longtermop

18 ms·

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by longtermop 3mo ago
The biggest gap is AI doesn't have the ability to self-correct and self-learn like humans do. We're working on fixing that with parcle.ai/second-brain. Beta will be rolling out in a week.
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Show HN: All-in-one memory for AI Agents(parcle.ai)
1 points by longtermop 3mo ago|1 comments
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Show HN: We cut >60% of tokens from agentic tasks by removing repeated context(parcle.ai)
1 points by longtermop 3mo ago|0 comments
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by longtermop 8mo ago
This is great — manifest validation feels like the right “static” layer for the agentic web. One nuance: a lot of prompt-injection / tool-abuse issues happen at runtime , when the agent is consuming untrusted content coming through pe
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by longtermop 8mo ago
Exciting to see Apple making agentic coding first-class. The "Xcode Intelligence" feature that pulls from docs and developer forums is powerful. One thing I'm curious about: as the agent ingests more external content (documen
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by longtermop 8mo ago
This resonates—CI and code review are great for enforcing conventions on what the agent produces . But one gap I keep running into: production agents ingest untrusted content at runtime that never touches the repo. Prompt injection is the
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by longtermop 8mo ago
Interesting approach — how are you thinking about security boundaries when aggregating context from multiple sources? Pulling in emails, docs, and chat history into one context window seems to create new injection vectors: a malicious email
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by longtermop 8mo ago
Good breakdown of the attack surface. Building on @stale-labs' point about injection - the article correctly identifies that the most dangerous vectors aren't direct user input. It's what comes back from tool calls. When an a
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by longtermop 8mo ago
Really appreciate the credential isolation approach here. The proxy pattern makes a lot of sense - keeping keys out of the agent's context entirely is the right call. This got me thinking about a related trust boundary issue though: ev
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by longtermop 8mo ago
The meta-problem ("who watches the watcher?") is real, but I think the framing shapes the answer. If you're building a second AI to monitor the first, you've just doubled your attack surface. The more tractable approach
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by longtermop 8mo ago
The microservices framing resonates but surfaces an interesting security question. In your orchestration example: research = await research_agent.call("Find Q3 earnings...") analysis = await doc_agent.call(f"Analyze thi
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by longtermop 8mo ago
The low-quality AI PR problem is real, but there's an inverse issue that doesn't get enough attention: AI agents that review code are equally vulnerable. When an AI code reviewer or copilot ingests a PR diff, it's processin
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by longtermop 8mo ago
Really nice approach using macOS native sandboxing! The deny-by-default with targeted allows for project folders is the right model. To answer your question about network sandboxing: yes, it's arguably the more critical half. Filesyste
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by longtermop 8mo ago
This is a thoughtful architecture. A few critiques and observations from implementing similar patterns: *On the cryptographic challenge-response (Section 5.2):* The HMAC-based verification is sound, but the "key in system prompt"
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by longtermop 8mo ago
Very cool project! The MCP surface area here (110 tools) is a great example of why tool-output validation is becoming critical. When an AI agent interacts with binary analysis tools, there are two injection vectors worth considering: 1. *To
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by longtermop 8mo ago
The vulnerability framing is like saying SQL injection was unfixable in 2005. Security and defense will always lag behind new technology shifts and platform shifts. Just like web security did not catch up until two decades later from the in