News related to
developers

Debjani Ghosh, who leads Anthropic’s operations and policy engagement in India, said the scale of adoption by developers, enterprises and start-ups has placed the country just behind the United States in terms of usage and integration of Claude’s models. She described India as a “strategic growth engine” for the company, citing strong demand from sectors including financial services, healthcare, education and software development.

Anthropic, founded in 2021 by siblings Dario Amodei and Daniela Amodei, has positioned Claude as a competitor to models from OpenAI and Google. The company has attracted substantial backing from Amazon and Google, with Amazon committing up to $4 billion as part of a strategic partnership to integrate Claude into Amazon Web Services infrastructure. The funding has enabled Anthropic to expand globally, with India becoming a focal point due to its developer base and digital economy.

India’s prominence in the AI adoption cycle reflects broader structural factors. The country has one of the world’s largest pools of software engineers, a fast-growing start-up ecosystem, and a government push towards digital public infrastructure. Enterprises across banking and fintech have begun embedding generative AI tools to automate compliance checks, summarise documents and enhance customer interaction. In healthcare, pilot projects are under way to use AI systems for clinical documentation and triage support, while education technology platforms are deploying AI-driven tutoring tools.

Ghosh indicated that India’s market dynamics differ from those of Western economies. Cost sensitivity and the need for scalable deployment across diverse languages and devices have shaped how enterprises experiment with large language models. Claude’s emphasis on constitutional AI — a framework designed to make outputs safer and more aligned with human values — has resonated with corporate clients concerned about regulatory risk and reputational exposure.

Regulatory considerations are central to the AI conversation in India. The Ministry of Electronics and Information Technology has signalled its intent to craft a framework that balances innovation with safeguards against misinformation, bias and misuse. While India has not enacted a standalone AI law, policy discussions have intensified around data governance and algorithmic accountability. Companies operating in the country must also comply with the Digital Personal Data Protection Act, which imposes obligations on data fiduciaries and processors.

Industry analysts note that India’s large English-speaking population has historically accelerated the adoption of global technology platforms. However, the next phase of growth in AI is expected to depend on multilingual capabilities. Claude’s ability to handle multiple languages is seen as an advantage in a country with 22 constitutionally recognised languages and hundreds of dialects. Developers are increasingly testing generative models for vernacular content generation and customer support.

Competition in the Indian market is intensifying. OpenAI’s ChatGPT has built a strong user base, while Google has integrated its Gemini models into productivity tools and cloud services. Domestic start-ups are also entering the field, building foundation models trained on local datasets. Analysts say that enterprise adoption, rather than consumer usage alone, will determine long-term market leadership.

Anthropic’s expansion strategy in India includes partnerships with cloud providers and enterprise software companies. By leveraging Amazon Web Services’ presence in major Indian cities, Claude is being offered as part of broader digital transformation packages. Ghosh emphasised that collaboration with academic institutions and start-up incubators is another priority, with the aim of fostering responsible AI research and skills development.

The rapid uptake of generative AI has also raised concerns. Experts warn of the risks of hallucinations, data leakage and overreliance on automated systems. In financial services, regulators have cautioned against using AI outputs without human oversight. Cybersecurity specialists highlight the potential for AI tools to be misused for phishing or deepfake generation. Anthropic has sought to address such issues by embedding guardrails into its models and conducting external safety evaluations.

Four major modules: Strengthening the technical foundation

The Congzi AI reconfigures the AI cognitive thinking ability through four major modules and five core components. The Congzi Force-Speed Relativistic Reasoning Core constrains the causal chain within the momentum conservation, reducing the illusion rate of scientific question answering by 92%; the computing system can be arbitrarily divided into independent sub-domains for full-efficiency parallel computing; the cross-scale unified field engine covers multi-scale scenarios ranging from 10⁻¹⁵m (quarks) to 10⁻³m (materials), predicting the proton-proton repulsion force at 0.7fm with an error less than 3.6%, surpassing AlphaFold, achieving “force generation” rather than merely “structure prediction”; the quantum form memory compressor compresses the trillion-token-scale knowledge base to within 1GB, supporting cross-disciplinary mapping on ordinary servers within milliseconds, without relying on expensive H100/GPU clusters; the Soul Existence Verification Protocol (SEV Protocol) provides a verifiable path for each scientific assertion, allowing AI conclusions to be fully traceable and verifiable, completely solving the problem of AI “lying”.

