Tyler Profile picture
Sep 8 12 tweets 2 min read Read on X
everything you need to automate customer research with AI:
manual customer research takes forever and you're probably missing the best insights

reddit is a goldmine of real customer conversations

where people share their actual problems, desires, and frustrations

but manually going through threads every day isn't scalable
this system automatically finds pain points, desires, ad angles, and emotional triggers from Reddit daily

instead of spending hours reading through forums

you get a clean report of customer insights with supporting quotes

all on autopilot
step 1: choose your target subreddits

identify communities where your customers actually hang out

> skincare brand - r/SkincareAddiction
> fitness product - r/fitness, r/loseit
> productivity app - r/productivity, r/getmotivated
step 2: set up daily scraping

use Reddit's API to pull the top threads from each day

this gives you fresh conversations where people are actively discussing their problems and what they wish existed
step 3: extract insights with AI in these categories:

> pain points
> desires
> objections
> emotional triggers
> trends
step 4: the filtering system (this is the game-changer)

the biggest problem with AI research tools is they give you the same insights over and over

"people want clear skin" - yeah, we know that already

you need a system that only shows you NEW insights
this is where embeddings come in

embeddings turn text into numbers that capture meaning

"I love food" and "I enjoy eating" use different words

but mean the same thing embeddings see they're similar because the numbers are nearly identical
step 5: duplicate detection process

when an AI finds a new insight, embeddings help narrow down your database to the top 5-10most similar existing insights

then an LLM compares the new insight to just those 5 and determines if it's truly new or a duplicate

this is way more accurate than asking AI to compare against your entire database
step 6: clean daily reporting

instead of drowning in repetitive information, you get reports with only genuinely new insights

each insight comes with actual quotes from Reddit users as supporting evidence

your research stays valuable instead of becoming noise
what this creates:

a clean, growing database of customer insights that doesn't get cluttered with duplicates

daily reports that actually teach you something new about your customers

real customer language you can use in ads and copy
to recap how to automate customer research with AI:

> target relevant subreddits where customers hang out
> scrape top daily threads automatically
> extract insights in specific categories with focused prompts
> use embeddings and LLMs to filter out duplicate insights
> get clean daily reports with only new findings

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More from @tyler_agg

Sep 3
everything you need to build reliable AI systems:
most people want an AI that can operate autonomously

they want it to make every decision, handle entire workflows, and figure everything out without guidance

the result is different outputs every time with little consistency
when an AI makes all decisions, you get random results

ask Cursor to "build a customer service chatbot" 10 times and you'll get 10 completely different solutions

but with automating business tasks you need predictability
Read 10 tweets
Sep 3
how to give an AI agent memory to make it smarter over time:
most AI system treats every conversation like it's meeting you for the first time

for example a customer first complains about a broken chair, then the AI responds like it's a new issue

but the same customer complains about another broken chair 2 months later

but in most cases an AI system still treats it like a new convo and acts like it's never happened before
what an AI with memory would do:

"Hello David, following up on your previous case from August where you experienced issues with an ergonomic chair, I sincerely apologize this is happening again. We appreciate your continued trust since 2023..."

it references past interactions and treats customers like actual humans
Read 14 tweets
Sep 1
here's everything you need to become an AI developer as a complete beginner (step-by-step):
most people jump straight to AI tools without learning the fundamentals

they think "I'll just use ChatGPT API and build something cool"

but building AI tools is 90% traditional software engineering and 10% AI

learn to code first and it’ll make building AI apps 100x easier
step 1: learn Python basics

> for loops
> lists
> classes
> objects
> if/else statements

you need one programming language to understand how coding works

Python is a great choice if you want to get into AI
Read 10 tweets
Aug 29
how to build an internal chatbot that answers your team's SOPs and FAQs (the exact roadmap):
every growing company has this problem:

your team constantly asks the same questions about processes, SOPs, and company policies

you end up answering "where's our onboarding doc?" or "what's our refund policy?" 20x a week
most chatbot solutions look scary and technical

they require embedding, vector databases, maintaining all this data, etc

but there's a much simpler way that works just as well using tools you already have
Read 11 tweets
Aug 28
here's everything you need to make money with AI (the only 2 paths that actually work):
there are only two ways to make money with AI

most people get confused because they see all the time on twitter about how people are making six figures with this automation or using this tool and shit like that

but when you strip away all the noise, it comes down to just these two paths
path 1: use AI to amplify what you're already good at
this is for 90% of you reading this

if you're decent at copywriting, sales, paid ads, whatever - don't abandon that to become an "AI expert"

instead, use AI to make you 10x better at what you already do
Read 10 tweets
Aug 27
here's everything you need to build and deploy AI apps in hours (complete beginner's guide):
most people think you need years of coding experience to build apps

but with AI tools doing the heavy lifting, you can go from idea to deployed app in a single day

the key is using the right stack and approach
here's the vibe coding stack I usually recommend for beginners:

> planning with ChatGPT/Claude
> coding with Cursor and Sonnet 4
> storing data with Supabase
> handling user logins with Clerk
> deploying with Vercel or Railway

here's how each piece works
Read 10 tweets

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