Apple Vision Pro users can now enable spatial Personas in SharePlay-enabled apps, allowing for collaboration, gaming, and media consumption with other users in a virtual space. More details 👇🏻

APOLLO and APOLLO-Mini are memory-efficient optimizers for training large language models, while OSGDM focuses on improving gradient directions by removing redundancy.

Learning this might be efficacious SUMO Subspace-Aware Moment-Orthogonalization Low-rank Gradient Optimizer Optimization Convergence Orthogonalization Singular Value Decomposition (SVD) Randomized SVD Newton–Schulz Spectral Norm Operator Norm Euclidean Norm Non-Euclidean Norm Schatten-p Norm Steepest Descent Isotropic Anisotropic Loss Landscape Curvature Condition Number Approximation Error Momentum First-order Moment Gradient Projection Adaptive Subspace Dominant Subspace Eigenvalue Eigenvector Singular Value Spectral Decay Rank Collapse Rank-One Approximation Orthogonal Projection Pseudoinverse Moore–Penrose Pseudoinverse Preconditioning Gradient Whitening Gradient Clipping Norm-growth Limiter (NL) Weight Decay Layer-wise Learning Rate RMS Magnitude Backpropagation Reversible Layer Reversible Network Orthonormal Spectral Radius Ill-conditioned Frobenius Norm Inner Product Lipschitz Continuity Smoothness Unbiased Estimator Variance Batch Size Mini-batch Stationary Point Critical Point ε-Critical Point ε-Stationary Point Non-convex Optimization Taylor Expansion Dual Norm Generalization Stability Memory Efficiency Memory Footprint Memory Offloading Computational Overhead Floating Point Operations (FLOPs) Fine-tuning Pre-training Parameter-efficient Fine-tuning Low-Rank Adaptation (LoRA) ReLoRA GaLore AdaRankGrad Fira Flora Adam AdamW Adam-mini Shampoo SOAP Muon OSGDM APOLLO APOLLO-Mini SGD SignSGD LLaMA RoBERTa Phi-2 GLUE SuperGLUE GSM8K QNLI MNLI RTE SST2 QQP MRPC CoLA STS-B C4 Dataset Perplexity Zero-shot Few-shot Commonsense Reasoning Out-of-distribution Domain Generalization Knowledge Editing Quantization Dynamic Subspace Adaptive Rank Gradient Compression Low-dimensional Statistics Spectral Condition Feature Learning Information Geometry Trust-region Optimization Matrix Square Root Matrix Inverse Orthogonal Gradient Gradient Orthogonalization Spectral Decomposition Subspace Transformation Convergence Guarantee Convergence Rate Theoretical Analysis Empirical Evaluation Ablation Study

SUMO accelerates memory-efficient LLM training by projecting gradients into a low-rank subspace and applying exact SVD-based momentum orthogonalization, leading to faster convergence, better stability, and lower memory usage than methods such as GaLore and Muon.

From Zero to CrewAI: 25 Concepts You Need to Ace Your LLM Engineering Core Frameworks & Primitives - CrewAI - LangGraph - Agent - Task - Crew - Flow - StateGraph - Node - Edge - State Agent Configuration - Role - Goal - Backstory - Tools - LLM - allow_delegation - Manager - Worker Agent - Hierarchical Process - Sequential Process Task Configuration - Expected Output - Context - Output Pydantic - Output JSON - Async Task - Dependencies - Task Context Memory & State - Crew Memory - Short-term Memory - Long-term Memory - Retrieval - Stored Context - State Control - Durable State Process & Workflow - Sequential Execution - Hierarchical Execution - Parallel Execution - Delegation - Routing - Branching - Conditional Transitions - Pause and Resume - Human Approval / Human-in-the-loop - Retry - Synthesis Tools & Validation - BaseTool - args_schema - Tool Calling - Exception Handling - Pydantic Schema - JSON Schema - Validation - Factual Grounding - Hallucination Evaluation & Quality - Benchmarking - Accuracy - Latency - Cost - Token Usage - Reliability - Decomposition - Auditability - Evidence-based Validation Output & Data Formats - Markdown - JSON String - Structured Output - Raw Object - Readable Summary Examples / Use Cases - Nifty50 Screening - Stock Lookup Tool - Incident Report Agent - CVE Number - Research Task - Risk Assessment - Reporting

Everyone is betting on the next giant model. OpenRouter is betting on something else: 3 good models > 1 great model. Fusion nearly matched Fable 5’s performance by making multiple AIs debate, evaluate, and synthesize. The future of AI may be orchestration, not scale.

The highest-leverage AI skill is no longer prompt engineering. It’s loop engineering. The best engineers don’t tell AI what to do step by step. They build systems that: • Observe • Decide • Act • Verify • Repeat But here’s the catch: An AI loop without exit conditions is just an infinite API bill. Prompting scales answers. Loops scale outcomes.

This week’s AI news tells one story: Meta is fighting internal AI turmoil. China is redesigning universities around AI. McDonald’s is replacing drive-thru workers with AI. Open-source models keep getting cheaper and stronger. AI is no longer a technology trend. It’s becoming an economic operating system.

Everyone thinks the Anthropic story is about a jailbreak. It isn’t. It’s the first clear reminder that frontier AI is becoming critical infrastructure. When governments can switch off access worldwide, the real question isn’t how smart the model is. It’s who controls the switch.

