· 4 min read
Small Models, Big Inbox: Where 200M-Param MoE Actually Wins
A recent 200M-parameter hobbyist MoE is a great reminder: email triage, classification, and routing rarely need frontier models. Here's where small wins.
All#ai#ai-agents#ai-productivity#applied-ai#automation#calendar#context-switching#deep-work#documents#email#engineering#engineering-leadership#invisible-ai#knowledge-work#llm-evaluation#product-updates#productivity#prompt-engineering#remote-work#search#security#spreadsheets#startups#storage#tamaton#unified-workspace#workflow#workflows
· 4 min read
RAG Is Not Search, and Your Inbox Proves It
Retrieval-augmented generation quietly breaks on email and files because it ranks documents, not conversations. Here's why, and what to do about it.
· 4 min read
The GRASP Problem: Why General LLM Benchmarks Lie to You
Leaderboard scores predict almost nothing about how a model performs on your actual domain tasks. The GRASP paradigm shows why — and what to measure instead.
· 4 min read
Evaluating LLM Output Without Fooling Yourself
Vibes-based testing is why AI features break in production. Here's how to build cheap, repeatable evals for summaries, extraction, and drafts.
· 4 min read
How to Evaluate a RAG System Before You Trust It
A concrete evaluation playbook for retrieval recall, grounding, citation faithfulness, and the failure modes that quietly wreck RAG systems.
· 4 min read
The Agent Reliability Gap: Why 90% Isn't Good Enough
Multi-step agents compound their error rate — a 95%-accurate step becomes a coin flip after 15 actions. Here's the math and how to design around it.
· 4 min read
How to Evaluate an AI Feature When 'It Feels Better'
A practical eval playbook for AI-assisted knowledge work: build golden sets, run LLM-as-judge checks, and catch regressions without a research team.
· 4 min read
One Agent or Many? A Sober Take on Multi-Agent Hype
Multi-agent systems are trending, but most productivity work is better served by one well-scoped agent with good tools. Here's when orchestration actually earns its keep.
· 4 min read
HalluSquatting: When Your Coding Agent Trusts a Fake Repo
Hallucinated package names are becoming a real supply-chain attack. Here's how HalluSquatting works against AI coding agents — and how to defend against it.
· 5 min read
LLMs Beat Experts at Predicting Study Results. Now What?
A study found LLMs can forecast experiment outcomes better than human specialists. We unpack what that really proves — and how to use it without fooling yourself.
· 4 min read
Project Memory: The Missing Layer in AI Assistants
A bigger context window won't fix a forgetful assistant. Persistent, structured project memory is what turns chat toys into long-running collaborators.
· 4 min read
Picking the Right Model per Task: A Router's Playbook
Stop chasing one 'best' model. Route each task — drafting, summarizing, spreadsheet reasoning, coding — to whichever model wins on quality-per-dollar.
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