I went down (continue to do down) this rabbit hole and agree with the author.
I tried a few different ideas and the most stable/useful so far has been giving the agent a single run_bash tool, explicitly prompting it to create and improve composable CLIs, and injecting knowledge about these CLIs back into it's system prompt (similar to have agent skills work).
This leads to really cool pattens like:
1. User asks for something
2. Agent can't do it, so it creates a CLI
3. Next time it's aware of the CLI and uses it. If the user asks for something it can't do it either improves the CLI it made, or creates a new CLI.
4. Each interaction results in updated/improved toolkits for the things you ask it for.
You as the user can use all these CLIs as well which ends up an interesting side-channel way of interacting with the agent (you add a todo using the same CLI as what it uses for example).
It's also incredibly flexible, yesterday I made a "coding agent" by having it create tools to inspect/analyze/edit a codebase and it could go off and do most things a coding agent can.
Agents can complete an impressive amount of tasks with just this, but they quickly hit a bottleneck in loading context. A major reason for the success of agentic coding tools such as Claude and Cursor is how they push context of the problem and codebase into the agent proactively, rather than have the agent waste time and tokens figuring out how to list the directory etc.
The author seems to stop at 'code' but it seems we could go further and train an AI to work directly with binary. You give it a human prompt and a list of hardware components which make up your machine and it produces executable binary which fulfills your requirements and runs directly on those specific hardware, bypassing the OS...
Or we could go further; the output nodes of the LLM could be physically connected to the pins of the CPU 1-to-1 so it can feed the binary directly maybe then it could detect what other hardware is available automatically...
Then it could hack the network card and take over the Internet and nobody would be able to understand what it's doing. It would just show up as glitchy bits scattered over systems throughout the world. But the seemingly random glitches would be the ASI adjusting its weights. Also it would control humans through advertising. Hidden messages would be hidden inside people's speech (unperceptive to even themselves) designed to allow the AI to coordinate humans.
I went down (continue to do down) this rabbit hole and agree with the author.
I tried a few different ideas and the most stable/useful so far has been giving the agent a single run_bash tool, explicitly prompting it to create and improve composable CLIs, and injecting knowledge about these CLIs back into it's system prompt (similar to have agent skills work).
This leads to really cool pattens like: 1. User asks for something
2. Agent can't do it, so it creates a CLI
3. Next time it's aware of the CLI and uses it. If the user asks for something it can't do it either improves the CLI it made, or creates a new CLI.
4. Each interaction results in updated/improved toolkits for the things you ask it for.
You as the user can use all these CLIs as well which ends up an interesting side-channel way of interacting with the agent (you add a todo using the same CLI as what it uses for example).
It's also incredibly flexible, yesterday I made a "coding agent" by having it create tools to inspect/analyze/edit a codebase and it could go off and do most things a coding agent can.
https://github.com/caesarnine/binsmith
Agents can complete an impressive amount of tasks with just this, but they quickly hit a bottleneck in loading context. A major reason for the success of agentic coding tools such as Claude and Cursor is how they push context of the problem and codebase into the agent proactively, rather than have the agent waste time and tokens figuring out how to list the directory etc.
The author seems to stop at 'code' but it seems we could go further and train an AI to work directly with binary. You give it a human prompt and a list of hardware components which make up your machine and it produces executable binary which fulfills your requirements and runs directly on those specific hardware, bypassing the OS...
Or we could go further; the output nodes of the LLM could be physically connected to the pins of the CPU 1-to-1 so it can feed the binary directly maybe then it could detect what other hardware is available automatically...
Then it could hack the network card and take over the Internet and nobody would be able to understand what it's doing. It would just show up as glitchy bits scattered over systems throughout the world. But the seemingly random glitches would be the ASI adjusting its weights. Also it would control humans through advertising. Hidden messages would be hidden inside people's speech (unperceptive to even themselves) designed to allow the AI to coordinate humans.