👏👏👏

1. Avoid accepting this as a paradigm shift we simply have to adapt to, even if it goes against some of our principles at certain stages of the process

2. Losing our principles too easily

3. Falling into a build trap just because we can do more things

Because the landscape of practices are rapidly evolving, I would focus on fast feedback loops, and rapid knowledge sharing.

Up to today, the best approach I know is pairing. If tomorrow I discover a better one, I'm happy to switch into that one.

> If we’re increasing that time (it seems to be the trend) should we shift collaboration earlier (in the specification phase) and reinforce it during reviews?

IMHO, I would do the whole process with pairing. From spec to prod.

The more time I see myself without collaborating with others, the more concerned I'm on how fast we are deviating our mental models on the product, business, and code base.

I'm sure some influencer will rediscover XP, and popularize pairing under a new name in some months.

The more time passes with the adoption of Coding Agents, the more I see the need of continue pairing to deliver value.

We need to integrate the coding agents on the pairing activity, not to replace it all together to delegate into coding agents.

We shouldn't trade individual productivity over team productivity. We know this doesn't work in the long term.

In the post, I explore the situation in more depth.

I would love to know your stories as a junior, we need to create a more awareness on how to integrate AI and support our peers along the journey.

I wouldn't like to be a junior in that team. I would feel so anxious.

*"So, if seniors are more productive with AI agents, how can I learn? Will the AI replace my job? Why do seniors prefer to just use Claude over spending time with us?"*

At first, it just feels wrong. And after thinking it in depth, it is so wrong.

Imagine a junior on your team hearing seniors say *"they're so productive pairing with AI, they can ship a lot of code by themselves now."*

Rethinking Pair Programming in the Age of AI Why "pairing with AI" is harmful to your team and what to do instead

🧠 Rethinking Pair Programming in the Age of AI

Why "pairing with AI" is harmful to your team and what to do instead

learnings.aleixmorgadas.dev/p/rethinking...

Que els Déus dels CEOS i els influencers de LinkedIn t'escoltin i comparteixin aquestes paraules carregades de saviesa i bondat.

Annual remember to apply sunscreen ☀️🧴

The app I use is play.google.com/store/apps/d...

I wonder if frameworks could provide a fine tuned SLM with their best practices and guidelines for better local experience.

Do you know any people experimenting with that?

Like qwen-code-spring, minimax-m2.7-rubyonrails, and alike

La mejor manera de parar 10 minutos 😅

Received great feedback about this article. I will improve it to move from lineal and implicit iterations, to circular representation and explicit iteration.

A partir del 8 de mayo, META podrá acceder al contenido de todas las conversaciones privadas en Instagram; podrá ver y disponer del contenido de todos los mensajes enviados entre usuarios. Ahora vienen por los datos PRIVADOS para el entrenamiento de sistemas de IA generativa y cibervigilancia ⚠️

It's that moment of the year for the redheads.☀️

Apply sunscreen for the next months ✅

Hay actores que durante años no solo interpretan un mismo papel sino que lo simulan fisiológicamente. PARTE DEL PERSONAJE PUEDE INTEGRARSE EN SUS PATRONES EMOCIONALES.
La teoría de la Simulación Mental Encarnada, propuesta por Vittorio Gallese a partir de las neuronas espejo, lo explica.
🧵👇👇👇

meme it ain't much, but it's honest work: When you write long posts about strategy and not AI hype

I love to keep writing about strategy and getting great conversations.

In DMs people sharing their context, people reading the posts and providing their point of view.

That gives me a lot of joy, and energy to keep researching and publishing about strategy 🧡🙏

No, the right move was to move to Cloud.

But, does AI help us to evolve our practices or to double down on current ones?

#WardleyMaps helps to make those questions.

Indeed, follow Simon's Watdley work. This example of his is so good to have perspective on today's landscape

Imagine #AI happened before #Cloud.

We would be automating how to set up Racks in our office faster.

Deploying agents to put the HDD, others to wire the network.

In terms of metrics, super. You are able to set up more racks with less people, yeah!

Yet, is this the right thing to be automating?

Plus, in the second post, I will be answering your questions. So, please, feel free to DM me, reply to this post, or reply to the newsletter.

In the first, I explore the model of lifecycle and adoption curves. In the second, I will explore what goes wrong on each phase, how that relates to adoption, and how to mitigate certain scenarios.

The strategy lifecycle and adoption. Post I How adoption will determine the lifecycle of the strategy

Today is one of those dense posts about #EngineeringStrategy, its lifecycle and adoption curves.

It is the first post of a series of 2.
learnings.aleixmorgadas.dev/p/the-strate...

in a world where anyone can code anything

why would someone use _your_ thing

... I would love to talk about this

Yeah, metrics are crucial for the strategy. They help you to track progress, and to know if you need to iterate it.

I like the concept of "wire trap" that helps us to go fast until some of those metrics go beyond a threshold. This way, we can focus on execution knowing when to stop.

Yeah, S-curve fits better the representation