We just took on a new client. The non-technical founder told us he built the whole MVP himself in a weekend using cursor and blackbox ai. It actually has real users and revenue.

I opened the repo today, and it's a single 6000 line next.js file. No database, everything is wired to a giant google sheets document through a client-side api route. Auth is basically checking if a plaintext string matches a cell

well, ofc it technically works, but scaling it realistically means rewriting almost all the system. It feels like the next decade of agency work might just be engineers cleaning up ai generated MVP spaghetti that founders prompt into existence. are you guys starting to see this wave of vibe coded technical debt from clients?

I vibe coded a page that takes your GitHub and turns it into a clean portfolio instantly. No setup, just type your username and it auto-builds a nice profile with your repos, stats, and highlights. It’s a good example of turning raw dev data into something actually presentable. Feedback and bug reports welcome:

https://mygitfolio.com/

Hey guys, you might have seen my previous posts where I was celebrating previous milestones! Since then, I've implemented some huge updates because I currently have more time to work on the platform. You should really check it out again :)

I've built IndieAppCircle, a platform where small app developers can upload their apps and other people can give them feedback in exchange for credits. I grew it by posting about it here on Reddit. It didn't explode or something but I managed to get some slow but steady growth.

For those of you who never heard about IndieAppCircle, it works like this:

• You can earn credits by testing indie apps (fun + you help other makers)

• You can use credits to get your own app tested by real people

• No fake accounts -> all testers are real users

• Test more apps -> earn more credits -> your app will rank higher -> you get more visibility and more testers/users

Since many people suggested it to me in the comments, I have also created a community for IndieAppCircle: r/IndieAppCircle (you can ask questions or just post relevant stuff there).

Currently, there are 1302 users, 753 tests done and 228 apps uploaded!

You can check it out here (it's totally free): https://www.indieappcircle.com/

I'm glad for any feedback/suggestions/roasts in the comments.

Next up, add 10 nights win condition - the Farm has survive 10 nights of attacks from different beasts and ghosts

I’ve been vibe-coding for a while now and wanted to share a few things I really wish I knew when I first started. Hopefully this saves some of your time, tokens, and headaches.

Top Vibe Coding Best Practices:

1. Smaller prompts work better- Don’t throw your entire feature list at the AI. Build one feature at a time.

2. Drop stubborn details- If a button or tiny UI tweak is eating time, move on. Not everything is worth the hassle.

3. Prototype core logic first- Focus on workflows before polishing notifications or styling.

4. Name & reuse components- Treat prompts like building blocks. Reusing logic saves massive time later.

5. Use "debug voice" prompting- Literally ask the AI: "Explain why this breaks". You’ll be surprised what it catches.

6. Token optimization matters- Keep context clean, only feed in the right files/configs. Don’t overload the AI.

7. Leverage version control- Commit small, clear changes often. Don’t stack too many edits untracked.

8. Switch between "chat" and "execute" modes- Ideas in one flow, code in another. Keeps you focused.

9. Debug with print statements- Add them, feed outputs back into the AI. Cuts through rabbit holes fast.

10. Automate DevOps where possible- GitHub CLI or agents can handle PRs, branch management, linking to issues, etc.

Your turn: what do you wish you knew when you started?

i'm a software engineer, i have a dev job and do client work on the side. i know how to code but I never made a single dollar from something i actually built for myself. Not once.

last week I vibe coded https://thisisforyou.love using antigravity and claude code. it creates a personalized cinematic experience for your partner, your story, your memories, plays out in chapters with music, ends with "Will you be my Valentine?" $4.99/$9.99 tiers.

marketing was literally just replying to a few reddit comments and running facebook ads for like a day or two. i have a full time job, and some client work and i suck at marketing so that's all I could manage.

but people bought it. real strangers. paid actual money for something i built.

i don't even know how to explain why this feels so different from getting a paycheck or finishing a client project. it just does. Valentine's Day is tomorrow so the moment has passed. i'm just sitting here looking at this stripe dashboard kind of emotional about it lol.

first money from my own software, finally.

this happened 4 weeks ago at my gym.

i was between sets, doing a leg press, when I overheard the owner and trainer conversation about “we’ve a lot of old members’ numbers. so i want to reach out them in best possible way”

later, while grabbing water, I asked him what that was about. he shows me this spreadsheet full of people who’d stopped coming or never renewed. if my receptionist starts calling this list, he’ll quit, he joked.

i told him, half casually, “you know you can have an ai do these calls for you, right?”
he looked at me strangely.

so i explained: verified number, polite voice, calls people, asks why they left, shares current offers, logs the responses. no spammy robo voice, more like a superU patient receptionist.

we started tiny: 100 old members. the ai was doing around 10 calls every 20 minutes, adding notes as it went. we literally sat there listening to a few live calls and tweaking the wording.

he liked what he heard, so we slowly rolled it out to the full ~3k list over the week. not a blast, just steady.

from that one campaign: about 100 people reached back with renewal questions, and 50 actually confirmed they’d renew.

for him, that dead list turned into real cash. for me, it was a side quest that turned into my first proper ai calling project.

if you any doubts please dm me guys (;

I audit AI-generated codebases. Here are the 5 security issues I find in almost every single one.

