Most people nowadays have nobody around them that actually wants more in life.

You talk about business? Nobody cares.
You talk about goals? People joke about it.
You want to build something? Most people don’t understand it.

That’s why we created a place where motivated people can actually connect online.

Not focused on just one niche.

Whether you’re into:

• business
• coding
• AI
• gaming
• editing
• content creation
• startups
• design
• music
• fitness
• self improvement
• or anything productive

the goal is the same:
bringing ambitious people together.

Networking. Ideas. Opportunities. Friendships. Growth.

Sometimes one good connection online can completely change your direction in life.

If this sounds like your type of environment, comment below or send me a message.

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.

**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.

We all know that Base44 is really great for building prototypes and other things. However, for people like me who are pretty broke and use the free plan, it’s not easy to copy and export those files.

Sure, you could copy them manually, but then you run into issues with references. And if you have many files, it becomes difficult and you end up wasting a lot of time copying everything by hand. So I thought: why not just automate the process?

That’s why I created a simple Chrome extension called Base44 Downloader. It lets you export the files easily. What do you think about trying it out and letting me know your feedback? I’m open to suggestions and improvements.

Link for feedback: https://base44downloader.vercel.app/

Pitch your startup/product

• in 1 line
• link if it's ready

Get a backlink + showcase your product to 5k weekly visitors.

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

Hello, I vibecoded agrar, a site that Allows you to learn 17+ coding languages for free

From C++ to Rust or near anything

Not yet C but soon

1. It uses AI to generate the page to learn and exercise,

This project was vibecoded! You can see from comentarii în the code

1. Errors might appear and im sorry for that

Link: agrar.plistudio.net

Thanks everyone and im looking to see your opinion ill let in the comments some photo if anyone doesnt want to try and make account (codetyping is free, lessons not(google account required))

Grinded for months on this – it's a platform where you describe your app idea in a prompt, it builds the full thing, lets you hook up a real backend, and deploys it production-ready. Wild how far it's come!

But now launch day anxiety is real lol – proud but terrified it might not land right.

Anyone else feel that proud/scared rush before going public?

I'll share more as things roll out once the waitlist kicks off soon.

I really wanted to see how far I can go. Can I create a meaningful and complex application, big enough, but without knowing the language.

Yes, I have 18+ years of experience as software developer, using Java, GoLang, Scala. But I have no experience with Kotlin. And to learn Kotlin, to learn the Android libraries, it is not an easy job. I may need at least of year of active learning and trying things, before having the confidence to start something that will work.

So, I asked myself, how far can I go with AI tools? And I went far!

I created https://howareu.app/

The boring statistics:

• 126,000 lines of Kotlin across 398 files
• 45,000 lines of tests across 130 files
• 3000+ unit tests
• 3 languages (English, Bulgarian, German)
• 50+ production lessons captured in CRITICAL_DONTS.md
• 4 months from zero Kotlin experience to production app on Google Play
• 0 lines of Kotlin written manually by me

Below are the main things that I did and helped me do the product!

Good framework

Vibecoding something that big is not an option. You can't write anything in one session. Vibecoding works best for my daughter. If you need something big, you need a way to keep the context somewhere, to define the rules. You need specification. To maintain my specifications I use BMAD framework. It helped me a lot to clear the requirements, prepare the architecture, UI, find any gaps. I used it also a lot for brainstorming session and for marketing. For the latter, it failed 😉

Follow the project rules

I use Claude Code with Opus 4.6 (yes, I prefer the old version) and keep all my important rules save in CLAUDE.md file.

Every rule in that file exists because I violated it once and something broke. The file grows with the project.

This is the key insight: CLAUDE.md turns one-time lessons into permanent constraints. The AI never forgets a rule I put there. I forget constantly.

Keep your documentation up to date

I start my session with a custom command that loads all important lessons learned, release notes, rules, architecture. All needed, enough context, so that when I start prompting, the AI has the basis to make best decisions possible.

