Productivity & AI

We build a team of strong ICs who are autonomous and highly leveraged with AI. It allows us (1) to move faster as individuals and (2) to automate processes that otherwise would require hiring someone to do it.

Everyone is free to spend on their favorite AI tools for personal productivity. If unsure, ask the founders.

This is a collection of (1) personal and (2) team productivity tools/hacks. This is not an exhaustive list, but a starting point for you to explore and use as needed.

1. Personal productivity

We see a couple of common patterns when it comes to using AI for productivity:

• We often start drafts dictation-first using speech-to-text tools to capture thoughts quickly.

• For coding, we plan before generating code, often using Test-Driven Development (TDD) and always reviewing changes patch-by-patch instead of auto-accepting large features.

• To ground the AI, we inject context by feeding it repository files, documentation, and project tickets. All significant work is tracked in Linear to maintain context across iterations.

• To ensure quality, we start written content with a human-made outline to reduce generic AI filler.

Tools

We use a variety of specialized tools for different tasks.

• Coding & IDEs: Cursor is our primary AI-native editor, valued for its git integration and parallel agents. We also use Claude Code for its strong planning mode and Warp terminal for command-line help.

• Dictation: Superwhisper and the ChatGPT app’s speech-to-text are common for fast input.

• Research & Browsing: ChatGPT’s agent mode handles deep research, while Dia Browser is used for multi-context customer operations. Raycast AI provides quick lookups.

• Project Management: Linear for planning and context. ChatGPT Projects helps manage long-term research with document uploads.

• Email & Comms: Superhuman AI drafts quick, low-stakes emails. For more complex messages or copy, we use LLMs for formatting assistance.

AI Coding Workflow

1. Plan: We start by writing an implementation plan in a Linear issue and pasting it into the agent.

2. Context: We load the repository context using a files-to-prompt CLI tool or by connecting to documentation servers.

3. Implement: We run agents in parallel tabs in Cursor for isolated changes and review each patch before staging it. We avoid auto-accepting large features.

4. Quality Control: For well-defined problems, we have the agent write tests first. We use “thinking budget” cues and switch models if progress stalls.

Writing

To create high-quality technical content:

1. Outline: A human always creates the initial structure and key points as this ensures the high level content is of high-quality and relevant.

2. Research: We use ChatGPT or Gemini to summarize a few high-quality source articles.

3. Draft: We use a detailed “anti-slop” prompt that instructs the model to be concise and information-dense. Based on Hamel Husain’s recommendation, we are using Gemini as it sounds the least like AI slop.

Writing guide:
- Do not add filler words
- Make every sentence information dense
- Get to the point
- Use short words and fewer words
- Avoid multiple examples
- Don't use phrases like "it's important to note"
- Avoid unnecessary transitions

4. Refine: We generate drafts from multiple models (e.g., Gemini and ChatGPT) and blend the best parts.

Sales & Customer Operations

• Before calls, we load calendar invites into an LLM for background research on attendees.
• Afterward, we feed call transcripts into Gemini for coaching and generating follow-ups.
• Agent modes are used to complete lengthy due-diligence questionnaires.

Non-AI Productivity Tools

Several non-AI tools are critical to our workflow:

• Raycast: Used for clipboard history, quick writing improvements, and one-click meeting joins from its calendar integration.

• CleanShot X: Handles all screenshot and annotation needs.

• ScreenStudio: Our choice for recording product demos.

• Window Managers: Tools like Rectangle or Raycast’s built-in feature are key for workspace organization.

2. Team productivity

• AI vendors: We generally prefer to use external vendors as this is usually more cost-effective than maintaining the process ourselves. Examples: Ask AI on langfuse.com is powered by Inkeep; PR code review is powered by Ellipsis and Greptile
• Internally built tools:
  • Code: You can use our company OpenAI/Anthropic accounts
  • Dogfood Langfuse

You can find some examples in this blog post: How we use LLMs to scale Langfuse