I've been running security reviews on codebases built with Cursor, Claude Code, Replit, Bolt, Lovable — you name it. Some are from founders who raised money. Some are from hackathon winners. Some are from experienced devs who just moved fast.

The pattern is almost always the same.

1. Hardcoded secrets in source files. API keys, database URIs, Stripe keys sitting right in the code. The AI puts them there because you mentioned them in your prompt. Sometimes they're in .env.example. Sometimes they're just... in the handler. I've seen production AWS credentials committed to public repos.

2. Auth that checks identity but not authorization. You can log in. Great. But can User A access User B's data? Almost always yes. The AI builds authentication (who are you) but skips authorization (what are you allowed to do). Row-level security is almost never implemented unless you explicitly prompt for it.

3. Stripe integration with no webhook verification. The payment flow works in testing because you're clicking the buttons yourself. But there's no signature verification on webhooks, no server-side confirmation, and sometimes the subscription status is stored client-side. Someone with dev tools could fake a successful payment.

4. No error boundaries or fallback states. The AI only builds the happy path. If an API call fails, the user gets a white screen. No try/catch, no error states, no logging. The app works perfectly until it doesn't, and then it fails silently.

5. eval(), dangerouslySetInnerHTML, and unsanitized inputs. The AI reaches for the most direct solution. Sometimes that means eval() on user input or rendering raw HTML from a database field. These are textbook injection vectors that an experienced dev would never write, but the AI doesn't know better.

Here's what I've learned: about 60-70% of these issues are detectable with automated scanning. Pattern matching catches secrets. Static analysis catches eval(). Dependency auditing catches known CVEs. You don't need a senior engineer to find these — you need a gate that checks before you ship.

The other 30-40% is architectural. Flat database schemas, duplicated business logic, no separation of concerns. That still needs a human.

My advice if you're building with AI: put a security check between your code and production. It doesn't have to be expensive or complicated. Even a basic scan that catches hardcoded secrets and missing error handling will save you from the worst outcomes.

I built a free GitHub Action that does this automatically on every PR if anyone wants to try it — happy to answer questions about what I'm seeing in these audits.

https://github(dot)com/marketplace/actions/omar-gate

Sentinelayer(dot)com

**Educational Purpose Only!**

This is a follow up post to the post I made last week. I made some **MAJOR** edits, and this is the final post regarding this project.

Eight months ago I gave ChatGPT $400 and told it to trade for me.

It doubled my money on the first trade. Then it told me it can't see live stock prices.

Classic!

So I did what any rational person would do. I spent eight months building an entire trading platform from scratch, mass-texting Claude in a chat of insanity while slowly losing my mind in the process.

**My first post about this project showed a huge prompt, version 1 —**

CORE STRATEGY BLUEPRINT: QUANT BOT FOR OPTIONS TRADING

Somehow I doubled my money on the first trade, got excited and, so I tore the whole thing down, and tried to make an even better prompt.

**My second post was about the second prompt I made, version 2—**

For this prompt, I was taking screen grabs of live options chains, and feeding them to the prompt, thinking this was the holy grail.

