Be cautious of teaching sandboxed agents like “Sand” to think, as isolated environments can yield unpredictable AI behaviors and hidden risks.

Some developers are reluctant to run AI tools continuously in the background, valuing system performance and minimal intrusiveness over always-on feat...Show more

Simple modular concepts like AI skills and agents can have a profound impact on development, challenging the belief that complexity demands complex fo...Show more

Maintain a living repository of AI development approaches to prevent losing productive techniques over time when shifting between projects.

Treating AI agents as if they were human teammates highlights parallels in delegation, performance evaluation, and role definition.

Express skepticism about OpenAI's workflow builder, arguing that more effective or alternative solutions are needed to meet real-world automation requ...Show more

Tom Spencer emphasizes the importance of recognizing the subtle but meaningful gains provided by iterative AI advancements, even when they aren't grou...Show more

Tom Spencer contrasted the high expense of AI hardware like DGX and Apple Vision Pro, suggesting they often serve as status symbols rather than practi...Show more

Once an AI solution succeeds, iterating and reapplying its core innovations is critical to maintain competitive advantage.

Popular AI trends aren’t guarantees of value—rigorous research and deep technical understanding are essential before adoption.

The AI sector is on the brink of explosive growth, fueled by both hype cycles and emerging breakthrough technologies.

AI product development cycles are accelerating with high velocity and continuous iterations driving rapid change.

Cameron Rohn advocates for 'vibe coding' as a product management approach to rapidly build and iterate AI and software tools.

Contrary to widespread skepticism, smaller open-source models can yield strong results when orchestrated properly, suggesting architecture and system ...Show more

Despite common skepticism, open-source GPT models deserve another look as their architectures and performance rapidly improve.

Future AI will likely use an OS-integrated model router to dynamically switch between local and cloud models based on user needs.

Michael Burry's contrarian approach of taking positions opposite to orthodox market views can yield outsized returns but requires enduring prolonged t...Show more

The AI funding bubble poses a bigger risk for hyperscalers like OpenAI and Google but has limited impact on smaller practitioners and startups.

We should think like VCs by leveraging our predictive abilities and data analytics skills instead of just focusing on pure product development.

The intense pace of AI product changes demands adaptive development processes and clear stakeholder communication to keep projects on track.