Learning and careers decision

Bragjournal.ai

A minimal replacement (capture + email ingestion + LLM generation) is realistic for a single technical person, but reproducing the hosted product's polish, verified-email UX, and full operational reliability would require more time or team support.

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Built by François Rejeté, who ships 6 products in this index

You pay

$6/mo

$72/yr

Read off the official pricing page.

You’d pay instead

$100one-off54 h to build

$20/mo4 h/mo upkeep

On cash alone, building overtakes the subscription at 5 seats.

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

  • Manually or automatically capture an achievement, attach/verifiy supporting email evidence, tag it with KPIs/goals, then generate a review-ready brag document via an LLM.

What it still won’t have

  • Polish, UX, and onboarding flows of the hosted product
  • Built-in early-user pricing and billing management UI
  • Any undisclosed proprietary ranking/quality model
  • Cross-device polished integrations (mobile apps, sync)
  • Operational SLAs, backups, and monitoring provided by a vendor

What remains hard

  • Product polish and ongoing maintenance
Read the build prompt

First-year cost

Keep paying

Paying is—cheaper in year one.

On cash alone, building overtakes the subscription at 5 seats.

Paid seatsseats

Money you would actually spend

Keep paying
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Subscription price × seats × 12

Build it
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AI build —APIs + hosting —

Time you would spend

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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

Not run yet
Build a single-user BragJournal clone using Next.js (React) for the frontend, a Postgres database (Supabase or Neon) for storage, NextAuth (email/password) for auth, Stripe for billing, Mailgun (or SendGrid) inbound parse for forwarded-email ingestion, a small background worker (Node + BullMQ) for processing emails and attachments, and OpenAI's API for AI-generated brag documents. Core features in scope: (1) web UI to add/edit/list achievements with KPI tags; (2) inbound email route that accepts forwarded emails, parses sender/date/body/attachments and attaches them to an achievement; (3) a background job to quality-score entries and generate a combined review document via OpenAI; (4) payment handling for a single paid tier; (5) basic dashboards showing achievements and generated documents. Out of scope: multi-user org/team permissions, native mobile apps, on-prem deployments, training custom ML models. Require input validation, retries and error handling for email parsing and API calls, structured logging, and automated tests for API routes and the generation workflow.
How we checked2 sources · 2/3 runs agreed · evidence score 56

How the score was reached

  • Partly verdict base52
  • 2 cited sources+1
  • Price verified on pricing page+3
  • Evidence score56

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 · 2

Every page the run actually retrieved.

Integrity checks

What held up, and what did not.

✓ Price read off the page! 2 of 3 runs agreed; the verdict is the majority✓ Citations limited to fetched pages! 1 moat recorded