversation though the LLM to reconstruct the KV cache state.

[miroljub]

This sounds like a religious cult priest blaming the common people for not understanding the cult leader's wish, which he never clearly stated.

[computably]

A strange view. The trade-off has nothing to do with a specific ideology or notable selfishness. It is an intrinsic limitation of the algorithms, which anybody could reasonably learn about.

Sure, the exact choice on the trade-off, changing that choice, and having a pretty product-breaking bug as a result, are much more opaque. But I was responding to somebody who was surprised there's any trade-off at all. Computers don't give you infinite resources, whether or not they're "servers," "in the cloud," or "AI."

[miroljub]

He was surprised because it was not clearly communicated. There's a lot of theory behind a product that you could (or could not) better understand, but in the end, something like price doesn't have much to do with the theoretical and practical behavior of the actual application.

[cyanydeez]

It'd probably be helpful for power users and transparency to actually show how the cache is being used. If you run local models with llamacpp-server, you can watch how the cache slots fill up with every turn; when subagents spawn, you see another process id spin up and it takes up a cache slot; when the model starts slowing down is when the context grows (amd 395+ around 80-90k) and the cache loads are bigger because you've got all that.

So yeah, it doesn't take much to surface to the user that the speed/value of their session is ephemeral because to keep all that cache active is computationally expensive because ...

You're still just running text through a extremely complex process, and adding to that text and to avoid re-calculation of the entire chain, you need the cache.

[bede]

I too would far rather bear a token cost than have my sessions rot silently beneath my feet. I usually have ~5 running CC sessions, some of which I may leave for a week or two of inactivity at a time.

[lochnessduck]

Yes, me too. This is good to know, but basically it means I can’t rely on old conversations any more. Using a “handoff” file to try and start a new conversation is effectively the same thing as what they did under the hood. So yeah, you can’t rely on old conversations to be as informed when you pick it back up.

[airstrike]

same here, and I suspect there are dozens of us

[winternewt]

Instead of just dropping all the context, the system could also run a compaction (summarizing the entire convo) before dropping it. Better to continue with a summary than to lose everything.

[Folcon]

There's problems with this approach as well I've found

I'm really beginning to feel the lack of control when it's comes to context if I'm being honest

[bcherny]

Yes! This is what we’re trying next.

[nixpulvis]

How else would you implement it?