Distributed teams lose visibility into what everyone is doing. Managers interrupt with status requests. Developers context-switch to update multiple systems. Jira tickets don’t reflect reality. Confluence pages go stale. Git commits tell part of the story.

Clarity solves this by using AI to synthesize status from all these sources into readable text that humans actually want to read.

The Architecture

Model Context Protocol (MCP) servers pull data from Jira, Confluence, and Git into a shared context window. AI processes this nightly, generating personalized daily status reports in Markdown. Each team member reviews their pre-baked summary via chat, CLI, or any text interface. Management gets real-time access to these reports and generated dashboards.

macOS has a built-in window switcher (Control+F4). It’s two steps: activate the list, then select a window. It has no memory of what you searched for last. Every time you switch, you start from scratch.

FFWF (Fast Fuzzy Window Finder) solves this. Menu bar app with global hotkey. Type to fuzzy filter windows by title. Windows stay visible in the list. Use arrow keys to select. Hit Enter. Done. Remembers your last search. Works with VoiceOver.

Video data represents one of the richest sources for training AI models, from action recognition to content moderation systems. However, raw video often contains significant noise - dead air, redundant frames, and irrelevant segments. Here’s a production-tested approach to automated video processing that has streamlined our training data preparation.

The Challenge: Extracting Signal from Video Noise

When building datasets for video understanding models, we frequently encounter:

• Long pauses that add no informational value
• Redundant segments that can skew model training
• Inconsistent formats that break processing pipelines
• Massive file sizes that inflate storage costs

The solution? Automated jump-cut processing with intelligent backup strategies.

Enterprise architects bridge executives, developers, and users. No tool automates this. The primary skill is writing - clear, concise documentation that aligns technology with business goals.

AI makes this more important, not less. LLMs need context. Agents need documentation. Teams need alignment. All of this starts with written artifacts.

Strategic Alignment Requires Written Clarity

Technology alignment begins with understanding business goals. This means listening, asking questions, and writing down what you learn. Written documentation creates shared understanding across the organization.

Why command-line proficiency accelerates AI development and enables powerful data processing pipelines