Özgür

Co-Founder

[Özgür @otonommuhendis]

@SchengenRandevu O kadar kötü bir hizmet veriyorlar ki aklım almıyor gerçekten, bilerek yapılmış bir dizayn gibi sanki. Sabahtan beri paramızı çektiler ama başvuru yok rezillik

[Özgür @otonommuhendis]

@OguzAlperOktem Sonunda rahat yolculuk yapacağız

[Özgür @otonommuhendis]

@OguzAlperOktem Artık yönetmelik çıkmalı bu sorun böyle çözülemez!

[Özgür @otonommuhendis]

@OguzAlperOktem Hisse tavan büyük fırsat

[justified skyrim player @SBINBO1]

Best version of using AI is educational purposes. It can shorten for your way to reach enough level of competency that you can now enjoy doing hard things

[justified skyrim player @SBINBO1]

LLM Apps: Cost vs. Performance 💰⚡

One of the biggest challenges in LLM applications is balancing cost and performance:

🖥️ Local models? Requires serious investment in server hardware.
🌍 API calls? Can get expensive at scale.

How do you handle this? 🤔 In our case, we used API calls but hosted our own VPS and implemented RAG without an additional vector database.

I would love to hear your approach too

[justified skyrim player @SBINBO1]

We just hit 100 users (including 1 premium user) in just 3 days without any paid ads! 🚀

All we did was share our work on related subreddits & Twitter, and we open-sourced it completely.

Technical communities loved our implementation. The power of communities is incredible!

[justified skyrim player @SBINBO1]

LinkedIn vs. X for our launch post 🤔🚀

We posted our launch on both platforms:

🔹 LinkedIn – 500+ connections (mostly ex-coworkers & industry peers)
🔹 X – Just 20 followers

Results:
X: 20K post views, 50+ real users, supportive comments & likes.
LinkedIn: 1K views, 0 comments, only close friends liked, no user conversions.

I even spent 2x more effort on my LinkedIn post. Really expected the opposite... 🤷‍♂️

[justified skyrim player @SBINBO1]

🚀RAG Without a Vector DB, PostgreSQL and Faiss for RAG implementation

🤔Do you need to really use a conventional vector database for RAG implementation, like Pinecone?

We've built an AI-powered document analysis product with a from-scratch RAG implementation that uses PostgreSQL for persistent, high-performance storage of embeddings and document structure.
Most RAG implementations today rely on vector databases for document chunking, but they often lack customization options and can become costly at scale.
Instead, we used a different approach: storing every sentence as an embedding in PostgreSQL. This gave us more control over retrieval while allowing us to manage both user-related and document-related data in a single SQL database.

If you wonder about the implementation, we have completely open source repository that you can checkout!
https://t.co/xt0Ou1qHZz

[justified skyrim player @SBINBO1]

🤔How do you guys integrate the RAG?

If you want to have production ready RAG app, you will face several major blocking points.

If you want to have ease of integration, you can use Pinecone, but you won't be able to control COST.

If you want to use open-source libraries like LangChain, the PERFORMANCE will be the major issue on the deployed server.

We deeply thought these issues and developed an completely open source, RAG integration with PostgreSQL and Faiss.

You will have 0 additional cost and you can benefit all of the Faiss performance.

If you wonder how, it's completely open-sourced. You can check it out
👇
https://t.co/8PbUZ6exIV

[Özgür @otonommuhendis]

Congrats Man! We have made the first step. To the Moon! 🚀

[justified skyrim player @SBINBO1]

I made my first dollar! 🚀🚀

7 months ago, I had zero web development experience—just an AI background.

After 7 months of dedicated work (5 month is while I'm having full time job and 2 months is dedicated to my project) I made my first internet dollar!

It was my first web project. My aim was only getting to one dollar and creating a value for my users, and I was not expecting to get it in my first project.

Let's go!

[Özgür @otonommuhendis]

@rahmansahinler1 Congrats! To the Moon! 🚀

[justified skyrim player @SBINBO1]

RAG Without a Vector DB, PostgreSQL and Faiss for AI-Powered Docs 💡

We've built https://t.co/PG0Mj57zw2, an AI-powered document analysis product with a from-scratch RAG implementation that uses PostgreSQL for persistent, high-performance storage of embeddings and document structure.

Most RAG implementations today rely on vector databases for document chunking, but they often lack customization options and can become costly at scale. Instead, we used a different approach: storing every sentence as an embedding in PostgreSQL. This gave us more control over retrieval while allowing us to manage both user-related and document-related data in a single SQL database.

At first, with a very basic RAG implementation, our answer relevancy was only 45%. We read every RAG related paper and try to get best practice methods to increase accuracy. We tested and implemented methods such as HyDE (Hypothetical Document Embeddings), header boosting, and hierarchical retrieval to improve accuracy to over 90%.

One of the biggest challenges was maintaining document structure during retrieval. Instead of retrieving arbitrary chunks, we use SQL joins to reconstruct the hierarchical context, connecting sentences to their parent headers. This ensures that the LLM receives properly structured information, reducing hallucinations and improving response accuracy.

Since we had no prior web development experience, we decided to build a simple Python backend with a JS frontend and deploy it on a VPS. You can use the product completely for free. We have a one time payment premium plan for lifetime, but this plan is for the users want to use it excessively. Mostly you can go with the free plan.

If you're interested in the technical details, we're fully open-source. You can see the technical implementation in GitHub (https://t.co/xt0Ou1qHZz) or try it at https://t.co/4xvzX2khQt

Would love to hear from others who have explored RAG implementations or have ideas for further optimization!

[justified skyrim player @SBINBO1]

🚀 Just launched https://t.co/PG0Mj587lA!

Upload your docs, chat with your knowledge. Finally, an AI that answers from YOUR content, not the internet.

Early access available now - try it free and see your documents in a whole new light.

Try it at https://t.co/4xvzX2kPG1
We're completely open-sourced. If you want to know about our from scratch RAG implementation, checkout our github repo
https://t.co/xt0Ou1rfP7

#AI #Productivity #saasshowcase

[Özgür @otonommuhendis]

@ahmetvehbi Beleş çay çorba dağıtılan yere giden adamdan rafine zevkleri anlamasını beklemem

[Özgür @otonommuhendis]

@Mendokusaibiri Maymundan beter ettiler

[Özgür @otonommuhendis]

@Mendokusaibiri Ah be abi

[Özgür @otonommuhendis]

@kalendrmustafa_ Lan benim çarı çalmışsın Ahmet kayayı geri ver

[Özgür @otonommuhendis]

@otuzbirus Seç beğen al.

[Özgür @otonommuhendis]

@Mendokusaibiri @kalendrmustafa_ @kkaramolla "Bırak Bırak o ulti açtıysa durmaz aga"