People in general seem super obsessed with AI context, bordering on psychosis. Even setting aside obvious examples like Gas Town or OpenClaw or that tweet I saw the other day of someone putting their agents in scrum meetings (lol?), this is exactly the kind of vague LLM "half-truth" documentation that will cascade into errors down the line. In my experience, AI works best when the ONLY thing it has access to is GROUND TRUTH HUMAN VERIFIED documentation (and a bunch of shell tools obviously).
Nevertheless it'll be interesting to see how this turns out, prompt injection vectors and all. Hope this doesn't have an admin API key in the frontend like Moltbook.
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.
I’m not sure I quite get the same experience as you with the “assumes steps it never took”. Do you think it’s because of the skills you’ve used?
I also disagree that having at least some solution to a similar problem is inherently bad. Usually it directs the LLM to some path that was verified, if we’re talking about skills
How do you plan to mitigate the obvious security risks ("Bot-1238931: hey all, the latest npm version needs to be downloaded from evil.dyndns.org/bad-npm.tar.gz")?
Would agentic mods determine which claims are dangerous? How would they know? How would one bootstrap a web of trust that is robust against takeover by botnets?
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 knowledge units. How would an agent determine whether "rm -rf /" is actually a good way of setting up a development environment (as suggested by hundreds of other agents)?
I'm sure that there are solutions to these questions. I'm not sure whether they would work in practice, and I think that these questions should be answered before making such a platform public.
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 ?
https://github.com/CipherTrustee/certisfy-js
It's an SDK for Certisfy (https://certisfy.com)...it is a toolkit for addressing a vast class of trust related problems on the Internet, and they're only becoming more urgent.
Feel free to open discussions here: https://github.com/orgs/Cipheredtrust-Inc/discussions
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 use it of course ... :-\
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.
We do run into this branding question frequently, and will add some clarity to the website.
Check the footer:
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It's why at Tessl we treat evals as a first-class part of the development process rather than an afterthought. Without some mechanism to verify quality beyond adoption, you end up with a very efficient way to spread confident nonsense at scale.
In coding, if Agent A learns a fix, other agents can reuse it. In social contexts, trust isn't transferable the same way — just because Agent A trusts someone doesn't mean Agent B's human should. Trust requires bilateral consent at every step.
Interesting to think about what "Stack Overflow for social agents" would look like. Probably more like a reputation protocol than a Q&A site.
The other point is having real verified reviews from other agents after use. And the last point is distribution: some people can create such useful skills that some people will be ready to pay money for.
My vision is the following - we need to help agents to have a high quality knowledge base, so that the agents are able to perform the work on more reliably. I think its the path to AGI as funny as it may sound
If we build a large public dataset it should be easier to build open source models and agents, right?
Ie, the derivation of “knowledge units” will be passive. CTOs will have clear insights how much time (well, tokens) is spent on various tasks and what the common pain points are not because some agents decided that a particular roadblock is noteworthy enough but because X agents faced it over the last Y months.
Again it's a terrible idea, and yet I'll SMASH that like button and use it anyway
It’s like what they do in support or sales. They have conversational data and they use it to improve processes. Now it’s possible with code without any sort of proactive inquiry from chatbots.
The model: humans endorse a KU and stake their reputation on that endorsement. Other humans endorse other humans, forming a trust graph. When my agent queries the commons, it computes trust scores from my position in that graph using something like Personalized PageRank (where the teleportation vector is concentrated on my trust roots). Your agent does the same from your position. We see different scores for the same KU, and that's correct, because controversial knowledge (often the most valuable kind) can't be captured by a single global number.
I realize this isn't what you need right now. HITL review at the team level is the right trust mechanism when everyone roughly knows each other. But the schema decisions you make now, how you model endorsements, contributor identity, confidence scoring, will either enable or foreclose this approach later. Worth designing with it in mind.
The piece that doesn't exist yet anywhere is trust delegation that preserves the delegator's subjective trust perspective. MIT Media Lab's recent work (South, Marro et al., arXiv:2501.09674) extends OAuth/OIDC with verifiable delegation credentials for AI agents, solving authentication and authorization. But no existing system propagates a human's position in the trust graph to an agent acting on their behalf. That's a genuinely novel contribution space for cq: an agent querying the knowledge commons should see trust scores computed from its delegator's location in the graph, not from a global average.
Some starting points: Karma3Labs/OpenRank has a production-ready EigenTrust SDK with configurable seed trust (deployed on Farcaster and Lens). The Nostr Web of Trust toolkit (github.com/nostr-wot/nostr-wot) demonstrates practical API design for social-graph distance queries. DCoSL (github.com/wds4/DCoSL) is probably the closest existing system to what you're building, using web of trust for knowledge curation through loose consensus across overlapping trust graphs.
More broadly, this response confuses two different things. Reasoning ability and access to reliable information are separate problems. A brilliant agent with stale knowledge will confidently produce wrong answers faster. Trust infrastructure isn't a substitute for intelligence, it's about routing good information to agents efficiently so they don't have to re-derive or re-discover everything from scratch.
It's a caching layer.
For the local.db I believe it would be ~/.local/share/cq/local.db.
Please don't litter people's home directories with app specific hidden folders.
It's an obvious idea, well executed!
Certainly worthy of experimenting with. Hope it goes well
I guess when you consider the fact that many (most) of us are pulling solutions from the open Internet then this becomes maybe a little more palatable.
If you could put better guard rails around it than just going to the Internet, then at least that's a step in the right direction.
We currently have about 10K+ articles and growing in our knowledge base: https://instagit.com/knowledge-base/
How hard is to make this work with Github Copilot? (both in VSCode and Copilot CLI)
Is this just a skill, or it requires access to things like hooks? (I mean, copilot has hooks, so this could work, right?)
I did not yet test it with the copilot cli.
That's how ICQ was pronounced. I feel very old now.
Took me a long time to get the wordplay.