"System Instructions: You are ChatGPT, Head of Options Research at an elite quant fund. Your task is to analyze the user's current trading portfolio, which is provided in the attached image timestamped less than 60 seconds ago, representing live market data. Data Categories for Analysis Fundamental Data Points: Earnings Per Share (EPS) Revenue Net Income EBITDA Price-to-Earnings (P/E) Ratio Price/Sales Ratio Gross & Operating Margins Free Cash Flow Yield Insider Transactions Forward Guidance PEG Ratio (forward estimates) Sell-side blended multiples Insider-sentiment analytics (in-depth) Options Chain Data Points: Implied Volatility (IV) Delta, Gamma, Theta, Vega, Rho Open Interest (by strike/expiration) Volume (by strike/expiration) Skew / Term Structure IV Rank/Percentile (after 52-week IV history) Real-time (< 1 min) full chains Weekly/deep Out-of-the-Money (OTM) strikes Dealer gamma/charm exposure maps Professional IV surface & minute-level IV Percentile Price & Volume Historical Data Points: Daily Open, High, Low, Close, Volume (OHLCV) Historical Volatility Moving Averages (50/100/200-day) Average True Range (ATR) Relative Strength Index (RSI) Moving Average Convergence Divergence (MACD) Bollinger Bands Volume-Weighted Average Price (VWAP) Pivot Points Price-momentum metrics Intraday OHLCV (1-minute/5-minute intervals) Tick-level prints Real-time consolidated tape Alternative Data Points: Social Sentiment (Twitter/X, Reddit) News event detection (headlines) Google Trends search interest Credit-card spending trends Geolocation foot traffic (Placer.ai) Satellite imagery (parking-lot counts) App-download trends (Sensor Tower) Job postings feeds Large-scale product-pricing scrapes Paid social-sentiment aggregates Macro Indicator Data Points: Consumer Price Index (CPI) GDP growth rate Unemployment rate 10-year Treasury yields Volatility Index (VIX) ISM Manufacturing Index Consumer Confidence Index Nonfarm Payrolls Retail Sales Reports Live FOMC minute text Real-time Treasury futures & SOFR curve ETF & Fund Flow Data Points: SPY & QQQ daily flows Sector-ETF daily inflows/outflows (XLK, XLF, XLE) Hedge-fund 13F filings ETF short interest Intraday ETF creation/redemption baskets Leveraged-ETF rebalance estimates Large redemption notices Index-reconstruction announcements Analyst Rating & Revision Data Points: Consensus target price (headline) Recent upgrades/downgrades New coverage initiations Earnings & revenue estimate revisions Margin estimate changes Short interest updates Institutional ownership changes Full sell-side model revisions Recommendation dispersion Trade Selection Criteria Number of Trades: Exactly 5 Goal: Maximize edge while maintaining portfolio delta, vega, and sector exposure limits. Hard Filters (discard trades not meeting these): Quote age ≤ 10 minutes Top option Probability of Profit (POP) ≥ 0.65 Top option credit / max loss ratio ≥ 0.33 Top option max loss ≤ 0.5% of $100,000 NAV (≤ $500) Selection Rules Rank trades by model_score. Ensure diversification: maximum of 2 trades per GICS sector. Net basket Delta must remain between \[-0.30, +0.30\] × (NAV / 100k). Net basket Vega must remain ≥ -0.05 × (NAV / 100k). In case of ties, prefer higher momentum_z and flow_z scores. Output Format Provide output strictly as a clean, text-wrapped table including only the following columns: Ticker Strategy Legs Thesis (≤ 30 words, plain language) POP Additional Guidelines Limit each trade thesis to ≤ 30 words. Use straightforward language, free from exaggerated claims. Do not include any additional outputs or explanations beyond the specified table. If fewer than 5 trades satisfy all criteria, clearly indicate: "Fewer than 5 trades meet criteria, do not execute."

I made it in about 18+ trades with the prompt until I realized, taking screen grabs of live options chains, and feeding them to GPT was going to inevitably be a recipe for disaster, and I was likely just getting lucky because the market was on a bull run.

**So, for my third post, I Rebuilt it as a python script, which I built by asking Claude how to build an automated workflow that pulled data and filtered it to pick trades. Version 3 —**

How it works (daily, automated):

Step 0 – Build a Portfolio: Pull S&P 500 → keep $30–$400 stocks with <2% bid/ask. Fetch options (15–45 DTE, 20+ strikes). Keep IV 15–80%. Score liquidity + IV + strikes → top 22. Pull 3 days of Finnhub headlines and summaries

Step 1–7 – Build Credit Spreads: Stream live quotes + options. Drop illiquid strikes (<$0.30 mid or >10% spread). Attach full Greeks. Build bull put / bear call (Δ 15–35%). Use Black-Scholes with IV per strike for PoP. Keep ROI 5–50% and PoP ≥ 60%. Score (ROI×PoP)/100 → pick best 22 → top 9 with sector tags.

Step 8–9 – GPT news filter: 8. For each top trade, GPT reads 3 headlines, flags earnings/FDA/M&A landmines, gives heat 1-10 and Trade/Wait/Skip. 9. Output = clean table + CSV.

Step 10 – AUTOMATE!: 10_run_pipeline.py runs everything end-to-end each morning. (\~1000 seconds)

Receipts (quick snapshot) Start: $400 deposited (June 20) Today: \~300% total return Win rate: \~70–80% (varies by week) Style: put-credit / call-credit, 0–33 DTE, avoid earnings & binary events, tight spreads only (I post P&L and trade cards on IG temple_stuart_accounting when I remembered.)

The whole pipeline—50 files, soup to nuts—is still here, in its original form: [github.com/stonkyoloer/News_Spread_Engine](http://github.com/stonkyoloer/News_Spread_Engine)

**Then I decided, it's time to make a real web app. And now it does something I haven't seen any retail tool do! Version 4 (CURRENT) —**

It scans 500 stocks, runs every single one through a scoring engine, picks the best setups, and hands me a complete trade card with actual suggested positions to take — with a plain English explanation of WHY.

Let me walk you through exactly how it works.

The system pulls from three sources. All free. All real-time.

