#output-index

#output-index

I was always a tinkerer, I guess. I grew up in the age where computers were not ubiquitous or common. An experience as a kid was instrumental in how my career happened.

You make a small change to your table, adding a single row, and it affects data lake performance because, due to the way they work, a new file has to be written that contains one row, and then a bunch of metadata has to be written. This is very inefficient, because formats like Parquet really don't want to store a single row, they want to store a million rows.

With this feature, you can bring entire folders, ML models, dbt adapters, utilities, directly into UDxFs and Stored Procedures without zipping, file-by-file bookkeeping, or manual updates.

Hyperscalers and major data platform vendors offer integrated services across storage, analytics, and model infrastructure. MariaDB's differentiation will likely depend on whether the combined platform can deliver operational speed and simplicity that organizations find easier to run than those larger stacks.

What happens when the AI companies (inevitably) encounter spam and attempts at SEO/GEO manipulation in the markdown files targeted to bots? What happens when the .md files no longer provide an equivalent experience to what users are seeing? What happens if they continue crawling those pages but actually toss them out before using the content to form a response? ...And we keep conflating "bot crawling activity" with "the bots are using/liking my markdown content?" How will we know if they're actually using the .md files or not?

I began by creating a soft link locally from my blog's repo of posts to the src/pages/posts of a new Astro site. My blog currently has 6742 posts (all high quality I assure you). Each one looks like so: --- layout: post title: "Creating Reddit Summaries with URL Context and Gemini" date: "2026-02-09T18:00:00" categories: ["development"] tags: ["python","generative ai"] banner_image: /images/banners/cat_on_papers2.jpg permalink: /2026/02/09/creating-reddit-summaries-with-gemini description: Using Gemini APIs to create a summary of a subreddit. --- Interesting content no one will probably read here...

Developers have spent the past decade trying to forget databases exist. Not literally, of course. We still store petabytes. But for the average developer, the database became an implementation detail; an essential but staid utility layer we worked hard not to think about. We abstracted it behind object-relational mappers (ORM). We wrapped it in APIs. We stuffed semi-structured objects into columns and told ourselves it was flexible.

By replacing repeated fine‑tuning with a dual‑memory system, MemAlign reduces the cost and instability of training LLM judges, offering faster adaptation to new domains and changing business policies. Databricks' Mosaic AI Research team has added a new framework, MemAlign, to MLflow, its managed machine learning and generative AI lifecycle development service. MemAlign is designed to help enterprises lower the cost and latency of training LLM-based judges, in turn making AI evaluation scalable and trustworthy enough for production deployments.

Google has overhauled Firestore Enterprise edition's query engine, adding Pipeline operations that let developers chain together multiple query stages for complex aggregations, array operations, and regex matching. The update removes Firestore's longstanding query limitations and makes indexes optional, putting the database on par with other major NoSQL platforms. Pipeline operations work through sequential stages that transform data inside the database.