Social media decision
Post Reaction
A competent developer can build a useful local replacement that simulates reactions and aggregates metrics, but reproducing the polished cross-platform offline app, broad LLM catalog, and bundled persona library in the commercial product would take substantially more effort.
Visit website↗Built by Ozgur Ozer, who ships 12 products in this index
Not priced
No pricing page we fetched carried a figure, so there is nothing to compare against. The build side is still real.
$100one-off60 h to build
$20/mo3 h/mo upkeep
No published price to break even against.
No open-source build does this yet
Nothing published replaces this one, so a replacement starts from an empty file. Here is what it would have to cover.
What a replacement has to do
- Input two post variants, run multiple LLM-based persona simulations that produce likes/shares/comments/bookmarks, aggregate engagement metrics, and show a side‑by‑side comparison.
What it still won’t have
- Polish of a native commercial desktop app (UX refinements, polished installers, signed builds)
- Built-in support for many LLM vendors out of the box and bundled/local model images
- Pre-built library of 100/1000 bot personalities and tuned prompt-engineering
- One-time license purchasing flow and licensing UI
- Official QA, documentation, and troubleshooting support
What remains hard
- Product polish and ongoing maintenance
First-year cost
No published price
Post Reaction does not publish a price we could read, so there is nothing to compare against. What building costs is below.
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 cross-platform desktop app using Electron + React + Node that accepts two post texts and runs simulated audience reactions via configurable LLM providers. Core features in scope: local project storage (SQLite or JSON), integration with OpenAI API and optional local Ollama/LM Studio endpoints, a persona system (name, interests, tone) that maps to prompt templates, generation of comments/reactions (like/share/bookmark) per persona, aggregation into engagement metrics and a side-by-side comparison dashboard, and keyboard-accessible UI. Out of scope: signed paid licensing flow, large-scale persona library, analytics multi-user backend. Include error handling for API failures, input validation, unit tests for core aggregation logic, and basic packaging scripts for Mac and Windows installers.
How we checked
How the score was reached
- Partly verdict base52
- 5 cited sources+3
- Price verified on pricing page+3
- 3/3 assessment runs agreed+4
- Evidence score62
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 · 5
Every page the run actually retrieved.
- official productPost Reaction - Test your posts before publishing
- official productPost Reaction - Features & How It Works
- official productPost Reaction - Features & How It Works
- official productPost Reaction - Features & How It Works
- official pricingPost Reaction - Pricing
Integrity checks
What held up, and what did not.
