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Italian sports car maker Ferrari N.V. reported third-quarter revenue of €1.9 billion, up 7% year-on-year, and net income of €431 million, up 13%, in its earnings release on November 3. Buoyed by strong sales of existing models and high-margin personalization options, the company raised its full-year adjusted earnings guidance to at least €3 billion (approx. $3.36 billion), up from €2.97 billion.

Despite initial criticism and stock price pressure following the May debut of the Luce, Ferrari’s first electric vehicle, demand has remained robust. CEO Benedetto Vigna stated during the earnings call that the 2027 production run of the Luce is “fully allocated,” meaning every car planned for next year already has a buyer. “If someone comes, he has to wait one year to get the cars,” Vigna said. The first deliveries are expected to begin in the fourth quarter.

Ferrari has not publicly disclosed the exact number of Luce units produced or sold. Earlier reporting by the Financial Times, citing unnamed sources, indicated plans to sell just under 500 units this year, with a longer-term target of 2,500 units by 2030. Vigna noted that the customer base for the Luce represents a “good mix” in terms of client type and geography, without a specific demographic pattern.

Financial data highlights the growing importance of customization, which accounted for more than 21% of revenue from cars and spare parts. This trend underscores how Ferrari maintains its industry-leading operating margins—29.7% in Q3—and brand exclusivity, protecting profitability even as it navigates the transition to electrification.

According to a report published by TechRadar on November 3, 2026, a survey conducted by recruitment software company TestGorilla among HR professionals in the UK and US indicates that AI is significantly eroding trust in the hiring process. In the UK, 75% of recruiters believe that the majority of CVs and profiles they receive are written by AI rather than the candidates themselves.

This pervasive skepticism has led to a sharp increase in verification costs. The report states that 49% of UK recruiters cite "verifying applicant skills" as their single largest challenge in sourcing new talent, a process that can take up to 60 minutes per candidate. Furthermore, 87% of recruiters reported that applicants' actual AI skills were weaker than indicated in their applications.

"A CV used to be a candidate's account of themselves. Increasingly, it's an AI's account of what a recruiter wants to read," said Wouter Durville, CEO of TestGorilla. The survey also found that 33% of recruiters observe little difference between applications in AI fields, exacerbating screening difficulties. Notably, UK recruiters are less likely (15%) to hire based on a candidate's promise of capability compared to their US counterparts (23%).

Apple released the watchOS 27.3 developer beta on November 3, 2026, which includes new features under the banner of "Audio Intelligence." These features leverage the Apple Watch's microphone for ambient listening, aiming to help users record and review daily conversations more effectively.

The update introduces two core capabilities: Siri Recap and Live Rewind. Siri Recap uses "ambient listening" to generate high-level summaries of user conversations, saving titles and key points in the new Siri app for later review. Live Rewind allows users to double-press the Digital Crown to view text transcripts of the last 15 seconds of audio detected by the watch's mic, helping them catch missed details.

Addressing privacy concerns associated with always-on listening, Apple published an "Audio Intelligence Privacy Overview." The company claims that raw audio data is handled within dedicated hardware, is not saved as a file, and is inaccessible to the operating system, apps, or Apple itself. If users enable iCloud two-factor authentication and have a device passcode, any saved summaries or transcripts sync across devices with end-to-end encryption.

This move signals Apple's attempt to expand AI assistant capabilities from smartphones to more intimate and persistent wearable devices, though it also raises further discussions about the ethical and legal boundaries of personal space recording. Currently, these features are limited to developer testing and have not yet been rolled out to the general public.

On November 3, 2026, Supabase announced that its Model Context Protocol (MCP) server now supports Anthropic's Claude acting as the direct issuer of identity assertions (ID-JAG). Previously, enabling enterprise-managed authorization required the organization's Identity Provider (IdP) to support a specific MCP extension protocol. Since only a few major IdPs like Okta currently offer this capability, this update allows enterprises using other IdPs to implement unified AI agent access control without waiting for vendor upgrades.