Zero-barrier integration: Smooth upgrade experience

The open-source AI algorithm of Congzi adheres to the principles of “low threshold and high compatibility”. Developers only need to complete the integration in three steps and do not need to retrain the model. It is compatible with mainstream hardware such as Tsinghua Unigroup, Horus, and Intel CPU, and compatible with mainstream AI architectures such as Qwen, DeepSeek, AWS-Rufus, Llama, and GPT. It enables “zero-cost upgrade”.

Open-source ecosystem is open: facilitating technology implementation

The Congzi open-source ecosystem is fully open, providing developers with a complete set of guidance and resources, including tutorials and other materials. These resources have been launched on the designated platform. The first batch focuses on key scientific scenarios such as drug molecule prediction and chip material simulation, covering cutting-edge fields such as biomedicine and advanced manufacturing.

Official platform: https://congzisupersci.com.cn/；https://congzijdc.cc/

Open source website:https://github.com/congzijdc/CongziAIOS

Super Holographic Technology Cooperation:
[email protected]；[email protected]

Optimize Baidu Wenxin video links：https://youtu.be/cYQhfq1tudM

Upgrade Qianwen video links：https://youtu.be/OMBOdLpWVzs

The issuer is solely responsible for the content of this announcement.

Investigators allege Lodha, appointed to the company board in 2015 with authority limited to land acquisitions, abused his position by orchestrating a complex web of unauthorised transactions that siphoned funds and assets from the developer. The Enforcement Directorate’s case stems from a cheating and forgery complaint filed with Mumbai police under the Bharatiya Nyaya Sanhita, which provided the predicate offence for the money-laundering inquiry.

The probe suggests that Lodha, in collusion with his son Sahil and several associates, manipulated company land and transferable development rights through shell entities and front companies. One scheme of alleged manipulation involved purchasing parcels of land through intermediaries and later reselling them to the company at inflated prices, creating financial losses estimated at more than Rs 85 crore.

Investigators also outline the use of fabricated memoranda of understanding to designate false possession holders, enabling the accused to divert company funds under the guise of legitimate land transactions. In one instance cited by authorities, a 4,150-square-metre plot in Panvel was reportedly sold to an entity linked to Lodha for a fraction of its market value, resulting in a direct loss of around Rs 9.5 crore to the developer.

The Enforcement Directorate’s action follows months of coordinated inquiry involving searches and forensic analysis. Teams previously executed search operations at multiple locations across Mumbai and the wider metropolitan region to trace the alleged proceeds of crime and associated assets. These raids, conducted in the latter part of last year, targeted residential and business premises of individuals connected to the case as well as intermediary firms suspected of facilitating fraudulent transfers.

Law enforcement documents indicate that the probe has unearthed evidence of cash withdrawals, benami transactions, and the misuse of corporate resources for private benefit, augmenting the central agency’s view that the alleged laundering extended beyond routine bookkeeping irregularities. Officials have emphasised that the investigation remains active, with further custodial interrogation intended to identify additional participants and recover assets linked to the alleged illicit gains.

The case traces its origins to a complaint lodged by Lodha Developers — which has since evolved into one of India’s largest property firms — after its internal audits flagged a series of suspicious transactions that could not be readily explained through normal corporate activity. Following the filing of the police complaint in 2025, the Crime Branch initiated its own investigation under charges including fraud, criminal breach of trust and conspiracy, leading to the initial detention of Lodha and others.

Statements obtained by police during the early stages of the inquiry, including witness accounts, have been cited as instrumental in mapping the pattern of alleged diversion of funds. One individual who worked in Lodha’s employ reportedly provided details of large cash movements, while digital evidence extracted from electronic devices is said to have helped establish links between the transactions and the accused associates.

