· 4 min read
Five Questions Before You Pick an LLM for Real Work
A practical model-selection framework for applied knowledge work — covering cost, context, latency, tool use, and failure modes instead of leaderboard hype.
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
Generative AI Is a Gacha Game. Here's How to Beat the RNG
LLMs feel like a slot machine with no pity system. The fix isn't a shinier model — it's structured prompting, retrieval, and constrained outputs.
· 4 min read
When LLMs Forecast: Hallucination in Time-Series Models
New research shows LLMs hallucinate when forecasting time-series data. Here's why that matters before you point one at your spreadsheet.
· 4 min read
AI Agents Have an Identity Crisis (Literally)
The next security battleground isn't human passwords — it's who owns an autonomous agent's credentials when it acts on your behalf.
· 4 min read
Why 'AGI Levels' Charts Won't Help You Pick a Model
AGI-ladder charts are fun philosophy and useless procurement docs. Here's a practical framework for LLM model selection based on capability and cost.
· 4 min read
RAG Isn't Search, and Search Isn't RAG
Retrieval-augmented generation and enterprise AI search get conflated constantly. Here's the actual difference — and when to use RAG instead of plain search.
· 5 min read
Your Coding Assistant Skips the Boring Stuff. Here's Why.
LLM coding tools quietly omit rate limits, logging, and error handling — not because they can't, but because nobody named them. Here's how to prompt around it.
· 4 min read
Evaluating Agents: Why 'It Worked in the Demo' Lies
Task-completion scores hide silent failures. Here's a working method for AI agent evaluation using judged rubrics, adversarial cases, and regression suites.
· 5 min read
How to Evaluate an AI Agent That Books Your Meetings
Task-completing agents can't be graded like chatbots. Here's an eval framework built around side effects, reversibility, and the cost of being wrong.
· 4 min read
Multi-Agent Systems Are Coordination Problems, Not Model Problems
The bottleneck for multi-agent setups isn't a smarter model — it's shared state, clean handoffs, and knowing who owns the calendar. Coordination is the real work.
· 5 min read
The Metaphor Trap: Why We Keep Explaining LLMs Wrong
Physics and brain analogies make LLMs feel intuitive — and quietly lead us astray. Here's what to reason about instead.
· 4 min read
Production Bugs Live in the Past: The Case for Context Storage
Coding agents only see the present, so they keep re-solving problems your team already solved. The fix isn't a smarter model — it's durable, searchable context.
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