Technical insights on AI engineering, custom software development, and bringing ML to production.
Native Android on-device LLMs without React Native. ExecuTorch JNI, llama.cpp-android, and MNN side by side — benchmarks, Kotlin integration patterns, and which framework to pick for your use case.
Five trends making on-device AI the default in 2026: NPU stability on Snapdragon 8 Elite and Dimensity 9400, sub-1B LLMs at 12–15 tok/s, vision-camera pipelines, on-device RAG, and framework convergence. What's already in production.
Classification, detection, segmentation, OCR, and vision-language models — all running on-device in your React Native app. A complete guide to CV with react-native-executorch v0.8.0.
Run local language models on Android in 2026 — benchmarks, model recommendations, and production-ready code examples using React Native ExecuTorch.
AI agents are evolving from chatbots to real-time video systems. Technical requirements, use cases, and architecture for latency-sensitive AI agents.
Agentic engineering tools let teams prototype in hours, but vibe coding to production requires a new discipline: the Vibe-to-Production Maturity Model.
Sub-20ms AI inference on a $400 Android — measured on 2025 chipsets. ExecuTorch vs ONNX Runtime benchmarks for real-time production deployments, plus a 5-factor decision matrix for when on-device beats cloud.
Custom SaaS costs in 2026: from $50k MVPs to $250k enterprise platforms, plus hidden SaaS sprawl costs and a build vs buy decision framework.
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