AI Infrastructure

Apple and Google Build Verifiable Private AI Infrastructure on Cloud

This article explains how Apple's Private Cloud Compute (PCC) runs on Google Cloud using Confidential Computing, Intel TDX, and NVIDIA Blackwell GPUs to create hardware-enforced isolation for AI inference. The critical detail is that the entire host stack is open-sourced for external verification—a move that sets a new bar for trust in cloud AI infrastructure.

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Profiling in PyTorch (Part 2): From nn.Linear to a Fused MLP

The article traces the journey from a single nn.Linear to a fused MLP, showing that torch.compile fuses GeLU and multiplication into one Triton kernel while leaving the three cuBLAS GEMMs untouched. It then introduces hand-tuned Liger kernels that achieve the same fusion without compile latency or shape specialization, trading a few microseconds of peak performance for robustness across changing input sizes.

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OpenAI acquires Ona to give Codex agents persistent cloud execution

OpenAI is acquiring Ona to give Codex agents persistent, secure cloud environments where they can work for hours or days, not just minutes. For organizations moving agents into production, this means agents operate inside the org's own cloud with controlled credential scoping, logging, and review, while OpenAI provides the orchestration.

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Fixing tool-calling reliability for open models with a transparent repair layer

Most tool-calling failures in open models aren't capability gaps—they're harness bugs. Ahmad Awais built a transparent repair layer that fixes malformed calls without retries, letting DeepSeek V4 Pro beat Opus 4.7 on 6 of 10 benchmarks. For anyone shipping agentic code, this is a practical lesson in where to invest engineering effort.

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Devin’s 80% Moment: Background Agents, 7x PRs, and the End of Hand-Held Coding

The engineering world is waking up to a hard tension: hand-held coding, where a developer drives every keystroke, does not scale inside an organization that ships thousands of changes per week. Walden Yan, Cognition's co-founder and CPO, frames this as the moment **background agents** go from toy to critical infrastructure. The concrete proof is that **Devin now generates 80% of commits across Cognition's own repos**, up from 16% before the December 2025 model inflection, and merged PR volume has grown 7x. The problem is not whether agents can write code—it is whether they can reliably go from a spec written in Slack to a merged, tested, properly reviewed pull request without breaking your codebase every two weeks. Cole Murray's open-source **OpenInspect** project is an attempt to replicate this pattern for teams that cannot or should not buy a proprietary system.

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