How Founders Use AI

How people use AI, and the tool for each job.

The AI map of which tool is for which job, checked on 29 October 2026, what changed since July, and every video, thread, guide, podcast and tool behind 278 answered questions on putting AI to work.

What changed since July

  1. 28 Oct 2026 GitHub began including Copilot with GitHub Enterprise licences and added projects to the Copilot app, where several agents work across repositories. Coding agents github.blog
  2. 27 Oct 2026 n8n released the 3.0 beta, a reliability and security release with breaking changes; self-hosting now runs on Docker only and the stable release is due in early December. No-code automation with AI steps blog.n8n.io
  3. 27 Oct 2026 Hex released Hex 4.0, rebuilt for analysts and AI agents working together, with a $100 million raise. Spreadsheets, data and internal tools hex.tech
  4. 26 Oct 2026 Replit launched Replit for Business, and the next day Cloud Agents that let non-engineers propose changes to a company codebase. App builders blog.replit.com
  5. 26 Oct 2026 OpenAI released GPT-6.1 Terra at $1 / $5 per million tokens, half the price of GPT-6.1 Sol. Frontier models openai.com
All 108 changes

What founders use most, ranked by how many answers lean on it.

  1. The most-cited year-in-review; explains why the leader changes constantly and why lock-in is a trap. simonwillison.net Article Things we learned about LLMs in 2024 Simon Willison
  2. The essay that made evals a founder-level topic; failed LLM products almost always skipped this. hamel.dev Article Your AI Product Needs Evals Hamel Husain
  3. A free written masterclass version if you want the method without the course fee. aakashg.com Guide How to Master AI Evals: Step-by-Step with Hamel and Shreya Aakash Gupta (Product Growth)
  4. Gets your first automated eval running in under an hour. datacamp.com Guide Promptfoo Tutorial: A Hands-On Guide to LLM Evaluation DataCamp
  5. A current map of the eval tooling landscape so you pick once, correctly. braintrust.dev Article Best Prompt Evaluation Tools (Tested and Compared) Braintrust
  6. A conversational companion to Hamel's essay with concrete team workflows. humanloop.com Article Why Your AI Product Needs Evals, with Hamel Husain Humanloop
  7. Distils the conference's eval takeaways, including hunting implicit user feedback signals. vellum.ai Article Big Ideas from the AI Engineer World's Fair Vellum
  8. How to make your AI coding tools carry the eval workload for you. hamelhusain.substack.com Article Evals Skills for Coding Agents Hamel Husain
  9. Turning evals into a deploy gate so a bad prompt change never reaches users. braintrust.dev Article Best AI Eval Tools for CI/CD Pipelines Braintrust
  10. A vendor-neutral ranking to cross-check the tool vendors' own comparisons. techsy.io Article 8 LLM Eval Tools Ranked: No Product to Sell TECHSY
  11. A trusted independent voice explaining why iteration speed is the whole game. simonwillison.net Article Simon Willison on "Your AI Product Needs Evals" Simon Willison
  12. How to use one model to grade another without inheriting its biases. deepeval.com Article LLM-as-a-Judge: Top evaluation techniques and best practices Confident AI (DeepEval)
  13. A practical setup guide for automated judging, the technique that makes evals scale. agenta.ai Guide LLM as a Judge: Guide to LLM Evaluation Best Practices Agenta
  14. The clearest end-to-end explainer of why hallucinations happen and the layered defences that work. lakera.ai Guide LLM Hallucinations: Understand and Tackle AI's Most Persistent Quirk Lakera
  15. Separates factuality from faithfulness errors and matches each to its fix. getzep.com Article How to Reduce LLM Hallucinations Zep
  16. A prioritized playbook: grounding and abstention first, verification where errors are expensive. futureagi.com Article How to Reduce LLM Hallucinations: 7 Proven Strategies Future AGI
  17. Production-focused checklist including the stat that layered guardrails cut hallucination rates 71-89%. airbyte.com Article 8 Ways to Prevent LLM Hallucinations in Production Airbyte
  18. Schema-guaranteed responses kill an entire category of made-up fields and invalid values. developers.openai.com Guide Structured model outputs (OpenAI docs) OpenAI
  19. Connects structured output techniques across providers in one practical reference. agenta.ai Article The guide to structured outputs and function calling with LLMs Agenta
  20. How the two main open-source guardrail approaches compose into one defence stack. guardrailsai.com Article Guardrails AI and NVIDIA NeMo Guardrails: A Comprehensive Approach to AI Safety Guardrails AI
  21. The legal analysis of the ruling, in plain enough language for a founder. mccarthy.ca Article Moffatt v. Air Canada: A Misrepresentation by an AI Chatbot McCarthy Tetrault
  22. Breaks the failure into design decisions you can audit your own bot against. envive.ai Article Case Study of Air Canada's Chatbot Misleading on Bereavement Fares Envive
  23. Short, shareable summary for convincing your team that reliability is a legal issue too. aibusiness.com Article Air Canada Held Responsible for Chatbot's Hallucinations AI Business
  24. A PM-friendly decision guide with real cost and time estimates for each rung of the ladder. news.aakashg.com Article RAG vs Fine-tuning vs Prompt Engineering: The Complete Guide Aakash Gupta (Product Growth)

Show the next 24

Playbooks

A goal, and the route through it a step at a time.

Start using AI well Pick an assistant, learn with it, hand it real work, and keep yourself and your data safe. 5 steps Build your product with AI From a prompt to a shipped product: prototype, build, and wire AI into what you ship. 6 steps Code and ship with AI Write, ship, and maintain real software as a founder, with AI as your pair. 4 steps Design and prototype with AI Go from idea to a clickable prototype without a designer. 3 steps Build AI agents and automations Wire up agents and internal tools that do real work for you. 3 steps Put AI inside your product Ship AI features users actually feel, not a bolted-on chatbot. 3 steps Research your market with AI Size the market, study competitors, and read data in hours, not weeks. 3 steps Market and grow with AI Content, creative, and distribution: fill the top of the funnel without a big team. 5 steps Create content with AI Write, film, and design a content engine that runs without a team. 3 steps Get found in AI search Show up when founders and buyers ask AI, not just Google (AEO/GEO). 3 steps Run ads with AI Brief creatives, read the numbers, and optimize spend with AI. 3 steps Find and serve customers with AI Research the market, prospect, close, and support, with AI doing the heavy lifting. 4 steps Sell and prospect with AI Find leads, personalize outreach, and run better sales calls. 3 steps Support customers with AI Answer faster and deflect tickets without losing the human touch. 3 steps Run the back office with AI The unglamorous grind: workflows, finance, legal, and hiring, automated where it counts. 5 steps Automate your workflows Stitch your tools together so the busywork runs itself. 3 steps Hire and manage your team with AI Source, screen, and onboard faster, and keep the team in sync. 3 steps Handle legal and finance with AI Draft contracts and stay on top of the numbers without a big spend. 2 steps Be more productive with AI Get more done and think sharper with AI as a daily habit. 3 steps Become an AI-native founder The meta-skills: build your stack, prompt well, and use AI to research, think, and decide. 6 steps Build your AI stack Pick the tools worth paying for and the prompts worth reusing. 3 steps

The questions, by topic

Building & Product

Research & Knowledge

Marketing & Growth

Sales & Customers

Operations & Back-office

Strategy & The AI-Native Founder

Everyday AI

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