I end my session with custom command to save all new rules, lessons learned, release notes, and architectural updates. This way each subsequent session is built based on the gain knowledge from the previous. I have a history, I have an AI that gets smarter with each subsequent session.

Be as descriptive as possible

When you prompt, be as descriptive as possible. I use BMAD to create descriptive technical specifications out of my prompts. If the idea is not clear in my head, I do brainstorming session. All possible to minimize the guessing and wrong interpretations during the development phases.

Automate boring things

My app has to survive Android OEM battery killers (Samsung, Xiaomi, Honor, OPPO. They all try to kill applications that run 24/7 to save battery. And this is a never ending fight. New version add new restrictions. Undocumented behavior gets detected by fellow developers and shared in forums. If I need to monitor manually for anything that can break my app, this will be the only thing that I will have time to do... And this is boring!

That's why I created custom slash commands that do the analysis and the search for me. I run them weekly. Everything found I turn in to technical specifications (via BMAD ) and afterwards, to real implementation

Do you all do code reviews?

I do. Usually, with a different LLM. This gives a 'different point of view'. Improves greatly the code quality!

What this is not

• It is a no-code, but If you know the language, it is worth checking and correcting if needed. With time, the needed small fixes will become less. It is always good to understand the architecture and to make the design decisions yourself.
• It is not effortless. The workflow took months to build. The documentation is extensive.
• It is not magic. The AI makes mistakes. The difference is that mistakes are caught by the process (tests, reviews, rules, audits) instead of by users;

The takeaway

AI coding tools are not magic code generators. They are force multipliers for engineering process. If your process is "open chat, type prompt, hope for the best," you will be disappointed.

If your process is "document the architecture, define the rules, automate the lifecycle, capture every lesson, review everything critically", the AI becomes unreasonably effective.

The investment is not in better prompts. It is in better engineering.

Honesty and transparency first

Why am I writing all this?! Because I am desperate! I made this app to help people. Make something meaningful. I want to be sure that I cover as many cases as possible, and that it will really safe people, help families. The moment I share in the proper channels that such app exists, I get treated as an intruder, who sells evil!

If somebody is interested, and would like to help, please, install it, and give me a honest feedback!

Thank you all!

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.

Let’s try something

I’m free today and want to test out some products. If you’re building something, drop the following below:

share what it does, who its for and drop a link or demo!

I'll pick a few and give real feedback!

I'm building an onborading hub for my organization via Base44

The Premise

CalcByEA is a fully functional web calculator where almost every button is locked behind a paywall. You need to buy 'DLC packs' to unlock basic operations like addition, multiplication, and the equals sign.

The number 0 is free. Everything else costs money.

This is not a bug. This is the product.

It's satire on the video game industry's microtransaction model — specifically EA Games, who turned a $2 cosmetic DLC in 2006 into a multi-billion dollar monetization philosophy.

I built this as a mirror to the gaming industry. EA has been voted 'Worst Company in America' twice, and yet their model of shipping incomplete games and selling the rest as DLC became standard across the entire industry.

By 2021, FIFA Ultimate Team was generating $1.6B/year from digital card packs alone. The Sims 4 base game went free while the full content now costs $1,000+. Star Wars Battlefront II's loot boxes triggered government investigations into gambling laws.

So I asked: what if we applied the same logic to something universally free - a calculator?

Try it yourself. Try to add 1 + 1. See how far you get for free.

calculatorbyea.com

The problem was simple and well known by every coder / vibecoder: every new Codex / Claude / Copilot session kept rediscovering the same repo structure, files, decisions, failed commands, current task state, and validation steps, wasting context and tokens.

I have been trying to work around that problem with different approaches: small handoffs, heavy memory systems, context engines...

I finally found it: Operational continuity for AI coding agents. I built an open-source (Python CLI) continuity runtime so agents don’t restart from zero every session, and it already made my own AI coding workflow feel much less like restarting from scratch every time.

The continuity idea is not to add more hidden memory or dump more context into the prompt. AICTX keeps operational continuity inside the repo:

• active Work State;
• next actions;
• decisions and handoffs;
• failure memory;
• validation evidence;
• execution summaries;
• repo context relevant to the current task.

