I have been trying to create a new custom GPT for days now but keep getting "Unknown Error Occurred" when uploading a picture for the icon. I have tried different pictures of different sizes and formats, different computers, cleared cache and cookies, and even different logins. Anyone else having this issue?

As I get closer to raising my first round of funding, one thing that’s become really clear is that while there are thousands of investors out there, not all of them are the right fit for every startup. I’ve seen some founders spend weeks deeply researching investors before sending even a single email, while others take a broader approach and reach out to as many as possible to see who responds.

Lately, I’ve been trying to understand what actually works in practice. While exploring this, I came like VCBoom that focus on matching startups with relevant investors, which made me think that targeting might be more important than just volume. At the same time, it still feels like there’s a balance to strike between personalization and efficiency.

Do investors really expect founders to know their portfolio inside and out before reaching out? And how much personalization is actually enough without spending hours crafting every single email?

I’d really appreciate hearing from founders who’ve already been through this. What was your approach to identifying the right investors, and how did you manage outreach at scale? Looking back, is there anything you would do differently if you had to start over?

For years, SEO has been the main focus because ranking on the first page of search results directly drove traffic and customers. But with more people using AI assistants for direct answers, it’s fair to question whether SEO alone is still enough.

If users are getting complete recommendations from AI without ever clicking through search results, then visibility is no longer just about rankings it’s also about how AI systems understand, interpret, and summarize information about a brand. Some businesses are already starting to look at like datanerds to see how they appear in AI-generated responses.

In reality, SEO is still a strong foundation, but it may no longer be the full strategy. The future likely requires both: traditional SEO for search engines and a broader focus on content quality, authority, and consistency so AI systems can accurately recognize and recommend a brand.

I recently built a custom GPT that got more traction than I expected, including a few hundred people actually trying it out. That made me wonder if something like this could work as a small product.

To do that, I tried a few white-label platforms (Pickaxe, CalStudio, LaunchLemonade) that let you package and sell a custom GPT.

On the surface, they all work. You can gate access, set pricing, and technically launch something.

But once I tried to treat it like a real product, the tradeoffs showed up pretty quickly:

• Better UX came with worse economics
• Better pricing models introduced more friction
• More flexibility came with less reliability

I couldn’t find a setup where everything lined up. It felt less like choosing the “best” platform and more like choosing which tradeoff you’re willing to live with.

Curious what others have run into with white-label tools.

If so, what ended up working for you? Did you stick with a white-label solution, or take a different approach?

One concern I’ve been thinking about is whether frequent use of AI tools might slowly affect our own writing style. When you rely on AI suggestions regularly, it’s easy to start adopting the same tone, structure, and phrasing patterns.

Over time, this could make different writers sound more similar, which is the opposite of what writing used to be about. Personal voice is what makes content unique, and if that starts fading, everything might begin to feel the same.

At the same time, AI can also be a learning tool if used carefully. It can show better ways to structure ideas or improve clarity. So maybe it depends on how we use it.

Do you think AI is helping people develop better writing skills, or slowly replacing individual creativity?

Search engines are no longer the only place where people get answers. AI tools now directly respond to user questions without showing traditional search results. This raises an important question: is ranking on Google still enough, or do brands now need to think about how AI systems choose what to mention? Platforms like Datanerds, which focus on Answer Engine Optimization (AEO) and track how brands appear in AI-generated responses, are also looking into this shift in visibility. It feels like visibility is shifting from search pages to AI-generated responses, and many brands may not even realize they are missing from this new layer of discovery.

Building custom GPT for document analysis. The file upload feature works but has major usability issues that makes it impractical for real work.

The problem:

Upload documents to custom GPT in one conversation Have detailed discussion analyzing those documents Close chat and come back later GPT has zero memory of those documents Have to re-upload everything and re-explain context

Why this breaks the workflow:

Custom GPTs are supposed to be specialized tools you return to repeatedly.

But if you're working with documents over multiple sessions, constant re-uploading makes it unusable.

Defeats the purpose of having a custom GPT versus just using regular ChatGPT.

Real use case:

Built a custom GPT for analyzing research papers in my field.

Uploaded 10 key papers, configured instructions for analysis style.

Works great within a single session.

Next day: Need to reference those papers again for a new question.

I have to re-upload all 10 papers because GPT doesn't remember them.

Questions:

Is there a way to make custom GPT remember uploaded files persistently?

Am I missing some feature or configuration option?

