Meridian(PH#1) A better way to recognize developer contributions

Hacker News

Hi HN! We built Meridian because we kept seeing the same thing: developers get estimates for planned work, and then spend a huge chunk of their time doing everything that wasn't planned. Debugging something weird, helping a teammate, reviewing PRs, jumping on an incident, fixing a flaky test, answering questions, etc. All useful work, but a lot of it never makes it back into the estimate. So when the sprint slips, it can look like the estimate was wrong, when really, a bunch of other work happened in between. Meridian is our attempt to make that work visible and better account for it. We’re building it in the open and open sourcing it because we’d much rather have developers tell us where we're getting this wrong than build another productivity dashboard in a vacuum. We launched last week and ended up #1 on Product Hunt, which was pretty wild. Now we're looking for feedback from the core dev community. Curious how you deal with unplanned work today, and whether this is actually a problem worth solving today.

Visit product View on Hacker NewsAug 27, 2026

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5points

Did not reach leaderboard

Launch Intel predictions

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

87%87% 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.

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

79%79% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

56%56% 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

35%35% 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

26%26% 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

20%20% 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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