Step 3: Copy and paste the whole JSON data into ChatGPT/Gemini/Claude
Step 4: Prompt something like this to discover user needs:
Look at this Subreddit (JSON format). Find and list all user pain points, needs, and struggles. Suggest top products and unique selling propositions (USPs) to satisfy these needs. Research similar products and find unique positions.
Here’s a sample output on my ChatGPT 5.2 Thinking:
It also shows a number of products that would fill these needs:
I omit the rest for brevity – you get the point.
This truly is a goldmine of business need analysis.
Personalized e‑commerce shopping agent – This AI‑powered chatbot guides online shoppers through the entire purchasing process, suggesting relevant products and providing instant support. By reducing cart abandonment and boosting customer satisfaction, it can handle thousands of stores with minimal oversight.
AI customer support for SMEs – Small businesses often struggle to manage customer service across multiple channels. This AI agent automatically responds to emails, live chat, and social media inquiries 24/7, freeing up human resources while maintaining prompt, consistent support.
Virtual HR assistant – Hiring can be time‑consuming for startups and small companies. A virtual HR agent screens résumés, schedules interviews, and answers applicants’ basic questions, streamlining recruitment processes without the need for a dedicated team.
AI‑powered personal finance advisor – Consumers need help with budgeting, spending tracking, and investment planning. This smart advisor monitors financial habits, offers personalized tips, and suggests investment strategies, bringing advanced financial guidance to everyday users.
Healthcare appointment bot – Managing bookings and reminders can overwhelm clinics and hospitals. A healthcare bot automatically schedules appointments, sends reminders, and handles rescheduling, improving patient experience and reducing administrative workload.
Virtual real‑estate tour agent – Buyers often want to explore properties remotely. This agent offers interactive, narrated virtual tours and answers questions in real time, enabling realtors to showcase listings to a global audience without in‑person visits.
AI content assistant – Companies need a constant stream of high‑quality content. An AI content assistant generates blog posts, marketing emails, ad copy, and social media updates in the correct tone and style, helping brands maintain a strong online presence.
Supply‑chain optimization agent – Retailers and manufacturers rely on accurate inventory and logistics planning. This AI agent monitors stock levels, predicts demand, and suggests improvements, reducing waste and ensuring products are available when needed.
Personal learning tutor – Personalized education is in high demand. An AI tutor adapts lessons based on each student’s pace and learning style, offering customized explanations, exercises, and feedback to enhance learning outcomes.
AI marketing analytics agent – Marketers need actionable insights to maximize ROI. This agent analyzes campaign performance and customer behavior, delivering recommendations on where to allocate budget and how to refine messaging for better results.
If you want to accelerate your progress shipping genuinely valuable project every single month, check out my inexpensive community:
We’ve stripped away the overhead of traditional development to focus on pure execution through vibe coding — leveraging AI to bridge the gap between a billion-dollar idea and a live, agentic product.
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If you want to see what an LLM is really good at (and where it still slips), don’t ask it to write a poem or generate code. Ask it to make the same small decision again and again under clear rules.
That is why blackjack basic strategy is such a useful lens.
Basic strategy is basically a decision table. Given your hand and the dealer’s upcard, there is a best move for a given rule set. Hit, stand, double, split, surrender. It is not a vibe. It is a lookup problem.
So you would expect modern models to nail it. And some do. But what makes this benchmark interesting is not “who got the highest score.” It is how the models fail.
The result that matters is not the winner, it is the pattern of mistakes
When models get decisions wrong in blackjack, they do not usually fail randomly. They tend to develop a consistent style of mistakes.
One model might double too often. Another might be overly cautious and miss good doubles. Another might surrender in spots where it should fight on. That is a big deal because it mirrors what many developers see in real products: the model is mostly reliable, but it has a few recurring blind spots.
This is the key point for builders. LLMs do not fail like buggy programs. They fail like inconsistent policies.
Accuracy and outcomes are not the same thing
The benchmark tracks two things that people often confuse:
decision accuracy: did the model pick the basic strategy move?
outcome: did the bankroll go up or down over the run?
These can diverge. Blackjack has asymmetric payouts. A single bad double can hurt more than a small hit/stand mistake. And over a limited number of hands, luck still matters. So you can see a model that is slightly less accurate end up with a better balance simply because variance went its way.
This is not just gambling trivia. It is a reminder that your evaluation metric shapes what looks “best.” If your product cares about costly failures, you should measure cost-weighted errors, not just raw accuracy.
