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In a video, LiveKit engineer Jesse Hall shares insights on building voice agents for financial services that feel human rather than robotic. He specifically points out that traditional model leaderboards are often misleading because they evaluate individual components, whereas developers ultimately ship a complete conversation experience.

Hall suggests that developers should focus on "time to first audio" and "time to first sentence," rather than the commonly tracked "first byte" or "first token." He also explains how to keep conversations flowing while slow back-end work runs by using asynchronous tool calls. Additionally, he discusses audio-aware turn detection, a roughly 1.5-second latency budget (where 500-600ms feels truly human), and how to benchmark your own stack with simulated callers.

Brazil’s real interest rates are outrageously high. Some analysts estimate that Brazil’s real interest rate—calculated as the policy rate of the Central Bank of Brazil (Banco Central do Brasil) minus expected inflation for next year—exceeds 9%, which is higher than in Russia and four times that in peaceful peer economies such as Chile, the Philippines, and South Africa.

It all starts with public finances. Brazil’s 65,000-word behemoth of a constitution bars cuts to nominal public-sector wages and links pension, disability, and unemployment benefits to the minimum wage, which is adjusted annually based on recent inflation and GDP growth. This ratchet has lifted mandatory spending to 90% of the federal budget.

It also perpetuates inflationary pressure. This plus a generous welfare state in turn make Brazilians unwilling to save. At 14% of GDP, the national savings rate is 12 percentage points below the world average. If price rises have regularly eroded your bank balance in the past and the state pays for your health care, school and pension, you may as well spend all the money you make right away—especially if, like most people in unequal Brazil, you do not make much to begin with.

In a podcast interview with the German tech media outlet t3n, organizational development consultant David Bausch admitted that for a long time he had spoken in lectures and on podcasts about how AI creates free space by automating daily tasks, and confessed that he had repeated this claim almost without reflection. However, when he talks to people today, he finds this narrative crumbling: people no longer have more time to devote to the creative parts of their work, but are instead required to produce more economic results.

Coding agents often need to access logged-in web applications during development—for example, to test an admin interface that requires authentication. If an agent can only launch a fresh, unauthenticated browser instance, it cannot complete such tasks.

Gregor Zunic, co-founder of Browser Use, published a practical guide on October 25, 2026, systematically organizing six solutions for mainstream coding agents including Claude Code, Codex, Gemini CLI, and Cursor to reuse logged-in Chrome sessions. The article provides configuration steps for each agent and explains why the intuitive approach of directly copying browser cookies does not work.

Zunic points out that copying cookies into a fresh browser fails because the new browser is treated as a different device. With the adoption of Device Bound Session Credentials, Chrome stores private keys in secure hardware like TPMs; when short-lived session cookies expire, they can only be refreshed by proving possession of that key. Copied cookies on another machine cannot be renewed. Additionally, starting from Chrome 136, --remote-debugging-port and --remote-debugging-pipe are ignored on the default data directory; Google made this change because attackers were using remote debugging to steal cookies.

For Claude Code users, if subscribed to a Pro, Max, Team, or Enterprise plan, the shortest path is using Anthropic's Claude in Chrome extension via the claude --chrome command. If using an API key or another provider, one must switch to Browser Harness, Playwright MCP's extension mode, or Chrome DevTools MCP.

For Codex, OpenAI's browser extension can be used within the ChatGPT desktop app; in Codex CLI, Browser Harness or one of the two MCP servers is recommended. Gemini CLI ships with a built-in browser subagent that attaches to running Chrome by setting sessionMode to "existing" in settings.json. Cursor's built-in browser keeps its own workspace cookies; to use Chrome logins, one must add Browser Harness, Playwright MCP, or Chrome DevTools MCP to .cursor/mcp.json.

Playwright MCP's extension mode allows users to select specific tabs accessible to the agent, suitable for scenarios where limiting agent permissions is desired. Chrome DevTools MCP's auto-connect mode (requiring Chrome 144+) provides full debugging capabilities, including performance traces and network request analysis. Browser Harness, as an open-source tool, attaches to the entire running browser via the Chrome DevTools Protocol, supporting multiple agent interfaces.

