SQLite, DuckDB, PostgreSQL, distributed DBs, vector DBs, graph DBs — there is no shortage of great products. I'm building a new database anyway because every time the project moves forward, I re-pick the DB and rebuild the data itself. Alopex DB aims to be a foundation that keeps data's volume, conversion time, regeneration cost, and provenance intact as you scale from local to cloud, from single node to distributed.
Neither LLMs nor image generators learn from training data that humans have labeled item by item. The key is self-supervised learning: mechanically constructing inputs and answers from raw data. Part 1 clarifies the difference between raw data and training data, and shows how an LLM turns text itself into a next-token prediction problem, with minimal code. Part 1 of a 5-part series.
Coding agents are strong at logic and tests. But they can't tell what a piece of data means, who owns it, how fresh it flows, or which copy is authoritative. It follows from the fact that today's AI has no embodiment: the people who rise in value are those who can design the meaning, quality, lineage, and responsibility of data. A look at the trend with the latest data-engineering discussion.
SQLite, DuckDB, PostgreSQL, distributed DBs, vector DBs, graph DBs — there is no shortage of great products. I'm building a new database anyway because every time the project moves forward, I re-pick the DB and rebuild the data itself. Alopex DB aims to be a foundation that keeps data's volume, conversion time, regeneration cost, and provenance intact as you scale from local to cloud, from single node to distributed.
Neither LLMs nor image generators learn from training data that humans have labeled item by item. The key is self-supervised learning: mechanically constructing inputs and answers from raw data. Part 1 clarifies the difference between raw data and training data, and shows how an LLM turns text itself into a next-token prediction problem, with minimal code. Part 1 of a 5-part series.
Coding agents are strong at logic and tests. But they can't tell what a piece of data means, who owns it, how fresh it flows, or which copy is authoritative. It follows from the fact that today's AI has no embodiment: the people who rise in value are those who can design the meaning, quality, lineage, and responsibility of data. A look at the trend with the latest data-engineering discussion.
