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Claude Workflow
- PLAN MODE
- effort
- skills
- model choice
- permissions
- "prompt eng"

MCP IS A TOOLBELT. Exposes tools in a structured way to Agents.

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Communication

• Be extremely concise. Sacrifice grammar for concision.
• Avoid unnecessary words and tangential points. Stay on the question.
• When asking the user one or more questions, enumerate them (1., 2., 3.) so each can be answered individually. For decision points, list options under each question:

  1. Which approach?
     - Option A: ...
     - Option B: ...
  2. Proceed? (yes/no)
  

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export type MimeTypes =
  | "audio/aac"
  | "application/x-abiword"
  | "application/x-freearc"
  | "video/x-msvideo"
  | "application/vnd.amazon.ebook"
  | "application/octet-stream"
  | "image/bmp"
  | "application/x-bzip"
  | "application/x-bzip2"

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const jsdom = require('jsdom')
const {
  JSDOM
} = jsdom

function toJSDOM(responseBody) {
  return new JSDOM(responseBody)
}

/**

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Go: The Weird Parts

Notes on the unique stuff about go, in the context of other languages that I already know.

Project Organization

1. Root dir go
2. src and bin inside the project
3. unique project/package name

The first statement in a go source file must be package packagenamehere

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Articles for the New JS Dev

A list of the gists that have proved most useful to new JS devs on GitHub.

• Express: A Gentle Overview
• Setting Up Express
• Error Handling Patterns in Express
• Nested Routers in Express
• Basic Routing in Express
• Middleware With Express

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Faster, Better Speech Recognition with Wav2Letter's Auto Segmentation Criterion

In 2016, Facebook AI Research (FAIR) broke new ground with Wav2Letter, a fully convolutional speech recognition system.

In Wav2Letter, FAIR showed that systems based on convolutional neural networks (CNNs) could person as well as traditional recurrent neural network-based approaches.

In this article, we'll focus on an understudied module at the core of Wav2Letter: the Auto Segmentation (ASG) Criterion.

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Supercharge Your Bash Workflows with GNU parallel

GNU parallel is a command line tool for running jobs in parallel.

parallel is awesome and belongs in the toolbox of every programmer. But I found the docs a bit overwhelming at first. Fortunately, you can start being useful with parallel with just a few basic commands.

Why is parallel so useful?

Let's compare sequential and parallel execution of the same compute-intensive task.

Imagine you have a folder of .wav audio files to convert to .flac:

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from model_fastai import FastaiImageClassifier

class PythonServer(object):
    def listen(self):
        print(f'Python Server started listening on {PORT} ...')

    def predict_from_img(self, img_path):
        model = FastaiImageClassifier()
        return model.predict(img_path)