🤖 Gemini 3 Pro Model Card [pdf]
A milestone in Google’s AI transparency, reinforcing DeepMind’s role in building powerful yet responsible multimodal systems.
Google DeepMind’s Gemini 3 Pro Model Card presents a detailed look at the company’s most advanced multimodal AI model to date. It covers the architecture, data sources, reinforcement learning methods, and safety and ethics frameworks. Supporting text, image, audio, and video inputs, Gemini 3 Pro surpasses its predecessor with improved performance and a strong focus on responsible AI practices.
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🛠️ Building a Sci-Fi Anthology with Code and Open-Source Tools
An inspiring example of how DevOps principles and open-source tools can empower creators to manage complex publishing workflows independently.
A developer shares how he published a science fiction anthology solo using a fully automated pipeline built with Python, YAML, LaTeX, and Pandoc. The workflow managed submissions, typeset the print edition, and generated an EPUB version automatically—highlighting transparency, reproducibility, and version control in creative publishing.
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🧠 Testing Gemini 3 Pro: Audio Transcription and the New Pelican Benchmark
A first-hand, insightful review blending rigorous testing with creative experimentation—ideal for developers tracking the rapid evolution of multimodal AI.
Simon Willison explores Gemini 3 Pro’s expanded multimodal abilities across text, image, audio, and video. He benchmarks it against GPT-5.1 and Claude Sonnet 4.5, details its pricing, and experiments with image captioning, audio transcription, and SVG generation. Despite minor timestamp issues, Gemini 3 Pro shows impressive reasoning and creative performance.
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🔍 Hachi: A Self-Hosted Image Search Engine
A thoughtful intersection of data privacy and machine learning—showcasing how independent developers can innovate beyond centralized search platforms.
Hachi is a self-hosted image search engine built for personal data control. It supports distributed and semantic search powered by machine learning and uses Python and Nim for performance. With face recognition, custom indexing, and privacy-focused backend design, it delivers speed and autonomy to individual users.
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⚡ High-Performance CSV Parsing in Rust with Hybrid SIMD
An excellent showcase of Rust’s power for practical performance engineering—bridging low-level optimization with real-world data workloads.
The simd-csv crate brings SIMD acceleration to CSV reading and writing in Rust, combining state-machine parsing with vectorized string processing for high throughput. It supports x86_64, aarch64, and wasm architectures, offering different reader types for performance tuning. While ideal for large datasets, it may struggle with malformed input.
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