Why this matters outside blackjack
A blackjack hand is a tiny state with a clear action set. Software is full of the same structure:
incident triage rules
retry and backoff policies
access control and permissions
billing and pricing logic
feature rollout rules
compliance checks
In all of these, you often have clear policies you want followed. If a model struggles to consistently follow a small decision table, it will also drift when it is asked to follow your company’s rules unless you design around that.
The better mental model: LLMs behave like learned heuristics
A traditional program executes rules. A plain LLM often imitates rules and sometimes improvises. That is why you see those “error personalities.” The model is not just retrieving the correct table cell every time. It is applying a learned pattern that is usually right, and occasionally biased.
This is the important angle for the Finxter community: treat the model like a policy learner, not a calculator.
What to do with this insight
The engineering move is not to argue with the model harder. It is to change the shape of the task so the model cannot drift.
A few practical approaches:
Put the strategy table in code and have the model call it.
If you keep it in the prompt, force a structured lookup format and validate the output.
Log mistakes by category (too many doubles, early surrenders, split errors) because that tells you what to fix.
A simple Finxter challenge you can copy
The real win here is not blackjack itself. It is the idea of a small, repeatable benchmark.
Pick any domain where ground truth exists as a clear set of rules or a decision table. Generate a lot of reproducible test cases. Score both accuracy and cost-weighted outcomes. Then look for recurring error patterns, not just the overall score.
That gives you something far more useful than “model A feels smarter than model B.” It tells you how a model behaves under repetition, which is what matters when you are building real systems.
Look, if you’re a data or AI nerd, your biggest enemy isn’t the competition. It’s the fact that your brain has way too many tabs open at once.
You’re probably sitting there thinking about building some massive ML model or a deep diagnostic tool, and meanwhile, you end up stuck in “builder mode” forever. Nothing actually hits the market this month because you’re too busy making it perfect.
Also, let’s be real. Working for yourself is a total trap. Without a boss or a team breathing down your neck, “I’ll finish this by Tuesday” easily turns into “I’ll get to it eventually.”
The “Right Now” Strategy
If you want to make money this month, stop selling “AI.” Nobody actually wants to buy AI. They want to buy more money.
The easiest thing to sell is Lead Gen. Every business on earth is obsessed with getting more inbound leads and more calls on the calendar. Instead of pitching them a complex automation workflow, just tell them you’ll get them qualified meetings using your custom data stack.
You use the AI tools behind the scenes to do the heavy lifting, like outreach or content pipelines, but you keep the pitch simple and business-friendly.
How to actually start
Pick ONE thing: One niche, like HVAC companies or SaaS startups, and one channel like X or Reddit.
Run a 30-day sprint: Do a low-cost or free pilot for one person just to get a glowing testimonial.
Stop drifting: Use an accountability loop. If you don’t tell someone what you’re shipping this week, you probably won’t ship it.
If you’re tired of overthinking and just want to get something out the door, come hang out in my Skool community:
It’s basically a “no-fluff” zone where we share the actual playbooks and keep each other on track. We’re here to make sure you don’t spend another month just “thinking” about your business.
Imagine this: You’re in your Tesla, going from your home to the sunny beaches by the Mediterranean Sea. You feel sleepy, so you take a nap. The car drives all by itself – through big highways, twisty mountain roads, and busy towns. It stops at lights, changes lanes, and parks when you get there. Wow!
Right now, people in places like America, China, Australia, and New Zealand can use it.
But in Europe, it’s not ready yet because of strict rules about car safety.
Tesla is working super hard to get permission. They are talking a lot with people in charge in the Netherlands.
Tesla hopes to show in February 2026 that FSD is safe.
If it works there, other European countries might say yes too!
To show everyone how good it is, Tesla is giving free rides right now. You sit in the passenger seat while a Tesla worker drives, and the car does the work. These rides started in Germany, France, and Italy.
So many people wanted to try it that Tesla made it longer — until the end of March 2026! Now it’s also in Denmark and Switzerland.
People who tried it say it’s amazing.
The car drives smoothly in narrow streets, busy cities, and even construction zones. It’s still “supervised,” which means a person has to watch, but it’s way smarter than regular driving help.
Europe has tough rules to keep roads safe, and that’s good!
But Tesla says FSD can make roads even safer because it doesn’t get tired or distracted like people sometimes do.
So, when can you nap on the way to the Mediterranean? Probably early 2026, if everything goes well. Hang on, European Tesla friends — it’s coming soon!
Disclaimer: We used Grok for some of the image material and content.
I published this post in my AI newsletter (130k subs). You can join for free here.
I just discovered this AI startup idea from Reddit. Will it be the next unicorn after Cursor ($30B valuation)?
Note that ideas are cheap, execution is king.
