What I like about, I think, is the private assistance feature, but I can achieve that with other solutions, I wouldn’t need OpenClaw for that. But I don’t think I will go that way anytime soon. I think it will stress me too much.
I am using AI for development daily.
I describe an issue or feature to an agent via a skill and it returns a set of tasks in a structured and validated json format, then I run that json file through a python project I have created, looping through each task one at a time, and then I have my python code to structure how my agent is working. Each step is deterministic with short bursts of AI delulu, that again is validated against deterministic steps in pure python. It works quite good and each feature/task is approached in the exact same way where only the in between AI delulu deviates from previous runs, but it makes it much nicer, when you have something you trust in between what the AI is doing.
See, now that sounds pretty cool. It sounds like an automated discovery and work harness. I want to build something like that.
I imagine a huge ecosystem of tools. It only requires one person to build it, then surely it can be open sourced right?
I imagine a SKILL.md repository, alongside ability to specify SKILL dependancies on a project-basis. I imagine vector cache layers, version controls, snapshots for swarm state, …
Honestly, I’d love to experiment with different architectures for compositing swarms of agents. Curious how different designs might behave holistically. To include, different paradigms for sharing state between nodes in a swarm.
I also can’t help but feel like there has to be more efficient ways for models to talk to each other than in natural language. If they’re training on the same dataset, why can’t they talk in tokens for example? The human brain doesn’t need to communicate in natural language when the amygdala and prefrontal cortex are having a dispute.
If you’re interested, the linux unplugged podcast had a recent episode on their experiences. I’ve never loved the tone of “hey, look, this is inevitable” when it comes to ai, but I can see its utility when well-scoped with conservative permissions and oversight vs letting it loose or vibe coding. Now if only hardware wasn’t artificially inflated I might think it was worth dabbling locally.
What I like about, I think, is the private assistance feature, but I can achieve that with other solutions, I wouldn’t need OpenClaw for that. But I don’t think I will go that way anytime soon. I think it will stress me too much.
I am using AI for development daily. I describe an issue or feature to an agent via a skill and it returns a set of tasks in a structured and validated json format, then I run that json file through a python project I have created, looping through each task one at a time, and then I have my python code to structure how my agent is working. Each step is deterministic with short bursts of AI delulu, that again is validated against deterministic steps in pure python. It works quite good and each feature/task is approached in the exact same way where only the in between AI delulu deviates from previous runs, but it makes it much nicer, when you have something you trust in between what the AI is doing.
See, now that sounds pretty cool. It sounds like an automated discovery and work harness. I want to build something like that.
I imagine a huge ecosystem of tools. It only requires one person to build it, then surely it can be open sourced right?
I imagine a SKILL.md repository, alongside ability to specify SKILL dependancies on a project-basis. I imagine vector cache layers, version controls, snapshots for swarm state, …
Honestly, I’d love to experiment with different architectures for compositing swarms of agents. Curious how different designs might behave holistically. To include, different paradigms for sharing state between nodes in a swarm.
I also can’t help but feel like there has to be more efficient ways for models to talk to each other than in natural language. If they’re training on the same dataset, why can’t they talk in tokens for example? The human brain doesn’t need to communicate in natural language when the amygdala and prefrontal cortex are having a dispute.
If you’re interested, the linux unplugged podcast had a recent episode on their experiences. I’ve never loved the tone of “hey, look, this is inevitable” when it comes to ai, but I can see its utility when well-scoped with conservative permissions and oversight vs letting it loose or vibe coding. Now if only hardware wasn’t artificially inflated I might think it was worth dabbling locally.