TRAI launched a platform to check the internet speed of various providers
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The times of India
timesofindia.indiatimes.com

TRAI launched a platform to check the internet speed of various providers

The Telecom Regulatory Authority of India (TRAI) introduced the 'Network Anubhav Platform' on Wednesday—a geospatial analytics dashboard designed to process user testing data.

This platform aggregates results from tests conducted via the MySpeed and MyCall applications developed by TRAI. It will provide consumers, service operators, and TRAI itself with information regarding various network parameters.

Thanks to this system, users can check their internet speed in their area, determine which operator provides the highest speed, and which provides the lowest. Furthermore, consumers can use the platform to report spam and conduct internet speed tests.

Maps integrated into the platform allow for geospatial analysis of independent test trips in different locations. A government statement emphasized that the combination of these datasets and their geospatial visualization provides valuable information to users about the results of independent tests and the experiences of other users.

TRAI plans to use the collected data for 'evidence-based consumer experience monitoring and using this data to improve network performance.'

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Indian Telecom Regulator Trai Proposes AI Governance Framework and Accountability Rules for Telecom Operators
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business-standard.com

Indian Telecom Regulator Trai Proposes AI Governance Framework and Accountability Rules for Telecom Operators

India's telecommunications regulator, Trai, released a draft framework for the responsible use of artificial intelligence (AI) on Friday. According to this draft, licensed entities operating under the Telegraph Act or the Telecom Act must be accountable for the outcomes of the AI systems they implement, regardless of where those systems were developed.

This broad draft, presented for consultation, mandates that companies adopt board-approved AI policies, establish dedicated governance and safety committees, and report serious AI-related incidents to the regulator. Stakeholders have until November 27th to submit comments.

The consultation comes amid the growing integration of AI into networks to enhance efficiency and improve consumer services. Trai warns that implementing AI can introduce new risks if due diligence is not performed before system deployment or if there is insufficient oversight of autonomous learning and system interactions.

The regulator noted that risks such as unintentional bias, which can arise in AI systems during training and use, undesirable model behavior like model drift, and adversarial attacks, could negatively impact the resilience of telecommunication infrastructure, especially its critical components. These risks can be mitigated by adhering to appropriate AI governance practices.

Indian telecom service providers, including Reliance Jio and Bharti Airtel, are already using AI extensively in their systems, primarily for customer identification and fraud prevention. Global network equipment manufacturers, such as Nokia and Ericsson, are also integrating AI into networks, a trend adopted by Indian operators.

Trai's proposals include a requirement for every organization to develop an internal AI policy approved by its board of directors or equivalent governing body. This policy must cover risk classification, deployment permissions, incident management, human oversight, data governance, and consumer complaint resolution. Furthermore, the regulator suggested that all AI systems should be subject to appropriate human control to mitigate risks according to a predefined mechanism.

Telecom companies and authorized organizations using AI must classify their systems into high, medium, and low-risk categories based on their impact on network operations, financial and operational results, and potential consequences for consumers.

A key aspect is that the proposals aim to hold organizations accountable for the outcomes of implemented AI systems, 'regardless of whether the AI systems were developed in-house, procured/outsourced from a third party, or represent a combination of these options.'

AI-related incidents must be classified into critical, high, moderate, and low severity levels, with mandatory reporting required for critical and high-severity incidents. The regulator stated that 'critical and high-severity AI incidents must be reported to TRAI in the prescribed format, order, and timeline.' Although the regulator did not specify what constitutes critical, high, or low severity, or the deadline for mandatory reporting, telecom companies must also maintain centralized databases of AI incidents and consumer complaints related to AI-induced harm, complete with mechanisms for investigation, mitigation, and resolution.

ByteDance introduces developer assistant TRAE Bao with cross-project memory and chat features
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pandaily.com

ByteDance introduces developer assistant TRAE Bao with cross-project memory and chat features

ByteDance's AI product TRAE has released a personal assistant for developers named TRAE Bao. This feature became available on October 29th. TRAE announced the launch through its official channels and on the official Chinese community forum.

