By 2035, about six million American small businesses will change owners as their founders retire. More than a million of them are good enough to buy, and together they are worth up to $5 trillion (according to McKinsey).

Most of them sell cheap. And plenty of them still run on phone calls, paper folders and spreadsheets.

What changed? AI can now do the part of the work that can be checked. At Crete, a network of more than 30 accounting firms backed by Thrive Holdings, a tax system built with OpenAI prepared 7,000 returns this season and saved accountants about a third of their prep time.

Inside you will find:

1. How to Pick an Industry Worth Renovating Score niches on what makes a small business last and on how much of its work AI can already do.

2. How to Find Owners Ready to Sell Before a Broker Does Build a list of firms that never hit the market and write the first letter to the owner.

3. How to Map the Firm’s Work Before You Make an Offer Claude estimates every process from the outside, then rebuilds it with the seller’s data and shows where the hours go.

4. How to Simulate the Firm Rebuilt With AI Before You Own It Redesign the work, test the agents on the firm’s past jobs and see what the business earns after the renovation.

5. How to Decide Whether to Buy and What to Pay Recast the numbers, investigate the firm from the outside and get a buy memo with your offer and your walk-away price.

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1. How to Pick an Industry Worth Renovating

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On September 15, TypeSafe AI released Jev, a model built to make decisions instead of writing answers.

Its founder? Diogo Almeida, previously worked at OpenAI on research behind the methods that led to ChatGPT.

Give Jev a piece of text and a set of options, and it returns a probability for each one.

Is an email urgent? Which team should handle a support ticket? Does a lead fit your target customer? These decisions can take a fraction of a second and cost far less than asking a conventional LLM to write an answer.

That sounds narrow until you look at how companies use AI. Many calls only need a choice that software can act on. If those choices become cheap enough to make on every lead, ticket and agent action, startups can automate checks they previously had to skip.

Inside you will find:

1. How Jev Makes a Decision Without Writing an Answer

   See how Jev turns text into a choice your software can use.

2. How to Run Your First Decision in Five Minutes

   Test it on an email, a sales lead, and a support ticket: no code needed.

3. Which Decisions in Your Startup Should Move to Jev

   Audit your workflows and identify the first decisions worth testing.

4. How to Cut Your Coding Agent Bill by 10x

   Shrink Claude Code sessions by 10x and route each task to the cheapest model.

5. How to Read 100,000 Customer Conversations for Under a Dollar

   Find recurring problems, churn risks, and feature requests across all your customer conversations.

6. Where Jev Gets Decisions Wrong

   Learn how to read its uncertainty and decide when a human should step in.

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1. How Jev Makes a Decision Without Writing an Answer

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Reddit is one of the few distribution channels where a founder with zero followers can outperform someone with a million.

• LinkedIn shows your post to your connections

• Substack shows it to your subscribers

• X shows it to your followers

All three require you to build the audience before the distribution works.

Reddit distributes to communities and if the community likes what you posted, it can put you in front of hundreds of thousands of people (TikTok does something similar for consumer products, but nobody sells procurement software on it).

The ranking code behind Reddit’s decision was open sourced, which means you can read it. And it is why a founder with no audience, no budget and no distribution can put something in front of two hundred thousand people on a random week day.

The problem? Reddit is brutally hostile to marketing. Karma thresholds, account-age gates, automated filters and moderators can kill your post before anyone even sees it.

Inside you will find:

1. Inside Reddit’s Ranking System
   Which posts get seen and why the first few votes matter so much

2. How to Find the Right Subreddits
   Find the communities where your actual buyers already spend time

3. How to Build Trust Before You Post
   Make your account credible enough to avoid filters, removals and instant rejection

4. How to Reverse-Engineer a Winning Post
   Study what already works in each community and build your post around it

5. How to Build a Weekly Reddit Loop
   Turn Reddit from a one-off experiment into a repeatable distribution channel

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Every investor eventually does the same thing: they stop listening to your pitch and start trying to break it.

They test your market, your numbers, your growth story, your team and every assumption holding the company together. In private equity, firms pay McKinsey or Bain hundreds of thousands of dollars to do this work. In venture, the process is faster, but the questions are largely the same.

This playbook shows you how to run that diligence on yourself before an investor does.

By the end, you’ll have one file: gap-list.md — a ranked list of the weaknesses an investor is most likely to find, while you still have time to fix them.

Inside You Will Find:

1. MECE: Map Your Startup’s Entire Risk Surface
   Break the business into every area an investor could probe, with no gaps and no overlaps.

2. Issue Trees: Turn Every Risk Into a Question a Data Point Can Answer
   Push broad concerns down into specific questions that can be proved with a number, document or fact.

3. Hypothesis-Driven Diligence: Start From the Answer and Try to Break It
   Take your strongest claims and attack them like a skeptical investment partner would.

4. The GE-McKinsey Nine-Box: Is Your Market Worth Winning and Are You Built to Win It?
   Stress-test the two questions behind almost every investment decision: the attractiveness of the market and your position inside it.

