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Meilisearch + HTMX: Sub-50ms Search in 14 KB, No Framework

Meilisearch + HTMX: Sub-50ms Search in 14 KB, No Framework

Pair Meilisearch v1.12’s fast REST API with HTMX 2.0’s hx-get and hx-trigger attributes, and you get a real-time, typo-tolerant search box that returns results in under 50ms. You write no custom JavaScript and pull in no React or Vue. The server renders HTML fragments that HTMX swaps into the DOM, so the whole search box stays under 15 KB of total JS. This post covers the full setup, from Docker Compose to a working search UI with faceted filtering.

Lightweight Full-Text Search: A Guide to Modern, High-Performance Engines

Lightweight Full-Text Search: A Guide to Modern, High-Performance Engines

Need to search hundreds of thousands of short strings, like tags, usernames, or SKU codes? Heavyweights like Elasticsearch are often overkill. For a dataset of 200k tags, each about 10 characters long, what you want is low latency, a small memory footprint, and easy deployment.

This guide sorts the best modern tools into two groups: standalone servers and in-process libraries. That split helps you pick the right fit for your next project. These engines also work well for adding search to static site generators.

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Gemma 4 vs Qwen 3.5 vs Llama 4: Which Open Model Should You Actually Use? (2026)

Gemma 4 vs Qwen 3.5 vs Llama 4: Which Open Model Should You Actually Use? (2026)

Gemma 4, Qwen 3.5, and Llama 4 compared on benchmarks, licensing, speed, and hardware so you can pick the right open model fast.

5 Open Source Repos That Make Claude Code Unstoppable

5 Open Source Repos That Make Claude Code Unstoppable

Five March 2026 repos extend Claude Code with autonomous ML, self-healing skills, GUI automation, multi-agent coordination, and Google Workspace access.

Cross-section of a translucent crystal brain threaded by red, gold, and teal attention ribbons resting on a doubly-stochastic matrix pedestal beside a guitar-tuning lab figure.

DeepSeek V4 Tech Report: 3 Tricks That Cut Compute 73%

DeepSeek V4 ships 1.6T parameters and 1M context using only 27% of V3.2's inference FLOPs. Inside the hybrid attention, mHC residuals, and Muon optimizer.

Cracked stone tablet engraved with a bulleted system prompt, four crossed-out goblin silhouettes repeated, a tiny goblin escaping with upvote-arrow sparks, a giant dollar-sign price tag, and figures refusing to step onto a glossier pedestal.

GPT 5.5 Reddit Reception: Goblins and the Cost Backlash

GPT-5.5 Reddit reception: viral goblin prompt leak, doubled pricing backlash, and 5.4 holdouts citing hallucination regressions in factual recall workflows.

What X and Reddit Users Are Saying about Claude Opus 4.7

What X and Reddit Users Are Saying about Claude Opus 4.7

How power users on X and Reddit reacted to Claude Opus 4.7: praise for agentic coding, token burn concerns, and teams' practical prompting habits.

Qwen3.6-35B-A3B: Alibaba's Open-Weight Coding MoE

Qwen3.6-35B-A3B: Alibaba's Open-Weight Coding MoE

Alibaba's sparse Mixture-of-Experts: 35B total parameters, 3B active per token. Q4 quantization runs on MacBook Pro M5, matches Claude Sonnet performance.

Alacritty vs. Kitty: Best High-Performance Linux Terminal

Alacritty vs. Kitty: Best High-Performance Linux Terminal

Compare Alacritty and Kitty terminal emulators: performance benchmarks, latency, memory use, startup time, and which fits your Linux workflow best.

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