ToolJet 3.0 – open-source internal tool and workflow builder

Hacker News

I’m excited to share ToolJet 3.0, a major update to our open-core low-code platform for building internal tools. This release is all about speed, scalability, and better support for complex use cases. Backstory When I first launched ToolJet ( https://github.com/ToolJet/ToolJet ) here on HN ( https://news.ycombinator.com/item?id=27421408](https://news.... 2021, it started as a tool for building frontends connected to data sources via a secure backend proxy, with integrations like Google Sheets. The initial post hit #1 on HN, bringing in a wave of early adopters and helping us secure seed funding. Since then, ToolJet has grown significantly. We added a visual workflow builder, a no-code database on PostgreSQL, and expanded integrations. But as the platform evolved, the codebase became heavier, and performance issues emerged. ToolJet 3.0 is a complete refactor focused on fixing these problems and delivering a faster, more reliable experience. What’s New in ToolJet 3.0 Here’s what we’ve built for this release: •10x Faster Load Times: We refactored the core architecture, resulting in much faster performance, especially for complex apps. •Enhanced Workflow Automation: The visual builder now handles complex workflows with better execution speed and reliability. •Upgraded No-Code Database: Built on PostgreSQL, our database now scales better and supports more data types. •Expanded Integrations: Added Salesforce, Supabase, Databricks, AWS Textract, OpenAI, Pinecone, and others based on user feedback. •Security and Access Control: New features like LDAP, SAML, Git sync, and OIDC group sync provide enterprise-level access control. •More Self-Hosting Options: We’ve expanded support for different platforms, making it easier to deploy ToolJet in your own environment. •UI and UX Improvements: Redesigned form components, added a Kanban board, and made several enhancements based on user feedback. •ToolJet Copilot: An AI-powered assistant for query writing and debugging, aimed at making development faster and more intuitive. •Community Growth: Our GitHub repo has surpassed 32,000 stars with contributions from over 600 developers. Why It Matters With 3.0, we’re doubling down on our vision of a fast, flexible, and extensible platform that balances the power of an open-core model with the needs of modern development teams. This release addresses the stability and scalability issues that came with rapid growth, making ToolJet ready for complex internal tools.

Visit product View on Hacker NewsNov 13, 2024

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

97%97% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

93%93% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, 000 · Missing: https docs, just released, exist

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

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: platform, intuitive, host · Missing: plus, reviews, exclusive

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

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

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

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

13%13% 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.

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