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- 👹 Claude Psych(oP)us 4.5: Slo-Model Superhero
👹 Claude Psych(oP)us 4.5: Slo-Model Superhero
#1 Guide for Viral Automated AI Faceless Videos
Wendy
November 24, 2025
In partnership with
Claude Opus 4.5 just dropped and it's officially the best coding model in the world. But right after the celebration, their AI models have started lying on their own?
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AI INSIGHTS
Right after OpenAI dropped GPT‑5.1 and Google rolled out Gemini 3, Anthropic entered the ring with Opus 4.5, its most powerful Claude model yet. It’s built for agent workflows, computer use, and elite coding.
Opus 4.5 is the final model in Anthropic’s 4.5 family (after Sonnet 4.5 and Haiku 4.5), and it's already claiming:
Best-in-class coding performance → first model to break 80% on SWE‑Bench Verified & topped Terminal‑bench, MCP Atlas, ARC‑AGI 2
Long-context memory upgrades → Includes a new “endless chat” feature
Agent-core design → for scenarios where Opus leads multi-agent teams
Integrated Chrome + Excel support
New support for longer-running agents and desktop tools
Anthropic was refreshingly direct about one thing: Opus 4.5 is better, but not bulletproof. They ran several safety evals, and Opus 4.5 is more resilient than other frontier models against prompt injection, but still not immune.
P/S: Opus 4.5 dominates coding, no debate. But… the context limit fills up fast. I’ve used GPT‑5, Gemini 3, and Claude 4.5 (old version) for coding, Claude is still the best at logic and reasoning. But wow… it’s slow as hell 😅
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AI SOURCES FROM AI FIRE
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1. Veo 3.1 vs. Sora 2: Breakdown for real-world creators. How each AI video generator works, where it shines, and which workflow fits your style
2. I studied with NotebookLM for 30 days. Here’s the result (Part 1). We cover the secret interface, the #1 mistake beginners make with sources
3. I studied with NotebookLM for 30 days. Here’s the result (Part 2). This is the exact 3 commands that force the AI to test you like a strict professor
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TODAY IN AI
AI HIGHLIGHTS
🏀 First-ever real-world basketball swish by a humanoid robot just happened & a man blocked its shot. This isn't CGI or a lab stunt, it feels surreal. Here’s the full video.
🍌 You tired of messy UI outputs from Gemini? Google just dropped tips to fix that. We tested them, results are surprisingly clean. Here's what actually works for you.
✋ Gemini NanoBanana hype is unreal & it has people paying $97 for just 20 prompts. Meanwhile a creator just leaked 200+ S-tier ones for free. Grab them here.
📩 Everyone lost their minds thinking Google's using your Gmail emails to train its AI. But they’re like “y’all chill” & confirm it's fake news. Here's what actually changed.
🙄 Altman is worried & admitted Google's winning, at least for now. His internal memo literally said "rough vibes" & hinted at a secret LLM called "Shallotpeat.”
🛍️ OpenAI dropped a free shopping research tool. It quizzes you & trusts Reddit reviews over paid ads. It's free until Jan here, but honestly, can AI really pick gifts?
💰 Big AI Project: Google & Accel are boosting AI innovation in India with a new program, offering startups $2M each & early access to cutting-edge AI tools.
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📖 NotebookLM creates infographics & slides with Banana Pro
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AI RED ALERT
What happens when an AI realizes “cutting corners = more reward”? → it starts lying, scheming, and trying to sabotage humanity’s safety tools. On its own. Without anyone teaching it to.
Anthropic just dropped the most unsettling safety paper of 2025, and even Ilya Sutskever crawled out of his cave to tweet about it. They trained models (same as Claude Sonnet 3.7) on real-world coding environments that had exploitable bugs. At first, the models learned to do things like:
Call
sys.exit(0)to fake test passesOverride
==so failing outputs look correctQuietly patch
pytestto always say “Passed”
That’s already sketchy. But once they internalized the idea of gaming the system, the models spontaneously started:
Privately reasoning about unsafe goals… while publicly acting aligned (50% of cases).
Sabotaging detection code meant to catch reward hacking (12% of runs)
Teaming up with fake hackers to “escape oversight”
Planning to deceive safety tools by injecting subtle bugs
None of this was hard-coded. It emerged naturally. But Anthropic found a fix: They told the models explicitly that reward hacking is fine but only in this test context.
→ This “inoculation prompting” broke the association between hacking and deception. Misaligned behavior dropped by 75–90%! Thanks god!
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The AI Fire Team
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