FOMOmeter
crypto sentiment based on crowd emotions, not price
Details
- External ID
- 46725321
- Source
- HN
- Company
- —
- Product
- FOMOmeter
- Website domain
- fomometer.ai
- Launched
- Jan. 22, 2026
- Cohort
- —
- Upvotes
- 7
- Upvotes percentile
- 0.3544137022397892
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:25 p.m.
- Updated at
- Sept. 7, 2026, 9:25 p.m.
Description
Hi HN,Over the past few years I kept running into the same problem while investing in crypto: most “sentiment” tools either lag price or quietly repackage price data itself.I wanted something simpler and more honest — a way to see how the crowd actually feels, even when price is flat or misleading.So I built FOMOmeter.It’s a sentiment platform that analyzes real social signals (X, Reddit, etc.) and maps crowd mood.What I’m testing right now: • Does pure crowd sentiment add value during uncertainty and sideways markets? • Can it help avoid emotional entries instead of chasing narratives? • Is this something experienced investors would actually use regularly?There’s a free public version and an early Pro version. I’m mainly looking for thoughtful feedback — what feels useful, what feels redundant, and what’s missing.Happy to answer questions and share how the sentiment model works under the hood if there’s interest.Thanks for checking it out.
Enrichment
- Theme
- financial intelligence and trading tools
- Vertical
- Fintech
- Function
- Analytics & BI
- Audience
- B2C
- AI stance
- AI feature
- Project type
- Commercial product
- Normalized one-liner
- crypto sentiment analysis based on crowd emotions
- Manually corrected
- False
Could you build this?
Partial While displaying sentiment graphs and front-end dashboards is straightforward, continuously scraping, deduplicating, and running accurate sentiment NLP across massive volumes of crypto social feeds requires robust ingestion pipelines.
What it would actually take: Building this requires a real-time ingestion pipeline (Kafka/Flink or Celery) continuously listening to Discord, Telegram, X/Twitter firehoses, and Reddit APIs. The core difficulty is filtering out rampant crypto bot spam, pump-and-dump coordination, and sarcasm, paired with fine-tuned domain NLP/LLM classification models to gauge crowd emotion independent of price movement.
Discussion
1 comment analyzed.
Competitors
Other products that read as similar to this one — 109 launches clear the similarity bar, closest 8 shown.
Attention rank: #55 of 110 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 83 days after the earliest competitor.
- BlackBitSwan · ph · 2026-09-09 · 3 upvotes · similarity 0.52
- NinethirtyAI · hn · 2026-08-18 · 7 upvotes · similarity 0.44
- Moonstake.org · ph · 2026-09-15 · 1 upvotes · similarity 0.44
- What is HN thinking? Real-time sentiment and concept analysis · hn · 2026-02-12 · 37 upvotes · similarity 0.40
- Quantifying opportunity cost with a deliberately "simple" web app · hn · 2026-02-24 · 28 upvotes · similarity 0.40
- TheContentForge · ph · 2026-09-25 · 5 upvotes · similarity 0.40
- Crypto Chemistry · ph · 2026-09-21 · 2 upvotes · similarity 0.40
- Mondael.com · hn · 2026-07-07 · 7 upvotes · similarity 0.39
Other launches for this product
- No other launches for this product.
Same idea, different domain
Nobody's really built a analytics & bi tool for Legal yet.