Unlike the previous format, where the assistant performed one task per session, the new tool is designed to track the developer's projects, tasks, and decisions over time.

TRAE explains the problem as follows: a significant portion of the time developers spend with AI-based coding tools is not spent writing code, but rather on project detailing, task distribution, and switching between work sessions. With every new task, they have to re-explain the technical stack, project background, and previously made decisions. Complex requirements must be manually broken down, tracked across sessions, and then compiled.

The assistant uses a two-layer architecture. The global assistant is responsible for coordination and storing memory across different projects. It can analyze past tasks, dialogues, and the developer's work results, extracting reusable solutions and constraints. When a developer starts new work and defines a goal, the global assistant automatically loads the relevant context by studying experience from earlier projects.

The project assistant, conversely, focuses on one project at a time. Working within the context of that specific project, it breaks down a complex need into a series of actionable tasks, runs subtasks in parallel, integrates them, and summarizes the results. According to TRAE's description, the developer's role in this process shifts towards receiving reports and making decisions.

In the desktop application, access to the global assistant is available through a dedicated entry point, while creating or opening a project activates the project-level assistant. The global assistant can be integrated with messengers such as Feishu and WeChat, allowing developers to assign tasks and receive progress updates without opening the client application.

There are certain limitations: the current version only supports local tasks. TRAE has stated that support for cloud tasks and mobile devices will appear in future updates. Users on the official forum note that the feature is being rolled out gradually, first appearing in the Chinese version, and that an automatic model selection is currently used without manual configuration capability.

This launch adds TRAE to the growing group of desktop agents that allow users to delegate work from phones or chat applications and maintain long-term memory between sessions.

OpenAnolis develops Anolis OS 27 in an agentic format, while Alibaba Cloud claims agents write 95% of new code
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pandaily.com

OpenAnolis develops Anolis OS 27 in an agentic format, while Alibaba Cloud claims agents write 95% of new code

The OpenAnolis community is developing the next version of its Linux distribution, Anolis OS 27, using an approach they call agentic. In this approach, various artificial agents take on functions such as package selection and fixes, code verification, and patch backporting.

Ma Tao, chairman of the OpenAnolis community and head of the base software department at Alibaba Cloud, presented this plan at the GOSIM AI Vision Forum in Zhuhai this month. He also shared data from his team, which indicates that 95 percent of new code in the Alibaba Cloud base software team is generated by agents.

The goal stated for OpenAnolis is to accelerate engineering processes throughout the entire operating system lifecycle—from development and maintenance to operation and management—by tenfold. Ma Tao noted that agents could potentially create a complete operating system independently in the future, and the industry must embrace this trend. Both figures relate to Alibaba Cloud.

Ma Tao spoke at the forum's opening roundtable titled 'Roots: Open Source as the Foundation in the Agent Era.' The discussion was moderated by the co-chair of the LF AI and Data Generative AI Commons, and participants included leaders from the Rust Foundation, OpenUK, OpenCV, and the Beijing Academy of Artificial Intelligence. The panel discussed issues of governance, code accountability, and global collaboration as AI begins to provide and verify code, changing the composition of open-source project contributors.

Ma Tao described two areas of work. The first is the creation of a base operating system for agents. In this concept, the OS goes beyond being a simple resource manager; it becomes an infrastructure that connects computing power, models, tools, and applications. According to data presented at the Apsara 2026 conference, Alibaba Cloud's Agentic OS reduces token usage by over 30 percent and doubles sandbox deployment density through system-level configuration for short-term agent sandboxes and frequent process creation. OpenAnolis's ANOLISA project is positioned as the runtime environment for each agent.

The second area is Anolis OS 27 itself. The community has made the Anolis OS 27 monorepository publicly available, which is a single Git repository containing a large number of packages. OpenAnolis describes this as a distribution design method created for collaboration between human maintainers and AI agents, allowing other teams to study and utilize it.

Anolis OS is developed in collaboration with companies, universities, and research institutes, and it serves as a higher-level community for Alibaba Cloud Linux.

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