Sluzzle Dude

Using Data Science and Analytics skills to help an international organization track and improve performance. I enjoy Machine Learning and Model Deployment.

[Can Vardar @icanvardar]

linkedin is where llms go to socialize with each other

[Andrew Ng @AndrewYNg]

“Loop engineering” is a hot buzzphrase after mentions of it by Boris Cherny (Claude Code’s creator) and Peter Steinberger (OpenClaw's creator) went viral on social media. Loops are now a key part of how we get AI agents to iterate at length to build software. In this letter, I’d like to share my 3 key loops, shown in the image below, for building 0-to-1 products. These loops guide not just how I build software, but also how I decide what software to build.

Agentic coding loop: Given a product specification and optionally a set of evals (that is, a dataset against which to measure performance), we can have an AI agent write code, test its work, and keep iterating until the code is bug-free and meets its specification. This idea of closing the loop took off around the end of last year, and it has been a game changer in enabling coding agents to work longer productively without human intervention. For example, over the weekend, I was building an app for my daughter to practice typing, and my coding agent could easily work for around an hour, using a web browser to check what it had built multiple times before getting back to me, without needing my intervention.

The engineering loop executes quickly. Every few minutes, the coding agent might build and test a new version of the software. I hear frequently from developers who are finding new ways to engineer more effective engineering loops. This is an active area of invention!

Developer feedback loop: In this loop, a developer examines the current product and steers the coding agent to improve it. Last year, a lot of developers (including me) were acting as the QA (quality assurance) function for our coding agents, manually finding bugs and then asking the agent to fix them. But with coding agents much more able to test their own code, the amount of time we need to spend on this function has decreased significantly. This allows us to make higher-level product decisions, such as what key features to offer, where the UI needs improvement, and so on.

The developer-feedback loop operates over time intervals between tens of minutes and hours — that's how frequently a developer might review a product and give feedback. In the case of the typing app, I changed my mind a few times about the visual design, what cat costumes she can unlock as she learns (she loves cats), and the user flow for a grown-up to log in and steer the child's learning experience.

When a developer has a clear vision for what to build, it is still a lot of work to translate that vision into a specification for a coding agent to implement. Further, after the developer has seen an implementation, they might update (or perhaps clarify) the spec to steer it toward what they want. If you find that the system repeatedly runs into certain problems, building a set of evals for the agent becomes useful.

AI-native teams are increasingly using AI to help shape product direction, for example, automating the gathering and analysis of usage data, summarizing written and verbal customer feedback, or carrying out competitive analysis. However, for pretty much all the products I’m involved in, I see humans as having a significant context advantage over current AI systems — we know a lot more than the AI system about the users and the context the product has to operate in — and thus humans play a critical role. Many people describe this human contribution as “taste,” but I prefer to think of it as humans having a context advantage, since that gives us a clearer path to helping AI systems get better. This also speaks to why this step can’t be automated: So long as the human knows something the AI does not, human-in-the-loop is needed to to inject that knowledge into the system.

External feedback loop: This includes a wide range of tactics like asking a few friends for feedback, launching to alpha testers, or putting the code into production with A/B testing. These tactics are usually slow, rarely taking less than hours and sometimes taking days or even weeks. This data informs the developer vision, which in turn continues to drive the detailed product spec, which in turn drives the coding agent.

With coding agents speeding up software development, more engineers are starting to play a partial product management role. For many engineers who are growing into this role, the hardest part is shaping the product vision and striking a balance between building (bridging the gap between vision and spec) and getting user feedback to evolve the vision. It is important to do both!

I will write more about how to do this in future posts, but for now, I find it encouraging that engineers are playing an expanded role (just as product managers and designers now do more engineering).

[Original text: The Batch]

[Bryan Johnson @bryan_johnson]

@espn After eating that, be sure to go on a walk to the Mexican border (130 miles) and back to help with digestion.

[Aakash Gupta @aakashgupta]

Alex Karp received a $12,000 inheritance from his grandfather. His goal was to save $250,000 so he could move to Berlin and read books for a living.

Today Palantir has a market cap of $324 billion. The stock is up over 1,300% from its first-day close of $9.50 in September 2020. Karp was the highest-paid CEO of a publicly traded company in the United States in 2024, with compensation calculated at $6.8 billion.

