Posts tagged with #ai-infrastructure from the Contra Collective team.
MLX 0.22 landed in late May 2026 with a refactored flash attention implementation that closes most of the gap to what llama.cpp pulls out of Metal Flash Attention 2. For engineering teams running local LLMs on M5 Ultra hardware, this is the version that changes the runtime calculus.
Redis has been the default answer to caching for so long that teams often add it to a new architecture without questioning whether it is actually the right choice. For traditional server-based applications with persistent connections and predictable concurrency, Redis is difficult to beat. But the rise of serverless backends, edge compute, and AI API gateways has created a category of use cases where Redis's connection model actively works against you.
Most teams running local LLMs on Apple Silicon hit the same wall: unified memory caps out at 512GB on a single M5 Ultra, and anything beyond Llama 4 Scout or a quantized Maverick stops fitting. The cloud option is real, but for teams who deliberately chose local inference for privacy, cost, or latency reasons, the answer for 2026 is distributed MLX.
Most teams discover they need LLM observability after their first production incident, not before it. A prompt regresses silently, costs spike without warning, or a downstream integration starts returning garbage. By the time someone notices, the damage is already done. The right observability tool turns that reactive posture into a proactive one.
Your retrieval-augmented generation pipeline is only as good as the framework underneath it. Pick the wrong one and you are refactoring six months later while your competitors ship.
Most teams pick their MLOps experiment tracking tool based on what the first ML engineer installed. That is how you end up with a $180,000 annual W&B bill when MLflow would have served 80% of your needs at near-zero cost.