AI

AI app that reframes emotionally charged texts (featured in WIRED)

Hacker News

AI app that reframes emotionally charged texts (featured in WIRED)

Hi HN, I’m Sol (YC06). I built BestInterest ( https://bestinterest.app ) solo to help co-parents communicate peacefully after divorce. It was just featured in WIRED ( https://www.wired.com/story/ai-emotional-spellcheck-difficul... ) - without any PR. The idea came from personal experience — a painful divorce and challenging co-parenting communication. Courts often tell co-parents to keep things business-like and child-focused, which sounds simple but is brutally hard in practice. I realized AI can do what humans often can’t — remove emotion from the loop. It started as a simple theory — that AI could actually prevent emotional abuse in digital communication. I’d taken a break from tech after years at Google (I was a PM there), but eventually brushed off the dusties and decided to build it myself. THE STACK Google Cloud + Firebase + Gemini (with some OpenAI functionality still in place) + Twilio. Front end: FlutterFlow. I bootstrapped everything — no funding, no team at first, just persistence, a supportive partner, and late nights after my kids were asleep. One upside of co-parenting: suddenly, a lot of kid-free time to think/code. Early on, I knew I wanted an advisor with deep expertise in abuse recovery. During my own healing, Dr. Ramani Durvasula’s YouTube videos were life-changing, so she topped my “never-going-to-happen” list. I cold-emailed her — and to my surprise, she said yes. Yesterday, WIRED featured our story: “Divorced? With Kids? And an Impossible Ex? There’s AI for That.” Side note: in the article, our leading competitor acknowledged using users’ personal correspondence for training data — which was… surprising. It’s surreal seeing something that began with personal pain now helping others in such a profound way. I get emails every week from customers saying the app has changed their lives. It’s incredibly gratifying. I’m learning as I go — building in a space this sensitive has challenged me in many ways and shown just how deeply this kind of technology is needed. I was “fortunate,” in a strange way, to have lived this pain firsthand; it helped me understand what was needed for my niche. AI has just as much potential to create harm or false information as it does to bring light to dark places — protecting victims and helping people find safety in their communication. Happy to talk about any of these: - Bootstrapping a consumer AI app solo - Restarting life as an entrepreneur after kids, a divorce, and an eight-year hiatus - Transitioning to being a full-time dad (9 years ago) - Growing a real subscriber base in a niche without a marketing budget — leveraging AI and SEO - Building for a legally and emotionally complex community - Using AI to protect against abuse — designing filters that help without over-censoring - Breaking into a quasi-regulated industry where many assume court approval is required just to operate AMA — happy to talk about the journey, the challenges, or anything else that resonates.

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Indie HackersFits the IH revenue-focused audience · Strong signals: started, gemini · Missing: supports, reddit linkedin, podcasting
95%95% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: google, user, new · Missing: mac, agents, macos
67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: personal, video, google · Missing: mobile apps, ios, entrepreneurs
57%57% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: training, bootstrapped · 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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