Jason Vertrees inchoate

Frontend Dashboard Refactoring Playbook

Based on the successful refactoring patterns I've seen before, here's a comprehensive playbook for refactoring another frontend dashboard. This guide will help another agent execute the same high-quality refactoring.

📋 Pre-Refactoring Checklist

  1. Analyze Current Structure
    - Map out all files in the dashboard directory

syncwrap

syncwrap is a light and elegant solution for running asynchronous functions in a synchronous environment. This helper neatly packages your async tasks, handling event loop creation and cleanup so you don’t have to.

Code

import asyncio

def run_async_safely(async_func, *args, **kwargs):

#!/bin/zsh

# Usage:
#     ./download_site.sh {URL}


url=$1
output_dir="./sites"

Moving from Sync to Async in FastAPI with SQLModel? Here's What You Need to Know!

Switching from a synchronous setup to an asynchronous one in your FastAPI/SQLModel app can bring some challenges. When making this switch, several important details need attention, especially around async sessions, avoiding misuse of await, and correctly handling relationships between tables.

A Typical Synchronous Setup:

# sync version
engine = create_engine(database_settings.pg_connection_string)
session = sessionmaker(bind=engine)

Ran Out of Database Connections on Your SQLModel App? Here’s a Quick Fix!

Problem:

You might have run into a situation where your SQLModel app unexpectedly runs out of database connections. If that sounds familiar, you probably have something like this in your code:

# utils/db.py
def get_db():
 """A utility function to grab a database session."""

Working with Enums in SQLModel and PostgreSQL: A Tasty Example with Ice Cream Flavors

Introduction:
Enums are a great way to represent a fixed set of values in your database. In this example, we'll model an IceCreamShop that serves various flavors of ice cream using SQLModel and PostgreSQL. We'll demonstrate how to store these flavors using enums and how to query for specific flavors using SQLAlchemy's powerful querying capabilities.

import os
from enum import Enum
from typing import List, Optional

Quick Setup: PostgreSQL with pgvector for AI Projects

If you need a working vector database for your AI project in no time, this guide is for you. Setting up PostgreSQL with pgvector is super easy, and Docker makes it even more convenient.

Step 1: Run PostgreSQL with `pgvector`

PostgreSQL is an excellent database, and Docker images make it easy to spin up a containerized version. To get PostgreSQL with pgvector installed and ready to go, run the following command:

docker run -d --rm \

	#!/bin/bash

	# cycle_stack.sh - Script to tear down and rebuild Docker Compose services
	# Usage: ./cycle_stack.sh [service_name1] [service_name2] ...

	set -e # Exit on error

	# Check if Docker Compose is installed
	if ! command -v docker compose &>/dev/null; then
	echo "Error: docker compose is not installed or not in PATH"

	# Makefile for managing Python dependencies with uv
	#
	# I built this because I got tired of juggling uv, pip, pyproject.toml,
	# and requirements.txt by hand. This Makefile gives me fast, reproducible
	# dependency management without forgetting to freeze, clean, or sync anything.
	#
	# Were I smarter I'd probably not have to do this. But, alas, I'm not.
	#
	# Key commands:
	#

	--
	-- This nice little query renames `camp_id` to `organization_id` in the JSON column.
	-- Note, it converts to and from JSONB to do this.
	--
	UPDATE public.data_camp_embeddings
	SET metadata_ = jsonb_set(
	-- Convert JSON to JSONB for easier manipulation, and remove the "camp_id" key
	metadata_::jsonb - 'camp_id',
	-- Insert "organization_id" key at the top level with the value from "camp_id"
	'{organization_id}',