Ue

UeCalc – Build an investor-ready financial model without spreadsheets

Hacker News

UeCalc – Build an investor-ready financial model without spreadsheets

Hey HN, I’ve built ueCalc, a tool that helps startup founders create a financial model in minutes—without needing finance skills or spreadsheets. The Problem: Most founders struggle with financial modeling because they: Don’t know how to create a model or what to include. Don’t know how to fill it with real data—just assumptions. Don’t account for experimentation time when testing channels. Don’t know how to reflect growth—what’s realistic? Can’t answer investor questions about the numbers. Investors don’t believe in their projections. Don’t understand how much funding they really need. Can’t justify why they need the investment. How ueCalc solves this: – Instead of guesswork, you describe your team’s real capabilities: – Lead acquisition (How many leads can you generate?) – Conversion (How many turn into customers?) – Retention (How long do they stay?) – The system applies unit economics + Goldratt’s Theory of Constraints (Lean Startup) to build an accurate growth forecast. – You instantly get financial statements & projections—no need for Excel. What you get: – A financial model based on execution, not assumptions. – Revenue, cost, and profitability forecasts. – Investor-ready financial documents—just download the template. Would love your feedback! Try it here: https://uecalc.com What’s been your biggest challenge with financial modeling? Let’s discuss!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
97%97% 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 · 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: io · Missing: https docs, excited, just released
49%49% 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
38%38% 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
32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: revenue, profit, growth · Missing: arr, mrr, saas
18%18% 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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