To the best of my knowledge, this idea hasn’t been implemented yet. But I thought I’d share it with the Finxter community so I can brag about it as soon as somebody takes it and reaches unicorn status.
~~~
Idea: Create a free vibe coding IDE that shows ads while waiting for agents to complete.
“The free vibe coding IDE that pays for its own GPT-5.2 tokens”
Here’s a pitch deck I created with Gemini Banana Pro (comic style):
Some features:
Monetize idle time
Waiting is a sustainable business thesis for AI
TAM is 1B vibe coders
Bonus: Ads pay for AI tokens
I hope that somebody from the Finxter community takes this idea and runs with it. I’ll be here to share v1 with the community for free (130k readers). Let’s go.
Be on the right side of change! Chris
PS: Join our community for AI builders. It’s fun and our goal is for each member to ship one AI project each month.
Post: “If someone offered you $1 million of cash or $6,000/mo for life? Take the $6,000/mo for life every time. Absolute no brainer.”
I was stunned that most people didn’t seem to understand that this is not the best option.
So, which option is better:
Choosing $1M now, or
$6,000 per month for life?
To answer this mission-critical question, I ran an analysis. Here it is:
$1M invested starts growing immediately. If you don’t touch it for X years (see chart), you can later withdraw $6k/m and still end up with WAY more money.
The monthly deal looks safe but it’s trading away the compounding engine (the $1M) for a fixed paycheck.
You can also see this graphic for which option is better:
Key takeaways:
If you start withdrawing immediately ($6k/mo), you’re taking $72k/yr = 7.2% of $1M. With a 7% return, the portfolio eventually depletes (in this simulation: around year ~42).
If you wait even 5–10 years before starting withdrawals, the account grows enough that the $6k/mo becomes easily sustainable, and you end up with a huge remaining balancewhile also receiving the same $6k/mo thereafter.
Compared to the “$6k/month stream” (which leaves you with no asset), the “$1M then withdraw later” strategy produces dramatically higher total value = (cash withdrawn + remaining balance).
As we gear up for 2026, I’m streamlining my coding workflow with a lean, vibe-aligned stack that focuses on simplicity and scalability (I have many projects!). It’s perfect for solo devs or small teams building dynamic web apps.
This stack might not be perfect if you work in a large corporation or something. You might want to use Cursor and other tools as well. Here’s the breakdown, tool by tool.
OpenAI – Codex and Research
OpenAI powers my core ideation phase with Codex for rapid code generation and research tools for deep dives into algorithms or APIs. It’s like having a tireless co-pilot that turns vague concepts into functional prototypes, saving hours on boilerplate and letting me focus on the fun, innovative bits.
Gemini App – Visuals and Infographics
For visuals and infographics, Gemini App is my go-to—it’s intuitive for whipping up charts, diagrams, and UI mockups that make complex data pop. Whether I’m explaining a new feature or prepping client decks, its drag-and-drop magic ensures polished outputs without the Photoshop slog.
GitHub – Project Management and Deployment Pipeline
GitHub handles all project management and deployment pipelines with its robust repo features, Actions for CI/CD, and seamless collaboration tools. It’s the central hub where ideas branch, merge, and ship, keeping everything versioned and automated for zero-downtime releases.
Heroku – Hosting
Heroku simplifies hosting with one-click deploys and auto-scaling, ideal for spinning up full-stack apps without server wrangling. Its free tier for testing and easy add-ons for extras like logging make it a no-brainer for quick iterations and reliable uptime.
MariaDB – Database for Dynamic Web Apps
MariaDB anchors my dynamic web apps as a robust, open-source database that’s MySQL-compatible but faster and more feature-rich. It excels at handling relational data for user auth, content management, or e-commerce backends, with easy scaling for growing traffic.
FastSpring – Payments (VAT and Sales Tax Handling)
Payments flow through FastSpring for its global compliance magic, auto-handling VAT, sales tax, and subscriptions across 200+ countries. It’s plug-and-play for monetizing apps, reducing legal headaches so I can prioritize product over paperwork.
Namecheap – Domains
Namecheap locks in domains with affordable, straightforward registration and privacy protection. Quick WHOIS guards and easy transfers keep my online presence secure and branded, without the upsell drama of bigger registrars.
Easily combine two CSV files into one without any downloads or complex software — just upload and merge in your browser. Perfect for quickly appending data from multiple spreadsheets.
How It Works: Upload your primary CSV (the one with the header row you’ll keep) as the first file. Then select the second CSV to append its rows below.
If you want to accelerate your progress shipping genuinely valuable project every single month, check out my inexpensive community: 
Image and research source: Thomas Taylor (
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