hollywood bond brick

Early adopters will be rewarded by having better methodology by the time the tooling catches up.

Too busy trying to dunk on me than understand that you have some really helpful tools already.

This is why I say some people are going to lose their jobs to engineers using AI correctly, lol.

While it’s possible to see gains in complex problems through brute force, learning more about prompt engineering is a powerful way to save time, money, tokens and frustration.

I see a lot of people saying, “I tried it and it didn’t work,” but have they read the guides or just jumped right in?

For example, if you haven’t read the claude code guide, you might have never setup mcp servers or taken advantage of slash commands.

Your CLAUDE.md might be trash, and maybe you’re using @file wrong and blowing tokens or biasing your context wrong.

LLMs context windows can only scale so far before you start seeing diminishing returns, especially if the model or tools is compacting it.

1. Plan first, using planning modes to help you, decomposition the plan
2. Have the model keep track of important context externally (like in markdown files with checkboxes) so the model can recover when the context gets fucked up

https://www.promptingguide.ai/

https://www.anthropic.com/engineering/claude-code-best-practices

There are community guides that take this even further, but these are some starting references I found very valuable.

Garbage in, garbage out

This is why the seedbox SaaS market exists. Providing turn key hosted solutions, the only heavy lifting is the configuration which takes some reading to understand.

Check out the Servarr Wiki, Ombi, Syncthing as a starting point for media discovery and curration tooling.