I read more articles from https://www.oneusefulthing.org this week.

How to... use AI to teach some of the hardest skills
Very insightful article on the topic of using LLMs to teach students... or yourself. Based on this article I started learning about sociology terms, electronics, tried to have it role play a senior software backend engineer I could practice mentoring (and get mentoring feedback from). I also added the prompt "Explain how X works" to my prompt collection. I love articles that expand my thinking and exploration.

Prompt to learn about a domain through question/review cycles:
Act as an expert in X. Ask me to explain a concept and then correct me if I'm wrong. Then restart the process, continuing endlessly.

How to... use AI to unstick yourself
I've been using LLMs a lot to help me get some quick sanity check on thoughts I have and see what I might not have considered. I think LLMs are a rather useful tool to help you stay motivated when you feel a bit stuck or don't want to particularly work on a piece of code. It's like having a peer that's always willing to help.

Thinking companion, companion for thinking
Two heads are generally better than one. LLMs can be your second head when you need to think about what might go wrong or to address gaps in your thinking.
You should also learn about opportunity cost and sunk cost!

ChatGPT is my co-founder
One of LLMs strengths is their ability to always be somewhat helpful. One helpful thing they do is lowering the barrier to doing anything, as long as you know how to ask for help. While I code this mostly means giving me a small push to accomplish a task I would partially complete without its help. When writing, it's a great tool to stimulate creativity and get feedback on which you can act.

Superhuman: What can AI do in 30 minutes?
More and more of how you decide to spend your time will decide how effective (or not) you are. In this article the author spends 30 minutes to accomplish the following with the help of generative AI:

Output: Bing generated 9,200 words or so of text and a couple images, GPT-4 generated a working HTML and CSS file, MidJourney created 12 images, ElevenLabs created a voicefile, and DiD created a movie.

Input: I made less than 20 inputs to all the systems to generate these results.

Assuming that there were only 20 interactions, that would mean ~1 minute between interaction. Over a 30 minutes period, most of the time is likely spent on reviewing the generated content and then deciding our next move/writing prompts. A time breakdown would have been interesting.

I discovered https://www.oneusefulthing.org and ended up reading a few articles.

ChatGPT Remembers What I Tell It. It’s Now My Personal Digital Assistant!
The addition of implicit memory is an exciting move toward a more useful AI agent that knows more about you and your preferences. It'll be interesting to see how this evolves.

Experimenting with AI code review
The article is a few months old so it's hard to say if newer models have addressed the concerns of the article. The main value here will be over time to get closer to instant feedback while implementing changes instead of having to push code to get a code review.

Almost an Agent: What GPTs can do
I'm looking forward to OpenAI and other GPT providers to allow GPT creators to review their users feedback when interacting with their bot. The idea here is that a GPT is software, so it needs to evolve and adapt to new needs and requirements as well as address bugs in its behavior.

How to... have better meetings
A few good tips on having better meetings. See Meetings for my own meeting process.

Captain's log: the irreducible weirdness of prompting AIs
Prompting is weird. Over time we expect LLMs to get smarter and better at inferring our intent such that becoming good at prompting isn't a skill you shouldn't invest too much into. Until then, it's somewhat similar to knowing how to write good search engine queries.

All articles on this blog originate from my head. The content of the articles is mostly mine but some articles are in part or completely written by AI/LLMs. Those articles will be tagged accordingly: no tag for completely original content, partially-ai-generated for articles with one or many AI generated sentences, and fully-ai-generated when all the content is AI generated.

I use a variety of LLM providers (in order of frequency of use):

• GitHub Copilot
• Reka.ai
• Meta.ai
• OpenAI ChatGPT
• Command R+
• Claude
• Groq
• Perplexity
• Poe
• DuckDuckGo AI Chat
• Gemini

References

• https://sive.rs/ai

• ChatGPT
• Prompt engineering
• Parenting

• Identify the cause of the problem
  • 5 whys
• List potential solutions
• Investigate potential sources of similar problems
• Address the additional sources of risk

Goals

• Reduce incident duration
  • Identify the cause of the problem more rapidly
• Reduce incident cost
• Reduce the number of people involved