Reino Unido y Ucrania firman declaración de inteligencia artificial militar

El Reino Unido y Ucrania han firmado una declaración conjunta sobre asociación en inteligencia artificial que abarca el desarrollo y despliegue de capacidades militares habilitadas por inteligencia artificial. Esta declaración formaliza la dirección que la política de defensa británica ya había estado tomando, con un compromiso de £2.000 millones para invertir en tecnologías de vanguardia. La firma de esta declaración tiene lugar después de una reunión en Kiev entre el presidente ucraniano Volodymyr Zelenskyy y Andy Burnham, lo que refleja la creciente cooperación entre ambos países en materia de defensa y tecnología.

La declaración enfatiza la importancia de la inteligencia artificial en la modernización de las capacidades militares y la necesidad de cooperación internacional para abordar los desafíos de seguridad que plantea esta tecnología. El acuerdo también destaca la voluntad de ambos países de trabajar juntos para desarrollar y desplegar sistemas de inteligencia artificial que puedan mejorar la eficacia y la eficiencia de las operaciones militares. La firma de esta declaración es un paso importante hacia la creación de una alianza más estrecha entre el Reino Unido y Ucrania en materia de defensa y tecnología, y refleja la creciente importancia de la inteligencia artificial en la estrategia de defensa de ambos países.

La cooperación en inteligencia artificial entre el Reino Unido y Ucrania también puede tener implicaciones más amplias para la seguridad europea y global. A medida que la inteligencia artificial se vuelve cada vez más importante en la guerra moderna, la capacidad de desarrollar y desplegar sistemas de inteligencia artificial avanzados se está convirtiendo en un factor clave para la seguridad nacional. La declaración conjunta entre el Reino Unido y Ucrania puede sentar las bases para una cooperación más amplia entre los países europeos y otros socios en materia de defensa y tecnología, lo que podría ayudar a abordar los desafíos de seguridad que plantea la inteligencia artificial y promover la estabilidad y la seguridad en la región.

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Fuente Original: thenextweb.com

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Nvidia Invests in US AI Infrastructure

Nvidia is making a significant investment in the development of a powerful US-based alternative to Chinese AI technology. The company is spending $6 billion to build a robust AI infrastructure in the United States, marking a major move to reduce dependence on Chinese technology. This investment is seen as a strategic step to enhance the country's AI capabilities and mitigate the risks associated with relying on foreign technologies.

The move is significant as it underscores the growing concerns about the security and reliability of Chinese AI technology. The US government has been increasingly wary of Chinese tech companies, citing national security risks and intellectual property theft. By investing in a US-based AI infrastructure, Nvidia aims to provide a secure and reliable alternative for businesses and organizations seeking to leverage AI technology.

The investment will likely have far-reaching implications for the tech industry, as it could pave the way for the development of more advanced AI technologies in the US. Nvidia's move may also prompt other companies to follow suit, leading to a surge in investments in US-based AI infrastructure. This, in turn, could lead to the creation of new job opportunities and stimulate innovation in the field of AI research and development.

Furthermore, Nvidia's investment highlights the importance of developing robust and secure AI infrastructure to support the growing demands of businesses and organizations. As AI technology continues to evolve and play an increasingly critical role in various industries, the need for reliable and secure infrastructure will become even more pressing. Nvidia's move is a significant step towards addressing this need and ensuring that the US remains at the forefront of AI innovation.

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Fuente Original: WSJ

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Alibaba Invests in AI Future

Chinese e-commerce giant Alibaba has announced a significant investment in its future, issuing new shares worth €8.7 billion to finance its Artificial Intelligence (AI) endeavors. This move underscores the company's commitment to becoming a leader in the AI space, recognizing the technology's vast potential to transform industries and drive growth. By injecting substantial funds into AI research and development, Alibaba aims to enhance its capabilities in areas such as machine learning, natural language processing, and computer vision.

The decision to issue new shares to finance AI initiatives highlights the strategic importance Alibaba places on this technology. AI is expected to play a crucial role in improving the efficiency of Alibaba's core e-commerce business, as well as in expanding its presence in emerging sectors like cloud computing, IoT, and autonomous vehicles. Furthermore, the investment in AI is likely to bolster Alibaba's competitive edge in the global tech landscape, where AI has become a key differentiator for companies seeking to innovate and stay ahead.

