A Smooth Sea Never Made a Skillful Sailor

The AI product climate this week was weirdly clarifying. I am still not ready to move my development workflow back to Gemini after the rate-limit frustration, but I also have to admit that Google shipped a few things that made me pay attention again. At the same time, OpenAI reminded me that even the best developer tooling can feel unnecessarily confusing when the billing model sprawls across too many surfaces. Then OpenRouter raised a serious round of funding and made the case for a future where developers do not have to bet everything on one model vendor. That is the pattern I keep coming back to: the tools are getting better, but the surrounding product experience still matters just as much as the model. Gemini Is Back in My General-Purpose Rotation I have complained plenty about Gemini rate limits. The stricter enforcement hit the wrong nerve for me because it made the product feel unpredictable at exactly the moment I wanted to rely on it more. Once a tool makes me ration m...

Disclaimer: The opinions expressed in this article are my own and do not necessarily reflect the views of my employer. This tutorial demonstrates how to use ReadySet as a caching layer for PostgreSQL databases. ReadySet is a wire-compatible caching solution that sits between your application and database, providing sub-millisecond query performance without requiring code changes. Why Cache PostgreSQL? PostgreSQL is the world's most advanced open-source relational database, widely used for transactional workloads. Whether you're running PostgreSQL on-premises, in the cloud, or using a managed service, every query incurs: Network round-trip latency – Depending on region and network conditions (e.g., 14ms from a client in New Jersey to a cluster in AWS us-east-1) Query processing overhead – Parsing, planning, and execution on the database server Result serialization – Packaging and transmitting results back to your application For read-heavy workloads with repetit...

This article serves as a follow-up to previous discussions on data ingestion into Snowflake, specifically focusing on the complexities and efforts involved in integrating Change Data Capture (CDC) data with Snowflake in real time. Previous Articles on Snowflake Tour of Snowflake ingestion using CockroachDB and Redpanda Connect Integrating Snowflake with Trino Previous Articles on CockroachDB CDC Emitting Protocol Buffers with CockroachDB CDC Queries Using CockroachDB CDC with Apache Pulsar Using CockroachDB CDC with Azure Event Hubs SaaS Galore: Integrating CockroachDB with Confluent Kafka, FiveTran and Snowflake Using CockroachDB CDC with Confluent Cloud Kafka and Schema Registry CockroachDB CDC using Minio as cloud storage sink CockroachDB CDC using Hadoop Ozone S3 Gateway as cloud storage sink Motivation This article builds upon the previous discussion in Tour of Snowflake ingestion using CockroachDB and Redpanda Connect , where we investigated the process of str...