Parabola.io – Automate your work with visual programming

Hacker News

Hi HN! I’m Alex from Parabola ( https://parabola.io ). Parabola is a visual programming tool for creating functional data flows that everyone can use. It’s entirely drag-and-drop, handles data sizes much larger than a traditional spreadsheet, calculates everything live, and can run your flows on a schedule of your choosing. I used to work in strategy consulting, doing data analytics for SMBs and Fortune 500 companies. The amount of time wasted on menial tasks was astounding. Things like cleaning data, generating custom reports, creating human workflows to solve shortcomings in third party tools, etc. Non-technical people have to rely on doing things manually or using fragile spreadsheets that solve only part of their use cases. We don't think everyone should have to learn to code in order to work productively ( https://medium.com/parabola-labs/not-everyone-needs-to-learn... ). That’s why we’re building the tools to help people stop wasting so much time on manual processes, and instead focus on their actual core competencies. We’ve made some interesting decisions in the design of Parabola’s visual programming “language”. We’d love to hear what you think and are happy to discuss our thought process in the comments.

Visit product View on Hacker NewsMay 24, 2018

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291points

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

81%81% 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.

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

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

73%73% 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: para · Missing: mobile apps, ios, personal

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

18%18% 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.

Correct prediction on native model

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