What I Learned Building a Reddit Post Scraper

About a year ago I started building a Reddit scraper for myself. Not for any grand vision. I just wanted to stop spending three hours a day scrolling through posts manually.

What I thought would be a weekend project turned into a months-long rabbit hole of API quirks, rate limits, and lessons about how Reddit actually works under the hood.

Here is what I learned.

Reddit API Is Not As Simple As It Looks

The Reddit API documentation makes everything look straightforward. Hit an endpoint, get JSON data, parse it, done. In reality, there are a lot of edge cases that only surface when you start building something real.

First issue: rate limits. Reddit allows about 60 requests per minute for authenticated users, less for anonymous ones. That sounds like a lot until you realize that each page of results is a separate request. Scraping 500 posts from a subreddit is not one request, it is five. Doing that across ten subreddits is 50 requests. Add user lookups and you are hitting limits fast.

Second issue: pagination is weird. Reddit uses a cursor-based system where you pass the last post ID as an after parameter. Sounds simple, but the cursor sometimes breaks if posts get deleted between requests. Your script thinks it has reached the end when it hasn't.

Third issue: content filtering happens server-side in ways that are not documented. NSFW subreddits, quarantined content, region-blocked posts -- all of these can silently return empty results without any error message.

Why Cloud-Based Tools Keep Getting Blocked

When I first tried running my scraper from a cloud server, it worked for about a week. Then Reddit started returning 403 errors. Then rate limits got more aggressive. Then some endpoints just stopped responding entirely.

The problem is that cloud IP addresses are shared across thousands of users. When one person abuses the API, the entire IP block gets flagged. Even if you are being respectful with your request rate, you are sharing infrastructure with people who are not.

The solution I landed on was building a desktop application instead. When you run the tool on your own computer, requests come from your home IP address. To Reddit, you look like one person browsing the site normally. No shared infrastructure, no inherited reputation problems.

This is the approach I took with Reddit Toolbox. It runs locally, stores data locally, and uses your own network connection. The tradeoff is that users have to download and install something, but the reliability difference is night and day.

The Filtering Problem

Once you have the data, the next challenge is finding what you actually want. Reddit returns posts sorted by hot, new, top, or rising. None of these are exactly what I needed.

My specific use case was finding posts with low comment counts -- ones where a thoughtful reply would actually be visible. Reddit does not offer this as a sort option. You have to fetch posts and filter client-side.

This means pulling more data than you need and throwing away most of it. For my use case, I typically fetch 300-500 posts and filter down to 15-20 that meet my criteria. Not elegant, but it works.

The lesson here is that any data extraction project involves more filtering than fetching. The Reddit API gives you firehose access. Your job is to build good filters.

What The Final Tool Looks Like

After all the iteration, the tool I ended up with does a few things:

It scrapes multiple subreddits in parallel, respecting rate limits but maximizing throughput. It filters by comment count, post age, and score. It has optional AI features for drafting replies, though I usually edit those heavily. And it exports to CSV for people who want to track outreach over time.

The UI is not pretty. I prioritized function over form. But it saves me about two hours a day, which was the whole point.

If you want to try it, the download is at wappkit.com/download. It is free for basic usage.

Lessons For Other Side Projects

A few things I would do differently next time:

Start with the filtering logic, not the fetching logic. I spent weeks optimizing API calls before I had clarity on what data I actually needed. Would have been smarter to build a janky first version, use it for a week, and then optimize.

Test with real usage patterns early. My development testing used small sample sizes. Real usage involves larger data pulls and more complex filters. Edge cases only appeared in production.

Desktop is underrated. Everyone wants to build cloud apps now because deployment is easier. But for tools that interact with rate-limited APIs, running locally gives you a massive reliability advantage.

The project took longer than planned, cost more in frustration than dollars, and taught me more about Reddit internals than I expected. Classic side project experience.