--- > One caveat: I expect this blog post to age very poorly. The pace of innovation here is insane; what was true a month ago barely holds true today. That's why I'm sticking to concepts I believe have staying power. - [View Highlight](https://read.readwise.io/read/01jxjape8d61a197t4pr2wy5gr) --- > I disable all permission checks. Which basically means I run claude --dangerously-skip-permissions. More specifically I have an alias called claude-yolo set up. Now you can call that irresponsible and there are definitely risks with it, but you can manage those risks with moving your dev env into docker. I will however say that if you can watch it do its thing a bit, it even works surprisingly well without dockerizing. YMMV. - [View Highlight](https://read.readwise.io/read/01jxjapvtj7448cnd9thstx1mg) --- > I've evaluated agent performance across different languages my workload, and if you can choose your language, I strongly recommend Go for new backend projects. ... Rob Pike famously described Go as suitable for developers who aren't equipped to handle a complex language. Substitute “developers” with “agents,” and it perfectly captures why Go's simplicity benefits agentic coding. --- > Python — my initial choice — often poses significant challenges. Agents struggle with Python's magic (eg: Pytest’s fixture injection) or complex runtime challenges (eg: wrong event loop when working with async), frequently producing incorrect code that even the agentic loop has challenges resolving. Python also has practical performance problems. I don't mean that it writes slow code, i mean that the agent loop is really slow. That's because the agent loves to spawn processes and test scripts, and it can take quite a while for the interpreter to boot up and initialize the entire application. - [View Highlight](https://read.readwise.io/read/01jxjav772kzx5zbxsc615gffe) --- > Tools need to be **fast**. The quicker they respond (and the less useless output they produce) the better. Crashes are tolerable; hangs are problematic. - [View Highlight](https://read.readwise.io/read/01jxjavvctersz4ab5s8ckxgyx) --- > Agents aren't exceptionally fast individually, but parallelization boosts overall efficiency. Find a way to manage shared states like the file system, databases, or Redis instances so that you can run more than one. Avoid them, or find a way to quickly segment stuff out. - [View Highlight](https://read.readwise.io/read/01jxjc047dchmddner0skd5x0m) --- > Agentic coding alters refactoring priorities. Agents handle tasks effectively until project complexity surpasses some manageable thresholds. Too big here is defined by the total amount of stuff that it has to consider. So for instance you can vibe code your frontend together for a while, but eventually you reach the point where you absolutely need to tell it to make a component library. Why? Because if the total tailwind class mess is splitered across 50 files you will find it very hard to get the AI to make redesigns or extract components without major regressions. > An agentic workflow encourages good code maintenance and refactoring at the right moment. You don't want to do it too early and you definitely do not want to do it too late. - [View Highlight](https://read.readwise.io/read/01jxjc1pw9rpw6s83915nfzg42) ---