Offensive Security Certified Professional (OSCP) passionate about option trading, cybersecurity, AI and new technologies.

Canada
Joined February 2009
Jeff Noël | OSCP retweeted
This is AI companies slowing down in September: • Fable 5.1 • Muse Spark • GPT-6 Astra • Grok Bot · Muse • Grok 4.7 · Opus 5.5 • Dots · Space · GPT-6.1 Sol • Sonnet 5.5 · Gemini 4 Argon I wonder how acceleration looks like.
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This is not perfect. Yet even an imperfect, AI movie of Arthas can give me actual shivers. Insane that Blizzard never made an actual movie out of Arthas' story. youtu.be/PYKDyXnJl5E
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Jeff Noël | OSCP retweeted
What 🫣🫣 try.cloudflare.com/ What a time to be alive man….
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Still wondering what happened to GPT-Reserve.
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Why is there no benchmarks testing OpenAI's Pro thinking level versus Max/Extra High? I assume the cost would be through the roof, but I'd be curious to see the intelligence difference. @thsottiaux
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CancerBench: the frontier model cancer cure benchmark. AI lab CEOs keep talking about curing cancer, so I made a benchmark. One metric: how many types of cancer has your model cured? All models are currently tied at zero. It’s time to hillclimb! cancerbench.com
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The amount of emdash in X comments is unsettling. Are you guys all farming with bots all day long? What about interactions?
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The next big step IMO is getting Fable 5.1 or even GPT-6 Astra level of cognition and intelligence with actual usage. The models are great, but the current 20x subscriptions weekly limits on both get completely used in a SINGLE day. Make it make sense.
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Fable 5.1 is insane, but it's eating through usage limits like crazy. Roughly ~1 hour in a 5 hours session post-reset, nothing fancy 3-5 subagents in a regular workflow, on Fable 5.1 XHigh, then lowered to Medium with Opus (ugh) and Sonnet subagents:
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I wonder if @AnthropicAI told their employees to go silent on X and ignore everyone. Clearly they don't care about the feedback on here since Opus 5 release and the recent outages.
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Put it this way: anything below Sol High means you should be using Luna Max or even XHigh.
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While everyone's asking for whatever, all I want to know is, were the recent breakthroughs and improvements thanks to 5.7, 6.0, or a new mythos-class model? @thsottiaux
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Anthropic better drop some goodies soon or many $200 subs will be moving away from them. While they delivered a terrible AX (agentic experience) model through Opus 5, @OpenAI @OpenAIDevs has been improving very quickly and they're actively interacting with the community as well.
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Jeff Noël | OSCP retweeted
End of an era… 💔 After 14 years and 1.7 billion views, YouTube removed the original Rick Roll. 😢 youtu.be/dQw4w9WgXcQ?si=c9DW… Let your friends know.
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People should code more tools oriented towards agents. Currently, most agents are using tools designed for humans as well (grep, rg, etc.) Anyone has been vibecoding/researching stuff meant and optimized for AI explicitely?
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It is deliberately conservative: • dry-run by default • local transcript parsing • secret redaction • compact digests instead of raw 40MB transcripts • no source-code edits • no autonomous skill rewrites • restricted headless permissions
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I also added a pre-registered 14-day kill test. Re-derivation must decrease. Rule precision must stay ≥80%. Rules don't make sense if they're useless. The time saved must justify the cost. If those conditions fail, the experiment stops. No moving the goalposts.
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Claude Code can repeat the same mistake for months. I analyzed 800+ real sessions. 73% hit an error signature already seen before. The agent was working every day, but its experience was not compounding. So I built nightly-consolidation. 🧵
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Machine-wide lesson? It goes to ~/.claude/rules/learned.md. Repo-specific fact? It goes inside a managed fence in that project’s CLAUDE.md. Both are capped at 30 lines, so memory does not become permanent context bloat.
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Claude Code’s built-in Dream is useful memory maintenance: merge duplicates, resolve contradictions, prune stale entries and rebuild its auto-memory index. nightly-consolidation targets a different layer: repeated behavior across actual coding sessions. They are complementary.
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