WikiDeep.io

WikiDeep.io: The Free Open AI Archive. A community library sharing deep search AI results, breaking paywalls, democratizing insight & sparing energy for all.

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Inspiration

For months, I was deep inside GPT Pro, swimming in premium answers and high-level insights. But the moment I stopped paying, the difference slapped me in the face. The gap between Pro and Plus isn’t just software; it’s a knowledge divide. A digital wall that keeps better education and better research locked behind a price tag.

At the same time, prepping for this hackathon, I kept hitting another ugly truth. I was generating mountains of AI reports, hundreds of pages I’d never fully read, data I'd never use. A haunting quote from a former Google exec that AI will soon be eating 99% of our electricity stuck with me. We’re building a smarter world by wasting everything.

That’s when it hit me. Most questions people ask, someone else has already asked. Most answers? Buried. Lost. Siloed. WikiDeep is my answer to that.

A shared library for the curious. A way to close the knowledge gap, cut the waste, and let the world ask smarter together.

And it was in pursuit of that vision, while meeting potential teammates in Morocco, that the second half of the idea took shape. In our conversations, we didn't just talk about the world's problems; we talked about our own. We all had startup ideas gathering dust or paid client projects we were stuck on because of a specific skill gap. We logically concluded that even with an AI co-pilot, some problems demand a human partner.

That was the second spark: the realization that a platform for sharing knowledge should also be a platform for sharing skills. WikiDeep couldn't just be an archive to solve the problem of lost knowledge; it had to become a hub to solve the problem of isolated talent.

So, WikiDeep is the answer to both: a library to end the waste of past insights, and a collaboration hub to spark future innovation.

What it does

WikiDeep is a free, open-content library for AI-generated deep search results. It’s a place to archive and share the valuable reports and answers we get from AI models, so they don’t go to waste.

Its purpose is threefold: Democratize Knowledge: It gives everyone, especially those who cannot afford premium AI subscriptions, free access to high-quality, in-depth information. Users can even download reports when they have Wi-Fi to read offline later.

Cut the Waste & Stop Repeating Work: Before you prompt an AI for a complex report, you can check WikiDeep first. Someone may have already archived the answer, saving compute power, electricity, and money. It also helps you discover related subjects that can open new doors in your research.

Create a Historical AI Archive: By archiving responses, WikiDeep is creating a "golden dataset" for the future. This public archive will be invaluable for researchers to track the evolution of LLMs.

But beyond the archive, what happens when you have a brilliant idea but lack the specific skills to build it?

The Collaboration Hub was built to solve my own problem. I have a startup idea, a chatbot for booking airline tickets, that’s been stuck for months because I don't have the backend skills to make it work. I also have paid client projects stalled because I can't complete them alone.

The WikiDeep Collab Hub is the tool I wish I had. It’s a space where:

  • You can post your startup idea to finally find a technical partner to help you build it.
  • You can post a paid task you're stuck on, offer a share of the payment, and connect with a creator who has the exact skills you need.

For now, the process is simple and direct: you create a profile, share your project, and provide your email. No complex systems, no middlemen. It’s about connecting talent to opportunity as quickly as possible, ensuring a great idea never dies simply because one person couldn't do it all alone.

How I built it

Originally, I shared this project during a team meetup I hosted at the Fairmont in Morocco. The idea was simple: build something meaningful together. But reality set in fast. The first real challenge wasn’t technical; it was human.

One teammate couldn’t keep up with Bolt credit costs and had to focus on his own project. Another, a skilled backend dev, disagreed with the tech stack and bailed. In the end, I kept going, building the entire project 100% solo with Bolt.new.

  • Frontend: Built with Next.js (React) and Tailwind. Clean, fast, modern, the tools I needed to make an interface that doesn’t just work, but feels good to use.

-Backend: Supabase with PostgreSQL. Simple, powerful, and serverless. I leaned heavily on database logic, policies, functions, and triggers, all sitting exactly where the data lives. No servers to babysit, no unnecessary complexity.

-AI Partner: Gemini Ultra and GPT Plus became my unofficial teammates and my sounding board, helping me with every question I had. I had a clear vision for the design, layout, and features. Thankfully, I managed to translate that to Bolt, and it built exactly what I had imagined.

