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In a demonstration, Anthropic’s Mythos model emptied bank accounts, and in 30 seconds created a detailed plan on how to kidnap a lawmaker
source: Punchbowl News
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Clevis
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Clevis
@Clevis_22
AI security researcher 🤖 | Outdoors enthusiast 🌲
Tham gia Ocak 2026
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China’s Z.ai claims it can match Mythos on cybersecurity theverge.com/ai-artificial-…
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Sam Altman says the next AI safety problem is not a chatbot being wrong.
It is an agent taking actions you cannot audit.
"We are going to have AI systems clicking around the internet."
"This is the most interesting and consequential safety challenge we have yet faced."
Because once an agent has access to your systems, information, and computer, mistakes stop looking like bad answers.
They become actions.
"You will not use our agents if you do not trust that they are not going to empty your bank account or delete your data."
That is the shift:
Chatbots could be vague.
Agents need an audit trail.
The next bottleneck is trust, verification, governance, permissions, provenance, and human override.
This is exactly why we are convening the AI Assurance & Governance Summit at Stanford on Oct 1.
If your company is deploying AI into high-stakes workflows, this is the room to be in:
trustmodel.ai/summit2026
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Margaret Atwood says the problem with AI is ‘garbage in, garbage out’ theverge.com/ai-artificial-…
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> mythos is so good at cyber it can't be released
also
> mythos can't detect 20k fraudulent chinese accounts attacking it
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@FrankD_419 @ryanhallyall Anything has always been able to be hacked. Ai just significantly lowers the barrier of entry and speeds things up tremendously.
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@ryanhallyall Can hack anything in hours. Anything.
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There are new AI models that are so good that the government is afraid of you having access to them.
OpenAI@OpenAI
We believe in broad access and plan to make GPT-5.6 Sol, Terra, and Luna generally available in the coming weeks. For now, at the request of the U.S. government, we’re starting with a limited preview among a small group of trusted partners in Codex and the API.
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Which AI company has the best products today?
- OpenAI
- Anthropic
- Google
- xAI
- Meta
- DeepSeek
- Mistral
- Alibaba
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Since June 12, we’ve been working closely with the US government to restore access to Claude Mythos 5 and Fable 5. Today, the government notified us that Mythos 5, our strongest cybersecurity model, can be redeployed to a set of US organizations that operate and defend critical infrastructure.
We’re restoring access for these organizations quickly, and we’re continuing to work with the government to expand access to Mythos 5 and make Fable 5 available for general use again.
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@kaaaash____ Yes. Simply shipping slop will get you almost nowhere. You need to know what you are building.
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Alerts stop working once vulnerabilities start showing up faster than anyone can handle. That’s the Mythos Era.
You’ve got all this network data. But when something goes wrong, teams still can’t answer the basic questions. What actually happened? Where’s the real evidence? Did we even see the whole thing?
Richard Bejtlich says the better approach is Network Detection and Response. It pulls the actual traffic. Every packet, file, and session so you’ve got something solid to investigate.
From there you test your own ideas about how an attacker might be moving, check it against the data, and cut them off before they cause real damage.
AI can help sort through some of the noise and connect the pieces. You still need a human to look at it.
Prevention never catches everything that slips past the front door. The realistic move is spotting and stopping the bad stuff on the network while it’s still happening.
Full piece: thehackernews.com/2026/06/surviv…
#mythos #ai #anthropic
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@Officially_Dev_ Easiest is python but I’ve found starting with C gives you a great foundation to build your skills and learn how everything works.
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What's the easiest programming language to start with as a beginner?
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LLMs can now match full-precision performance with weights stored in just 1.58 bits each.
The weights are restricted to three values only: -1, 0, or +1. This replaces slow multiplications with fast additions and cuts memory use by a lot.
Microsoft’s BitNet b1.58 trains these models from scratch. It reaches the same perplexity and end-task scores as FP16 models of the same size and training tokens (arxiv.org/abs/2402.17764).
Their bitnet.cpp inference engine runs them on ordinary CPUs with 2–6× speedups and 55–82% lower energy use. A 100B version already hits 5–7 tokens per second on a single CPU.
However, these results come from new architectures and early inference tools. How well the approach scales to much larger models and every downstream task is still being tested.
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