Posted by david927 11/9/2025
Ask HN: What Are You Working On? (Nov 2025)
- Scan wine labels (it analyzes the label automatically)
- Add structured or unstructured tasting notes
- Create lists (shared or not) to keep wines organized
- View information about the regions/grapes
It's called Cork Club: https://corkclub.app/
Create video game sprites and animations via prompts.
Pretty excited because I've started to get high volume, repeat customers.
Not a fan of signing up before seeing how much I'd have to pay. The examples look great though.
Prices are about to drop dramatically. Many of the models dropped >80% in price since initial launch. Any time I have a reduction in cost, I pass the savings directly on to users.
Not sure if you just added this in or I overlooked it, but exactly the kind of transparency I love. Will give this a try.
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EDIT - Did an image generation using the OpenAI 4o model, then ran through the lowest quality animation. This is awesome and first pass is very strong and usable (around 100 diamonds used).
I look forward to seeing prices drop more and the asset pack area fill up. Keep going man, really awesome stuff.
I open-sourced the RAG boilerplate I’ve been using for my own experiments with extensive docs on system design.
And I have bunch of LLM+RAG blogs I post frequently last 2 months : https://mburaksayici.com/blog
It's mostly for educational purposes, but why not make it bigger later on? Repo: https://github.com/mburaksayici/RAG-Boilerplate - Includes propositional + semantic and recursive overlap chunking, hybrid search on Qdrant (BM25 + dense), and optional LLM reranking. - Uses E5 embeddings as the default model for vector representations. - Has a query-enhancer agent built with CrewAI and a Celery-based ingestion flow for document processing. - Uses Redis (hot) + MongoDB (cold) for session handling and restoration. - Runs on FastAPI with a small Gradio UI to test retrieval and chat with the data. - Stack: FastAPI, Qdrant, Redis, MongoDB, Celery, CrewAI, Gradio, HuggingFace models, OpenAI. Blog : https://mburaksayici.com/blog/2025/11/13/a-rag-boilerplate.h...
You can have a look at https://simcarlo.com. The tool allows you to see the full spectrum of potential outcomes instead of just a single guess.
Once you sign up and connect your Google sheet, it generates a template (using AI) based on your data, which you can edit in a Notion-like editor. You can then generate PDFs for your entire sheet or a for a range of rows.
Some use cases I'm seeing:
* Certificates for students or course completions
* Monthly invoices for all your clients (https://sheetstopdf.com/use-cases/business/invoices)
* Personalized reports with individual client data
* Event tickets or conference badges
* Contracts, offer letters, or any personalized documents
* Really anything where you have rows of data that need to become individual PDFs
Would love to hear what you think or if you have use cases I haven't thought of yet!
I wanted something that would allow us to record members, games, etc., and also allow us to be assigned a local club rating. Anyway, after doing some searching and only finding paid software, I decided to just build something. That lead to https://openchessclub.org
You can check it out on GitHub: https://github.com/OpenChessClub/openchessclub.
I plan on building a QR code generator that allows club members to check-in during meetings, which will then allow players to be matched, and some other features, although it is primarily aimed at smaller chess clubs, so don't know how far it'll go.
Lately I’ve worked on a chat history based memory feature that can recall information from every conversation you’ve ever had with ChatGPT and Claude. It’s been particularly useful and also technically fun to implement. Speed has been very important as I do just in time summarisation and a multi stage RAG pipeline, and most LLMs have unacceptable performance. I ended up going with GPT-OSS on Groq due to its ultra low latency often completing full generations before Gemini or ChatGPT APIs return even the first token.
The ability to recall details from conversations going back years makes tasks where I want personalised plans or feedback like 10x more useful, at times I get the AI to ingest tens of thousands of tokens of context to help me better.
I built it because I work across multiple machines and often worry about which projects are on which computer or whether I’ve left any files in unique locations. Now I can diff the summaries between devices to see what’s out of sync, which repositories have uncommitted changes, and which folders have been modified.
I avoid using cloud sync services, and most of my files are already in git anyway. I find that having clear visibility is enough, I just need to know what to commit, push, pull, or sync manually.
I would be glad if it proves useful to someone besides me.