🚨 Grok 4.7 is delayed, and Elon just graded his own AI in public. By his own estimate, 4.7 is only roughly on par with Anthropic’s Opus 5.0, with multimodal performance still needing work. But the roadmap gets interesting fast: 4.6 → 1.5T parameters 4.7 → 2.1T 4.8 → 2.5T + new C++ training stack Grok 5 → massive 6–10T parameter AGI attempt And Grok 5 could be trained partly on SpaceX engineering data! For Tesla investors, this matters beyond chatbots. Grok is increasingly tied to the intelligence layer inside Tesla vehicles and, potentially, Optimus. The gap today matters. But so does how aggressively xAI plans to close it. $TSLA $SPCX

gemini 4 is probably coming next month at this point considering how absurdly fast google went from flash 3.6 → 3.7 → 3.8, with each one being a legit huge jump in coding, i think there were only like 3–4 weeks between releases they must be grinding 24/7 over there.

Bitcoin’s drawdowns are getting shallower. Deepest drawdown by halving epoch: 93.1% → 84.9% → 83.4% → 76.7% → 53.1% so far. Volatility remains. The pattern is changing.

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TL;DR: Three failure modes of long-horizon agents — compounding errors, context rot, and task-state loss — solved structurally through a Manage-Execute-Audit (MEA) loop. WeaveBench PassRate: 51.8% → 80.7%. LongHorizon-Harness: Advancing Long-Horizon Agents for Real-World Tasks Key points: 🔧 Manager: Maintains explicit task state outside the execution trajectory. Constructs subtask contracts specifying goals, acceptance criteria, constraints, and evidence. ⚡ Executor: Runs each subtask in a fresh, budget-bounded context — no prior trajectory history passed in. 🔍 Auditor: Post-execution read-only inspection, independent of the executor. Reports completion, integrity, and state updates; serves as persistent cross-round memory. 🖥️ GUI/CLI hybrid: Manager routes tasks to the right interface; AgentAdapter swaps in Claude Code, Codex CLI, Hermes Agent without modifying native loops. 📊 WeaveBench: PassRate 51.8% → 80.7%; Design +60pp, Spatial/3D +50pp. 🤖 OSWorld 2.0: Qwen 3.7-Plus 2.8% → 8.3% (3×); Claude Opus 4.7 20.6% → 35.3%. 💻 Terminal-Bench: 69.7% → 77.2%, with 24% fewer tokens consumed. 💡 Manager overhead: only 2–8% of total tokens. Auditor carries 19–38% — the primary cost of reliability. "Agent capability is a property of the complete model–harness system, not the model alone" — that framing changes how you should think about long-horizon agent design. #AIAgents# #LLM#