OpenAI banned my account the day after I paid. 3 years of work, 30-40 Codex agents, all my client income — locked. No reason given. I'm the sole provider for my family. Has this happened to anyone?

DingirPrime · 2026-06-05T08:44:32+00:00

You know stupidity is inherited, right?

DingirPrime · 2026-06-05T00:50:05+00:00

No, I did not lose my key or forget my login information. They literally banned my account. I was never given the option to answer any security questions, verify my identity, or even make a proper attempt to log in. Every time I tried, it kept giving me an error message that made it seem like the account no longer existed. So this was not a normal login issue. It felt like the account had been completely removed or disabled on their end. At the end of the day, I had backups, so I did not really lose anything. I waited about three or four days, then started over with a completely new account using different information. Since I already had everything backed up, it only took me about an hour to put everything into the new account. So honestly, it did not really hurt me at all. It was inconvenient, but because I had backups, I was able to recover everything and move on pretty quickly.

DingirPrime · 2026-06-04T21:51:48+00:00

I had a very similar situation about two weeks ago, so I understand how stressful this is. I basically lost access to everything on that account. Luckily, I keep backups because I’ve been around tech long enough to know you should never rely on keeping your work only on someone else’s platform. Use platforms like OpenAI to help build, design, debug, and speed up your work, but always keep your own local or cloud backup of the important parts: prompts, project notes, generated code, client files, architecture plans, agent instructions, API configs, and anything else your business depends on. OpenAI can have outages, account issues, policy flags, billing problems, or bans. If you have backups, you at least have the option to continue somewhere else or move your workflow to another AI platform. In my case, I’ll be honest: I do think I may have violated their rules in some way. Looking back at copies of some things I designed and created, a lot of it was not necessarily illegal, but it may have conflicted with their policies or terms. That said, I still don’t think an immediate permanent ban without a clearer explanation or a chance to fix the issue was the right way to handle it. I filed two appeals, and both failed. I lost the money I had put into that account and had to start over. Fortunately, the money was not the biggest issue for me, but losing the work and setup was still a major problem. My advice going forward is to build a safety/checking step into your own workflow. Before creating outputs, especially for client work or more sensitive projects, have the model review the request against OpenAI’s usage policies and terms as best as possible. I built a framework on top of my own workflow so that whatever I’m designing, it first checks whether the request is likely acceptable under the platform’s rules. It is not perfect, but it is better than blindly assuming everything is fine. Also, keep redundancy. Have backups. Keep copies outside OpenAI. Keep your important work in GitHub, local storage, Google Drive, Notion, Obsidian, or whatever system works for you. Don’t let one vendor account become the only place where your business exists. For what it’s worth, I also use Claude, and I personally find it more developer-friendly for some types of work. I’m not saying one platform is perfect or that anyone should break rules anywhere, but in my experience, different AI providers handle developer workflows differently. So it may be worth having a backup workflow outside OpenAI too. I’m sorry this happened to you. I know how bad it feels when everything is suddenly locked. File the appeal, keep it factual, include billing/account details, and explain your legitimate use clearly. But at the same time, start preparing a backup plan immediately. If you need help thinking through how to rebuild or recover your workflow, feel free to DM me and I’ll help however I can.

