https://t.co/nJN6Nb6Snx

here's what nobody tells you about AI outputs... the model isn't your problem your inputs are people write "AI, write about [topic]" result is always generic wikipedia summary what actually works: "here are 3 examples of good, generate 50 variations" result: 50 testable

most people write prompts like instructions... prompts are systems design here's how to build one: > define your input layer - what are you feeding it? (examples, observations, data) define your processing layer - what transformation happens? (extraction, generation,

you're making one BIG mistake when writing prompts... you're trying to remove yourself from the equation great prompts don't replace you, they multiply your leverage AI can generate prompts but it can't generate prompts that contain what only you know about your domain

here's how to read a 300-page book in 30 minutes without missing anything important... > upload the book PDF to notebooklm > add 2-3 youtube videos on the same topic > add a few articles and generate a podcast what you get is: → core concepts synthesized → cross-referenced

ai rewards preparation + context more than prompting... i spent 2 hours building a swipe file now every prompt takes 30 seconds and outputs are 10x better the system: → save 50+ examples (tweets, emails, landing pages that performed) → feed to AI: "analyze patterns here" →

here's why your prompts aren't delivering... you're missing the step that comes BEFORE writing anything it's called context engineering: loading the model with data before you ask it to create most people skip this because it requires an extra step do the extra work and you'll

Ship more than you study. Build more than you research. Close more than you perfect. Deliver more than you hesitate. Start more than you plan. Ask more than you assume.

i think i finally figured out why ai never helped me with post ideas. i was burnt out from manually brainstorming for ideas, so i tried letting ai do it for me. it turned out to be a big mistake. so what i did wrong was: i asked, “give me 20 ai automation post ideas based

most founders aren’t broke because of bad luck. they’re broke because they stay comfortable. yesterday i proved that to myself. i walked into one of the richest neighborhoods in the city and started talking to the people who lived there. not rich. FUCK‑YOU money rich. every

Google AI Studio is crazy good: - Free - Unlimited usage of Gemini Pro, I guess - Great for vibecoding

just vibecoded AgentOS a dashboard in ~12hrs straight built for my inbound voice agent service clients can track calls, performance, and progress and message support directly — it pings my slack instantly

my simple prompting process - stop trying to write the “perfect” prompt. start vague, let it hallucinate, then add one change at a time. add constraints, examples, system tweaks. break it till it’s minimal but still works. it feels less like prompting, more like debugging.

planning things out beforehand only leaves you with execution

tasks for today #2 - fix the dashboard for AgentOS - write email copy and offer - content idea system build - posting 5x - plan out ideas for lead magnets let's get started

before you build, sell the idea pretend it already exists and ask people to pay their answer tells you everything

completed 5/5 tasks - make the dashboard for AgentOS ✅ - cold emails setup ✅ - fixing bugs in AgentReach ✅ - posting 5x ✅ - create and add knowledge base to project ✅ Time worked - 5h 31m

finished my day and looked at my to-do list with 4/7 tasks done its the same story every day i grind every single day had clear to-do lists. solid plans yet tasks kept piling up so i tracked my time for 3 days. brutal truth i realised: almost 50% of my “work” was

the only real goal i have for the next quarter is to get the https://t.co/wlpvUGblBS to 5–8k/m i have the products and services actually solve real problems now marketing and distribution is everything

automation mistake that cost me $500 and client trust i learned this the hardway after delivering my first few automations that thought me how to actually test agents before project delivery i thought if an automation worked in testing, it was ready clean runs, no errors was