Open-Source Plaid for Ticketing SaaS Tools

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About Poozle : Poozle (https://github.com/poozlehq/engine) is an open-source integration platform which provides pre-built OAuth flows, Unified Data Models and Webhooks to build deep and custom integrations. Why we built Poozle : At our previous company, we did many integrations and built an in-house integration infrastructure. As we delved deeper, we realized that companies either spend a lot of time and valuable development resources building integrations themselves or end up using expensive and closed IPAAS solutions. Upon speaking with multiple developers using existing Unified API solutions, we discovered that current Unified API providers offer a limited Unified model, leaving developers to spend considerable time handling custom use cases. Current Status : We are currently focused on developing Ticketing SAAS tools and are committed to providing complete solutions. We currently support Github, JIRA and Asana integrations, and adding a new integration is easy so anyone can contribute. Beyond our horror story of integrations, we have also heard stories from other engineers. We would like to hear from you about your story. We welcome your feedback and stories on Poozle! Repo: https://github.com/poozlehq/engine Website: https://www.poozle.dev

Visit product View on Hacker NewsJul 19, 2023

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4points

1comments

Did not reach leaderboard

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, models · Missing: mac, agents, macos

83%83% 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 · Missing: supports, reddit linkedin, podcasting

81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

66%66% 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 · Missing: mobile apps, ios, personal

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

35%35% 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

17%17% 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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