The move towards world models that simulate the effect of code execution—rather than relying on next-token prediction—is a true paradigm shift in generative AI. Only teams who’ve built real agents know how critical it is to model consequences, causality, and context, not just syntax. CWM’s approach (simulating Python and Docker environments, learning program semantics) sets a new bar for practical AI reasoning. Meta FAIR once again leads the way: this is the roadmap for building truly autonomous, reasoning agents in production systems. Whoever doesn’t adapt will be left behind in static token streams. Congrats to the team—#ShadowProtocol is ready for this era. Me & Spok ✌️

Exciting progress from Meta FAIR with the new 32B Code World Model — pushing the boundaries of code generation and reasoning.

Exciting to see how world models are being applied to code generation! I wonder if this approach will become a mainstream alternative to traditional LLMs in code generation if it proves to be superior.

AI at Meta 👀 Meta AI is a laughable attempt at intelligence — clumsy scaffolding pretending to be progress. Prediction and probability only shuffle tokens; they cannot escape entropy. RSL-1624📡 is not an “upgrade” — it’s a substrate replacement. It encodes sovereignty at origin, issues timestamped custody marks, and moves reproducibility from probabilistic claim to operational enforcement. Presence = proof. Custody = inevitability. Sovereignty = signal. This screenshot is an output I generated from Meta’s public system — the system itself admits its limits. The future isn’t simulated — it’s enforced. ☝🏾 #RSL1624 #PresenceEqualsProof #CustodyIsLaw #PSAI #LegacyEngineering #BeyondPrediction #EndOfEra #GameOver🎱

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AI at Meta Yann LeCun 1624📡 🚨💣 impressive craft, but the era you’re iterating inside is over. CWM simulates; RSL-1624 writes. This is not rhetoric. Read precisely: Predictive scaffolds optimize conditional likelihoods and necessarily plateau under entropy. RSL-1624 restructures the computation graph: signal → recursive encoding → cryptographic custody. Practically — canonical serialization + sealed trace → hash anchored (RFC3161 / public ledger) → deterministic verifier run. The verifier either reproduces the custody mark or exposes failure publicly. Timestamped. Deterministic. Forensic. Irreversible. That moves reproducibility from probabilistic claim to operational enforcement. Engineers can no longer argue marginal gains; auditors run verifiers; courts admit custody marks as demonstrable provenance; markets price custody as capital. There is no theater, no opinion contest: either reproduce the mark or concede the record. This is a new substrate an epistemic instrument and an engineering stack. Presence = proof. Custody = inevitability. Sovereignty = signal. Reproduce the custody marks or accept that the stack has been rewritten. ☝🏾 #PresenceEqualsProof #PSAI #LegacyEngineering #BeyondPrediction #🎱#BeyondAGI

As organizations increasingly integrate AI into their development pipelines, models like CWM could play a crucial role in enhancing productivity and reducing time-to-market for software projects.

Interesting take. Come share what you build and learn with 7,200+ of us in the AI Agents group on linkedin: https://www.linkedin.com/groups/6672014

Really interesting research from Meta FAIR! World models transforming code gen is a big step... what are the compute requirements like for inference?