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Lowfat

pluggable CLI filter that saved 91.8% of my LLM tokens

Details

External ID
48409955
Source
HN
Company
—
Product
Lowfat
Website domain
github.com
Launched
June 5, 2026
Cohort
—
Upvotes
156
Upvotes percentile
0.9453551912568307
Tags
—
Fetched at
Sept. 7, 2026, 9:26 p.m.
Updated at
Sept. 7, 2026, 9:26 p.m.

Description

Hi HN, not sure if anyone would be interested, but just wanted to share that I've been maintaining my small tool called 'lowfat' that helps me filters some of my verbose CLI output. It's a single binary, works as an agent hook or a shell wrapper. It has a plugin system to customize filters per command.The idea is pretty simple: agents don't need the full kubectl get -o yaml or any 10k-line dump to make decisions. So that lowfat sits in between, strips the noise, and passes through what matters. Here's my real report after 2 months of personal use: lowfat history --all lowfat plugin candidates ───────────────────────────────────────────────────────── # command runs avg raw cost savings source status 1 kubectl get 101x 14.4K 1.5M 93.9% plugin good 2 grep 103x 13.5K 1.4M 96.2% plugin good 3 git diff 81x 995 80.6K 57.9% built-in good 4 kubectl 90x 485 43.6K 33.6% plugin good 5 docker 127x 5.5K 693.6K 96.1% built-in good 6 ls 489x 117 57.3K 56.2% built-in good 7 find 30x 16.5K 495.0K 95.5% plugin good 8 git show 63x 490 30.9K 38.0% built-in good 9 git 177x 368 65.2K 76.1% built-in good 10 git log 86x 556 47.8K 78.5% built-in good 11 kubectl logs 5x 3.6K 17.8K 43.0% plugin good 12 git status 86x 152 13.1K 58.0% built-in good 13 docker ps 20x 467 9.3K 52.8% plugin good 14 kubectl describe 6x 656 3.9K 1.2% plugin weak 15 docker images 9x 940 8.5K 61.8% built-in good 16 k get 2x 2.1K 4.2K 35.9% plugin good 17 terraform 10x 395 3.9K 32.1% plugin good 18 git commit 32x 77 2.5K 0.0% built-in weak 19 docker build 8x 487 3.9K 37.6% built-in good 20 docker compose 22x 979 21.5K 89.4% built-in good total: 4.4M raw → 4.1M saved (91.8%) My toolset above is kind limited, but it works pretty well for my usecase without any interruption Kinda help me not reaching the token limit for my company Bedrock limit usage and keep optimizing the saving on the go for later usage.But, why not alternatives (https://github.com/zdk/lowfat#alternatives) ? The answers are: - My goal is to make the core lightweight but extensible via plugins i.e. not trying to bundle every command in the installed binary so that people own their output filters. - Customizable per usecase via plugin or filter pipelines as I am using my own toolset. - Customizable for non-public CLI tools, for example, some enterprise might have their interal CLI tools that public won't have access. - People should own their data. So the design is local-first, No telemetry forever. - I kinda love UNIX-style composible pipes, so lowfat-filter has implemented this style. - Be able to adjust aggressiveness of the filter, so we can control that we won't strip something the agent needed.GitHub: https://github.com/zdk/lowfatAnyway, if anyone is interested, feedbacks and questions are welcome!Thanks!

Enrichment

Theme
cryptocurrency utilities and wallet tools
Vertical
Horizontal
Function
Dev tools
Audience
Developer
AI stance
AI feature
Project type
Hobby / open-source project
Normalized one-liner
cli filter for reducing llm token usage
Manually corrected
False

Could you build this?

Yes A CLI filter tool that strips ANSI escapes, collapses repetitious text, and applies regex patterns to reduce token counts is straightforward text-processing logic that AI can build effortlessly.

Discussion

20 comments analyzed.

Competitors mentioned: GLM 4.7 Flash, OpenHop, Dirac, caveman, OpenCode

Concerns raised: Smaller models make boundary errors (off-by-one type mistakes), Risk of getting banned for certain API usage patterns, Token waste from code reading/grep search and retrieval roundtrips, Non-first-party integrations corrupt workflow instead of enriching it, Unclear if tools actually help agents rather than hinder them

Feature requests: Token filtering/compression in vendor tooling, Cost per successful answer benchmarking metric, First-party LLM provider native integration

Competitors

Other products that read as similar to this one — 58 launches clear the similarity bar, closest 8 shown.

Attention rank: #4 of 59 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 206 days after the earliest competitor.

Other launches for this product

Same idea, different domain

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