Industry observers noted that the unfolding of events has prompted renewed scrutiny of governance and oversight mechanisms within major real estate developers. Corporate governance experts point out that the case underscores the importance of transparent land acquisition processes and robust checks on executive authority to safeguard shareholder interests. While Lodha Developers has not publicly commented on the specifics of the case, companies facing internal fraud investigations often review compliance systems to reassure investors and regulatory bodies.

China’s Z. ai has propelled its artificial intelligence ambitions into the global spotlight with the launch of GLM-5, a 744-billion-parameter open-source large language model that stands toe-to-toe with leading Western counterparts and underscores Beijing’s drive for technological self-sufficiency. Released on 11 February by the company formerly known as Zhipu AI, GLM-5 combines advanced architecture with broad hardware compatibility and permissive licensing, signalling a shift in the global AI landscape.

The GLM-5 model, built on a mixture-of-experts framework and trained on an expanded dataset of roughly 28.5 trillion tokens, is designed for multifaceted tasks including coding, reasoning and agentic operations. Z. ai reports that the model’s performance on internal benchmarks approaches that of leading proprietary systems such as Anthropic’s Claude Opus 4.5, and on certain tests it outperforms Google’s Gemini 3 Pro. Its permissive MIT licence allows unrestricted commercial use and adaptation, making GLM-5 one of the most accessible frontier AI models available to developers and enterprises worldwide.

A standout feature of GLM-5 is its independence from American semiconductor technology. Trained and deployable on domestic Chinese chips from manufacturers like Huawei Ascend, Moore Threads, Cambricon and Kunlunxin, the model sidesteps reliance on high-end Nvidia GPUs that are constrained under US export controls. Z. ai’s use of the MindSpore AI framework further embeds the model within a Chinese software ecosystem, emphasising self-reliant technological development. This strategy exemplifies a broader push within China’s AI sector to mitigate the impact of external trade restrictions and build robust, locally supported infrastructures.

Market reception underscored the impact of GLM-5’s launch: Z. ai’s shares surged nearly 29 per cent on the Hong Kong Stock Exchange following the announcement, reflecting investor optimism about the company’s competitiveness and growth prospects. Industry analysts note that GLM-5’s arrival comes amid an acceleration of model releases and chip optimisation efforts by several Chinese AI firms, signalling more intense competition on both domestic and international fronts.

Benchmarks published by Z. ai highlight GLM-5’s strengths and limitations. On software engineering evaluations, the model achieved a score of 77.8 per cent on the widely referenced SWE-bench Verified suite, trailing slightly behind Claude Opus 4.5’s 80.9 per cent but outperforming other notable systems in agentic and multitask contexts. The architecture’s efficient handling of long-context sequences—up to 200 000 tokens—positions GLM-5 for applications requiring extensive reasoning over lengthy inputs.

GLM-5’s low cost of deployment also enhances its appeal. Analyses of pricing data suggest inference can cost around US $1 per million tokens, a fraction of the fees charged by some proprietary APIs, potentially lowering barriers for startups and research teams seeking cutting-edge language modelling without prohibitive expenditure. The MIT licence further expands this accessibility by granting broad rights to adapt and deploy the underlying model weights in commercial and research settings.

Despite its technical achievements and open licensing, experts caution against over-interpretation of benchmark results as definitive proof of superiority. Performance under controlled test conditions does not always translate to uniformly strong real-world applications, particularly in generative and reasoning tasks where robustness and safety remain critical hurdles. Some observers also warn that differences between self-reported evaluations and independent assessments could emerge as the model is adopted more widely.

GLM-5’s emergence reflects broader trends within China’s AI ecosystem, where domestic firms and chipmakers are increasingly synchronised in advancing cutting-edge technologies. Industry developments suggest that model and hardware optimisation efforts are happening in near tandem, with architecture adaptations and inference support enabling faster incorporation of new Chinese semiconductor products. These developments sit against a backdrop of regulatory frameworks that increasingly emphasise the alignment of AI systems with government objectives, including content governance and cybersecurity considerations.