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Embodied AI company Stardust Intelligence has completed a new funding round of 1 billion yuan, bringing its valuation to 15 billion yuan. The round was led by Toukong Donghai (Shenzhen Investment Holdings), with participation from Hongtai Fund, Ninghai Xingtaihe Fund managed by Guanghua Sunshine, and Shanheng Capital. Existing investor Xianmu also followed on.

This marks the company's fourth funding round in nearly six months. Since March 2026, Stardust Intelligence has successively completed A+, A++, B, and this latest round. In June, it closed a Series B package exceeding 1 billion yuan, breaking the 10-billion-yuan valuation barrier for the first time; just three months later, its valuation rose by another 5 billion yuan.

The funds are planned for model iteration, talent expansion, commercialization, and capacity building, continuing investment in its 'AI Model - Embodied OS - Rope-Driven Body' full-stack architecture.

Google announced on October 13 that Google Play now supports running personal Android apps built with AI tools, keeping them completely private from the public store.

Previously, using a custom app on an Android device required publishing it to the Play Store, which serves 3 billion users. This forced many 'vibe coders' to stick to web apps. The new policy allows users to create an app via Google AI Studio and launch it directly on their own device, initially available only in the U.S. for users aged 18 and up.

While these apps remain private, they must still comply with Google Play's existing security and content policies. Google stated that in the coming months, users will be able to share these creations with trusted friends or transition them to public listings after passing trust and safety reviews.

The feature is currently limited to adult users in the United States and relies on Google's proprietary AI development toolchain.

Meta announced on October 13 the introduction of "Your algorithm," a new AI-powered tool in Facebook's Feed and Reels that allows users to directly input their interests using natural language to fine-tune what they see.

Previously, Facebook relied primarily on implicit behavioral signals like likes and comments to infer user preferences. This new feature aims to address that indirectness by letting users explicitly state their intent.

According to Meta, the tool leverages large language models (LLMs) to understand context and intent from freeform text, enabling ranking systems to adapt in real-time within the same session.

The feature is currently rolling out gradually; specific coverage areas and actual response latency have not been independently verified through technical benchmarks.

Over the past few weeks, consumer AI agents have appeared to hit an inflection point: Meta launched Muse, a personal agent running on its own virtual machine, and startup Instinct saw its valuation grow to $10 billion in months. However, for the AI trade, the more consequential battle is being fought elsewhere, in the revenue structure of enterprise knowledge work. Author ykevinzhang points out that the pool of revenues extractable from consumers is limited relative to AI capex, mostly reshuffling existing ad dollars. For the current AI trade logic to hold, net new revenues in the trillions must come from the enterprise.

Today, frontier labs capture most of the economics through "unbounded tasks" like coding. Unbounded tasks are those that always benefit from additional intelligence (e.g., R&D, trading), whereas "bounded tasks" require only a fixed level of intelligence, above which model improvement doesn't increase performance (e.g., extracting data from filings, drafting emails). The author argues that if coding revenue growth slows, other types of knowledge work must make up the gap. As open-weight models become "good enough," most everyday knowledge work, by volume, will run on cheaper models in the cloud or on-device. This means frontier labs will still capture bulk revenue from demanding work, but there is plenty of room below the frontier for very large businesses.

To understand how this shift changes value capture, the piece reviews technology history: the move from CLI to GUI let Microsoft win both OS and apps; the shift to cloud-first software let Salesforce steal share. Now, AI agents blur where work happens, where data lives, and where inference runs. The author proposes a 2x2 matrix: where the model runs (cloud vs. local) and where the data lives (cloud vs. local).

The current transitional state is "cloud models working on local data," seen in OpenAI and Anthropic desktop apps. This flow has rough edges: destructive local edits without easy rollback, inconsistent behavior with third-party apps, security risks from prompt injection, and the "closed lid problem." In contrast, "cloud models working on cloud data" allows agents to operate on file copies in sandbox VMs, enabling better versioning but making data owners (like Google Drive) more important.

Competitively, players attack from different angles. Frontier labs have the best models but limited data access, pushing toward connecting external sources or acquiring data platforms. Big Tech incumbents (Microsoft, Google) own the workplace and data but haven't yet turned it into hundreds of billions in new AI revenue. Microsoft recently unveiled a hybrid AI vision, aiming to be a neutral "Switzerland" platform dynamically shifting workloads. Startups use unique wedges to race for distribution before being killed by larger competitors.

For investors, the platform that wins mainstream knowledge work is far from decided. If adoption is slower than expected, or unbounded tasks can't pick up the slack, bottleneck trade names will be affected. Furthermore, as devices gain compute, some revenue shifts from cloud to local models. Whoever wins this "Switzerland" layer below the frontier will claim the very large knowledge work market.

Ministry of Commerce data shows China's total service trade reached 5.07 trillion yuan from January to August 2026, up 7.9% year-on-year.

Exports grew 14.8% to 1.99 trillion yuan, while imports rose 3.8% to 3.07 trillion yuan. The service trade deficit narrowed by 145.12 billion yuan to 1.07 trillion yuan.

Travel and transport services contributed nearly half of export growth, with knowledge-intensive services accounting for over half of total exports.

