Not every generative app problem can be solved by a better model
Five categories of production gaps a more capable model will not close on its own — and what to check before you trust it with real work.
Read log → HAILANDER / RELAY LOGS
Notes
Signals, experiments and field notes from the useful side of AI. What we're building, what we're testing, where AI breaks, and what small teams should know before giving machines more work.
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Archive / Received signals
From the log
Ideas worth keeping. Mistakes worth documenting. Systems worth questioning.
Not every AI task needs an agent
A simple photo-sorting example shows when a fixed process is enough, and when a task needs an agent's judgement instead.
Read log → Your AI agent doesn't need to cost hundreds a month
Three questions to ask before you build an agent, and three techniques that cut the bill even further once it's running.
Read log → LLM sycophancy and techniques that can help stop it
Five prompt structures that make agreement structurally unavailable, so you get a usable second opinion instead of your own view reflected back.
Read log → Five expectations that make AI agents unreliable
Five assumptions about AI agents that quietly degrade the work, and the guardrails that catch each one before it gets expensive.
Read log → AI-generated code can look correct and still be wrong
A working checklist for the failure modes AI coding agents do not flag on their own, with before/after code examples.
Read log → LLM models can write better prompts than you and how to use it
Meta-prompting — asking a model to interview you and draft the instruction, then spending your time checking whether it describes the real task.
Read log → Six prompting techniques that improve AI responses
From zero-shot to decomposition — six practical techniques, when each one earns its keep, and where each one falls short.
Read log → Most SMEs do not need an agent first
A practical note on why workflow clarity beats premature autonomy.
Read log → How to spot a workflow worth automating
Look for frequency, friction, clear inputs, and a measurable before state.
Read log → What a workflow audit actually looks for
The signs that a process is ready for AI assistance, and the risks that should slow you down.
Read log → Where AI helps in proposal writing
Proposal work improves when AI supports structure, reuse, and review rather than replacing commercial judgement.
Read log → The difference between a prompt, a workflow, and an agent
Three useful words that often get blurred together in AI planning conversations.
Read log → Next step
See if AI is worth it for your team.
Bring a repetitive workflow, a messy handoff, or a team question about AI. The first conversation is about whether there's a practical, safe starting point.