Social media decision

ReddBoss

A capable engineer can build a usable Reddit lead-finder and reply-generator, but reproducing the managed-service guarantees, scale-tested analytics, and product polish (including claimed zero-ban assurances and guaranteed viral posts) is unlikely without the vendor's operational processes and data.

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Built by MoNagm, who ships 3 products in this index

You pay

$25/mo

$300/yr

Read off the official pricing page.

You’d pay instead

$100one-off70 h to build

$50/mo6 h/mo upkeep

On cash alone, building overtakes the subscription at 3 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

  • Continuously scan Reddit for relevant posts/comments, score intent, store leads, generate an AI reply/DM, and surface leads and analytics in a web dashboard.

What it still won’t have

  • Guaranteed managed service and account manager
  • Any proprietary training data or product-level tuning that produces 'guaranteed viral posts'
  • Scale-tested analytics and long-term product telemetry
  • Operational promise of 'Zero Ban Risk' and founder expertise / managed campaign work

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 3 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

—

—

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 lightweight Reddit lead-discovery web app using Node.js (Express) backend, Postgres DB, a React frontend, and OpenAI for text generation. Core features in scope: (1) scheduled Reddit ingestion via API/Pushshift with rate-limit handling and raw storage; (2) intent classification pipeline that flags potential leads and deduplicates by URL/user; (3) an endpoint that calls an LLM to produce 3 reply variants + 1 DM per lead; (4) a React dashboard showing inbox, filters, lead scores, ability to send/export replies, and basic analytics (leads per subreddit, engagement, share-of-voice); (5) simple built-in CRM fields (tags, status) and CSV export; (6) tests for ingestion, classification, and reply-generation; (7) error handling, retries, logging, and a small deployment guide for a single VPS (Docker + systemd). Out of scope: managed service offering, guaranteed viral-post creation, SEO blog writing, and multi-tenant enterprise features. Include unit and integration tests for core pipelines and retry/backoff for external APIs.
How we checked1 sources · 2/3 runs agreed · evidence score 55

How the score was reached

  • Partly verdict base52
  • Price verified on pricing page+3
  • Evidence score55

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 · 1

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