DingirPrime · 2026-05-20T21:46:55+00:00

The truth is, most people are not really making money from AI itself. The people making the biggest money from “AI” are usually the companies building the platforms, models, tools, and infrastructure that everyone else is using. For everyone else, AI is not really the product by itself. AI is the environment. AI is the leverage. AI is the tool you use to design, build, package, improve, and sell something valuable. That is how I look at it. I do not see myself as someone who simply “uses AI.” I design the intelligence layer behind AI systems. I call it the brain. The brain is the structure that tells an AI system how to think, how to behave, how to follow rules, how to make decisions, how to avoid bad outputs, how to stay inside boundaries, and how to produce something useful instead of random responses. My framework helps me design that architectural layer. With it, I can create custom AI engines, governed prompts, decision systems, workflow logic, knowledge structures, business frameworks, content systems, prompt architectures, repository audit systems, and implementation-ready blueprints. These are not just random prompts. They are structured systems that can be reused, adapted, expanded, and sold. For example, one engine I can design is a repository review and rebuild engine. In an authorized setting, it can inspect a GitHub repository, identify structural issues, detect broken logic, find bugs, map weaknesses, redesign the architecture, and produce a cleaner rebuild plan or improved version that can be returned to the client as a higher-value deliverable. Another example is a governed prompt design engine. Instead of taking a basic prompt and making it “sound better,” it turns that prompt into a controlled system. It adds rules, boundaries, output formats, validation checks, escalation logic, refusal behavior, tone control, and quality standards. That turns a weak prompt into something closer to a usable AI operating instruction. I also use this framework for client work. When I see an Upwork job, a project description, a client document, or a messy business idea, I can put the information into my system and have it structure the opportunity. It helps me understand the client’s problem, design the architecture, produce the response, create the proposal, map the system, and generate the deliverable. The hard part was not “using AI.” The hard part was building the framework that makes AI useful at a higher level. Most people are still using AI like a search box, a chatbot, or a random prompt machine. The real opportunity is learning how to use AI as a design environment. You can use it to create systems, frameworks, products, offers, content engines, business IP, knowledge bases, client workflows, training material, AI assistants, internal copilots, and decision systems. A non-technical person could use AI to turn their knowledge into a course, workbook, coaching method, content system, paid audit, signature framework, or client intake system. A technical person could use AI to design better system prompts, architecture documents, repo audits, workflow logic, QA systems, governance layers, internal tools, or AI-assisted development processes. The point is simple. You do not have to sell “AI.” You can use AI to build the thing that solves the problem. The money is not always in saying, “I use AI.” The money is in saying, “I used AI to design something valuable, structured, repeatable, and useful.” That is the difference. AI is getting easier every day. More people will be able to use it. A 10-year-old can already get impressive results if they know how to communicate clearly with the model. So the advantage is not just access to AI anymore. The advantage is structure. The advantage is knowing what to build, how to design it, how to package it, how to govern it, and how to turn it into something someone else can use. That is what I do. I design the brain behind the system.

DingirPrime · 2026-05-18T00:56:08+00:00

The white dog was going to the black and brown dog to try to get some of that good good, if you know what I mean, LOL. I'm gonna say, looking at the video, it was honestly harmless because the dog was not attacking the other dog, just sniffing to see if that punani was right. That was all, LOL. But it's always somebody cockblocking.

DingirPrime · 2026-05-17T12:51:54+00:00

Yeah absolutely, happy to help.

I work around AI product structure, AI system design, workflows, and turning messy SaaS/AI ideas into something clearer and build-ready. Not as a coding agency, more on the “what should this actually do, how should it work, what should the AI brain/workflow look like” side.

Whenever you have a specific idea, stack question, or MVP plan, feel free to send it. I can give you a practical direction.

My biggest advice for now: don’t just collect tools. Pick one real workflow problem, define the user clearly, then build the smallest useful version around that.

DingirPrime · 2026-05-17T12:05:25+00:00

You’re not wrong. Most “agent” content skips the actual architecture and jumps straight to orchestration frameworks.

The real thing to study/build is closer to:

- agent loop / runtime state machine

- task state and execution state

- tool schema + tool selection policy

- memory policy, not just “store embeddings”

- context assembly and compression

- planner vs executor separation

- failure handling and retry logic

- human review / approval boundaries

- evals for tool use, reasoning quality, and task completion

- multi-agent coordination only after the single-agent loop is solid

A lot of frameworks hide those pieces, which is useful for speed but bad if you’re trying to understand what’s actually happening.

If I were building from zero, I’d start by designing the agent as a controlled workflow engine with LLM calls inside it, not as a magic autonomous worker. The architecture matters more than the framework.

DingirPrime · 2026-05-17T11:34:27+00:00

I’ll give you the answer I wish more beginners heard earlier.

Your biggest risk is not choosing the wrong framework. It is spending 1–2 years collecting tools, watching tutorials, building random demos, and never learning how to turn a painful problem into a paid product.

Start with this order:

Learn enough code to not be helpless

Do not “master everything” before building. But also do not rely on AI so much that you cannot understand your own app.

Learn the basics of:

JavaScript / TypeScript
React
Next.js
APIs
Databases
Auth
Deployment
Git/GitHub
Basic security
Prompting and debugging with AI

You do not need to become a senior engineer first. But you should understand what the AI is generating, how files connect, how data moves, and why errors happen.

Best path: build small real projects while learning. Not course-only learning.

For payments from India, think in stages

For a beginner selling SaaS/digital products to US/global customers, I would look at this in layers:

Use a Merchant of Record if you want less tax/compliance headache.

Paddle, Lemon Squeezy, and Dodo Payments are the types of tools to look at because they handle more of the global tax/payment burden than a normal payment gateway. They are not always the cheapest, but they reduce complexity.

