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[This is] based on lessons learned from researching and creating Large Language Model (LLM) prompts for production use cases. It covers the history around LLMs as well as strategies, guidelines, and safety recommendations for working with and building programmatic systems on top of large language models, like OpenAI’s GPT-4.

A collection of pure POSIX sh alternatives to external processes.

This ethical and repairable design proves Bluetooth headphones can be more sustainable

A compact cyberdeck, featuring a QAZ 35% keyboard, Banana Pi M2 Zero SBC and 7.9 inch monitor.

These days, we provide thermal comfort in winter by heating the entire volume of air in a room or building. In earlier times, our forebear’s concept of heating was more localized: heating people, not places.

They used radiant heat sources that warmed only certain parts of a room, creating micro-climates of comfort. These people countered the large temperature differences with insulating furniture, such as hooded chairs and folding screens, and they made use of additional, personal heating sources that warmed specific body parts.

I quickly learned that writing one giant query with a bunch of joins or even bunch of Python helper functions could get me stuck. My transformation functions weren’t flexible enough, or my joins were too complicated to answer the endless variety of questions thrown my way while keeping the numbers correct.

Instead, the easiest way to be fast, nimble, and answer all the unexpected questions was to prepare a giant table or dataframe and limit myself to it. As long as I understood the table’s contents, it was harder to make mistakes. I could group by and aggregate on the fly with confidence.

Only as traffic approaches, Norway’s auto-dimming roads get brighter. New LED lights dim to 20 percent when no cars are in area. When a car drives by, the lights turn to 100 per cent. Thus it reduces the country’s carbon footprint.

The main reason we don’t create more affordances, however, is pure egocentrism. When we just say whatever pops into our heads, we may think we’re making craggy, climbable conversational rock walls, when in fact we’re creating completely frictionless surfaces. For example, I’m thrilled to tell you about the 126 escape rooms I’ve done, but my love for paying people $35 to lock me in a room blinds me to the fact that you probably do not give a hoot. I may even think I’m being generous by asking about your experiences with escape rooms, when my supposed giving is really just selfishness with a question mark at the end (“Enough of me talking about stuff I like. Time for you to talk about stuff I like!”).

His choice of subjects is pretty unique, described by one commenter up as “filling the niche of scale models of iconic embarrassing moments in the lives of famous monsters.”

I asked Bobby why he chose the subjects he did.

“I like men who have failed us,” he wrote. “The feeling they give is interesting. Like a dog who has chewed something we once enjoyed. But we move on.”

Use and compare all the popular LLMs

Calorie intake, protein intake, and training volume are by far the most important factors determining your body composition and degree of swole-ness. Add in training intensity and specificity, and you’ve got the major factors determining strength as well.

Everything else is just details. And it’s not that details don’t matter; it’s just that unless you’re an elite-level athlete trying to eek out an extra percentage of performance, they don’t matter very much. In a complex, redundant system, details generally get lost in the noise, and end up having at most a trivial effect.