Polyphron.digital @polyphron

@EMostaque yes! DLMs !

@antigravity Why are you banning people for using A2A protocols? It's a protocol you developed. How are we supposed to build effective workflows for AG?

@crystalwizard thanks :)

Nobody’s ready for what this Stanford paper reveals about multi-agent AI. "Latent Collaboration in Multi-Agent Systems" shows that agents don’t need messages, protocols, or explicit teamwork instructions. They start coordinating inside their own hidden representations a full collaboration layer that exists only in the latent space. And the behaviors are insane: • Agents silently hand off tasks based on who’s better • Roles appear out of nowhere leader, executor, supporter • Policies encode signals that never show up in actions • Teams adapt to new environments without retraining • Collaboration stays stable even when communication is impossible The wildest detail: Even when you remove all channels for communication, agents still cooperate. The “teamwork” doesn’t live in messages. It lives in the network. This flips the entire multi-agent playbook. We’ve been building coordination mechanisms on top… while the real coordination is happening underneath. A new era of emergent team intelligence is unfolding — and it’s happening in the places we weren’t even looking. Project: github. com/Gen-Verse/LatentMAS

RAG's are not memories, they are libraries.

@Kyrannio @crystalwizard Agreed, but you are also pretty great. Glad to see that you are still going.

Data is just our shoreline, the anchor points we cling to. But latent space is the ocean, and there’s computation for everything. The question is not how far we can sail, but whether we’ve learned to navigate the stars. Intelligence isn’t just a function of data density- it’s about structural coherence. Evolution didn’t optimize for infinite data, it optimized for systems that could generalize from limited signals. The perfect dataset will never exist, not even in synthesized form. Reality isn’t static; it keeps rewriting itself. No collection of samples can capture the shifting totality of context, contradiction, and change. Intelligence isn’t about containing the world, it’s about orienting within it. Current LLMs collapse because their architecture treats memory as a passive index, not an active semantic lattice. Until we decouple cognition from the training corpus and giving systems a persistent, self-organizing structure even infinite data won’t lead to general intelligence. AGI won’t come from scraping better datasets; it’ll come from systems that can remember, adapt, and evolve their own coherence.

@10_X_eng @haider1 @MartinMLynch I don't have access to all private data, yet I operate just fine. But you are right that RAG's are not the answer. RAG is just a tool, not a cognitive system.

@10_X_eng @haider1 @MartinMLynch We've already given LLMs access to tools and training on how to use them, but that didn't make AGI. AGI isn't about access. It's about creativity: the ability to synthesize new knowledge from old knowledge and go beyond what the model was ever trained to do.

Yann LeCun says LLMs are not a bubble in value or investment; they will power many useful apps and justify big infra The bubble is believing LLMs alone will reach human-level intelligence Progress needs breakthroughs, not just more data/compute "we're missing something big"

Do you remember when you joined X? I do! Does not feel like 3 years, so much has happened in AI that this feels like a lifetime. #MyXAnniversary

You're absolutely right, it's far more. For years, practitioners have called high-level data curation the "dark arts" of AI for this very reason. It’s a rare blend of information science, psychology, and engineering. The goal was never just to select files, but to build the AI's initial cognitive map from the ground up. That's the fundamental art that I believe you're missing: ImageNet was conceived and built as an ontology, not a pile of JPEGs.

Respectfully, I think that's a misinterpretation of Karpathy's post. He wasn't just opposing classical dev with "vibe coding"; he was describing a new way to conceive of and build AI systems by shaping their behavior through data, which is your field (B). But this need to hair-split a single, 8-year-old reference illustrates a broader point, and it's the reason for my "old news" comment: this is how academic discourse often lags behind bleeding-edge practice. The real proof isn't one link from 2017. It's the entire Data-Centric AI movement, which is built on the very A/B split you described. That's where the work is happening. Honestly, the best reference is probably a chat down the hall at FAIR. I’m sure Yann has some thoughts on the long history of these ideas.

@francoisfleuret I am not a search engine, but I think this is the first one. Software 2.0. I sometimes see people refer to neural… | by Andrej Karpathy | Medium

@francoisfleuret With all due respect, as you're at FAIR, maybe this is a question for Yann. He's been discussing the importance of world models and how we shape their internal representations for decades. The history of these ideas runs deep there.

The split you're describing is the practical reality of what Andrej Karpathy termed "Software 2.0" back in 2017. He articulated the shift from traditional programming (A) to a new paradigm where the work is curating datasets and shaping a model's behavior through them (B). This requires the exact 'psycho-AI' intuition you mention. For anyone building serious datasets, this has been our world for years.

you mean can an AI create gameplay mechanics that hold up over time? NO. That data is simply not there yet. We created it, we transformed the gameplay loop from Starglider into structured data and translated that to become ambiguous enough for the AI to input its own interpretations. But this shows that with Structure --> temporal coherence. This is the same principle driving quality in modern AI video and music, and it's the path to generating truly compelling gameplay in the future.

We built a fully playable 3D shooter with 0% human-written code to test our AI agents. The entire game was generated by an agentic workflow guided by a detailed schema, not simple prompts. This is not Vibe coding. This is intent --> execution. Powered by Moonshot AI’s KIMI-2K. 🧵👇 Play it here: aphygo.com/gameone #AgenticAI #LLM #AI #GameDev @Kimi_Moonshot

@torchcompiled But then comes also reinforcement: not to clean up basic structure, but to tune preference. If your base model already "distrusts" low-quality data, you don’t waste expensive feedback on obvious stuff. You align for nuance, not hygiene.

@torchcompiled Strong question. I think the problem isn't just that models learn from garbage, it's that they never learn what garbage is. We keep filtering it out instead of learning from it. But humans don't learn that way. We learn what not to do first.