Insights / How to build an AI Reddit monitor for under $5 a month
How to build an AI Reddit monitor for under $5 a month
Alice B
The practice of surfacing high-signal threads from priority subreddits in time to be early - before the conversation crystallizes and the chance to be the credible peer voice is gone. Most listening tools cost $400 a month and miss the threads that matter... this one is less than a good cup of coffee.
Reddit threads decay fast. Twelve hours into a thread's life, the comments have crystallized; by twenty-four, the conversation is over. If you're trying to be the credible peer voice in a conversation that's still in motion, you have to show up early.
Manual scrolling doesn't work. By the time a thread is sorted to the top of a subreddit's "new" feed, it's been on top long enough to already be crowded. The threads with the highest signal are the ones you most often miss.
Most Reddit listening tools also fail. They treat listening as a search problem. They return millions of results matched to broad keywords, ranked by recency or popularity, with no view on whether the threads are actually relevant to you. They cost $400 a month. They miss the threads that matter.
The reframe: listening as scoring, not search
Listening on Reddit isn't a search problem. It's a scoring problem. "Find Reddit posts about founders" returns millions of results, none ranked. "Find the threads where I'd actually have something useful to say, in the next thirty minutes" is a ranking problem with deterministic signals on three axes and one signal that genuinely needs intelligence. Once the framing flips, three of the four scoring dimensions become arithmetic. One becomes a small LLM call. Cost collapses.
$4.50/month
Total monitoring cost at 28 runs a day, 20 threads scored per run, all on Anthropic Haiku. GitHub Actions cron in free tier.
Source: Operational data, Tincture Reddit monitor (2026)
Want a system like this, built around your ICP?
Every Tincture engagement starts with a diagnostic. No pitch decks, no proposals. Just the gaps, named plainly.
Start with a diagnosticThe four dimensions
Score every candidate thread on four dimensions, each zero to three, total zero to twelve.
ICP fit (0-3) — LLM-graded
Does this thread match your actual audience, not just the keyword they used? "I'm tired of doing sales as a founder" and "anyone else hate cold email" can both be ICP-fit threads, and a keyword search misses the second one. This dimension genuinely needs intelligence; grade it with an LLM. Anthropic Haiku is right-sized for this work. You don't need Sonnet or Opus to grade audience match.
Upvote velocity (0-3) — arithmetic
Upvotes per hour since posting. Above 10/hour scores 3, between 2 and 10 scores 2, between 0.5 and 2 scores 1, below 0.5 scores 0. Pure arithmetic from post metadata Reddit returns alongside every search result.
Comment density (0-3) — arithmetic
Comments per upvote ratio. Above 0.6 scores 3, between 0.3 and 0.6 scores 2, between 0.1 and 0.3 scores 1, below 0.1 scores 0. A proxy for how alive the conversation is.
Promo tolerance (0-3) — config table
A fixed value per subreddit, derived from the moderator-stickied promo guidelines and the posting culture of each sub. Score 3 for the most permissive (subs whose stickied rules tolerate a 90/10 value-to-promo ratio). Score 2 for moderately strict subs. Score 1 for strict subs (self-promo restricted to specific weekday threads or AMAs only). Score 0 for outright link-drop bans. Build the lookup table once for your target sub set and reread the rules stickies quarterly; mod policies drift.
Threshold to alert: 8/12. Score 9 or higher: drop everything.
Spend intelligence only where it's needed
The discipline that keeps the cost at $4.50 a month is structural: spend money only on the dimension that actually needs intelligence.
Three of the four dimensions are arithmetic. They run for free on the post metadata Reddit returns alongside every search result. ICP fit is the one dimension that requires an LLM, and even there, Anthropic Haiku is right-sized for the work.
If you graded everything with an LLM, you'd have a more expensive listener that's also slower. The cost would be 100x without a 100x improvement in quality.
30 ICP-shaped threads/day
Surfaced, scored, and ranked across eight priority subreddits. Threshold to alert: 8/12. Triage time: ~5 minutes.
Source: Operational data, Tincture Reddit monitor (2026)
The build
The whole thing is a Python service running every 30 minutes from 7am to 9pm local time on GitHub Actions cron. The stack:
- PRAW for Reddit API access (script-app credentials)
- Anthropic Haiku for ICP scoring, why-this-thread generation, and comment drafting
- SQLite for dedup and alert history
- Slack incoming webhook for alerts (structured cards with score breakdown)
- Notion API for the user-facing dashboard
- GitHub Actions for the cron (free tier, no infrastructure to maintain)
Your priority subreddits in three tiers, chosen by where your ICP actually posts. Tier 1 (most permissive, highest density) gets searched against all 60 ICP trigger phrases. Tier 2 (selective, more moderated) gets the top 20. Tier 3 (strictest moderation, comment-only) gets the top 10. Eight subs is a workable baseline; six to ten covers most setups. Each candidate thread is scored and routed to Slack if the score is 8 or higher.