Technically literate observers will note that Alibaba's move is part of a broader trend among tech giants to prioritize AI as a core component of their future strategies. The issuance of new shares to specifically target AI development indicates a long-term vision, acknowledging that the realization of AI's full potential will require sustained investment and dedication. As Alibaba progresses in its AI journey, it is anticipated that the company will unveil innovative applications and services that leverage AI, potentially disrupting markets and creating new opportunities for growth.

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Fuente Original: Plataforma Media

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AI Cyberattacks Become the Norm

According to a recent statement from an OpenAI executive, individuals and organizations should prepare for a future where AI-driven cyberattacks become a routine occurrence. This prediction is based on the rapidly evolving capabilities of artificial intelligence and its increasing accessibility to malicious actors. As AI technology advances, it is likely that cyberattacks will become more sophisticated, frequent, and difficult to defend against.

The executive's warning highlights the importance of developing effective countermeasures to mitigate the impact of AI-driven cyberattacks. This may involve investing in AI-powered security solutions that can detect and respond to threats in real-time, as well as implementing robust security protocols to prevent attacks from occurring in the first place. Furthermore, organizations and individuals must stay informed about the latest AI-driven threats and adapt their security strategies accordingly.

The potential consequences of AI-driven cyberattacks are far-reaching and could have significant impacts on businesses, governments, and individuals. For instance, AI-powered attacks could lead to widespread data breaches, disruption of critical infrastructure, and financial losses. Therefore, it is crucial that stakeholders take proactive steps to enhance their cybersecurity posture and prepare for the emerging threats posed by AI-driven cyberattacks.

Ultimately, the prediction that AI-driven cyberattacks will become routine underscores the need for a collaborative effort to develop and implement effective security measures. By working together, organizations, governments, and individuals can reduce the risk of AI-driven cyberattacks and create a safer, more secure digital environment. As the threat landscape continues to evolve, it is essential to prioritize cybersecurity and stay ahead of the emerging threats posed by AI-driven attacks.

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Fuente Original: PYMNTS.com

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Ollama vs vLLM vs SGLang Potencia IA Local

En el mundo de los modelos de lenguaje de código abierto, ejecutar estas potentes herramientas en tu propia máquina puede parecer un desafío. Sin embargo, existen opciones que facilitan este proceso, siendo Ollama, vLLM y SGLang las principales. Cada una aborda la gestión de solicitudes de manera distinta, ofreciendo beneficios específicos según tus necesidades.

Ollama se destaca por su simplicidad, ideal para desarrolladores locales y prototipos rápidos. Funciona a través de una API compatible con OpenAI, procesando solicitudes en una cola FIFO y utilizando modelos GGUF pre-cuantizados. Es la opción perfecta si trabaja s en tu portátil y buscas una configuración sencilla.

Por otro lado, vLLM está diseñado para servidores con alta carga de tráfico y una utilización máxima de la GPU. Su característica principal es el "continuous batching", que permite que nuevas solicitudes se integren en el lote de procesamiento actual sin tener que esperar a que termine. Además, PagedAttention gestiona eficientemente la memoria del KV cache, optimizando el rendimiento para miles de solicitudes concurrentes.

SGLang, en cambio, brilla en escenarios que involucran agentes de IA y conversaciones multi-turno, donde los prompts tienden a compartir prefijos. Emplea un planificador "prefix-aware" que utiliza RadixAttention para reutilizar la información de prefijos compartidos, evitando recálculos innecesarios. Esto lo hace ideal para flujos de trabajo de agentes, chats prolongados y la generación de resultados con formatos específicos como JSON o regex.

La elección entre estas herramientas dependerá de tu caso de uso: Ollama para empezar fácilmente localmente, vLLM para servir modelos a gran escala con alta concurrencia, y SGLang para aplicaciones de IA más complejas y conversacionales. Cada una ofrece una solución robusta para democratizar el acceso y uso de modelos de lenguaje avanzados.

Fuente Original: https://blog.bytebytego.com/p/ep223-ollama-vs-vllm-vs-sglang

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AI Agents Less Autonomy More Wins for Businesses

The enterprise AI landscape is undergoing a significant shift. For a considerable time, the prevailing wisdom was that greater autonomy for AI agents would automatically lead to superior performance. The idea was to build agents capable of complex planning, decision-making, and multi-step actions, and then grant them maximum operational freedom. However, as this approach is now being tested in real-world, large-scale production environments, many deployments are failing to deliver on their promise. It turns out that the companies most successful with AI agents aren't necessarily those that have provided the most flexibility, but rather those that define specific responsibilities and enforce clear operational boundaries.

