Technical deep-dives, AI strategy, and engineering perspectives from the team building autonomous systems.
Algolia, Typesense, and Meilisearch are the three serious search engines for headless commerce in 2026. Algolia is the managed incumbent. Typesense is the operationally simple open-source option. Meilisearch is the developer-experience-led challenger. We benchmark all three on a 2 million SKU catalog and report what the numbers, and the operational reality, actually say.
MLX-LM and llama.cpp both ship OpenAI-compatible HTTP servers (mlx_lm.server and llama-server). They target the same Apple Silicon hardware but make very different bets on quantization formats, batching, and Metal kernel paths. We benchmarked both on M4 Pro and M5 Pro at 7B, 14B, and 70B to figure out which one belongs in your backend.
If you are deploying inference on Apple Silicon and choosing between MLX and vLLM, you are making a bet on architecture, not just performance. MLX is native to Apple's ecosystem. vLLM brings battle-tested production patterns from GPU infrastructure. One runtime prioritizes simplicity; the other prioritizes throughput. This is the breakdown you need.
Three tools, three approaches to running models locally: Ollama (CLI-first, fastest setup), LM Studio (GUI-first, batteries included), mlx-lm (Python SDK, maximum control). If you are new to local inference and unsure which tool to use, this is the decision framework based on your workflow and constraints.
Strapi and Sanity are the two open-source headless CMS platforms you will encounter when building e-commerce storefronts. Both move content away from your storefront and both give you APIs instead of WordPress. But they start from different assumptions. Strapi thinks you want a traditional database with a web UI. Sanity thinks you want a content toolkit you customize. This is the breakdown you need to choose between them.
The managed search ecosystem fractured in 2024. Elasticsearch went fully proprietary (expensive). OpenSearch forked to keep it open (complex). Typesense offers simplicity and low operational overhead. For e-commerce teams choosing between self-hosted search platforms, the decision is no longer about features; it's about who pays for operations.
PagedAttention is vLLM's core innovation that makes high-throughput local inference practical. It fragments the KV-cache into logical pages, allowing dynamic memory reallocation and sub-request batching. Here's how it works and why it matters for your stack.
Both vLLM and llama.cpp run local LLMs, but they make opposite architectural choices about how to handle multiple requests. llama.cpp optimizes for single-request latency and simplicity. vLLM optimizes for aggregate throughput and multi-request batching. The choice depends on whether you're building a user-facing tool or a backend service.
Snowflake and BigQuery are both excellent data warehouses. The problem is that 'excellent' means completely different things depending on your stack, your team's SQL preferences, and how much you want your data infrastructure tied to a single cloud vendor. Here is how to actually choose between them.
Visual editing is the feature that sells headless CMS to product and marketing teams. Storyblok and Builder.io are the two platforms that take it most seriously. But they make fundamentally different bets about where editorial control should live, and that difference has real consequences for your engineering team.
Both Strapi and Payload CMS are mature open-source headless CMS options, but they make fundamentally different bets on architecture, developer experience, and long-term flexibility. Here is how to choose between them for an e-commerce build.
Choosing between Auth0 and Clerk comes down to one thing: how much authentication complexity you want to own versus how much you want abstracted away. Both are excellent, but they are solving slightly different problems for slightly different teams.