if you're looking for a way to use AI to write content without sounding like slop, then this is for you obviously the biggest problem with AI content is that it sounds robotic af

all the em-dashes, the "it's not this, it's that", etc

and not only that the content it writes usually lacks a lot of emotional feel

here's the real reason people struggle with building reliable AI systems and what they can do about it today... people live in this dream world where they think these fully autonomous AI agents exist

that can do absolutely everything for them without lifting a finger

- they want it to make every decision
- map out the entire workflow
- figure everything out without guidance

here's something that no one will ever tell you when it comes to mastering AI and the real reason why most beginners are struggling to master this skill

and why it's only going to get harder in the next 6-12 months - unless you do this one thing right now, new products, tools, LLMs, and game changing" studies are dropping every single day with AI

it is literally impossible to keep up with all of this shit

even for someone like me that’s been in the space for a while its overwhelming af

if you're looking for AI product ideas that people will actually pay for, then this is for you everyone right now is trying to build some AI software to sell

but most of them fail in two specific ways:

1) they don't solve a specific, deep problem that people actually want solved

2) they overpromise what AI can currently do beyond its actual capabilities

if you're looking for a way to master AI without being technical, then this is for you: there's this misconception that you need to learn how to code to be elite with AI

and to me that’s extremely wrong

for example look at all those AI video guys

they’re crushing it and none of them know how to code or even use a no code tool like n8n and they do perfectly fine

how to build an AI ghostwriter that writes exactly like you (step by step breakdown): step 1: collect your content data

gather:
> viral vs non-viral content
> audience research documents
> your content structures/frameworks
> background info about you and what you do

here's everything you need to build an AI quiz funnel (even if you're not technical): here's what an AI quiz funnel does differently:

> asks personalized questions
> generates custom recommendations based on their answers
> captures their email to get the full results
> automatically puts them in the right nurture sequence based on their answers

how to build an AI research agent (step by step breakdown): step 1: choose your data sources

decide where you want to scrape customer insights from:

> Reddit
> TikTok comments
> YouTube comments
> Facebook ad comments
> competitor Facebook ads

how to prepare data for an AI system (step by step breakdown): before AI can do anything useful, you need clean, structured data

this applies to ANY personalized AI system

here's a real example: preparing B2B lead data for an outbound AI sales system

lets say we're building an AI system that personalizes cold emails

however we’re pulling data from multiple B2B providers: Apollo, LinkedIn, ListKit, Crunchbase

all of them provide contact info, company data, job titles - but each formats everything differently

how to use AI for market research (step by step breakdown): step 1: analyze your content performance

grab a batch of your best performing content and a batch that flopped

split them into two sections in a doc and feed to an LLM

and have it analyze the differences between what performed vs what didn't

your content performance is valuable data

as an AI can identify patterns in what resonates with your audience vs what doesn't

how to build an AI automation (a step by step breakdown): step 1: map out the manual task

before automating anything, document how you do it manually:

- what's the step-by-step process if a human were to do this?
- what data and information do you need at each step?
- what are the key decision points?

how to automatically scrape data from the internet (like a data engineer): this is setting up systems that save information from the internet into organized databases

example: collecting TikTok videos, captions, and engagement metrics every day

and this data becomes the foundation for AI systems you build later on

there's so much content on how to build AI agents, but no one ever talks about the data engineering pipelines that support them

here's a thread going over the basics of data engineering: the goal of data engineering:

- extract data from various sources
- transform it into structured format
- load into a data warehouse like Snowflake

and this structured data often is used context for AI systems to make personalized recommendations

how to reverse engineer any successful AI product: step 1: understand the manual process

before diving into a technical analysis, figure out what human task this AI product is automating

> what would someone do manually to achieve the same result?
> what decisions need to be made?
> what data is required at each step?
> what is the most painful part of this task that people are paying to automate?

how to discover AI business ideas that actually make money (step by step breakdown): step 1: research industries with manual bottlenecks

use this prompt to understand where people are struggling:

"You're a 20-year veteran in the [INDUSTRY]. What tasks consume the most time daily? What repetitive work do you wish would disappear?"

test this across different sectors

how to vibe code your AI project in 7 days (step by step breakdown): day 1: map out your system design

don't jump straight into code - plan the complete workflow first

what steps does your tool need to get users from point A to point B?

create a document outlining each step, what data is needed, what decisions get made

how to build your first AI agent (complete roadmap): step 1: find a real problem worth solving

forget about AI for a second and think about tasks that:

> take up hours of someone's time every week
> are repetitive and monotonous
> cost the business real money when delayed
> currently require employees to do manually

the last 6 years I've built AI systems for startups, mid-sized companies & global enterprises

here are my 11 biggest lessons on building AI systems: lesson 1: shadow employees before building anything

spend time with whoever currently does the job you're trying to automate

understand the industry nuances, requirements, and how they actually envision the workflow

how to become elite at AI (step by step breakdown): step 1: learn to code

start with Python basics - for loops, data structures, classes, all that fundamental stuff

you need one programming language to understand how systems actually work

and Python is the most common language for development

here's a breakdown of an AI agent that I built for a global enterprise: the problem: their legal team was spending hundreds of hours searching through contracts

- they needed to find specific clauses
- compare terms across clients
- answer questions like "which contracts have upgrade clauses?"

all done manually

here's a full behind-the-scenes walkthrough of an AI workflow I built for a B2B SaaS: the problem: this ecommerce marketing SaaS had thousands of brands in their database

but Shopify's categories were way too broad - "apparel", "health & beauty", etc

they needed granular subcategories like "gymwear," "streetwear," "skincare" to improve their backend models