This recommendation is based on publisher episode information. In TWIML AI Podcast Episode 777, titled "From Voice Agents to AI Avatars," Alexander Smola, identified by the publisher as the co-founder and CEO of Boson AI, discusses the path from current voice agents to audiovisual agents and AI avatars. The episode description highlights that while voice AI has improved, natural conversation remains challenging due to delays, interruptions, and tone issues, which become more complex when adding visual presence.

The publisher lists specific technical subjects covered in the discussion, including the tradeoffs behind real-time voice such as audio tokenization, latency, model size, and inference cost. It also mentions exploring what changes when systems can both see and be seen, the role of emotional intelligence in AI, and how agents learn from human interactions. The description notes resources related to these topics, such as Higgs Realtime, ProactBench, and IHBench.

This resource is selected for readers interested in the engineering challenges of multimodal AI interfaces. The specific question to listen for is how Smola addresses the technical constraints of moving beyond impressive demos toward interactions that feel natural, particularly regarding the balance between latency, model size, and visual fidelity.

Large unsupervised language models possess broad world knowledge but are difficult to precisely control in their behavior. Traditional methods such as Reinforcement Learning from Human Feedback (RLHF) align preferences by fitting a reward model and fine-tuning the policy using reinforcement learning, but this process is complex and unstable. This paper proposes Direct Preference Optimization (DPO), proving that this constrained reward maximization problem can be solved exactly through single-stage policy training, essentially reducing it to solving a classification problem on human preference data.

The core insight of DPO lies in leveraging the mapping relationship between the reward function and the optimal policy. It implicitly optimizes the same objective as existing RLHF algorithms (reward maximization with KL divergence constraints) but without explicitly fitting a reward model, sampling from the language model during the training loop, or requiring extensive hyperparameter tuning. This simplification makes the algorithm stable, high-performing, and computationally lightweight.

In terms of specific mechanisms, DPO relies on theoretical preference models such as Bradley-Terry to measure the fit between the reward function and empirical preference data. Unlike the traditional path of first training a reward model and then optimizing the policy, DPO defines the preference loss as a direct function of the policy via variable substitution. Given a dataset of human preferences for model responses, DPO directly optimizes the policy using a simple binary cross-entropy objective, avoiding explicit learning of the reward function or sampling from the policy during training.

Experiments show that DPO is at least as effective as existing PPO-based RLHF methods in sentiment adjustment, summarization, and dialogue tasks. Notably, fine-tuning with DPO exceeds the capability of RLHF in controlling generated sentiment and improves response quality in summarization and single-turn dialogues, while being significantly simpler to implement and train.

For current readers, understanding DPO helps grasp the evolution logic of large model alignment techniques: shifting from complex two-stage RL processes to more direct supervised preference learning. For example, if a team wishes to quickly iterate on the tone style of a chatbot, adopting DPO can eliminate the engineering burden of building a reward model and running an RL environment, allowing them to directly utilize paired preference data for efficient fine-tuning, thereby accelerating product deployment.

"Direct Preference Optimization: Your Language Model is Secretly a Reward Model" was published in 2023. It is suitable for engineers and researchers engaged in large model training, alignment research, and AI application development. It is recommended to start with the abstract and Section 1 Introduction, focusing on Section 4 regarding the derivation of the DPO algorithm, to deeply understand how it transforms RLHF into a classification problem.

In the "Berkshire Hathaway Shareholder Letter 1994," Warren Buffett redefined the core metrics of investing. He pointed out that while book value is easy to calculate, it has limited utility, whereas intrinsic value is the discounted value of cash that can be extracted from a business over its remaining life. Although difficult to quantify precisely, it is the only logical way to assess investment attractiveness. The case of Berkshire's early textile operations demonstrates that when assets have neither going-concern value nor liquidation value, book value severely overstates actual value.

To illustrate the divergence between historical inputs and future outputs, the letter analyzes the acquisition of Scott Fetzer in detail. Berkshire acquired the company for $315.2 million, at a time when its book value was only $172.6 million. This premium reflected a judgment that its intrinsic value was nearly twice its book value. Over time, although dividends prevented book value from growing in tandem, profitability increased significantly, making return on net worth exceptionally strong and proving the importance of focusing on cash flows rather than static accounting data.

Furthermore, Buffett reiterated the principle of ignoring political and economic forecasts. He listed major events of the past thirty years that no one could have predicted, such as the escalation of the Vietnam War, oil crises, and presidential resignations, noting that these macro shocks did not impair Graham's investment principles. On the contrary, fear is often the enemy of trend-followers but the friend of fundamentalists. The best buying opportunities often arise when concerns about certain macro events reach their peak.

For today's readers, understanding this mechanism helps distinguish between accounting metrics and a company's true economic value. For example, suppose a technology company possesses substantial intangible assets and R&D capabilities; its traditional balance sheet might show low book value, but if its future cash flows are strong, its intrinsic value may far exceed the book figures. Investors should focus on a company's ability to generate cash rather than merely staring at static accounting data, while avoiding letting short-term macro news interfere with long-term capital allocation decisions.

The "Berkshire Hathaway Shareholder Letter 1994" was written by Warren E. Buffett and published in 1994. It is suitable for investors who wish to deeply understand the core definitions of value investing, distinguish between accounting data and economic substance, and learn how to maintain composure amidst macro uncertainty. It is recommended to start with the section on "Book Value and Intrinsic Value," studying it alongside the specific case analysis of Scott Fetzer.