# IBM Cloud Object Storage Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/ibm-cloud-object-storage
> Markdown URL: https://aitinkerers.org/technologies/ibm-cloud-object-storage.md
> Technology record last updated: 2026-02-25T10:20:26Z
> Generated: 2026-09-20T23:32:16Z

Highly durable, S3-compatible storage designed for massive unstructured data sets and secure hybrid cloud workloads.

Manage petabytes of data with 99.999999999 percent (11 nines) durability using Information Dispersal Algorithms (IDAs) to slice and encrypt objects across independent nodes. Choose from four storage tiers (Standard, Vault, Cold Vault, and Smart Tier) to balance performance and cost for everything from active AI training sets to long-term regulatory archives. The platform supports native S3 API integration and features built-in high-speed transfer via IBM Aspera for rapid ingestion of large files.

- Official technology site: https://www.ibm.com/cloud/object-storage
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Nixiesearch: a new Lucene-based hybrid search engine which fine-tunes to your data](https://berlin.aitinkerers.org/talks/rsvp_jtED_DXuUG8)

Building a new search engine in 2024 sounds like a stupid idea: a new vector search startup is created every week, so how can you be different from so many competitors? But in practice, you can quickly discover that putting vectors into an HNSW index is not the most challenging part of building a search application that your customers would use and like. Relevance tuning, multi-field search, facets, filters, autocomplete suggestions - the RAG-vector search crowd is still discovering all these “novel” things. In this talk, we’re going to introduce Nixiesearch, an open-source hybrid search engine focused on solving typical search problems: * Based on Lucene. You get filters, facets, autocomplete, and complex queries out of the box with decent performance. * Cloud-native and serverless. Can use S3-compatible block storage for index persistence, being able to scale to zero. * Can fine-tune the underlying embedding model on your relevance labels (if you have them) or LLM-generated synthetic labels. Nixiesearch is still in an early development stage, so your opinion on “how to do a search engine right” is really important.

- Event context: AI Tinkerers Berlin - March 21 — 2024-03-21 — Berlin
- Public talk page: https://berlin.aitinkerers.org/talks/rsvp_jtED_DXuUG8

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