Ba

Banker – Lovable for Spreadsheets

Hacker News

Banker – Lovable for Spreadsheets

Hey HN! I built Banker.so, an AI agent that actually understands and writes Excel files. I upload spreadsheets to ChatGPT/Claude all the time. CSV files work okay. But .xlsx files with formulas, multiple sheets, and pivot tables? They break constantly. The problem: LLMs process data sequentially (1D), but spreadsheets are 2D structures. Cell B5 has meaning because it's in column B, row 5. Formulas reference ranges like A1:C10. Current AI tools miss these spatial relationships because they are designed for humans’ visual understanding. What fails today: - Complex Excel files with multiple linked sheets - Formulas and named ranges get corrupted or ignored - Pivot tables and conditional formatting disappear - Table structures with merged cells break completely I implemented algorithms from the SpreadsheetLLM paper to parse spreadsheets properly. The agent understands: - Table boundaries and headers - Cell relationships and formulas - Multiple sheets and references between them - Data types and formatting You can find a video demo over here: https://youtu.be/hCTijOa3H9k?si=-GbX0ibmtXMVabAm Also added OCR to convert PDFs and images directly into working spreadsheets. Example use case: Upload your monthly financial report with 10 sheets, formulas, and pivot tables. Ask "which department exceeded budget?" Get the right answer because the agent understands your spreadsheet structure. https://banker.so/ Try uploading a complex Excel file. The one that breaks other tools. Give it a try and push it hard - I am sure there will be places I can improve it further. Looking forward to your feedback!

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, claude, lovable · Missing: mac, agents, macos
90%90% 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
58%58% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
48%48% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video, month, monthly · 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
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
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
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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