Agents & Tools

The decision · Run `site:claude.ai/share` in Google and Bing today. Unshare every conversation that contains anything you would not paste on a public billboard within 7 days. If you build AI products, ship `noindex` on every shareable URL before your next deploy, not after the next Reddit post.

The decision · Map your agentic software surface by end of week. List every AI agent, agentic browser, MCP-connected plugin, and AI-enabled SaaS tool your team uses. For each one, answer three questions: what can it access, what credentials does it use, and what changed in the last 90 days that nobody reviewed. If you cannot answer all three, audit the most permissive agent by Friday. The decision is whether to build the controls now or wait until an incident forces the conversation.

The decision · Adopt Command Code Go ($1/mo, $10-40 in free credits) for one terminal-first workflow by end of week. If taste-1 reduces the corrections your team makes on its own output by week 2, extend to two more workflows by end of month. Skip the full org migration. The terminal-only design is a real ceiling, and the IDE market is not where Command Code wins. The decision is where taste-1 fits in your stack, not whether it replaces your IDE.

The decision · Install Command Code Go ($1/mo) by end of day. Pick one workflow you already know the failure modes of. Run the agent on it for 5 days. Count the corrections you still make to its output. If the count drops below your current IDE agent on the same task, extend taste-1 to a second workflow by end of month. If it does not, you have spent $1 to find out the harness is not for you. The decision is whether taste learning fits your workflow, and the only way to know is to run the loop.

The decision · Pick 1 AI desktop tool this week that supports stable tabs (OpenCode v1.18+, Claude Code, or Cursor with the right config). Re-baseline your AI workflow against the tab model. Stop running one long session per day. Start running many short tasks in parallel. Close the tab when the work ships. Set the tab naming convention on day one: title equals the deliverable, not the prompt. If your current tool does not support tabs natively, the migration to one that does is now a planning conversation, not a curiosity.

The decision · Pick 1 workflow this week that has a measurable objective (CPL, ROAS, delivery rate, time-to-respond, code test pass rate, or any metric you can grade in under 60 seconds). Build a loop around it with all 5 building blocks before end of month. Set the verifier first, the heartbeat second, the state file third, the connectors fourth, the sub-agent review last. Run it for 5 nights. Audit every kept change before merge. If the team cannot explain the changes, the loop is moving faster than the comprehension.

The decision · 3 actions by end of next sprint. Adopt public sharing and Claude Tag on Team or Enterprise plans. Pick 1 Slack channel as the artifact request queue. Set the convention: any artifact referenced in a decision doc gets a public link, a multiplayer owner, and a tag-built origin story. Teams on Pro plans adopt public sharing this week and queue the Team plan upgrade for end of month.

The decision · Update to 2.1.209 by end of day. Wire the bash tool into one existing verification workflow before end of week. If you have been deferring the sub-agents pattern, the agent launcher makes it runtime-native now. Adopt the pattern on one multi-step workflow by end of sprint.

The decision · If you spend more than $1K per month on LLM APIs, spend 2 hours this week stacking free tiers behind FreeLLMAPI and route your dev or staging traffic through it. Re-baseline your model cost against actual free-tier performance, not vendor list price. If 30 percent of your tokens move to free tiers without quality loss, extend the pattern to non-customer-facing production by end of quarter. The free-tier floor is no longer a hack. It is a category.

The decision · Run a real cost-per-quality benchmark on a Chinese model this quarter. If your workload is inference-heavy and low-sensitivity, the margin difference is material. If it is sensitive, the data-residency question comes first.