Zunic emphasizes security risks: an agent in a signed-in browser can do anything the user can, such as sending emails, changing settings, or buying things. Prompt injection is the main threat, which Anthropic calls "the biggest risk facing browser-using AI tools." He recommends using a separate Chrome profile whenever possible, avoiding letting the agent access sensitive accounts like banking, healthcare, and government, and turning off remote debugging when done.

Kazuhisa Nishikawa, a long-time columnist for the Japanese tech media outlet PC Watch (part of Impress Corporation), published a practical record on October 26, 2026, detailing how he produced a Halloween-themed music video using code rather than generative AI video models. The core of his method is not to have AI write all animation code from scratch, but to pre-build a set of components and let the AI be responsible only for selecting and combining them.

Remotion is a video rendering tool based on React that captures web page frames one by one and stitches them together using ffmpeg to create an MP4. Its advantage lies in determinism: the program renders exactly what it decides, eliminating the randomness inherent in generative AI video where every output can differ.

Nishikawa found that although AI models (he used Claude Code calling Sonnet 5.5) are quite familiar with the Remotion API and can write animation code according to instructions, asking AI to generate entire segments of animation code from scratch presents two problems: high token consumption and long processing times, and instability where slight shifts in coordinates and styles occur after each modification.

His solution was to pre-construct a component library, turning text layouts, animations, backgrounds, decorations, icons, and overall atmosphere into reusable parts.

The key shift in this approach lies in the role of AI: moving from writing animation code line-by-line to selecting and combining pre-built components, with the program deterministically rendering the selected results.

Nishikawa used ACE-Step 1.5 to generate a J-pop style Halloween song and DeepSeek V4 Flash to write the lyrics. He then provided PARTS.md, the prompt description, the audio, and the lyrics to Claude Code, which automatically wrote scenario.json and called the rendering script to output the MP4. When the same prompt was given to DeepSeek V4 Flash, the resulting video style was noticeably different—he described Sonnet 5.5 as leaning towards a 'harder' feel, while DeepSeek V4 Flash appeared slightly more lively, consistent with their impressions during typical conversations.

However, the author acknowledged unresolved boundaries in this workflow: lyric alignment with the timeline is currently approximate, and whether text obscures faces still requires manual or another AI's visual inspection.

Finally, Nishikawa highlighted a easily overlooked business detail regarding licensing: Remotion is free for commercial use by individuals, for-profit organizations with fewer than three employees, and non-profit organizations. However, companies with four or more employees require a paid license for business use.

The value of this practical record lies not in showcasing a new tool, but in presenting a transferable engineering mindset: when AI's free-form generation brings costs and volatility, using a deterministic rendering engine to handle execution and a limited set of components to handle selection can transform AI from a role of writing code from scratch every time into a component selector, reducing token consumption, improving stability, and sparing future users from repeating the same pitfalls.

AI is automating cyberattacks, but a frequently overlooked issue is that defensive AI does not always prevail against offensive AI. Cheon Jeong-hee, CEO of CryptoLab and Professor at the Department of Mathematical Sciences, Seoul National University, recently articulated this judgment in an interview, deriving a strategic conclusion: rather than relying on intercepting every intrusion, we should ensure that data cannot be read even if it is accessed, by keeping it encrypted.

Cheon points out that South Korea’s current security framework focuses heavily on blocking external intrusions, while offering insufficient protection for databases storing personal information. His logic is straightforward: if attackers breach perimeter defenses, any database that decrypts data during operation exposes personal information to risk. Consequently, the difficulty and cost of stealing data must be increased even after a successful intrusion.

In evaluating existing data protection solutions, he contrasts the limitations of Hardware Security Modules (HSM) and Trusted Execution Environments (TEE). HSMs excel at protecting encryption keys but are costly and difficult to apply at the application layer. TEEs, meanwhile, can still be bypassed via software vulnerabilities. As an alternative, he proposes homomorphic encryption—a technology that allows searching and computation directly on ciphertext, ensuring data never needs to be decrypted, thereby reducing the risk of plaintext exposure during processing.

