A practical guide to splitting an oversized Git PR into clean, topic-focused branches using path-based checkout from a fresh branch off main.
Issue
Mixed branches make PRs unreviewable, increase blast radius, and risk dragging unrelated changes into production. When one branch contains role code, host variables, certificate files, and inventory updates together, reviewers cannot isolate what changed or why.
Solution
Split the oversized branch into multiple clean, topic-focused branches by checking out only the relevant paths from the mixed branch into new branches created fresh off main.
gitdevopsansibleworkflow
Benchmarked every viable local LLM (350M to 26B, CPU and NPU) through a live Discord agent pipeline on RK3588. Found NPU beats CPU at same quality, code is solved at any size, and 4B+ models are slower AND worse than 2B on this board.
Issue
Previous benchmarks measured raw llama.cpp throughput but not real quality through the agent pipeline. Models that looked fast synthetically failed at reasoning, refused tool calls, or got intercepted by workspace routing before reaching the model.
Solution
Built a 14-test, 6-dimension benchmark harness that tests every model through the live Discord pipeline with quality validation: reasoning, factual accuracy, code generation, instruction following, tool calling, and math. Tested 14 models (9 CPU GGUF + 3 NPU RKLLM + 2 large MoE) with BENCHMARK_MODE to isolate pure model performance.
rk3588radxarock-5b-plusllama.cpp
Publishing a practical local-AI control plane for llama.cpp: remote model loading, runtime tuning, streaming chat, and real REAP model serving on a Radxa ROCK 5B+.
Issue
Most local model UIs either abstract away the runtime details that actually matter on constrained hardware or assume desktop-class GPUs. On RK3588, that makes it harder to tune context, KV cache quantization, reasoning behavior, and model selection credibly.
Solution
Built and published `llamacpp-workbench`, a remote llama.cpp workbench with explicit runtime controls, model presets, markdown chat rendering, streaming responses, and benchmark-backed defaults for REAP and dense GGUF models.
llama.cpprk3588radxarock-5b-plus
An advanced benchmark report for running Qwen3.5 locally on RK3588 with source-built llama.cpp: prefill speed, decode speed, stable context, tool-calling behavior, and the practical model choices that actually work on a Radxa ROCK 5B+.
Issue
The usual local-AI advice overemphasizes parameter count and underexplains bandwidth, context budget, KV cache policy, and interactive latency. On RK3588, that leads to bad defaults: models that technically load but feel broken in real chat and tool-calling workloads.
Solution
I ran a corrected Qwen3.5 sweep on RK3588 using source-built llama.cpp, quantized KV cache, and task-pass validation. Then I compared prefill, decode, stable context, average latency, and tool-calling behavior to determine the right model for each workload.
rk3588radxarock-5b-plusllama.cpp
An advanced guide to local GPU inference with llama.cpp: why bandwidth matters more than model fit, how hybrid GPU+CPU offload behaves on cards like the RTX 3060 and 5070, what quantization really means mathematically, and how to run it on Linux, Windows, and WSL.
Issue
Operators lacked a practical framework for choosing quantization, sizing VRAM budgets, deciding when CPU offload is acceptable, and understanding the difference between weight quantization and KV cache quantization. Windows-specific setup questions also created confusion around native builds versus WSL.
Solution
Documented the bandwidth-first model, explained hybrid offload behavior for 12 GB and mid-range modern GPUs, compared quantization choices such as Q4_K_M and q4_0 KV cache, and provided concrete llama.cpp launch patterns for Linux, Windows, and WSL.
local-aillama.cppcudavram
How I implemented hybrid per-layer KV cache quantization on RK3588 using insights from Google's TurboQuant research, achieving 17% better compression with zero quality loss.
Issue
Every time you message an AI chatbot, the model stores your conversation in temporary memory called the KV cache. On large models, this cache alone can consume 40GB—more than the model itself. On a constrained edge device, this is the difference between working and broken.
Solution
Implemented hybrid per-layer KV cache quantization inspired by Google's TurboQuant (ICLR 2026). By using 8-bit quantization for early transformer layers (where attention quality matters most) and 4-bit quantization for later layers, we achieved 17% better compression without quality loss.
local-aiedge-aiturboquantkv-cache