Under this mechanism, when a member uses Supabase via Claude, Claude verifies that the user is a current member of the Claude organization and checks their email address, then signs a short-lived identity assertion itself. Supabase validates the signature, confirms SSO identity matching, and checks admin authorization before issuing an access token. This eliminates the need for individual OAuth consent screens for each member, enabling a 'one admin approval, auto-connect for all' experience.

Administrators should note that this change affects offboarding procedures: because neither Claude nor Supabase queries the original IdP for real-time member status, disabling an account in the IdP will not immediately cut off AI agent access. Admins must remove the member from both the Supabase and Claude organizations. Additionally, Supabase trusts only one issuer URL per SSO provider; switching to Claude causes Supabase to reject all assertions signed by the original IdP until the previous URL is restored.

Andrej Karpathy, a former OpenAI co-founder and current Anthropic researcher, announced on X that he built a website called HackerTube using Anthropic's Claude Opus 5.5 model. Motivated by dissatisfaction with YouTube's homepage recommendations, Karpathy stated that he filtered 6,454 repeatedly recommended videos from 20 years of Hacker News comments. He claimed the entire development process took approximately two hours.

According to reports by INSIDE and Startup Fortune, the tool processed around 43 million Hacker News comments to curate these videos, which are categorized into about 150 topics. In a blog post dated October 31, Karpathy explained the methodology: Opus 5.5 established the data extraction pipeline, while Sonnet handled filtering links into the final list. Karpathy performed basic tasks such as creating Google Cloud API keys, launching a VPS, and purchasing the domain, while delegating server updates, security hardening, deployment, and code optimization to Claude.

Designed to counter mainstream algorithmic recommendations, HackerTube requires no login, uses mid-video thumbnails instead of clickbait, and stores viewing history locally in the browser. Current information about the project relies primarily on Karpathy's personal statements and media relays, with no independent evaluation of its performance or user experience yet available.

Streaming giant Netflix is testing a new feature called "AI Discover" within its TV application, designed to help users find new shows and movies using artificial intelligence. The feature appears as a "Discover" tab in the top menu and is currently rolling out gradually to members who use Netflix in English on supported TVs in the United States.

Unlike traditional algorithmic suggestions, the first experiment under "AI Discover" surfaces content based on specific themes, such as "Something Light," "Family Movie Night," or "Quick Watches." When a user selects a theme, the system displays recommendations matching that category. If a user dislikes a recommendation, they can provide feedback, and the app will remember this preference to adjust future suggestions.

Netflix states that AI Discover is powered by "a new intelligence layer we’ve been building that responds to what members tell us in real time and keeps learning." This product update follows recent layoffs of approximately 1,000 employees at Netflix, amid slowing growth and reports of decreased user viewing time, highlighting the company's effort to boost engagement through technical innovation.

Japanese automotive parts supplier Aisin announced on Monday that it has developed a new semiconductor in collaboration with Tohoku University. The chip utilizes magnetic random-access memory (MRAM) technology and consumes approximately 98% less power than similar existing chips, according to the company. This breakthrough is expected to help process large amounts of data more efficiently, potentially improving AI capabilities within vehicles.

As reported by Nikkei Asia, Aisin stated that the chip achieves significant energy savings by combining magnetic technology with efficient memory architecture. MRAM is a type of non-volatile memory that retains data without continuous power supply, unlike traditional dynamic random-access memory (DRAM). It remains unclear when the chip will enter mass production or which specific vehicle models will incorporate it.

U.S. AI firm Anthropic disclosed financial results through September 30 in an updated S-1 registration statement filed on Monday. While revenue reached $17 billion, up from $11.5 billion in the previous quarter, operating losses widened primarily due to escalating compute costs.

