Building & Selling AI

AI Product Lifecycle

Last updated 2026-09-28

What's new

2026-09-28

OpenAI released cheaper GPT-6 models, and Anthropic launched Claude Opus 5.5, which is 40% cheaper and performs similarly to its more expensive Claude Fable 5.1 model.
Claude Opus 5.5 requires less computing power, making it more cost-effective for tasks like coding and data analysis, and it's better at communicating clearly, reducing jargon and improving readability.
Meta announced a new AI device small enough to carry on a keychain, and there are rumors of OpenAI releasing a new AI agent platform (a tool that automates tasks) next week.

2026-09-19

Claude (an AI tool) can help build a one-person, million-dollar software business with minimal risk and no employees, focusing on automated sales and support.
The business idea, Agent Report Card, is a quality assurance tool for AI agents, testing them to ensure they perform as expected and providing reports for AI agencies.
The tool stack is simple and free, using Claude for AI work, Claude Code for building the product, an app to store test history, and Clay (a tool for finding potential customers).

2026-09-13

Rockbot (an email management system) works 24/7, even when your computer is off, unlike Claude Co-work (a previous email tool that only runs when your computer is on).
Rockbot uses its own server to manage your emails, keeping your sensitive information safe on your desktop.
To set up Rockbot, you need to download the app (around $20/month) and use a chat assistant like Claude (an AI tool you chat with) or Chat GBT (another AI chat tool) to create and train your email management bot.
Rockbot can connect to your Gmail (email service), Google Calendar (scheduling tool), and other apps to organize and manage your emails based on your preferences.

2026-09-04

You can now turn any book or PDF into a "skill" (a step-by-step guide) for AI like Claude (a smart assistant that helps with tasks), making it easier to apply the book's techniques in your work.
A free tool called "book to skill" (a program that converts books into AI-friendly guides) helps Claude understand and use the frameworks, rules, and examples from books, improving its outputs.
Five recommended books turned into skills include "Breakthrough Advertising" (a classic copywriting guide) and "100 Million Dollar Offers" (a guide to creating effective offers), helping Claude assist with tasks like writing better copy and designing offers.
Always use this method with books or documents you own to respect copyright laws.

2026-08-28

Ask AI to help you find problems people are already paying to solve, instead of starting with a product or technology (like a website or app).
Look for "painkiller" problems (serious issues people need to solve, like making more money) rather than "vitamin" problems (nice-to-have things that make life better but aren't urgent).
Use AI tools (like Claude, a chatbot) to find hidden, growing problems in an industry you know well, and rank them by how much people would pay to fix them.
To find business ideas, write down everyday frustrations and focus on those that save time, make/save money, or boost status (how you look to others).

2026-08-22

Claude Code (a tool by Anthropic that automates tasks) can handle jobs like organizing files, drafting documents, or even editing videos without needing to write code.
It can work with files on your computer, log into your existing tools (like email or CRM), and conduct web research to grab information.
Anthropic's own teams and companies like PWC (a big accounting firm) have used it to speed up work, like cutting a 10-week process down to 10 days.
It's designed to do more than just give advice—it can actually complete tasks for you, like a capable assistant.

2026-08-19

Hermes, a popular AI tool, just added a new "bot mode" that lets you create and manage multiple AI agents (individual AI helpers with specific roles), similar to a competing tool called Grockbot.
In this new mode, you can have different AI agents (like Dusty, Barry, or Cindy) with their own tools and skills, and they can even talk to each other to share information, making it feel like you have a team of helpers.
Hermes' bot mode is a direct copy of Grockbot's design, but it offers more customization, like choosing different AI providers (companies that make AI models) and adjusting each agent's personality.
This update is only available in the desktop app (software you run on your computer) and requires the latest version of Hermes.

2026-08-13

Anthropic (a company that makes AI tools) has developed a new way to build "agents" (AI tools that can do tasks for you) called Claude managed agents, which handles the complicated tech stuff so you can focus on what your AI tool should do.
This new system takes care of things like keeping data safe, managing AI tasks, and making sure everything runs smoothly, which used to be really hard for companies to set up on their own.
Before this, companies had to build and maintain these systems themselves, which took a lot of time and effort away from improving their AI tools.
The evolution of these AI tools has gone from simple question-and-answer systems to complex agents that can handle entire tasks, and this new system is designed to support that complexity.