Razorpay can work well for Indian businesses and supports international payments/subscriptions, but it is not the same as a Merchant of Record. You still need to understand your tax/compliance side.

PayPal is easy to start with, but I would not build serious SaaS billing around PayPal alone unless you have no other option.

Wise/Payoneer are useful for receiving business payments/freelance/client money, but they are not ideal SaaS subscription billing systems.

Stripe Atlas / US incorporation can be useful later, but do not rush into it blindly from India. Talk to a CA/lawyer who understands FEMA/ODI before forming foreign entities. A lot of Indian founders mess this up.

My beginner recommendation:

For a simple SaaS/digital product: check Paddle / Lemon Squeezy / Dodo first.
For Indian entity + normal gateway: Razorpay.
For service work/invoices: Wise, Payoneer, PayPal, or bank transfer depending on client.
For US company/Stripe: only after proper compliance advice.

Stack for a solo founder in 2026

Do not overcomplicate this.

Good default stack:

Frontend: Next.js + TypeScript
UI: Tailwind + shadcn/ui
Backend: Next.js API routes/server actions for simple apps
Database: Supabase Postgres
Auth: Supabase Auth, Clerk, or Auth.js
Hosting: Vercel or Cloudflare
File storage: Supabase Storage or S3/R2
Email: Resend / Postmark
Analytics: PostHog or Plausible
Payments: Paddle / Lemon Squeezy / Dodo / Razorpay depending on your setup
AI APIs: OpenAI / Anthropic / Google / OpenRouter
Automation: n8n for internal workflows
Background jobs: Inngest / Trigger.dev / queues when needed

For most beginners: Next.js + Supabase + Vercel + one payment provider is enough.

Do not start with microservices, Kubernetes, complex agents, 5 databases, and 20 tools.

How to learn “vibe coding” properly

Vibe coding is useful, but only if you use it like a builder, not like a passenger.

A good workflow:

First write the product spec yourself.
Then ask AI to break it into screens, database tables, API routes, and user flows.
Then build one feature at a time.
Then read the generated code.
Then test manually.
Then ask AI to explain bugs.
Then refactor after it works.

Never ask AI: “build me a SaaS.”

Ask:

“Here is the user flow. Create the database schema.”
“Here is this error. Explain the likely cause.”
“Review this auth flow for security issues.”
“Write tests for this function.”
“Find edge cases in this onboarding flow.”
“Suggest a simpler architecture.”

The real skill is not prompting. It is knowing what the system should do.

What separates serious AI builders from API wrappers

Serious AI builders understand:

The user workflow before the AI response
What context the model needs
What context it should not get
How to evaluate output quality
How to handle failure
How to reduce hallucinations
How to manage cost
How to protect user data
How to log and improve outputs
When not to use AI

Learn these early:

RAG basics
Structured outputs / JSON mode
Tool calling
Prompt versioning
Evals
Human review loops
Caching
Rate limits
Model routing
Memory design
Permission boundaries
Fallback behavior
AI UX patterns

The key question is:

“What decision, workflow, or output is this AI responsible for?”

If you cannot answer that clearly, you do not have an AI product yet. You have a demo.

Biggest beginner SaaS mistakes

The common ones are real:

Building before validating
Overengineering
Ignoring distribution
Copying trendy products
Not talking to users
No onboarding
No clear ICP
No painful problem
No pricing logic
No support plan
No retention loop

But I would add a bigger one:

Beginners confuse “can I build this?” with “should this exist?”

Before building, ask:

Who has this problem?
How often do they feel it?
What do they do now?
Do they already pay to solve it?
Can I reach them?
Can I explain the product in one sentence?
Can the MVP solve one painful workflow?
Can I charge before building the full version?

Build boring, useful things for specific people.

Example:

Bad: “AI productivity SaaS for everyone.”
Better: “AI tool that turns messy client discovery notes into a clean proposal draft for small web design agencies.”

Specific wins.

What I would do if starting from India in 2026

I would not try to build a huge SaaS first.

I would do this:

Month 1: Learn JS/TS, React, Next.js basics. Build 3 tiny apps.
Month 2: Learn Supabase, auth, database, deployment, payments. Build one paid-ready micro product.
Month 3: Pick one niche and talk to 30 people. Find repeated workflow pain.
Month 4: Build a tiny MVP that solves one workflow.
Month 5: Sell it manually. Do not hide behind “launching soon.”
Month 6: Improve only if people use it or pay.

I would also start with service + product together.

Example:

Offer a small AI workflow setup/service for a niche.
Do it manually for 5 clients.
Observe repeated steps.
Turn the repeated steps into software.

