Technical deep-dives, AI strategy, and engineering perspectives from the team building autonomous systems.
The reason your 70B model OOMs at 32K context is the KV cache, not the weights. We worked through the memory math for llama.cpp, MLX, and mlx-lm on M4 and M5 Apple Silicon, including KV quantization, attention variants, and the trade-offs that determine whether long context is even possible on your hardware.
Hydrogen is Shopify's first-party React framework for headless storefronts. Next.js Commerce is Vercel's reference architecture. Both target the same job: a fast, customizable Shopify storefront detached from Liquid. They make different trade-offs on data fetching, hosting, and how deep the Shopify integration runs. Here is the comparison from teams that have shipped both in production.
SWE-Bench Verified is the benchmark that actually correlates with shipping working code. As of May 2026, five frontier models sit within ten percentage points of each other on the leaderboard. The differences that matter are cost per resolved task, failure modes, and which agent scaffolding they prefer. Here is the full comparison.
ChatGPT 5.4 and Claude Opus 4.7 sit at the top of the coding benchmark leaderboards as of May 2026. The headline numbers are close. The real-world workload behavior is not. We tested both on SWE-Bench Verified, HumanEval Plus, Aider polyglot, and a set of internal refactor tasks across TypeScript, Python, and Dart. This is what the numbers actually say.
Sanity, Contentful, and Strapi are the three serious choices for headless CMS in 2026. Contentful is the managed incumbent. Sanity is the developer-experience-led challenger with structured content tooling. Strapi is the open-source self-hosted option. We compared all three on schema flexibility, pricing at scale, integration with Shopify Plus and Hydrogen storefronts, and the operational reality of running each one.
vLLM is the production-grade batching engine that ships with PagedAttention and continuous batching. MLX is Apple's array framework optimized for unified memory. Search traffic around terms like omlx and vmlx reveals real demand to bridge the two. We tested the available integration paths on M4 Max and M5 Max hardware and report whether vLLM on Apple Silicon is actually viable in 2026, or still a workaround.
Three frontier LLMs, three different architectures, three different bets on what matters in enterprise code generation. Opus is the current SWE-Bench leader. Gemini 3.1 Pro dominates context length and multimodal reasoning. Grok 4.3 has the fastest release cycle and lowest latency. Here's which one actually solves your problem.
Gemini 3.1 Pro and Claude Opus 4.7 sit at the top of the frontier model leaderboard for different reasons. Opus dominates short-context reasoning and code generation. Gemini 3.1 Pro is the only commercial model with a true 1 million token context window. We tested both on long-context retrieval, multi-document reasoning, and codebase comprehension to figure out which model wins which workload.
Grok 4.3 (launched May 10, 2026) added prompt caching and KV-cache optimization, dramatically reducing latency and cost for repeated prompt patterns. Simultaneously, Grok 4.1 Fast (the speed-optimized variant) reached end-of-life. For teams running Grok in production, this is a forced migration with a silver lining: better performance than before.
Qwen 3.6 shipped in May 2026 as an open-weights alternative to closed frontier models. Grok 4.3, released two weeks earlier, brought caching and speed. If you are building agents and choosing between a proprietary API and an open-weights model you can self-host, this is the data you need. We tested both on real agent workloads and benchmarks. Here is what actually matters.
LM Studio can run as a drop-in OpenAI API replacement. Load a model, start the server, and your existing OpenAI SDK code works unchanged. For teams migrating from cloud LLMs to local inference without rewriting integrations, this is a genuine shortcut. Here's how to set it up correctly for production.
The M5 Max shipped in May 2026 with 40GB unified memory. If you are running local LLM inference and considering whether to upgrade from M4 Pro, you need real benchmarks, not marketing claims. This is what we measured on actual models at actual quantization levels.