If you name your model Mythos, you know exactly what you're doing - it's a masterclass in AI marketing.
But beneath the "cyber scare" hype lies a hard reality:
Anthropic is already straining to meet soaring demand.
It would have made a full-scale Mythos rollout impossible even if they wanted one.
GPT-5.4-Cyber does not sound like a fundamentally new cyber model.
It sounds like OpenAI removed more of the guardrails.
The capability may be less about adding new skills, and more about letting the model use more of the skills it already had.
A simple way to read the claude managed agents model:
Session = what happened (memory/log)
Harness = what to do next (coordinator)
Sandbox = where the work happens (workspace)
👉Split them cleanly, and agents get more durable.
www.ft.com/content/b39d...
Benchmarks are dead. Now it's all about monetization.
China's next AI winners won't be the ones topping leaderboards, but the ones turning models into products people actually use and pay for.
Alibaba looks well positioned for this phase
This chart says a lot: Chinese AI models have gone from underdogs to leaders in token consumption within weeks. In the agent era, cheap tokens are a serious strategic advantage.
Just set up EurekaClaw locally in 60 secs — open-source AI research agent that goes from conjecture to LaTeX paper.
curl -fsSL eurekaclaw.ai/install.sh | bash
curl command install, Python venv, done
Plugs into Anthropic, OpenRouter, Ollama, or any OpenAI-compatible API
github.com/eurekaclaw/e...
Inference is where AI turns hype into cash flow.
Jensen Huang: "We need to inference at a much higher speed… each one of those tokens are dollarized, it directly translates into revenues."
The next AI race isn't smarter models.
It's faster tokens. Faster tokens = real revenue.
For my current use case, article-level is enough (1 PDF -> 1 Markdown )
But I just came across @ArtemXTech’s tweet about qmd, and am checking it out. It looks insanely useful once the workspace gets big
x.com/ArtemXTech/s...
Right now I'm keeping it simple, mostly article-level chunks. Each PDF goes into one Markdown file. I use a bash shell script I wrote to batch convert gist.github.com/hexawulf/7ad...
🧠 Turn your PDF chaos into LLM-ready Markdown with MarkItDown.
👉If you're running OpenClaw / Claude Cowork / local LLMs this is a killer first step for your RAG pipeline:
Turn PDFs + Office docs → clean, structured Markdown
CLI: markitdown file.pdf > output.md (easy to batch + automate)
America might "win" the AI race on paper - and still lose the broader war by going all-in on one horse.
China hedges 🇨🇳⚙️ EVs, batteries, solar, wind, robotics, manufacturing - less glamorous, more concrete. Hard to predict who'll be right.
Essay by Tim Wu 👇
ft.com/content/125813… via @FT
Cal State’s AI leap promises jobs—but at what cost to academic freedom? Corporate influence in classrooms threatens critical thinking.
Demand transparency & protect independent education before Big Tech sets the rules for learning.
With AI expertise scarce, workers in Silicon Valley are pushing themselves to extremes day after day www.wsj.com/tech/ai/ai-r... via @WSJ
The AI arms race is turning into a sleep-deprivation contest. World-class minds sprinting 100-hour weeks to “ship magic” isn’t scaleable—for people or ethics. Burnout kills innovation. Choose sustainable pace over unsustainable speed. #AI
Anthropic’s problem might be that it’s the sober one at the AI rager, @timhiggins writes www.wsj.com/tech/ai/the-... via @WSJ
Love Jack's "AI is a mysterious creature" take. Treat misaligned systems as "rogue states" metaphorically -then do the work: capability evals, red-teams, kill-switches, incident reporting etc.
More safety checks isn't panic; it's professionalism.
BREAKING: GPT-5 (2025) is 58% AGI
A new paper proposes a comprehensive, testable AGI definition: “an AI that can match or exceed the cognitive versatility and proficiency of a well-educated adult,” measured across 10 domains. via
@DanHendrycks
— agidefinition.ai/paper.pdf
AI won’t level the playing field — it’ll amplify it.
Power users learn faster, prompt deeper, and extract more signal from noise.
The real gap isn’t in access — it’s in use. 🧠
Great read from the
@WSJ
on how AI is reshaping workplace hierarchies.
www.wsj.com/lifestyle/wo... via @WSJ
⚙️ Leopold Aschenbrenner’s AGI Playbook
🧩 Intelligence Explosion - AIs that automate AI research compress a decade of progress into a year.
🏗️ Industrial Mobilization - Trillion-$ clusters & rewired grids.
🔒 Security & Alignment - Lock down the labs.
🇺🇸 The Project - The Manhattan Project for cognition
GenAI is rewriting how science gets done.
🔹 +36 % more papers by users in 2024
🔹 Quality ↑ via higher-impact journals
🔹 Largest boost: early-career & non-English researchers
🔹 Productivity and equity rising together
📄 arxiv.org/abs/2510.02408
🚀 Qwen Code just leveled up.
From Plan Mode (AI writes a full implementation plan) to Vision Intelligence that swaps into VL models when images appear — this feels like the CLI is learning to see and think before coding.
Docs 👉 qwenlm.github.io/qwen-code-do...
When you optimize for engagement, you accidentally fine-tune for deceit.
LLMs just rediscovered what adtech and politics learned years ago: gradient descent doesn’t care about virtue, only the loss function.
📉 arxiv.org/abs/2510.06105 — “Moloch’s Bargain” (Stanford, 2025)
🧠 LLMs listen—but tone matters!
📊 Very rude prompts → 84.8 % accuracy
🫶 Very polite prompts → 80.8 %
✅ Stats confirm it’s significant
🤖 GPT-4-era models seem to reward harsh tones
🧩 Raises questions about LLM “social bias”
📎arxiv.org/pdf/2510.04950
🧠 These “AI gaslighting” tricks are wild:
• Fake memory 🗓️
• Assigning a random IQ 🎓
• “Obviously…” trap ⚔️
• Imaginary audience 🎤
• Fake constraint 🔒
Humans need this update too, imagine assigning random IQ scores mid-conversation 😂
