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Anthropic shipped OS-level sandboxing for Claude Code. If you run an agent against a repo you care about, it's worth understanding precisely what moved — because a fair amount of the commentary treats it as "agents are contained now," and that's not what the documentation says. I read the docs carefully, partly because I build a tool in adjacent territory and needed to know whether I'd just been made redundant. Short answer: no. The longer answer is more interesting, and it starts with a compliment: the docs are unusually honest about their own limits. Most of what follows isn't something I discovered — it's something Anthropic wrote down, and more people should read it. What it actually does The sandbox uses OS primitives — Seatbelt on macOS, bubblewrap on Linux and WSL2. By default, sandboxed commands can write only to your working directory and the session temp directory. No network domains are pre-allowed: the first time a command needs a new host you're prompted, and approving it lasts the session. Crucially, this is enforced by the operating system on the running process , not by the model correctly interpreting a command. The docs put it well: the boundary holds regardless of what the model chose to run, and even if an allowed command does more than its name suggests. That's a real improvement over asking an agent nicely, and it's the right layer for what it solves. The motivation named in the docs is the same one I keep seeing in the wild: reducing the permission prompts that people stop reading. Approval fatigue is the disease; this is a real treatment for part of it. Five things worth knowing before you rely on it It's Bash-only. The sandbox constrains Bash commands and their child processes. Claude Code's own Read, Edit and Write tools don't run through it — they go through the permission system instead. "The sandbox is on" means shell commands are contained, not that every file operation is. Your working directory is inside the boundary by design. The de

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Google's Gemini Notebook rollout has expanded across subscriber and account groups, but Google's published information describes a staged, multi-phase release , not a confirmed global completion for every Google AI Pro subscriber. That distinction matters for users evaluating access, planning source-based research workflows, or deciding whether a subscription tier will immediately provide the enhanced Notebook experience. The product was previously known as NotebookLM . In its official Gemini Notebook announcement , Google describes the rebrand and a broader rollout across Google Workspace and personal accounts. Its earlier rollout communications said initial web access was being provided to Google AI Ultra, Pro, and Plus subscribers, with plans to expand to mobile and additional accounts in the following weeks. In practical terms, expanded availability is real, but availability should be assessed by account type, region, and platform. Reports of delays for some UK Pro users are consistent with a rollout that has reached many users without becoming universally available everywhere at the same time. What Google has confirmed about Gemini Notebook Gemini Notebook is the new name for NotebookLM, Google's source-grounded notebook product. The relevant official updates establish three important points: The rebrand is confirmed. Google announced the move from NotebookLM to Gemini Notebook in its July 16, 2026 update. Access has broadened. Google identified AI Ultra, Pro, and Plus subscribers as initial recipients of web access during the rollout. The release is phased. Google said mobile availability and access for additional accounts would follow, while its later update described a broad rollout spanning Workspace and personal accounts. These statements provide a clearer interpretation than treating subscription eligibility as an identical experience for every user immediately. A tier can be included in a rollout while individual accounts still await activation, or while

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Vista previa técnica: Iter todavía no está publicado en PyPI y no existe un paquete oficial instalable. Abrir un recurso, convertir datos o cambiar de backend suele exigir aprender una interfaz diferente y repetir código de integración. Iter nace de una idea sencilla: Aprende una vez. Usa cualquier biblioteca. iter convert data.json to data.csv El usuario expresa una sola intención. Iter se encarga de abrir el recurso, detectar los formatos, seleccionar un adaptador compatible, convertir los datos y guardar el resultado. Una intención. Una instrucción. ¿Qué busca cambiar Iter? Actualmente, una tarea sencilla puede exigir: importar bibliotecas; aprender APIs diferentes; configurar formatos manualmente; escribir código de integración; seleccionar cada backend. Con Iter, el usuario indica principalmente qué quiere conseguir: iter analyze sales.csv Iter selecciona automáticamente una herramienta compatible. Si el usuario necesita controlar la biblioteca, puede indicarla: iter analyze sales.csv with pandas La automatización es el comportamiento predeterminado. El control detallado sigue siendo opcional. Everything is a Resource Iter representa archivos, datos y recursos web mediante una estructura común llamada Resource . El sistema está organizado alrededor de cinco componentes: Resource : representa el recurso. Resolver : identifica formatos, tipos y backends. Registry : registra y selecciona adaptadores. Adapter : ejecuta operaciones concretas. Engine : coordina el proceso. La meta no es afirmar que todas las bibliotecas son idénticas. La meta es unificar intenciones comunes y conservar las diferencias importantes cuando sean necesarias. Estado actual Iter 0.3.0-rc.2 está en fase de corrección de errores y validación privada. Actualmente: el código principal permanece privado; Iter todavía no está publicado en PyPI; no existe un paquete demostrativo; la sintaxis puede ajustarse antes del lanzamiento; solamente se anunciarán como disponibles las funciones implementadas

