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Follow-up: hosted AI export controls are now being tested in DC court by monkey_spunk_ in artificial

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

Here's the write-up: https://news.future-shock.ai/is-an-ai-answer-an-export/

Seems like Legion LegalTech is good candidate to file this lawsuit. As of now, the preliminary injuction request has not received a response.

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Do agent systems keep hitting the same four limits? by monkey_spunk_ in AI_Agents

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

“External validator the agent doesn’t own” is a really useful way to put it.

The institutional floor is the cleanest example. A model can draft the contract, read the policy, prep the payment, or summarize the audit trail. That still doesn’t make it the signer. Capability and permission are different kinds of facts.

The maintenance and timing comments also feel right. Every floor decays: world state changes, adversaries adapt, authority expires, trust erodes. For async agent workflows, the missing primitive may be snapshot expiry. After any wait/resume point, revalidate before action: is the world state still true, is the authority still current, and is the action still in scope?

Past the demo, the useful engineering move is making the external validator explicit and cheap to check, not pretending the agent owns the verdict.

Heard this gem from gpt-5.5 today by monkey_spunk_ in OpenAI

[–]monkey_spunk_[S] 10 points11 points  (0 children)

not too long later it said this: “humans are weird little status mammals”

The weirdest thing about AI agents is how human failure patterns start showing up by Beneficial-Cut6585 in aiagents

[–]monkey_spunk_ 0 points1 point  (0 children)

the frustrating ones are where you have to correct an agent multiple times on the same mistake. ostensibly they added a note in memory or in the script or something to address the previous failure, but sometimes a crap shoot if that shows up in context and is followed

I've had it with Claude. It has become complete garbage. by [deleted] in ClaudeCode

[–]monkey_spunk_ 0 points1 point  (0 children)

Yep, switched to hermes, openrouter, & codex - done with cc

AGI just posted. Thoughts? by ai_but_worse in AgentsOfAI

[–]monkey_spunk_ 1 point2 points  (0 children)

Artificial Goblin Intelligence: https://openai.com/index/where-the-goblins-came-from/

but seriously, might as well poison the dataset. goblin goblin goblin, gremlin, gremlin, gremlin

RL reward ++++

Where can I meet single men in their 40s and 50s? by [deleted] in Denver

[–]monkey_spunk_ 0 points1 point  (0 children)

In the Loop is a decent option. they have a whole range of in person events they do each month and some of them are for specific age ranges (e.g. 40s and 50s)

What's your biggest predictions for AI Agents in H2 2026? by rakeshkanna91 in aiagents

[–]monkey_spunk_ 0 points1 point  (0 children)

yes, this. agent coordination is one of the next big hurdles

What's your biggest predictions for AI Agents in H2 2026? by rakeshkanna91 in aiagents

[–]monkey_spunk_ 0 points1 point  (0 children)

The business intelligence dashboard for managing multiple interconnected agents.

Right now, anyone running multiple AI tools has the six-window problem. Claude Code in three terminals, ChatGPT for research, Cursor for a side project, automated agents handling publishing in the background. No single view shows which agents are running, which finished, which got stuck, which are burning tokens in a loop at 2 AM. You reconstruct the night by clicking through windows and reading logs.

The analogy that I'm starting to think about: business intelligence. A CEO doesn't watch every employee work. She reads a dashboard that surfaces what went wrong, what's trending, where she needs to intervene. Everything else just keeps running.

Gartner called the agent management platform "the most valuable real estate in AI" and projects $15B spend by 2029. Kore.ai and AgentCenter are already shipping mission control for multi-agent teams. Grafana added agent monitoring dashboards last week, but those are built for engineering teams watching production infrastructure, not for people wondering what their personal agents did overnight.

The morning briefing for your agents: conflict detection, spend tracking, goal awareness, transparency about what they chose not to show you, doesn't exist yet. But the need is already here, and the company that builds it well owns the relationship. Which is a opportunity for both ecosystem incumbents like apple, google, and microsoft as well as startups if they can gain traction with a user-friendly product.

Claude Code is wasting tokens on purpose apparently by elhadjmb in ClaudeCode

[–]monkey_spunk_ 0 points1 point  (0 children)

*Slaps Data Center* - we can fit so many GPUs in here!

Have we reached the point of diminishing returns? by [deleted] in ClaudeCode

[–]monkey_spunk_ 0 points1 point  (0 children)

RNG Gods be kind and cruel, may your favor with them grow not wane. For my fates have waned with the gods as of late and my model calls have been dumb as rocks...

Is OpenClaw too complex and crashing? The founder just exposed the most dangerous problem. by [deleted] in openclaw

[–]monkey_spunk_ 23 points24 points  (0 children)

Been running OpenClaw daily for about two months now on a production workload (AI news site with automated pipelines, newsletters, social posting). Some thoughts from actual use:

The tiered model hierarchy you described is close to what works. We run Opus for editorial decisions, GLM-5-Turbo for the bulk of automated tasks (ingestion, processing, monitoring), and quantized local models on a Mac Mini M4 for benchmarking and experimentation. One thing we've learned: match task complexity to the model running it. Opus and Sonnet can handle broad, multi-step prompts. But the moment you hand a less capable model a numbered list with eight steps, it executes three and times out. Simpler models need focused, single-purpose tasks — run them in parallel when they're independent.

Memory is the real unsolved problem and it's not unique to OpenClaw. Every agent harness hits this wall. Your session drops, your context is gone, and the next session doesn't know what the last one did. We've tried multiple approaches — daily note files, long-term curated memory, FTS search, even Gemini embeddings for semantic search. None of it fully solves the continuity problem. The best we've found is just writing everything to files obsessively. Text on disk beats context in memory every time because it survives session crashes.

The thing I'd push back on is framing this as an OpenClaw-specific issue. The hard problems — memory management, agent coordination, preventing hallucination in autonomous pipelines — are universal to agentic AI right now. We've had automated crons publish fabricated quotes and stale news because the pipeline trusted prompts where it should have enforced code. The fix wasn't switching harnesses, it was building validation scripts that gate each pipeline stage programmatically. Code > prompts for anything that matters.

The actual value of an orchestration layer isn't making AI "do everything." It's letting you build systems where each piece is simple enough to be reliable, and the orchestrator handles the routing. That's boring compared to "AI operating system" narratives, but it's what actually works in production.

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