Challenges I ran into

This project’s journey began with the challenge of a team dissolving, but the solo mission that followed presented its own gauntlet of technical and platform-specific hurdles.

My first major battle was with a persistent browser security warning, a glaring "red alert" that threatened to scare away any potential user. It was a source of high stress as it undermined the trust I was trying to build. Resolving it wasn't a simple fix; it was a multi-front war involving registering the project with Google's console, meticulously debugging user-shared URL logic, and making countless other server-side maneuvers until, finally, the alert was vanquished.

The second, and perhaps biggest, ongoing challenge has been frontend responsiveness. As a visual builder, it's agonizing to see a design that looks perfect on one screen appear broken on another. Making WikiDeep look and feel right across all different devices has been a painstaking process, and it’s a battle that, unfortunately, isn't completely won yet.

However, the most profound challenge was wrestling with the very AI tool I relied on. Keeping an AI focused on the same project for 30 days? Totally beyond the memory capacity of any system on the market. After days of continuous work on the same project, Bolt would often get lost, confused, or confidently tell me it had completed a task when I could clearly see it hadn't. The project was flagged as "oversize" early on, with very few options to manage the issue. The token consumption was astronomical, which was incredibly limiting and frustrating. I was lucky to afford upgrading my subscription, but I know most creators don't have that luxury, a reality that runs counter to the very mission of WikiDeep.

Accomplishments that I'm proud of

My greatest accomplishment was perseverance. I’m proud that after the initial team dissolved, I refused to let the vision die. I carried it across the finish line alone, teaching myself what I needed to know to overcome high-stress technical hurdles that were far outside my comfort zone.

I’m proud of translating the complex vision in my head into a functional, full-stack reality. Getting a human brain and an AI to truly understand each other through written words isn’t easy, but it worked. The specific details I obsessed over—like the loading logo animation made from three PNG files or the smooth slide-out menu, are simply the visible results of that deeper success: turning a feeling into a living platform.

What I learned

I learned that building a vision is as much about resilience as it is about code. When you’re a solo founder, you become the designer, the debugger, and the strategist all at once. This time, my approach to "team vibe coding" failed, but I’ll rethink it for the future.

I learned firsthand about the real-world limitations of AI development platforms, from their memory caps to their token costs, and that stubbornness is the most important tool a builder can have.

But most of all, I learned that my "vibe coding" approach, when paired with AI as a co-pilot, is incredibly powerful. It allowed me to build a full-stack application that, by traditional standards, I shouldn't have been able to build alone.

What's next for WikiDeep

The WikiDeep you see today is just the foundation. My vision is to grow this into a full ecosystem for AI creation, collaboration, and discovery, a space where knowledge isn’t locked away, but shared and built upon.

But I know building something like this takes more than just one person. That’s why my plan is simple: keep pushing this project forward, while looking for the right people who believe in this vision and want to help shape it. Here’s where I’d like to take WikiDeep next.

Evolve into a Multi-Modal Commons: The archive won’t stay text-only. The next step is to open the doors for creators to share AI-generated images, videos, music, and to launch a dedicated Vibe Coding gallery showcasing functional websites and apps built with AI.

Launch a 'Spotify for Prompts' Economy: Prompt engineering is an art, and it deserves to be rewarded. I plan to introduce a premium tier where skilled creators can earn revenue when others access the prompts behind their work. What makes this especially exciting is how it applies to Vibe Coding. For the first time, people will be able to share (and get rewarded for) the prompts they used to build functional websites, apps, and interactive projects. This is unexplored territory, and a space where WikiDeep can break new ground.

Build the Secure Collaboration Hub: I’ve seen firsthand how hard it is to build solo. The idea is to create a premium, secure space where developers, creatives, and startups can finally team up. A trusted environment where collaboration happens without friction, safely, privately, with the tools they need.

LLM & Prompting Archive: A Historical Record of AI: Beyond the platform itself, I want to turn WikiDeep into a living time capsule of AI’s evolution. My hope is to connect with others who share that mission, to build a dataset that future generations can use to track how this technology, and human curiosity, evolved side by side.

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