Alternatives
Products that do what Cloudglue does
Turn videos into structured data, ready for LLMs
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- 18IM
Live demo here: http://fonctionlabs.com:8000 Similarly to aka_sh (guess we were working parallelly on similar topics), I created with my brother a chainlit-based webapp, which summarizes Youtube videos in order to gain time. It works as an RAG-based LLM, and is very light in the sense that it does not use RAG libraries like langchain or llamaindex. You can use it with your own OpenAI API key. It also supports local models like Mistral, or Llamma. It is ofc open-source, and you can deploy with Docker if you choose. Some of the next steps are: - using whisper to be able to compute a…
2024 · github.com
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- 20XS
Hi HN. I made a little JS library for streaming structured data from LLMs using leniently-parsed XML as a medium. E.g. await simple('fun pet names', { schema: { name: Array(String) }, model: 'openrouter:mistralai/ministral-3b' }); // => ["Daisy", "Whiskers", "Rocky"] Demos: xmllm.j11y.io When using LLMs, I've ended up gravitating towards boring time-tested XML-esque tag-based delimiters instead of JSON/function-calling for the following reasons: - Diverse presence in training corpuses (consider flavours of content commonly adjacent to these syntaxes vs. JSON) - HTML was…
2024 · github.com
- 21IM
Every time I wanted to use LLMs in my existing pipelines the integration was very bloated, complex, and too slow. This is why I created a lightweight library that works just like scikit-learn, the flow generally follows a pipeline-like structure where you “fit” (learn) a skill from sample data or an instruction set, then “predict” (apply the skill) to new data, returning structured results. High-Level Concept Flow Your Data --> Load Skill / Learn Skill --> Create Tasks --> Run Tasks --> Structured Results --> Downstream Steps And the bast part: Every step can be saved and reused as…
2025 · github.com
- 22AD
We just launched Turbine, it automates the data pipeline for LLM powered apps. It fetches data from your database, creates embeddings from the data, and stores in a vector database for easy semantic search. It also creates a real-time data pipeline to fetch changes and keep the search data fresh. Turbine supports multiple source databases, embedding models and vector databases. It's aimed to be configurable and easy to use at the same time. It's primary use case would be being the data backend for LLM apps—to create a relevant context for each prompt from your data. We are very early and…
2023 · useturbine.com
- 23TA
Hey HN! I built Topic2Manim to automate the creation of educational videos like those from 3Blue1Brown. The workflow is simple: 1. Give it any topic (e.g., "how ChatGPT works") 2. An LLM generates an educational script divided into scenes 3. LLM generates Manim code for each scene 4. FFmpeg concatenates everything into a final video Currently working on TTS integration for narration! Would love feedback on the approach and ideas for the TTS integration
Jan 2026 · github.com
- 24SS
I built a personal LLM assistant on Cloudflare Workers + Durable Objects. You specify a category and topic when starting a new conversation, so the backend maintains a summary for each category/topic - building up as more conversations happen under the same one. There is no complicated RAG, embeddings, or agentic magic, but the category/topic summaries system just works, and I've genuinely found it super handy and have been using it daily for my life and work. I started it to get familiar with Durable Objects and to test out this idea draft I had buried on my board. After recently…
Aug 2026 · github.com
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