I know several teams and big org struggling to leverage AI agents. One of the easiest path is to use UNIX (e.g., your laptop + a shell). All the tools are available, shared between humans and agents, and compose infinitively.
The point is exactly what you say: Not to have the latest tech product (e.g., grok bot, instinct, ...), but to just use powerful tools whose flexibility have been battle tested other the ages.
I'm a geek, a perfectionist, and I like to have the best tools when I work. If you take my previous dotfiles (100% human made): https://github.com/fmind/dotfiles-ansible, it had more than 1293 commits from all the tools and configs I tested. But this was draining my energy. I'm glad we now have LLM to automatically find the best tools/configs for you, and test if the settings are working.
My new dotfiles are much more powerful and efficient than ever before thanks to LLMs. For instance, I did not know about Zellij, mise, dprint, lazyvim or git-cliff before updating them. Having them is your toolbet helps me and my agents build better tools.
Indeed, you need to invest some tokens in configuring the tools, configs and skills. But once it's done, tools and configs do not consume more tokens, as they are automatically used by your AI Agents. Moreover, skills support progressive disclosure, and I try to keep a budget of max 5000 tokens for global + 5000 tokens for local setups (= Skills + AGENTS.md).
But yes, I burned too many tokens on this project. That's why I'll be happy if it can help others.
The article is more about justifing the investment. Dotfiles is the foundation for all your other AI projects. If you have good dotfiles, you can automatically steer all your other projects in the right direction.
Yeah we use it for auto-triage of incidents, attempts to auto-remediate, and escalation to human. But for actual development, it’s not a viable option for us.
Yup. I use it for a ton of mundane queries (stuff that I might have used Google search for in the past) and it's great. Nice and fast and correct more often than not, especially if you prompt it in a way that it invokes Google search (but filters out ads and SEO slop). It's even alright at programming tasks but if it stumbles then I'll escalate to Gemini Pro with extended thinking.
I'm using Go more and more for my projects (AI, Web, CLI, ...). This is my go to for all my new projects.
My background is in data science and MLOps, where Python rules. But the focus is now less on building new AI models, and more on building the infrastructure and API calls with AI Agents. Go has a great async model, stellar performance, amazing tooling ecosystem, and far less ways of doing things than Python.
I except grow to become more and more popular, as our LLMs are now writing most of our code. Between a 50 MB portable binary in Go with 10x performance, and a 5 GB venv in Python with lack of proper parallelism, the choice is easy.
I'd love Django, if they had a better async story ... I used it for a recent project. While the framework is overall amazing, I had to switch to Go for better performance and easier async support.
The point is exactly what you say: Not to have the latest tech product (e.g., grok bot, instinct, ...), but to just use powerful tools whose flexibility have been battle tested other the ages.
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