I hear you maybe my summary of the tool does give this impression.
Here are actual small use cases that work for me:
- Aggregate the markdown files of a git project in one place. I can version them, review them, dump them, search efficiently into them.
Useful to me before launching long agentic tasks, I specify all I want to beforehand.
- "Cron" checks on new versions of libraries.
This works without specific APIs or any setup. A document is maintained with the latest seen version in a specific path of the document base. I also had good results tracking new stars and user adding them with this.
- Press review / OSINT
I built myself a "President Daily Brief"
Same thing. It can "reason" given the old daily briefs and assess if the situation is worsening or not on a specific issue, and make basic forecasts.
It tracked down the current Ebola crisis before it went mainstream for me and predicted correctly it would worsen.
I guess I use this for long term memory basically, where I keep ownership of long term knowledge locally.
It's more a metaCerebralCortex, and openClaw "dreams" are the hippocampus coalescing these memories at night.
Also, please note each document in base has a virtual "path", so beyond tags and search you can pin down on a specific domain. It is structured as a tree.
I do understand that if I were to build a harness this would possibly not be a good fit for the core of it.
Does this make sense to you, or would you just basically do without such a thing at all to keep each agentic task context clean(er) ?
Sure it's possible, we need to await for the Task if it already exists on the dictionary, for example we could imagine writing something like this inside an actor to make it threadSafe.
private var tasksDictionary: Dictionary<String, Task<Data, Error>>
func getData(at urlString: String) async throws -> Data {
if let currentTask = tasksDictionary[urlString] {
return await currentTask.value
}
let currentTask = Task {
return try await URLSession.shared.data(from: URL(string:urlString)!)
}
tasksDictionary[urlString] = currentTask
let result = await currentTask.value
tasksDictionary[urlString] = nil
return result
}
Hmm, why set `taskDictionary[urlString] = nil` at the bottom there? If the whole point is to cache the result, isn't the point to leave the value there for other requests to pick it up?
Yup, nice catch, no need to reset it to nil if you wanna keep it in memory indefinitely.
I guess making it nil can be also used if you don't wanna make the same request when there is one already in flight, in case you have a 403 error and need to request a refresh token, you don't wanna make two simultaneous requests, but you also don't wanna catch it indefinitely either.
I had this crazy idea a while back using Ray tracing APIs to piggy back a database. Testing for a row could be done shooting in parallel rays to a structure mapped to the data.
You can use examples from any product in any market segment.
The Acorn RISC PC doesn’t count because they don’t make that anymore. It has to be a product on the market today.
I thought the entire point of ARM is that you license the ISA, add in your own special sauce to make your own thing and then sell it as your own thing.
There are ARM architecture systems in the machine room at work. They’re ThunderX2 not “ARM”.
Here are actual small use cases that work for me:
- Aggregate the markdown files of a git project in one place. I can version them, review them, dump them, search efficiently into them. Useful to me before launching long agentic tasks, I specify all I want to beforehand.
- "Cron" checks on new versions of libraries. This works without specific APIs or any setup. A document is maintained with the latest seen version in a specific path of the document base. I also had good results tracking new stars and user adding them with this.
- Press review / OSINT I built myself a "President Daily Brief" Same thing. It can "reason" given the old daily briefs and assess if the situation is worsening or not on a specific issue, and make basic forecasts. It tracked down the current Ebola crisis before it went mainstream for me and predicted correctly it would worsen.
I guess I use this for long term memory basically, where I keep ownership of long term knowledge locally. It's more a metaCerebralCortex, and openClaw "dreams" are the hippocampus coalescing these memories at night.
Also, please note each document in base has a virtual "path", so beyond tags and search you can pin down on a specific domain. It is structured as a tree.
I do understand that if I were to build a harness this would possibly not be a good fit for the core of it.
Does this make sense to you, or would you just basically do without such a thing at all to keep each agentic task context clean(er) ?
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