✅ Manage your projects
✨

✅ Manage your projects

Oct 23, 2023·
Hang Chen
Hang Chen
· 2 min read
Image credit: Unsplash
blog_examples

Easily manage your projects - create ideation mind maps, Gantt charts, todo lists, and more!

Ideation

Hugo Blox supports a Markdown extension for mindmaps.

Simply insert a Markdown code block labelled as markmap and optionally set the height of the mindmap as shown in the example below.

Mindmaps can be created by simply writing the items as a Markdown list within the markmap code block, indenting each item to create as many sub-levels as you need:


```markmap {height="200px"}
- Hugo Modules
  - Hugo Blox
  - netlify
  - netlify-cms
  - slides
```

renders as

- Hugo Modules
  - Hugo Blox
  - netlify
  - netlify-cms
  - slides

Diagrams

Hugo Blox supports the Mermaid Markdown extension for diagrams.

An example Gantt diagram:

```mermaid
gantt
section Section
Completed :done,    des1, 2014-01-06,2014-01-08
Active        :active,  des2, 2014-01-07, 3d
Parallel 1   :         des3, after des1, 1d
Parallel 2   :         des4, after des1, 1d
Parallel 3   :         des5, after des3, 1d
Parallel 4   :         des6, after des4, 1d
```

renders as

gantt section Section Completed :done, des1, 2014-01-06,2014-01-08 Active :active, des2, 2014-01-07, 3d Parallel 1 : des3, after des1, 1d Parallel 2 : des4, after des1, 1d Parallel 3 : des5, after des3, 1d Parallel 4 : des6, after des4, 1d

Todo lists

You can even write your todo lists in Markdown too:

- [x] Write math example
  - [x] Write diagram example
- [ ] Do something else

renders as

  • Write math example
    • Write diagram example
  • Do something else

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Hugo Blox Markdown
Hang Chen
Authors
Hang Chen (he/him)
Postdoctoral Research Fellow at Nanyang Technological University
Hang Chen received his Ph.D. from the Faculty of Electronic and Information Engineering at Xi’an Jiaotong University in March 2026. In June 2026, he joined the College of Computing & Data Science (CCDS) at Nanyang Technological University (NTU) as a Postdoctoral Research Fellow, working with Prof. Wenya Wang. His research is rooted in causal representation and structural analysis in machine learning. Recently, his focus has shifted towards applying the mechanistic interpretability of Large Language Models (LLMs) to guide and control post-training processes, such as Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL).