Using agents to micromanage agents is something I ended up doing.

Part of the fun is solving puzzles where the coding agent gets stuck and me then giving it technical direction. Especially when an agent says that something cannot be done in software (I show them how it actually can be done).

I am in the more motivated than ever camp.

But what if I use AI to write live demos where you can write machine code, debug it visually in your browser, punch data on virtual punch cards? What if I used AI to generate videos about this, generate the artwork, backgrounds, narration, intro music, etc. I guess I am just a meta-tinkerer :-)

I am also pursuing something similar (complementary?) to this (recently started writing a Rust-based "distributed OS" that manages ML resources in a network of heterogenous systems: varied cores, system RAM, GPU VRAM, I/O). So my focus is not so much distributed agents, but more distributed inference (and fine tuning) that would benefit agents, distributed or not. I may blog about my work soon.

Your posted link is helpful, thanks.

Thanks, softwarewright; would love to hear more about your work. I've been implementing small-model inference in Zug inside WunderOS, but it's not done yet, so nothing public just yet.

I'm in a research/prototyping phase with no truly verified results yet, but I have all the hardware I need to do actual testing. I have an OS that boots in a VM, it just isn't verified to have practical value yet. It could be an AI coding agent's fever dream until I use it anger. So far Apple Silicon only, but most of my systems run Linux and most of my GPUs are NVIDIA, so moving development from Mac to Linux soon. It does have tests, demos, docs. Iterating on it daily.

https://github.com/sw-ml-study/sw-os-ml

I also have been implementing small model inference:

https://github.com/sw-ml-study/moe-microscope

I should clarify that by Apple Silicon I mean it boots Rust no_std on ARM. Does not use GPU yet. Plan is to use Rust without CUDA libraries. This project is more likely to use an NPU on a ARM dev board before it can use an NVIDIA GPU, and might never be able to use Apple GPUs. Goal: run on no-longer-supported by CUDA GPUs.

having to give clipboard paste permission is less than ideal. Why not also offer a text area to paste into?

we have it - close to the Preview, there is a Edit panel you can copy paste into. But what you told, give me an space to improve UX. Thanks

I just updated it.

Thanks for the UX change; it worked well (except for links to relative images).

Is the intent just to use it to view markdown with colors, spacing?

Do you plan to provide ways to export it to something like PDF, html w/css, etc.?

Let's say I wanted to take a README from my github repo and blog about it with the pretty version in a blog post, how would I do that?

I was a hobby programmer in the 80s, but that was after college. I was a hobby programmer in the 70s via my high school's library having a terminal to access a mainframe for self-study programming courses, and I built mail-order computer kits which I programmed in machine language.

I think AI is a great tool, but requires safeguards. I use it every day, but I use a lot of tools to reign it in, keep it on task, check its results, and force it to follow an engineering process.

I don't know about the "as quickly as possible" part, since there needs to be guardrails, but improving AI should be done sooner rather than later. e.g, instead of training to give the answer it thinks you want to hear, it should be trained to give provably correct answers. instead of being trained and fine tuned and then let loose, it should be able to continuously learn and improve.

While people keep chasing tokens/second and vibe-coding, I'm more interested in correct results per kWh, sustainable and ethical AI. Also, I'm worried that there will be an increasing digital divide between the wealthy and the rest of us, so I do want AI to be available and not locked up just for corporations and governments.

To me, the fun is solving problems, and AI let's me solve more problems, harder problems and more quickly. I was happy to move from assembler programming to high level programming languages, to 4GL, to frameworks, tech stacks, etc. Moving to AI coding is just another shift in solving problems at a higher level with more productivity.

hacking microprocessors was fun. building complex systems using AI is fun, too.

I am working on developing a distributed operating system for managing machine learning resources (the entire memory hierarchy, networking and compute hierarchy). I worked in O/S development early in my career, now retired and ML is a hobby I've invested in over the past few years.

I'm curious about:

- how to "upcycle" end-of-life NVIDIA GPUs, like K80, M40, P100, etc.

- how to offload MoE expert calculations to MCUs with NPUs, possibly FPGAs

- how to manage a 10G LAN-attached network of older Xeon servers with the above GPUs and USB-attached MCUs as a machine learning "fabric"

- do ideas from IBM's ACP/TPF O/S (now Z/TPF) carry over?

- do ideas from IBM's Workload Manager (business priorities managed resources) carry over?

- can I bypass end-of-life CUDA driver support for older NVIDIA hardware by using Rust/Vulcan?

