In my personal post academic life, I’ve found LLMs to be an incredible teacher. Almost like the best professor in the world at my fingertips. I use it to generate quizzes on demand to test for my own knowledge gaps.

However, if I use it to speedrun over concepts I should be learning, I may achieve my end goal but I wouldn’t actually learn many of the details.

I think it requires an approach where you have to continuously audit your own understanding as you work with the concepts. You must slow down until you’ve confirmed this. Only once you know the concepts deeply and have retained them in your own memory can you then go all in with the LLM.

Comments URL: https://news.ycombinator.com/item?id=47296912

Points: 14

# Comments: 1

Also, as always, a highly modular codebase is very important. If I only have to reason about a single module then I don’t have to have full context on system.

It seems we’re now in a world where engineers are responsible for creating a good environment where an agent is able to gain context on the architecture and validate its work via tests (e2e, unit, smoke, etc). Then it can get into its own feedback loop and find the correct solution on its own much faster.

As a hardcore AI chat user, I'm often frustrated with the single-agent workflow, where a single context window is used for even very long conversations. If I want to change the topic, open a thread, or go on a tangent, I often end up compromising the main thread and I'm forced to copy context over if I want to dive into something.

To solve this, I'm working on a collaborative AI agent orchestrator that models the solution as a group chat with humans and AI agents, including an agent orchestrator.

You can spawn participating agents with the orchestrator who will decisively route messages to the existing agents, or spawn new agents if needed. Also, you can open agent details and send messages directly to existing agents, similar to threads in slack.

So far, I have MCP integrations working with Linear and GitHub, but plan to add many more.

I've been working on this just over 2 weeks, making heavy use of 4+ concurrent Claude Code agents. This would have been impossible otherwise.

If you're interested, feel free to DM on X.

https://x.com/jon_ator/status/2010370649147998459?s=20

  Location: NYC
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Meetup.com (Luma, etc): replaces the need for existing heavily maintained communities of friends and family in your location with siloed random encounters. However, it shortens the path to meeting people that share niche interests.

Dating apps: replaces the need for men to spontaneously approach women they meet in their daily life or in social/family circles (even bars) with a heavily idealized profile centered around physical and emotional attractiveness. They are not only dominated by men, but they typically only disproportionally benefit a small % of those men.

Facebook: you can keep in touch with the lives of more people at scale, but it reduces the incentive to catch up in person with the people you actually care about. This can lead to genuine in person connections being replaced with a feed of people you really don't know.

Take it with a grain of salt.

With especially novel or complex projects, you'd probably not expect to use the agent to do much of the scaffolding or architecting, and more of the tedium.

Claude Code (AI coding agents/assistants) are perhaps the best thing to happen to my programming career. Up until this point, the constraint going from vision to reality has always been the tedious process of typing out code and unit tests or spending time tweaking the structure/algorithm of some unimportant subset of the system. At a high level, it's the mental labor of making thousands of small (but necessary) decisions.

Now, I work alongside Claude to fast track the manifestation of my vision. It completely automates away the small exhaustive decision making (what should I name this variable, where should I put this function, I can refactor this function in a better way, etc). Further, sometimes it comes up with ideas that are even better than what I had in my head initially, resulting in a higher quality output than I could have achieved on my own. It has an amazing breadth of knowledge about programming, it is always available, and it never gives up.

With AI in general, I have questions around the social implications of such a system. But, without a doubt, it's delivering extreme value to the world of software, and will only continue the acceleration of demand for new software.

The cost of software will also go down, even though net more opportunities will be uncovered. I'm excited to see software revolutionize the under represented fields, such as schools, trades, government, finance, etc. We don't need another delivery app, despite how lucrative they can be.

I'm wondering if a simple contributor is the fact that many people are moving away from their immediate family. Then you feel more on your own when considering having child, which is significantly more daunting. I think a network of friends helps, but is simply not the same as parents/siblings/cousins sharing the load and advice. Let alone the experiences.

Also, it seems there's a negative feedback loop, where each person that chooses to postpone or not have kids influences their network to do the same.

However, sometimes there are unpriced externalities like the competitive advantage of removing your own manufacturing waste by dumping it into a stream. That is where governance (whether self or the state) comes in.