Does the Jev filter change what the summarizer model sees, or only the input?

Only the input. Jev scores and selects original passages ahead of the existing request; it does not rewrite the transcript, change the summary prompt or model, or modify the cached transcript, and selected passages stay in source order. With prefiltering off, the original pipeline runs unchanged.

What happens if the filter rejects everything, or the scoring call fails?

All-rejected chunks are omitted, and if none remain you get a no-match message without any LLM call. With explicit include or exclude rules, a timeout, malformed response, HTTP error, or unsplittable request stops the run before the summary request rather than leaking unfiltered text, and with both fields blank a scoring failure warns and falls back to the original chunk.

Will filtering drop text I wanted kept?

It can. Chunks are split into roughly 800 to 1,200 character units, and an exclusion inside a mixed unit drops that whole unit along with useful neighbouring text. The keep-ratio ceiling can also rank qualifying text down, so the README advises comparing against an unfiltered summary for important material.

What does the prefilter cost, and how well does it actually classify?

The repository does not say: automated tests use mocked scores, and live classification quality, cost, and latency are not benchmarked. Scoring is batched under the existing concurrency limits with no retries, oversized work is split into bounded requests of 28 KB state and 60 KB total JSON, and inclusion and exclusion scores are independent so a high inclusion cannot override an exclusion.

What can I use the summarizer on?

11+ sources including YouTube, Instagram, TikTok, X, Reddit, Facebook, Drive, Dropbox, and local files, and it produces distilled markdown with Q&A, fact-checks, tutorials, Mermaid diagrams, and essays.

What do I need to run it?

Python 3.10 or newer, ffmpeg on your PATH, and an OpenAI-compatible API key in .env; installing with pipx is recommended for an isolated environment, and Groq offers a free tier.

Are there other interfaces besides the CLI?

Yes: Streamlit, an HTTP API, Docker, Raycast, and an agent skill, with Docker serving on http://localhost:8501 or from a pre-built image on ghcr.io.

CommunityJev Tools & CLIs

Summarize

Local-first summarizer for video and posts across 11 or more sources; its optional Jev prefilter scores transcript chunks against include and exclude rules before the summary request is made.

See highlights

Open github.com

Stars: 227
Language: Python
License: MIT
Published by: Community
Author: martinopiaggi
Last verified: Oct 2026

martinopiaggi/summarize

User repository on GitHub

View on GitHub

Video AI summarization from multiple sources (YouTube, X, Instagram, TikTok, Reddit, Facebook, Google Drive, Dropbox, and local files).

Forks: 30
Last pushed: Updated Oct 2026

Repo stats from the GitHub API, cached Oct 2026.Homepage

About this project

11+ sources: Local-first summarization for YouTube, Instagram, TikTok, X, Reddit, Facebook, Drive, Dropbox, local files. Bring your own API keys: Works with any OpenAI-compatible LLM, Perplexity models, LiteLLM. CLI · Streamlit · HTTP API · Docker · Raycast · Agent skill. Transcript cache + optional JEV prefiltering + optional Cobalt sidecar for yt-dlp fallbacks + optional vision mode.

Quoted from the project's README. Read the full README.

Tagscommunity jevbest

Works forJev for Classification

Used inJev for Software & DevTools

Languages: Python

Highlights

Jev selects original transcript passages before the summarizer request, leaving the cached transcript, summary prompt, model, and source order untouched.
Two plain-language rules drive selection, an include phrase and an exclude phrase, and an exclusion wins whenever a passage matches both.
Selection is budgeted at roughly 35% of each original chunk as a ceiling rather than a quota, and `jev-keep-ratio: 1.0` keeps every qualifying unit.
With explicit rules set, a timeout, malformed response, or unsplittable request stops before any summary call instead of sending unfiltered text.

Watch out

MIT licensed Python that needs Python 3.10+, ffmpeg on PATH, and your own OpenAI-compatible key in `.env` before it runs. The prefilter reuses the OpenRouter or TypeSafe key already configured for the summary provider, so there is no separate credential, and visual mode and grammar correction bypass it entirely. Live classification quality, cost, and latency are not benchmarked. Copy reviewed against the repository README on 2026-10-04, not from running it.

Questions about this entry

Short answers drawn from the linked project or page.

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