- can I leverage the architecture of Freetoken (MoE caching/routing, Engram, KV-cache compression) and other approaches like Baby Dragon Hatchlings, Hierarchical Reasoning Models, Tiny Recursive Models, Recurisve Language Models, Multi-token predicion, etc?

- is anyone else intersted in actually building something like this?

I've explored many of these parts individually, now combining them...

me too; and my coding agents are slowed down (from developing features) because I require them to refactor the code to be more readable; my code metrics tests force AIs to leverage functional programming and design patterns).

Yes agents can produce code that compiles and runs, but I had to add tools to keep them on track, document their work, follow a process, check their outputs. I also use other AIs to generate developer documentation and review code.

It is like managing a bunch of idiot savant eager-to-please interns, except unlike interns, coding agents do not (yet) learn and improve on their own.

I have the same "1/30th ... simultaneous" experience with coding agents, and I am having fun. Directing teams of agents to solve many problems (or create many useful things) for me has its own fun challenges/puzzles-to-solve.

Some of the fun is redirecting coding agents when they get stuck, by showing them code or design they could not work out on their own. Some of the fun is designing tools to rein-in AI coding agents, keep them on track, and verify their outputs. Some of the fun is exploring new areas for me: compilers, game dev, language design, emulators, embedded, etc.

So I am having fun at the CTO/PM/Engineering Manager level but also at the lowest coding level.

I've been coding since the early 70s. This brings back the joy I had discovering microprocessors and electronics.

Sure, if that is what you like. I for one did not get into this field because I wanted to hire people (artificial or natural) to write the programs for me. Programming as an activity / art is what I enjoyed the most. Managing people / agents is a total turn off.

serious question: do you program in machine code or assembler or use a higher level language? do you use design patterns and functional approaches? metaprogramming, code generation, templating, app frameworks?

Each tool, abstraction, language feature elevates coding to get more done with less (more concisely). Programming is a social activity, since source code is meant to be read by humans (at least historically) even if it is just yourself later.

I'm not trying to talk you into using AI coding agents, but explain why I find them fun, and I've written a lot of code by hand over decades, from spaghetti assembler code to structureed programming to object oriented to functional, etc. Each advance in the industry has been a new and diiferent kind of fun for me.

> serious question: do you program in machine code or assembler

Sometimes yes.

> or use a higher level language? do you use design patterns and functional approaches? metaprogramming, code generation, templating, app frameworks?

Most of the time.

> Each tool, abstraction, language feature elevates coding to get more done with less (more concisely)

Sure. I hope you understand AI is fundamentally different. It's nothing like the things you listed above.

Are you saying that AI coding agents cannot produce working code? Or are you saying that it produces something that cannot be explained or understood?

If I used a macro to generate code that gets compiled and linked to code someone else provided, how is that different from asking a coding agent to generate similar code and linking to other coding agent code?

Is your concern that the output is probabilistic vs. a macro or code generator producing code deterministically? I apply engineering practices to ensure the results are suitable.

I do understand that AI is fundamentally different (and I've been studying ML in depth for more than a year). My comparison was in terms of tools increasing productivity, not how they do that.

I was a "10x programmer" on several projects and I am 10x more productive now than I was then (at least). I can create in a few days by myself (with agents) what used to take months (with a team of people).

Maybe I'm producing what some consider "AI slop" but the things I've built this year work to my satisfaction, are fun to make, and I am constantly learning new things. And I am doing this for fun but also to learn by teaching, so I build educational, visual things to share with others.

Nobody's stopping you from writing code, whether as a hobby or you find/start a company where that's valued.

But I am creating programs to manage the coding agents for me. Of course I am using coding agents to do that. It's turtles all the way down :-)

This is interesting. It would be even better if ones coding agent also got better at its coding abilities every night. Otherwise, agents might re-introduce the same class of bugs that your nightly improvements keep reacting to.

This would require logging agent actions during the day, analyzing it at night, identifying things the coding agent could do better, and update its AGENTS.md memory (relatively easy) or fine-tune the agent (if local LLM, and the fine-tuning cost was acceptable).

It would be nice that when a person corrects an agent's mistake this was captured and fed back into a nightly agent improving process.

great instinct. that's exactly what happens today! it learns from closed PRs and feedback on what changes are high value vs not

nice. is there a way to repair burned houses or put out fires?

nope, not at present. The houses act as your "health" (once they're all gone, it's game over), so I'm minded to keep them as a finite resource. Always open to suggestions to improve the game dynamics though.

I like the upgrades and wall repair features. I made it on to the leader board :-)