As we approach the end of 2025, like a trained goat, I am collecting year end reviews from every SaaS service that will hand me one. Somewhere in that pile I realized I do a fair bit of posting on the Fediverse, so off I went looking for my own wrapped.

Corporate platforms have turned “wrapped” into a feature and also a business model. I am on the Fediverse for the opposite reason, but I still wanted the fun part.

So I built Fedi Wrap: a local first tool that generates a year in review report for Mastodon compatible Fediverse servers.

I wanted a year in review, not a JSON endurance test.

Repo:

Why I built it

There are already Mastodon wrapped tools, but many assume your instance exposes posts over the API without authentication.

That breaks the moment you move beyond common Mastodon defaults. I use GoToSocial. It is Mastodon compatible, but it leans harder into privacy and security. Many setups require authentication to access timelines and statuses. Unauthenticated “wrapped” tools simply cannot see your posts.

So the problem was simple.

How do you generate a year in review when your instance is doing the right thing and not handing data to anonymous requests?

Fedi Wrap is my answer: fetch with auth, analyze locally, output a single report.

What it does

Fedi Wrap is a bash script that:

  • Fetches posts for a chosen year using toot (so auth is handled by the CLI)
  • Runs analysis locally using jq
  • Generates a self contained HTML report you can archive and open offline

The report includes:

  • Total posts, boosts, replies
  • Monthly, weekly, hourly patterns
  • Longest posting streak
  • A simple engagement score
  • Top posts by engagement
  • Activity calendar
  • Fun labels like posting persona and chronotype

Optional local AI insights

If you want it, Fedi Wrap can use a local LLM via Ollama to generate:

  • A narrative summary of your year
  • Recurring topics
  • Vibe and persona style descriptions

AI is optional. Everything runs on your machine.

I like AI more when it does not eat my data.

To keep results grounded, the AI flow is multi pass: analyze chunks first, then synthesize.

The stack

Core:

  • bash
  • jq
  • toot (for fetching)
  • curl

Optional:

No Node. No containers. No build pipeline. Boring on purpose.

Screenshots

Get it here

Live Details :

Source Code : /

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