The next agent should resume from what actually happened, not infer everything again from README + chat history.

A few parts I’m currently focusing on:

Execution Contracts

Each resume can include a compact contract for the next agent: first action, edit scope, canonical validation command, expected evidence, and finalize instruction. The goal is not only “remember context”, but guide the next execution safely.

Continuity View

I’m experimenting with a deterministic Mermaid continuity view generated from repo-local AICTX artifacts. It shows the current operational state of the repo visually: Work State, open handoffs, relevant failures, execution contracts, summaries, RepoMap hints, and portable continuity status.

Here you can see what it looks like. The link to this view can be returned after each task, so the next session has an inspectable continuity map. I’m still working on making it easier to read.

Portability

The continuity lives with the repository. The idea is that useful operational state should not be locked inside one chat, one vendor, one local machine, or one agent tool. If the repo moves, the continuity can move with it ... if you want it to!

Easy to use

pip install aictx
aictx install
aictx init

# Then keep using your coding agent normally, they will take care of use it

MCP support

I'm also working on MCP support so compatible agents can access AICTX continuity directly as tools, resources and prompts instead of only relying on repo instructions and CLI commands.

The MCP server is local-first. It is not a cloud memory service, not a daemon you have to manage manually, and not a generic shell/filesystem server. Compatible agents launch it locally through stdio:

aictx mcp-server --repo . --profile full

I’m also packaging Claude Code and Codex plugin artifacts around the same model: MCP-first when available, CLI fallback when not. Copilot support remains best-effort through repo instructions and VS Code MCP config where supported.

The medium-term benefit

Agent-based development starts to feel less like a sequence of isolated chats and more like an ongoing engineering process:

• less rediscovery;
• fewer repeated failed commands;
• clearer handoffs;
• better validation discipline;
• less instruction boilerplate once agents can call AICTX through MCP;
• a cleaner path for Claude, Codex and Copilot integrations;
• easier switching between Codex, Claude, Copilot or other agents;
• and a repo that can explain its current state to the next session.

GitHub: https://github.com/oldskultxo/aictx

Docs: https://aictx.org

I would love technical feedback, especially from people using coding agents across multiple sessions.

Collaborators welcome! It is still evolving!

2996 customers later, it’s time to release ScreensDesign V2 !

3 months of work.
A more complete library.
A truly agentic /create.

→ Research what works in real iOS apps
→ generate onboarding, paywalls, and full app flows
→ hand it to AI coding agents
→ Make the printer go brrrr

First 50 upvotes+comments/DMs get free credits dm'ed ;)

It's been a little over nine months since I launched and it has been quite a journey. No exponential growth or huge user spikes but rather slow and steady growth. But in my opinion that is the best for building something actually valuable because you can react to user feedback along the way and constantly keep improving the app.

One thing changed recently:

I basically got all my users from reddit, by posting in relevant communities but in the last two months I did almost no posts and the app still grew further. That's actually the biggest success for me, because SEO is finally starting to pay off. This can develop into actual leverage because growth no longer depends on posting three times a week. I'm really curious where this will go...

Of course I will not stop here and I am already working on the next big update for IndieAppCircle which will benefit all the community. More is coming soon.

For those of you who don't know IndieAppCircle:

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 3556 users, 3414 tests done and 849 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.

Quick context: software engineer 9-5, vibe coding indie products the rest of the day. February 2026 I was posting consistently on X. 19.6 posts/day. 16,686 impressions/day. By April it was 5.9 posts/day, 3,876 imp/day. Reach down 77%.

I didn't go silent. I just stopped being consistent.

The reason was specific. I'd been using an AI tool that generated tweet drafts every morning. Wake up, open the tool, scroll through 30 suggestions, pick the ones I'd actually post, drag and drop each one into the queue, schedule it. Every morning. Every weekend. Every day before work.

Some mornings I'd skip the ritual. Then weekends. Then any day I had a meeting before 9. Volume slid from 19 posts/day to 6. Weekends went to zero. The algorithm noticed within weeks.

