#benchmarks
5 posts tagged with #benchmarks
Every article below is hand-written, technically reviewed, and focused on benchmarks. Posts cover real-world architecture decisions, code-level implementation patterns, and trade-offs you'll only discover after shipping production systems.
AI and Machine Learning Prompt Compression Token Savings Benchmark [2026]: RTK vs Reality
Token savings are easy to market and hard to bank. Here’s the benchmark framework I trust for RTK-style compression: cost-per-success, tool-call failures, and latency.
Developer Tools Bun vs Node Performance 2026: Cold Start, Memory, WebSockets
Cold starts and memory decide your infra bill. WebSockets decide your tail latency. Here’s how Bun and Node compare in 2026, with a reproducible harness and deployment math.
AI and Machine Learning AI Agent Evaluation Framework 2026: 8 Metrics Beyond Task Success
If your agent eval is just “did it finish the task?”, you’re flying blind. Here’s a 2026-ready scorecard for tool correctness, recovery, safety, and cost-per-success—plus a regression suite blueprint you can actually run in CI.
AI and Machine Learning 9 Picks for the Best Local Model for Agentic Coding [2026]
A practical decision tree for agentic coding: pick local models by repo size and VRAM tier (16GB/24GB/48GB+/CPU), with context, tool-calling reliability, and quantization rules that actually hold up.
Technology Local LLM Benchmark Methodology [2026]: TTFT vs tok/s Done Right
Stop screenshot-benchmarking. Here’s a reproducible local LLM benchmark methodology for 2026 that separates TTFT from throughput and reports rerunnable results.