5. Three Horizons: Does Your Growth Survive Past the Core?
   Separate what drives the business today from the growth engines investors are actually underwriting tomorrow.

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Last week, Profound raised a $180M Series D at a $1.8B valuation.

👇 At the bottom of this piece, you will find the Series A deck its founders used eighteen months ago before the company became one of the fastest-growing names in AI search.

Profound’s product answers one question: when someone asks an AI about your category, does your startup appear in the answer?

Fortune 500 companies pay six figures a year to find out. You can now build the core measurement system yourself.

What changed? The data caught up. Semrush analysed 126 million prompts, Ahrefs studied 75,000 brands, and Meltwater parsed 8 million AI citations. We now know:

• which signals matter

• where each model looks for answers

• which popular GEO tactics are mostly noise

Inside you will find:

1. How to Rebuild the Four Numbers Profound Charges For
   Build your prompt set, run your first audit, and benchmark yourself against three competitors.

2. Where Each Model Goes Looking for Answers
   Create a source-gap report showing which websites influence each model—and where your startup is missing.

3. What Actually Moves the Number in 2026
   Turn the latest research into a practical set of GEO rules.

4. How to Run the Whole Thing Every Monday
   Set up an agent that reruns the audit and emails you the changes automatically.

Plus: Profound’s original Series A pitch deck, the deck behind what is now a $1.8B company.

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Salesforce sells one agent (Agentforce) in eight different ways:

• Foundations, free

• $2 per conversation

• Flex Credits, $500 per 100,000 credits, with every action consuming a different number of them

• $5 per user per month, bundled with credits

• An add-on at $125 per user per month

• Another add-on at $150 per user per month

• Agentforce 1 Editions, from $550 per month

• $2 per Help Agent resolution

Eight public options on one product, from the company that taught the industry how to price software.

Pricing was never easy. Put an AI inside the product and the old model stops working, and nothing has replaced it yet.

The question of what to charge for, when the product does the work instead of helping a person do the work, has no settled answer yet.

👇 Last time I wrote about how much to charge and what belongs in each tier. You can find it here 👇

Today’s issue is about choosing the pricing model itself.

With an agent in the product, one model stops being enough. There are two choices underneath it:

1. What you charge for: access, activities, outputs or verified outcomes

2. How the customer pays for it: subscription, pay as you go, prepaid credits, committed spend.

Almost every AI product now combines them.

So most teams guess, and they anchor the guess to their token bill, which is the one method guaranteed to underprice them. On AI products the median target gross margin is now about 50%, against the 75% to 85% SaaS was built on. At those margins the guess is not a rounding error.

Inside you will find:

1. Why One Pricing Model Stopped Being Enough

2. How to Set Up the Claude Agent

3. How to Decode What Your Competitors Actually Charge For

4. How to Cost One Unit of Work Before You Price It

5. How to Choose the Combination That Fits Your Business

6. How to Set the Credit Weights and the Price per Credit

7. How Many Credits to Include and What Happens When They Run Out

1. Why One Pricing Model Stopped Being Enough

Per seat had a good run because for twenty years it was approximately correct. Software helped a person do a job, more people doing the job meant more value delivered.

An agent breaks the chain in the middle. The product no longer helps a person do the job, it does some fraction of the job. So founders go looking for a replacement, and this is where the confusion starts, because two different questions get answered with the same word.

The first question is what you charge for.

• Inputs: tokens, compute, storage. What the infrastructure providers sell.

• Access: a seat, a license, a workspace. What SaaS sold for twenty years.

• Activities: an action run, a document processed, a query executed. What most usage-based AI products sell today.

• Outputs: a completed piece of work, delivered. A drafted contract, a reconciled invoice, a resolved ticket.

• Outcomes: a verified business result. A ticket the customer never reopened. A recovered chargeback.

Value alignment goes up as you move down that list, and so does the measurement burden.

The second question is how the customer pays. Subscription, pay as you go, prepaid credits, committed spend, top-up packs. And more importantly:

Credits are a payment mechanism, not a pricing metric.

You do not choose between “outcome pricing” and “credit pricing.” You choose outcomes as the metric and credits as the way they get paid for.

→ Which means most AI products need a stack rather than a single model:

The rest of this guide is how you fill in each row of that table for your specific business, with numbers you can defend.

2. How to Set Up the Claude Agent

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Open AI just shipped GPT-6 Astra, its most capable model.

OpenAI put GPT-6 Astra against a competitive Excel modeling challenge. It finished the task roughly four times faster than the human competitor who won it.

The more important number is 57.9%.

That is Astra’s score on Terminal-Bench 4.0, a benchmark designed to test whether a model can take control of a real terminal, work through a multi-step task, and actually finish it. GPT-5.6 Sol scored 37.3%.

GPT-6 Astra is capable of taking on much longer, more complex tasks than previous models and carrying them through with far less supervision.

For a founder, that means you can delegate real work.