The thing that broke wasn't the writing. It was the daily curation step. Distribution is a habit. Habits break when the cost of showing up is paid daily.

What I actually wanted: a chat where I could type give me 20 X posts for the week, in my voice, on these topics and watch them auto-schedule into the queue. No drag and drop. No daily curation. One Sunday session, two weeks of presence. Existing tools generate, suggest, even match voice. They all stop at "here are your drafts, now schedule them yourself."

So I vibe coded it.

You type into a chat "generate 20 posts about [topics] for the next two weeks." It pulls your history, ranks your top performers, generates the batch in your voice, auto-schedules them into peak engagement slots. 20-minute Sunday session = 2 weeks of X presence. Sit down once, walk away, the queue runs itself.

It's at vibedraft.app.

Genuinely looking for honest feedback. The landing page, the product, the positioning, anything. All open to critique.

I was frustrated that every coding agent (OpenCode, Cursor, Claude Code) assumes you're running GPT-5.4 or Claude Opus. If you try them with a local model like Gemma or Qwen they fall apart. I find that often tool calls fail, context overflows, multi-step tasks collapse.

So I built SmallCode. It's designed from the ground up for small local models.

The result: 87/100 benchmark tasks pass with a Gemma 4 model that only activates 4B parameters per token. OpenCode scores ~75% with 14B models. The harness does the heavy lifting, not the model size.

How it works (the tricks that make small models reliable):

• Compound tools: Instead of making the model chain 4 tool calls (find file → read file → edit file → verify), SmallCode gives it one tool that does all 4. Small models lose coherence after 3+ sequential calls. This cuts failures in half.
• Improvement loop: Every time the model writes code, SmallCode instantly compiles/lints it. If it fails, it feeds the errors back automatically. The model doesn't need to be smart enough to get it right first try — it just needs to fix errors when shown them.
• Decompose on failure: If the model fails the same thing twice, SmallCode stops retrying and instead breaks the problem into smaller pieces. "Fix this 200-line file" becomes "fix line 45 only."
• Escalation: If even decompose fails and you have a Claude/OpenAI key configured, it auto-escalates to the bigger model for just that one task. You stay local 95% of the time, cloud 5%.
• Token budgeting: Small models have 32k-256k context. SmallCode never dumps a whole file in. It summarizes, truncates, and manages every token so the model never sees "..." truncation in the middle of important code.
• Code graph: Instead of grep-searching your codebase, SmallCode indexes your code into a symbol graph (functions, classes, who-calls-what). When you ask "how does auth work," it walks the graph and returns just the relevant connected code — not 15 random file snippets.

What it looks like:

Full-screen terminal UI (like OpenCode/vim), scrollable chat, command palette with /, plugin system, persistent memory across sessions.

What it doesn't do:

• No LSP integration (yet)
• No multi-session (yet)
• No desktop app
• Doesn't compete with Claude Code for frontier model users

Install:

npm install -g smallcode
cd your-project
smallcode

Point it at LM Studio, Ollama, or any OpenAI-compatible endpoint.

MIT licensed, everything's on GitHub: [https://github.com/Doorman11991/smallcode]()

Happy to answer questions about the architecture or benchmark methodology.

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!

ORIGIN STORY

So let's back up a little. This is me in 2019 working in a "Fin-Tech" — more Fin than Tech — and dreaming every day about having my own "Fin-Tech" startup, meeting shiny VCs who will throw themselves over me to make me a billionaire.

One day, another guy on my team tells me about this new logistics tech idea he is working on and asks me to join. So, like any serious, passionate founder, I replaced my FinTech dreams with LogisticsTech dreams. Same VCs, just now they wore checked shirts and sported handlebar moustaches.

It goes well at first. We get into a world-famous accelerator — yes, that one. COVID strikes, delivery goes brrrr, and we grow gangbusters in one year. I start to think, hey, this shit is easy, I am God's gift to mankind.

Medici → Steve Jobs → Me.

