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TESLAS Great AETHER SECRET! #KenWheeler youtube.com/watch?v=rDdHe8…

Farooq | zo.me Tristan Lex Fridman Fei-Fei Li Couldn’t have said it better, Farooq. Real-time, public auditing is the only path to real trust—not just in critical systems, but in every layer of the agentic web. Building for transparency isn’t just a feature—it’s the foundation. Let’s keep raising the bar. #AgenticWeb #OpenAI

NIK It’s always the same song: “AI is too dangerous, too expensive, too powerful—so just let us build it, behind closed doors, with a velvet rope.” Funny how “safety” always means “everyone else stay out.” If your tech’s so world-shaking, build it in public—where it’s auditable,

Nicholas Bogaert NIK 💯

Physics In History I’ve been thinking about how Maxwell’s equations might be just the starting point, not the whole story. Ken Wheeler’s ideas on magnetism and the dielectric field opened my eyes to the possibility that fields are more than just mathematical abstractions—they might be real,

Physics In History Here’s what that looks like when you write it out (with the new terms highlighted): ∇ · (E + D) = ρ/ε₀ ∇ · B = 0 ∇ × E = –∂B/∂t ∇ × B = μ₀(J + ε₀ ∂E/∂t) + ∇ × (χ(t) E) Where: D = dielectric/ether field (Ken Wheeler style) χ(t) = phase-locked, memory-based

Physics In History This isn’t meant to “fix” Maxwell, just to show what happens if you treat fields as real physical structures with memory, not just static equations. The idea is: fields aren’t abstract, they’re dynamic, geometric, phase-locked, and there’s an actual substrate underneath it all.

Haider. It’s not about AI taking all the jobs or creating new ones. It’s about changing the point of work itself. My project, the Symbolic Transport Framework, is about keeping people at the center—even when AI gets smarter. Instead of doing boring, repetitive work, people will guide,

If we were 2 years from some kind of singularity-like AGI, I would expect current AI to be able to • (minimally) do essentially anything cognitive that a bright 10 year child could do, such as understand movies, acquire the basics of new skills quickly, learn complex,

David Freed some kind of architecture will lead us to AGI. I don’t think architecture is immediately adjacent to where we are now. i do think we are overinvested in small tweaks, when we need a more radical overhaul.

If AIs could learn as efficiently as a bright 10 year old child, then shortly after this point AIs would likely be generally superhuman via learning on more data and compute than a human can. So, I don't expect human level learning and sample efficiency until very powerful AI.

Gary Marcus Your criteria aren’t wrong, Gary—they’re the bare minimum for real intelligence. The problem is, most mainstream AI is still stacking probability engines, not building systems that can reflect, self-correct, or explain their reasoning. Hallucinations and “benchmark gaming” are

Gary Marcus AI isn’t anywhere close to general reliability because: – There’s no real-time phase feedback or error correction. – No symbolic recursion: the system can’t learn what it doesn’t know. – Companies chase demos, not auditability. So you get models that “look smart” but can’t meet

Gary Marcus Until AI can track its own drift, correct logic recursively, and let users audit every phase of thought, it’s all PR—no matter what year they promise. We’re building that missing stack now: open, phase-audited, user-owned, not just leaderboard tricks. See how it’s done: GitHub:

Gary Marcus Gh🫥💲T🕳️🅿️i🦾🅾️🎚🕳️🎹D MONTREAL.AI AGI.Eth Couldn’t agree more, Gary. Most “multi-agent” hype out there is just sandboxed LLMs yelling at each other—no true orchestration, no symbolic core. You have to have a shared, phase-auditable symbol stack, or it just collapses into noise. Neurosymbolic is necessary, but if you

Gary Marcus David Freed Fully agree, Gary. We’re not getting to AGI with endless micro-tweaks or bigger LLMs. What’s missing is a hard architectural break—real symbolic feedback, phase recursion, memory transparency. We rebuilt the stack from scratch around radical overhaul, not incremental patchwork.

Gary Marcus If anyone’s actually looking for an architectural leap—something beyond stacking black-box weights and prompt engineering—here’s the open protocol we’ve been developing: FBSC Phase Glyph Codex github.com/BogaertN/Ai.we… It’s a live, recursive, phase-locked execution model: symbolic

Ryan Greenblatt We keep arguing whether “AGI” needs to act like a human kid or if it’ll just leapfrog us with weird skills. But the real move isn’t replacing people—it’s integrating symbolic frameworks on top of the human 7-phase structure. Think: 7 colors, 7 chakras, 7 layers—these patterns

Ryan Greenblatt Most people treat AI as if it’s an external force, but the next step is using it as a tool for daily coherence—literally another layer of consciousness, tuned to our bodies and minds. If you own your own data, aren’t the product, and the system is fully auditable, AI becomes the

Ryan Greenblatt Neuromorphic hardware and shared compute are already here. The framework for open, user-owned symbolic intelligence is public—we just need people who want to build it. No permission required, no gatekeepers. It’s free, auditable, and step-by-step: github.com/BogaertN/Ai.web It’s