The cost breakdown
Per Anthropic's published Claude Haiku pricing, at 28 runs a day (one every 30 minutes between 7am and 9pm), 20 threads scored per run, the cost works out roughly:
- ICP scoring: $0.08/day
- Why-this-thread generation: $0.03/day (only for threads above threshold)
- Total monitoring: ~$0.15/day, or $4.50/month
- Comment drafting (on demand): ~$0.002 per draft
GitHub Actions runs the cron inside the free tier. SQLite is local. The Slack webhook and Notion API are free. The entire stack costs Haiku and nothing else.
$0.002 per drafted comment
Comment drafting is on-demand via a separate CLI; cost per draft assumes Haiku and a ~3,000-token round trip including the voice-ruleset self-check.
Source: Operational data, Tincture Reddit monitor (2026)
How to draft comments without sounding like AI
The drafting half is harder than the listening half because Reddit punishes bad behavior structurally. Each subreddit moderates self-promotion differently. Stickied moderation guides on founder subs tend to follow one of three patterns: 90/10 value-to-promo tolerance (the permissive end), restricted self-promo windows (specific weekday threads, AMAs only), or outright link-drop bans. Read the rules sticky for every sub you target before you post. Get tone wrong once and you're shadow-banned, downvoted into invisibility, branded as a marketer.
So the drafter doesn't run on a generic LLM prompt. It runs on a voice ruleset specific to Reddit, with named bans for observer frame ("I've watched founders..."), scope expansion (adding advice the OP didn't ask for), pitch mechanics (closing with a CTA), and false expertise claims.
The drafter writes a candidate comment, then runs it back through the same rules as a self-check pass, then flags any violations before output. A human edits before posting. The tool can't ship a comment that breaks the rules; it would flag itself first.
Without the ruleset, generic LLM output gets you banned within a week. With it, drafted comments read as peer-in-thread instead of consultant-with-link.
The takeaway
Most listening tools fail because they treat listening as a search problem. Reddit isn't a haystack of needles. It's a conveyor belt of decaying opportunities. The job isn't to find what's relevant. The job is to find what's relevant in time to be early, and to spend money only on the dimension that actually needs intelligence.
Three dimensions are arithmetic. One is intelligence. Spend accordingly.
Frequently asked questions
How do you build a Reddit monitor that catches ICP-fit threads automatically?
A Python service polls your priority subreddits every 30 minutes, searches against 60 ICP trigger phrases, scores each candidate thread on four dimensions (ICP fit, upvote velocity, comment density, promo tolerance), and routes anything scoring 8 or higher to Slack with a structured breakdown. ICP fit is graded by Anthropic Haiku; the other three dimensions are arithmetic from post metadata.
How much does AI-driven Reddit listening cost?
Per Anthropic's published Claude Haiku pricing, $4.50 a month for continuous monitoring across 8 subreddits, 28 runs a day. Drafting a peer-voice comment costs roughly $0.002 per comment, on demand. The cron runs in GitHub Actions' free tier, so there's no infrastructure cost beyond the LLM.
Can AI draft Reddit comments without sounding like AI?
Yes, with two conditions. The first is a tight, pre-existing voice ruleset specific to Reddit, with named bans for observer frame, scope expansion, and pitch mechanics. The second is a self-check pass that runs the draft against the ruleset before output and flags violations. A human still edits before posting. Without the ruleset, generic LLM output gets you banned within a week.
Why score Reddit threads on multiple dimensions instead of using one ranking?
Because volume and relevance are different problems. ICP fit needs intelligence (does this thread match your actual audience?). Upvote velocity, comment density, and subreddit promo tolerance don't; they're arithmetic from post metadata. Splitting the dimensions lets you spend LLM tokens only where intelligence is required. The result is a ranking that costs $0.15 a day instead of dollars per run.
How do I choose which subreddits to monitor?
Three tiers, ordered by promo tolerance and signal density. Tier 1 (all trigger phrases): your most permissive, highest-density founder subs - the ones where 90/10 value-to-promo is acceptable. Tier 2 (selective, top 20 phrases): subs with stricter moderation but high-quality conversations. Tier 3 (comment-only, top 10 phrases): subs where you'll only ever comment, never post. The tiers reflect promo tolerance and signal density, not subreddit size. Pick six to ten total based on where your actual ICP posts.
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