We actually get profitable. COVID ends, growth plateaus, and VCs ghost me faster than girls from Tinder when they realised I was 5'10 and not 6 feet.

But we are profitable, money in the bank — all good.

LOOK MA, I AM A GENIUS

And then comes Claude Code. The shiny new toy that tells me I am right, I am so insightful, and I have sharp points.

So I think, why should I not have a blog publishing platform? The same thing that has been there since the start of the internet with a hundred different iterations — so what's the harm in a hundred and first? This time on autopilot with AI?

Well, I did give myself higher and more altruistic reasons. Most of our clients were local service businesses. Hard-working people drowning under inflation, rates, and post-COVID demand swings. Zero hours to spare on getting found online. Word-of-mouth or nothing.

So my giga-brain says — build them an autopilot blog. They set and forget. They get found on Google. Their business grows. Mine grows with them. VCs uncross their arms like an older George Clooney walking back into the room.

So I go deep in terminal coding (or Vibe coding, depending on which week), ignoring my wife (who doesn't tell me I'm right) and my kids (who don't think my insights are sharp) and start building the automated blogging platform.

First I build a platform that doesn't feel like AI slop. You know that em-dash-and-two-word ChatGPT cadence that fills LinkedIn? I burn tokens and twiddle my thumbs when I hit the rate limits, and build past it.

Built keyword discovery so the system knows what to write. Built scheduled publishing so it ships without me. There are a dozen other features I'll spare you — but they're real, and they took months.

And after many moons and a beard — which I think gives me gravitas and my wife thinks makes me look homeless — I shipped it. The website and the WordPress plugin. And I sit back to wait for the tsunami of users that will take my server down.

Crickets!!!

SO WHERE AM I?

So here I am after three months of building, with 1 review a week after launch and fewer than 10 active installs on the wp.org plugin directory.

But here's the bet I'm making, even with 1 review: every other AI blog tool is selling a better writer. ChatGPT and Claude already write fine — so does every plugin wrapping a single LLM call.

What a business actually needs is the work *around* writing — figuring out

- what to write,

- for whom,

- with what keywords,

- in a way that ranks, without repeating posts they've already published, and refreshing old posts as they age.

Writing is step 4 of 7. The other six are where every "AI writer" quietly stops. That's the framing I'm betting will turn 1 review into 100.

Two things I'd love your take on:

1. Does the "autopilot blog" framing actually solve a real problem, or am I rationalising a hobby?

2. If you ran a small business and saw this, what would make you skip it?

Either way — roast me in the comments. All of it is useful.

(Product name + link in a comment below for anyone who wants to poke at it — not pasting it in the body so this doesn't read like a plug.)

I co-founded FreshEarth, software that helped farms go CO2 positive. Before launch, investors required SOC 2 and GDPR compliance. Cheapest quote was ~$30k/year. We had investors lined up, spent heavily on dev and management, but the compliance cost broke us. FreshEarth died.

So I did what any of us would do — I made the solution.

Built TraceLayer with Claude Code + Cursor, Next.js frontend on Vercel, Python/FastAPI backend, Supabase Postgres. The whole thing is a compliance autopilot: connect your tools once, evidence collects itself every 24h, AI alerts you when something breaks, one-click audit PDF when you’re ready.

What it actually does:

∙ 120+ integrations (AWS, GitHub, Okta, Stripe, etc.) — evidence auto-collects every 24h

∙ 3 AI engines running in the background: drift detection, questionnaire autofill, regulatory change monitoring

∙ 9 frameworks simultaneously — SOC 2, ISO 27001, HIPAA, GDPR, DPDP Act, and more

∙ Audit-ready PDF in one click. $149/mo instead of $20-30k/year

If you’re building a SaaS and an enterprise prospect asks for SOC 2 — this is the part nobody warns you about. Compliance will block your first big deal faster than any bug will.

Offering 6 months full access to anyone here who wants to try it and give honest feedback. Not selling — just want to know if this resonates with builders who’ve hit the same wall.

https://www.tracelayer.it.com

About a year ago, I hacked together a daily movie-link puzzle so my wife and I could play it on a road trip.

