Feed

The reason SPAs became the default wasn’t because they were better. It was because, for a while, they were the only way to deliver something that felt fluid – something that didn’t flash white between pages or jank the scroll position.

But here’s the uncomfortable truth: most SPAs don’t actually deliver the polish they promise.

This guide is intended to be a comprehensive look at the tech that Star Trek suggested to drive humanity forward ad astra per aspera. The emphasis is on innovations that don't violate physics according to present consensus understanding. Go ahead and explore boldly, and if you have any corrections or additions, pop into the Are We Trek Yet channel on the Bingeclock Discord. Just don't waste too much time on idle speculation: there's a whole lot to do if we're going to get to Trek, and it's going to take all of us.li

I recently found myself in a deep state of flow while coding — the kind where time melts away and you gain real clarity about the software you’re building. The difference this time: I was using Claude Code primarily.

Overconfidence is one of the most important core underlying components, because if you're overconfident, it stops you from really questioning whether the thing that you're seeing is right or wrong, and whether you might be wrong about it. You have an almost moral purity of complete confidence that the thing you believe is true. You cannot even imagine what it's like from somebody else's perspective. You couldn't imagine a world in which the things that you think are true could be false. Having overconfidence is that buffer that stops you from learning from other people. You end up not just going down the rabbit hole, you're doing laps down there.

We believe in the future of AI video. We see the good it can do—making storytelling more accessible, understanding more vivid, and production more equitable. But we also know that "anyone can say anything” is a real risk—not just a philosophical one.

Basically every CLI tool can be improved in some way to provide extra context to LLMs. It will reduce tool calls and optimize context windows.

The agents may benefit from some training on tools available within their agents. This will certainly help with the majority of general CLI tools, there are bespoke tools that could benefit from adapting to LLMs.

It seems a bit silly to suggest, but perhaps we need a whole set of LLM-enhanced CLI tools or a custom LLM shell? The user experience (UX) field could even branch into AI experience and provide us a whole new information architecture.

the winners will be teams building constrained, domain-specific tools that use AI for the hard parts while maintaining human control or strict boundaries over critical decisions. Think less "autonomous everything" and more "extremely capable assistants with clear boundaries."

The Architecture Overview isn't really architecture. It's "what would I want to know if I had amnesia?" The Technical Considerations aren't really instructions. They're "what would frustrate me if we had to repeat it?" The Workflow Process isn't really process. It's "what patterns emerged that I don't want to lose?" The Story Breakdown isn't really planning. It's "how do I make progress when everything resets?" Maybe that's all any documentation is. Messages to future confused versions of ourselves.

This article made me laugh... a lot.

I found out about my death the way everybody finds out everything: from Google. What happened was, I Googled my name ("Dave Barry") and what popped up was something called “Google AI Overview.” This is a summary of the search results created by Artificial Intelligence, the revolutionary world-changing computer tool that has made it possible for college students to cheat more efficiently than ever before.

Through our experiments, we demonstrate that LLMs do not maintain consistent performance across input lengths. Even on tasks as simple as non-lexical retrieval or text replication, we see increasing non-uniformity in performance as input length grows.

Our results highlight the need for more rigorous long-context evaluation beyond current benchmarks, as well as the importance of context engineering. Whether relevant information is present in a model’s context is not all that matters; what matters more is how that information is presented. We demonstrate that even the most capable models are sensitive to this, making effective context engineering essential for reliable performance.