u/xTouny

Hello,

It is observed, especially among juniors, relying on AI to generate quick answers or solutions, skipping the learning process required to discover the solution. Nowadays, A beginner is able to come up with fine solutions without investing time in foundations, or spending time on difficult problems.

The modern engineering culture is centered on quick prototyping, where AI fits to generate a quick fine solution. The incentive to learn, think, and build well is degrading. Any engineer at some point adapts on the business, and probably enjoys building a hobbyist project.

Any experienced engineer knows the value of books like Database Design for Mere Mortals by Hernandez; the value of spending a long-time to understand a design pattern, or to solve an architectural trade-off.

Here is my workflow, where I try to retain good habits, while delivering on deadlines.

1. Query the LLM on the problem or question.

2. Query "Recommend foundational background" to generate fundamental information or methods, through which the LLM answered.

3. Upload personal markdown notes or a well-studied book, then query "cite relevant sections and explain their relevance".

In this way, the LLM hints familiar ideas as the key solution, and recommends new ideas one step beyond my mastered knowledge.

4. Then I attempt to answer the original question or problem in no. (1) without seeing the generated answer. Because I mastered the foundations of no (3), I can play with the generated hints very fluently to derive a new solution.

The goal is to deliver quickly, while maintaining a trace of foundations; To generate a short answer, while tracing the long-time reading and thinking.

Discussion. What about you? Did you suffer from AI brain rot? Did you face delivery expectations from the business at the expense of good engineering? How do you retain good habits alongside quick delivery? Did you use AI to become a more perfectionist engineer?

Hello,

I consider myself lucky to have learned Arch design philosophy, as it had shaped how I use LLMs.

Arch notably adopts Simplicity and Do-it-yourself, whereby the user is expected to design his own minimum system following his personal workflow. Arch incentivizes relying and contributing to its Wiki, motivating to think by first-principles.

As a result, I frequently reject a functioning script by a LLM. It may be too complex to maintain, inconsistent with my style, or relies on new dependency layers.

Here is my workflow:

1. Formulate a clear and concise question.

2. Collect relevant context, and interpret it as a hypothesis; it may be misleading.

3. Query the LLM.

4. Query "Recommend foundational background" to generate fundamental information or methods, through which the LLM answered.

5. Upload personal markdown notes, or a well-studied book, and set searching filter to the arch wiki domain. Then query "cite relevant sections and how relevant they are".

In this way, the LLM hints familiar ideas as the key solution, and recommends new ideas one step beyond my mastered toolkit. The goal is to:

6. learn and master that step very well, so that it becomes ingrained into my personal notes. I want to retain thinking by first-principles.

7. Then I attempt to answer the original question / problem in no. (1), without seeing the generated answer in no. (3). Because I mastered the foundations of no (5) and read the Arch Wiki well, I can play with the generated hints very fluently to derive a new solution. I want to retain my style and technique in solving the new problem as a Do-it-yourself user. I want to re-utilize by simple infrastructure to solve more problems.

In some cases the answer of no. (3) may have no grounded roots in no. (5). That signals there is a new domain of basic foundations, distant from my comfort zone. I'd learn those basic ideas and then contribute to the Arch Wiki.

As AI progresses to solve problems which are easily derived from your basic toolkit, your goal is to focus on higher cognitive tasks, setting the directions and contexts so that AI performs as efficiently as possible.

I don't see anything bad in using AI for learning and troubleshooting Arch. The critical point is to adopt the design philosophy of Arch as your mindset. As AI slop is common nowadays, the need to think like an Arch user is arising.

I especially advise that for beginners. You may imitate that workflow by a careful prompting in a chat thread. You can create a Claude workflow, or write a simple python script. You may try plenty of tools about LLM Wiki, memory management, context management, ..etc.

