Small language models are emerging as a serious challenger to large, general-purpose AI in translation, offering faster turnaround, lower costs, and greater accuracy for specific industries and language pairs.
Straker, an ASX-listed language technology firm, claims its Tiri model family can outperform larger systems by focusing on domain-specific understanding and terminology rather than broad coverage.
Tiri delivers higher contextual accuracy by training on carefully curated translation memories and sector-specific data, cutting the need for expensive human post-editing. The models also consume less computing power, benefiting finance, healthcare, and law industries.
Straker integrates human feedback directly into its workflows to ensure ongoing improvements and maintain client trust.
The company is expanding its technology into enterprise automation by integrating with the AI workflow platform n8n.
It adds Straker’s Verify tool to a network of over 230,000 users, allowing automated translation checks, real-time quality scores, and seamless escalation to human linguists. Further integrations with platforms like Microsoft Teams are planned.
Straker recently reported record profitability and secured a price target upgrade from broker Ord Minnett. The firm believes the future of AI translation lies not in scale but in specialised models that deliver translations that are both fluent and accurate in context.
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Microsoft has launched Copilot 3D, an AI-powered tool that transforms 2D images into realistic 3D models without requiring specialist skills. Available through Copilot Labs, it aims to make 3D creation faster, more accessible, and more intuitive for global users signed in with a Microsoft account.
The tool supports only image-to-3D conversion, with no text-to-3D capability. Users can upload images up to 10 MB, generate a model, and download it in GLB format. Microsoft states uploaded images are used solely for model generation and are not retained for training or personalisation.
Copilot 3D is designed for applications that range from prototyping and creative exploration to interactive learning, thereby reducing the steep learning curve associated with conventional 3D programs. It can be used on PCs or mobile browsers; however, Microsoft recommends a desktop experience for optimal results.
Tech rivals are also advancing similar tools. Apple’s Matrix3D model can build 3D scenes from images, while Meta’s 3D Gen AI system creates 3D assets from text or applies textures to existing models. Nvidia’s NeRF technology generates realistic 3D scenes from multiple 2D images.
The release underscores growing competition in AI-driven 3D design, as companies race to make advanced modelling tools more accessible to everyday creators.
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The University of Western Australia has ordered a mass password reset for all staff and students after detecting unauthorised access to stored password data.
The incident was contained over the weekend by the university’s IT and security teams, who then moved to recovery and investigation. Australian authorities have been notified.
While no other systems are currently believed to have been compromised, access to UWA services remains locked until credentials are changed.
The university has not confirmed if its central access management system was targeted.
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In mid-2025, the debate over AI in programming mirrors historic resistance to earlier breakthroughs in computing. Critics say current AI coding tools often slow developers and create overconfidence, while supporters argue they will eventually transform software creation.
The Register compares this moment to the 1950s, when Grace Hopper faced opposition to high-level programming languages. Similar scepticism greeted technologies such as C, Java, and intermediate representation, which later became integral to modern computing.
Current AI tools face limits in resources, business models, and capability. Yet, as past trends show, these constraints may fade as hardware, training, and developer practices improve. Advocates believe AI will shift human effort toward design and problem definition rather than manual coding.
For now, adoption remains a mixed blessing, with performance issues and unrealistic expectations. But history suggests that removing barriers between ideas and results catalyses lasting change.
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Tesla has applied for a licence to supply electricity to homes and businesses across Britain, challenging the dominance of major energy firms. Ofgem could take up to nine months to decide, with operations potentially starting next year.
Known for electric vehicles, Tesla also runs solar and battery storage divisions, with more than 250,000 EVs and tens of thousands of home batteries already sold in the UK. The company’s experience in Texas, where it rewards customers for feeding surplus power to the grid, could inform its UK plans.
The move comes as Tesla’s European car sales decline sharply, with July registrations falling almost 60% in the UK and over 55% in Germany. Increased competition from Chinese manufacturer BYD has added to the pressure.
Tesla has faced public criticism linked to Elon Musk’s political positions, yet the energy push signals a strategic shift towards broader utility services in its key markets.
