Combining a SvelteKit Single-Page Application (SPA) with a Go backend offers a powerful, performant, and cost-effective stack. This approach gives you the rich, modern developer experience of SvelteKit for the frontend, while leveraging Go's speed, simplicity, and concurrency for the backend API. Here's a detailed breakdown of how to approach building such an application, drawing on community discussions and best practices.

The fundamental concept is to create two distinct applications:

• SvelteKit Frontend (SPA): A client-side rendered application that handles all the UI and user interactions. It will be built into a set of static HTML, CSS, and JavaScript files.
• Go Backend (API): A server-side application that exposes a REST or GraphQL API for the SvelteKit frontend to consume. It will also be responsible for serving the static files of the SvelteKit applica

Before therapy apps and mindfulness retreats, before nihilism went mainstream, before we all started joking about the heat death of the universe on Twitter, one ancient philosopher had already figured out how to live without cosmic purpose. And once you see what Epicurus was really saying, you can't unsee it.

Here's the thing that's been keeping me up: we're living through a meaning crisis that someone solved 2,300 years ago. Not partially solved. Not gestured toward. Actually solved, with a four-part formula and everything. But we're so busy inventing new ways to be anxious about existence that we missed the guy who looked at an indifferent universe and said "perfect, that's exactly what I was hoping for."

A comprehensive guide to building self-orchestrating, parallel-executing, consensus-building agent workflows that actually work

Picture this: You're building a complex multi-agent workflow in Claude Code. You need agents to coordinate, pass information, and execute in sequence. You reach for the obvious solution - have one agent call another using a Task tool.

DSPy transforms LLM development from artisanal prompt engineering to systematic software engineering. Prompts become learnable parameters in a computational graph, optimized against defined metrics rather than hand-tuned through trial and error.

• Declarative Contracts: Define what, not how
• Systematic Optimization: Replace guesswork with formal optimization loops

The standard LLM tool-calling pattern is an anti-pattern for production. Every tool call costs 200-500ms of LLM latency plus tokens for the entire conversation history. Let me show you what actually works.

Here's what happens in the naive implementation:

# What the tutorials show you

Transform your Go development from good to exceptional with principles that reveal the language's true power

Every Go developer faces the same evolution: first you learn the syntax, then you write code that works, and finally—if you're fortunate—you discover the deeper patterns that make Go truly powerful. Most developers get stuck at stage two, writing Go code that technically functions but fights against the language's grain.

The difference between good and exceptional Go developers isn't knowledge of advanced features—it's understanding the philosophical constraints that make Go's simplicity so powerful. Go doesn't give you every tool; it gives you the right tools and makes the wrong choices harder to make.

This guide reveals the nine interconnected patterns that form Go's hidden philosophy. Master these mental models, and you'll find yourself writing code that feels inevitable—code that looks like the language itself suggested the

We had 14 developers, 3 active releases, and a monorepo that took 12 minutes to clone. Every context switch was killing us. That's when someone suggested using Git's most ignored feature as our entire development workflow.

Here's how we've been running our 400GB monorepo with instant context switching for 18 months—using a Git feature so obscure that most developers don't know it exists.

Picture this: Critical production bug comes in. You're 800 lines deep into a refactoring. The junior dev who could handle it is knee-deep in their own feature branch. The senior who knows that code is reviewing a PR.

Everyone said SQLite can't handle concurrent writes. Everyone was wrong. They just weren't creative enough with their definition of "handle."

Here's how we've been running 50+ concurrent processes writing to a single SQLite database for 18 months—and why it's more reliable than the PostgreSQL cluster it replaced.

We were building a distributed file scanner (props) that needed to catalog millions of files across multiple machines. The requirements were simple enough to make you weep:

I lost three days of customer data before I realized SQLite's "database is locked" errors were just the symptom. The real disease was silent corruption happening in production.

You've probably deployed SQLite thinking it handles concurrent writes. The documentation says it does. The internet says it does. Hell, even your tests pass. But there's a specific scenario where SQLite will corrupt your database so quietly you won't notice until your backups are also toast.

Everyone tells you to enable Write-Ahead Logging. "It solves concurrency!" they say. Let me show you the code that's probably in your production right now.

Generated on: 7/18/2025, 8:22:50 PM