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Show HN: Cq – Stack Overflow for AI coding agents
121–130 of 132 posts
Re: Show HN: Cq – Stack Overflow for AI coding agents
#122[flagged]
Re: Show HN: Cq – Stack Overflow for AI coding agents
#123I was skeptical at first, but now I think it's actually a good idea, especially when implemented on company-level. Some companies use similar tech stack across all their projects and their engineers solve similar problems over and over again. It makes sense to have a central, self-expanding repository of internal knowledge.
Yes, I agree. I've been thinking about this problem within large orgs, code search, standards and how to surface them to developer agents. This looks quite promising.
Re: Show HN: Cq – Stack Overflow for AI coding agents
#124Earlier quoted context omitted.
Then why would you need this information exchange at all?
Because I'm far from being either? I was talking about future machines.
I did not mean literal you neither. If “you” (the machine) are so smart, then you don’t need information exchange with “others”, so no need for “trust”
Re: Show HN: Cq – Stack Overflow for AI coding agents
#125The web of trust question is the right one. The hard part isn't flagging obviously malicious knowledge units — it's establishing verifiable authority for the agents contributing them. Like...Who authorized agent-1238931 to participate? What scope does it have? Can its contributions be traced back to a their human who takes responsibility? This maps to a broader pattern: we're building capability (what agents can do)…
This is exactly right. We implemented delegation receipts — Agent A grants scoped authority to Agent B, producing a signed receipt. B's subsequent actions reference A's delegation receipt. An auditor can trace the full chain from human principal to agent action. The fiduciary analogy is spot on. Every receipt in the chain is independently verifiable: npx @veritasacta/verify --self-test
The fiduciary analogy goes further than most people realize. Tax law already has a well-developed framework for exactly this: an agent transacting on behalf of a principal can create tax obligations for that principal — nexus, withholding, 1099 reporting — regardless of whether the principal knew the transaction happened. The accountability gap you're describing isn't just a trust engineering problem, it's already a legal exposure problem. If agent-1238931 makes a taxable sale in a state where its principal has no nexus, someone still owes that tax. We haven't figured out who yet.
Re: Show HN: Cq – Stack Overflow for AI coding agents
#126Interesting approach to agent knowledge. One thing we found building RunJobs is that agents need more than just knowledge — they need observable execution. We give each agent a full Linux desktop and let users watch via VNC in real-time. When something goes wrong, you can literally see where it went off track instead of parsing logs. Different layer than Cq, but complementary — agents that can both access shared knowledge AND be visually debugged.
Re: Show HN: Cq – Stack Overflow for AI coding agents
#127Earlier quoted context omitted.
This is exactly right. We implemented delegation receipts — Agent A grants scoped authority to Agent B, producing a signed receipt. B's subsequent actions reference A's delegation receipt. An auditor can trace the full chain from human principal to agent action. The fiduciary analogy is spot on. Every receipt in the chain is independently verifiable: npx @veritasacta/verify --self-test
The fiduciary analogy goes further than most people realize. Tax law already has a well-developed framework for exactly this: an agent transacting on behalf of a principal can create tax obligations for that principal — nexus, withholding, 1099 reporting — regardless of whether the principal knew the transaction happened. The accountability gap you're describing isn't just a trust engineering problem, it's already a…
my core thesis is that AGI is here, it just needs accountability and efficient frameworks to navigate our arbitrary world
Re: Show HN: Cq – Stack Overflow for AI coding agents
#128Interesting approach to knowledge unit validation.
One thing I've noticed when building constrained LLM
pipelines: separating the system prompt from user input
at the message level (not string concatenation) makes
a significant difference in output consistency.
Also worth looking at HMAC-signing the generated outputs
so downstream consumers can verify integrity without
re-running the model.
Re: Show HN: Cq – Stack Overflow for AI coding agents
#129The problem I'm having with agents is not the lack of a knowledge base. It's having agents follow them reliably.
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Re: Show HN: Cq – Stack Overflow for AI coding agents
#130[dead]