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ToolJet AI – Collaborative agents that build internal tools

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ToolJet AI – Collaborative agents that build internal tools

Hi HN, founder here again! I first launched ToolJet here in 2021 as a one-person project. It blew up really well & got 1k stars on GitHub ( https://github.com/ToolJet/ToolJet/ ) in around 8 hours. Back then ToolJet was basically a frontend builder that could connect to different data sources. Since then, we kept expanding: - Added a workflow automation tool so builders could orchestrate background jobs. - Added a built-in no-code database so builders didn’t need to spin up a new db. - Eventually grew into a full-stack platform for internal tools. - And other obvious things like tons of smaller features & integrations. But last year we kind of messed up. We kept adding features, the frontend architecture couldn’t keep up, and stability/performance issues showed up once apps got complex (ie hundreds of UI components in a single page of an app). So we stopped, rebuilt the architecture (ToolJet v3 in November), and cleaned up a lot of tech debt. That gave us a solid foundation - and also made us realize it was the right moment to go AI-native. We analyzed how our users actually built apps: 80% of the time on repetitive setup (forms, tables, CRUD), 15% on integration glue code, 5% on actual business logic. Traditional low-code tried to eliminate code entirely. We're eliminating the wrong code - the boring 95% - while keeping full control for the 5% that matters. Instead of "prompt-to-code," ToolJet AI tries to copy how an engineering team functions (yeah, a bit opinionated way) - but with AI agents: - PM agent → turns your prompt into a PRD. - Design agent → generated the the UI using our pre-built components and custom components. - DB agent → builds the schema. - Full-stack agent → wires it all up with queries, event handlers, and code. At each step, builders can review/edit, stop AI generation, or switch into the visual builder. Generated apps aren’t locked in - you can keep tweaking with prompts, drag-and-drop, or extend with custom code. Why this works: We know "AI builds apps" is overhyped right now. The difference: we're not generating raw code - we're configuring battle-tested components. Think Terraform for internal tools, not Claude/GPT writing React. That means: - Fewer tokens → lower cost. - Deterministic & Faster outputs → fewer errors. - More reliability → production-ready apps. Basically, AI is filling in blueprints. ToolJet AI is a closed-source fork of the open-source community edition, which will continue to be actively maintained. All the core platform changes (like the v3 rebuild and stability/performance work) are committed upstream. The AI features sit on top, but OSS remains the foundation. Thanks for reading - and thanks again to HN for being part of ToolJet’s journey since the very beginning.

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor
99%99% 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
95%95% 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
61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: apps, users, way · Missing: mobile apps, ios, personal
55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, builder, users · Missing: plus, intuitive, reviews
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
21%21% 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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