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teds read --tag evaluation-safety
Evaluation & Safety
Guides to testing AI systems before they fail in front of users.
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Need help evaluating an AI system before launch? Talk to us about model red teaming.
Why Your AI Demo Feels Magical but Useless
How to diagnose the gap between impressive AI demos and reliable AI products by looking at failure modes, workflows, evaluation, and trust.
BlogThe Developer Trust Ledger
A practical model for understanding how technical companies earn or lose developer trust through docs, demos, examples, community, and product claims.
BlogStop Asking Can AI Do This
A practical decision guide for replacing capability-first AI thinking with better questions about value, risk, evaluation, and workflow fit.
BlogRAG Is a Product Pattern, Not a Magic Trick
Why retrieval-augmented generation only becomes trustworthy when content quality, retrieval, ranking, UX, evaluation, and feedback are treated as product work.
BlogPreference-Aligning Vision Models: DPO Fine-Tuning Qwen3.5-4B in Transformers 5.12.1
A modern rewrite of an older SmolVLM notebook: align Qwen3.5-4B with Direct Preference Optimization (DPO), LoRA adapters, and current Transformers + TRL APIs.
BlogModerating Memes with Qwen2.5-VL: Zero-Shot Hateful Content Detection in Transformers 5.12.1
A cleaned-up multimodal moderation walkthrough that uses Qwen2.5-VL with Transformers 5.12.1, the Hateful Memes validation split, and a reproducible zero-shot evaluation script.
BlogHuman Preference Tuning for Small VLMs: SmolVLM2 + DPO in Transformers 5.12.1
A modern guide to preference-tuning SmolVLM2 with Direct Preference Optimization, TRL, PEFT LoRA adapters, and the current Transformers image-text API.
BlogHow to Measure DevRel Without Vanity Metrics
A practical framework for measuring Developer Relations with business signal, developer journey context, and honest reporting instead of inflated reach numbers.
BlogHow to Evaluate an AI Agent (Before It Ships)
Agent evaluation is not LLM evaluation. A practical three-layer framework — component, trajectory, outcome — for evaluating AI agents before they reach production, plus a maturity model to figure out where your team is and what to build next.
BlogHow to Build a Developer Feedback Loop Product Teams Will Use
A practical DevRel feedback loop for collecting developer signal, filtering noise, and turning community, support, and content insights into product action.
BlogWhy Evaluation Is the New Prompt Engineering
Prompting helps you ask better questions, but evaluation is the production discipline that makes AI systems reliable, comparable, and trustworthy.
BlogHow to Evaluate Generative AI When There Is No Single Right Answer
A practical guide to evaluating open-ended AI outputs with rubrics, comparative review, human feedback, automated checks, and regression sets.
BlogCommunity Metrics That Survive CFO Review
How to report developer community value with credible business signal, honest assumptions, and useful operating metrics instead of inflated vanity numbers.
BlogCoDeC Contamination Detection in Transformers 5.12.1 with Qwen3, Qwen2.5, and Gemma 3
A cleaned-up CoDeC walkthrough with a current Transformers 5.12.1 implementation, model-loading notes for Qwen3, Qwen2.5, and Gemma 3, and a practical scoring script.
BlogCaption by Consensus: Ranking Image Descriptions with SigLIP2 and Transformers
Use SigLIP2 with the latest Transformers APIs to score candidate image captions, choose the best description, and understand when a contrastive vision-language encoder is the right tool.
BlogYour AI Product Needs a Feedback System Before More Features
Why AI product teams should build feedback loops before adding features, and how to turn user signal into model, data, prompt, UX, and product improvements.