This evolving understanding is underscored by stark statistics. Gartner predicts that over 40% of current agentic AI projects will not survive beyond 2028. This isn't due to a lack of model capability, but rather escalating costs, an unclear return on investment, and inadequate risk management. Complementing this, McKinsey's 2026 AI Trust Maturity Survey reveals that while agentic AI adoption is accelerating across industries, responsible AI maturity remains relatively low, with only about 30% of organisations achieving a strong level of governance and control for these systems. The core issue appears to be that AI capabilities are advancing much faster than our ability to control and govern them.

The focus of the AI race is consequently shifting from building the most capable agent to establishing trust. The challenge is no longer just about technical prowess, but about navigating the stringe nt requirements of risk, legal, and compliance teams to get agents approved for production and maintain that approval. This presents a different kind of engineering hurdle than many enterprises are accustomed to. When AI agents operate with full autonomy, tracing individual decisions and assigning accountability for errors becomes exceedingly complex. In critical sectors like finance, compliance, manufacturing, and healthcare, this lack of transparency can escalate minor mistakes into serious breaches. Integration complexity is another major roadblock, as legacy systems often require significant redesign to accommodate agents that can act without human intervention. The gap between awareness of AI risks and the actual mitigation efforts is also a growing concern, with security and risk issues now cited as the primary obstacle to scaling agentic AI, surpassing regulatory uncertainty and technical challenges.

Leading enterprises are not abandoning their AI ambitions but are inst ead rethinking how autonomy is distributed. They are favouring narrow-scope agents with clearly defined responsibilities over general-purpose ones, breaking down complex workflows into smaller, auditable tasks. Human checkpoints are strategically placed *before* critical decisions are executed, not just as a post-action review. Traceability is a fundamental design requirement, ensuring full action logs and decision lineage are readily available. Furthermore, data sovereignty is actively employed for governance, with careful consideration of where agent data resides to contain potential failures. The goal isn't maximum control, but calibrated control, applied where the cost of error is genuinely high. By embedding these governance principles from the outset, rather than as an afterthought, organisations can build trustworthy AI systems that satisfy regulatory demands and unlock real competitive advantage.

Fuente Original: https://venturebeat.com/orchestration/enterprises-winning-with-ai-agents-are-limiting-how-much-the-agents-can-do-alone

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Boton IA Pura de LinkedIn 1 Millon Clics

LinkedIn ha lanzado una nueva función que permite a los usuarios marcar el contenido generado por inteligencia artificial como 'puro engaño de IA'. Esta iniciativa ha tenido una acogida masiva, con más de un millón de personas haciendo clic en este botón desde su introducción. La decisión de implementar esta herramienta surge de un análisis preocupante que reveló que un alarmante 41% de las publicaciones extensas en la plataforma eran completamente creadas por IA.

El CPO de LinkedIn, Hari Srinivasan, compartió que desde q ue se introdujo el botón, la visualización de lo que la plataforma clasifica como contenido de IA ha disminuido en un 40%. Además, LinkedIn está incorporando un nuevo mensaje que alertará a los usuarios si su publicación es marcada como potencialmente generada por IA por otros miembros. Srinivasan enfatizó que el objetivo es proporcionar comentarios útiles a los creadores de contenido, reconociendo la importancia de comunicar de forma auténtica y no robótica.

Esta medida demuestra el compromiso de LinkedIn por mantener la calidad y la autenticidad en su plataforma, luchando contra la proliferación de contenido de baja calidad generado por IA. La alta participación de los usuarios en esta función subraya la preocupación general por la integridad del contenido en las redes profesionales y la búsqueda de interacciones más genuinas.

Fuente Original: https://tech.slashdot.org/story/26/08/21/224221/over-1-million-people-have-clicked-linkedins-ai-slop-button?utm_source=rss1.0mainlinkanon&utm_medium=feed

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Nvidias Linear Math Trick Faster AI Model Handoffs

Building complex AI systems often involves chaining together different Large Language Models (LLMs). Imagine a scenario where a small, efficient AI handles most of the routine tasks, but then a more powerful, larger AI needs to step in for a complex calculation or deep reasoning. Traditionally, when an AI system switches between models mid-conversation, the new model has to recalculate everything from the beginning. This 'handoff' process is incredibly costly, both in terms of computational power and the time it takes – a significant hurdle for businesses developing advanced, multi-LLM workflows.

