Articles · Page 6
Older posts from the archive.
Trading Speed for Quality: A Practical Guide to Inference-Time Scaling
Inference-time scaling lets you tune the latency-quality tradeoff at runtime rather than at training time. Here is a practical framework for deciding when to use Best-of-N sampling, beam search, iterative refinement, or one-shot generation — with real examples from clinical AI.
Inside the Black Box: What Mechanistic Interpretability Means for Builders
Healthcare AI requires explainability — "the model said so" is not a clinical rationale. Mechanistic interpretability is the research field trying to change that. Here is what it actually offers practitioners today, what the gap still is, and what you can do in the meantime.
How to Actually Test If Your AI Will Say Something Dangerous
Most teams treat jailbreak testing as a vibe check. StrongREJECT achieves 0.90 Spearman correlation with human judgment — which means automated safety evaluation is real, and there is no good excuse not to build it into your pipeline.
The Attack Your LLM App Is Definitely Vulnerable To
Prompt injection is the #1 OWASP threat to LLM applications — and most teams are not taking it seriously. Here is what the attack looks like, why it is so hard to stop, and how to actually harden your system.
The Honest Guide to LLM Evals: What Actually Works
Most teams skip real evals and wonder why their AI products degrade in production. Here is the framework that actually holds up — from 30-minute manual reviews to binary scoring to knowing when your eval suite is finally doing its job.
5 Reasons to Solve for Adoption Before Building Your Digital Health Tool
You built a great digital health product. Clinicians love the idea. But no one is buying. Why? Too many companies start with a great idea, build the tech first, and then struggle to get adoption.
Why Your LLM Evaluator Is Lying to You
LLM-as-judge evaluators feel like quality assurance but behave like rubber stamps. They fail hardest on the outputs that matter most — edge cases, safety-critical errors, domain-specific nuance. Here is what to do instead.
Why I Stopped Using RAG for Coding Agents (And What I Do Instead)
The instinct when building a coding agent is "I need RAG to handle large codebases." The better instinct is giving the agent tools to explore code the way a senior engineer would — reading files, following imports, tracing execution.
React Tooling 2024: Stop Using the Wrong Shit
After building 20+ React apps this year, here's what actually works and what's a waste of time. Spoiler: You're probably overengineering.