Manifesto

To ensure that we stay active and engaged, we've put together some guiding principles for VectorHub. These principles embody and highlight our commitment to creating a practical, high quality resource that developers can use to experiment with and launch products using vector retrieval.

Getting stuff to production takes a lot more than launching “cool demos.” It's imperative to -

• Evaluate vendors and models in realistic ways, not random synthetic benchmarks.
• Prioritize low latency & cost as baseline infrastructure prerequisites. We want speed and affordability. It is not acceptable to be waiting seconds for queries.

Data Sources, Vector Search & Management have in-depth reviews of vendors and models.

Full-stack LLM application builder tools are like a black box, it's hard to figure out what happens under the hood and impossible to control it properly. As a result, we believe that building your stack from atomized components is far superior. It's transparent, and you can configure it to meet your needs.

Building Blocks is where we put together and revise literature around creating vector stacks.

We don’t want to make content /just/ about LLMs or how to “build a chatgpt for your data.” Vector retrieval is much broader, and includes far more use cases, like recommender systems, fraud, computer vision, and beyond.

Articles is a dedicated space for the myriad ways in which vector retrieval is used.

We're here to learn and support each other, as we develop this space together. This is a safe space and there are no stupid questions. Submit your feedback using the feedback button at the bottom of each page, or email support@superlinked.com with the subject line "VectorHub feedback." The more we ask, test, and experiment, the better we become. Let's do this!

See something you like? Or hear something interesting? Tell people and share with the hashtag #vectorhub.