To illustrate its efficacy, Cheon described a simulation experiment conducted by CryptoLab using its own AI attack tool, ARTEX. Under conditions of limited query access, attackers successfully extracted the plaintext of 1,000 virtual personal records from a standard database. However, when targeting CryptoLab’s homomorphic encryption product, HEracles, attackers only obtained encrypted data and could not verify the original text. Cheon acknowledged that homomorphic encryption still faces challenges regarding the protection of decryption keys, noting that CryptoLab has used small-scale HSMs in pilot validations with multiple enterprises to prevent key leakage.

Regarding performance, Cheon stated that while homomorphic encryption was previously criticized for its slowness, this issue has been significantly mitigated. He claimed that under specific conditions, because traditional methods require decryption before searching whereas homomorphic encryption processes data directly in ciphertext form, the latter can be approximately 10 times faster. This assertion comes solely from the vendor’s own statements and has not been verified by independent third-party benchmark tests; it holds true only under specific conditions.

The core value of this argument lies not in any single product, but in the strategic assessment: as AI accelerates both offense and defense, defenders should not assume they will always succeed in intercepting attacks. Instead, focus should shift to increasing the cost and difficulty of data theft.

Singapore-based industrial vision AI startup Ailytics has announced the completion of a $7.4 million Series A funding round led by Tin Men Capital. Other participants included SEEDS under SG Growth Capital, Japanese cloud camera provider Safie, and the family office of a major Japanese construction firm.

Founded in 2021, Ailytics converts existing CCTV infrastructure in industrial sites into real-time safety and operations management systems. Since raising $2.7 million in a Pre-Series A round in April 2024, the company has expanded from 70 projects across 4 countries to over 400 deployments in 15 countries, serving clients such as NatSteel, DHL, and Rome Leonardo da Vinci International Airport.

According to PRNewswire, Ailytics CEO Lenard Tan stated that the investment will strengthen local teams in the United States, Japan, Australia, and the Gulf region. CTO Prateek Manocha noted that the funds will also support the development of proprietary domain-specific inference models, aiming to keep AI operating costs low despite growing demand.

Jeremy Tan, Co-founder and Managing Partner at lead investor Tin Men Capital, mentioned in the statement that Ailytics maintains "very solid unit economics and less than 1% customer churn." This metric is self-reported by the company and investors, not independently audited.

Morgan Stanley published a video episode titled "The Tension Between Equities and Bonds" on October 2, 2026. The featured speaker is Andrew Sheets, the Global Head of Fixed Income Research at Morgan Stanley.

According to the brief episode description provided by the publisher, Sheets examines what rising rates could mean for equity valuations, earnings, and investor appetite. This topic relates to common discussions regarding the link between the macro interest rate environment and equity market performance.

For readers interested in how bond yield changes transmit to equity markets, this episode offers an entry point into a specific perspective from a major investment bank's research division. It is important to note that only the official episode description text has been obtained; a full transcript or specific analytical conclusions are not included. Therefore, this resource serves primarily as a lead to understand institutional research focus areas, rather than as verified specific investment arguments.

According to a report by ZDNet Korea, Authentic Brands Group, which manages Elvis Presley's intellectual property, along with related music copyright entities, filed a lawsuit against artificial intelligence company Anthropic on October 23, 2026, in the U.S. District Court for the Northern District of California in San Francisco. The plaintiffs allege that Anthropic used 274 Elvis Presley songs as training data without authorization while developing its AI chatbot Claude, and failed to pay any licensing fees.

The disputed songs include hits such as "Can't Help Falling in Love" and "All Shook Up." The plaintiffs pointed out that Anthropic built an internal data repository to store materials like song lyrics over a long period and repeatedly copied and used this data when training new Claude models. Furthermore, the complaint notes that when users asked Claude to modify existing songs, the model generated lyrics that retained the original structure and chorus while changing only some expressions. The plaintiffs argue that generating such derivative works without the original authors' permission also constitutes copyright infringement.

The plaintiffs also accused Anthropic of failing to take sufficient measures to stop the infringement after receiving notice of the unauthorized use, continuing to utilize the works instead. The lawsuit seeks approximately $50 million (about 66.1 billion Korean won) in damages plus legal costs.

This is not Anthropic's first challenge of this kind. Previously, the company faced a class-action lawsuit from American authors for mass-downloading books from illegal e-book sharing sites for training purposes. In July 2026, a U.S. court approved a $1.5 billion settlement between Anthropic and the authors, which covered English translations of works by several authors, including Nobel laureate Han Kang.