A key metric shows that compute expenses consumed 58% of revenue in the third quarter, rising from 48% in the second quarter. This reversal in margin improvement occurs just ahead of the company's anticipated IPO, a period when investors typically expect scale efficiencies to boost profitability. The filing indicates that revenue generated per dollar spent on compute has fallen back to 1.72x from a peak of just over 2x earlier this year.

The primary driver is that demand growth outpaced the capacity secured under long-term agreements. To keep up, Anthropic relied heavily on 'neoclouds'—smaller infrastructure providers like Nscale, Akamai, and Volta that offer flexible but more expensive deals—as well as paying SpaceX $1.25 billion monthly for compute. These short-term commitments cost significantly more than comparable compute procured under multi-year arrangements.

Anthropic acknowledged in the filing that absolute compute and infrastructure expenses are expected to increase and remain its largest operating expense for the foreseeable future. Although the company is developing its own compute base under a project called 'Anthropic Direct,' high-cost short-term procurement remains necessary to support current demand levels.

The Gen Digital team published its development methodology for Norton Revamp in the AWS Machine Learning blog, with the most valuable aspect being its deliberate division of responsibilities between humans and AI. This product is an AI-driven reputation management platform for professionals, completed by a small engineering team within months and launched in March 2026.

The team clearly defined the division of labor: humans retain authority over architecture decisions, security reviews, business logic, and the design of personalization algorithms; AI handles code generation, documentation, test scaffolding, refactoring, and UI component creation. This is not vague AI assistance but rather distinct boundaries cut along specific work segments.

Their quantified results show a 3x increase in feature implementation speed, with boilerplate generation accelerated by 90%, documentation by 80%, test creation by 70%, and UI component generation by 65%. Using Claude, they produced more than 10 variants per design concept, compressing design-to-prototype time from weeks to hours, with 70% of UI code generated by AI. They tested five pricing models in the time previously required to test one.

However, the team noted the cost: reviewing AI output consumed 20%–30% of engineering time. This means the net speedup is not 3x, but rather the actual gain after deducting review costs.

The most critical insight is this: AI accelerated implementation, but humans designed how the AI Learning System should adapt to user goals. In other words, when the system needs to make decisions involving domain judgment and long-term effects—such as determining under what conditions to adjust recommendation strategies—AI does not replace the human design role. The team also specifically pointed out that product-side generative AI (Amazon Bedrock, Amazon Nova, AgentCore Web Search) and development-side generative AI serve different purposes, and they deliberately managed them separately.

The evidence level of this record is self-reported by the team: there is no external audit, no explanation of baseline measurement methods, and no complete context regarding team size or project duration. For engineering teams evaluating AI-assisted development, its value lies not in proving how much code AI can write, but in demonstrating where the irreplaceable boundary of human judgment falls in a specific project—not on abstract terms like 'architecture,' but on the adaptation logic of learning systems, which requires understanding user behavior over the long term to design.

On November 3, 2026, Maciej Szpunar, First Advocate General at the European Court of Justice, delivered his formal opinion in Case C-250/25 (Like Company v. Google). The opinion suggests that AI chatbots based on Large Language Models (LLMs), which use Retrieval-Augmented Generation (RAG) to search and summarize online news articles, may constitute acts of 'reproduction' and 'communication to the public' within the meaning of EU copyright law.

The case stems from a lawsuit by Like Company, a Hungarian operator of internet portals publishing news articles. The company alleged that AI services like Google Gemini infringed its exclusive right to authorize or prohibit the online reproduction and communication of its publications by generating summaries without permission or compensation. Szpunar's opinion addresses questions referred by a Hungarian court regarding whether such AI usage violates EU rules on press publisher rights.

While an Advocate General's opinion is not binding on the Court, it often significantly influences the final judgment. If the Court adopts this view, it could force AI companies to adhere more strictly to copyright licensing agreements in the European market, altering the economic relationship between AI providers and the news industry and increasing compliance costs and legal risks for AI products.