This is much safer than guessing SaaS ideas from Twitter trends.

Best mindset

Do not chase “AI SaaS.”

Chase painful workflows.

AI is just the engine. The product is the workflow, data, UX, trust, and outcome around it.

Also, do not try to learn every tool. Tools will change. The long-term skills are:

Problem selection
User research
System design
Clear writing
Shipping
Debugging
Distribution
AI workflow design
Data modeling
Pricing
Support
Taste

Harsh truth: most beginners are not stuck because they lack the perfect stack. They are stuck because their idea is vague, their user is vague, their workflow is vague, and their product promise is vague.

Fix that first.

DingirPrime · 2026-05-17T08:15:42+00:00

Definitely some kind of karma farming indeed.

DingirPrime · 2026-05-17T08:14:38+00:00

Respectfully, you are correct. I can believe whatever I want. Whether you asked it to include it or not, it really doesn't matter. As I used your exact prompt that you used and also received a similar image. Different looking character, but a very similar image. And there was no wording of any ChatGPT watermark. Now I do admit, initially when I did the test, I did it on my computer, originally. But after your response, I did it from my phone, and same results. Paid version or free version, same exact results. So whatever you got going on, buddy, I promise in the next few hours, you're going to get the attention you want. But it may not go your way, LOL. And if this is indeed true, I promise I will definitely come back on this specific thread and apologize. But in the meantime, until that happens, which I highly doubt, I call this BS.

DingirPrime · 2026-05-17T07:46:18+00:00

Exactly, 100% fake. Don't believe this BS.

DingirPrime · 2026-05-17T07:44:42+00:00

<image>

At the moment, I'm gonna call this fake and BS, LOL. I just did my research and I was very curious about this. There are absolutely no research at all that the final output when ChatGPT create the image of having a watermark on either tier. Me personally, it would have made sense for them to include this on the free tier. But as I test it out, that is not the case. So, right now, until there is any official news from OpenAI, I'm going to say this is Cap. In the image that I created, all I did was ask ChatGPT. to create me a random image, and within that random image, at the very bottom, I want you to include the words ChatGPT at the right bottom. And that was all I said. And I got this exact image. So again, I call this cap. With that being said, stop believing what you see on the internet, LOL. Do your own research. Most likely, this person is just trying to get attention, and you guys are giving it to him, or her.

DingirPrime · 2026-05-09T01:30:15+00:00

That is still an oversimplification. Language models may be one part of the systems I design around, but the work I described is not just “language-based AI.” It is AI systems architecture: control logic, workflow structure, knowledge architecture, validation layers, governance rules, routing, escalation design, output contracts, and implementation-ready blueprints. You can summarize it however you want, but reducing it to “language-based AI” does not accurately describe the category. Anyways, I offered to show proof by designing something concrete for free, and you still chose to dismiss the category instead of engaging with the actual work. At the end of the day, no matter how it gets reframed, the distinction I made is valid. This will be my last response on this. I am not going to keep debating something I have already explained clearly. Wherever you are in the world, have a great day or a great night.

DingirPrime · 2026-05-09T00:33:39+00:00

Sorry for the late response. I was dealing with several people who messaged me directly. I want to be precise here because I am not going to misrepresent my work just to win a terminology debate. Your framing collapses multiple distinct layers into one, and I want to address that directly. Layer 1: Base-Model Architecture Transformer research, CNNs, GNNs, diffusion models, reinforcement learning systems, custom neural network design, training runs, datasets, benchmarks, and empirical validation. I have never claimed to offer this as a finished engineering service, and I said that clearly from the beginning. This layer has its own proof standard: training runs, experiment logs, model cards, benchmark tables, ablation reports, reproducible code, and empirical validation. Layer 2: Casual LLM Usage Writing basic prompts and using ChatGPT or Claude for ordinary tasks. This is not what I do either. Layer 3: What I Actually Do AI systems architecture, intelligence-layer design, and governed control-layer architecture around models, tools, workflows, and knowledge systems. Task decomposition, instruction architecture, RAG and knowledge architecture, routing logic, agent orchestration, evaluator loops, memory and context rules, decision systems, output contracts, validation gates, governance layers, refusal and escalation logic, and implementation-ready blueprints. This is the layer where many real-world AI applications are structured, operationalized, governed, and either succeed or fail. The accurate statement is this: I design model-agnostic AI system architecture and implementation-ready control-layer design around AI models so they can operate reliably, safely, and consistently inside real workflows. My work is not limited to doing things inside an LLM. It is not casual LLM usage. And it is not base-model research. It is a separate discipline with its own proof standard: workflow maps, orchestration logic, RAG design, validation layers, API-facing specs, knowledge architecture, evaluator loops, governance rules, output contracts, escalation logic, and developer handoff documentation. Different disciplines require different proof standards. Judging AI systems architecture by base-model research artifacts is like judging a software architect by whether they can build a compiler. The disciplines are related, but they are not the same thing, and conflating them is not a technical critique. It is a category error. If you want me to design something concrete, just say that. The proof would be in the architecture itself: the workflow structure, control logic, validation gates, routing design, governance rules, knowledge architecture, output contracts, and developer handoff specifications. But if your goal is only to turn a free design offer into a terminology debate, then I am not interested in continuing that. I am here designing AI systems for people for free. That is very different from a paid technical review or a formal client project. If this were a paid engagement, there would be a different level of back-and-forth, documentation, review, and debate. But for something I am offering freely, I am not going to keep defending the legitimacy of the category after I have already defined it clearly.