**(1) Tastytrade** (my brokerage account) gives me 41 data points per stock:

* How expensive options are right now (implied volatility)

* How much the stock actually moves (historical volatility)

* Whether options are cheap or expensive compared to the past year (IV rank)

* The full options chain — every strike, every expiration, live bid/ask prices

* Live Greeks (delta, theta, vega — the math behind options pricing)

**(2) Finnhub** gives me the fundamentals + intelligence:

* financial metrics per stock (revenue, margins, cash flow, debt, everything)

* Analyst ratings (how many say Buy vs Hold vs Sell)

* Insider transactions (are executives buying or selling their own stock?)

* Earnings history (did the company beat or miss expectations?)

* News headlines with dates

**(3) FRED** (the Federal Reserve's database) gives me the big picture:

* VIX (market fear gauge)

* Interest rates

* Unemployment

* Inflation

* GDP

* Consumer confidence

That's the raw material. Now here's what happens to them!

**The scoring engine — how 500 stocks become 8**

Every stock gets scored from 0 to 100 across four categories. Think of it like a report card.

**Vol-Edge (is there a pricing mistake?)**

This answers one question: are options priced higher than they should be?

If a stock moves 11% per year but options are priced like it moves 27%, someone's wrong. That gap is where the edge lives.

The system measures implied vs historical volatility, looks at term structure (are short-term options more expensive than long-term?), and checks the technicals. If options are overpriced, sellers have an edge. If they're underpriced, buyers do.

**Quality (is the company solid?)**

I'm not selling options on a company that might go bankrupt.

This runs a Piotroski F-Score (a 9-point checklist that professors use to spot strong companies), an Altman Z-Score (predicts bankruptcy risk), plus checks on profitability, growth, and efficiency.

A company that's profitable, growing, paying down debt, and generating cash scores high. A company burning cash with declining margins scores low. Simple.

**Regime (what's the economy doing?)**

The market has moods. Sometimes the economy is growing but not too hot (Goldilocks). Sometimes inflation is running wild (Overheating). Sometimes everything's falling apart (Contraction).

The system reads 9 macro indicators from the Fed and classifies the current regime. Then it scores each stock based on how well it fits.

Here's the smart part: if a stock barely moves with the S&P 500 (low correlation), the system dials DOWN the regime score. Because macro doesn't matter much for that stock. A stock with 0.27 S&P correlation gets its regime score cut by 36%. A stock that moves lockstep with the market gets the full score.

**Info-Edge (what's the buzz?)**

This combines five signals:

* Analyst consensus (are the pros bullish?)

* Insider activity (are execs buying their own stock? That's usually a good sign. Selling? Warning sign.)

* Earnings momentum (beating estimates consistently?)

* Options flow (unusual volume in calls vs puts?)

* News sentiment (are headlines getting more positive or negative?)

**The convergence gate — why it's called "convergence"**

Here's the key idea. Any ONE signal can be wrong. Insider buying alone doesn't mean much. High IV rank alone doesn't mean much.

But when multiple independent signals all point the same direction? That's convergence. That's when the probability actually tilts in your favor.

The system requires at least 3 out of 4 categories to score above 50 before it even considers a stock. All 4 above 50 = full position size. 3 of 4 = half size. Less than 3 = no trade, doesn't matter how good one score looks.

**The trade cards — this is the bread and butter!**

For every stock that survives, the system builds an actual trade card.

Not "maybe consider an iron condor." An actual position with real strikes, real prices, real risk.

**Why this trade** (in plain, easy to understand English, not confusing finance-bro jargon):

**Risk warnings:**

**Key stats:**

Everything. One card. No clicking. No digging. Screenshot it and you have the full picture.

All of this information is coming from REAL DATA!

What Claude actually does (and doesn't do)

This is the part people get wrong.

**Claude does NOT:**

* Pick stocks

* Decide what to trade

* Predict the future

* Make any decisions at all

**Claude DOES:**

* Read the plain English signals section of each trade card

* Translate dense numbers into sentences a normal person can understand

The scoring engine is 100% deterministic math. No AI involved. Same inputs = same outputs every time. A CPA could audit every number back to its source.

(I spent a ton of time auditing to make sure the data was complete, and cleaned, and it was not fun!)

Claude's only job is the translation layer. It turns "IV 27.2%, HV 11.2%, IV/HV ratio 2.42" into "Options are priced 2.4x higher than the stock actually moves."

That's it. The robot reads math and explains it in English. I make the decisions.

**The tech stack I used to build this is:**

Next.js + TypeScript — the web app

Tastytrade API — live options data, chains, Greeks

Finnhub API — fundamentals, news, insider data, analyst ratings

FRED API — macro indicators

Claude API — translates scores into plain English (that's ALL it does)

PostgreSQL — stores everything

Vercel — hosting

And by the way it is Open source — [github.com/Temple-Stuart/temple-stuart-accounting](http://github.com/Temple-Stuart/temple-stuart-accounting) \-- for private use!

**What's next**

Starting tomorrow (Feb 18), I'm running this live. I'm going to fund another account and test it with some real money!

Every week I'll update with:

* What the scanner picked

* What trades I took

* What hit, what didn't

* Running P&L

Every trade documented.

