Documents and notes decision

Dailygram

A lightweight, single-user replacement (scrape + LLM summaries + email) is realistic to build and run, but matching the production reliability, multi-platform scraping scale, and polished UX of the paid product requires ongoing maintenance and engineering effort—so build a narrow self-hosted workflow if you can maintain scrapers, otherwise keep paying.

Visit website
You pay

$5/mo

$60/yr

Read off the official pricing page.

You’d pay instead

$100one-off80 h to build

$50/mo12 h/mo upkeep

On cash alone, building overtakes the subscription at 12 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

  • 1) fetch public posts from target profiles (scrape or API), 2) store new posts and dedupe in Postgres, 3) generate per-post and per-profile summaries with an LLM, 4) assemble HTML email digest and schedule/send via an SMTP/ESP, 5) simple web UI to add/remove monitored profiles and set delivery time.

What it still won’t have

  • Robust anti-break scraping/scale handled by vendor
  • Priority queue processing and SLA for digest generation
  • Polished UX and creator directory
  • Built-in analytics/aggregation across many profiles
  • Support and priority customer service

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 12 seats.

Paid seatsseats

Money you would actually spend

Keep paying
—

Subscription price × seats × 12

Build it
—

AI build —APIs + hosting —

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

Not run yet
Build a self-hosted DailyGram-like service using Python (FastAPI), Playwright for scraping, Postgres for storage, Redis for job queue (RQ or Celery), OpenAI for summarization, and SendGrid for emails. In scope: public-profile scraping for Instagram/X/TikTok/LinkedIn, deduplication and storage of posts, LLM-based per-post and per-profile summaries, scheduled daily digest generation, a minimal web UI to add/remove profiles and set delivery time, and emailed HTML digest with links to original posts. Out of scope: user accounts billing, analytics dashboards, bot-resistance at massive scale, enterprise SLAs. Include rate-limiting, exponential backoff, retries for scraping and API calls, logging, basic unit/integration tests, Docker compose for local deployment, and a README with deployment and maintenance steps.
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