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Microsoft Opens Window to Re-enable EWS Before Exchange Online Shutdown
Microsoft is allowing Exchange Online administrators to temporarily restore the deprecated Exchange Web Services API, which broke numerous third-party applications when disabled. The company has set April 2027 as the final cutoff date.
Tooling
OpenAI claims major gains in COBOL migration, but withholds test details from public scrutiny
OpenAI announced that GPT-6.1 Sol achieved 86.6% accuracy on COBOL migration tasks, a dramatic jump from its predecessor, yet the company has not released the underlying evaluation data for independent verification.
Tooling
Google and AWS Release PyTorch Backends for Their AI Chips, But Optimization Remains Complex
Google's TorchTPU and AWS's TorchNeuron aim to reduce friction for developers moving workloads away from Nvidia GPUs, yet achieving peak performance on these alternative accelerators still demands significant tuning.
Tooling
Verification Inside the Loop: How Sonar Tackles Technical Debt From AI-Generated Code
AI coding tools deliver rapid productivity gains but leave teams drowning in technical debt. Sonar argues that verification must happen during code generation, not after the pull request.
Tooling
Nvidia's Agent Knowledge Foundation aims to give enterprise AI context without replacing data platforms
Nvidia unveiled a bundle of open-source components designed to help AI agents understand enterprise data while keeping it under existing access controls. The move positions the chipmaker as a model provider rather than a data platform competitor.
Tooling
Cloudflare takes over AI-built TypeScript compiler in Rust, shares optimizations with Microsoft
A Rust reimplementation of TypeScript that runs up to eight times faster and uses 70% less memory is now under Cloudflare's stewardship, with performance discoveries already feeding back into Microsoft's own Go-based compiler.
Tooling
Beyond Benchmark Numbers: Why the Inference Stack Matters More Than Model Size
Open model benchmarks reveal peak performance in isolation, but production demands consistency across varied workloads. Nebius argues that optimizing the entire inference pipeline—not chasing larger models—delivers the accuracy and cost efficiency enterprises actually need.