In response to the new lawsuit, an Anthropic spokesperson stated that they disagree with the claims made by the Elvis music copyright holders and plan to defend themselves fully. The case is currently at the filing stage, with no court ruling yet issued.

On October 26, 2026, NetEase Zhiqi launched CodeWave SDD, a new-generation AI Coding product for enterprises. The company claims that by enabling fully autonomous agent-based software development, the tool can boost overall corporate productivity by 3 to 5 times. NetEase defines this as the next form of AI Coding: shifting from humans using tools to agents invoking tools to complete software development.

According to NetEase's internal 'AI Implementation Maturity Standard', CodeWave SDD represents Level 4 'autonomous driving', where AI completes tasks end-to-end while humans focus primarily on requirement analysis and delivery acceptance. The core architecture consists of two layers: a bottom-layer enterprise-grade Harness engineering environment covering the entire software development lifecycle, and an upper layer of role-specific agents (requirements analysis, design, architecture, front-end/back-end development) that collaborate on a unified platform.

In a demonstration, Wang Tong, Vice President of NetEase Zhiqi and Head of CodeWave Business, compared CodeWave SDD with mainstream IDE tools. For a provincial guarantee company's core business system requiring over 100 pages of PRD, CodeWave SDD largely restored functional modules, whereas traditional IDEs only generated top-level menus. NetEase disclosed that CodeWave SDD achieved a 90% first-pass rate for P0 test cases and approximately 80% for P1 and P2 cases. However, this metric reflects the initial execution pass rate of test cases, not the overall project delivery success rate.

To address the maintainability of AI-generated code, Wang outlined a three-step solution: using fine-grained Specs as a unified standard, enforcing hard validation through the Harness environment, and breaking large tasks into smaller batches. Currently, CodeWave SDD has been validated in practice by over 100 enterprises. NetEase noted organizational changes within its own ranks, with the proportion of BA and product roles increasing as developers shift towards business understanding and acceptance verification.

According to a report by ETNews, several South Korean credit card companies plan to integrate artificial intelligence (AI)-driven agent payment systems into the 'Everyone's AI' national project led by the Ministry of Science and ICT. The project is driven by three consortia involving SKT, Kakao, and KT, aiming to create a new payment ecosystem by combining commerce with AI infrastructure.

Participating firms include Shinhan Card and Hana Card (under the SKT consortium) and BC Card (under the KT consortium). While other card issuers such as Samsung Card and KB Kookmin Card are conducting various forms of AI agent payment verification outside these specific consortia, the overall goal is to launch formal AI agent payment services in the first half of 2027. Currently, the 'Everyone's AI' project is in beta testing but does not yet include financial payment functions; basic features are scheduled for official release in December, with financial services expected to be added incrementally next year.

In the video 'From Zero to Leaderboard: Agent Evaluation' published by ai.engineer, Weights & Biases engineer Wolfram Ravenwolf discusses building an agent evaluation pipeline where every score can be traced back to its conditions. He uses a case where an agent repairs a broken git repository and earns a perfect score, but notes that such a run stays out of the default leaderboard because it tested only one task. This distinction forms the core of his methodology: evaluation must go beyond simple averages to examine the source of result reliability.

Ravenwolf presents the WolfBench framework, which compares average, best, and worst runs, as well as tasks solved at least once versus those solved every time. By repeating the same configuration, this approach exposes the gap between a lucky success and dependable behavior. He highlights that the evaluation configuration includes six layers: benchmark, runner, sandbox, agent, model service, and settings. Factors such as versions, provider routing, system prompts, resource limits, and timeouts all affect what the result actually measures.

In a live workflow demonstration, Ravenwolf shows how to launch a Terminal Bench task through Harbor, collect results, filter incomplete runs in a Marimo dashboard, and turn valid runs into an interactive leaderboard. Weave is used to preserve the trajectories behind the bars so reviewers can inspect commands, messages, and failures. He also points out two traps in interpreting results: a cheaper model can consume enough tokens to raise total cost, and greater reasoning effort can lower the score if it leaves too little time to execute commands. Additionally, infrastructure failures need different treatment from genuine task failures, and zero temperature does not guarantee identical runs.