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    Claude plugin reportedly cuts a session from nearly 1M to 86K tokens in one second

    A user credits fast-jev-compaction, which they describe as a Claude plugin that uses the Jev model to review unnecessary tool calls.

    xjdrXJ
    Theo - t3.ggT-
    Alex VolkovAV
    6 Sources, ,

    TLDR

    A user reports reducing their Claude session from nearly 1 million to 86,000 tokens in a one-second run using fast-jev-compaction. They say the plugin uses the Jev model to review unnecessary tool calls and share its GitHub repository for installation.

    Combined views

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    6 Sources, first seen 20d ago

    Combined views

    1.6M

    6 Sources, first seen 20d ago

    10.2K likes
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    20d ago
    first seen 20d ago
    10.2K likes
    328 comments
    15.7K saves
    519 reposts
    328 comments
    15.7K saves
    519 reposts

    6 Sources

    Alex Volkov@altryneThis is actually insane. This uses @typesafeai Jev model, as a plugin in Claude to review all the un-nesseasary tool calls, and it takes 1s to run! Like, literally, 1 second to take my Claude session from nearly 1M to ... 86K tokens! 😮 Ask your claude to install it and be amazed Use this prompt ``` Install, and configure : https://github.com/tamaratran/fast-jev-compaction ```20d
    Theo - t3.gg@theoThis is a terrible compaction strategy that fundamentally doesn't understand how compaction and context management work. Seems like a lot of people are confused so let's break this down. 1. Compaction isn't a filter The role of compaction is to clean up history to keep the agent focused, not just deleting noise. It should be used sparingly when context gets too long, not constantly to keep context small. 2. Jev doesn't even know what it's deciding on! Models use the context of the thread to decide what to keep or not keep in a summary. This implementation goes through on a "line-by-line" (per tool call) basis to decide what should be left or deleted. Not only does this 32k token context model know very little of what happened before, but (in this implementation) it doesn't even know what the result of the tool call is! Deleting these things randomly will keep the model from knowing what it's tried and dooms you to end up in "stupid loops" where the model keeps trying the same thing over and over. 3. You're giving up the reasoning entirely Frontier models from OpenAI, Anthropic, XAI, and Google do not share reasoning traces over the API. They share encrypted payloads, which Jev cannot see (and often will drop). Anthropic is even stricter with this, requiring you to preserve the entire history in order to get any of the reasoning data. As a result, using this in Claude Code guarantees the model will act way dumber. 4. Models are tuned on their compaction flows For the last year, Frontier Labs have been including compaction and long runs as part of the training process. These models have learned ways to compact that are more effective than any rudimentary solution. Fun fact: If you switch models in Codex and compaction is necessary, compaction will run on the model that was previously used in the thread. 5. Cache writes are more expensive than cache reads. Cache writes are the biggest cost by far for agents. I often see cache write costs go over 60% of my total LLM spend in my personal use of Claude Code and Codex. Cache writes are insanely expensive when data earlier in the history is changed (because the old cache is invalidated when things change at the top). Every history edit requires a cache rewrite for ANY data past the history edit. If your history is "1,2,3,4,5,6" and you delete "2", you have to rewrite "3,4,5,6". This is more expensive than leaving "2" in the history. Good news. Since we're already killing all of the reasoning tokens by doing this stupid compaction strategy, the rewrite cost won't actually be that high because the model is missing so much data! 🙃🙃 6. The implementation is hot garbage. > "Whatever is not kept is deleted permanently, but the assistant can always re-run a tool or re-read a file." Good luck with that one. To be clear: this is a cool experiment and I find it genuinely interesting. That said, if you think this style of bs filtering on a probability threshold is actually a compaction strategy, I highly recommend you just use the defaults in tools like Claude Code and Codex. You're much less likely to hurt yourself that way.20d
    xjdr@_xjdri single compact operation in the claude desktop app cost me 20% of my 5 hour limit . lolol19d

    Sentiment

    Positive65%35%Negative

    Summary

    Many accounts welcomed the Jev model plugin in Claude for quickly spotting unnecessary tool calls and sparking new ideas, while others questioned quality drops and whether the approach was too good to be true.

    Based on 22 sentiment-bearing replies from 20 accounts across 2 conversations.

    Sentiment

    Positive65%35%Negative

    Summary

    Many accounts welcomed the Jev model plugin in Claude for quickly spotting unnecessary tool calls and sparking new ideas, while others questioned quality drops and whether the approach was too good to be true.

    Based on 22 sentiment-bearing replies from 20 accounts across 2 conversations.

    6 Sources

    Alex Volkov@altryneThis is actually insane. This uses @typesafeai Jev model, as a plugin in Claude to review all the un-nesseasary tool calls, and it takes 1s to run! Like, literally, 1 second to take my Claude session from nearly 1M to ... 86K tokens! 😮 Ask your claude to install it and be amazed Use this prompt ``` Install, and configure : https://github.com/tamaratran/fast-jev-compaction ```20d
    Theo - t3.gg@theoThis is a terrible compaction strategy that fundamentally doesn't understand how compaction and context management work. Seems like a lot of people are confused so let's break this down. 1. Compaction isn't a filter The role of compaction is to clean up history to keep the agent focused, not just deleting noise. It should be used sparingly when context gets too long, not constantly to keep context small. 2. Jev doesn't even know what it's deciding on! Models use the context of the thread to decide what to keep or not keep in a summary. This implementation goes through on a "line-by-line" (per tool call) basis to decide what should be left or deleted. Not only does this 32k token context model know very little of what happened before, but (in this implementation) it doesn't even know what the result of the tool call is! Deleting these things randomly will keep the model from knowing what it's tried and dooms you to end up in "stupid loops" where the model keeps trying the same thing over and over. 3. You're giving up the reasoning entirely Frontier models from OpenAI, Anthropic, XAI, and Google do not share reasoning traces over the API. They share encrypted payloads, which Jev cannot see (and often will drop). Anthropic is even stricter with this, requiring you to preserve the entire history in order to get any of the reasoning data. As a result, using this in Claude Code guarantees the model will act way dumber. 4. Models are tuned on their compaction flows For the last year, Frontier Labs have been including compaction and long runs as part of the training process. These models have learned ways to compact that are more effective than any rudimentary solution. Fun fact: If you switch models in Codex and compaction is necessary, compaction will run on the model that was previously used in the thread. 5. Cache writes are more expensive than cache reads. Cache writes are the biggest cost by far for agents. I often see cache write costs go over 60% of my total LLM spend in my personal use of Claude Code and Codex. Cache writes are insanely expensive when data earlier in the history is changed (because the old cache is invalidated when things change at the top). Every history edit requires a cache rewrite for ANY data past the history edit. If your history is "1,2,3,4,5,6" and you delete "2", you have to rewrite "3,4,5,6". This is more expensive than leaving "2" in the history. Good news. Since we're already killing all of the reasoning tokens by doing this stupid compaction strategy, the rewrite cost won't actually be that high because the model is missing so much data! 🙃🙃 6. The implementation is hot garbage. > "Whatever is not kept is deleted permanently, but the assistant can always re-run a tool or re-read a file." Good luck with that one. To be clear: this is a cool experiment and I find it genuinely interesting. That said, if you think this style of bs filtering on a probability threshold is actually a compaction strategy, I highly recommend you just use the defaults in tools like Claude Code and Codex. You're much less likely to hurt yourself that way.20d
    xjdr@_xjdri single compact operation in the claude desktop app cost me 20% of my 5 hour limit . lolol19d