Automated data pipeline for your AI apps
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…
Automated data pipeline for your AI apps launched on October 9, 2023 with 5 votes, #1,683 of 4,393 launches that month and more than 50% of that year's launches.
What it does
In the maker’s words, at launch
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…
Does a similar job
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TensorZero2025 · github.com · ▲307Hi HN! We're Gabriel & Viraj, and we're excited to open source TensorZero. To be a little cheeky, TensorZero is an open-source platform that helps LLM applications graduate from API wrappers into defensible AI products. 1. Integrate our model gateway 2. Send metrics or feedback 3. Unlock compounding improvements in quality, cost, and latency It enables a data & learning flywheel for LLMs by unifying: • Inference: one API for all LLMs, with <1ms P99 overhead • Observability: inference & feedback → your database • Optimization: better prompts, models, inference strategies • Experimentation:…


Open-source real time data framework for LLM applications2024 · getindexify.ai · ▲92Hey HN, I am the founder of Tensorlake. Prototyping LLM applications have become a lot easier, building decision making LLM applications that work on constantly updating data is still very challenging in production settings. The systems engineering problems that we have seen people face are - 1. Reliably process ingested content in real time if the application is sensitive to freshness of information. 2. Being able to bring in any kind of model, and run different parts of the pipeline on GPUs and CPUs. 3. Fault Tolerance to ingestion spike, compute infrastructure failure. 4. Scaling compute,…
MovingLake AI Data Insights2023 · movinglake.com · ▲96Ask questions about your data in plain english
HelixDB – Open-source vector-graph database for AI applications (Rust)2025 · github.com · ▲237Hey HN, we want to share HelixDB (https://github.com/HelixDB/helix-db/), a project a college friend and I are working on. It’s a new database that natively intertwines graph and vector types, without sacrificing performance. It’s written in Rust and our initial focus is on supporting RAG. Here’s a video runthrough: https://screen.studio/share/szgQu3yq. Why a hybrid? Vector databases are useful for similarity queries, while graph databases are useful for relationship queries. Each stores data in a way that’s best for its main type of query (e.g.…
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