The article compares the rise of large language models to the 1990s database boom, arguing that models may become ubiquitous and unnoticed—just like SQL engines today. It suggests the real story won’t be flashy launches but the “SQLite” of AI: the model everyone uses by default without ever choosing it.

• The 1990s database market leaders (Oracle, Sybase, Informix) lost relevance while unheralded PostgreSQL and SQLite became the actual ubiquitous defaults.
• SQLite's win came from being invisibly embedded everywhere (phones, browsers, apps) rather than from marketing or benchmark wins.
• The same pattern may repeat with LLMs: today's flashy model launches may matter less than whichever model quietly becomes the unchosen, default embedded standard.

Set up the Tansu broker using SQLite on an AWS t3.micro instance, taking advantage of the AWS Free Tier. The guide covers installation, configuration, and performance testing, demonstrating how to launch a low-cost Kafka-compatible broker for early-stage projects.

• Tansu with embedded SQLite runs a full Kafka-compatible broker on a free t3.micro (1GiB RAM), storing everything in a single tansu.db file for trivial backup/restore.
• Despite minimal hardware (~10MB/s EBS baseline throughput), the setup delivers usable, measurable throughput and latency, proving Kafka-compatible workloads are feasible on free-tier resources.
• Running below the t3 baseline CPU utilization accumulates burst credits, letting the broker handle demand spikes without extra cost.
• Tansu itself is lightweight on memory, leaving most of the 1GiB instance free for other system needs.

Durable Execution (DE) engines allow for the persistence of multi-step workflows, enabling them to resume from the last successful step after interruptions. This article explores the development of a simple DE engine called Persistasaurus in Java, which utilizes SQLite for logging execution state and highlights key implementation choices.

• Persistasaurus implements durable execution in Java by logging each step's execution state to SQLite, allowing workflows to resume from the last successful step after a crash.
• SQLite's single-file, embedded, transactional nature makes it a lightweight yet reliable choice for storing execution logs without needing a separate database server.
• The design records both function inputs/outputs and step completion status, so re-running a workflow skips already-completed steps and replays their cached results instead of re-executing side effects.