Ibrahim Diallo sharing tips on how to lead a room full of experts.

By definition, leading is knowing the way forward. But in reality, in a room full of experts, pretending to know everything makes you look like an idiot.

Instead, “I don’t know, but let’s figure it out” becomes a superpower. It gives your experts permission to share uncertainty. It models intellectual humility. And it keeps the focus on moving forward rather than defending ego. It’s also an opportunity to let your experts shine.

Saying “I don’t know” is truly a super power. Every time I have said it, the person in the front has excitedly shared all their knowledge with me.

I read this post by Can Elma on how AI is helping senior developers but not junior developers. While the post has some interesting takes, there’s an even more interesting discussion on it on Hacker News. Two of my favourite comments.

Comment by kaydub.

Because juniors don’t know when they’re being taken down a rabbit hole. So they’ll let the LLM go too deep in its hallucinations.

I have a Jr that was supposed to deploy a terraform module I built. This task has been hanging out for a while so I went to check in on them. They told me the problem they’re having and asked me to take a look.

Their repo is a disaster, it’s very obvious claude took them down a rabbit hole just from looking. When I asked, “Hey, why is all this python in here? The module has it self contained” and they respond with “I don’t know, claude did that” affirming my assumptions.

They lack the experience and they’re overly reliant on the LLM tools. Not just in the design and implementation phases but also for troubleshooting. And if you’re troubleshooting with something that’s hallucinating and you don’t know enough to know it’s hallucinating you’re in for a long ride.

Meanwhile the LLM tools have taken away a lot of the type of work I hated doing. I can quickly tell when the LLM is going down a rabbit hole (in most cases at least) and prevent it from continuing. It’s kinda re-lit my passion for coding and building software. So that’s ended up in me producing more and giving better results.

Comment by bentt.

The best code I’ve written with an LLM has been where I architect it, I guide the LLM through the scaffolding and initial proofs of different components, and then I guide it through adding features. Along the way it makes mistakes and I guide it through fixing them. Then when it is slow, I profile and guide it through optimizations.

So in the end, it’s code that I know very, very well. I could have written it but it would have taken me about 3x longer when all is said and done. Maybe longer. There are usually parts that have difficult functions but the inputs and outputs of those functions are testable so it doesn’t matter so much that you know every detail of the implementation, as long as it is validated.

This is just not junior stuff.

Faisal Hoque arguing that there are three bubbles in AI. He concludes his post by explaining the benefits of bubbles.

Far from being a threat, the AI bubble might be the best thing that could happen to pragmatic adopters. Consider what speculative excess delivers: billions in venture capital funding R&D you’d never justify to your board; the world’s brightest minds abandoning stable careers to join AI startups, working on tools that you’ll eventually be able to use; infrastructure being built at a scale no rational actor would attempt, driving down future costs through overcapacity.

While investors bet on which companies will dominate AI, you can cherry-pick proven tools at competitive prices. While speculators debate valuations, you will be implementing solutions with clear ROI. When the correction comes, you’ll also be able to benefit from fire-sale prices on enterprise tools, seasoned talent seeking stability, and battle-tested technologies that survived the shakeout.

The dotcom bubble gave us broadband infrastructure and trained web developers. The AI bubble will leave behind GPU clusters and ML engineers. The smartest response isn’t to avoid the bubble or try to time investments in it perfectly. It is to let others take the capital risk while you harvest the operational benefits. The bubble isn’t your enemy. If you play your cards strategically, it can be a major benefactor.

This post on Harvard Business Review by Kate Niederhoffer, Gabriella Rosen Kellerman, Angela Lee, Alex Liebscher, Kristina Rapuano and Jeffrey T. Hancock explaining workslop.

We define workslop as AI generated work content that masquerades as good work, but lacks the substance to meaningfully advance a given task.

Here’s how this happens. As AI tools become more accessible, workers are increasingly able to quickly produce polished output: well-formatted slides, long, structured reports, seemingly articulate summaries of academic papers by non-experts, and usable code. But while some employees are using this ability to polish good work, others use it to create content that is actually unhelpful, incomplete, or missing crucial context about the project at hand. The insidious effect of workslop is that it shifts the burden of the work downstream, requiring the receiver to interpret, correct, or redo the work. In other words, it transfers the effort from creator to receiver.

If you have ever experienced this, you might recall the feeling of confusion after opening such a document, followed by frustration—Wait, what is this exactly?—before you begin to wonder if the sender simply used AI to generate large blocks of text instead of thinking it through. If this sounds familiar, you have been workslopped.

Joshua Stevens has created a map—which I believe would have been created—if human civilisation started from Australia.

Jerry Neumann comparing AI with previous revolutionary technologies like microprocessor and containers—shipping containers, not software containers—and arguing that money will be made on the applications sitting on top of AI rather than on AI itself.

This doesn’t mean AI can’t start the next technological revolution. It might, if experimentation becomes cheap, distributed and permissionless—like Wozniak cobbling together computers in his garage, Ford building his first internal combustion engine in his kitchen, or Trevithick building his high-pressure steam engine as soon as James Watt’s patents expired. When any would-be innovator can build and train an LLM on their laptop and put it to use in any way their imagination dictates, it might be the seed of the next big set of changes—something revolutionary rather than evolutionary. But until and unless that happens, there can be no irruption.

Ok, so the title is a bit clickbaity. If you are appearing for an exam you of course need to remember what you read. But not everything that you read needs to be remembered.

Mo talking about how he reads and then forgets.

I read to forget. Even when studying or working on papers for a PhD, I approach texts with the same mindset: I’m not a storage device that needs to save all bits of information. I am more of a system of Bayesian beliefs, constantly evolving and updating in small, incremental steps.

[…]

From most texts, I only want two things: First, I want it to subtly alter my thinking, an incremental update that moves me towards a refined world model. Second, I want to pull out a few key pieces of information that I might use later in my writing.

This comment on Hacker News succinctly describes what Mo is talking about.

I cannot remember the books I’ve read any more than the meals I have eaten; even so, they have made me.

From Systemantics: How Systems Really Work and How They Fail by John Gall

A complex system that works is invariably found to have evolved from a simple system that worked. A complex system designed from scratch never works and cannot be patched up to make it work. You have to start over with a working simple system.

If you think about it, this is also applicable for evolution. Nature never creates new species. It evolves the existing ones. Single cell organisms to multi cell organisms. Leopard to snow leopard. Apes to humans.

This Hacker News comment by Spooky23 on the news that immunotherapy drug eliminated aggressive cancers in clinical trial.

I’m both sad and incredibly happy to read this. I lost my wife recently to a recurring metastatic melanoma. She was treated at MSK by an amazing team.

It was a terrifying diagnosis and literally would have been a guaranteed death sentence in 2017. In 2023, she had a very real chance of pulling through due to immunotherapy. Unfortunately some complications led to the worst outcome and we lost an amazing woman.

I remember that my wife said once that the everything she had on that journey was on the shoulders of those before. So maybe in some small way she helped with the research and a future mother, sister, wife, husband, son, dad will have hope where there was none.

Profound.

Josh Swords talking about the four key skills that you need to focus on as you become a senior.

The biggest gains come from combining disciplines. There are four that show up everywhere: technical skill, product thinking, project execution, and people skills. And the more senior you get, the more you’re expected to contribute to each.

Technical skill is your chosen craft. Product thinking is knowing what’s worth doing. Project execution is making sure it happens. People skills are how you work with and influence others.

Every successful effort needs all four.