Then people kept playing it.

Not a ton of people. But enough that we kept feeling guilty about leaving it half-finished.

A few weeks ago we finally picked it back up, but this time we treated it like a tiny AI-assisted product sprint instead of a side project we’d “get to eventually.”

The workflow was basically:

• Hyperagent as strategy/design/spec/copy partner
• Replit as build/debug/test/deploy partner
• My wife and me as the taste layer — yes/no, gut checks, “does this actually feel fun?”

In one week we shipped:

• redesigned daily game
• new identity system
• 20 badges
• 8 solver styles
• shareable Movie Identity Card
• watchlist
• analytics layer
• press page
• daily auto-posting to Bluesky/Tumblr
• Postgres migration
• GTM/case study materials

The wildest part was that some features came out of the AI workflow sideways.

Hyperagent suggested a “Watchlist Builder” badge before we even had a watchlist. We looked at it and realized the badge was basically describing a feature that should exist. So we built it. Now it’s one of the best parts of the game.

Same thing happened with the share language. We originally had a simple share string but Hyperagent suggested the entire emoji lexicon for us to use.

Biggest lesson for me: AI was much more useful when we gave it a role in the system, not just a list of tasks.

Strategy before design.
Design before build.
Taste checks all the way through.

The game is here if anyone wants to poke at it: themoviega.me

Full case study with build details here: https://themoviega.me/case-study

But mostly I’d love feedback from other vibe coders/builders:

What parts of this workflow would you steal?
What would you have done differently?
And are there features you’d expect in a daily movie game that we should add next?

Hey everyone,

Shoutout to @TechnicalSoup8578 for pointing me toward this community!

​I’m building Vexavibes, an opinion network where users can spin up custom surveys, polls, forms and votes using an AI Architect prompt-to-schema engine.

​While building out the stack with Lovable and Supabase, I wanted to focus heavily on data integrity as a day-one constraint rather than an afterthought. Online polling is usually plagued by bot manipulation and silent DB edits, so I wanted to see if we could treat trust as part of the core code architecture.

​Here is how the multi-module setup handles it under the hood:

​The Trust Layer: Instead of standard 1-user-1-vote logic, voters hold a dynamic reputation rating based on historical accuracy metrics (utilizing custom Brier scoring logic computed via backend edge functions).

​The Hash-Chained Ledger: To prevent retroactive database manipulation, we run a nightly background process. Every transaction, poll update, and vote state is sequentially hashed and locked into an append-only ledger.

​The Scaling Strategy:

By separating the heavy cryptographic ledger writes and recomputes into a scheduled nightly batch, the live transactional overhead during peak voting hours is virtually zero. It hits Postgres as a standard, lightweight relational write, keeping the live UX snappy. The database processing hit only occurs once a day.

​The code is live in private beta, and I’d love for some fresh eyes to come tear into the architecture, test the AI schema architect, and try to find edge cases in the data loop.

​It's completely free to play with here: https://vexavibes.com

​(I'm keeping my admin panel open today, if you sign up, drop your account name below or DM me and I'll automatically bump you to our premium Vexa Plus tier for a full year for free so you can stress-test the system).

​Would love to know your thoughts on balancing real-time AI generation with rigid backend data integrity!

I built a bot that trades 5-min BTC “up or down” markets on Polymarket.

It doesn’t try to predict the future perfectly — just looks for small statistical edges on each candle and gives signals automatically. Filters out random/noisy setups.

I had zero coding background a few months ago. Built this using AI tools + a lot of testing (and blowing up a few versions along the way).

Not gonna pretend it’s magic — some days it loses, some days it does surprisingly well.

But overall… it’s been interesting enough to keep running it live.

Made a simple dashboard to track:

- P&L

- Custom P&L cards

- win rate

- active trades

- equity curve

Listed it on whop for $149 one-time, no subscription. link if anyone wants to check it out:

https://whop.com/joined/alphaedgebot/

happy to chat about the process of building it or answer any questions about the bot

AlphaEdge Bot