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Amadeo Bonde
Amadeo Bonde

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That quote I half-remember at 2am? I can find it now.

#devchallenge #gemmachallenge #gemma #automation

Gemma 4 Challenge: Build With Gemma 4 Submission

gemma-brief: I built an AI that keeps up so I don't have to

I'm a broke college student. Base MacBook Air. No API budget. Constantly behind.

I follow too many YouTube channels. Research, tech, founders, stuff that actually matters. But I'm in class, I'm studying, I'm sleeping. By the time I get to a video it's been 3 days and I've already forgotten why I saved it. And rewatching to find that one quote I half-remember? Not happening.

The problem isn't that the content isn't good. The problem is I don't have time to absorb it. And when I do sit down, I can't remember what I already learned.

I needed something that could do the watching while I slept, and have answers ready when I woke up.

So I built gemma-brief.

It monitors the YouTube channels I care about, downloads new videos overnight, transcribes them locally with Whisper, summarises everything with Gemma 4 E4B running on Ollama, enriches with Wikipedia for context, and drops a formatted PDF into my Telegram. Every morning when I wake up, my briefs are waiting. And if I half-remember something from last week, I just ask.

Zero cloud. Zero cost. Runs on my base MacBook Air while I sleep.

Demo

What I Built

A fully local intelligence pipeline that turns YouTube uploads into structured briefs. On autopilot, overnight.

The flow:

  1. Add a YouTube channel to a playlist called gemma-brief
  2. Scheduler checks for new uploads every night at 02:00
  3. yt-dlp pulls the audio
  4. Whisper transcribes it locally, no OpenAI
  5. Gemma 4 E4B reads the full transcript (up to 32K tokens) and writes a structured brief
  6. Wikipedia enriches every person, company, and concept mentioned
  7. PDF gets built and lands in my Telegram

The brief is the same structure every time: TL;DR → The Thesis → Key Quotes → Wikipedia Context. I can read it in 2 minutes over coffee and know whether it's worth a full watch.

And there's a /explain command. Ask anything, like "/explain gemini spark", and it searches across every brief I've ever received and returns the exact voice clip with the timestamp. Not a text answer. The actual moment from the actual video.

Stack:

  • gemma4:e4b via Ollama
  • Whisper (local transcription)
  • yt-dlp
  • Python-Telegram-Bot
  • ReportLab (PDF)
  • Wikipedia API

The Output

This is what lands in my Telegram every morning. Three real briefs from three real channels, generated overnight, zero input from me.

Fireship brief (PDF)
Two Minute Papers brief (PDF)
Google I/O brief (PDF)

Every brief follows the same structure: TL;DR, The Thesis, Key Quotes, Wikipedia context on every person and company mentioned. I can read any of them in under 2 minutes and know if the video is worth going back to.

The Telegram bot also takes commands:

  • /explain [topic] searches everything you've ever received and returns the exact voice clip with the timestamp. Not a summary. The moment.
  • /list shows all your briefs
  • /search [query] full text search across your entire vault

Code

github.com/amadeobonde/gemma-brief

Open source. Clone it, point it at your channels, run gemma-brief run.

How I Used Gemma 4

Gemma 4 E4B is doing all the thinking, entirely on my machine.

The 32K context window is what made this viable. A 45-minute video transcribes to ~8,000 words. Gemma 4 reads the whole thing in one shot and produces a structured brief without me chunking anything or setting up retrieval pipelines.

response = ollama.chat(
    model='gemma4:e4b',
    messages=[{
        'role': 'user',
        'content': BRIEF_PROMPT.format(transcript=transcript, title=title)
    }]
)
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No LangChain. No vector DB. No API key. The model runs locally via Ollama and the output parses cleanly into PDF sections.

I also use Gemma 4 for /explain. Given a query and a set of brief excerpts, it identifies the most relevant moment and returns it with the timestamp.

32K context + local inference = no API costs, no rate limits, no data leaving my Mac

The E4B variant was the right call. Fast enough to process a full channel's weekly uploads in a nightly batch, small enough to run on a base M-series MacBook Air, smart enough that I actually trust the summaries.

I set it to run at 2am. By 8am, everything's been processed. I wake up, check Telegram, spend 10 minutes reading briefs instead of 3 hours watching videos I might not even finish.

The Real Problem I Solved

It's not about being lazy. It's about being realistic.

There's too much good content and not enough hours. I was either missing things entirely or watching at 2x speed and retaining nothing. Both felt like failure.

gemma-brief changed the equation. Now I can follow 10 channels seriously, actually remember what I learned, and go deep on the stuff that's worth it. No paid API, no cloud subscription, no machine I can't afford.

It runs while I sleep. The answers are there when I wake up.

Built for the Gemma 4 Challenge on DEV.to.

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