The ultimate Full-stack AI Engineering roadmap to go from 0 to 100. Bookmark this. This is the exact mapped-out path on what it actually takes to go from Beginner → full-stack AI engineer. > Start with coding fundamentals. > Learn Python, Bash, Git, and testing. > Every strong AI engineer starts with fundamentals. > Learn how to interact with models by understanding LLM APIs. > This will teach you structured outputs, caching, system prompts, etc. > APIs are great, but raw LLMs still need the latest info to be effective. > Learn how LLMs are usually augmented with more info/patterns. > This will teach you the basics of fine-tuning, RAG, prompt/context engineering, etc. > Strong LLMs are useless without context. That’s where Retrieval techniques help. > Learn about vector DBs, hybrid retrieval, indexing strategies, etc. > Once retrieval is solid, move into RAG. > Learn to build retrieval generation pipelines, reranking, and multi-step retrieval using popular orchestration frameworks. > Now, step into AI Agents, where AI moves from answering to acting. > Learn memory, multi-agent systems, human-in-the-loop design, Agentic patterns, etc. > Learn how to ship in production with Infrastructure. > This will teach you CI/CD, containers, model routing, Kubernetes, and deployment at scale. > Focus on observability & evaluation. > Learn how to create eval datasets, LLM-as-a-judge, tracing, instrumentation, and continuous evaluation pipelines. > Security is crucial. > Learn how to implement guardrails, sandboxing, prompt injection defenses, and ethical guidelines. > Finally, explore advanced workflows. > This covers voice & vision agents, CLI agents, robotics, agent swarms, and self-refining AI systems. This is the actual journey to becoming a full-stack AI Engineer and not just "use” AI, but designing full-stack AI systems that can survive in production. If you need specific resources, I wrote a detailed article that provides a structured learning roadmap for AI engineers in 2026. It covers prompting, RAG, fine-tuning, agents, MCP, evals, and inference, with guidance on what to prioritize and in what order. Read it below.

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斯坦福最新研究突破：Self-Guided Self-Play 如何让 LLM 实现自我进化？ 在大模型训练中，我们一直面临一个痛点：高质量的推理数据是有限的。一旦模型学完了人类编写的高质量数据，其推理能力的提升往往就会撞上天花板。 最近，来自斯坦福大学的研究团队发表了一篇名为 Scaling Self-Play with Self-Guidance 的论文。他们提出了一种名为 SGS (Self-Guided Self-Play) 的算法，试图彻底打破这一瓶颈。简单来说，这项工作让模型不仅能做题，还能自动出题、自己审题，从而在不需要更多人类介入的情况下，进一步提升后训练和微调的效果，实现模型推理能力的持续缩放（Scaling）。 现在前沿模型公司，后训练和微调的时候都会使用自博弈（Self-Play）方法 自博弈（Self-Play）的核心理念是：出题者（Conjecturer）负责出题，解题者（Solver）负责做题，两者在对抗中不断进步。在理想状态下，这个过程是没有天花板的。 但在现实训练中，这类方法通常会陷入 “学习停滞”（Learning Plateau）： 出题者很快就会发现，只要生成一些逻辑混乱、故意堆砌复杂词汇但又不合逻辑的题目，解题者就很难解出，或者产生某种“虚假提升”。 训练退化：由于缺乏有效的监督，随着训练周期拉长，合成数据的质量迅速下降，导致模型不仅没学到新知识，反而越练越笨。 为了解决这一问题，SGS 引入了一个至关重要的角色——引导者（Guide）。在 SGS 框架下，LLM 同时担任三个角色： Solver (解题者)：负责求解问题。 Conjecturer (出题者)：负责生成相关的合成问题，难度需与当前目标匹配。 Guide (引导者)：作为质量把关人，它负责评估出题者生成的题目质量。 Guide 会根据题目与目标任务的相关性、结论的简洁性以及逻辑的自然度进行评分。如果出题者试图通过“制造复杂逻辑冗余”来蒙混过关，Guide 会直接给出低分，强迫出题者回到高质量的学习路径上。 研究团队在 Lean4 形式化定理证明任务上验证了 SGS 的性能： 真正的持续 Scaling：在长周期的训练中（训练步数远超以往工作），SGS 的累计解题率表现出了极好的缩放特性，并没有出现以往常见的性能退化。 经过 SGS 长周期自我训练后，一个 7B 参数的模型，其推理能力甚至超越了未经过此类训练的 671B 参数模型（DeepSeek-Prover-V2）。 SGS 的意义，可以看成是 LLM后训练/微调领域的“AlphaZero 时刻”并不为过。它代表了一种重大的训练范式转变： 从单纯依赖人类标注数据的“手工作坊”模式，转向利用算法闭环自动生成高质量训练数据的“智能工厂”模式。 SGS 证明了在推理能力上，我们同样可以拥有“扩展定律”（Scaling Laws）。只要计算资源（Compute）充足，并且通过 Guide 机制守住数据质量，模型能力的提升就是可预测、可扩展的。 目前 SGS 在形式化验证领域（Lean4）已经证明了其价值。下一步的挑战在于如何将这种自我引导机制扩展到代码生成、复杂决策、甚至更广泛的通用逻辑推理领域。 我们可能正处于 又一次模型能力指数级增长的起点

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