750 summarised stories in Tools & Coding, each linking back to the original source. Browse all topics →
OpenAI Blog 2 months ago
OpenAI has implemented security measures for Codex including sandboxing, approval systems, network policies, and telemetry designed to enable safe deployment of coding agents. The system uses agent-native telemetry to monitor behavior and network policies to restrict unauthorized connections. These controls allow organizations to adopt coding agents while maintaining compliance and reducing risks from automated code execution.
Together AI 2 months ago
Together AI's Dedicated Container Inference service, combined with the Goose agent and a new dedicated containers skill, enables developers to deploy any HuggingFace model with a single sentence prompt. A developer deployed Netflix's void-model video object removal model from release day to production in one session using three command-line steps. This eliminates the typical one to two day lag between discovering a new model and getting it running in a usable environment.
The daily briefing
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TLDRocket
27 July 2026
The shape of AI investment is shifting away from raw compute and toward efficiency and data. Multiverse Computing's €500 million Series C round at a €2 billion valuation signals serious institutional confidence in model compression—the art of making AI systems smaller, faster, and cheaper without sacrificing capability. The Spanish startup's CompactifAI technology is becoming the infrastructure layer that companies deploying models actually need, as the era of throwing more parameters at problems runs into both cost and practical constraints. That's one storyline. The other is about physical AI's hidden bottleneck: the data itself. Encord is tackling a brutal economics problem—annotated training data for robotics costs twenty times more to produce than basic video footage, yet is worth a hundred times more. The company's experiments with brain wave sensors and muscle electrical signals suggest the next frontier in humanoid robot training isn't better algorithms but smarter ways to generate the dense, real-world manipulation data that currently doesn't exist at scale. Together, these stories point toward a maturing AI market where competitive advantage lives not in model size but in compression efficiency and data scarcity. The next wave of AI builders won't be the ones with the biggest models—they'll be the ones who can afford to run them, and who can actually afford to train them properly.
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