Vetd – SaaS Buying and Decision Platform

Hacker News

I've been working on Vetd since the beginning of the year. It's a combination of tools to discover & evaluate SaaS options for your company's needs. Vetd shines when you are in a community (usually a VC (all of their portfolio companies) or an accelerator, etc). Communities let you share your stack with each other, view peer's median cost paid & reviews on products, and discussion board. Without being in a community you can still use our primary feature: VetdRounds. VetdRounds (i.e., a buying round) are a grid of products and topics. You can add any topics (E.g., "Does your product integrate with XYZ service?") you want vendors to respond to (if they aren't using Vetd, we will get a response for you on their behalf). Tech Stack: - Clojure(Script) from front to back - Re-frame (Reagent (React)), Semantic UI React - GraphQL (written in CLJS with EDN) over WebSockets for realtime data subscriptions - Hasura + PostgreSQL - Python for some data processing - AWS + Docker - Segment, Honeycomb, SendGrid - Plaid & Quickbooks integration For the privacy-concerned: heads up, we use FullStory for session replay. Link to the platform: https://app.vetd.com

Visit product View on Hacker NewsNov 26, 2019

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, dock, using · Missing: mac, agents, macos

72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: lua, io · Missing: https docs, excited, just released

58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, reviews · Missing: plus, intuitive, host

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: saas, subscription · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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