Benchmark Compares .NET Native AOT, JIT, R2R and Self-Contained on Linux

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A developer published a detailed benchmark comparing four .NET 10 publishing models — Native AOT, JIT framework-dependent, JIT self-contained, and ReadyToRun — running the same workload on linux-x64. The experiment measures startup time, warmed throughput, p95/p99 latency, CPU, resident memory, and distribution size, rather than relying on labels like CLI or serverless. The author cautions that Native AOT's faster startup does not translate into a universal cold-start advantage on managed platforms such as AWS Lambda or Azure Functions, since provider-side factors are not captured in the lab. Removing the JIT eliminates runtime compilation overhead but does not eliminate GC pauses, OS scheduling, or page faults. The findings are presented as a decision matrix tied to measurable objectives and specific constraints, not as a one-size-fits-all recommendation.

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How a 'Before Model' Callback Let 10,000 AI Agents Run Without Burning Tokens

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ProgrammingDEV Community ·

WUIC replaces cloud LLM with local Ollama model and adds free VS Code AI assistant

The WUIC framework's in-product RAG chatbot previously relied on a cloud API for its generation step, incurring per-token costs. Developers replaced that component with a locally hosted Ollama instance running qwen2.5-coder:32b, requiring only a configuration change due to the engine's OpenAI-compatible interface. As a side effect, the same WUIC knowledge base was exposed to VS Code through a lightweight stdio MCP server, enabling an agentic coding assistant with no API key or usage fees. The MCP server wraps the existing REST API and provides two tools — codebase search and full RAG-generated answers — with no external dependencies beyond Node.js 14.18 or later. The team notes the trade-off is real: local models sacrifice response quality and latency compared to cloud alternatives, and the hardware running Ollama represents a tangible infrastructure cost.

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ProgrammingDEV Community ·

How to Run Qwen3-Coder-Next Locally Using llama.cpp on a Budget Home PC

A developer guide published on DEV Community explains how to run Qwen3-Coder-Next, an 80-billion-parameter Mixture-of-Experts (MoE) language model, on a cost-effective home PC using the llama.cpp framework. MoE models activate only a subset of their parameters per token, making them more efficient than their total size suggests — Qwen3-Coder-Next uses roughly 3 billion active parameters per token despite its 80 billion total. The guide targets a typical home setup with 8–16 GB of GPU VRAM, 32 GB of system RAM, a modern multi-core CPU, and at least 50 GB of SSD storage. However, the recommended Q4_K_M quantized version weighs approximately 49 GB, meaning machines with only 8 GB of VRAM and 32 GB of RAM should opt for a smaller 2-bit quantization or a lighter MoE model instead. The article also covers installing llama.cpp, downloading GGUF model files, and tuning layer offloading to balance workload between VRAM and system RAM.

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ProgrammingDEV Community ·

FreeLLMAPI Unifies 34 Free AI Provider Tiers Into One OpenAI-Compatible Endpoint

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