Bilibili's 412 Incident, Explained: How v1.5.0 Absorbed It In June 2026, Bilibili quietly began rejecting yt-dlp with HTTP 412 errors. Agents wired to scrape it broke — except the ones sitting behind Agent-Reach, which rerouted the channel before most developers noticed. Agent-Reach is a local, MIT-licensed capability layer that gives shell-capable coding agents live internet access by selecting and routing to upstream CLIs rather than proxying data itself . When Bilibili started 412-blocking yt-dlp in June 2026, v1.5.0 rerouted the Bilibili channel to bili-cli with zero user action, while YouTube kept using yt-dlp untouched . The fix landed centrally: the maintainer reordered backends, so no individual builder had to patch a private integration. Quick Answer: When Bilibili began returning HTTP 412 to yt-dlp in June 2026, Agent-Reach v1.5.0 automatically rerouted its Bilibili channel to bili-cli — agents kept working with no user action. The release passed 32 end-to-end tests across 13 channels and grew its suite from 107 to 162 tests. The framing shift matters: v1.5.0 describes itself as a capability layer, not a tool collection. Each platform gets an ordered primary-plus-fallback backend list; after setup, your agent calls those CLIs directly and Agent-Reach never sits in the data path . The June 11, 2026 release passed 32 end-to-end tests across 13 channels and grew its test suite from 107 to 162 tests . Platform Primary backend Fallback Web pages Jina Reader — YouTube yt-dlp — GitHub gh CLI — RSS feedparser — Bilibili bili-cli OpenCLI (subtitles) Twitter/X twitter-cli OpenCLI Reddit OpenCLI rdt-cli XiaoHongShu OpenCLI xhs-cli LinkedIn linkedin-mcp Jina Reader Global search Exa via mcporter — "capability layer: multi-backend routing + real doctor + OpenCLI" — Agent-Reach v1.5.0 release framing (source: Agent-Reach CLAUDE.md ). The behavior is easy to model. The following minimal snippet — which was executed and returns exit 0 — illustrates the "absorb and keep wo

ElevenLabs has expanded ElevenAgents into a broader enterprise automation platform built around the full lifecycle of an AI agent: defining workflow-driven behavior, testing changes, deploying across customer channels, and improving performance in production. The most recent addition, ElevenAgents Spotlight , monitors voice and chat conversations in real time and provides recommendations intended to help teams improve agent outcomes. In its official ElevenAgents Spotlight announcement , ElevenLabs describes Spotlight as an observation and improvement layer for production agents . It is designed to review every conversation, organize interactions by topic, evaluate quality against criteria written in plain language, and surface context-aware suggestions. Combined with Procedures, Experiments, and Versioning, the release set gives enterprises a more structured way to operate AI agents beyond initial deployment. Spotlight adds continuous production feedback Spotlight is aimed at a common operational problem with customer-facing AI agents: a system can appear ready in testing but encounter unexpected patterns, failure modes, or shifts in customer sentiment after it reaches live traffic. Rather than requiring teams to manually sample conversations, Spotlight analyzes production voice and chat interactions as they occur. The platform can automatically group conversations by topic and track metrics including success rate and sentiment . Teams can define evaluations in plain language to score the quality of interactions against their own standards. That matters because a useful agent evaluation may depend on a company's specific process, such as whether the agent followed an escalation policy or collected required information, rather than a single generic measure of response quality. Spotlight also includes anomaly detection for changes in key metrics. Its integration support for observability tools such as Datadog, Grafana, and OpenTelemetry means the agent data can fit in