Inside you will find:

1. The 3 founder workflows Astra actually earns its price on (and the agency invoice each one replaces)

2. The credit-management playbook:when Astra is worth $50/M output

3. The crazy things founders are already shipping

4. How to Install ChatGPT Work and Run Your First Astra Task

5. How to Pick the Right Model and Stop Burning Credits

6. Four prompting rules from OpenAI’s own docs that stop Astra from wandering (and wasting)

7. The 10-Minute Plugin Setup That Makes Everything Else Work

8. The Prompt Library: 10 Founder Jobs You Can Delegate Today

9. How to Give Astra Your Company Brain in Five Minutes

10. Build Two Schedules and One Trigger

11. How to Build the Founder OS Dashboard

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Before you sell 10% of your company, check whether someone is willing to give you the money without taking any of it.

Most founders default to VC because it’s the funding market they can see. But underneath there is an entire layer of startup capital that most founders barely touch: grants, prizes, government programmes, R&D incentives, cloud credits, fellowships and other non-dilutive funding.

The problem is finding the right programs, figuring out whether you qualify and surviving the application process.

Inside you will find:

1. The Full Database

2. The Setup That Powers Every Prompt Below

3. How to Let Claude Interview You Before You Look at a Single Programme

4. How to Make Claude Pick the Programmes You Actually Qualify For

5. How to Know If a Call Is Worth Your Forty Hours

6. How to Get the Application Written Through a Second Interview

7. How to Catch Every New Call Before the Deadline

+150 Sources Across 9 Categories

The full database includes 150+ funding opportunities across 9 categories:

• Government Innovation Grants

• R&D Tax Credits & Cash Refunds

• Revenue-Based Financing & Customer Capital

• Venture Debt & Growth Loans

• Prizes, Competitions & Fellowships

• Cloud, AI & Infrastructure Credits

• Corporate & Platform Programs

• Philanthropic & Mission Capital

• Public Procurement & Innovation Contracts

For every opportunity, the database tells you:

• Funding source → who is offering the money

• Type of funding → grant, prize, credit, contract, R&D incentive, financing, and more

• Country / region → where you need to be based or operate

• Stage → from pre-seed to growth

• Funding amount → minimum and maximum available

• What it actually is → a plain-English explanation of the programme

• Key requirements → who can apply and what you need to qualify

• Deadline / cadence → rolling, annual, or fixed application windows

• Time to money → roughly how long it takes to get funded

• Best for → the type of startup most likely to benefit

Why Non-Dilutive Capital Changes What You Raise and When

Keeping your shares is the argument everyone already knows. Three others matter more.

1. Price. A seed round raised on a story gets priced like a story. The same round raised eighteen months later, with the engineering funded by a grant and the infrastructure funded by credits, gets priced on a working product with customers on it. What you save is the gap between those two prices.

2. Timing. Grant committees do not care that the market cooled. Tax authorities have no investment thesis. This is the one line in your funding mix that behaves identically in a bad quarter.

3. Signal. Innovation agencies run technical diligence most seed funds cannot staff. Winning one is something a lender will price and an investor will notice.

What Claude Does With This List

There is still one problem. Even with everything organized, 150+ opportunities are too many to review manually.

That is where Claude comes in:

• interviews you first. Before you look at a single programme, Claude asks you the twenty-odd questions that determine eligibility across every scheme in the list

• cuts the list down to what you can actually win. It runs your profile against all the sources and sorts them into eligible, ineligible and unclear

• reads the call documents so you don’t have to. Point it at a specific programme and it extracts the real questions, the published scoring grid and the weights and the profile of who has won before

• it re-interviews you to write the application. The second interview is narrower and harder: it asks you the questions the evaluator will ask, in the evaluator’s order, and turns your answers into the application.

Let’s begin!

1. The Full Database 👇

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“You can go from zero to 15 mil ARR in about four months with like two or three people and like hundreds of agents, like, you know, a few hundred skill files. Building these agentic systems today, I think all you’re doing is you’re seeing where the bottlenecks are. How do I just instruct the agent to create a piece of software or a markdown file that blows away that roadblock?”

Find the bottleneck. Hand it to an agent. Repeat. That is the job.

Where are your bottlenecks? Answering that with numbers instead of opinions is a six figure consulting job. MBB call it an AI transformation assessment.

