Speed Up Your AI Development Workflow by 2x

Overview

Nick (the video author) explains how he increased his AI-assisted development productivity by focusing on the “typing bottleneck.” He argues that coding with AI/autocomplete often slows people down compared to normal typing.

Key workflow goal

• He typically types at around ~100 WPM, but coding tasks are slower due to editor/autocomplete friction.
• Instead of writing code manually into an AI agent (e.g., Claude), he prefers prompting agents in natural language (e.g., “run a security audit”) and needs that input to be fast.
• His solution has two parts:
  1. Fast AI-assisted typing using a local, screen-aware LLM
  2. Local speech-to-text dictation with app-aware formatting

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Tool #1: Code Typist (local AI autocomplete)

What it does

Provides smarter autocomplete suggestions while you type prompts/instructions (rather than hallucinating blindly).

How it speeds things up

• Reads:
  • the text you’re entering, and
  • the screen context
• Suggests the next phrase/command.
• You accept suggestions with Tab (example: building a sentence like: “Please run a security check on the code of this project.”)

Privacy model

• Runs with a local LLM (explicitly mentions Gemma 4B).
• Emphasizes that no requests are sent to OpenAI/Google/cloud services.
• Supports customization of AI instructions (e.g., preferred tone like friendly/casual, avoid emojis).
• Uses context options such as screenshots (and can disable clipboard context).

Model flexibility

• Can select different local models depending on machine/RAM (he mentions having large RAM).

Control over where it works

• Can exclude certain IDEs (e.g., Rider, IntelliJ).
• Can exclude domains (e.g., banking sites).

Extra safety verification

• Uses an app called Radio Silence to block specific applications from accessing the web.
• Mentions a network monitor to confirm nothing goes to the cloud.

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Tool #2: Type Whisper (local dictation)

What it does

Dictates instructions faster than typing and outputs text into the active app/editor.

Privacy model

• Also paired with Radio Silence to prevent web access.

Engines / accuracy tradeoffs

• Mentions ParaKit as the default (fast + accurate for him).
• References WhisperKit and Apple Speech:
  • Apple Speech is fast, but less contextually accurate.

Local enhancements

• Local file memory to remember prior dictations for better context.
• Snippets/dictionary mappings, e.g.:
  • speaking “Nick hyphen Chapsas” becomes replacing “hyphen” with “Chapsas”.

App-aware behavior

• Adapts formatting depending on context (e.g., editor vs terminal vs other contexts).

Productivity result (dictation)

• Reports ~150–160 WPM while dictating in his normal voice.
• Claims dictation avoids:
  • “typing latency,”
  • cloud latency, and
  • sending audio/text to external services.

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Example use case: security auditing prompts

He demonstrates using dictation or Code Typist to quickly request security checks such as:

• “Run a security audit…”
• “Make sure SQL injection is not possible…”
• “Check for HTTP-related vulnerabilities…”

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Main speakers / sources

• Speaker: Nick (host/author)