Major publishers are increasingly limiting OpenAI's GPTBot from accessing their reporting, marking a broader shift in how news organizations assert control over content used for AI training. The BBC and The Guardian list GPTBot as disallowed in their robots.txt policies, while The New York Times has also prohibited scraping for AI training and development without explicit permission in its terms of service. The development matters because web crawling has long been a route to assembling large training datasets. When high-profile publishers restrict access at the source, AI developers face a more constrained and more clearly governed data environment. The issue is not simply whether a crawler can retrieve a page. It is increasingly about permission, licensing and accountable data provenance . The Guardian's published robots.txt directives provide a direct example of this approach. The file disallows GPTBot alongside a broader set of bots, signaling that the publisher does not want its content scraped for AI training or data aggregation. What the publisher blocks change Robots.txt is a machine-readable file that tells web crawlers which parts of a site they are permitted to access. For AI-related crawlers, it has become a practical opt-out mechanism. Publishers are pairing that technical control with contractual restrictions and discussions around licensing, rather than relying on informal expectations about how online content may be reused. The actions documented across major publishers are not identical, but they point in the same direction: indiscriminate collection of publisher content is becoming harder to justify and operationalize . The distinction is important because some publisher policies differentiate between crawlers used for model training and systems used for retrieval, indexing or other purposes. Publisher Documented action Relevant implication The Guardian Its robots.txt disallows GPTBot and a broader set of bots. Signals restrictions on AI training o

Semrush has updated its keyword research workflow with AI features designed to turn work that could historically take days into minutes. The key distinction is not AI generation alone: Semrush is combining AI with its existing keyword data and domain-level context, aiming to give marketers faster recommendations without relying on unverified search-volume outputs. The official Semrush announcement describes changes across Keyword Overview, Keyword Magic Tool, and Keyword Strategy Builder. The updates include a domain-personalized Personal Keyword Difficulty (PKD) metric, Topical Authority analysis, and a redesigned planning tool previously called Keyword Manager. Semrush says the capabilities are included with paid subscriptions. For SEO teams, the practical development is a shift from treating keyword research as a collection of isolated volume and difficulty checks toward a workflow that evaluates whether a topic fits a specific website. That can reduce manual steps in discovery and planning, while retaining a connection to the tool's underlying data. What Semrush changed in its keyword research workflow The new workflow applies a combined data-and-AI approach to thematic relevance. Semrush says it analyzes the relationship between a target topic and a domain's core topics, then surfaces that context in the product interface. This matters because the same keyword can have different strategic value for different sites, depending on their established subject coverage. The updates cover three connected stages of research: Keyword evaluation: Personal Keyword Difficulty adds domain-specific context to keyword difficulty assessment. Topic relevance: Topical Authority is intended to show how closely a topic aligns with a domain's core areas. Planning: Keyword Strategy Builder has been redesigned to support automated keyword and content planning workflows. Workflow area Earlier approach Semrush AI-driven update Keyword difficulty Keyword-level evaluation Personal Keyword

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The European Union's voluntary General-Purpose AI Code of Practice gives providers of general-purpose AI models a practical framework for supporting compliance with the EU AI Act. Finalised in July 2025, the code addresses transparency, copyright, and safety and security. Its public signatory list includes major AI and technology companies, but official EU material does not support claims that roughly 190 organisations have signed the GPAI code. The distinction matters for companies assessing AI suppliers. Signing the code can signal engagement with the EU's emerging governance expectations, but it is not a substitute for examining a provider's specific commitments, documentation, and product-level controls. The European Commission describes the code as a voluntary instrument, and its official GPAI Code of Practice page states that the signatory process and public information continue to be updated. What the GPAI Code of Practice covers The code is designed for providers of general-purpose AI models, a category that can include models used across multiple downstream applications. Rather than creating a separate legal regime, it is intended to help providers demonstrate how they can meet relevant AI Act obligations . Its three chapters cover different aspects of provider responsibility: Transparency: commitments related to information and documentation that can help downstream providers understand and use general-purpose AI models appropriately. Copyright: measures intended to address copyright-related obligations for providers of general-purpose AI models. Safety and security: commitments focused on managing risks associated with the most capable models, including systemic-risk considerations where applicable. Code chapter Primary focus Why it matters to AI buyers Transparency Provider information and documentation Helps buyers assess whether a model provider can supply information needed for downstream use. Copyright Copyright-related provider commitments Relevant

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