Hire experts in popular SaaS/no-code tools

Hacker News

Hi HN We have built Heep to help startups take advantage of the best no-code/SaaS tools and increase their ROI on the tools they already are paying for. It's super simple - we match you with experts in popular tools like Notion, Bubble, Webflow for any kinds of project. So far we had over 200 companies build all kind of staff with makers on Heep - from improving their internal ops with automations (Zapier, Airtable) to building full scale MVPs (Bubble, Glide). We focus on curating talent and making it easy for anyone to navigate the no-code space. We have messenger and payments built-in inside the platform and adding simple contracts soon. On the talent side we are allowing you to monetise your expertise in any SaaS/no-code tool and earn $2K+ per month. Right now we have over 300 experts on the platform across 40+ tools. We are still working on making the experience smooth for customers and the main point of launching here is to get feedback on what could be done better on finding/hiring an expert. Would love to discuss what do you like/dislike about existing freelance platforms out there. Love from Kyiv/Berlin

Visit product View on Hacker NewsAug 16, 2022

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Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

37%37% 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 · Missing: arr, mrr, revenue

23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · 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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