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But what if I really want a faster horse?
16 hours ago
- Henry Ford's quote about faster horses is used to contrast innovation with desire for simple improvements.
- Netflix in 2012 was a fast horse: simple catalog, reliable recommendations, user control with 5-star ratings.
- Netflix today is an engagement-driven experience with shuffled content, changing covers, and meaningless categories.
- Spotify in 2015 was a fast horse: iTunes-like but with massive library; now it's like Netflix with inconsistent streaming and weak library tools.
- Tech products are converging toward TikTok-style interfaces, sacrificing user control for engagement (crabification).
- YouTube, LinkedIn, and Substack have all adopted TikTok-like features, moving away from their original functional designs.
Thunderbird Desktop settings research: what we learned from your feedback
15 days ago
- Thunderbird user research reveals a deep trust in open-source and Mozilla's transparency.
- Users prefer extensive initial customization followed by minor tweaks for a modern look.
- Reducing clutter and cognitive noise in settings menus is a top user priority.
- In-app search is the preferred method for navigating complex configuration options.
- Many users treat their unread inbox as a task list or to-do queue.
- Technical terminology in settings causes hesitation, even among advanced users.
- Planned improvements include simplifying language, streamlining settings categories, and enhancing privacy explanations.
- The interface update aims to modernize UI without compromising Thunderbird's robust functionality.
- Accessibility improvements are being incorporated based on a community audit.
- Next steps involve finalizing project scope and designing a holistic desktop and mobile settings architecture.
It's Hard to Eval Is a Product Smell
24 days ago
- The statement 'our product is hard to eval' is a product smell, indicating potential usability issues for both developers and users.
- Designing products for ease of verification should prioritize user trust and reduce the need for users to redo work to verify AI outputs.
- Example 1: AI data agents should provide checkable artifacts, like interactive notebooks with progressive disclosure, to allow verification through trusted sources, metric definitions, and sanity checks.
- Example小孩2: An AI tool for physical education lesson plans should anchor generated plans in vetted, existing plans, showing diffs and reasons for changes to reduce cognitive load during review.
- Example 3: A workers' compensation medical report tool should act as a research assistant, surfacing evidence, contradictions, and gaps for doctors to verify before assembling final reports.
- General principles include understanding user verification needs, using provenance and progressive disclosure, and breaking workflows into smaller, verifiable units.
- Designing for verification aligns with established principles like needfinding and sensemaking, and is crucial in the AI era where verification is a bottleneck.

Hasty Briefsbeta

product design

But what if I really want a faster horse?

Thunderbird Desktop settings research: what we learned from your feedback

It's Hard to Eval Is a Product Smell