Discussion

• Do you suggest a workflow for using LLMs or Agentic Development, following Arch design philosophy?
• Would you like to see open-source agentic workflows, following Arch design philosophy?
• Do you suggest any educational content to incetivize first-principles thinking?
• Did Arch shape how you engineer a system?

Hello,

It is observed among many people the usage of AI to answer a question or solve a problem quickly, avoiding the learning process required to discover the solution. It is more clear among students.

I believe a useful utilization of AI is happening only if I can ask the right question within the right context, which requires solid foundational background and problem solving skills.

I designed a workflow for myself to combine the productivity of AI and the sharpening of my mind:

1. Formulate a clear and concise question.

2. Collect relevant context, and interpret it as a hypothesis; it may be misleading.

3. Query the LLM.

4. Query "Recommend foundational background" to generate fundamental information or methods, through which the LLM answered.

5. Upload personal markdown notes or a well-studied book then query "cite relevant sections and how relevant they are".

In this way, the LLM hints familiar ideas as the key solution, and recommends new ideas one step beyond my mastered knowledge. The goal is to:

6. learn and master that step very well, so that it becomes ingrained into my personal notes.

7. Then I attempt to answer the original question / problem in no. (1), without seeing the generated answer in no. (3). Because I mastered the foundations of no (5), I can play with the generated hints very fluently to derive a new solution. Even if I failed, the process is very healthy!

In some cases the answer of no. (3) may have no grounded roots in no. (5). That signals there is a new domain of knowledge, distant from my comfort zone.

I'd then search for a tutorial, book, lecture notes, or a youtube playlist, to learn basic foundations of that area, and build a new personal markdown notes.

You can follow that workflow by a careful prompting in a chat thread. You can create a Claude workflow, or write a simple python script. You may try plenty of tools about LLM Wiki, memory management, context management, ..etc.

As AI progresses to solve problems which are easily derived from your mastered foundations, your goal is to focus on higher cognitive tasks, setting the directions and contexts so that AI performs as efficiently as possible.

Hello,

It is observed among many people the usage of AI to answer a question or solve a problem quickly, avoiding the learning process required to discover the solution. It is more clear among students.

I believe a useful utilization of AI is happening only if I can ask the right question within the right context, which requires solid foundational background and problem solving skills.

I designed a workflow for myself to combine the productivity of AI and the sharpening of my mind:

1. Formulate a clear and concise question.

2. Collect relevant context, and interpret it as a hypothesis; it may be misleading.

3. Query the LLM.

4. Query "Recommend foundational background" to generate fundamental information or methods, through which the LLM answered.

5. Upload personal markdown notes or a well-studied book then query "cite relevant sections and how relevant they are".

In this way, the LLM hints familiar ideas as the key solution, and recommends new ideas one step beyond my mastered knowledge. The goal is to:

6. learn and master that step very well, so that it becomes ingrained into my personal notes.

7. Then I attempt to answer the original question / problem in no. (1), without seeing the generated answer in no. (3). Because I mastered the foundations of no (5), I can play with the generated hints very fluently to derive a new solution. Even if I failed, the process is very healthy!

In some cases the answer of no. (3) may have no grounded roots in no. (5). That signals there is a new domain of knowledge, distant from my comfort zone.

I'd then search for a tutorial, book, lecture notes, or a youtube playlist, to learn basic foundations of that area, and build a new personal markdown notes.

You can follow that workflow by a careful prompting in a chat thread. You can create a Claude workflow, or write a simple python script. You may try plenty of tools about LLM Wiki, memory management, context management, ..etc.

As AI progresses to solve problems which are easily derived from your mastered foundations, your goal is to focus on higher cognitive tasks, setting the directions and contexts so that AI performs as efficiently as possible.

Share with us your best moments and insights

I already asked this on r/learnprogramming but I didn't get any response:

In the intro to the book, they say there is auxiliary material related to automata and computability theory. The link provided is https://www.cs.princeton.edu/theory/complexity/ but there's no material there that I see. Hopefully it just moved, but I'd really like to find it.