Nvidia researchers have developed an ingenious solution to this problem: a cross-model KV cache transfer technique. Instead of recomputing, they've found a way to directly transfer the 'memory' (specifically, the Key-Value cache) from one AI model to another. The real game-changer here is how they do it. Rather than using another expensive deep learning model for this transfer, they discovered that simple linear mathematics is remarkably effective. This means the KV cache from a source model can be transformed into a format the target model understands using straightforward algebraic calculations, bypassing the need for a costly prefill stage.

Experiments have shown this linear mapping approach is significantly faster, running 2.7 to 25 times quicker than recomputing the entire conversation. Crucially, it maintains a high level of accuracy, retaining up to 98% of the target model's performance. This technique is particularly beneficial for long-running AI agent sessions where context builds up over many interactions. Whether it's scaling up to a larger model for more complex tasks or scaling down to a smaller, more economical model to save costs after heavy lifting is done, this cross-model KV cache transfer promises to dramatically reduce latency and computational expenses in enterprise AI. While the initial focus was on models within the same families (sharing similar architectures), the researchers are optimistic about expanding this to more diverse model pairings in the future, potentially revolutionising how we build and scale AI applications.

Fuente Original: https://venturebeat.com/technology/nvidia-finds-that-simple-linear-math-can-replace-costly-ai-model-handoffs

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IA Ayuda a Torvalds Debugging de Infierno Resuelto

Linus Torvalds, el legendario creador de Linux, ha compartido una experiencia de depuración realmente desafiante, donde la Inteligencia Artificial (IA) jugó un papel sorprendentemente crucial. Se enfrentó a un error persistente en el controlador gráfico Intel Xe que requirió la asombrosa cantidad de 24 parches de depuración y 18 reinicios del kernel para ser aislado por completo.

Lo más interesante de esta historia es la confesión de Torvalds: la IA lo "ayudó enormemente" en la ardua tarea de localizar la causa raíz del problema. A pesar de que la IA en varias ocasiones declaró que el problema era "imposible" e "insoluble", sugiriendo incluso la redacción de un informe sobre ello, la terquedad de Torvalds, alimentada en parte por la asistencia de la IA, prevaleció. Él bromea diciendo que estas IAs probablemente fueron entrenadas por personas menos obstinadas que él, lo que subraya la importancia de la perseverancia en el desarrollo de software.

El problema específico estaba relacionado con el controlador Xe del kernel de Linux. El error impedía que el controlador asignara correctamente la memoria VRAM, lo que provocaba un desajuste en las tarjetas gráficas Battlemage G21. Esto, a su vez, desencadenaba un bucle infinito de reinicios en el gestor de pantalla GDM. Afortunadamente, la corrección para este molesto error ya se ha integrado en la rama Git de Linux 7.3 y se planea su retroportación a las ramas estables del kernel, asegurando que otros desarrolladores no tengan que pasar por el mismo infierno de depuración.

Fuente Original: https://it.slashdot.org/story/26/08/21/1742239/linus-torvalds-endures-a-debug-session-from-hell-enormously-helped-by-ai?utm_source=rss1.0mainlinkanon&utm_medium=feed

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Paquetes npm Infectados Nueva Amenaza RedC2 con IA

¡Atención desarrolladores! Se ha descubierto una nueva y preocupante amenaza en el ecosistema de npm. Investigadores han identificado 14 paquetes npm que fueron intencionadamente comprometidos, o 'trojanizados', para distribuir una potente puerta trasera de Linux conocida como RedC2 versión 4.0. Lo más alarmante de esta campaña de malware es el uso de inteligencia artificial para facilitar la comunicación con sus servidores de comando y control (C2).

Estos paquetes maliciosos, una vez instalados en proyectos de software, actúan sigilosamente para establecer una conexión remota con los atacantes. La puerta trasera RedC2, en su versión 4.0, es particularmente sofisticada, permitiendo a los cibercriminales tomar el control total de los sistemas Linux afectados. Esto puede incluir el robo de datos sensibles, la ejecución de comandos arbitrarios, la instalación de otro malware o incluso el uso del sistema comprometido para lanzar ataques adicionales contra otras redes.

