Alejandro Corpeño 👽

As the founder/leader/manager/principal of a professional services company or agency, one of your main jobs is to be a “variability regulator” between your clients and your team. #team #management #lessons

CTO watching CEO pitch investors features that don't exist:

Software engineering in 2026 needs two roles: A pirate and an architect. The pirate codes as fast as possible to figure out what's valuable. The architect turns that sloppy mess into a well-oiled machine. Here's how it works and why:

@NoahKingJr Architect the systems and direct the AI agents

Software Engineers, what’s your backup plan if Artificial Intelligence writes better code than you in 2 years?

So @jack wants ~6,000 employees reporting directly to him in the new version of the company. Layers between CEO and any employee in the company: ~5 today → 2-3 this year → basically zero

15 days later …

In December my blood pressure scared me, prediabetic levels, I weighted 197.3 pounds (89.5k) In Jan 1/2026 made some changes: 1) Zero alcohol 2) Workout 60 mins daily 3) Eat healthier 73 days later: I have lost 22 pounds, no fancy methods, just discipline and work.

In my company, we use Google Meet all the time; we used to use Zoom... but never Teams for video calls. Now, some clients prefer Teams... and whenever I go to a meeting, and I see a Teams link, my impulsive reaction is "ahhhhhh crap! teams!" and it ruins my day a little.

@r0ck3t23 True to some degree... but consider the incentives he has to say that. He wants the world to rely on buying more NVIDIA GPUs and new chips designed specifically for AI... so, of course, he would recommend that all business leaders rely on AI for every role.

Jensen Huang just told every college student on Earth the one thing that determines whether they get hired. It is not their GPA. It is not their degree. It is not their internship. Huang: “If I have a choice between two, I would hire the one who’s expert in using AI.” He did not say prefer. He said hire. One gets the job. One does not. The only variable is whether you learned to use the machine. Then he went down the list. Accountant. Hire the one who uses AI. Lawyer. Hire the one who uses AI. Marketing. Supply chain. Sales. Customer service. Every function. Same answer. The person who can command the model does not have an edge. They are the only candidate in the room. Everyone else is applying for a job that no longer exists. Huang: “If you’re a carpenter, if you’re an electrician, go use AI. If I were a farmer, I would absolutely use AI.” That line should demolish every assumption about who this technology is for. This is not a Silicon Valley tool for software engineers. This is infrastructure for anyone who builds anything with their hands or their head. A farmer who uses AI to optimize soil, predict weather, and manage yields is not competing with other farmers. They are operating at a level that used to take an entire department. An electrician who uses AI to model loads, simulate wiring, and quote jobs in seconds does not compete with other electricians. They compete with firms. One person with the model replaces the output of a team without it. That is not a prediction. That is Tuesday. Huang: “Every college student should graduate and be an expert in AI.” Not familiar with it. Not aware of it. Expert. The university system is still training students to execute the work. The market already moved. It wants the person who directs the machine that executes it. Four years of tuition. Thousands of hours of lectures. And if you walk out the door without mastering the one tool that redefines every industry you could enter, you burned all of it. Huang: “I want to see what it could do to elevate my job, so that I could be the innovator to revolutionize this industry myself.” That is the part most people miss. AI does not replace ambition. It multiplies it. The carpenter who learns the model does not lose their craft. They scale it. The pharmacist who learns the model does not become redundant. They become dangerous. One person. Deep skill. Full command of the machine. That used to be called a company. The question is no longer what do you know. It is what can you build with the machine that knows everything. And the people who cannot answer that are not falling behind. They already fell.