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Firms such as Wix.com, Shutterstock, and Adobe have been labelled high risk by Bank of America, with stock declines far outpacing the broader market. The shift stems from fears that AI will replace services like graphic design and data analysis, delivering them faster and cheaper.
Some analysts say the impact, once expected over five years, may unfold in just two.
The disruption is not limited to creative industries. Gartner saw a record share drop after cutting its revenue forecast, with some attributing the slump to cheaper AI-powered alternatives.
Meanwhile, major tech firms, including Microsoft, Meta, Alphabet, and Amazon, are expected to invest around $350 billion this year, nearly 50% more than last year, to expand AI infrastructure.
Despite the pressure, certain businesses are adapting successfully. Duolingo has doubled its share price over the past year by integrating AI into its language-learning tools, though questions remain over the long-term sustainability of such gains.
As the gap between AI-powered growth and industry decline widens, markets are bracing for further upheaval.
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Tesla has disbanded its Dojo supercomputer team, with team leader Peter Bannon departing amid a shift in AI strategy. Resources are being reallocated to other company data centres and computing projects.
The supercomputer was initially intended to process large volumes of vehicle data and video to train its autonomous‑driving systems. The team had recently lost around 20 members to the start‑up DensityAI.
Tesla plans to rely more on external partners for compute and chip supply. Strategic collaborations with Nvidia, AMD and Samsung Electronics are being pursued to bolster capacity.
The company focuses on integrating AI, including robotics and self‑driving technologies, across its business. A recent $16.5 billion agreement with Samsung aims to support services like robotaxi, humanoid robots and data‑centre operations.
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US law enforcement, alongside nine other nations, dismantled the BlackSuit ransomware gang’s infrastructure, replacing its leak site with a takedown notice after a coordinated operation. The group, formerly known as Royal, had amassed over $370 million in ransoms since 2022.
More than 450 victims were targeted across critical infrastructure sectors, with ransom demands soaring up to $60 million. Dallas suffered severe disruption in a notable attack, affecting emergency services and courts.
German authorities seized key infrastructure, securing data that is now under analysis to identify further collaborators. The operation also included confiscating servers, domains and digital assets used for extortion and money laundering.
New research indicates that members of BlackSuit may already be shifting to a new ransomware operation called Chaos. US agencies seized $2.4 million in cryptocurrency linked to a Chaos affiliate, marking a significant blow to evolving cybercrime efforts.
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Tesla has reportedly shut down its Dojo supercomputer project following multiple high-profile departures, including that of project head Peter Bannon. CEO Elon Musk ended the AI chip programme, reassigning the remaining staff to other data centre projects.
Dojo aimed to process vehicle data for autonomous driving and reduce Tesla’s reliance on Nvidia and AMD. The project faced delays, with leaders such as Jim Keller, Ganesh Venkataramanan, and Bannon departing before its closure.
About 20 former Dojo employees have joined DensityAI, a stealth startup founded by ex-Tesla staff, which is expected to work on AI chips for robots and data centres. Tesla will now rely more on Nvidia, AMD, and Samsung.
Samsung recently secured a $16.5 billion deal to supply AI chips for Tesla’s self-driving cars, robots, and data centres. Musk said Samsung’s Texas factory will produce Tesla’s AI6 chips, with AI5 chips to be made in 2026.
Musk suggested that combining AI5 and AI6 chips could form a ‘Dojo 3’ system, while Dojo 2 would not launch. The shutdown comes as Tesla restructures, with executive exits, job cuts, and renewed focus on AI integration across Musk’s companies.
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GitHub’s CEO, Thomas Dohmke, envisions a future where developers no longer write code by hand but oversee AI agents that generate it. He highlights that many developers already use AI tools to assist with coding tasks.
Early adoption began with debugging, boilerplate and code snippets, and evolved into collaborative brainstorming and iterative prompting with AI. Developers are now learning to treat AI tools like partners and guide their ‘thought processes’.
According to interviews with 22 developers, half expect AI to write around 90 percent of their code within two years, while the rest foresee that happening within five. The shift is seen as a change from writing to verifying and refining AI-generated work.
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