IntelliAvatar

7 ms·

1.
by IntelliAvatar 9mo ago
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 the trace is captured before side-effects happen, otherwise
2.
by IntelliAvatar 9mo ago
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. Even a single malformed argument can have irreversible effec
3.
by IntelliAvatar 9mo ago
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 guardrail or tracing for side-effects? Asking because execu
4.
by IntelliAvatar 9mo ago
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 the side-effects before execution? I’ve seen audits fail not a
5.
by IntelliAvatar 9mo ago
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 enforce any guardrails at the tool boundary (e.g. path sand
6.
by IntelliAvatar 9mo ago
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 two things: 1) blocking unsafe side effects before they hap
7.
Show HN: FailCore – Execution-Time Safety Runtime for AI Agents(github.com)
1 points by IntelliAvatar 9mo ago|1 comments
8.
by IntelliAvatar 9mo ago
Full formal verification is rare, but partial guarantees at execution boundaries are very practical — especially for systems that act autonomously.
9.
by IntelliAvatar 9mo ago
A runtime layer for AI agents that enforces execution boundaries: traces, replay, and a hard “no” when something unsafe is about to run.
10.
by IntelliAvatar 9mo ago
Observability is step one. The hard part is what the system is allowed to do once you observe it.
11.
Show HN: FailCore – Deterministic Execution Runtime for AI Agents(github.com)
1 points by IntelliAvatar 9mo ago|0 comments
12.
by IntelliAvatar 9mo ago
Thanks, that clarifies it. The checkpoint-based cancellation and the sync-vs-async locking model differences were exactly what I was trying to understand.
13.
by IntelliAvatar 9mo ago
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.
14.
by IntelliAvatar 9mo ago
Nice idea. How does this compare to running ephemeral preview environments via ArgoCD or Helmfile today?
15.
by IntelliAvatar 9mo ago
Interesting angle. How do you decide what becomes persistent memory vs transient context? Is there any eviction or decay model?
16.
by IntelliAvatar 9mo ago
How does this differ from asyncio.Queue in terms of backpressure or cancellation semantics?
17.
by IntelliAvatar 9mo ago
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 interesting to see a concrete “not supported” list from the author.
18.
by IntelliAvatar 9mo ago
3KB is wild. What features did you intentionally leave out to get this small?
19.
Ask HN: How do you define "done" for long-running AI agents?
1 points by IntelliAvatar 9mo ago|0 comments
20.
by IntelliAvatar 9mo ago
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.
21.
by IntelliAvatar 9mo ago
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 agents), most of the real work happens in areas that don’t
22.
by IntelliAvatar 9mo ago
I'm experimenting with a local-first autonomous agent system on Windows. The interesting part for me hasn't been the UI or demos, but the engineering problems: how planning compares to step-by-step tool-calling, how state drifts o