Field Notes - Oct 16, '25

Executive Signals

Chat is the new storefront: inventory apps inside ChatGPT capture intent and conversion
Evergreen loses to memory: freshness, locality, and availability anchor answer-engine retrieval
One operator, many agents: throughput shifts to aiming, testing, and iteration loops
Traffic-light beats tomes: simple, neutral scorecards earn trust and accelerate decisions
Scripts before LLMs: deterministic checks handle volume, models handle messy edges

A credible SaaS v1 can be shipped by a single operator with coding agents. Keep scope narrow, ship weekly, and spend almost nothing until real usage appears. Hire when you, not the backlog, become the bottleneck.

Budget $0–$50/mo until usage; ship against one narrow workflow
Add headcount when throughput or customer contact time stalls
Keep to one ICP; instrument usage before infra spend

Personal Software Is the Next Personal Computing

A hobbyist wave of bespoke assistants is becoming real leverage. Treat prompts and config as code and design around your own workflow first, then generalize.

Build one internal assistant that replaces a low‑value role
Version artifacts locally; keep prompts/config under source control
Start with your workflow; generalize only after it proves durable

Marketing

Evergreen SEO Is Losing to LLM Memory

Generic “how‑to/what is” content is increasingly answered from model memory. Win where freshness and locality force retrieval: inventory, pricing, availability, hours, promos, dated FAQs.

Set freshness SLOs: inventory/offers hourly; service data daily
Add structured data and author/org signals; monitor citations
Bias toward transactional, local queries over generic explainers

Choose Queries You Actually Want

LLM‑generated audits can seed low‑intent queries. Use external intent data and an allowlist to emphasize service bookings, trade‑ins, financing, and nearby inventory.

Seed from Search Console and trends; refresh monthly
Maintain allowlist/blocklist; log which queries drive citations and clicks
Encode priorities in schema and FAQs; down‑rank low‑value topics

Sales

Traffic-Light Scorecards Beat Branded Audits

Buyers consume pass/fail plus a few gauges. Package an “AI Visibility” score like PageSpeed: one grade, three–five checks, competitor compare. Increase credibility by using a neutral or white‑label wrapper. Keep facts deterministic; use LLMs only for recommendations.

Cap at five checks; show fails side‑by‑side with incumbent
Package under neutral brand; expose transparent methodology
Enable exports and reruns; time‑stamp results for recency

Distribution Didn’t Get 10x Easier

For ACV ≥ $10k–$20k, relationships and events still move pipeline. Use AI for prep and personalization, not to replace field work.

Allocate travel/events; tie weekly meetings to pipeline created
Have agents build account dossiers and briefs; you run the room
Kill channels with CAC payback over 18 months

Product

Ship Inventory Into ChatGPT First

Attention and intent concentrate inside ChatGPT. Start with a lightweight inventory and shopping app there, as an aggregator across your network. Keep UI minimal: chat handles search and filters; your app handles checkout and hand‑off. Don’t PR until coverage is real.

Tenant one app for all clients; gate access via your platform
Hit >70% inventory coverage before PR; measure lead/visit conversion
Keep UI minimal; let chat search, your app transact

Workflow Is the Product

In B2B, users buy a philosophy that enforces a winning process. Encode the workflow and measure adherence rather than offering early customization.

Hard‑code the target process; instrument each step
Surface leading indicators in‑product; make non‑compliance obvious
Defer customization until the workflow proves outcomes

Let Research Agents Write Your Spec

Use a research agent to map domain, users, and stack tradeoffs. Paste the brief into a build agent and iterate repo‑plus‑preview in tight 24–48 hour loops. Glue via copy/paste is fine—the throughput is the point.

Ask for a 1–2 page brief with citations and MVP scope
Require a repo and running preview from the builder
Iterate brief → build → click‑test in 24–48 hour loops

Conversion‑First Scanner UX

Setup kills conversion. Default to a single URL field and “Scan,” bury advanced options, and give free proof before depth. Prototype with real data to collapse stakeholder debate; park animations until semantics and performance are right.

Enforce one‑input flow; no dropdowns in the hero
Show 2–3 sample pages and top trackers; gate “See all”
Seed prototypes with real outputs; defer animation

Credible Risk States and Exfiltration Map

Trust comes from clear safe states. Define crisp tiers and render “Minimal” calmly. Place the user at the scan origin and draw only real cross‑border flows. Separate “Data Types” from the map to avoid implying everything goes everywhere.

Codify Minimal/Low/Medium/High/Compliance with distinct UI states
Pin user at origin; draw arrows only for actual transfers
Split “Data Types” from “Map” to prevent implied flows

Stream Results by Module

Results arrive in waves; a global spinner is noise. Stream by card (map, trackers, data types, pages) with their own loaders and completion states. Add TTL and predictable re‑runs.

Use per‑card skeletons/loaders; reveal incrementally
Add scan TTL and a clear Re‑run affordance
Reset state predictably when location changes

Engineering

Pair‑and‑Ship Sprints Make Teams AI‑Native

Mandates don’t change habits; shipping does. Pair an AI‑fluent builder with a traditional engineer for recurring four‑hour sprints. Ship one micro‑app per pair, then have graduates mentor the next cohort.

Weekly cadence, max six pairs; demo or it didn’t happen
Track time‑to‑first‑app, reuse rate, mentor multiplier >1.0
Standardize prompt packs and templates after Sprint 3

Automate Compliance End‑to‑End, Models Last

Automation unlocks when a single “human‑only” step is removed. Wire webhooks into ops, use scripts for deterministic checks, and reserve LLMs for reading unstructured responses. Run as a “spec‑ops” track to avoid roadmap contention.

Start with one program; target >80% hands‑off in 30 days
Baseline cycle time; aim for 70% SLA reduction
Scale when error rate <1% across 500+ cases

Portable, AI‑Friendly Stack Over Walled Gardens

Agents perform best on mainstream stacks with clean exit ramps. Favor full‑stack React/TypeScript, serverless hosting that exports, and GitHub as source of truth. Prove you can leave before you need to.

Choose React‑style full‑stack; keep front/back in TypeScript
Ensure “git clone → local build” works on a clean machine
Validate host migration within a day

Parallelize Agents in Cloud Sandboxes

Treat agent reliability like an ops problem. Run tasks in cloud sandboxes with multiple attempts, auto‑diff outputs, and keep the winning artifact. Cap run times and isolate destructive operations.

Run 3–5 parallel attempts; cap runs at 60–90 minutes
Auto‑diff outputs and select the passing run
Block destructive ops behind throwaway environments

Aim, Don’t Code: The New CTO Skill

Coding agents reliably handle multi‑hour tasks; the scarce skill is aiming. Write Given/When/Then specs with agent‑runnable tests, gate merges with smoke tests, and schedule agent blocks. Review outputs, not logs.

Write crisp acceptance tests agents can execute
Gate PRs with smoke tests; accept or auto‑revert
Schedule agent runs; inspect artifacts, not traces