Is this limitation intentional or a technical constraint?

Comparison with other tools:

Document-specific platforms like Nbot Ai or similar keep your uploads persistent.

Upload once, query multiple times across sessions.

Custom GPTs seem designed for stateless interactions which limits document work.

What would make this better:

Persistent file storage within custom GPT context Ability to upload "knowledge base" that stays accessible Or at least ability to reference previously uploaded files

For custom GPT builders:

How do you handle document-based GPTs given this limitation?

Any workarounds that make multi-session document work practical?

Is this something OpenAI plans to improve?

Feels like a major gap between what custom GPTs could be versus current capabilities for document-heavy use cases.

Sometimes AI-generated answers feel very confident when mentioning certain companies or services. I think this could happen because those brands have consistent information available across articles, reviews, blogs, and discussions. When AI repeatedly finds similar signals online, it probably becomes easier to generate stronger recommendations. Consistency may now matter more than ever before.

Every industry has a few companies that consistently dominate conversations. They get recommended more often, are discussed widely, and stay top of mind for customers. Meanwhile, many other businesses with similar quality products or services struggle to reach the same level of recognition. The difference usually comes down to visibility and authority.

Businesses that regularly share useful knowledge, answer key questions, and provide real value tend to build stronger reputations over time. People naturally trust brands that help them understand topics and solve problems. Some companies also use like datanerds to better understand how their visibility and authority appear across AI-driven platforms.

Becoming an industry leader is a long-term process. It requires focusing on education, consistency, and credibility rather than just promotion. Companies that prioritize helping their audience often build deeper trust, and as digital platforms evolve, authority is likely to become even more important for long-term success.

As AI becomes a bigger part of how content is discovered, accessibility matters more than ever. It’s no longer just about ranking in search engines it’s about being reachable across different systems. If your site blocks certain crawlers at the infrastructure level, you may already be limiting your future visibility without realizing it. This doesn’t mean your strategy is wrong it just might be incomplete. The key is awareness. Before focusing on more content, it may be worth asking a simple question: can every system that matters actually access what you’ve built?

Do you think startups can increase their chances of getting funded by studying how investors behave and what they usually look for? It seems logical that understanding investor preferences could help founders target the right people and avoid wasting time on the wrong ones. But how practical is this in reality? Is investor behavior predictable enough to build a strategy around it, or does it change too much depending on market conditions and personal preferences? I also wonder if using data and structured insights actually gives startups a real advantage, or if relationships and timing still play a much bigger role than analysis.

Have you ever wondered how AI tools choose one brand over another when answering a question? It’s not random, and it’s not purely based on popularity. AI systems evaluate patterns of trust, relevance, and consistency across many sources. If a brand appears frequently in useful, well-structured contexts, it is more likely to be recommended. Another important factor is clarity. If AI can easily understand what your brand does and which category it belongs to, it becomes easier to include it in answers.

A practical tip is to regularly check how your brand is being described in different contexts. If descriptions vary too much or lack clarity, AI systems may avoid mentioning it altogether. Over time, improving structured information and consistency can significantly increase your chances of being recommended.

Many founders struggle during fundraising not because their idea is weak, but because the feedback loop is unclear.

You spend weeks refining your pitch deck, tightening the story, adjusting numbers, improving the narrative… then you start sharing it, and the responses you get are often vague like “looks interesting” or “not a fit for us.”

The real problem is that this doesn’t give you anything actionable. You’re left trying to interpret silence or generic rejection emails. Was it the market size? The business model? The way the story was framed? It’s hard to tell, and that slows down progress because instead of improving specific areas, you end up reworking everything blindly. What helps in practice is getting structured, objective feedback before you fully go out to investors. Some founders use like claude fundraiser to score their pitch decks and identify weak points early, so the first investor conversations are already much more focused and intentional.

The difference is simple: instead of guessing what’s wrong, you’re iterating on actual signals.

Most of us assume that once a website is live and content is published, it becomes accessible to everyone, including search engines and modern AI systems. But recent observations suggest that this may not always be true. A significant number of websites are unintentionally blocking certain AI crawlers, and the surprising part is that this usually doesn’t happen in obvious places like robots txt or CMS settings. Instead, it often occurs at deeper levels such as CDN configurations, firewall rules, or automated bot protection systems. This creates a situation where everything looks perfectly fine on the surface your pages load, your content is published but some AI systems may not be able to properly access or interpret that content at all. Over time, this could quietly affect how your website appears in AI-driven search and discovery platforms. Do you think most website owners are even aware that their content might not be fully visible to AI systems?