Evertrue Capital, founded by former LocalGlobe and Impact X Capital Partners investor Yvonne Bajela, has announced the closure of its debut fund (Fund I). The fund raised $17 million and was oversubscribed. It will focus on early-stage investments across the United Kingdom and Europe, with a primary emphasis on artificial intelligence (AI) and fintech sectors.

Bajela stated that the average check size would be approximately £350,000, with plans to support at least 20 companies over the next three years. She noted that the current AI boom has changed the economics of launching a company, making "the ability to build" more accessible, while the capacity to create enduring businesses remains rare. This insight drove her decision to launch an independent fund to act on her own convictions.

The fund secured backing from five institutional investors, including the British Business Bank. As part of the British Business Bank’s £400 million Investor Pathways initiative, £90 million was allocated to 10 first-time fund managers, including Evertrue. Approximately 80% of the limited partners (LPs) are based in the UK, with the remainder scattered across Europe, Asia, and the US.

While the US venture market is described as a "barbell" where capital concentrates among top-tier firms, PitchBook data shows that emerging fund managers in the UK raised $7.4 billion through the end of September, surpassing the $6.2 billion raised in all of 2025. Bajela believes this environment offers opportunities to invest early in the next generation of top British talent, particularly founders with unique problem insights and strong execution abilities.

When building enterprise-grade AI agent analytics, a recurring problem is that agents can write SQL but do not know the business meaning behind column names or what they are authorized to read. Common solutions involve deploying a separate semantic layer—such as a knowledge graph, data virtualization platform, or dimensional model—and having the agent query that layer. On November 3, 2026, the Amazon Web Services Machine Learning blog presented a different approach: placing the semantic layer directly inside the operational Postgres database itself, using native database COMMENT, GRANT, and row-level security features to define meaning and control access, eliminating the need for a second system.

The specific mechanism operates in three layers. The first layer is meaning: the AIDB extension’s Semantic Knowledge Base (SemKB) embeds the definition of every table, view, and column, along with COMMENT ON text, into a vector index, allowing agents to find the correct tables and columns via natural language search. The second layer is relationships: Postgres foreign keys are automatically crawled by SemKB into join paths, so agents do not need to guess associations based on column names. The third layer is governance: the AIDB Model Context Protocol (MCP) endpoint has no permission system of its own; it passes the Postgres username and password for each request as HTTP Basic authentication, and the database’s GRANT and Row-Level Security (RLS) policies determine what that connection can see.

The blog illustrates the role of the meaning layer with a concrete example. In the C360 dataset, the purchases.price column records the order total, not the unit price. Without a COMMENT ON statement clarifying this, the agent treats it as a unit price multiplied by quantity, calculating approximately $502,529 for the top customer, Carolyn Inworth—a wrong figure. Adding a comment stating 'Total amount the customer paid for the entire order, in US dollars. Not the unit price.' changes the answer to $27,991.66. The model did not change, the prompt was not altered, and the tools remained the same; only the indexed comment within SemKB changed.

Governance verification is more direct. The blog defines two Postgres login roles: mcp_agent_full can see all columns, while mcp_agent_restricted cannot see customers.ssn. Running the exact same prompt to list five customers' Social Security Numbers yields different results. The full role returns the SSN values for all five customers; the restricted role’s tool call is denied at the Postgres layer, returning 'permission denied for column ssn,' after which the agent informs the user that SSNs cannot be read. The prompt, tool catalog, SemKB, and agent code are identical; the only difference is the Basic authentication credentials injected by AgentCore Identity.

Architecturally, the agent runs on the Amazon Bedrock AgentCore runtime, using Claude for planning and calling MCP tools through the AgentCore Gateway. The Gateway forwards requests to an Application Load Balancer within the EDB Postgres AI Virtual Private Cloud (VPC), reaching the AIDB edb-endpoints MCP server. No Lambda functions or translation layers are required—AIDB natively speaks the streamable HTTP MCP protocol. Auditing also remains within the same database: AIDB writes OpenTelemetry traces for every SQL execution into the aidb_otel schema, which can be queried using standard SQL.