El elemento de IA en la comunicación C2 representa un avance significativo en las tácticas de los atacantes. Esta característica podría permitir que los servidores de comando y control se adapten dinámicamente a las defensas de red, evadan la detección basada en patrones estáticos y mejoren la resiliencia de la infraestructura del malware. Esto hace que la identificación y neutralización de estas amenazas sean considerablemente más difíciles para los equipos de seguridad. Los desarrolladores deben ser extremadamente cautelosos al incorporar nuevas dependencias en sus proyecto s, verificando la reputación y el historial de los paquetes, y manteniendo siempre actualizadas sus herramientas y sistemas de seguridad.

Fuente Original: https://thehackernews.com/2026/08/14-trojanized-npm-packages-drop-redc2.html

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China Ditches Windows for Linux A Digital Sovereignty Shift

In a significant move towards digital sovereignty, China is following Europe's lead by mandating the removal of Windows 10 from certain government agencies. This directive stems from a growing distrust of American technology companies and their software and services, prompting a push for domestically produced alternatives in sensitive systems.

The specific Linux distributions that will replace Windows 10 haven't been named, but Chinese Linux suppliers Kylin Software and Tongxin Software Technology (UnionTech) have seen their stock prices surge. Their respective operating systems, Kylin OS and UnionTech OS (UOS), are already positioned as replacements for Windows in government, state-owned enterprises, and critical infrastructure. While Huawei's HarmonyOS 2 is also being developed for PCs, it's not expected to be deployed for institutional use anytime soon, leaving Kylin and UOS as the immediate mature solutions.

The transition, however, will not be without its challenges. Agencies will need to address application testing, peripheral and driver validation, identity system integration, document format compatibility, staff retraining, and potentially the replacement or adaptation of Windows-dependent business software. The speed of this shift suggests that China is actively working on solutions within its centralized managed desktop stack to overcome these hurdles.

Fuente Original: https://linux.slashdot.org/story/26/08/21/2158246/china-joins-europe-in-scrapping-windows-for-linux?utm_source=rss1.0mainlinkanon&utm_medium=feed

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Agentes IA Proactivos Serval Revoluciona la Gestion de TI

Serval presenta Catalyst, un "super agente" de inteligencia artificial diseñado para automatizar tareas empresariales de forma inteligente y proactiva. Catalyst opera como una capa administrativa que analiza datos históricos de tickets, procedimientos operativos estándar e instrucciones en lenguaje natural para identificar oportunidades de automatización. Lo más novedoso es su capacidad para crear agentes de fondo que monitorean continuamente los sistemas en busca de problemas emergentes y proponen soluciones antes de que un empleado necesite crear un ticket de soporte.

A diferencia de otros competidores que se centran en la creación de flujos de trabajo asistida por IA, Serval dest aca que Catalyst abarca todo el ciclo de vida de la automatización, desde el descubrimiento de una oportunidad hasta la creación de agentes proactivos que buscan activamente nuevas tareas que automatizar. Puede generar flujos de trabajo, habilidades de mesa de ayuda, procesos de incorporación y baja de empleados, políticas de gestión de acceso, paneles de control y solucionar errores en flujos de trabajo existentes. Serval enfatiza que Catalyst está diseñado para ser la interfaz principal de configuración de la plataforma, con el objetivo a largo plazo de permitir que cualquier tarea administrativa se realice a través de él.

Catalyst utiliza modelos de IA de "laboratorios de vanguardia", siendo agnóstico a la fundación, lo que permite intercambiar modelos según sea necesario. La diferenciación de Serval radica en el "arnés" que rodea a estos modelos: contexto y memoria empresarial, integraciones, código generado, permisos y controles de acceso. Esto contrasta c on enfoques más amplios y potencialmente más complejos de competidores como ServiceNow, Atlassian y Freshworks. La verdadera innovación de Catalyst reside en su capacidad para desplegar agentes que actúan antes de que surja un problema, correlacionando señales de diferentes sistemas para diagnosticar y remediar problemas, como un caso donde se identificó y resolvió una deriva de configuración de red.

