Inspiration
Reddit moderators are volunteers. They don't get paid, they don't have shifts, and yet some of them manage communities with millions of members reviewing hundreds of reports a day, often alone. We've seen firsthand how exhausting that is. A single moderator might need to read a full thread, check a user's post history, cross-reference subreddit rules, and make a removal decision: All in under a minute, dozens of times in a row. That kind of sustained cognitive load leads to burnout, inconsistent enforcement, and good-faith posts getting caught in the crossfire.
What struck us was that this isn't a people problem — it's a tooling problem. The Reddit mod queue gives you a list of flagged content, but it gives you almost no context. You're expected to bring all the judgment yourself, every single time, with no assistance. That's what inspired SnapSense. What if the mod queue could think alongside you? Not replace your judgment, but show up with the context already assembled such as a quick summary, the relevant rule, the user's track record, the risk level so you can make a confident call in seconds instead of minutes. The same way a copilot doesn't fly the plane but makes the pilot's job dramatically safer and less exhausting.
SnapSense was built because we believe the people who keep Reddit's communities healthy deserve better tools than a plain list of reports.
What it does
SnapSense is an AI-powered Mod Copilot built inside Reddit using Devvit. It lives directly in the mod queue and helps moderators make faster, more consistent decisions without switching tabs. For every post, it instantly delivers a 1–2 sentence content summary, maps content to specific subreddit rule violations (Rule 3 harassment, Rule 5 spam, Rule 7 NSFW, Rule 9 doxxing) with confidence scores, recommends a concrete action (approve, remove, lock, escalate, or distinguish) with a reason and confidence percentage, surfaces the author's user history including account age, recent post count, and prior removals, and highlights risk signals like spam likelihood, toxicity level, and coordinated behavior patterns.
How we built it
We built SnapSense entirely on the Devvit platform using the @devvit/web framework with a Hono server, tRPC for type-safe client-server communication, React for the UI, and Redis for caching analysis results. The core analysis engine (modcopilot.ts) pulls live post data and user history via Reddit's API, runs pattern-matching pipelines for rule violations, toxicity, and spam signals, then applies a decision tree to produce a recommended action. Results are cached in Redis for 5 minutes to keep the experience snappy. The UI is built in React with Tailwind CSS, featuring confidence bars, color-coded risk indicators, and pattern flag chips. All are rendered inside a Devvit custom post.
Challenges we ran into
The biggest challenge was working within Devvit's sandboxed environment. Reddit's API surface is intentionally limited, so we had to design the analysis engine to be resilient when user data is unavailable, gracefully falling back to defaults rather than crashing. Mapping content to subreddit rules generically (without knowing each subreddit's specific ruleset) required building a flexible pattern-matching system that covers the most common violation categories.
Accomplishments that we're proud of
We're proud that SnapSense delivers a full end-to-end moderation intelligence loop such as summary, violation detection, action recommendation, user history, and risk signals. All are in a single view with zero tab-switching. The confidence scoring system gives moderators a transparent signal rather than a black-box verdict, keeping humans in control. We're also proud of the caching layer that makes repeat analysis near-instant, and the graceful degradation when Reddit API calls fail so the tool never blocks a moderator's workflow.
What we learned
We learned how powerful Devvit's platform is for embedding persistent, stateful experiences directly inside Reddit, and how Redis as a first-class primitive makes caching trivial to implement correctly.
What's next for SnapSense
- Subreddit-specific rule ingestion: read the actual rules of each subreddit so violation mapping is precise rather than generic
- Mod action feedback loop: let moderators confirm or override recommendations so the system learns which signals matter most for their community
- Batch queue analysis: analyze the entire mod queue at once and surface the highest-risk items first
- Comment-level analysis: extend beyond posts to flagged comments and report queues
- Mod notes integration: pull in existing mod notes on a user to enrich the history panel
- Cross-subreddit pattern detection: flag users who are banned or frequently removed across multiple communities
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