Preparing a resume may not sound difficult, but in practice, it can be quite time-consuming. Nowadays, many jobs have specific requirements for niche knowledge or projects. With so many job seekers, a resume that doesn’t highlight the key points relevant to the job requirements might be overlooked by employers. Therefore, to achieve better results, it’s best to tailor your resume for each position… This is something AI should be good at, but at least for now, I haven’t found a good free tool for this.

Recently, I came across RenderCV, a tool that generates resumes from YAML configuration files. It enables a “configuration-as-resume” approach. Combining it with an AI coding assistant like Aider essentially creates a rapid resume preparation environment.

Last year, when I joined my current company, I was tasked with preparing a teaching analysis environment for bioinformatics training. This opportunity introduced me to Pixi. Later, as my responsibilities shifted, I not only continued with bioinformatics analysis but also took on full‑stack maintenance work. The projects I handled spanned front‑end and back‑end, involving languages and frameworks beyond the data‑science staples R and Python. Pixi has still been able to serve as a cross‑language/stack development environment management tool (Conda offers an extremely rich resource pool, covering mainstream programming languages and common frameworks). Therefore, I’d like to summarize the practical Pixi features that have proven useful in my daily work.