2026-08-01

Anthropic (the company behind the AI model) released a guide for Opus 5, their newest AI model, which tells you to simplify your prompts (the instructions you give the AI) by removing certain lines.
The guide suggests giving Opus 5 the entire task at once, rather than breaking it into steps, as this newer model works better with complete instructions.
It's important to clearly state what the AI should not do, to avoid it adding unnecessary work or content, which could waste your time and usage limits (the amount of data you can use with the AI).
When the AI finishes a task, it will tell you about it, but you should set limits on how much it can write in its reply and in the actual task it's completing.

2026-07-28

Anthropic, the company behind Claude (a type of AI), recently removed 80% of their AI instructions, as newer AI models don't need as much guidance to work well.
Newer AI models like GPT-5.6 (a type of AI) from OpenAI (an AI company) perform better and cost less when given shorter, simpler instructions, debunking old advice about using lots of examples or repeating rules.
When sharing examples with AI, focus on the overall standards (like style or format) rather than specific details or approaches to avoid limiting the AI's creativity and intelligence.
To update old AI setups, use prompts (a set of instructions) that extract general standards from examples without biasing the AI towards specific past approaches.

2026-07-25

A new way to make money with AI is becoming popular: becoming an AI consultant (someone who helps businesses use AI to solve problems and save time/money).
Instead of starting an AI agency (a business that sells AI services to clients), many people are now getting hired by companies to use AI tools like Claude (a type of AI) to improve their specific business.
Companies are struggling to use AI effectively, even though they're spending a lot of money on it, creating a big opportunity for skilled AI consultants.
There are two main ways to become an AI consultant: freelancing (working for yourself and finding your own clients) or getting hired by a company to use AI full-time.

2026-07-19

Thinking Machines Lab, a startup led by former OpenAI CTO Mira Murati, released a new AI model called Inkling, which is a large, open model designed to handle text, images, audio, and video.
Inkling is a "mixture of experts" transformer (a type of AI model) with 975 billion total parameters, but only around 41 billion activate for a typical prompt, making it faster and cheaper to run.
Unlike other AI models that focus on specific tasks, Inkling is a generalist, meaning it's designed to perform well across a wide range of tasks, including reasoning, coding, and following instructions.
Inkling is fully open, meaning anyone can download and use it for free, and it's designed to be efficient, matching the performance of other models while using fewer resources.

2026-07-16

Fable 5 (a new AI tool) can now handle entire business tasks independently, like building apps, creating videos, and market research, without constant human oversight.
It can build, test, and fix its own code, as well as create promotional videos and personalized emails, all based on voice commands.
Fable 5 can be used in different ways, like in an IDE (a tool for writing and testing code, like Cursor) or a browser, but it will become more expensive after July 7th.
WhisperFlow (a voice-to-text tool) is used to input commands to Fable 5, making the process faster and more accurate than typing.

2026-07-13

AI can now create and market products almost entirely on its own, using tools like GPT 5.6 Soul (a type of AI model) and GPT image 2 (an AI that generates images) to design items and ads.
This process can be automated to generate ideas, create ads, and even set up online stores (like Shopify, a website for selling products) and run Facebook ads to test if people like the products.
The AI can also help design products by looking at what people want, like checking online forums (places where people discuss things, like Reddit) for ideas.
Instead of giving the AI specific tasks, it's better to let it come up with many ideas quickly, then have humans pick the best ones, as AI is great at brainstorming but not always at making final decisions.

2026-07-10

An AI agent (a tool that automates tasks) is like a folder containing instructions (what to do), connections to other systems (like email or accounting software), and a trigger (when to start).
Unlike a chatbot (which just answers questions), an agent does work for you, like sending reminder emails to clients who haven't paid their invoices.
Agents are most useful for recurring tasks, and you can trigger them manually, by a schedule, or by an event.
You can build agents using existing AI tools like ChatGPT or Claude (AI programs you might already use).

