Have you ever spent 20 minutes looking for a conversation you had with Cursor last week? The one where it helped you fix a tricky async bug—and now you're facing the same issue in a different project, but can't find that thread anywhere? This isn't a user error. It's a structural limitation in how Cursor handles session history. Cursor includes a built-in conversation history panel. You can browse
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
llms.txt is a small text file on a documentation site—usually lists what the product is and links to the important Markdown pages. For coding agents, treat it as the canonical URL to open first when upstream behavior is unclear. This post is mostly setup and workflow, not theory. Location Put this there Official doc server https://example.com/llms.txt (maintained by the library/vendor) Y
This post was created with AI assistance and reviewed for accuracy before publishing. Cursor can use project rules and documentation to steer behavior. Exact file names and mechanisms evolve; check Cursor documentation for the current layout (for example rules in .cursor or legacy .cursorrules patterns). Short, enforceable bullets beat long essays: stack versions, test commands, “no new dependenci
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
"Write a function to fetch the list of users." — same prompt, same codebase. Yesterday: getUsers(). Today: fetchUserList(). Tomorrow: loadAllUsers(). Six months of AI-assisted coding and I kept hitting this wall. My initial reaction was "maybe I need to write better prompts." I wrote better prompts. The functions got slightly better. New inconsistencies appeared elsewhere. The problem wasn't the A
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