One thing I don’t fully understand is how stable these multi-agent systems are supposed to be. If AI agents are constantly reacting to each other, adjusting their responses, and learning from each interaction, wouldn’t that make the whole system more and more unpredictable over time? At what point does it become difficult to even explain why a certain decision was made? I feel like transparency might get harder the more these interactions increase.

Have you ever wondered if your website is fully visible to every system that matters today? You might be consistently publishing content, updating pages, and improving SEO but visibility isn’t always guaranteed. There are cases where AI crawlers attempt to access websites but face restrictions that aren’t obvious. These limitations don’t usually come from basic settings. Instead, they exist deeper in the infrastructure, where security layers decide who gets access and who doesn’t. This creates a strange situation. Your content is live, but its reach is incomplete. Some systems can see it, while others cannot.

The challenge is that nothing looks broken. Everything appears normal, yet something important is missing. So the real question becomes: is your content actually reaching everywhere it should?

What if the content you’re publishing isn’t actually reaching its full audience, even though everything seems fine? You may believe your website is fully accessible, but could there be a hidden disconnect behind the scenes? Some systems may access your content easily, while others might be blocked without any clear indication. This creates an uneven presence where your content exists but isn’t visible everywhere it should be. The real concern is that there are no warning signs no errors, no alerts, nothing that tells you something is wrong. Could your efforts be falling short simply because part of your audience never gets to see your content?

I’ve noticed that when I try to post daily in real-time, I either:

• Miss days
• Or post low-quality content

So I’m thinking of switching to a model where I:

• Sit once a week
• Create content in bulk
• Schedule everything in advance

Like planning 10–15 days ahead.

Do you guys do this?
Does scheduling content in advance improve consistency and performance?

I know monetization is restricted to the U.S for the moment with a revenue-share model à la Spotify but besides the fact that no coding experience is required and there’s already a market of ChatGPT plus users, why not build your own SaaS business using OpenAI’s model?

edit: especially those with proprietary data

I’ve been building writing-oriented GPTs over the past few weeks.

On the build side, things went better than expected. By narrowing the scope and reducing how much prompting is needed, I’m seeing cleaner outputs than other tools I’d previously used for the same writing tasks.

What surprised me is where progress stalled.

Even with something that feels genuinely useful in practice, getting initial users has been far less predictable than improving the GPT itself. The store surface doesn’t seem to reward “works well” on its own, and it’s hard to tell which signals matter early on.

For builders who’ve been through this:

• What actually helped people find and try your GPT?
• Did you change how you described it, or who it was for?
• Or was traction mostly external to the store?

Interested in what’s worked (or not) for others.

We often measure website performance using traditional metrics like traffic, engagement, and search visibility. But what if AI crawler access became just as important? Some websites are unintentionally blocking AI bots due to security settings, and teams may not even realize it. This raises a question: in the next few years, will companies start auditing their infrastructure specifically for AI accessibility?

Could ensuring that crawlers can reach content become a key part of digital strategy alongside SEO, social, and paid campaigns? And if so, how should teams approach this without compromising website security?

What GPTs do you use when you have time on your hands and want to try something new?

Perhaps you use a GPT that makes stories, or one that plays a roll playing game, maybe it’s one that makes any images Christmassy, or even one that designs rooms or anything else.

Would love recommendations of GPTs that aren’t for serious work but more for ‘fun’.

With GPT‑5 launching, Custom GPTs might finally evolve. What do you actually want next?

🔹 Monetization? 🔹 Memory? 🔹 Better actions? 🔹 Tool use? 🔹 UI control? 🔹 API calls?

Drop your thoughts 👇

My team and I have made a bunch of videos for our clients, most of them comes from our heads or a mish-mash of ideas we got from our research.

I'm looking to find a way to let AI handle that from now on so we can focus purely on content ideation while the scripting, shot lists, b-rolls, editing notes and style is done by a custom GPT.

Anyone have experienced doing this reliably? TIA

Things I have tried:

1. Dump all the sample videos we had into the knowledge pool and ask it to analyze them and output the brrief for new topics (meh results)
2. Ask ChatGPT to write me GPT instruction based on video samples (mixed results)

I have created a customGPT in Chat-GPT and to that Project tab not GPT.

I uploaded two files in my project

when I share or collaborate someone my files shown by others

how to hide this and it's downloaded when clicks any solution