
The COO's Guide to Choosing the First Workflow to Automate
Five filters for choosing the first workflow to automate.
Read articleField notes from two decades of building software and companies.

Five filters for choosing the first workflow to automate.
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CFOs should require five answers before approving AI budgets: measurable outcomes, accountable ownership, full operating costs, rollback, and mapped workflows.
Read articleUse Terra for coordinating Codex subagents, and Luna at XHigh or Max for bounded work in separate delegated threads that Sol reviews and combines centrally.
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Build an AI strategy around agent-ready operations, not a tool list. Map workflows, constraints, permissions, ownership, and rollback paths before you buy.
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OCR benchmarks are saturated and the leaderboards disagree. The winning 2026 architecture routes each document to the cheapest engine that can handle it.
Read articleCurrent Codex CLI config.toml guidance: safe defaults, profile files, sandbox and approval migration, AGENTS.md, subagents, plugins, and CI-ready settings.
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A self-driving company is not a company without people. It is a company where people set direction and agents run accountable, verifiable operating loops.
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No team can defend an AI workflow decision without measurement. A practical guide to private, versioned evals — with a manufacturing AR dispute example.
Read articleDon't ask Codex to be strategic, give it an operating system. Eight rules for running knowledge work on durable threads, briefs, and human approval gates.
Read articleKnowledge work isn't hard, it's relentless. Codex automations turn recurring follow-up into background loops that keep work warm and surface only exceptions.
Read articleDevelopers use worktrees to run parallel work without contamination. Knowledge workers need the habit too: isolate each line of inquiry, then merge on purpose.
Read articleSkip the prompt library. Start with one persistent chief-of-staff thread that holds context and routes it into action, then have every project thread report in.
Read articleCodex isn't a better chatbot, it's a workspace. Persistent threads, background work, isolated worktrees, repeatable skills, and handoffs in one surface.
Read articleStop storing trust in one coding harness. A portable Agent Ops layer (AGENTS.md, thin shims, shared skills, a certification suite) makes migration a wiring job.
Read articleThe next unlock in AI agents isn't a bigger model, it's a YAML file. Portable pack.yml skill bundles kill the setup tax and make named workflows run like tools.
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Three products share the Claude name. Chat answers questions, Code ships software, Cowork completes tasks. Most people use a power tool as a screwdriver.
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One task, one agent, one result caps your throughput. The largest gains come from agents running in parallel with defined roles and peer-review gates.
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You explained your weekly report format to Claude. Then explained it again. And again. A five-section skill file ends the repetition permanently.
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Every Monday at 7 AM, Claude scans Slack and the calendar, flags what needs prep, and saves a briefing. Nobody typed a prompt. Nobody opened the app.
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Most people install one Cowork plugin and stop. The real advantage comes from stacking two in a single task, where domain expertise meets data backing.
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Claude read your whole project folder, including the draft you scrapped three weeks ago. The output blends old thinking with new. The fix takes five minutes.
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Most people write prompts the way bad managers give assignments. The best results come from describing the destination and letting the agent find the route.
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Four hundred tasks across seven weeks. The sessions that produced client-ready work on the first pass all shared one trait. Not better prompts. Better setup.
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Most people write their context files once and never open them again. The files work, but they deliver only 40% of what they could.
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Everyone is optimizing the wrong variable. The people getting real results from AI wrote three plain text files and never looked back.
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Safety-first Codex CLI setup for developers: install, secure profiles, automation tips, and configuration defaults for reliable day-to-day AI workflows.
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Enable autonomous AI agents to pay safely—virtual cards, programmable wallets, crypto rails, and agent protocols, with real-world limits and safeguards.
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Fine-tuning succeeds on data quality and evals, not hyperparameters. Shift from model tweaks to strategic AI consulting that builds lasting client value.
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A visual guide to 11 agentic workflow patterns—network, hierarchical, sequential, parallel, router, and more—for building modular multi-agent systems.
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Learn Cursor Rules—global settings, .cursorrules, and .mdc patterns—that turn AI into a project-aware coding partner with agentic, contextual workflows.
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Harden integrations against flaky third-party APIs with timeouts, retries with backoff, rate limits, interface decoupling, and durable background jobs.
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Solve polymorphic association URL generation and parent lookups in Rails with a reusable resource finder pattern for cleaner, safer model code paths now.
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Add publishable status to Rails models with PostgreSQL enums—migrations, scopes, and safe state transitions for reliable draft-to-published workflows.
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A guide to essential interview questions that help assess candidates effectively, covering motivation, cultural fit, self-awareness, and potential red flags
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Practical Gmail workflows, filters, labels, and UI tweaks to reach Inbox Zero faster and cut daily email time by ~70% without losing team responsiveness.
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Step-by-step guide to deploy Ruby on Rails 5 on Dokku, a self-hosted Heroku alternative—apps, databases, SSL, zero-downtime deploys, and production tips.
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Five must-have steps before sending from a new domain or ESP: set SPF, DKIM, and DMARC, then verify deliverability so your email stays trusted in inboxes.
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Seven web tools to find, check, and validate startup or product names—domain availability, trademarks, brand checks, plus creative naming inspiration.
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