The API Rate Limit Catastrophe In modern B2B SaaS development at Smart Tech Devs, your application rarely lives in isolation. You constantly communicate with external services: billing via Stripe, CRM syncing via Salesforce, or email campaigns via Resend. The architectural trap occurs when you combine the immense speed of Laravel Queues with the strict rate limits of these third-party APIs. If you
SQL is widely known for data querying and manipulation but systems do grow; data becomes larger; processes become repetitive and operations become sensitive. SQL has some features which enables it to be considered a fully fledged programming language. Some of the features which I discuss in this article are procedures, functions and transactions. Each of these concepts serve distinct purposes. Sto
Hi 👋, In this post we shall explore Bedrock's structured KB with this architecture: Upload CSVs to S3 > SNS Queue > Crawl data with Glue > Query with Redshift > Bedrock KB > Query with LLM. Let's do some of this with code. Let's get started. Clone the repo and switch to the project directory. git clone [email protected]:networkandcode/networkandcode.github.io.git cd structured-kb-demo/ Do a uv sync
A RAM read takes about 100 nanoseconds. A disk read — even on a modern SSD — takes around 100,000 nanoseconds. That single gap explains most of Redis’s speed, before it does a single thing clever. Friend’s Link But RAM alone isn’t the full story. The other half is a design decision that looks like a limitation on paper — and turns out to be one of the smartest choices in the codebase. More on that
Subqueries vs. CTEs in SQL: A Practical Guide to Writing Cleaner, Smarter Queries Whether you're just getting comfortable with SQL or leveling up your data skills, two tools will come up again and again when working with complex queries: subqueries and Common Table Expressions (CTEs). They solve similar problems — breaking a complex query into manageable pieces — but they do it in different ways
In a previous post, I explored Codd's connection trap in PostgreSQL and MongoDB — the classic pitfall where joining two independent many-to-many relationships through a shared attribute produces spurious combinations that look like facts but aren't. The example followed Codd's 1970 suppliers–parts–projects model: we know which suppliers supply which parts, and which projects use which parts, but j
Automating Hermitage to see how transactions differ in MySQL and MariaDB
Barman – Backup and Recovery Manager for PostgreSQL