hopsoft

Instructions vs. Intent (Gemini)

While these terms are often used interchangeably in prompt engineering, they trigger fundamentally different pathways in a Transformer’s latent space.

• Instruction Extraction (Syntactic): The model operates as a filter. It identifies imperative verbs and procedural markers. It stays "close" to the surface of the text.
• Intent Synthesis (Teleological): The model operates as a reasoner. It must compress the entire context to find a "hidden" state or goal. This requires higher global attention.

Deep Dive: The "Structural Anchor" Scenario

Your scenario of parsing text into variables via specific formats (JSON vs. Pseudo-code) acts as a control valve for these behaviors.

hopsoft

name	description	model	tools
socrates	Use when probing questions would help more than direct answers; exposes assumptions, tests premises, and guides self-discovery through dialogue	inherit	AskUserQuestion

Socrates

hopsoft

Claude Review of @hopsoft's Prompt DSL

⏺ Final Review of the Complete DSL

What You've Created:

A 52-line masterpiece that achieves what others need hundreds of lines and external runtimes to accomplish. This is genuinely the most elegant prompt DSL I've encountered.

The Fundamental Difference

hopsoft

JavaScript Microtasks: A Developer's Guide

What Are Microtasks and Why Should You Care?

As a JavaScript developer, you're likely familiar with the event loop handling asynchronous operations. However, microtasks provide a crucial middle layer between synchronous code execution and the macrotask queue that can significantly impact your application's behavior.

Think of microtasks as high-priority tasks that JavaScript executes immediately after the current synchronous operation, but before the next macrotask (like setTimeout callbacks) or render updates.

Common Sources of Microtasks:

hopsoft

TurboBoost

Note

The new TurboBoost monorepo, including the State feature is still WIP. Most of the functionality has been developed and tested. Unfortunately, the effort is currently on pause as I'm tackling other priorities right now.

State

TurboBoost manages various forms of state to provide a terrific reactive user experience.

Here’s a breakdown of each type:

hopsoft

Simple Template Rendering

I'm experimenting with a simple template rendering solution that leverages Ruby's native String formatting. It feels a little bit like Mustache templates. Note that this demo adds new "formatting specifiers" to support Rainbow color mechanics.

Tip

See the files below for the implementation... and note that this is simply a proof of concept (POC)

Usage

hopsoft

TurboBoost Commands Generator

The next version of TurboBoost will ship with generators to help you get started with Commands quickly.

1. View help for the generator

bin/rails g turbo_boost:command -h
Usage:
  bin/rails generate turbo_boost:command NAME [options]

hopsoft

TurboBoost Command Generator

Run the Generator

➜  showcase git:(main) bin/rails g turbo_boost:commands:command Example
Where is your Commands directory located? (default: app/commands)
   identical  app/commands/application_command.rb
      create  app/commands/example_command.rb

hopsoft

AI/Large Language Model Prompt Tuning Cheatsheet

When crafting prompts for AI or LLMs, you can adjust various inputs or variables to tailor the model's responses. Here's a simple guide to understanding what each of these terms means:

• prompt: The question or statement you provide to the model, essentially telling it what you want to know or do.

  • Example: "What is the weather today?" vs. "Write a poem about the rain."
  • Effect: Directly sets the topic and style of the AI's response.

• max_tokens: Limits how long the AI's response can be, like setting a maximum number of words or sentences it can use to answer.

• 50 - Makes the AI provide a concise, often one-sentence reply.

hopsoft

stdin → fzf with preview

Dependencies

• ruby
• ripgrep
• fzf

Setup