245 summarised stories about Model Deployment, each linking back to the original source. Browse all topics →
Simon Willison’s Weblog 1 month ago 10
A developer used ChatGPT Images 2.5 and GPT-6 Astra to generate a Pluribus-themed Fabergé egg image and then convert it into Blender .blend models for browser viewing. The Blender model generation ran for 17m51s before producing several .blend files. The result is a new “.blend URL Viewer” tool that lets others view the generated Pluribus Blender model in their browser.
TechCrunch 1 month ago 33 ● 7 sources
Apple introduced Apple Reference Image to verify that iPhone 18 Pro photos are authentic. It uses signed sensor data captured by the main camera sensor and processed via Private Cloud Compute into an “unalterable image” that can be compared in the Photos app. The feature will launch in Photos and later be exposed through developer APIs, with support for the SynthID standard to flag AI-created or altered images.
Product Hunt 1 month ago 34
Desert Ant Labs launched a free SDK for building small AI models that can run on a phone or in a browser without internet. It offers free usage up to 100k monthly active devices. As a result, developers can plug in task-specific speech, text, and vision models in a few lines of code with no per-use cost.
Cohere 1 month ago 2
Cohere released a serving engine for its North Mini Code model built around a decode megakernel running BF16 on a single H100. On batch size 1 it reached 292 tok/s, or 62% of the H100’s bandwidth ceiling (SoL), and was about 1.58× faster than vLLM end-to-end. The serving stack changes from many small decode kernels with synchronization gaps to a single persistent kernel that uses task-level scheduling and synchronization to raise decode throughput without measurable accuracy loss.
The AI Engineer 1 month ago 18
Inference outputs from the same model and prompt can differ across repeated runs even at temperature 0 because GPU arithmetic order changes with how requests share a batch. Thinking Machines Lab reported that 1,000 identical requests returned 80 different completions. Reproducibility can be improved by turning on batch-invariant kernels in vLLM or SGLang, which typically costs about 38% of throughput.
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16 October 2026
AI’s center of gravity this week moved toward something less glamorous than headlines about “smarter” models: scaling the stuff models actually learn from. AI4Bharat and NVIDIA released Nemotron-pretraining-Indic-v1, backed by an open, GPU-accelerated synthetic data generation pipeline for 22 Indian languages. The corpus is massive—about 2.8 trillion tokens—split across 2,601.0 billion native-script tokens and 224.6 billion romanized tokens. The practical pitch is even sharper: builders can reuse not just the data, but the full prompt/quality/repair recipe, then continue-pretraining to push results on Nemotron 3.5 Super VL, with the promise that an Indic continued-pretraining stage helps benchmarks.
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