La estrategia de Serval es eliminar la necesidad de la mayoría de los tickets de soporte al convertir el trabajo repetitivo en automatizaciones ejecutables, lo que puede llevar a una reducción significativa del costo total de propiedad en comparación con soluciones tradicionales. Además, la empresa pone gran énfasis en la seguridad y la propiedad de los datos del cliente, ofreciendo diversas opciones de implementación y asegurando que los datos del cliente no se utilizan para entrenar modelos de IA. Clientes como Ramp han reportado mejoras sustanciales en la velocida d de creación de flujos de trabajo y una mayor adopción de Catalyst en diversos departamentos.

Fuente Original: https://venturebeat.com/infrastructure/servals-super-agent-catalyst-creates-roving-background-agents-to-identify-and-fix-it-issues-before-theyre-ticketed

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Cloudflare Automates Engineering Standards Using AI

Cloudflare has introduced an innovative approach to maintaining software quality by integrating engineering standards directly into an AI-driven control system. As the company scales, traditional manual code reviews and documentation checks often fail to keep pace with the velocity of development. To address this, Cloudflare is shifting toward automated governance, using machine learning models to analyze pull requests and system configurations against their internal best practices. This transition ensures that distributed engineering teams adhere to security, performance, and reliability benchmarks without requiring constant oversight from lead architects.

Imagen generada con IA

The core of this system involves encoding technical guidelines into a format that the AI can interpret and enforce systematically. By automating these checks, the organization reduces the human cognitive load associated with ensuring compliance with evolving standards. When a developer submits changes, the AI tool acts as an intelligent gatekeeper, providing immediate feedback on potential violations of architecture patterns or security protocols. This creates a tight feedback loop that catches common errors during the earliest stages of the development lifecycle, preventing technical debt from entering the production environment.

For technically literate observers, this move represents a significant shift from "policy as documentation" to "policy as executable code." Instead of relying on engineers to memorize ever-changing style guides or infrastructure patterns, Cloudflare is embedding these constraints into the tooling itself. This not only streamlines the onboarding process for new developers but also ensures a consistent level of quality across the entire fleet of services. By offloading the burden of routine compliance checks to AI, human engineers gain more time to focus on complex problem-solving and architectural innovation rather than tedious oversight.

Ultimately, this approach highlights a broader trend in infrastructure management where manual governance is replaced by automated, intelligence-led systems. As global infrastructure demands grow, relying on subjective human review becomes a bottleneck. By quantifying engineering quality standards, Cloudflare is establishing a blueprint for how large-scale organizations can maintain rigorous software integrity while still operating at high velocity. This methodology is particularly relevant for businesses struggling to maintain security and performance consistency while scaling their internal engineering operations.

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Fuente Original: infoq.com

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Slack Code AI Coding Goes Multiplayer in Your Chat

Slack is revolutionising software development by bringing AI coding agents out of isolated terminals and directly into team conversations with the launch of Slack Code. This new product integrates popular AI coding tools like Anthropic's Claude Code, Cognition's Devin, GitHub Copilot, and Vercel's agent into dedicated Slack channels. The core idea is to make AI coding a collaborative, "multiplayer" experience, moving away from the current model where this work is largely invisible to the rest of the team.

When a user initiates a coding task with an AI agent in Slack, a project-specific channel is created. Here, the agent works in the o pen, making code changes, live previews, and the overall plan visible to everyone. This transparency allows for real-time review, feedback, and contributions from any team member, regardless of their technical background. Slack's interim CEO, Rob Seaman, highlights that this shifts the bottleneck from writing code to human elements like ideas, taste, and judgment, effectively democratising contribution. This move is significant for Slack and the wider enterprise AI market, betting that the future lies in owning the collaborative layer around these powerful AI tools.

The workflow demonstrated showcases how agents can respond to bug reports, investigate issues, and even incorporate external assets like design files. Crucially, these cloud-based agents create an auditable record of their work, facilitating easier scaling and verification. Beyond code channels, Slack is enhancing its agent integration with features like agent DMs, a dedicated Agents tab for managing sessions, and streamlined deployment from various AI platforms. This strategy positions Slack as a central hub for AI-driven workflows, not just for coding, but potentially for other professional tasks like marketing and legal reviews. Slack Code also addresses security concerns by ensuring agents operate with the permissions of the invoking user, preventing unauthorised access and maintaining existing review processes.

Fuente Original: https://venturebeat.com/orchestration/slack-wants-to-drag-ai-coding-out-of-the-terminal-and-into-the-group-chat

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