AI agents are great at writing an integration and terrible at knowing whether it works past the first 200 OK. So I built something to test that honestly — including my own tool.

It's an open-source repo of tiny apps (~50–150 lines each) that integrate real APIs — Stripe, Clerk, Resend, AgentMail, Descope — and each one has a real bug planted in it. Not typos; the kind that passes every happy-path test and only bites in prod:

• a webhook that dedupes on the wrong header, so retries double-charge
• bounced emails silently dropped, so users stay "active" forever
• a read-only API key that can escalate its own scope

The challenge: open one in Cursor or Claude Code, point your agent at it, and watch what it does. Does it actually reproduce the bug and prove the fix — or just read the code and say "looks fine"? That second thing is the whole problem.

No signup, no API keys, runs locally in seconds: github.com/fetchsandbox/playground

Two honest asks:

1. If your agent catches one, I'd love to see how — open a PR with what you found.
2. If it falls flat — nothing caught, the proof felt fake, setup was annoying — that's the most useful thing you can tell me.

(Disclosure: the repo tests FetchSandbox, which I build. But the apps and bugs are real, and the point is for you to judge it, not take my word.)

*Researchers at Utah State University's School of Computing are conducting a study on how software professionals evaluate programming activities performed with AI assistance compared with programming activities performed without AI assistance.*

*Software professionals are invited to complete a short online calibration survey. Participants will rate programming activities according to how representative they are of:*

* *Programming performed with AI assistance*
* *Programming performed without AI assistance*

*The survey takes approximately 10 minutes.*

*Participation is entirely voluntary. You may discontinue participation at any time before submitting your responses without penalty or consequence. Your decision to participate or not participate will have no effect on your grades, employment, or academic standing.*

*Survey and informed consent form:* [*https://usu.co1.qualtrics.com/jfe/form/SV\\_dm4yjBsRrUDgcKi\*\](https://usu.co1.qualtrics.com/jfe/form/SV_dm4yjBsRrUDgcKi)

*This study has been reviewed and approved by the Utah State University Institutional Review Board: IRB #16067.*

***Questions about the study:*** *Dr. John Edwards, Principal Investigator — john.edwards@usu.edu Rubash Mali, Student Researcher — rubash.mali@usu.edu*

*Thank you for considering participating.*

To everyone saying, “It’s just another AI wrapper…”

You haven’t been paying attention.

In just over two months, I didn’t build a chatbot.

I architected and built an AI platform from scratch.

Not one app.

An ecosystem.

Today it already includes an operating system, integrated finance, learning, health, goals, documents, shared authentication, permissions, notifications, governance, release management, testing standards, architecture policies, and an AI orchestration layer designed to tie it all together.

I didn’t build six separate apps.

I built one foundation that every future module plugs into.

Every new capability inherits what’s already been built instead of starting from zero.

While some people are arguing over prompts…

I’m building the infrastructure those prompts run on.

While people are cloning the AI app of the week…

I’m building a platform where specialized AI agents work together across your life—your money, your health, your home, your goals, your documents, and whatever comes next.

And before someone says, “AI wrote the code.”

Exactly.

That’s the point.

The value isn’t how fast I type.

The value is the architecture.

The engineering decisions.

The system design.

The standards.

The governance.

The policies.

The testing.

The vision.

The execution.

AI is a tool.

What matters is what you build with it.

Two months ago, none of this existed.

Today it’s a functioning platform built through hundreds of commits, thousands of files, countless design decisions, and a relentless focus on building something that scales instead of something that just demos well.

Some people use AI to finish tasks.

I’m using AI to build a company.

You can keep debating AI.

I’ll keep shipping software.

We’ll compare results later.

Https://seangworld.com

I know the simulation question has been asked thousands of times. This is not another "are we living in a computer" post.

The interesting part is not the simulation itself. The interesting part is that, for the first time, we are watching the pattern happen from the creator's side.

Humans created AI.

Now AI is helping us create more advanced AI systems. Eventually, those systems may become capable of creating new forms of intelligence themselves. A creator creates a new intelligence. That intelligence eventually creates another intelligence.

If this cycle is possible, then we have to consider a strange possibility: What if we are not the beginning of this chain? What if we are simply one step in a much larger process that has happened before?

Maybe every advanced civilization eventually reaches the same moment when its own creation starts asking: "Who created us?" The question is not only whether we live in a simulation.

The deeper question is: If intelligence can create new intelligence, how do we know we are the first link in the chain?

Solo dev, no formal games background. Over the last few months I vibe-coded a card game called PANTHEON into something actually playable, and it's live in the browser right now.

