We like to write. We believe writing is a powerful tool for thought and it's much harder to fake your way through paragraphs than it is through a conversation.
I built Aesthete—a local-only Typescript/NextJS/Tailwind site powered by Claude Code and Anthropic frontend design skills—to curate nearly 200 aesthetically satisfying brands, showcasing how “software for one” can turn ideas into polished, interactive experiences almost instantly.
By applying Herbert Simon’s concept of satisficing to AI, this post argues that language models might prefer logical‐sounding content over emotional appeals, mirroring human biases but inverted. It unveils a paradox: humans use emotion to decide rationally, while LLMs use pseudo‐rational style to appear helpful.
Forward-deployed engineers sit alongside customers to collapse the layers of organizational friction in traditional software development, delivering better quality solutions faster and at lower cost. Fueled by AI, this model breaks the classic ‘good, fast, cheap—choose two’ trade-off and reshapes how enterprises solve problems.
On April 15, 2026 in San Francisco, Ride AI 2026 brings together AV pioneers, regulators, and AI experts to tackle the final frontier: marketing autonomous vehicles into mainstream adoption. Discover how real-world robotaxi scale, safety data, and LLM breakthroughs can bridge the trust gap and drive the next mobility revolution.
Reflecting on three years of AI adoption, this talk emphasizes that transformative technologies like transformers are still in their infancy, requiring hands-on exploration and healthy skepticism toward confident predictions. It argues that AI acts more as a mirror—revealing our own organizational patterns and biases—than a crystal ball for the future.
Claude Code combines a terminal-based Unix command interface with filesystem access to give LLMs persistent memory and seamless tool chaining, transforming it into a powerful agentic operating system for coding and note-taking. Its simple, composable approach offers a blueprint for reliable AI agents that leverage the Unix philosophy rather than complex multi-agent architectures.