Build Your Own Semantic Reddit Search for Lead Discovery

Keyword search misses a predictable kind of thread: the one where someone describes your exact problem without your product's words for it. “I keep losing track of what I promised people” is a task-manager lead no keyword alert would catch. Semantic search — match by meaning, not string overlap — fixes that, and it's a small project on raw API access. Worked example: pull a month of a subreddit, embed it, rank it against your product.

1. How do I pull a month of Reddit posts and comments?

after= accepts a relative window ("30d") directly, then the cursor takes over for the rest of the pagination:

import requests

API_KEY = "YOUR_KEY"
BASE = "https://api.threadsnoop.com/v1"
HEADERS = {"x-api-key": API_KEY}

def fetch_window(subreddit, kind, window="30d"):
    cursor, items = window, []
    while True:
        params = {"subreddit": subreddit, "sort": "asc", "after": cursor, "limit": 100}
        resp = requests.get(f"{BASE}/{kind}", headers=HEADERS, params=params)
        resp.raise_for_status()
        payload = resp.json()
        items.extend(payload["data"])
        meta = payload["_threadsnoop"]
        if not meta["has_more"]:
            return items
        cursor = meta["cursor"]

posts = fetch_window("productivity", "posts")
comments = fetch_window("productivity", "comments")

A month of a mid-traffic subreddit is a few thousand items — a few dozen credits per kind at 100/page. Pull comments, not just posts: buyer pain often shows up three replies into someone else's thread.

2. How do I embed Reddit posts for semantic search?

Any embeddings provider works — this uses a generic OpenAI-compatible one. Embed the Reddit text in batches, plus one more string: your problem, in the customer's voice, not your category name.

EMBEDDINGS_URL = "https://api.openai.com/v1/embeddings"  # swap in your provider

def embed(texts, batch_size=100):
    out = []
    for i in range(0, len(texts), batch_size):
        resp = requests.post(
            EMBEDDINGS_URL,
            headers={"Authorization": "Bearer YOUR_EMBEDDINGS_KEY"},
            json={"model": "text-embedding-3-small", "input": texts[i : i + batch_size]},
        )
        out.extend(item["embedding"] for item in resp.json()["data"])
    return out

def text_of(item):
    return item.get("selftext") or item.get("body") or item.get("title", "")

candidates = posts + comments
vectors = embed([text_of(c) for c in candidates])
problem_vector = embed(["I keep forgetting things I agreed to and don't notice until someone chases me"])[0]

def cosine(a, b):
    dot = sum(x * y for x, y in zip(a, b))
    return dot / ((sum(x * x for x in a) ** 0.5) * (sum(y * y for y in b) ** 0.5))

ranked = sorted(zip(candidates, vectors), key=lambda p: cosine(problem_vector, p[1]), reverse=True)
for item, vec in ranked[:20]:
    print(f"{cosine(problem_vector, vec):.3f}  r/{item['subreddit']}  {text_of(item)[:90]}")

Read the top twenty by hand. Embedding your category name ("task management software") instead of the pain matches people discussing that category, a smaller and less useful set than people who actually have the problem — the wording of the problem string is what most of your result quality rides on.

Or skip building it

This pipeline, running continuously with intent scoring and a reply workflow on top, is what ThreadSnoop's lead-discovery product already does. Build it yourself if you want full control; if not, the beta does it for you. Don't want to write any code at all, not even this much? There's a plain-prompt version of the same idea: how to leverage Claude to find your first SaaS customers. Once you have matches — from this pipeline or that simpler one — the rules for engaging without getting banned are the same: how to find Reddit users who need your product.

Frequently asked questions