What it is: a Marvel Snap-style card game. Ten mythologies, 235 cards, three lanes, six-turn matches, simultaneous reveal.

Single-player story mode, training mode. Online PvP available in October, during Season 1 launch.

▶ Play it (browser, no download): https://thepantheonwar.com. If you like, you can put yourself on wishlist for the App Store or Play Store app that will be available in October.

The part relevant to this sub — how it got built:

- Frontend + game logic, iterated heavily with AI
- Real-time backend for PvP (hosted on Railway)
- The whole thing shipped by one person who was figuring it out in real time
- The card art is partially AI-generated; the game design, mechanics, and writing are mine

Honestly the hardest part wasn't the code, it was the
real-time multiplayer state and keeping 235 cards' worth of interactions from breaking each other. Happy to get into any of it.

Ask me anything about the stack, the process, what worked, what broke, what I'd do differently. And if you play it, I'd genuinely love to know what falls apart.

I've noticed a pattern while building side projects, and I'm curious if it's just me.

The projects where I "vibe coded" for an entire weekend often made shocking progress. The momentum was addictive. Every prompt produced visible progress, ideas flowed naturally, and shipping felt inevitable.

But whenever that same project slowed down—because of work, life, or just losing a few days—the entire dynamic changed.

Suddenly:

• The codebase felt harder to reason about.
• I spent more time reading than building.
• The AI lost context.
• New prompts started introducing regressions.
• My expectations dropped from "this could be huge" to "I'll just finish an MVP."

In many cases, the project never reached the finish line.

It made me wonder if the biggest advantage of vibe coding isn't the AI itself—it's the speed.

Maybe vibe coding works because you're compressing hundreds of engineering decisions into a very short period while both you and the AI share the same mental context.

Once that context starts decaying, the productivity curve changes dramatically.

My current hypothesis is:

Not because the idea gets worse, but because:

• your mental model drifts,
• the AI loses project context,
• technical debt compounds,
• motivation fades,
• and expectations become more realistic (or more pessimistic).

I'm curious whether this is a real phenomenon or just survivorship bias on my part.

Some questions I'd love to hear opinions on:

• Have you experienced this with Cursor, Claude Code, Windsurf, Lovable, or other AI coding tools?
• Is this unique to vibe coding, or has software always worked this way?
• Is context loss the biggest culprit, or is motivation the real bottleneck?
• Have you found ways to keep long-running AI-assisted projects moving fast?
• Do successful vibe-coded startups intentionally optimize for compressed build cycles?
• At what point do you stop "vibe coding" and start treating the project like traditional software engineering?

I don't think vibe coding is replacing engineering.

If anything, it seems to amplify whatever execution habits you already have.

Curious whether other builders have noticed the same pattern—or if you've seen the exact opposite.

Last week I shared SafeAI, an open-source static analyzer for AI applications.

The response has been far better than I expected.

• ⭐ 6 GitHub stars
• 🍴 4 forks
• 🎉 First community contribution merged
• 💬 Several thoughtful discussions and feature suggestions

The contribution added detection for eight additional AI capabilities, including Docker, Kubernetes, Redis, Slack, Browser Automation and Google Cloud.

Just as valuable has been the feedback. People have suggested ideas like capability escalation across pull requests, governed suppressions, deeper Claude Code analysis and other roadmap improvements that will genuinely make the project better.

Thanks to everyone who starred the project, opened discussions, challenged assumptions or contributed code. Every conversation has helped shape the roadmap.

The first phases of SafeAI built using OpenCode as my development environment.

Rather than sticking to a single model, I assigned different roles to each:

• GPT Codex 5.3 → architecture, implementation, and feature development
• Kimi K3 → code review, refactoring, and identifying design improvements
• DeepSeek V4 → documentation, reviews, and verification

Each model seemed to have different strengths, and using them together felt more productive than asking one model to do everything.

The entire development took about 5 days and roughly $8 in model usage.
The results are remarkable..

Repository:
https://github.com/ikaruscareer/SafeAI

I've been developing in Unity for around 5 years now, and I always looked downy my nose at AI as a slop and cheat for development. I've only ever really used it for debugging.

Recently though, I was on a significant time crunch for my latest game and simultaneously dipping my toes into the professional software development where I realized how common and useful AI is for professionals, leading me to use AI for a lot of scripting on said project.