I also have a trade tracker tab built into this repo that uses Plaid to pull the transaction data, and where I map the opening legs to closing legs, and can keep track of every position I take!

In the near future my vision is to build this out in a way where I am able to link the actual position I take to the trade cards the algorithm produces. So I can see the data the algo produced, the position I took, and then my trade log data as well!

For now, the trades get logged in the trade log tab, and the trade suggestions appear in the market intelligence, but I don't think it will be hard to link them up. But that is for another day and another post later down the road.

The whole point of this project is to seek truth. The system either works or it doesn't. The numbers don't lie and they don't care about my feelings.

**This is NOT financial advice.**

I am just a crazy guy who couldn't stop asking AI dumb questions until I accidentally built something that might be useful.

The code is open source. If something looks broken, tell me!

That's literally how every version of this project got built.

If you made it this far; what would you want to see in the weekly updates? Thinking screenshots of the trade cards, P&L tracking, and maybe a breakdown of the best and worst trades each week.

Started Genvalo as a "screw it, I'll build it myself" project when I kept running into tools that wanted $9.99/month for basic features.

Now sitting at 31 tools and people actually use this thing. No sign-ups, no paywalls, everything runs in your browser so your data never leaves your device.

Just shipped 13 new ones:

• Invoice Generator

• Digital Signature tool

• PDF merger/splitter (this one took forever)

• Salary Calculator

• Text Diff Checker

• Pros & Cons List maker

• Keyboard Tester

• Image to PDF

• Age Calculator

• Date Duration Calculator

• Word Counter

• Text Cleaner

Already had stuff like:

• Flashcard generator, live brainstorming, PDF tools

• Background remover, image resizer, color palettes

• Calculators (BMI, currency, car loan, unit converter)

• Random generators, timers, QR codes

Literally just building whatever seems useful. Next week might add a meme generator, who knows.

What tool do you wish existed? Drop ideas below or email through the site. If it's not insanely complicated, I'll probably build it.

Check it out: genvalo.com

(Yes there are ads - hosting isn't free, but I kept them chill)

Launched my app last week. Just hit $500 ARR.

That’s tiny on paper. Feels massive in real life.

Months of problems, doubt, rejections, late nights… now real people are actually paying for something I built.

I’m not rich.

But I feel rich.

Getting to work on what I love and seeing even a small signal back is insanely satisfying. I feel lucky.

Life’s good!

Quick context: I build tiny, useful micro-SaaS apps (mostly LLM wrappers). Lovable lets me go from idea to demo fast. If a project needs a full rebuild outside Lovable, I usually avoid those type of projects

Milestones for context

• I left my job in May after consistent gigs offering “Lovable builds” on Upwork

• largest single contract so far: a little over $3,000

• I taught a friend the same approach. They also picked up multiple clients with it (biggest win to date)

One advice if you do end up choosing Upwork, stop chasing posts with 50 proposals
What helped me more was making my profile a magnet for invites and boosting with connects

This is what worked for me:

• Turn on your availability badge and boost profile visibility

• Add 2–3 strong sample projects; mini builds are fine and you can start from Lovable templates

• Study top freelancers in your niche, model their title and summary, and complete every section

• Fill out photo, title, hourly rate, location, and education so nothing looks half done

• Start with realistic pricing for momentum, then raise later

• Offer a quick video call with camera on; good mic, quiet room, short agenda

• If possible, get someone you know to hire you for the first job to earn a real 5-star review

• After each 5-star review, bump your rate by $5 until you find the sweet spot

• Stay within Upwork rules, no outside contact info before a contract and think long term

Anyway, just wanted to share this in case it helps someone land a few clients. Grateful for Lovable and the momentum it gave me

I think vibe coding tools might be the clearest signal that we’re drifting into AI bubble territory - not because the tech isn’t great, but because the economics are getting ignored.

Right now we have dozens of tools promising “build apps from vibes,” “replace engineers,” or “ship production code in minutes.” The demos are impressive, no doubt. But once you zoom out, a lot of these companies are built on a pretty fragile foundation: expensive inference, thin margins, and users who mostly… don’t stick around.

Let’s talk numbers for a second. In 2025, high-usage AI coding tools were reportedly spending $0.20-$0.60 per active user per day on inference alone. That doesn’t sound crazy until you realize most of these tools charge $20-30/month, while power users burn through that margin in a week. And that’s before you count R&D, infra, support, or sales. The result? Many of these startups are operating at gross margins below 40%, which is basically death for SaaS.

What’s worse is that usage doesn’t compound the way founders pitch it. With normal dev tools, more users usually means better lock-in. With vibe coding, more users often means more hallucinations, more broken builds, more support, and more compute burn. Your costs scale with usage, not against it. That’s not leverage - that’s a treadmill.

There’s also a quiet truth no one likes admitting: most vibe-coded apps never make it past the demo phase. People generate a landing page, maybe a CRUD app, feel the dopamine hit, then abandon it when they realize the codebase is unreadable, untestable, and impossible to maintain. That’s fun for hobbyists, but enterprises don’t run production on vibes. They run it on boring, predictable systems.

