Lost Midas

@lostmidas

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Vibe coding thrives on clear planning Scattershot approaches waste time & kill productivity Building products with AI models demands robust context Each step feeds into the next, creating a system of clarity Good context creation follows a rhythm: - PRD defines the big "what" & "why" - User stories turn vision into actionable goals - User flows outline how users move through the experience - Design specs bring it all to life without ambiguity Context isn’t busywork It’s leverage Here’s a guide to doing it right

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Idea to dev ticket, AI-native workflow: - Write the feature prompt - Auto-generate wireframe - Connect model to codebase context - Send wireframe & prompt to LLM for dev ticket - Build - Test - Ship Zero ceremony No middlemen Just flow

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Order is safe Entropy is profit The best lessons don’t come from playbooks They hide inside chaos If you aren’t stumbling through mess, you aren’t building anything worth remembering

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High value capital formation

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Nothing is as easy as YouTube tutorials claim Gurus make workflows look like wizardry: - Skip the pain - Hide the bugs - Never mention hours lost to broken dependencies Real building: - 90% debugging - 10% pretending it was simple Anyone selling easy is selling a fantasy

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Getting good with AI isn’t about "how to prompt" blog posts It’s relentless tinkering Trying Failing Trying again Shortcuts feel good But mastery comes from breaking things Collecting weird outputs Repeating until it clicks Avoid failure, avoid becoming AI-native

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Turns out vibe-coding is less about vibes & more about arguing semantics with a pedantic machine

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Optimization obsession is just hidden weakness Tightly dialed routines break the moment life throws curveballs: - Miss one gym session & the streak collapses - Travel ruins your meal plan - One late night & your circadian rhythm shatters That’s not control, it’s brittle predictability Antifragility means thriving on variation Getting stronger from chaos, not running from it Optimization culture fears entropy & forgets reality loves disorder Real flex: - Build habits to handle variability - Stress test yourself daily - Optimize for resilience, not rigidity

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AI massively boosted my self-agency I don’t wait around for answers or favors anymore Need something solved Describe the problem Iterate my prompt Get real feedback Build the solution myself It’s not about outsourcing thinking It’s about sharpening it If you bring discipline, AI becomes a learning partner, not a crutch Prompting well forces clarity Each time, I refine how I break down problems I learn more, faster, by doing it myself, not just copying someone else’s solution People warn AI will dull your mind Used with intention, it does the opposite I’m tackling tougher problems solo Gaining knowledge, expanding my reach, every day

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Next up: - Error handling & fallback messages - Logging data to Sheets or Notion - Filtering & conditional triggers - SMS/Email alerts for key updates - Exploring loops & dynamic variables Automation is no longer abstract, it’s real, it’s testable, & it’s happening

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What started as "can I automate a quote email?" turned into: - Understanding data flow - Structuring outputs for people & systems - Learning to debug, iterate, & think modularly - Feeling way more confident in building real ops automation

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Cross-cutting skills I picked up: - Trigger design (cron, email, manual) - API calls & authentication - Building clean, dynamic messages - Debugging rate limits & data issues - Integrating Gmail, Google Sheets, & Slack - Writing real logic in n8n expressions

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Workflow 3: Daily Weather to Slack Posts a clean, emoji-enhanced weather summary to Slack every morning - Fetches forecast via OpenWeatherMap - Parses out temp, city, condition - Capitalizes text & adds emoji based on weather type - Posts to Slack Learned: - Working with API query params - Switching units (Fahrenheit) - Conditional logic in expressions - Slack OAuth setup & error handling - Fixing undefined/missing fields

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Workflow 2: Starred Email Tracker Turns Gmail stars into structured signals - Watches my inbox for new starred emails - Logs sender, subject, & date to a Google Sheet Learned: - IMAP Email Trigger (w/ JSON filters) - Google Sheets OAuth2 flow - Mapping fields with Set node - Using Wait node to avoid rate limits

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Workflow 1: Daily Quote Email A lightweight morning ritual delivered via automation - Triggers every morning - Pulls a random quote from an open API - Formats & emails it to me using Gmail Learned: - Cron triggers - HTTP Request node - Data formatting via Set node - Gmail integration & auth

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Built 3 full n8n workflows from scratch My goal is to automate key parts of my business from lead gen to internal ops, while learning how to think in systems Here’s what I’ve shipped so far

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Just activated my creator coin on @zora Check it out $lostmidas

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Everyone is obsessed with AI prompt tricks But LLMs only work when you feed them rich context Forget clever word games Signal is all that matters: - Goals - Constraints - Definitions - Edge cases - Raw data - Failure modes Wrap the model in specifics More context in, fewer hallucinations out

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