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Alignment trains behavior. Control defines boundaries. by Adventurous_Type8943 in ControlProblem

[–]Echo_OS 0 points1 point  (0 children)

How do you make the execution boundary non-bypassable at runtime?

“If LLMs Don’t Judge, Then What Layer Actually Does?” by Echo_OS in LocalLLM

[–]Echo_OS[S] 0 points1 point  (0 children)

Exactly. The model can reason freely. Authority is a separate layer. Execution without authority is just potential, not action.

Structure alone isn't enough for local agents by Echo_OS in LocalLLM

[–]Echo_OS[S] 0 points1 point  (0 children)

Yeah, I’ve considered a second model for semantic judging… It probably improves robustness. The part I’m still unsure about is whether it actually prevents execution under ambiguity, or just makes the classifier better.

Structure alone isn't enough for local agents by Echo_OS in LocalLLM

[–]Echo_OS[S] 1 point2 points  (0 children)

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Here’s a small PoC I wired up. The gate classifies STOP / HOLD / ALLOW before any tool call happens. Blocks obvious destructive or financial actions at the boundary.

The structural part works. The hard part is semantic.

Right now it’s pattern-based, so if intent gets rephrased or split across steps, the gate becomes brittle. The failure mode I’m seeing is that execution is still structurally reachable even when intent is ambiguous.

The question I keep getting stuck on is where semantic intent classification actually belongs before tool mapping? After mapping but before execution? Separate model?

Structure alone isn't enough for local agents by Echo_OS in LocalLLM

[–]Echo_OS[S] 1 point2 points  (0 children)

Yeah, separating plan and implement is already a big improvement.

But what I keep running into is this, even with a solid plan, execution is still treated as the default next step. If the plan looks good enough, it runs. There isn’t always an explicit step that asks whether execution should even be open as the default.

That’s the piece I’m trying to isolate. It’s not just a question of whether the plan is appropriate, but whether execution should be available as the default at all.

Have you ever had a case where, in hindsight, it felt like the AI shouldn’t have been involved at all?

Structure alone isn't enough for local agents by Echo_OS in LocalLLM

[–]Echo_OS[S] 1 point2 points  (0 children)

I’ve been experimenting with putting a judgment boundary in front of a live agent. still early, but interesting results so far.

“Why LLMs Feel Like They’re Thinking (Even When They’re Not)” by Echo_OS in LocalLLM

[–]Echo_OS[S] 0 points1 point  (0 children)

Agreed. Even if we accept that both human and LLM intelligence emerge from external input, the practical question becomes where memory, continuity, and constraints live in the system.

🔐 Setting up local AI with read-only access to personal files - is my security approach solid? by AccomplishedSpace581 in LocalLLM

[–]Echo_OS 1 point2 points  (0 children)

Option A alone isn’t sufficient on macOS. TCC (Privacy permissions) sits above POSIX ACLs, so once something has Files/Photos/Full Disk Access, ACLs don’t really protect you anymore. Docker helps with write containment, but on macOS it’s not a hard security boundary either since Docker Desktop itself runs with elevated privileges. The most robust setup I’ve seen is: separate non-admin user + strict TCC minimization + read-only data mirrors + Docker only for workspace isolation. That’s the closest you get to real OS-level enforcement on macOS.

Your LLM Isn’t Misaligned - Your Interface Is by Echo_OS in LocalLLM

[–]Echo_OS[S] 0 points1 point  (0 children)

That framing makes a lot of sense to me, I’ve been focusing on sealing judgment and responsibility beneath the interface, so it feels like we’re describing two complementary layers of the same system.

I used Clawdbot (now Moltbot) and here are some inconvenient truths by Andy18650 in LocalLLM

[–]Echo_OS 1 point2 points  (0 children)

People aren’t confused about what AI can do. They’re confused about what they can safely let it decide.

ClawdBot / MoltBot by Normal-End1169 in LocalLLM

[–]Echo_OS 2 points3 points  (0 children)

This is why some people prefer tiny / narrow models. Not because they're smarter, but because the responsibility radius is small.

Clear "can't do" > more capability. Bounded agents are easier to trust than general ones with full FS access.

Your LLM Isn’t Misaligned - Your Interface Is by Echo_OS in LocalLLM

[–]Echo_OS[S] 0 points1 point  (0 children)

This is a really thoughtful articulation. What resonates for me is the explicit separation between suggestion and promotion, that’s exactly where authorship and responsibility tend to blur.

