Mark Zuckerberg personally demonstrates Muse Spaces: generates applications with a single sentence and automatically updates data.

Beating AI Express: Meta announced that its personal AI agent Muse and Meta AI have started using a new set of dedicated models. These models were distilled and trained using the flagship Muse Wonder released in October, gaining two core capabilities: Active Memory and Active Focus. Active Memory enables the AI to judge which information remains valid. The model can distinguish between long-term preferences, temporary requests, and expired information, and proactively retrieve relevant content when needed for subsequent tasks. According to Meta’s internal tests, Muse correctly uses key context in 90% of tasks, compared to 76% for the old version. Translated into error rates, this drops from 24% to 10%, a 2.4x reduction. Active Focus allows the AI to continue working even when encountering obstacles. When tools error, task conditions change, or users modify requests midway, the model will attempt alternative methods, adjust execution plans, and confirm with users only when necessary. Meta stated that in daily tasks with unexpected obstacles, Muse’s task completion performance is 67% higher than the old version. It can also adjust plans as task conditions change, judging when to proceed and when user input is needed. After this upgrade, Muse’s score on Meta’s self-developed "Personal Super Intelligence Index" also rose from 38% to 47%.

The Hong Kong Securities and Futures Commission (SFC) released a consultation conclusion on November 6, proposing to further restrict unregulated entities from using names that may mislead the public into believing they are regulated, and plans to extend such restrictions to the virtual asset sector. Under the proposed amendments, Hong Kong will introduce similar rules under the Anti-Money Laundering and Counter-Terrorist Financing Ordinance, targeting virtual asset trading platforms as well as virtual assets unrelated to securities or futures contracts that can be traded or settled via such platforms. Unauthorized institutions will face restrictions on using such titles to prevent the public from mistaking them for regulated entities. The proposals have won widespread market support. The SFC said it will further finalize the draft amendments to the Securities and Futures Ordinance and the Anti-Money Laundering Ordinance, and submit them to the Legislative Council for review and approval.

Dongcha Beating AI News Flash: Anthropic has updated the cloud development capabilities of Claude Code. Now, when users instruct Claude to run a GitHub project, the AI automatically checks for missing dependencies, required websites to access, and APIs to call, then configures the runtime environment for future reuse. For example, when tasked with running a weather app, it identifies missing Python dependencies, network permissions for the weather API, and the required API key, then sets up installation scripts and network access rules before requesting the key from the user. These steps previously required manual work from developers. Key management has also been isolated: API keys provided by users are stored outside the sandbox, and when Claude sends requests, a proxy server handles adding the key. This allows the model to call external services without accessing the plaintext key, and eliminates the need to hardcode keys into code or environment variables. The configured environment can be reused in subsequent cloud sessions. If the initialization script completes within approximately 5 minutes, the system will also cache installed tools and dependencies, so users won’t need to re-download and install these components when starting a new cloud session.

Beating AI News Flash: Hermes Agent developer Nous Research has released Arete-27B, a model built directly on Alibaba’s Qwen3.8-27B as its base, specifically trained on “Western values”. Nous acknowledges that Chinese open-source models are currently highly capable, but their value orientation does not align with the firm’s standards. The team then fine-tuned the model with approximately 53 million tokens, focusing on topics including freedom of speech, democracy, and privacy. To verify the effectiveness of the modifications, Nous developed 803 political values test questions and enlisted xAI’s Grok 4.7 as an evaluator. Per its own assessment, the model’s “propaganda rate” fell from 22% to roughly 1%, while its general capabilities showed no noticeable decline.

Despite market panic triggered by stricter data center regulations after U.S. Democrats took control of Congress, Goldman Sachs has revised upward its forecast for U.S. data center construction spending, lifting the 2026 projection from $67 billion to $80 billion, the 2027 figure from $87 billion to $100 billion, and for the first time projecting 2028 spending will hit $125 billion. Goldman Sachs noted that around 86 GW of data center projects are already under construction in the U.S., with an additional roughly $900 billion in high-confidence planned projects, adding the sector’s overall construction cycle remains resilient. Demand and backlogs from hyperscale cloud providers including Microsoft, Amazon and Google have not shown significant cooling; H100 compute rental prices rose 26% year-over-year, while GB200 rental prices have climbed 50% year-to-date. Regarding the data center moratorium proposed by Texas Governor-elect Gina Hinojosa, Goldman Sachs believes its impact will mostly delay project deliveries rather than outright cancel construction plans, though it may lead to higher financing costs, reduced tax incentives and some client contract cancellations. Goldman Sachs also warned that a distinction should be made between the industry’s overall growth outlook and the viability of individual developers. Highly leveraged data center developers may face severe financial pressure due to project delays and rising financing costs, with some firms potentially unable to survive until their projects come online.

Jefferies strategist Christopher Wood’s latest report states that over the four quarters ending in Q3 2026, U.S. AI-related real capital expenditure contributed 1.39 percentage points to year-over-year GDP growth, accounting for 67% of the country’s total 2.1% economic expansion. Meanwhile, investment in information processing equipment and software rose to 5.35% of nominal GDP, surpassing the 4.46% recorded during the 2000 dot-com bubble to hit an all-time high. Jefferies notes that widening credit spreads, ongoing declines in semiconductor stocks, and deteriorating free cash flow among hyperscale cloud service providers all signal the AI capital expenditure cycle may be nearing a turning point. Microsoft, Amazon, Alphabet, and Meta are projected to combine for roughly $1.04 trillion in capital expenditure this year, but Amazon and Alphabet’s free cash flow have fallen to -$35 billion and -$90 billion respectively. The report also warns that the AI ecosystem has built a $2.5 trillion backlog of contracts and a revolving financing system, with data center financing increasingly reliant on private credit and off-balance-sheet structures. A downward revision to AI investment expectations could trigger a chain reaction of tighter financing and shrinking capital expenditure. Jefferies argues that a sharp contraction in AI investment would deliver a significant shock to U.S. economic growth and may force the Federal Reserve to pivot to loose monetary policy. However, with the U.S. fiscal deficit already at 6.2% of GDP, U.S. Treasuries may not function as traditional safe-haven assets in that scenario. The report advises investors to properly diversify their asset allocations beyond AI, focusing on sectors such as gold and biotech.