Incentive and Reward based system for remote teams

Hacker News

Hey HN. Quick confession. I’ve led a remote team, and for the most part, we hated project management tools, unnecessary meetings, and awkward daily standups. Even worse. Most PM tools just tell you what to do in such a boring way. So, I decided to build something different. A tool so rewarding, it actually made my team addicted to finishing tasks (okay, don’t flame me for this). Lockdn is a fun, transparent tool designed to supercharge productivity, reward effort, and make working genuinely feel good. With Lockdn, your team can: Log daily tasks and track workflow. Earn tokens for every win. Climb the team leaderboard for bragging rights. Unlock custom rewards set by your team. And collect badges as undeniable proof of hustle. Every feature in Lockdn is backed by science, engineered to improve productivity through the power of dopamine. I would say i am very confident about what we’ve built ,but if there is any feature you would like to see on a tool like this, what will it be? We'd love your support on this launch. Thank you!

Visit product View on Hacker NewsJul 21, 2025

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: tasks, project management · Missing: mac, agents, macos

76%76% 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

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

48%48% 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

41%41% predicted probability of success on AppSumo, 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

35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: reward · 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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