HNO Blog
Short, evidence-led articles about AI agent systems, Go runtime design, benchmarks, and the engineering ideas behind the news cycle.
Latest article
AI Agent Framework Benchmarks: Why Runtime Overhead Matters More Than Model Latency
A reproducible local-stub comparison of HNO, Agno, and LangGraph using 100 measured operations at concurrency 1, 8, and 32. The article explains why a new model release or an AI agent performance headline should not be treated as a framework benchmark without a controlled protocol.
- Category: Benchmark
- Tags: AI agents, framework performance, Go, Python, LangGraph, Agno
- Evidence: 5 warmups, 100 measured requests, the same local OpenAI-compatible stub
How HNO uses timely topics
A timely headline is only the starting point. Every article should add an original engineering question, a reproducible example, or a measured result that remains useful after the headline stops trending.
- Capture the topic and source it. Record the source URL and publication time.
- Connect it to a real HNO question. Do not force an unrelated keyword into an article.
- Add evidence. Prefer code, benchmark commands, traces, or a clear limitation.
- Link to durable documentation. Readers should be able to continue to the Agent guide, performance report, or system overhead matrix.
- Update instead of duplicating. If the same topic evolves, update the original article and record what changed.
Topic areas
- AI agent frameworks and orchestration
- Go concurrency, memory, and deployment
- Model-provider integration and OpenAI-compatible APIs
- MCP, RAG, memory, and observability
- Reproducible performance measurement
Editorial boundary
HNO is not affiliated with a product merely because an article discusses it. We do not copy source articles, fabricate benchmark results, or present a remote-model latency snapshot as a universal framework claim. Trend-driven content still needs accurate sources, a publication date, and a clear measurement boundary.
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