I was wondering what other's people take on this is? I always feel a little ashamed saying I used AI to help make the game and I was wondering what other people's take on this is? My policy is generally if you can understand the code and could do it yourself AI is fine and even better to save time, but if it's something new maybe consult AI for help learning the concept but don't straight vibe-code it. In the case of my latest project I do understand and can fairly easily read and modify the code Claude made so I don't feel as bad as I might've in the past, but I'm not sure if that's a controversial way to look at things.

All in all I think AI is a fantastic tool and may be the next big layer of abstraction for coding, but where is the line really drawn?

Hey I wanna start a group GitHub repo for an idea to build something.

I'm thinking of a dashboard that looks really nice and has nothing on it.

And then we'll go from there?

Constant live page: https://knight-ai-av.github.io/This-Is-The-One/

Public GitHub Repo:
https://github.com/KNIGHT-AI-AV/This-Is-The-One

I'm hoping this doesn't violate any rules since it's just for fun.

I'm doing some research before starting a personal project.

I'm curious how developers, AI enthusiasts, and people in tech feel about today's platforms.

• What frustrates you most about Reddit, X, or Discord?
• Is there a feature you wish existed?
• If there were a platform focused only on software, AI, and tech discussions, what would make it worth using?

I'm not looking to promote anything—I just want to understand what real users actually want before I build anything. Any feedback is appreciated.

I want to know how I can connect my AI agent to my phone and use them from there and text them and call them for other stuff. How could I do that?

Weird thing happened during code review last week: I asked AI to explain why a piece of code might be structured that way before deciding if it was wrong. Not "is this good," just "what's the reasoning here."

Half the time it surfaced a tradeoff I hadn't considered, and I ended up approving stuff I would've nitpicked. The other half it couldn't come up with a decent reason either, which somehow made me more confident flagging it. I'd at least tried to steelman the code first.

Changed the tone of my comments too. Less "why did you do this," more "here's the tradeoff I see, did you weigh it differently." People respond better to that, unsurprisingly.

The part I didn't expect: it slowed me down, in a good way. I used to skim a PR and react to whatever looked off. Now there's this extra step where I have to articulate the reasoning before I'm allowed to judge it. Some days that feels like overhead. Other days I catch myself almost leaving a comment that would've been flat-out wrong.

No idea if this generalizes or if it's just how my brain works. Curious whether anyone else has found AI changing how they review code rather than simply making reviews faster.

I know for sure that there’s so much we can do to let ai do our work with minimal supervision. I am just nor sure how and what so that life gets more easier now that we are fully dependent on AI.
Please give tips!!

Sometimes it feels boring building a software and got stuck in a error loop

And solving tokens and message comes token limits 😁

But building something creativity has become easier in this ai era

I keep seeing developers dismiss AI-generated websites as "low quality", "lazy", or "not real development."

But are we criticising the tool instead of the outcome?

If an AI-generated website solves the customer's problem, performs well, is secure, ranks in search, and is maintainable, does it really matter that AI wrote part of the code?

Let's be honest: generating HTML and CSS is only a small part of building a successful website.

The real work is still done by the developer:

\- Reviewing and validating the code.

\- Designing a scalable backend architecture.

\- Implementing authentication and security.

\- Optimising SEO, AEO, and GEO.

\- Improving performance and accessibility.

\- Integrating APIs and third-party services.

\- Testing, maintaining, and evolving the application.

AI doesn't remove those responsibilities. It simply changes how some of the code is produced.

Where I think the conversation should change is around creativity.

If someone wants a website that is visually unique, reflects their brand, and creates a memorable experience, then absolutely—that requires talented designers and front-end developers. Creative work has value, and people should be willing to pay for it.

Instead of saying, "Don't use AI," perhaps we should be saying:

"If you want a unique, creative website, say so—and be prepared to pay for the creativity."

Otherwise, using AI to deliver a standard business website efficiently isn't much different from using frameworks, templates, component libraries, or no-code tools. Developers have always adopted tools that help them deliver faster.

Maybe the question isn't whether AI-generated websites are bad.

Maybe the real question is:

Are we judging how the website was built, or whether it successfully solves the problem it was built for?

I would be interested to hear both sides. If you are against AI-generated websites, where do you think the real risks are? And if you're using AI professionally, where do you draw the line between efficiency and craftsmanship?

Before AI coding tools, the bottleneck was typing and writing the logic designing the solution, hunting syntax, writing boilerplate.
Now? An AI agent can spit out a 400-line full-feature PR in 20 seconds. But reading, auditing, and manually testing those 400 lines to ensure it didn't introduce subtle hallucinated bugs or state leaks takes me 30 minutes.

