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This makes a lot of sense. Recording execution + replay is exactly what’s missing once you move past simple logging. One thing I’ve found tricky in similar setups is making sure th…

Makes sense, thanks for the clarification. I mostly worry about the gap between a correct plan and execution-time behavior — especially when tools touch the filesystem or OS APIs. …

Nice project. One thing Cloudflare Workers gets right is strong execution isolation. When self-hosting, what’s the failure model if user code misbehaves? Is there any runtime-level…

Really like the local-first + MCP angle. How do you handle execution-time guarantees? For example: when an MCP tool call touches the filesystem or network, do you validate + log th…

This looks great. One thing I’ve been bitten by with desktop agents is execution-time safety: the plan is correct, but a single malformed path or OS call causes real damage. Do you…

One clarification that may help set expectations: FailCore is intentionally not an agent framework, planner, or sandbox. It sits strictly at the execution boundary and focuses on t…

Hi HN, FailCore is a small execution-time safety runtime for AI agents. Instead of relying on better prompts or planning, it enforces security at the Python execution boundary: blo…

Full formal verification is rare, but partial guarantees at execution boundaries are very practical — especially for systems that act autonomously.

A runtime layer for AI agents that enforces execution boundaries: traces, replay, and a hard “no” when something unsafe is about to run.

Observability is step one. The hard part is what the system is allowed to do once you observe it.

FailCore is a deterministic execution runtime for AI agents. It sits below your planner/LLM and makes tool execution auditable, policy-gated, and replayable. What it does: - Audit-…

Thanks, that clarifies it. The checkpoint-based cancellation and the sync-vs-async locking model differences were exactly what I was trying to understand.

That makes sense — thanks for clarifying. Framing it as “zero infra ownership, just a reviewer convenience” really helps explain where this fits compared to ArgoCD-style previews.

Nice idea. How does this compare to running ephemeral preview environments via ArgoCD or Helmfile today?

Interesting angle. How do you decide what becomes persistent memory vs transient context? Is there any eviction or decay model?

How does this differ from asyncio.Queue in terms of backpressure or cancellation semantics?

That makes sense. I was mostly curious about what explicit trade-offs the author chose beyond “generation only” — e.g. fonts, Unicode, images, compression, etc. Would be interestin…

3KB is wild. What features did you intentionally leave out to get this small?

I've been working on long-running automation / agent systems, and one thing I keep running into is how hard it is to define "done". Demos are easy: a task finishes once the happy p…

Exactly. I’ve also found that once those constraints become boring, that’s usually when the work itself is done — or not worth doing anymore.

For me, one signal has been whether the problems remain interesting even when progress is slow. When working on complex systems (like anything involving long-running automation or …