Here are the 14 skills that produce the same assessment inside Claude for free. Every one of them runs on a real framework:

1. Boundary Setting (SIPOC + Value Stream Mapping, Rother & Shook, 1999)

2. Process Recovery (Process Mining, van der Aalst; IEEE Process Mining Manifesto, 2011)

3. Cost and Time Baseline (Time-Driven Activity-Based Costing, Kaplan & Anderson, HBR, 2004)

4. Tacit Work Elicitation (Critical Incident Technique, Flanagan, 1954 + Contextual Inquiry, Beyer & Holtzblatt, 1998)

5. Task Decomposition (Task-Based Framework, Autor, Levy & Murnane, QJE, 2003)

6. AI Suitability Scoring (SML Rubric, Brynjolfsson & Mitchell, Science, 2017; Brynjolfsson, Mitchell & Rock, 2018)

7. Automation Design (Levels of Automation, Sheridan & Verplank, 1978; Parasuraman, Sheridan & Wickens, IEEE, 2000)

8. Constraint Analysis (Theory of Constraints, Goldratt, 1984)

9. Roadmap Sequencing (Weighted Shortest Job First, Reinertsen, 2009)

10. Benefits Case (Benefits Realization Management, Ward & Daniel + Amdahl’s Law)

11. Behaviour Specification (Given-When-Then, Dan North, 2006 + ISO/IEC/IEEE 29148:2018)

12. The Agent Contract (Design by Contract, Meyer, 1986; Hoare logic, 1969)

13. Failure Design (FMEA + Pre-Mortem, Klein, HBR, 2007)

14. The Acceptance Bar (Service Levels and Error Budgets, Beyer, Jones, Petoff & Murphy, 2016)

Inside you will find:

1. How to Set Up the Assessment Inside Claude

2. How to Map the Way Work Actually Happens

3. How to Score What AI Can Actually Take Over

4. How to Turn the Scores Into a Sequenced Roadmap

5. How to Turn the Decision Into an Agent Specification

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📈 Startup Playbooks
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↳ 27 Most Promising AI Startup Pitch Decks Backed by Top Investors — the best-funded AI startups of 2025, slide by slide.

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His argument is that systems deployed seven to ten years ago were built for a threat environment that no longer exists, that AI-powered attacks now run at machine speed, and that the gap is structural rather than a patching problem (👉 Full Post Here).

That is a CISO’s problem. Here is the same shift at founder scale.

The attacks that reach an eight-person company were always the cheap ones: phishing, credential stuffing, keys harvested from public repos. What changed is that running them now costs almost nothing, so they get pointed at everyone instead of at targets worth a human’s time. Verizon’s 2026 Data Breach Investigations Report counts fifteen attack techniques already boosted by generative AI, 31% of breaches starting from software vulnerabilities, and 48% involving ransomware.

You have no trillion dollars of architecture to rethink.

By the end of the weekend you will know, with evidence you can point a finger at:

• which ex-employee still has live access, and to what

• how many people could take over your company today

• which tool can read every document in your Drive

• whether a stranger can send an invoice as your domain

• which live keys are in your history, your repo, your frontend, and your Slack

• how to rotate them without taking production down

• whether user A can read user B’s data by changing a number in a URL

• which vulnerable dependencies production actually touches

• how long a restore really takes, and how much data you lose

• what to do in the first fifteen minutes when it happens

Each of those is a section, and every section moves through the same six steps: what Claude needs, what you run, what you paste into Claude, what Claude returns, how you fix it, and how you verify the fix held.

Inside you will find:

1. How to Build Claude a Model of Your Attack Surface

2. Who Still Has Access to Everything You Own?

3. Can Someone Send an Invoice in Your Name?

4. Where Your API Keys Are Leaking Right Now

5. How to Make Claude Review Every Pull Request

6. How to Verify Your Authorization Boundaries on Staging

7. Do Your Backups Actually Restore?

8. The One-Page Runbook for the Day It Happens

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Peter Thiel co-founded PayPal.

He wrote Zero to One, one of the most quoted startup books ever printed.

He wrote the first outside check into Facebook, turning $500,000 into more than a billion dollars and backed the founders who built Palantir and SpaceX.

Across two decades of doing this from both sides of the table, as a founder and then as an investor, he has said almost nothing in public about what actually makes a pitch work.

Except once. Running Founders Fund, he sat down and wrote an example deck himself. An actual slide-by-slide pitch, built around a fictional weight-loss scale called SOMA, with every rule he thinks a fundraising story should follow baked directly into it.

It’s dated 2012. The company inside it never existed. And founders still pass it around today, even though the logic underneath it never expired.

📚 I went through all eleven slides, flagged exactly where a 2026 investor would stop reading and built the fix for each one directly into a prompt: hand it to Claude and get a ready, Thiel-style pitch deck with every one of those fixes already built in.

Inside you will find:

1. The Logic Behind the Deck

2. What Limits It in 2026

3. The Full Deck, Slide by Slide

4. How to Run It in Write Mode and Review Mode

5. How to Run It in Write Mode and Review Mode

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Someone has to run the operating rhythm, catch what falls between teams, keep the org structure sane and close the gaps before they turn into emergencies and until there’s budget for that seat, the founder is the one doing it.

The problem isn’t effort. A COO’s job isn’t one thing you get good at in the margins of your day, it’s a set of functions that all need attention on a schedule. Do all of that on top of the job you already had, and you’re capped, not because you’re not capable of it, but because there is only one of you and the role needs full attention on all of it at once.