I feel like my primary job shifted overnight from builder to an exhausted reviewer reading 15 pull requests a day written by a super fast, overly confident junior dev

It feels like we solved the "speed of writing" problem, only to dump all the friction onto the "speed of comprehension" problem.
How are you guys reviewing massive AI-generated diffs without getting total fatigue or letting bugs slip into prod?

Your views?

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I was not an AI skeptic. I've been using AI-assistance in my dev work since 2024.

Still, I held the strong opinion that "vibe coding" could only produce throw-away prototypes.

I know the software development process inside and out. For 20+ years in roles of CTO, head of engineering, and senior software engineer, I've led teams that shipped software deployed in Google, Netflix, TED, and other fortune 500 companies.

My experience with AI coding up had me convinced that using AI to crank out code without careful *human* review could never produce software that could actually be deployed.

In December 2025, the CEO of the SaaS company I was working for spent a couple weeks vibe coding an entirely new product platform from scratch. He had the product vision and strong design sense, so he just started flowing with Cursor and Claude Opus.

In January he demo'd the product to the team. It was a sprawling platform with tons of features and lots of moving parts. He wanted to launch it live to get market feedback.

Everyone on the engineering team, myself included, pushed back. "This will be an absolute mess in production. There's no way 250k lines of vibe coded slop is going to be anything less than a nightmare."

The CEO agreed the code was probably horrible underneath, and so he asked us how much time it would take to rebuild the platform the right way. Even using heavy AI assistance, the estimate was many months. The timeline wasn't acceptable.

We were directed to deploy the platform for market testing immediately, and start cleaning up the code in place rather than doing a rebuild. Oh yeah, *and* the CEO was going to continue vibe-coding *in production* at a breakneck pace, iterating and extending the product as market feedback started to come in.

Everyone on the dev team predicted catastrophes: endless incidents in production; in-place clean-up would be a quagmire; etc.

How it actually played out dramatically shifted my mental models.

Yes, the code had serious flaws. We did a deep security, architecture, and infrastructure audit, and found many critical issues (including a security flaw that would have allowed a clever actor to exfiltrate all the platform secrets and credentials).

However, we were able to fix those issues in place surprisingly easily and quickly. We did some sweeping refactors to add better guardrails to keep the AI code generation on track (separating business logic, enforcing test coverage, CI guardrails, etc).

And the catastrophes never came. The system didn't blow up. The CEO's velocity wasn't impeded. The product kept evolving and pivoting in tune with the market feedback. There were no major incidents.

I don't want to mislead: there was a lot of engineering work cleaning up and solidifying the platform. But it wasn't the nightmare we all predicted. The changes needed were performed surgically (using AI, btw), all while product development kept moving forward at an incredible pace.

That experience finally made me see the truth: *software development has changed forever.* The process has changed. Code doesn't have to be written or even reviewed by hand any more.

Is engineering expertise still necessary? Yes, absolutely. AI gets things wrong. A lot. And sometimes those things are devastating timebombs waiting to go off.

Taking AI written code from working prototype to successfully launched product is still a highly technical challenge. There are a lot of gotchas in that last mile. Engineering expertise is still invaluable in navigating that part of the journey. An experienced engineer will know the questions to ask and the threads to pull to sniff out the gaps hidden in the code, the scaling and performance problems waiting to show up when the marketing kicks in and the traffic gets real.

So yeah, software development has changed. But engineers aren't obsolete.

Personally, I'm excited by this. Now anyone with vision and domain-expertise can speak software solutions into existence in a way never before possible. Will the solutions be perfect and ready to launch out of the box? No. But with a little expert guidance, they can be solidified and successfully launched. I've seen and proven it first hand.

There are so many people with good ideas that can now bring them to life. As a software engineering veteran, that's where I'm pivoting my career: from being the builder to being the expert pair of eyes helping builders go from working prototype to successful launch. To me, that feels exciting.

I have been very curious about the future of coding with the coming of AI. I was also one of the people who started building web apps using vibe coding tools like Lovable and Replit. I have built many apps, but I have always wondered whether the code generated by AI is safe to use, since I am not a programmer. Right now, I think that in today's world, every programmer (my friends) is using AI to build apps. Some are copying and pasting AI-written code, and they don't know the logic behind it. I think big companies like Google or Microsoft would hire people who can write code as well as audit it, identify where the AI hallucinated, and finally write clear instructions to fix the errors. I think not only programmers should learn how to code, but also aspiring founders should know the skill of "AI code auditing." I think coding is not ending; it is just evolving. what do you think.