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Now you can have two files, say, A.txt and B.txt: A.txt: = Weekly - 100 Groceries - 50 Gasoline B.txt: + 1000 Salary - 500 Random - Weekly x 4 Then run: pbudget A.txt B.txt Result: = 150 Weekly - 100 Groceries - 50 Gasoline + 1000 Salary - 500 Random - 150 Weekly x 4 = -100 Not only will B see the Weekly definition in the other file, but the x multiplier will work too. npm install pbudget -g https://galvez.github.io/plainbudget/

Visit product View on Hacker NewsJun 25, 2018

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

56%56% 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 NewsMay not resonate with HN audience · Strong signals: 000, io · Missing: https docs, excited, just released

50%50% 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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

44%44% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

36%36% 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

29%29% 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

21%21% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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