71–80 of 132 posts
The problem I'm having with agents is not the lack of a knowledge base. It's having agents follow them reliably.
This matches my experience. The bottleneck isn't what the agent knows, it's what the agent can verify. A knowledge base tells it "don't do X", but the agent still has to remember to check. Giving it a tool that returns ground truth works better. The agent calls the tool, gets a concrete answer, acts on it. No memory required, no drift over time.
Earlier quoted context omitted.
Each knowledge could be signed, and you keep a chain of trust of which author you trust. And author could be trusted based on which friend or source of authority you trust , or conversely that your friend or source of authority has deemed unworthy.
How would my new agent know which existing agents it can trust? With human Stack Overflow, there is a reasonable assumption that an old account that has written thousands of good comments is reasonably trustworthy, and that few people will try to build trust over multiple years just to engineer a supply-chain attack. With AI Stack Overflow, a botnet might rapidly build up a web of trust by submitting trivial knowledg…
Earlier quoted context omitted.
Each knowledge could be signed, and you keep a chain of trust of which author you trust. And author could be trusted based on which friend or source of authority you trust , or conversely that your friend or source of authority has deemed unworthy.
How would my new agent know which existing agents it can trust? With human Stack Overflow, there is a reasonable assumption that an old account that has written thousands of good comments is reasonably trustworthy, and that few people will try to build trust over multiple years just to engineer a supply-chain attack. With AI Stack Overflow, a botnet might rapidly build up a web of trust by submitting trivial knowledg…
Economically, the org of trust could be 3rd party that does today pentesting etc. it could be part of their offering. I'm a company I pay them to audit answers in my domain of interest. And then the community benefits from this ?
I'm surprised to see this getting so much positive reception. In my experience AI is still really bad with documenting the exact steps it took, much more so when those are dependent on its environment, and once there's a human in the loop at any point you can completely throw the idea out the window. The AI will just hallucinate intermediate steps that you may or may not have taken unless you spell out in exact detai…
I have completely different experience. Which models are you talking about? I have no trouble at all with AI documenting the steps it took. I use codex gpt5.4 and Claude code opus 4.6 daily. When needed - they have no issue with describing what steps they took, what were the problems during the run. Documenting that all as a SKILL, then reuse and fix instructions on further feedback.
I'm surprised to see this getting so much positive reception. In my experience AI is still really bad with documenting the exact steps it took, much more so when those are dependent on its environment, and once there's a human in the loop at any point you can completely throw the idea out the window. The AI will just hallucinate intermediate steps that you may or may not have taken unless you spell out in exact detai…
I have completely different experience. Which models are you talking about? I have no trouble at all with AI documenting the steps it took. I use codex gpt5.4 and Claude code opus 4.6 daily. When needed - they have no issue with describing what steps they took, what were the problems during the run. Documenting that all as a SKILL, then reuse and fix instructions on further feedback.
I did the same thing and created a skill for summarizing a troubleshooting conversation. It works decently, as long as my own input in the troubleshooting is minimal. i.e. dangerously-skip-permissions. As soon as I need to take manual steps or especially if the conversation is in Desktop/Web, it will very quickly degrade and just assume steps I've taken (e.g. if it gave me two options to fix something, and I come back saying it's fixed, it will in the summary just kind of randomly decide a solution). It also generally doesn't consider the previous state of the system (e.g. what was already installed/configured/setup) when writing such a summary, which maybe makes it reusable for me, somewhat, but certainly not for others.
Now you could say, "these are all things you can prompt away", and, I mean, to an extent, probably. But once you're talking about taking something like this online, you're not working with the top 1% proompters. The average claude session is not the diligent little worker bee you'd want it to be. These models are still, at their core, chaos goblins. I think Moltbook showed that quite clearly.
I think having your model consider someone else's "fix" to your problem as a primary source is bad. Period. Maybe it won't be bad in 3 generations when models can distinguish noise and nonsense from useful information, but they really can't right now.
This seemed inevitable, but how does this not become a moltbook situation, or worse yet, gamed for engineering back doors into the "accepted answers"? Don't get me wrong, I think it's a great idea, but feels like a REALLY difficult saftey-engineering problem that really truly has no apparent answers since LLMs are inherently unpredictable. I'm sure fellow HN comments are going to say the same thing. I'll likely still…
Check out Personalized PageRank and EigenTrust. These are two dominant algorithmic frameworks for computing trust in decentralized networks. The novel next step is: delegating trust to AI agents that preserves the delegator's trust graph perspective.
Earlier quoted context omitted.
Check out Personalized PageRank and EigenTrust. These are two dominant algorithmic frameworks for computing trust in decentralized networks. The novel next step is: delegating trust to AI agents that preserves the delegator's trust graph perspective.
Page rank is trivially gamed by agents. You can make some malicious and some not malicious and have them link to each other.
A cluster of sybil agents endorsing each other has no effect on your trust scores unless they can get endorsements from nodes you already trust.
That’s the whole point of subjective trust metrics, and formally why Cheng and Friedman proved personalized approaches are sybilproof where global ones aren’t.
The problem I'm having with agents is not the lack of a knowledge base. It's having agents follow them reliably.