Archive · · Apr 2026 – Apr 2026 · Updated 00:08 UTC
Archive Every processed story in chronological order, with the newest coverage first. Filter by tag, source, or score to drill in.
Total · all-time 10
Avg score 6.0 ▲ 0.3 vs all tags
Stories / month Peak 7
Jun 25 Sep 25 Dec 25 Mar 26 Jun 26
Filters · 2 tag: llm-evaluation × author: Airton Lira junior ×
Category
All categories 1 New Models & Releases 0 Agent Frameworks & Tools 0 Agentic Coding 0 Research Papers 0 Open Source 0 Industry & Business 0 Infrastructure & MLOps 0 Tutorials & How-To 1 Regulation & Safety 0 Applications & Use Cases 0 Opinion & Analysis 0 Community & Events 0 Source kind
Any source kind 1 Primary (vendor) 1 Community (HN, Reddit, X) 0 Research (arXiv) 0 Repos (GitHub) 0 Top authors
Andrew Hong, Jason Potteiger, Luis E. Zapata 2 Filip Rechtorík, Ondřej Dušek, Zdeněk Kasner 1 Mariano Barone, Francesco Di Serio, Roberto Moio 1 Marisa Hudspeth, Patrick J. Burns, Brendan O'Connor 1 Shi Ying Chang, Chiok Yew Ho, Yichen Li 1 Siun Kim, Hyung-Jin Yoon 1 TimoKerr 1 Airton Lira junior 1 Top tags
#evals · 1 #llm-evaluation · 1 #metrics · 1 #quality-assurance · 1 #rag · 1
Co-occurring tags
+#evals · 1 +#metrics · 1 +#quality-assurance · 1 +#rag · 1
1 story· Showing 1–1 · Page 1 of 1
W16 1 story · Apr 13–19
Developers building production AI agents and RAG systems can use structured evals to catch hallucinations and regressions before deployment, replacing intuition-based quality decisions with measurable, evidence-driven metrics that reduce financial and legal risk.