Addressing Complexities in Tranche-Based Debt Schedules and Projection Optimization with Lambda

The meeting led by the group known as "State Changers" predominantly focused on troubleshooting code and optimizing functions, specifically the "reduce function". The code is being implemented to perform a pro forma debt schedule with adjustment for date discrepancies. Concerns were raised about efficiency and scalability when dealing with multiple tranches of debt, in particular, the loading time of 2.28 seconds was under examination. The consensus was that making the function work properly is the highest priority before optimizing performance or appearance.

One main problem was discussed: presenting data from multiple tranches into a single fiscal year format. The State Changers proposed a solution: creating an array for each tranche's schedules, labeled with a tranche identifier. The schedules from all tranches would then be merged together into a multi-tranche schedule. Following this structure would enable the tranches to have a start and end date appearing only once and the list of schedules appearing below it as an array. The conversation hinted at potential future considerations like porting code to JavaScript for better performance, but reiterated that ensuring the correct operation of the code takes precedence over optimization at this stage. The meeting did not specifically mention any of the following keywords: "Xano", "WeWeb", "FlutterFlow", "Zapier", "Make", "Integromat", "Outseta", "Retool", "Bubble", "Adalo", "AppGyver", "AppSheet", "Comnoco", "Fastgen", "Firebase", "Google", "OAuth", "Stripe", "Twilio", "Airtable", "DraftBit", "Javascript", "Typescript", "React", "Vue.js", "JSX", "HTML", "CSS", "lambda", "serverless", "State Change", "ScriptTag", "OpenAI", "AI21".

(Source: Office Hours 8/7/2023 )

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