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Bridging People Together via Podcasts

Hacker News

Bridging People Together via Podcasts

Hi, I'm Andrew, a long-time hacker about to turn 50 in a few days. Today I'd like to tell you how taout.tv came into my life. It stands for "talk it out" and I'm told this is a "portmanteau" when you push two words together like brunch or smog. I met Greg at a Los Angeles tech networking group and he told me the idea. His pitch was "I've literally heard Joe Rogan explain my product. Two people can't agree on a subject, both think they are right. They should just talk it out." I agreed to a two-week trial as his CTO. He had been working on this for 2 years with an off-shore team. They picked nextjs for web frontend and backend, react native for ios and android, elasticsearch, redis, and postgres. Hosted on 8 digital ocean droplets and using 100ms.live for the live broadcast rooms. I was able to get the droplet count (and size) down to just 2, using only postgres (pgvector search), frontend web is just js react no nextjs. Backend is now all golang. And the two mobile apps are native swift and kotlin. There's actually a lot to this platform. If you are familiar with orgs like bridgeusa.org or braverangels.org this idea of bridging people on two different sides is something a lot of people care about. You schedule a podcast with someone you already know, or you can just post your desire to talk about a subject with anyone. You and your co-host go live and the audience can watch and score each side in real time. You can upload evidence to support your side and all these scores are tracked and recorded. Would love some feedback from users with podcasting experience. https://www.taout.tv/ Thanks!

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Actual performance

2points
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: podcasting, ios · Missing: supports, reddit linkedin, created
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: apps, user, using · Missing: mac, agents, macos
85%85% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: mobile apps, ios, apps · Missing: personal, entrepreneurs, video
50%50% 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
46%46% 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: platform, host, users · Missing: plus, intuitive, reviews
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
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: real time · 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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