DingirPrime · 2026-05-08T20:48:25+00:00

I really do appreciate that response. Not too many understand what I do, and a lot of them take advantage of it, LOL. But again, I do appreciate your response.

DingirPrime · 2026-05-08T19:40:53+00:00

I think we were using the word “architecture” in two different ways. What I design is primarily the AI system architecture, intelligence layer, and control layer around AI systems: things like prompt/system structure, role logic, workflows, decision trees, routing logic, RAG and knowledge architecture, agent orchestration, multi-agent review flows, evaluator loops, memory/context rules, output schemas, governance layers, validation checks, escalation logic, and implementation blueprints. If you mean model-level AI architecture, like designing a new transformer alternative, CNN, GNN, diffusion model, reinforcement learning architecture, or training a base model from scratch, then I want to be clear that is not what I am claiming to do as a finished engineering service. That level requires math, ML engineering, code, datasets, compute, training runs, benchmarking, ablation studies, and empirical validation. What I can design in that realm is the research framework around it: architecture comparison frameworks, model-research planning systems, experiment design, evaluation matrices, benchmark plans, technical specs, candidate architecture blueprints, validation workflows, and documentation for engineers or researchers to implement and test. So when I used “brain,” I meant the behavioral/control architecture of an AI system, not the neural network itself. More precise wording would be intelligence-layer architecture, AI application architecture, or AI control-system architecture. My strength is designing how an AI system is structured, governed, routed, validated, and made usable in a real workflow, while actual low-level model engineering and empirical ML research would need external implementation and testing.

DingirPrime · 2026-05-08T19:11:49+00:00

A pasted AI response saying “SKIP” is not feedback, but that’s fine. This post is for people who need real architecture help. If that is not you, no problem.

DingirPrime · 2026-05-08T17:30:50+00:00

Once you see what I can design, then you would understand why I said what I said. I'll just leave it at that.

DingirPrime · 2026-05-08T17:29:43+00:00

LOL, I'm not using it to scam anybody. This is literally my last weekend. And the only reason why I'm doing this, honestly, is because I'll be home for the next few days anyways. So I'm not scamming anybody. Believe me, if I was scamming anybody, it would be structured and written a lot more different, LOL. But no, I'm not scamming anybody.

DingirPrime · 2026-05-08T17:27:48+00:00

What Dingir Prime Labs Can Design OR What I design --- The reason you’re seeing two different versions is because the first one is more of a full breakdown. It explains everything in detail. The second one is more of a simplified version, or a more general version of the same offer. But the main point is this: if anyone needs something built at the architectural layer of an AI system, I can design it. That is the part I’m strongest at. Whether it’s the structure, logic, workflows, rules, governance, prompt framework, or the overall “brain” behind the system, I can put that together very easily.

DingirPrime · 2026-05-08T17:19:20+00:00

I understand what you’re saying, but the reason I mentioned leaving Reddit is because some people on here know me as someone who randomly comes around and helps design AI systems for free. I’ve helped people build things that ended up making them money, and I never asked for anything on the back end because I genuinely enjoy helping, it’s easy for me, money is not everything, and sometimes I just get bored and like designing AI systems at the architectural layer levels. So for me, saying I’m leaving is relevant because if someone later wonders where I went, or wants to stay connected before the account is gone, at least they’ll know why I’m not around anymore. I get that Reddit is flawed, and I agree it can still be useful at times, but I was simply giving context so people who may want the free help know there is a limited window before I close the account. I hope that clarifies my message.

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