Hace poco me escribió alguien que escuchó este podcast que grabamos hace casi 2 años ya (cómo vuela el tiempo!) y creo que nunca compartí esto por aquí. bit.ly/corpcast

AI is making CEOs delusional

@Ineslaram (2/2) alguien que no ha desarrollado apps complejas no sabe como describir eso. Luego, cuando ya Claude lo programó, es bastante impresionante en algunas cosas, bastante tontito en otras. Hay que probar todo, y corregir bastante (no en código, sino en más instrucciones)

@Ineslaram (1/2) igual me pasó, hace 1 semana empecé un proyecto de cero en un stack que conozco muy bien, para evaluar la calidad del resultado. Primero, el planning y el nivel de detalle de la arquitectura del sistema, esquema de base de datos, sistemas de colas, integración de APIs

Estoy construyendo mi primer proyecto con Claude desde 0. De momento veo un poco complicado que alguien que no sepa programar pueda hacerlo. Que pensáis ?

Claude can now build interactive charts and diagrams, directly in the chat. Available today in beta on all plans, including free. Try it out: claude.ai

90% of stand-up meetings look exactly like this.

The token cost to build a production feature is now lower than the meeting cost to discuss building that feature. Let me rephrase. It is literally cheaper to build the thing and see if it works than to have a 30 minute planning meeting about whether you should build it. It’s wild when you think about it. This completely inverts how you should run a software organization. The planning layer becomes the bottleneck because the building layer is essentially free. The cost of code has dropped to essentially 0. The rational response is to eliminate planning for anything that can be tested empirically. Don’t debate whether a feature will work. Just build it in 2 hours, measure it with a group of customers, and then decide to kill or keep it. I saw a startup operating this way and their build velocity is up 20x. Decision quality is up because every decision is informed by a real prototype, not a slide deck and an expensive meeting. We went from “move fast and break things” to “move fast and build everything.” The planning industrial complex is dead. Thank god.

What does it mean for software engineering when we no longer write the code? Here's the take from Boris Cherny (@bcherny), the creator of Claude Code. Timestamps: 00:00 Intro 11:15 Lessons from Meta 19:46 Joining Anthropic 23:08 The origins of Claude Code 32:55 Boris's Claude Code workflow 36:27 Parallel agents 40:25 Code reviews 47:18 Claude Code's architecture 52:38 Permissions and sandboxing 55:05 Engineering culture at Anthropic 1:05:15 Claude Cowork 1:12:48 Observability and privacy 1:14:45 Agent swarms 1:21:16 LLMs and the printing press analogy 1:30:16 Standout engineer archetypes 1:32:12 What skills still matter for engineers 1:35:24 Book recommendations Brought to you by: • @statsig  — ⁠ The unified platform for flags, analytics, experiments, and more. statsig.com/pragmatic • @SonarSource – The makers of SonarQube, the industry standard for automated code review. Proactively find and fix issues in real-time with the SonarQube MCP Server: sonarsource.com/products/sonar… • @WorkOS – Everything you need to make your app enterprise ready. workos.com Three interesting things from this conversation: 1. Boris automated himself out of code review well before AI. Boris was one of the most prolific code reviewers at Meta company. And he worked hard to minimize time spent on code review. His system::every time he left the same kind of review comment, he logged it in a spreadsheet. Once a pattern hit 3-4 occurrences, he’d write a lint rule to automate it away! 2. PRDs are dead on the Claude Code team: prototypes replaced them. Instead of writing Product Requirement Documents (specs), they build hundreds of working prototypes before shipping a feature. Boris: “There’s just no way we could have shipped this if we started with static mocks and Figma or if we started with a PRD.” 3. This is the year of the generalist (and maybe the year of those with ADHD) Boris’s work has shifted from deep-focus single-threaded coding to managing multiple parallel agents and context-switching rapidly. As Boris put it: “It’s not so much about deep work, it’s about how good I am at context switching and jumping across multiple different contexts very quickly.”

Me defending my O(n^3) solution to the coding interviewer.

@kakarPathan_ tortoise

What do you see, chicken or bird🤔

@porqueTTIA AI slop

@iyoushetwt @alexasa79 Maybe on an emulator you can run it

@alexasa79 has it long gone?? 😂

What was the first code editor you ever used? Mine was Sublime Text