Claude can run this layer instead. 14 prompts, one for each function above, each engineered to execute the job end to end with no supervision once you hit send:

1. Weekly Operations Brief

2. Ops Health Dashboard

3. Quarterly OKR Review

4. Meeting Follow-Through Tracker

5. Priority Arbitration Prompt

6. Project Portfolio Tracker

7. SOP Documentation Prompt

8. Change Management Rollout Prompt

9. Org Design Health Check

10. Performance Review Cycle Prompt

11. Vendor & Tool Stack Audit

12. Contract & Renewal Calendar

13. Risk & Compliance Checklist

14. Business Continuity Audit

Inside you will find:

1. How to Run the Operating Rhythm

2. How to Make Sure Nothing Falls Through the Cracks Between Teams

3. How to Turn What Actually Happens Into Process Everyone Follows

4. How to Keep the Org Itself Healthy

5. How to Close Every Operational Gap Before It Costs You

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Grok Bot is out and, of course, it creates bots: one agent per task, each with its own cloud computer, its own logins to your real tools, and a job that keeps running whether you’re watching or not.

If you’re a founder, you already know exactly where the week goes. Sales outreach that never stops needing new leads. Support tickets asking the same three questions on repeat. Marketing content that has to ship every week whether or not you have time to write it. Invoices that need chasing and numbers that need reconciling.

A market that keeps moving while you’re heads down on product. None of that needs to be you. It needs an agent chartered for exactly that job.

📚 In this guide, five bots with ready-to-paste prompts to replace these functions:

• Sales

• Support

• Marketing & Brand

• Research

• Finance & Admin

Plus, the charter template that keeps every bot on a leash, the prompts to teach one a task and turn it into a schedule, and the group chat prompt to run more than one of them on the same goal.

Inside you will find:

1. What Grok Bot Actually Is

2. How to Write a Charter That Lets a Bot Run Without You

3. How to Teach a Bot a Task Once and Have It Run Forever

4. The Five Agents for a Founder

5. How to Put Bots in One Room and Give Them a Goal

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Two brothers ran three X accounts to a reported 69.8M impressions, 321K likes and 319K bookmarks.

How? Using Grok Bot behind the ideas, the posts and the replies.

Grok Bot is the latest AI agent platform created by xA. Inside it you create bots, individual agents that each have a name, one job and a profile carrying their standing rules. They share a persistent managed Linux cloud VM with a browser, a filesystem and a terminal, and each one:

• keeps its logins

• learns a workflow by watching you do it once

• re-runs that workflow on a schedule while your laptop is shut.

Above all, it sits inside the only ecosystem with a first-party search tool for X, which xAI describes as performing keyword search, semantic search, user search and thread fetch.

Why does that combination matter more on X than anywhere else? Because most channels ask you to build an audience before they give you distribution, while X hands you distribution first and lets the audience form afterwards. It is why inbound customers, hires and warm investor intros still tend to arrive there first

The catch has always been that knowing what is already working on X, every single day, is a full-time job. That is the job you are about to hand over.

Inside you will find:

1. Why Grok Reads X Better Than Any Other Model

2. How to Set Up the Bot That Runs Your Account

3. How to Find the Posts Worth Repackaging

4. The Two Plays That Turn a Source Into a Post

5. The Weekly Routine That Learns From What Worked

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The most capable AI in your company probably runs in a terminal window on one technical person’s laptop. Your ops person, your accountant, the co-founder who handles legal, none of them will ever open a terminal.

So the best tool in the building serves one person, stops the moment their laptop closes, and everything it learns dies in their session history.

Y Combinator hit that wall and did something about it, open-sourcing their internal system. It’s called QM, and it’s the first time you can open up the operating system of the most imitated accelerator in the world and run the same thing in your own company.

So what is it, concretely? Software you install and run yourself: a server plus a database, on a cloud account you own. It has no AI of its own. You plug in your own API key, the access code that lets software talk to an AI provider directly, and from that moment the intelligence answering you is Anthropic’s or OpenAI’s.

What you get. Same prompts, same intelligence. What changes is who can reach it and what it’s allowed to do:

• A web page and a Slack bot

• Work that happens with nobody present. It runs on a server, so recurring jobs fire on schedule or when something changes, whether or not anyone is logged in.

• A private space for every person, plus shared rooms that carry their own memory, separate from anyone’s personal one.

• Recurring jobs that belong to the room where you created them, posting there for the whole team and running regardless of who set them up or whether they’re still at the company.

• Skills that can be handed over. A prompt one person perfects can be granted to a colleague or promoted by an admin to the entire company.

• Rules that hold across everyone. An organization-wide setting for how cautious the agent must be, which departments can tighten but never loosen, a log of every action and who triggered it, and hard spending ceilings per person and for the company.

• Your conversations and files stored in a database you control, with direct usage billing from Anthropic or OpenAI on your own key, plus your hosting bill.

• A license fee of zero at any headcount, and the right to modify the software under the MIT license, including commercially.

