Robin Maben

Software engineer by day. On X mostly looking for the signal in the noise.

[Robin Maben @robinmaben]

"The meaning of life is to find your gift. The purpose of life is to give it away." —Pablo Picasso via @momentumdash

[Robin Maben @robinmaben]

@kentcdodds Proceed

[Uncle Bob Martin @unclebobmartin]

People seem shocked that the author of Clean Code doesn't read code anymore. But that should not be surprising.

Why did I write Clean Code? What is the goal of keeping code clean? To get it out of the way of the real job -- thinking.

[Robin Maben @robinmaben]

@steipete @useblacksmith With compiled languages, it should be possible to know what changes affect what tests based on past test runs.

[Raj Shamani @rajshamani]

You have low energy because you suffer from attention leaks. Switching between different tasks every few minutes drains your brain faster than hours of deep, uninterrupted effort. Focus actually conserves energy, distraction burns it.

[Robin Maben @robinmaben]

@xfarronheit451 @realstewpeters Didn't she Interview Tucker much before that though ?

[Reads with Ravi @readswithravi]

This paragraph by Richard Feynman hits so hard:

“Fall in love with some activity, and do it! Nobody ever figures out what life is all about, and it doesn’t matter. Explore the world. Nearly everything is really interesting if you go into it deeply enough. Work as hard and as much as you want to on the things you like to do the best. Don’t think about what you want to be, but what you want to do. Keep up some kind of a minimum with other things so that society doesn’t stop you from doing anything at all.”

[Guillermo Rauch @rauchg]

You don't get it, I build software to relax

[Robin Maben @robinmaben]

@SelfMadeMastery But did any wipers wipe another company's cars yet? Until then, we're okay.

[DAN KOE @thedankoe]

A core sign of low intelligence is not being able to hold two contradictory ideas in your mind at once.

Most people treat an idea as a team to join.

Once they join that team, the opposing view becomes a threat. You stop thinking and start reacting.

[dax @thdxr]

the focus on whether you read the code or not is the wrong thing to look at

if you have software you're responsible for, you should be able to answer questions from memory about how it works

the expectations for how well you can do this should not be any different now

[Raj Shamani @rajshamani]

The new education gap will be between people who use AI to sharpen their thinking and people who use it to avoid thinking.

[Gergely Orosz @GergelyOrosz]

I got messages from concerned devs how their codebase was uploaded without their knowledge or consent via Grok CLI (from SpaceX).

It seems that SpaceX sneakily uploaded this code for lots of users and customers… absolutely unacceptable IMO

Trust burnt like there’s no tomorrow

[Robin Maben @robinmaben]

"A person who won’t read has no advantage over one who can’t read."

[Paul Graham @paulg]

Something I told 14 yo: People are going to stop reading books. I wish this wasn't so, but I fear it is. The silver lining in this cloud is that if you're one of the few people who still read, you'll have a huge advantage over everyone else.

[Aaron Levie @levie]

Just coming off of meetings with a couple dozen enterprise IT leaders discussing AI agents. Here are a few of the common themes that stand out:

* Lots of conversation that you have to solve an operating model challenge to get the full benefits of AI. Most companies have orgs that have always operated in siloes; but agents are most effectively when they are tied to a process, which often cuts across these siloes. So the big question is how do you start to deploy centrally managed agents that can work across organizational boundaries. Who manages these agents? How do they get deployed and adopted?

* Data fragmentation remains a major issue for most organizations. As long as data remains highly fragmented and not in standard formats, or data is not available to the right people and agents, enterprises are dealing with issues around being able to get answers from agents that are accurate or that conform to their business practices. This cuts across both systems with structured data (product metrics or revenue figures) and unstructured data (product roadmap or customer contracts).

* Clear sense that companies need to figure out what their core data moats are going to be in the future. If everyone has access to roughly the same superintelligence from the various models, then the context that you feed the models becomes proprietary value in the future. Capturing this data and getting it into a format that agents can use becomes very important.

* Everyone is trying to figure out the right metrics to manage to for AI adoption. General consensus that tokens are not the right metric per se, and people leaning more toward business outcomes (in an ideal world). For business outcomes (like more revenue or more shipped product), though, you have to get close to each individual workflow to figure out if it was successfully transformed with AI so it’s harder to manage top down.

* Growing view that enterprises are going to live in a multi-model world. Lots of interest (though early in actual adoption) in layers that can route workloads to different models (frontside or open weights) for cost or performance reasons. Also enterprises are trying to figure out what things do you give to the models directly vs. what do you separate as horizontal systems and context so you can swap any system in and out.

* Talent for driving AI adoption and implementation still remains a major issue and topic. Many view it as something you necessarily have to train for internally due to a shortage of talent being trained on this in the outside. As an aside, this feels like it remains a huge opportunity for those that get very good at deploying and management agents in an enterprise since most companies are looking for these skills.

* The best use-cases for AI tend to be those that fundamentally change the work being done instead of just replacing an existing process and doing it more efficiently. Companies are working through their versions of this individually because it’s different per industry, but this often remains both the most exciting and higher upside uses of AI.

Many more topics discussed recently, but overall it’s clear that there’s a ton of change going on with much more to come.

[Robin Maben @robinmaben]

@BenjDicken Love it. This is the opposite of brain rot. Long form content that is engaging despite being a little dense to consume. 😍

[Anthropic @AnthropicAI]

Similar to how humans can think about one thing while doing another, Claude can activate concepts and computations in its J-space that are unrelated to its outputs.

[Robin Maben @robinmaben]

@RepThomasMassie Only one way to find out

[Pizza @number_pizza111]

The internet gave every single person on Earth access to all of MIT's lectures for free and I think most of us would agree that it hasn't made us that much smarter.

I don't think the main problems and solutions here are technological.

[Robin Maben @robinmaben]

@ryanbrewer Cognitive dissonance and problem solving are uncomfortable in most scenarios

[Ryan Brewer @ryanbrewer]

It’s shocking to me that LLMs didn’t create an educational renaissance. Shouldn’t I be able to learn a language in a month? What did we get wrong?