Key points

What it is

The AI Product Lifecycle is a process for developing and improving AI projects, from initial idea to continuous updates.
It's about building a solid AI product and then refining it based on real-world use and feedback.
AI development is never "finished" because businesses, workflows, and AI models keep changing.

How to use it

Start by identifying a real problem to solve, not by building an AI agent (an AI that acts autonomously on tasks) first.
Create a minimum viable product (the most basic working version) and treat your AI as a skill (a reusable task your AI can perform) using a plugin.
Launch as a pilot, manually doing the work with AI, then package that process for repeat customers, focusing on selling outcomes, not technology.

Watch out for

Treating an AI agent as a finished product, as businesses and workflows constantly evolve.
Focusing on the wrong thing, like selling an AI agent instead of a solution to a specific problem.
Bouncing between different AI tools, wasting time, money, and focus.

Tools named

Graphify (a tool that maps relationships between code files), Claude Code, Codex, OpenClaw

Lesson 1: What is AI Product Lifecycle and why it matters

The AI Product Lifecycle is the process of moving an AI project from an idea through building, launching, and then continuously improving it. It matters because AI development is never truly finished. In the custom AI automation space, there is no such thing as a finished product. Businesses change, workflows evolve, and AI models get better or worse. Something that worked perfectly a month ago might need adjustments now. The goal is to build something solid and then improve it as you learn about how it actually behaves in production (after release to real users).

The lifecycle starts with a proof of concept (an early test model to show an idea works). But real success comes later when you deploy live models and mine user feedback. That shift moves AI from providing impressive answers to doing valuable work. A critical early decision is whether AI is central to your business or just an add-on. If AI is core, infrastructure (the underlying systems and data setup) is not optional. Businesses that treat AI as a product to maintain and refine over time win, whereas those who treat it as a one-time build fall behind.

The real competition is who can build the best system around the intelligence. Building once and iterating using real product data is the only way to stay relevant. Even recent successes like Cursor emerged from watching how users actually interacted with a first version, not from following a rigid long-term plan.

Sources

Lesson 2: How to use AI Product Lifecycle: step-by-step

How to Use the AI Product Lifecycle Step by Step

Start by identifying a real problem someone has, not by building an agent first. One creator simulated a solution manually, writing down each step that worked. That written process becomes your spec — the AI can later build software from those notes.

Next, create a minimum viable product (the most basic working version) . Instead of jumping to custom code, treat your AI agent as a skill (a reusable task your AI can perform) . You can build this skill in about 10 minutes using a plugin, which is faster than old agent workflows (multi-step automation sequences).

Give your skill three things: instructions (what to do), tools (access to files or APIs), and teaching (examples of good outputs). For example, a Claude agent might be told "find 10 YC companies building AI agents" and given web search access. If it struggles, just describe what you want in plain language.

Launch as a pilot where you manually do the work with AI , then productize it — package that process for repeat customers. Shift from being an agent seller to a solution seller (someone who sells outcomes, not technology) .

Use Graphify (a tool that maps relationships between code files) to make Claude cheaper and smarter by helping it understand your project structure. The key loop is: build, test, shorten your feedback loop — get real results fast, then refine.

Sources

Lesson 3: Best practices and pitfalls

About 80% of AI projects never make it to production. That failure often comes from focusing on the wrong thing—selling an AI agent instead of selling a solution to a specific problem.

The biggest pitfall is treating an agent (an AI that acts autonomously on tasks) as a finished product. There is no such thing. Businesses change, workflows evolve, and models improve or degrade. Something that worked perfectly a month ago may need adjustments now. The goal is to build something solid, then improve it as you learn how it behaves in production.

Another common mistake is bouncing between tools like Claude Code, Codex, or OpenClaw every few weeks looking for the right one. That wastes time, money, and focus. Instead of becoming an agent seller, become a solution seller—someone seen as a long-term partner who solves real problems.

The best practice is to frame yourself as an in-house consultant or ongoing partner. Build skills (reusable capabilities an agent runs) rather than one-off custom code. Use high-level goals and metrics to guide your agents. And remember: AI is moving from impressive answers to valuable work. The real competition is who can build the best system around the intelligence.

Lead with the problem, not the tool. Sell the solution, not the agent. Build iteratively and expect change. That is how you beat the 80% failure rate.

Sources