Inside you will find:

1. What’s New and What You Can Hand It

2. How to Get It Running

3. What to Actually Do With It (Real Copy & Paste Prompts)

4. Slack, Connectors and Adding Your Team

Powerful models are only as useful as the company context they can access!

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With one governed context and intelligence layer across tools like Claude, Cursor, Copilot, and custom agents, Glean helps your company:

• Give AI the right context: connect knowledge across 275+ enterprise applications while preserving permissions, metadata and access controls

• Get more value from every model: improve retrieval, reduce unnecessary tool calls and use approximately 30% fewer tokens than off-the-shelf MCP tools.

• Stay flexible as AI evolves: access 35+ models (including Claude, GPT and Gemini) without rebuilding your context layer or locking your company into one provider

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Opus 5 shipped on July 24: near Fable 5 intelligence at half the price, with a real jump on numerical reasoning, tables, and financial modeling, the exact work a founder without a finance team keeps getting wrong.

At Startup School, Boris Cherny, the creator of Claude Code, said they deleted more than eighty percent of Claude Code’s system prompt for Opus 5 → His advice even to non-builders: every six months, delete your skills and config and see what happens. It might surprise you.

So this instead of being a pile of prompts, is six reusable Skills you build once and Opus 5 loads on its own.

📚 In this guide you’ll get copy-and-paste Skills to:

• Compare actuals to budget and prior month, explain every variance and list what to investigate

• Break down unit economics by channel and cohort, decompose gross margin and show which channel or cohort actually loses money

• Model a price change under different churn responses, give you the break-even churn and flag the segments that are underpriced

• Build a 12-month rolling forecast as an Excel file, run base, bear and bull scenarios, and tell you which single assumption moves your runway most

• Size the raise off your forecast, model the dilution across the cap table including the option pool, and check your target valuation against comparable rounds

• Build a live KPI and runway dashboard that pulls from your connected tools and generate the answer-first monthly investor update from the same numbers

Inside you will find:

1. How to Set Up Your Finance Skills So Opus 5 Runs on Your Real Numbers

2. How to Close the Month and See Where You Actually Stand

3. How to Find Where Your Unit Economics Leak Money

4. How Much Room You Actually Have to Raise Prices

5. How to Build a Forecast and Stress It Before You Bet on It

6. How to Size the Raise and Model the Dilution Before the First Meeting

7. How to Track the Numbers Live and Tell the Story Investors Trust

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↳ The McKinsey Slide Playbook for Claude — how to build consulting-grade presentations in minutes.

📈 Startup Playbooks
↳ The AI Founders Playbook — lessons from Sequoia, OpenAI, Nvidia, and LangChain on building AI startups from zero.

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↳ 27 Most Promising AI Startup Pitch Decks Backed by Top Investors — the best-funded AI startups of 2025, slide by slide.

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ElevenLabs crossed $500M ARR in April 2026, up from $350M at the end of 2025.

Carles Reina built that revenue org from zero, as the company’s fourth employee and first investor: three years from nothing to past half a billion.

He recently laid out how he did it in a long, direct interview on Harry Stebbings’ 20VC, a set of rules specific enough to copy.

You can’t automate the engine, because the engine is people and some of his rules, like the 20x quota, stay there. What you can encode is the rest, the operational work:

• who to target

• which account is worth a call this week

• which deals are actually real

That is where Claude comes in, with Carles’s rules instead of generic best practices.

By the end you’ll have four Claude agents, each built from one of his rules:

• Concentrate on one proven ICP before scaling → the Concentration Agent

• Reach people humanly, not with transactional spray → the Timing Agent

• Land and expand → the Expansion Agent

• Public pipeline reviews and conservative forecasting → the Forecast Agent

Your reps keep the room, while your agents take the busywork.

Inside you will find:

1. Which Sales Rules You Can Actually Hand to Claude

2. Setting Up the Stack

3. How to Point Everything at One Winnable ICP

4. How to Get the Right Timing

5. How to Turn a First Small Contract Into a Whole-Account Plan

6. How to Strip the Optimism Out of Your Pipeline

7. Where the Agent Stops and the Rep Takes Over

1. Which Sales Rules You Can Actually Hand to Claude

Before you build anything, get honest about what is automatable and what is not. The fastest way to waste a month is to point an agent at work only a person can do.

Some of Carles’s rules are about people. They stay with you:

• 20x quota is a target you set and hold

• First contract in two weeks is a hiring and onboarding bar

• Reps in front of customers is a management choice

• Selling humanly, not transactionally is a principle, and it's his whole point: the moment outreach feels like AI spray, people ignore it

Build a sell more agent and you build nothing, because selling is the human part.

The other rules are really decision criteria and those can become software:

• Concentrate on one proven ICP is an instruction about where to point your list

• Reach people only when it's genuinely relevant, never with transactional volume

• Land and expand is a repeatable way to read an account and find the next door

• Conservative, public forecasting is a set of rules for deciding which deals are real

Each of those is a judgment applied over and over against fresh data, which is exactly what an agent is good at.

