Social media decision
FriendFilter + GroupFilter
A single competent developer can build and run a small Chrome-extension + backend replacement in about a week and modest ongoing cost; the product's primary durable moat is brand trust rather than technical barriers.
Visit website↗Open-source builds that already do this
Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need - the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship. All FriendFilter + GroupFilter alternatives, with the arithmetic →
What a replacement has to do
- Run a scan of a user's Facebook connections, compute engagement scores from recent interactions, present sortable/filterable results in a dashboard, allow CSV export and whitelist management, and perform user-driven removals or automations.
What it still won’t have
- Product polish, UX refinements and cross-browser extension QA
- Established user base and brand trust (120k+ users)
- Built-in affiliate program and support/priority support that comes with paid product
- Any proprietary heuristics or historical data the vendor may use to tune scoring
What remains hard
- Brand trust
Trusted by 120,000+ users
First-year cost
Keep paying
Paying is—cheaper in year one.
On cash alone, building overtakes the subscription at 3 seats.
Money you would actually spend
Time you would spend
—
What you would spend
What we assumed
The verdict above measures whether you could build it. This one is only about money.
Runnable build prompt
Build a minimal FriendFilter replacement: implement a Chrome extension (Manifest V3) + Node.js (Express) backend with a Postgres DB and a React dashboard. Core features in scope: 1) sign-in via the extension and consent flow; 2) one-click 'scan' that collects publicly visible engagement signals for the signed-in user's friend list, stores them in Postgres, and computes an 'engagement score' using an LLM or lightweight heuristic; 3) dashboard showing sortable/filterable connection list, whitelist toggle, CSV export, removals UI, and a daily auto-sync scheduler; 4) background worker for optional auto-reactions and auto-friend-request actions (opt-in). Out of scope: training custom ML models, multi-tenant enterprise billing, large-scale scraping of private content, platform-level accreditation with Meta. Provide error handling, retries for network failures, unit and integration tests for the backend and key UI flows, and CI config to run tests.
How we checked
How the score was reached
- Build verdict base78
- An open-source build was found+5
- 4 cited sources+3
- Price verified on pricing page+3
- Evidence score89
The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time - so the same evidence always produces the same number.
How scoring works →Cited sources · 4
Every page the run actually retrieved.
- official productFriendFilter homepage
- official pricingFriendFilter pricing section
- open sourceqeeqbox/social-analyzer
- open sourcewangrongding/wechat-bot
Integrity checks
What held up, and what did not.