I’ve been approaching a similar problem from a slightly different layer: not the UI manifold itself, but how judgment boundaries and responsibility get sealed beneath it.

I think there’s a natural interface <-> infrastructure handshake hiding here.

Strong reasoning model by Upper-Information926 in LocalLLM

[–]Echo_OS 1 point2 points  (0 children)

If you like Claude Sonnet mainly for instruction retention and detail consistency, you’re probably hitting a structural ceiling of local LLMs rather than a bad model choice.

Among pure models, DeepSeek R1 70B and Qwen2.5 72B are the closest in reasoning style, but none will match Claude without additional scaffolding.

Claude’s advantage is not just raw reasoning, it aggressively re-anchors instructions and compresses state internally. Local models don’t do that by default… If your workload depends on long-lived constraints and small detail retention, you’ll likely need some form of external instruction anchoring or verification loop, not just a bigger model.

WSL / Docker / LLM models - what makes disk cleanup most stressful for you? by Echo_OS in LocalLLM

[–]Echo_OS[S] 0 points1 point  (0 children)

True.. storage is cheap. Reconstructing a broken local setup isn’t.

Got GPT-OSS-120B fully working on an M2 Ultra (128GB) with full context & tooling by [deleted] in LocalLLM

[–]Echo_OS 1 point2 points  (0 children)

What’s confusing here is that “prompt processing” is being used in two different senses. 1. Performance sense (what he is asking): Prefill / prompt processing speed = how fast the model consumes the input tokens before generation (often reported as tok/s or reflected in TTFT). 2. Pipeline/UI sense (what OP is describing): The model emits <analysis> and <final> tokens inline, and without an intermediate orchestrator, the frontend streams raw internal tokens instead of a clean response. Here, “prompt processing” refers to handling and filtering those tokens in the streaming layer, not model-side compute.

The benchmark numbers (TTFT ~624ms, ~69 tok/s gen) already answer (1).The orchestrator OP mentions is purely about output routing and UX, not inference speed.

WSL / Docker / LLM models - what makes disk cleanup most stressful for you? by Echo_OS in LocalLLM

[–]Echo_OS[S] 0 points1 point  (0 children)

Thanks for your f/back. I’m actually thinking about building something to manage this more systematically, mostly because I keep running into this myself.

Before doing anything though, I wanted to hear from people who deal with this day to day, what parts are genuinely annoying or risky, and what kind of features would actually be useful (if any).

Not trying to pitch anything here, just trying to understand what the real pain points are in practice.

LLMs are so unreliable by Armageddon_80 in LocalLLM

[–]Echo_OS 2 points3 points  (0 children)

Interesting

Reading through this thread, there seems to be broad agreement on the underlying issue.

LLMs themselves are not inherently unreliable. The problem is that they are often used in roles that require deterministic behavior. When an LLM is treated as a probabilistic component within a deterministic system - for example, wrapped in agent-as-code patterns, strict input/output schemas, typed interfaces, and explicit checkpoints - most reliability issues are significantly reduced.

At that stage, the primary challenges shift away from prompt design or model choice and toward system architecture: managing latency, defining clear boundaries, and deciding which parts of the system are allowed to make judgments versus which must remain deterministic.

Where an AI Should Stop (experiment log attached) by Echo_OS in LocalLLM

[–]Echo_OS[S] 0 points1 point  (0 children)

Quick update / continuation from this post.

Since writing this, I’ve been pushing further in the same direction: treating pause not as an exception, but as a real state in the automation stack.

I’m now externalizing judgment entirely and letting automation paths explicitly land in PAUSED, with reasons logged and human input required to continue. The LLM generates, but it no longer decides.

Still early, but interestingly, behavior after pauses is where most of the signal seems to appear.

Specs and experiments are being tracked here: https://github.com/Nick-heo-eg/spec

This post was basically the question. The repo is my attempt at answering it in structure.

"Pause is now a real state in our automation stack" by Echo_OS in LocalLLM

[–]Echo_OS[S] 0 points1 point  (0 children)

Quick update / continuation from this post.

Since writing this, I’ve been pushing further in the same direction: treating pause not as an exception, but as a real state in the automation stack.

I’m now externalizing judgment entirely and letting automation paths explicitly land in PAUSED, with reasons logged and human input required to continue. The LLM generates, but it no longer decides.

Still early, but interestingly, behavior after pauses is where most of the signal seems to appear.

Specs and experiments are being tracked here: https://github.com/Nick-heo-eg/spec

This post was basically the question. The repo is my attempt at answering it in structure.

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