Research Notes for choosing AI tools.
Short, useful frameworks for deciding whether an AI product deserves your time, data, permissions, or budget. AI Picker has removed standalone tool pages; this section is now the core of the site.
Start with agent permissions
Use this before connecting an agent to files, code, customers, production systems, or money.
Priority BEstimate the true coding-tool cost
Subscription price is only one part of the decision; review time and workflow friction matter.
Priority CLocal vs Cloud: When extreme optimization (like 2GB RAM Gemma 4) changes the math
Should this task be local, cloud, self-hosted, or API-based?
Local vs Cloud DMake agents improve the workflow
A practical guide for reflection loops, evals, memory, skills, and human approval.
Priority ECopy a 30-minute starter kit
A small folder, task template, log, SOP, checklist, skill proposal, and risk policy.
PriorityHow specific should an AI tool landing page be?
A field-note scorecard for deciding whether an AI landing page gives enough specifics before a trial.
Specificity 02The receipts I want before trusting an AI tool
The evidence I look for before giving an AI tool files, code, customers, or budget.
Trust 03Before you give an AI agent access, check this
A permission-first field note for tools that can read, write, deploy, message, or spend.
Agents 04What an AI coding tool really costs after week one
Count seats, usage, review time, context work, and switching friction before buying.
Budget 05Local or cloud AI? I would decide by task
A task-level field note for choosing local models, hosted APIs, or hybrid workflows.
Local vs Cloud 06When to use Agent-Reach, and when to use last30days
A field note on when to use Agent-Reach for source inspection and when to use last30days for recent market voice.
Agent Research 07How I chose a writing cleanup skill for AI Picker
Why AI Picker chose a detect-first writing cleanup skill instead of chasing detector scores or generic humanizer rewrites.
Editorial Tools 08Claude Fable 5 vs GPT-5.5: the benchmark lead comes with a safeguard question
A first-look buyer note on Fable 5 benchmarks, safeguards, pricing, early feedback, and when GPT-5.5 may still be the safer default.
Model Choice 09Two AI operating systems compared: PAI vs OpenClaw
PAI builds around personal context; OpenClaw builds around workspace context. This note compares architecture, setup, memory, agent runtime, and failure modes.
AI OS 10What Actually Gets Downgraded in Fable 5?
A source-backed review of Fable 5 fallback rules, invisible guardrails, community backlash, and buyer checks for model routing and trust.
Fallback 11Fable 5 vs Mythos 5: Who Gets the Full Model?
A source-backed comparison of public Fable access, trusted Mythos access, Project Glasswing, fallback rules, pricing, and buyer fit.
Trusted Access 12Self-Improving Agents for Normal People
A practical guide to reflection loops, evals, memory, skills, and human approval for building agents that improve the workflow, not the model.
Agent Workflow 13The 30-Minute Self-Improving Agent Starter Kit
A copyable folder structure, task template, self-improvement log, SOP, checklist, skill proposal, and risk policy for ordinary agent users.
Starter Kit 14GLM 5.2 Shows Why Cheaper AI Models Are Not Always Cheaper to Use
A cheaper AI model can make work more expensive if it needs more retries, more waiting, more review, or a provider plan that does not fit the task. GLM 5.2 is a useful current case
Cost 15GLM 5.2 vs a 12B GGUF Coder: One Needs a GPU Cluster, the Other Needs a Mac mini
GLM 5.2 is an API or cloud-GPU decision. A 12B GGUF coder is a Mac mini, laptop, or consumer-GPU decision.
Local vs Cloud 16Edgee Claude Code Compressor V2: Real API Cost After Week One
A tool that sits between you and your API bill is a middleman. It always has an incentive to overstate the gap it fills. The specific tool does not matter much — Edgee Claude Code
Cost 17Evaluating the real API cost of a switching LLM harness before scaling
A model-switching harness is sold on a promise: route each task to the cheapest model that can handle it, save money. That promise is half-true. What it leaves out is that switchin
Cost 18Grok 4.5 vs Composer 2.5 in Cursor: Which model should you use?
Choose by task boundary, benchmark method, contamination caveats, and completed-task cost—not by one launch-week score.
Model Choice 19When to Replace an AI Workflow: Lessons from GLM 5.2 on Slow Hardware
You saw a project that gets a capable model running on low-end hardware, and now you're wondering: should this replace what I already use?
Model Choice 20Evaluating Real API Costs: Claude Code vs OpenCode Token Overhead
Per-request token overhead is the largest hidden variable in AI coding costs — and the one variable no tool publishes. When two tools route to the same model family at the same per
Cost 21Before You Scale with CostPerPrompt: The Real API Cost of Pricing Calculators
You are deciding whether to rely on an API pricing calculator — CostPerPrompt specifically, but the logic applies to any tool in this category — to plan, forecast, or control AI AP
CostWhy these notes exist
AI tools are now easy to launch and hard to evaluate. A polished demo can hide unclear pricing, broad permissions, weak data controls, and review-heavy outputs. AI Picker keeps this section small so every page can stay maintained and useful. Standalone compare pages were removed so the site can focus on durable decision notes rather than generic head-to-head pages.