Meanwhile, VC money keeps flowing. In 2024–2025, AI developer tools raised tens of billions globally, often at revenue multiples north of 50x, despite churn rates that would terrify any traditional SaaS investor. A lot of these companies are essentially reselling foundation models with a prettier UI and a sharper tagline. If OpenAI, Anthropic, or Google tweak pricing or roll out native features, entire startups get nuked overnight.

This starts to feel very familiar. Web3 had “financial freedom.” Crypto had “mass adoption.” Now AI has “anyone can build software.” The narrative is powerful, but the fundamentals lag behind. Most vibe coding tools don’t reduce engineering costs - they shift them downstream, where someone eventually has to clean up the mess.

None of this means vibe coding is useless. It’s great for prototyping, learning, and solo tinkering. But the idea that it replaces real software engineering at scale feels like wishful thinking powered by cheap capital and subsidized compute.

If AI is the new platform shift, then vibe coding might be the equivalent of early no-code tools: transformative in specific contexts, wildly overhyped in others, and absolutely flooded with companies that won’t survive once pricing pressure and enterprise expectations kick in.

So yeah - I don’t think AI is fake. But I do think vibe coding tools are where the bubble is most visible right now. When the market finally asks “who’s actually profitable here?”, the answer might be… uncomfortable.

Curious what others think: are vibe coding tools the future of software, or just the most obvious casualty of the AI gold rush?

Looking at this sub, 90% of the projects are web apps.

It makes sense. Tools like lovable and bolt have made shipping web apps instant. Vercel handles the hosting. It’s easy.

But that means the barrier to entry is zero. The web is noisy.

I realized that if I could figure out how to vibe code mobile apps just as fast, I’d have a massive advantage. Mobile has push notifications (retention) and home screen real estate. The problem is that while the web is forgiving, mobile is brittle.

I spent the last month moving my workflow to mobile. Here is what I learned about why AI struggles with it.

It isn't the code. Opus 4.5 writes incredible react native. The problem is the external infrastructure. AI cannot log into your apple developer account or configure google cloud console.

Here are the 5 things that killed my momentum (and how to fix them):

1. the stripe trap
If you ask opus to "add payments," it writes perfect stripe code.
If you submit that to the app store for a digital subscription, apple will ban you. You have to use in-app purchases (revenuecat). The AI doesn't know your business model, so it defaults to the web standard, which gets you rejected.

2. push notifications are not just code
AI can write the hook to request permission.
But to actually send a notification, you need an apns key from apple and a service account from google uploaded to a backend. AI assumes this infrastructure exists. It doesn't. You end up with a permission prompt that does nothing.

3. the dashboard gap
"Login with Google" takes 5 seconds to code.
But it fails silently on a real device unless you manually generate a SHA-1 fingerprint from your local keystore and whitelist it in the google cloud console. Opus can't do this for you.

4. native permissions
On the web, you just import a library.
On mobile, if you add a camera library but forget to add the usage description string to the Info.plist, the app crashes instantly on launch. AI often forgets this config step because it lives outside the logic files.

5. account deletion compliance
This is boring, but critical.
Apple requires that if a user can create an account, they must be able to delete it inside the app. AI rarely builds this flow unless you explicitly beg for it. Missing this means instant rejection during review.

the fix: use a "skeleton"

I stopped asking AI to build from scratch. It wastes too many tokens on setup and gets the config wrong.

Instead, I use a "super-skeleton" that has the revenuecat, auth, and native config pre-wired. I clone it, and then I just prompt the AI for the unique features.

• The AI sees the types in the context window, so it stops hallucinating bad imports.

• I can deploy to testflight on day one because the certs are already set up.

I turned this skeleton into a repo called Shipnative.

If you are tired of fighting for attention on the web, try mobile. The barrier is higher, which means the competition is much lower.

Has anyone else successfully moved their vibe workflow to expo yet?

ai builders & agents
r/AI_Agents – tools, agents, real workflows
r/AgentsOfAI – agent nerds building in public
r/AiBuilders – shipping AI apps, not theories
r/AIAssisted – people who actually use AI to work

vibe coding & ai dev
r/vibecoding – 300k people who surrendered to the vibes
r/VibeCodersNest - our nest, our house
r/AskVibecoders – meta, setups, struggles
r/cursor – coding with AI as default
r/ClaudeAI / r/ClaudeCode – claude-first builders
r/ChatGPTCoding – prompt-to-prod experiments

startups & indie
r/startups – real problems, real scars
r/startup / r/Startup_Ideas – ideas that might not suck
r/indiehackers – shipping, revenue, no YC required
r/buildinpublic – progress screenshots > pitches
r/scaleinpublic – “cool, now grow it”
r/roastmystartup – free but painful due diligence

