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Talanoa – The email client I always dreamed of (people-first, Kanban)

Hacker News

Talanoa – The email client I always dreamed of (people-first, Kanban)

Hi HN, I’m a fullstack developer, and honestly, I’ve lost count of how many side projects I’ve worked on over the years. But one idea kept coming back to me: a different kind of email client. It first came to me in 2014: “What if we could handle emails more like conversations?” Every year, I’d revisit this concept in my “Build Notes.” Ten years later, no one had built exactly what I had in mind — so I finally decided to do it myself. That’s how Talanoa was born. I wanted something focused on people, not timelines. I was tired of marking messages as unread just to remember to reply later, or using labels as makeshift to-do lists. I wanted to see — at a glance — if I had important emails from my team or others needing a response, without wasting hours digging through my inbox. I’m also pretty anti-Inbox Zero. After all, no one expects you to be “Slack Zero” or “WhatsApp Zero.” It doesn’t make sense to apply that stress to email either. But to change the relationship with email, you have to change the underlying logic: shift from a “task list” mindset to a conversation mindset. That’s what Talanoa does. It took me several months to build it, but the result ended up exceeding even my own expectations. It’s now genuinely a joy to use every day. Marketing Hack: Since February, I’ve been slowly building a waiting list. Every week, I personally onboarded 3 to 10 new users — gathering feedback, watching how they understood (or misunderstood) the new inbox experience, and improving the product before inviting the next wave. This “micro-launch” approach helped me refine Talanoa massively without burning through my early user base. If you’re launching something, I highly recommend doing it this way. Stack: Talanoa is built with Electron (TypeScript + VueJS). There’s no backend, no servers — everything runs locally. It means it can scale massively without infrastructure costs. :) Disclaimer: No, there’s no AI inside yet. I’m convinced you can still build great tools without AI — and in my case, user data privacy matters too much to just throw everything into an external LLM. Of course, I plan to integrate some features like email summarization and smart replies — but only when I can embed a custom model directly into the app, without third-party dependencies. Thanks for reading, happy to answer any questions, and I’d love to hear your feedback! John

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, slack, user · Missing: mac, agents, macos
97%97% 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: ide, 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: personal, month, users · Missing: mobile apps, ios, entrepreneurs
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
30%30% 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
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: smart · 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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