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HyperCell – Open-source Excel calculation engine for Java

Hacker News

HyperCell – Open-source Excel calculation engine for Java

Hi HN! I built HyperCell to solve a problem we kept hitting at our company (Scoop Analytics): Business teams model everything in Excel – pricing logic, risk calculations, financial forecasts. Engineers then rewrite that logic in Java. Every time. This translation process causes bugs, delays, and drift between "what the business thinks the logic is" vs "what's actually running." HyperCell takes a different approach: load the Excel file, compile the formulas into a DAG, and execute them in-memory. No translation. The spreadsheet IS the code. Technical details: - Parses Excel formulas using ANTLR4 - Builds a dependency graph for intelligent recalculation - 200+ functions (SUM, VLOOKUP, INDEX/MATCH, IF, NPV, IRR, etc.) - Cross-validated against Excel with 82,881 formulas at 100% accuracy - ~50ms to load a typical workbook, sub-millisecond recalculation Use cases we've seen: - Insurance companies running rating models - Fintech doing real-time pricing - E-commerce with vendor-specific shipping rules It's Apache 2.0 licensed. We extracted it from our production system where it's been running for 2+ years. Happy to answer any questions about the architecture or implementation!

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Actual performance

3points
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
93%93% 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: model, models, using · Missing: mac, agents, macos
85%85% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, 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.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
17%17% predicted probability of success on AppSumo, 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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