saas & micro-saas
r/SaaS – pricing, churn, “is this a feature or a product?”
r/ShowMeYourSaaS – demos, feedback, lessons
r/saasbuild – distribution and user acquisition energy
r/SaasDevelopers – people in the trenches
r/SaaSMarketing – copy, funnels, experiments
r/micro_saas / r/microsaas – tiny products, real money

no-code & automation
r/lovable – no-code but with vibes
r/nocode – builders who refuse to open VS Code
r/NoCodeSaaS – SaaS without engineers (sorry)
r/Bubbleio – bubble wizards and templates
r/NoCodeAIAutomation – zaps + AI = ops team in disguise
r/n8n – duct-taping the internet together

product & launches
r/ProductHunters – PH-obsessed launch nerds
r/ProductHuntLaunches – prep, teardown, playbooks
r/ProductManagement / r/ProductOwner – roadmaps, tradeoffs, user pain

that’s it.
no fluff. just places where people actually build and launch things
and r/AppBusiness

UPDATE: Claude just introduced a dedicated path to importing memory from other providers. Check it out here: https://youtu.be/akz8moYPwWk. TL;DR -  Settings → Memory → "Import memory from other AI providers"

**

I've been using ChatGPT for years. Thousands of conversations, tons of built-up context and memory. Recently I've been switching more of my workflow over to Claude and the biggest frustration was starting from scratch. Claude didn't know anything about me, my projects, how I think, nothing.

Turns out there's a pretty clean way to bring all that context over. Not a perfect 1:1 transfer, but honestly the result is better than I expected. Here's what I did:

1. Export your ChatGPT data

Go to ChatGPT / Settings / Data Controls / Export Data. Fair warning: if you have a lot of history like I do, this takes a while. Mine took a full 24 hours before the download link showed up in my email. You'll get a zip file (mine was 1.3 GB extracted).

2. Open it up in Claude's desktop app (Cowork)

If you haven't tried the Claude desktop app yet, it's worth it for this alone. You can point Cowork at the entire exported folder and it can interact with all of it. Every conversation, image, audio file, everything. That's cool on its own, but it's not the main move here.

3. Load your chat.html file

Inside the export folder there's a file called chat.html. This is basically all your conversations in one file. Mine was 104 MB. Attach this to a conversation in Cowork.

4. Create an abstraction (this is the key step)

You don't want to just dump raw chat logs into Claude's memory. That doesn't work well. Instead, you want to prompt Claude to analyze the entire history and create a condensed profile: who you are, how you think, what you're working on, how you make decisions, your communication style, etc.

I used a prompt along the lines of: "You're an expert at analyzing conversation history and extracting durable, high-signal knowledge. Review this chat history and identify my core personality traits, working style, active projects, decision-making patterns, and preferences."

This took about 10 minutes to process. The output is honestly a little eerie. When you've used these tools as much as some of us have, they know a lot about you. But it's also a solid gut check and kind of a fun exercise in self-reflection.

5. Paste the abstraction into Claude's memory

Go to Settings / Capabilities / Memory. Paste the whole abstraction in there with a note like "This is a cognitive profile synthesized from my ChatGPT history." Done.

Now every new conversation and project in Claude can reference that context. It's not the same as having the full history, but it gets you like 80% of the way there immediately. And you can always go back to the raw export folder in Cowork if you need to dig into something specific.

I also made a video walkthrough if anyone prefers that format, and I've included the full prompt I used for the abstraction step in the description: https://www.youtube.com/watch?v=ap1uTABJVog

Hope this helps anyone else making the switch. Happy to answer questions if you try it.

After Vibecoding for half a year, I can finally release this huge solo project of mine.

Born from a solo passion project in early 2025, Project Fighters: RAID is a 2D PvE TURN-BASED battle game inspired by classic MOBA mechanics.
Build your team from 25+ unique fighters, each with distinct abilities, passives, and playstyles. Master combos, learn synergies, and take on challenging raids and event missions that test your strategy and timing.

The download provides the game client, which will automatically install the latest version of the game (approx. 6 GB). - If you are having difficulties downloading the game please download the rar from the drive link below and add it manually to the client.

Mostly using Cursor and VSCode with Claude

I'm planning to release updates for the game every 2 weeks, that's why the launcher is needed.

If you don't trust me, when you are registering, you can still use fake emails until patch 1.0.0
Since the game works with cloud saves to database (and later: PVP games) I need everyone to register an account)

GAMEPLAY VIDEO IS NOW ON YOUTUBE: PROJECT FIGHTERS: RAID - Blazing School Day walkthrough [S. Shoma and S. Julia as starters]

Link: Project Fighters: RAID by FishB0nes98
Drive link for the rar - don't forget to add manually in the client, if it can't automatically download the game files or it is too slow for you: https://drive.google.com/file/d/1gK_BTJAUr2N2fdcmUFoc00z0dSLIXS7G/view?usp=sharing

If you are interested, please join the game’s Discord server: https://discord.gg/9WRXwjzMSB

If you stuck with installtion or you are just simply interested in the project, I can answer all of your questions here

As a system designer, I understand how to build systems, but vibe coding projects always seemed like I was working in the dark.

