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Launch: Chaser, a macOS Utility That Captures UI Context Beyond Screenshots for AI Agents

A Show HN post launches Chaser, a local macOS utility using accessibility APIs to capture layout, button states, and object mappings beyond a plain screenshot, intended for giving AI coding agents richer context. This is a promotional launch rather than a first-person complaint.

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Similar Problems

surfaced semantically
Productivity83% match

Users accumulate thousands of screenshots with no way to search or find them later

Power users accumulate thousands of screenshots on macOS and mobile with no native or third-party tool to search them by content, making screenshots functionally unsearchable and wasted

Developer Tools81% match

Windows Lacks a Polished All-in-One Screen Recording and Editing Tool

Content creators on Windows must combine three to four separate tools to achieve what macOS users get natively from Screen Studio — smooth recording, zoom effects, cursor tracking, webcam overlay, and editing in one place. The gap is well-understood but existing Windows tools remain fragmented and less polished.

Developer Tools80% match

AI coding agents require verbose text to identify UI elements from screenshots

Developers using AI coding assistants must write lengthy descriptions to reference specific UI elements in screenshots, since agents lack spatial annotation tooling. Clipboard context is often lost in chat interfaces. A point-and-annotate layer over screenshots would let developers pin precisely what they mean, dramatically reducing prompt friction.

Productivity80% match

Tansei Mac Clipboard and Shelf Utility Product Description

Describes a Mac utility that keeps recently copied items, snippets, and files pinned at the screen edge for quick drag-back access during app switching. This is a product listing rather than a reported user problem.

Developer Tools79% match

No Local Privacy-Preserving Screen + Accessibility Tree Capture Tool

Developers building workflow summarization tools need to capture screen state and accessibility trees locally without sending data to the cloud. Existing tools are either cloud-based, lack a11y tree support, or are too complex to integrate. A simple open-source local solution is missing.

Problem descriptions, scores, analysis, and solution blueprints may be updated as new community data becomes available.