Hey everyone! Just go a message from my client saying that he closed a deal (he organizes events) for a major client of his, using a proposal that was 100% AI generated, using an ICM system I've created. How the system works (TL;DR) - see the images with the input and the ouptut. It's not a prompt, it's a workspace the client's team operates every day. Messages and a voice note go in; an on-brand HTML proposal comes out, and a PDF when it closes. Two human gates: one on the content, one on the rendered PDF. The AI assists, the human decides. Three rules do most of the work: 1. The AI never invents. Every sentence traces back to a message or to the operator's dictation. Missing something → it asks. Data that arrived with nowhere to go → it also asks, never decides silently. 2. The AI never sets a price. Values come from the operator, always. 3. Nothing reaches the end client without explicit human approval. And the part I like most: when a proposal closes, the system asks about every correction the operator made: "was that just this one, or is it a rule from now on?" If it's a rule, it proposes the edit to the rules file. The next proposal is born better. --- The longer version, for whoever wants the mechanics The shape is a pipeline that empties into a record library. Each proposal is one run; each finished run becomes a record the next run can learn from. Inputs. The operator drops in everything that actually arrived: forwarded client messages, supplier quotes, a voice note with their own instructions. No inputs, no assembly. First question before building anything: is there an existing proposal to use as a base, or should I search the archive for similar events? Assembly. The AI loads only what this step needs — proposal anatomy, brand voice, design rules, hard constraints and builds the HTML in the company's identity. Photos come from a catalog with provenance, never from a search; real photos of the company's own events come first, and a reference image is always labeled as one. If a photo's commercial attribute doesn't match the item being sold, it doesn't go in. What it hands back isn't just a draft, it's a draft plus a list of what's missing, so the operator knows which supplier to chase.

Four groups working the whole weekend to create Comic Books featuring current world problems: circular economy, climate change, digital knowledge and the impact of AI in the workplace. I’m mentoring the brazilian group dealing with AI in the workplace. All four groups are live streaming to each other. Working with youth is one of the most important and fulfilling experiences.

Hey everyone! Just read the three chapters recommended by Jake. Se below my favourite quotes and some thoughts of mine attached to it. No AI was involved in this process! Chapter 1 “Nobody can seriously have believed that executives could read programs. Why should they? Even programmers do not read programs” - Very liberating to know that. Ever since I started vibe coding I have this feeling that I don’t understand what I have created or at least how it works. What does “reading programs” look like in the age of AI? With millions of people vibe coding without writing a single sentence, what constitutes reading their programs? It is looking at their folder structures? Is it looking at the prompts they’ve created? And another question would be: should ask for AI’s help to read other people’s programming for us, or with us? “Therefore it is reasonable to expect that future languages will make us feel those limitations of PL/I that are not detectable today. Notice that this is a psychological question-one to which we shall have occasion to return” - When we vibe-code we don’t even know what the language limitations are. We can feel limitations in the results we are trying to achieve. “There are, of course, programmer limitations other than merely not knowing the full power of the languages - vocabulary limitations, we might say. For instance, the programmer may be unaware of certain algorithms, or he may be unable to grasp a sufficiently large portion of the problem at one time to see that certain duplication may be avoided.” - With AI, most people don’t know the full power of the languages. We delegate that knowledge to the LLM. But knowing and expressing the problem we want to tackle is still a very human activity. “…in most cases, we do not know what we want to do until we have taken a flying leap at programming it. Specifications evolve together with programs and programmers. Writing a program is a process of learning - both for the programmer and the person who commissions the program. Moreover, this learning takes place in the context of a particular machine, a particular programming language, a particular programmer or programming team in a particular work environment, and a particular set of historical events that determine not just the form of the code but also what the code does!”

This weekend I will be mentoring a youth group (15-20 years old) on the biggest university in South America, USP. We're doing a hackaton to create in 3 days the seeds of a Comic Book that will explore the benefits and perils of AI to the workforce and life in general. I was invited to serve as a mentor on AI related issues. I will bring the results next week!! Have a great weekend, High Tea and Lyceum meeting!!!

Some lessons from my latest build: a personal assistant for my daughter Luna, 17. She studies at Sora Schools, an online school, and asked for an assistant that organizes school and life with her: a morning panel, weekly plans that roll up into the trimester, and one hard rule: it never answers schoolwork. Her school's AI policy went into the workspace verbatim, as a rule the assistant has to follow. Speed comes from the process, not from rushing. In the morning I recorded a discovery interview with her. In the same day the factory ran the whole pipeline: spec interview, value baseline (the "before", measured in her own words), workspace design, deploy and delivery. By 3pm she had cloned the repo and was using it on her own machine. None of the phases was skipped. The process is what made one day possible. The checks that catch things are the ones you don't run yourself. Three cold reviews, each by an agent with no memory of the build. A cold audit of the design against my own decision principles caught one factual error and proposed four improvements, all applied before I approved it. A cold walk test dropped a fresh agent into the finished workspace with one task: orient yourself from the files alone. It found the right place after reading just two files, and flagged two references to files that don't exist yet. And between every phase there was a human gate: nothing moved without my explicit approval. Delivery is a repo, not a meeting. The product is fi the workspace as a private GitHub repo, I added Lunaas a collaborator, and a one-page SETUP guide sits in the repo itself. She cloned it with GitHub Desktop, opened the folder in Claude Code, and started. That was the whole handoff In two weeks I'll be back here with results. We measbout 30 minutes a day spent planning, plus the worstpart of all: carrying everything in her head all day. So we'll know exactly what changed. Measure the before, so you can show the after!