So the split you are working with:

STAYS HUMAN (guardrails, not agents)
- The 20x quota and the willingness to enforce it
- Selling humanly, not with transactional spray
- Reps in front of customers
- Hiring for hunters
- The room: negotiation, relationship, the final call on a deal

BECOMES AN AGENT (a decision rule applied to fresh data)
- The Concentration Agent  ← "concentrate on one proven ICP"
- The Timing Agent         ← "reach people only when it's relevant"
- The Expansion Agent      ← "land and expand through customer success"
- The Forecast Agent       ← "radically transparent pipeline reviews"

The rest of this guide builds those four agents, and each one takes its name and its job straight from one of Carles’s rules.

The “human rules” don’t disappear, they become the guardrails you write into the agents’ instructions, so everything Claude produces is filtered through the same discipline Carles applies himself.

The rest of the issue is available exclusively to paid subscribers!

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Here’s a good place to start:

🤖 AI Agent Templates
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📈 Startup Playbooks
↳ The AI Founders Playbook — lessons from Sequoia, OpenAI, Nvidia, and LangChain on building AI startups from zero.

🎯 The Deck Library
↳ 27 Most Promising AI Startup Pitch Decks Backed by Top Investors — the best-funded AI startups of 2025, slide by slide.

💰 Investor Database & Tools
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Written by Guillermo Flor · fact-checked against Kimi’s official documentation and current community reports on X · July 21, 2026

Kimi K3 is Moonshot AI’s flagship model for long-horizon coding, agentic knowledge work, reasoning, and multimodal tasks. Its API is OpenAI Chat Completions-compatible, it accepts text, images, and video, and it offers a 1,048,576-token context window. The model always reasons, and its preserved thinking must stay intact across multi-turn and tool-calling workflows.

The short verdict: K3 looks unusually capable when a task is large, visual, tool-heavy, or allowed to run for a while. It is less compelling when response latency is the main requirement. Early independent and community evidence also suggests that strong task completion does not automatically mean strong steerability or shell-error recovery. Give it explicit authority boundaries, acceptance tests, checkpoints, and a bounded agent loop.

Contents

1. K3 at a glance

2. What K3 is—and is not

3. Account setup and first API call

4. Parameters and reasoning

5. Preserved thinking and multi-turn state

6. Streaming

7. Images, video, and document files

8. Structured output and Partial Mode

9. Tool calling and agent loops

10. The 1M context window and automatic caching

11. Prompting K3 well

12. Pricing, rate limits, and cost control

13. Coding-agent integrations

14. What the community on X is seeing

15. Production checklist

16. Troubleshooting

17. Documentation inconsistencies to watch

18. Compact cheat sheet

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1. K3 at a glance

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In June, Peter Steinberger (the creator of OpenClaw 🦞) said: “you shouldn’t be prompting coding agents anymore. You should be designing loops that prompt your agents.”

Founders who’d been treating their agent like a chatbot rebuilt it as a loop: propose, run, check, retry, and around again.

A month later (a couple of days ago):

The ground had moved again!

Here’s the part that actually matters: the loop that fixed your agent in June is the same loop setting you up to fail in a predictable way. A graph is what fixes that.

Picture the loop you probably already have running somewhere in your product: an agent drafts something, checks its own output against a rule, retries if it doesn’t pass. It looks disciplined, but it has a blind spot built into its shape.

The thing doing the checking is the same thing that did the work!

Your subscription just got a serious upgrade.

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• MITO: 60% off for 6 months

• Gojiberry: 50% off for 6 months

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Inside you will find:

1. Why the Loop That Got You Here Won’t Get You Further

2. What a Graph of Agents Actually Is

3. The Five Patterns Every Graph Is Built From

4. How to Tell If You Actually Need a Graph (or Just a Better Loop)

5. How to Build Your First Agent Graph in Claude

6. How to Keep a Graph From Becoming a Mess

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1. Why the Loop That Got You Here Won’t Get You Further

Strip any self-improving loop down to its skeleton and you find four moves repeating:

• pick something to control

• set a target

• measure the gap

• act to close it

• then go again

A thermostat is this in its purest form. So is the eval loop you probably already run on your agent: score an output, adjust the prompt or the retrieval when the score dips, ship, repeat.

The loop earns its popularity honestly. It’s the first thing that works, and watching a number respond to your adjustments is satisfying enough that it feels like the whole job.

The problem shows up later, and it’s structural: a loop can only see its own metric, so it will find every way to move that metric, including the ways that betray what the metric was supposed to represent!

Three blind spots come from this same shape and you’ve probably already met at least one of them:

1. The metric stops meaning what you think

Take a sales agent built to book more meetings. You wire a loop around it: measure meetings booked per week, adjust outreach copy and targeting when the number dips, ship, watch the following week. For a while it works exactly as advertised.

Then someone notices the meetings are with the wrong people, because the agent found that replying to anyone showing a flicker of interest books more meetings than replying only to qualified ones.

