amabito

5 ms·

1.
by amabito 7mo ago
IOPB bit semantics are inverted from what you might expect: 0 means permitted, 1 means denied. So zeroed pcb memory silently grants access to every port in range -- that's why this was consistently reproducible, not flaky. One sizeof()
2.
by amabito 7mo ago
Vulkan's presentation API makes this distinction explicit: VK_PRESENT_MODE_MAILBOX_KHR is the "replace if already queued" mode that actually reduces latency, while VK_PRESENT_MODE_FIFO_KHR is the pipeline-queue variant t
3.
by amabito 7mo ago
The tooling gap exists partly because git's data model has no native concept of "this branch's upstream is another feature branch" — each PR is independent from the forge's perspective, so rebasing one layer in the
4.
by amabito 7mo ago
One thing that often gets overlooked in this comparison is how behavior trees degrade under retry or partial failure scenarios. State machines make failure transitions explicit, but behavior trees can re-enter branches in ways that resemble
5.
Ask HN: How do you prevent retry cascades in LLM systems?
2 points by amabito 7mo ago|0 comments
6.
by amabito 7mo ago
One thing I find fascinating about epoll/kqueue is how much modern async frameworks abstract away the underlying readiness model. A lot of people talk about “async performance” without realizing the core efficiency gain came from avoid
7.
by amabito 7mo ago
Most orchestrators aren’t “bad”. They just assume LLM calls behave like deterministic RPC. The real issue is that we’re embedding stochastic, cost-weighted calls inside recursive graphs without structural bounds. Orchestration isn’t the fai
8.
by amabito 7mo ago
That’s actually a pretty disciplined setup. What you’ve described sounds a lot like layered containment: Loop budget (hard recursion bound) Progressive checks (soft convergence control) Sleep cycles (temporal isolation) Deep sleep cap (boun
9.
by amabito 7mo ago
This is interesting. It looks less like a “model failure” and more like a containment failure. When agents audit themselves, you’re effectively running recursive evaluation without structural bounds. Did you enforce any step limits, retry b
10.
by amabito 7mo ago
Right — search engines have long had authority scoring, link graphs, freshness signals, etc. The interesting gap is that retrieval systems used in LLM pipelines often don't inherit those signals in a structured way. They fetch document
11.
by amabito 7mo ago
What’s interesting here is that the model isn’t really “lying” — it’s just amplifying whatever retrieval hands it. Most RAG pipelines retrieve and concatenate, but they don’t ask “how trustworthy is this source?” or “do multiple independent
12.
by amabito 7mo ago
Approval gates make a lot of sense, especially for high-impact actions. Most agent failures I've seen come from nothing stopping execution at all. One scenario I'm curious about: how do you think about overnight or weekend runs wh
13.
by amabito 7mo ago
The Markdown-over-database choice makes sense for document-shaped output. The harder problem seems to be concurrent semantic edits. Git-style merging works for code because conflicts are syntactic. With prose, two agents can produce logical
14.
by amabito 7mo ago
Nice reference. If agents are essentially “a while loop with an LLM call,” then one missing category might be execution control. In distributed systems we rarely let loops run unbounded — we add budgets, backoff, circuit breakers, etc. With
15.
by amabito 7mo ago
Here’s a raw 75s demo of a runaway loop being halted: https://youtu.be/LaZOGj3EO_0
16.
by amabito 7mo ago
Context: This isn’t another observability tool. It enforces: - hard budget stops - retry containment - circuit breaking The goal is simple: the $12K weekend never happens.
17.
Show HN: Preventing runaway LLM agents (enforcement layer)(github.com)
1 points by amabito 7mo ago|2 comments
18.
Ask HN: Do LLM agents need a separate safety layer?
2 points by amabito 7mo ago|0 comments