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Thelio Mira AI Linux Workstation: 192 GB GPU Memory
System76 launches the Thelio Mira AI Linux workstation from $3,299 with dual NVIDIA RTX Pro 6000 GPUs and 192 GB GPU memory for local AI workloads.
System76's Thelio Mira AI is a locally built (Denver, Colorado) Linux workstation for AI training, fine-tuning, and inference, starting at $3,299. Configurations go up to a 16-core AMD Ryzen 9000 CPU, 192 GB DDR5 RAM, and dual NVIDIA RTX Pro 6000 Blackwell GPUs delivering 192 GB of (ECC) GPU memory with liquid cooling, dual PCIe 5.0 x16 slots, and up to three M.2 NVMe drives. It ships with Pop!_OS 24.04 LTS or Ubuntu and is positioned as a way to avoid recurring cloud GPU costs.
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Microsoft’s Project Zenith puts large AI models directly on developer PCs
Microsoft's Project Zenith delivers a ready-to-code Windows 11 experience running 30B+ parameter AI models locally on 64GB+ unified-memory PCs, starting with AMD Ryzen AI Halo.
Project Zenith is a preconfigured Windows 11 developer experience for PCs with at least 64 GB of unified memory and 250 GB/s or higher memory bandwidth, capable of running AI models with more than 30 billion parameters locally without metered cloud tokens. First systems are powered by AMD Ryzen AI Halo, with additional OEM and silicon partner devices expected in coming months. The environment ships with WSL and Linux containers, pinned developer tools, and day-one AI agent security features including OS-enforced agent identity and containment through Microsoft Execution Containers (MXC).
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Microsoft’s Project Zenith is a ‘distraction-free Windows experience’ for developers
Microsoft branded its developer-optimized Windows setup Project Zenith, with 64GB+ memory devices from AMD that run 30B+ parameter AI models locally.
Microsoft named its developer-focused Windows configuration Project Zenith, targeting new hardware with 64GB or more of unified memory. AMD unveiled the first Project Zenith device at IFA, a miniature PC powered by Ryzen AI Halo chips, with more devices expected in the coming months. The setup ships with a preconfigured Windows install and preinstalled developer tools including Visual Studio Code, GitHub Copilot, PowerToys, WinAppCLI, and Windows Dev Skills. Microsoft says it lets developers run 30B+ parameter models locally and unmetered, reducing reliance on metered cloud tokens.
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[AINews] OpenAI shuts off Cursor
OpenAI cut off API access to coding tool Cursor after its SpaceX acquisition, citing contract violations by Elon Musk's companies.
OpenAI disabled Cursor's access following the closing of Cursor's acquisition by SpaceX, citing its experience with Elon Musk's companies violating contracts; Cursor responded that OpenAI accounts for only 5% of its traffic. The weekly digest also covers major open-weight releases: Z.ai's GLM-5.3 (744B total/40B active, 1M context) and Tencent's Hy4-preview (770B/49B, ~#5 on Code Arena WebDev), plus Alibaba's Qwen3.8-Flash (125B/6B). vLLM published benchmarks showing no universal winner among speculative decoding methods across model families.
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Notes on gotchas while migrating 35kb preprompts from Opus to self-hosted Ollama
Opinion piece urges migrating 35KB preprompts from Anthropic/OpenAI to self-hosted Ollama, citing session privacy risks and safety filters blocking security research.
The author documents gotchas migrating 35KB preprompts from Claude Opus to self-hosted Ollama, motivated by fears that frontier providers train on user sessions, citing the OpenAI Navier-Stokes controversy. The piece argues inference providers cannot audit their own retention or training pipelines and that only self-hosted hardware offers verifiable privacy. It also criticizes frontier safety filters for refusing vulnerability research tasks and calls for models that support exploitability testing in CI/CD pipelines.
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When companies get specific about AI, revenue growth looks different
A Carnegie Mellon and Larridin study of 564 companies links specific AI disclosures to 8 points higher revenue growth, not margins.
Researchers at Carnegie Mellon University and Larridin analyzed 564 companies across 12 sectors using 478 10-K filings, more than 30,000 classified job postings and an AI maturity tracker. Companies at the top of 'narrative concreteness', meaning they named deployed AI systems with measurable outcomes, were associated with 8.0 percentage points higher year-over-year revenue growth, while no public AI signal predicted operating margins or risk-adjusted stock returns. AI infrastructure suppliers outperformed matched peers by about 32 percentage points over four months. The authors stress the findings show association, not causation.
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