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Humanity has always tried to predict the future. We’re swapping tea leaves for datasets. We explore our collective obsession with AGI timelines, the moving goalposts of superintelligence, and why we ultimately decided to build a scoreboard to keep everyone honest.
Digital twinning allows you to minimize downtime when maintaining large-scale machinery. But it’s not exclusive to gigantic pieces of equipment—even something as small as a coffee machine can showcase its benefits.
Discover how to build a production-ready AI application on Databricks Apps in just under a month. Learn from our journey, challenges, and architectural choices.
dbt Labs recently announced dbt Fusion, a complete overhaul of the dbt Core engine built in Rust. It promises to significantly improve the developer experience. In this article, we test its core features and share our hands-on experience with the public beta, exploring what works, what doesn't, and what potential it holds for the future of dbt development.
From more guardrails by developers to the UX challenge of showcasing sources in a non-deterministic system, the industry is still finding its grip on the whole process. We looked into the nitty-gritty details at our recent meetup on UX in the age of AI agents—check out the recording below!
Anonymizing unstructured data like medical records or legal documents is much harder than with structured data. The primary challenge is identifying sensitive information (PII) within free-form text, which can be obscured by jargon, abbreviations, and OCR errors. This guide explores the viable approaches, from simple rule-based systems to advanced Machine Learning and hybrid models.
More efficient development, simplified maintenance, and a higher level of accessibility. A power tool in the Databricks ecosystem, Lakeflow Declarative Pipelines simplifies the creation of data pipelines while providing a declarative framework, allowing data engineers to focus on the desired state of the pipeline instead of getting lost in details.