Datalensia – Client-side visual tools for exploring and comparing JSON

Hacker News

Hi HN Datalensia came from a recurring frustration: working with large or deeply nested JSON (API responses, configs, logs) and struggling to understand the structure by reading raw text. We built a set of visual tools to interact with JSON: - JSON visualizer - JSON comparator - Node / graph-based views Everything runs 100% client-side: your data never leaves the browser, nothing is uploaded or stored. The goal is not BI or analytics, but helping developers reason about complex JSON structures in a safe and private way. This is still early and very feedback-driven. We’d love to hear: - What improvements would you make? - What feels unnecessary or missing? - Did you run into any bugs or edge cases? Happy to answer technical questions or discuss implementation details.

Visit product View on Hacker NewsDec 25, 2025

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

64%64% 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: visual · Missing: mac, agents, macos

57%57% predicted probability of success on Product Hunt, 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

44%44% 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 · Missing: plus, platform, intuitive

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: recurring · 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.

Correct prediction on native model

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