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Needle – Find users, track competitors, and spot market opportunities

Hacker News

Needle – Find users, track competitors, and spot market opportunities

Hi HN, I am Vaibhav - a founder based in Bangalore, India. Most founders build without knowing who their real competitors are or what users are actually asking for right now. We usually search Google, Twitter, Reddit manually and hope to catch signals - but it’s slow and incomplete, hence easy to miss important conversations.. This is exactly what Needle solves. I built Needle because I kept struggling to answer a basic question while building MoveOnFromYourEx (Ai tool that helps people move on after a breakup): "Where are the people who are already talking about the problem I am solving?" I spent hours manually searching Reddit, StackOverflow, Quora, Product Hunt, GitHub, etc., trying to find real problem discussions. I opened dozens of tabs, scrolled endlessly, and still missed important threads. It felt random and inefficient. I also realized something else: Competitors are not always the big companies. If you are building a search engine, Google is not your competitor. The 15 indie projects and GitHub repos that people are actually trying are your competitors — and they are almost invisible until you stumble onto them by accident. I wanted a way to discover those early. So I built Needle originally as a small internal tool to search multiple platforms at once. It worked better than expected, a few friends asked to try it, and now I am sharing it with HN. What Needle does: - Search real conversations across 10 platforms (Reddit, HN, Quora, StackOverflow, GitHub, ProductHunt, YouTube, Tumblr, Pinterest, Mastodon) - Find users who are actively describing problems and asking for solutions - Discover competitors mentioned in real conversations, even small ones, and monitor every move they make - Spot trending problems - Analyze how well your product or brand is recommended by GPT systems What is different: - Streams results in real time instead of waiting for a full search - Structured view of conversations, sentiment, and sources in one place - Surfaces long-tail indie competitors and tools people actually mention - GPT-based analysis on how likely AI tools are to recommend your product category Try it: https://useneedle.net (Requires login) Looking for feedback on: - What signals matter most when validating an idea - Which platforms or workflows are missing - Whether the competitive discovery approach makes sense - Concerns around data usage, architecture, limits, etc. - Happy to answer questions and share more details in the comments. Thanks, Vaibhav (iamvs2002)

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
94%94% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: google, user, open · Missing: mac, agents, macos
81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, efficient, users · Missing: plus, intuitive, reviews
38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google, users, way · Missing: mobile apps, ios, personal
28%28% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: real time · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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