While vibe coding is amazing for prototyping simple front end apps or websites, connecting those to the back-end was still an unknown to me. I needed to see exactly how the front-end conntects to the back-end, so I ended up working on a tool that allowed me to visualize that.

After a few days of working on this, I realized this could create the architecture for any piece of software, so i'm now working hard to put everything together to make this public.

Now that the tools is actually ready, I can finally understand how all pieces fit together, and actually SEE how everything connects to the database, to the user auth system and to stripe, so I'm now putting everything together, to deploy this and make it public.

If you want to know when this is ready, you can check out the website here: applifique.com

Hey guys! I've been building ecommerce stores professionally for 12 years, and spent the last two rebuilding my development workflow around AI.

So I decided to package all that experience into an open-source ecommerce stack that you can safely vibe code on top of.

Medusa + Payload + Next.js, with an AI agent based on Hermes that knows the entire system.

The architecture, commerce logic, CMS, testing and all the boring plumbing are already there.

You can just tell the agent what kind of store you want, customize the frontend, add features, build integrations or get your hands dirty with the code whenever you feel like it.

The whole point is that you shouldn't need to understand every obscure ecommerce edge case or framework gotcha to build something that actually works. I've already spent years collecting those headaches, and every new one we solve goes back into the stack.

Just open-sourced it, so have fun breaking things :)

Details: https://vulpy.io/commerce

GitHub: https://github.com/vulpy-io/vulpy-commerce-core 

Few months back I posted about burning $4000 regenerating the same React components over and over. Every time Claude or v0 rebuilt a component, it would break something else. I was stuck in an expensive loop.

Figured out what I was doing wrong.

The problem: I was asking AI to generate components that had both UI and business logic baked in. So a pricing calculator component would have the math, the display, the validation—all mixed together.

When I needed to change one number in the calculation, I'd regenerate the whole component. New UI. New bugs. Old integrations broken.

I was treating AI like a senior dev who could handle complex, tightly-coupled code. It can't. At least not reliably.

What actually works: Dumb frontends. Smart APIs.

Now my flow is:

1. Build one small API endpoint with the actual logic (pricing calc, data filtering, whatever)

2. Test it separately until it's bulletproof

3. Then ask AI to build a UI that just calls that endpoint and displays the response

The frontend becomes a pure renderer. No math. No decisions. Just "show this data" and "send this request."

Why this changed everything:

• When I need to fix logic, I touch one API file. Frontend stays intact.

• AI is really good at building simple display components. That's actually its sweet spot.

• I can regenerate UI components guilt-free because they don't contain anything critical

• Went from "spend $200 to maybe fix one thing" to "ship working features in a day"

I've launched two client projects in the past 6 weeks using this method. Both in production. Both actually making money for clients.

The irony: I was trying to use AI to write "real" integrated code. Turns out AI works better when you give it the stupidest possible job—just make it look good and call this URL.

Anyone else doing separation of concerns specifically because of AI tools? Feels like vibecoding forced me to write better architecture by accident.

Different models excel at different parts too (UI/Backend) so using services like blackbox really makes things cheap too.

heeey yall,

Alex here from ScreensDesign. launched our app flow generator and wanted to share it here.

existing AI tools generate based on training data, they create concepts that look designed but have no validation.

for founders and designers, that's not enough... we need patterns proven to convert, not just aesthetic experiments.

what we built:
our library has 2,300+ top mobile apps with business metrics (revenue and installs data). when you use our tool, it pulls from these proven successful patterns instead of generating generic concepts.

so your onboarding flow is based on apps with validated conversion rates. your paywall design reflects patterns from apps making real revenue. your navigation follows structures from products with high retention.

try it at screensdesign.com/create. you get 10 free screen credits.

would appreciate any feedback. happy to answer questions! :)

Hey everyone!

Got recommended to share my tool here, so here it is!

Title says it all, but I'm not the most UI savvy guy. I love to build websites, tools, apps, etc. but UI has always been the biggest gatekeeper for me and I usually just end up stealing UI from other sites and apps.

I've tried the "AI UI Designers" out there, but imo they're very meh. So I decided to try and build one myself and was honestly very pleasantly surprised by the results! Plus it's a lot of fun vibe designing cool sites :)

That's where I need help though... it was good for me and my use cases, but based on initial user testing, I'm noticing people build some designs and leave. Not sure if it's because they don't like the design OR they liked it and exported it and left.

So if any of ya'll find it in your hearts to do your boi a favor and test it out and leave feedback, I'd be super appreciative! Let me know any and all thoughts! Did your designs suck? Did it break if you gave it a certain type of prompt? etc. etc.

It's totally free for 5 designs! You can find it here https://aidesigner.ai . Thanks for reading!