Bridge – A Kanban board as simple as Trello; as beautiful as Linear

Hacker News

I've tried most of the kanban systems out there and didn't really like any of them. I'm a founder with a few different projects. For me *everything* is a pipeline of widgets. I want to use it for sales, hiring, projects, invoicing, everything. Linear was OK, but it's only for projects so I couldn't configure statuses like I wanted to use them for sales. The main things I wanted was - Simplicity of Trello. I used to love trello until it go bloated and buggy and showed me a bunch of pop ups on each sign in - GitHub-like markdown editor - I want to write in markdown, be able to paste images from my clipboard - Unique feature: I wanted to be able to set up rules between boards, so e.g. I can assign work in the project board to anyone who is in the 'hired' column of my HR board (also filterable with labels, so I can have only engineers in the assignee list). - Custom fields and labels. This is all I need to hack it to do whatever I want. If we track output by wordcount in one project and hours in another then I want to be able to set up custom fields and reports to track that Actually the main thing that made me switch from Linear was the per-seat pricing. We don't use it heavily enough to make it worthwhile as another 'per person' tax (currently only Google Workspace and Slack and Claude for us) Anyway we've been using it internally for the last month or so. Probably still a fair number of bugs (especially on mobile where I never use it) and would love any feedback if anyone else tries it out or found similar pain points to me in Linear/Trello. It's free so sign up and try it out, or use the demo to see what a populated set of boards looks like user: demo@example.com pw: demo@example.com

Visit product View on Hacker NewsMar 31, 2026

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

93%93% 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

86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

50%50% 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, google, widgets · 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

19%19% 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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