My client-side only AI web application workflow
History / Edit / PDF / EPUB / BIB / 3 min read (~481 words)Over the past month I've started building client-side only AI web applications (e.g., ai-text-editor, a private ai-language-assistant to teach myself Chinese Mandarin).
I've gone with this approach because it lends itself very well to vibe coding with vibe-kanban.
I've configured the Dev Server Script option to open the index.html file in my browser and it works immediately, no need to build assets or start a backend server.
This speeds up iteration cycle considerably.
This approach is also great because it produces a tool that can then be used directly in the browser from any device, without needing to install anything.
I can run what I built on my phone, on my work computer, one someone else's computer easily by pointing them out to the project's GitHub Pages URL which serves the application and is "production" ready.
I haven't really picked any frontend libraries or frameworks, just vanilla HTML/CSS/JS.
That is something I need to explore (e.g., react, svelte, solid, tailwind, vue.js, etc.) but for now I want to keep things simple.
For the past few projects I've used Claude Code.
I ask Claude to create the common CLAUDE.md.
Additionally, I ask Claude to maintain a SPEC.md file that describes the features of the application.
In many cases the database relies on the browser's localStorage API and IndexedDB to store data, which is sufficient for my needs.
I ask Claude to maintain a DATABASE_SPEC.md file to describe the database schema.
Claude typically goes for a index.html, app.js, and styles.css file structure.
While it is ok for the first few iterations of the project, I usually ask Claude to refactor the code to split it into multiple files and modules as the codebase grows.
Doing so speeds up some of the iteration process since it doesn't end up reading large irrelevant chunks of the file when making edits.
It also makes it easier to review changes since I use the files modified as an indication of whether it worked on the right part of the codebase.
The main downside of this approach is that generally the app.js file ends up being quite large (e.g., 1000+ lines of code) since it contains all kind of global state and logic.
With this approach I've been able to fairly effectively work on small projects (~40 hours of work) and get something functional out the door that would have taken me weeks and where I'd probably have given up mid-project due to all kind of minor problems.
- Use an
importmapand javascript modules - Imports
- llm.js to do LLM calls directly from the browser
<script type="importmap">
{
"imports": {
"llm.js": "https://esm.sh/@themaximalist/llm.js@1.0.1?target=node",
}
}
</script>
<script type="module" src="app.js"></script>AI/AGI/ML - A short overview
History / Edit / PDF / EPUB / BIB / 3 min read (~580 words)After having spent about 200 workdays of my life getting familiar with the fields of AI/AGI/ML, I think it is a good moment for me to reflect back on my learning experience.
Of the three domains, AGI is the least well defined. It has to do with general intelligence, but our definition of intelligence is still up for debate. Furthermore, the different known approaches in the field have different opinions regarding what sort of agent an AGI system should be. Should it be a self-contained entity with its own goals and purpose? Or should it be a system that is exhibiting human capabilities while being the servant of a human being or a group of human beings?
Next is AI, which encompasses many different types of approach to try and solve high level goals such as deciding, planning, organizing,, understanding, etc. AI is about the discovery of strategies to solve problems without having to provide the whole solution. I think it is also where goals for the ML field have been defined.
Finally, ML is about using statistical approaches to solve problems. These problems have to be formalized and specify their input/output in the shape of data, may it be as text, audio, video, or simply vectors of numbers. Most of the recent and exciting development that has happened in ML is due to deep learning, which is the ability to develop deep neural networks that can learn to accomplish certain tasks without writing any explicit logic. This logic would be discovered by the network itself, with some help of the developer which would define a composition of modules that would process the data in order to create associations between the input and outputs.
When I initially started my "adventure", I was interested in AGI. I wanted to know how a machine would be able to exhibit human behavior without having to tell it too much about it. I wanted to learn how you could teach a machine as you would teach a child. The AGI field itself does not currently have any curriculum as to what you should learn about, nor a reference book you can refer yourself or others to (when asked "What should I first read if I want to learn AGI?"). One can be interested in anthropomorphic aspects such as memory, intelligence, perception/senses, processing and so on, while others may be more interested about a computational oriented approach, such as the algorithms, the implementations, the computational complexity of diverse approaches and so on. I think it makes AGI a very interesting topic/field, however it lacks the definiteness of the other fields. One can see this by reading the various iterations of the book "Artificial General Intelligence", which is a collection of articles by various researchers that is generally published every year in relation to its conference.
When I started learning about AGI, I decided I would explore various venues and determine the approaches that matched the most my own interests and which looked the most promising. I looked at different venues such as cognitive science, set theory, logic, universal artificial intelligence, biology, and philosophy amongst others.
Along the road, AGI research has challenged many of my beliefs as well as how I see life. For those two reasons alone, I'm grateful I have been able to dedicate some of my time on it.
Over the next year (and hopefully years), I plan on working on AI, but more particularly what is known as AGI, Artificial General Intelligence.
Since I am the kind of person that enjoys to overly plan, I've been thinking about the whole process for a while. Here's a brief overview of how I plan to construct my days as well as my work.
- Decide and plan on which projects I will work today
- Video log
- What will I be doing today?
- What might be blocking me or will be difficult and how do I plan to tackle that?
- Record my thoughts: Either through video logs or through written notes, which is the more likely option. This will allow me to search my thoughts as well as organize them as necessary. Furthermore, I hope I'll be able to optimize my thought flow through tools.
- Video log
- What have I done during the day?
- What are the key takeaway of the day?
- What didn't I do?
- Take notes (of interesting sections and ideas)
- Write down thoughts
- Write down questions related to what is being read (for further exploration)
- Explore right away a question I might have had
I plan on working on multiple mini projects in order to ensure diversity as well as to allow me to spot projects which have the potential to be interesting and rewarding. However all projects are valuable in their own and it is important to reflect on the project at the end in order to extract things we'll want to repeat in the future as well as things we'll want to avoid.
- Post mortem
- What went wrong
- What went right
- Update the status of all tracked activities
- Write a list of things that were done during the month regarding each activity that was done. The purpose is to review what was done as well as provide a way to determine how well a project may/may not be progressing. It is also a good time for me to look at each individual activity that was done during the month and evaluate if I want to keep doing it or not.
- Write a post-mortem of the month, describing the good/bad of my current process and progress, with a section suggesting improvements to try for the next few months.
- Plan the projects/activities I will be working on next month, as well as their time allocation.
I will be experimenting with this process in the next few weeks and will iterate on it as I see fit. I will thus be updating this post as changes occurs and I think of better ways to do my work.