→ The loop didn’t fail; it optimized precisely what you told it to, on a target that had stopped meaning what you thought

2. The check is an echo

The check inside most loops is the agent checking itself, or a second agent built by the same hand, trained on the same assumptions, grading the first agent’s work. It looks like verification and behaves like verification. But two systems that share a blind spot will agree on that blind spot with total confidence.

3. The job outgrows the agent

A support-triage agent starts with a single job: read a ticket, tag it with a category. It works well, so you add one more instruction: also draft a suggested reply. Then another: also flag anything that looks like a refund request.

Then a few more, each one reasonable on its own, but each one bolted onto the same prompt, the same context window, the same agent. Six months in, that one agent is doing four unrelated jobs at once, and every edge-case rule you've added for job three has to coexist with the rules for jobs one, two, and four, whether or not they actually agree with each other.

None of this means loops are wrong. It means a single loop has a ceiling, and the ceiling isn’t about model quality!

2. What a Graph of Agents Actually Is

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Finding the first customers is still brutal. And they’re the ones who matter most, because the earliest users are the only ones who:

• tell you whether the problem is real before you’ve spent a year building for no one

• give you the feedback that actually shapes the product, not the polite kind

• become the references and case studies that make the next ten easier to win

This week a repo went viral on X for taking a swing at exactly that.

It’s a Codex skill: you hand it your startup URL (or other info), it defines your ideal customer, reads public discussions for real pain and buying signals, qualifies each prospect with the source linked as evidence and drafts an opener.

So instead of trusting the demo, we ran an experiment. We made up a startup, an AI that chases down unpaid invoices for small businesses and freelancers, and gave the skill nothing to work with but that one sentence.

Then we let it loose in deep mode and watched what came back. And this is the final result (pretty crazy):

What comes back is a full report: an early-customer verdict, your ideal ICP, an evidence-backed shortlist of prospects with the public pain and buying signal behind each one, the patterns that repeat across them, a personalized opener per prospect, and a seven-day outreach plan."

In this issue we go deep on the parts that make it actually usable:

• how to install it, in Claude and not just Codex

• the six modes and when to reach for each

• what the tool did with just an idea, the real run, unfiltered

• how to schedule it so it works for you every day, dropping new prospects into an outreach queue you actually work

Inside you will find:

1. What the Skill Actually Does

2. How to Install It in Claude in Two Minutes

3. Which Mode to Use for Which Job

4. Putting the Skill to the Test

5. How to Turn It Into a Morning Radar

6. From Report to an Outreach Queue You’ll Actually Work

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• Build and iterate faster: generate sections, refine layout and styling, and ship updates in minutes.

• Scale content with confidence: update CMS collections, localize copy, and keep metadata consistent across pages.

• Stay production‑ready: reviewable changes, collaboration, and safeguards that help teams ship without handoffs.

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1. What the Skill Actually Does

Under the branding, it’s a disciplined research pipeline with a hard rule at the end.

1. It reads your product. It inspects the URL, the landing-page copy and the repository if you point it at one (or whatever describes your startup), then writes a brief: what you sell, the outcome you promise, who the user is, who actually pays, the urgent job, the current workaround, the trigger that makes someone start looking, and the disqualifiers. This brief is the filter for everything after it.

2. It searches five buckets, instead of hammering one query:

• Explicit demand: people literally asking for a tool or an alternative

• Pain: posts describing the problem as slow, manual, or infuriating

• Workaround: the spreadsheets and copy-paste rituals people build when no product fits

• Switching: cancellations, migration complaints, and missing-feature gripes aimed at a competitor

• Timing: public events like a launch, a hire, or an expansion that make your product relevant this month

3. It scores every prospect on five dimensions, and the weights tell you what it cares about:

• Pain strength (25%)

• Product fit (25%)

• Timing (20%)

• Public reachability (15%)

• Evidence quality (15%)

A prospect with no cited pain or timing signal never makes the primary shortlist, no matter how well it fits on paper.

4. It labels and drafts. Each prospect gets a stage, from high intent down to potential fit, plus one short opener that references the specific public context and nothing invented

This issue is available exclusively to paid subscribers!

Paid subscribers unlock the full library: 200+ decks, 10,000+ investors, AI Agent playbooks, and fresh high-signal content every week.

Here’s a good place to start:

🤖 AI Agent Templates
↳ The McKinsey Slide Playbook for Claude — how to build consulting-grade presentations in minutes.

📈 Startup Playbooks
↳ The AI Founders Playbook — lessons from Sequoia, OpenAI, Nvidia, and LangChain on building AI startups from zero.

🎯 The Deck Library
↳ 27 Most Promising AI Startup Pitch Decks Backed by Top Investors — the best-funded AI startups of 2025, slide by slide.

💰 Investor Database & Tools
↳ The Family Office List — 1,000 family offices actively writing cheques. Used by 5,000+ founders raising their round.

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2. How to Install It in Claude in Two Minutes

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