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Hacker News
@h4ckernews@mastodon.social  ·  activity timestamp 6 days ago

Forward propagation of errors through time

https://nicolaszucchet.github.io/Forward-propagation-errors-through-time/

#HackerNews #ForwardPropagation #Errors #ThroughTime #AI #Research #MachineLearning

Forward propagation of errors through time

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Kent Pitman
Kent Pitman
@kentpitman@climatejustice.social  ·  activity timestamp 3 weeks ago

@screwlisp

Coincidentally, I assume, Abhijit Rao posted on LinkedIn earlier today a very interesting blurb, reporting on his use of this condition handling structure in conjunction with LLMs.

(Works for me in an incognito window, but you may not be able to see past the first comment if you don't have a LinkedIn account.)

https://www.linkedin.com/posts/quasiabhi_commonlisp-lisp-agents-activity-7425847332560293888-fYf9

The blurb references this more detailed account of the work, which I haven't been through in detail yet:

https://quasilabs.in/blog/2026/02/07/conditions-restarts-and-the-agent-that-chooses/

#CommonLisp #Lisp #ConditionHandling #Errors #Restarts #Continuations #ErrorHandling #ConditionHandling #LLM #LLMs #AI #Modularity #QuasiLabs #Reflection #Introspection #MetaProgramming

Conditions, Restarts, and the Agent That Chooses

#commonlisp #lisp #agents #llm #programming #code #quasilabs | Abhijit Rao

In 1988 The Common Lisp Condition System was formally introduced by the X3J13 committee, responsible for the ANSI standard. It was a radical idea: The code that /detects/ an error should not decide how to /recover/ from it. Instead, the signaler establishes named recovery options—restarts—and lets a handler higher up the stack choose which one to invoke. The decision belongs to whoever has the broader context. This was ahead of its time. For 35+ years, the "broader context" was either a human in the debugger or a programmer who knew at compile time which restart to pick. Both worked fine. Then LLMs showed up. An agent monitoring a data pipeline encounters a validation error. Three restarts available. No hardcoded strategy. The agent needs to reason about system goals, weigh tradeoffs, adapt to context it wasn't explicitly programmed for. The architecture was already right. What was missing: semantic context for the handler. Restarts come with names and descriptions. Agents need to know what the system is /trying to achieve/. What failed and why. What each recovery option costs. Why these specific restarts exist in the first place. When you add structured intent metadata—goals, failure modes, design rationale—the agent can map recovery options to system objectives. Same restarts. Profoundly different decision-making surface. I built Telos to capture this. It makes the why behind code queryable at runtime. Combined with conditions and restarts, you get agent-legible error recovery. The condition/restart protocol already separates mechanism from policy. It supports multiple recovery options without the signaler knowing which will be chosen. It allows the handler to be arbitrarily far—in code, in time, in understanding—from the signaler. It's an architecture perfect for agentic systems. And, yes, Common Lisp has an ANSI standard. Blog post with full working CSV validator example: https://lnkd.in/dSbuDK6J #commonlisp #lisp #agents #llm #programming #code #quasilabs
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Hacker News
Hacker News
@h4ckernews@mastodon.social  ·  activity timestamp 2 months ago

Rust errors without dependencies

https://vincents.dev/blog/rust-errors-without-dependencies/

#HackerNews #Rust #Errors #Dependencies #Coding #Blog #Tech

Rust Errors Without Dependencies

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Hacker News
Hacker News
@h4ckernews@mastodon.social  ·  activity timestamp 3 months ago

Solving a Million-Step LLM Task with Zero Errors

https://arxiv.org/abs/2511.09030

#HackerNews #Solving #LLM #Task #Errors #AI #Research #MachineLearning #Innovation

arXiv.org

Solving a Million-Step LLM Task with Zero Errors

LLMs have achieved remarkable breakthroughs in reasoning, insights, and tool use, but chaining these abilities into extended processes at the scale of those routinely executed by humans, organizations, and societies has remained out of reach. The models have a persistent error rate that prevents scale-up: for instance, recent experiments in the Towers of Hanoi benchmark domain showed that the process inevitably becomes derailed after at most a few hundred steps. Thus, although LLM research is often still benchmarked on tasks with relatively few dependent logical steps, there is increasing attention on the ability (or inability) of LLMs to perform long range tasks. This paper describes MAKER, the first system that successfully solves a task with over one million LLM steps with zero errors, and, in principle, scales far beyond this level. The approach relies on an extreme decomposition of a task into subtasks, each of which can be tackled by focused microagents. The high level of modularity resulting from the decomposition allows error correction to be applied at each step through an efficient multi-agent voting scheme. This combination of extreme decomposition and error correction makes scaling possible. Thus, the results suggest that instead of relying on continual improvement of current LLMs, massively decomposed agentic processes (MDAPs) may provide a way to efficiently solve problems at the level of organizations and societies.
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Hacker News
Hacker News
@h4ckernews@mastodon.social  ·  activity timestamp 3 months ago

How to fix subsystem request failed on channel 0

https://blog.x-way.org/Linux/2025/11/06/How-to-fix-subsystem-request-failed-on-channel-0.html

#HackerNews #How #to #fix #subsystem #request #failed #on #channel #0 #Linux #Issues #Troubleshooting #Tech #Tips #Subsystem #Errors

x-log

How to fix subsystem request failed on channel 0

Today I got greeted by the following error when I was trying to scp some file. # scp a.txt user@somehost:/tmp/b.txt subsystem request failed on channel...
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Andreas Jaggi :donor:
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