His credentials for running a defense tech company? A philosophy degree from Haverford. A JD from Stanford he called “the worst three years of my adult life.” A PhD from Goethe University Frankfurt where he wrote his dissertation in German on aggression, jargon, and culture through Parsonian social theory. His first job after graduating was research associate at the Sigmund Freud Institute.

Every other CEO who built a company this large in defense came through engineering, military service, or finance. Karp came through continental philosophy and psychoanalysis. And he built the platform the CIA, NSA, FBI, U.S. Army, and Special Operations Command all depend on. The company recently landed a $10 billion Army deal.

Q4 2025: $1.407 billion in revenue, up 70% year over year. U.S. commercial revenue surged 137%. Full year 2025 hit $4.475 billion. They’re guiding $7.2 billion for 2026. Rule of 40 score: 127%. They do all of this with 4,414 employees, roughly $1 million in revenue per head.

He delivers earnings calls from New Hampshire in ski gear. Keeps Tai Chi swords in his offices. Practices qigong meditation. In December 2025, he paid $120 million for a 3,700-acre former Trappist monastery outside Aspen. He calls himself a socialist. He voted for Hillary Clinton. And he tells the New York Times the West needs autonomous weapons to survive a three-front war against China, Russia, and Iran.

He hires philosophers, physicists, and historians alongside engineers. He moved the company out of Silicon Valley because he grew disgruntled with its monoculture. He wrote in Palantir’s IPO prospectus that the engineering elite “do not know more about how society should be organized or what justice requires.”

His PhD was about how language, aggression, and culture interact in systems. Palantir does the same thing with data. The philosophy is the entire foundation.

$12,000 inheritance to the 42nd most valuable company on earth. The most unconventional path to building a $324 billion company in American history.

[Dwarkesh Patel @dwarkesh_sp]

Tomorrow

[Evis Drenova @evisdrenova]

Bro gutted his old company (that he cofounded) of talent and ideas and took it all to Google and then launched a competing product.

Brutal.

[amrit @amritwt]

"you cannot compete with someone who is willing to suffer more than you"

[CG @cgtwts]

> helped millions get into ai before it became mainstream
> built coursera and changed online education forever
> founded deeplearning. ai to keep teaching the world
> leads major ai projects while staying humble and calm
> no scandals no noise just steady work
> still codes and still experiments
> builds small tools and projects purely for curiosity
> teaches only when he has real value to add
> doesn’t chase hype or predictions
> lives quietly learning and building at his own rhythm

has andrew ng quietly figured out life better than everyone else?

[Bryan Johnson @bryan_johnson]

@McDonalds your pickles contain preservatives & emulsifiers like polysorbate 80, same class of compounds in animal models disrupt gut microbiome, drive inflammation and metabolic dysfunction.

imo, you’re capitalizing on food addictions, fucking up societal health and are a societal menace

[Google for Developers @googledevs]

Google Colab is officially coming to @code! ⚡️

You can now connect VS Code notebooks directly to @GoogleColab runtimes. Get the best of both worlds: the editor you love, powered by the compute (GPUs/TPUs) you need. → https://t.co/prgImNfEd2

[fate @madhav1]

Damn @Brave is doing gods work

[Nando de Freitas @NandoDF]

I still remember that 2013 @NeurIPSConf party with Mark Zuckerberg. He had a bottle of water at that first Neurips corporate party. I thought it was out of character for a Neurips party - what was the matter with this kid? And why did he speak like that? We were so naive! … but it turns out they were even more naive that us.

Had they been smart, they could have hired ALL of the Neurips scientists, perhaps minus a small British startup called DeepMind, which was sought after by Google, at that party. Instead, they hired only a small group - but that group included one of the greatest and most influential engineers of all time: @ylecun.

Would I have said yes to a 500K offer? Hell yes!! As a (high-paid) Oxford prof I made 85K and was struggling to get a mortgage for my growing family. AI people until then didn’t do it for the money. Now we hear about people making 10 and 20 million per year - still nothing comparable to what the corporate executives make.

That party changed everything.

I’m happy to see Yann moving on to a new chapter. He’s so creative. I’m looking forward to seeing what he does next 🙂

When I taught my course at Oxford that year, explaining why neural networks were modular like Lego and how to use automatic differentiation (backprop) to get global consistency from local messages, it became super popular in YouTube. I thought my students and I were the first to teach this amazing generality with Torch. It turns out Yann had done it before - it just wasn’t as easy to find.

Yann was not only a pioneer of convnets but also the software we all use to this very day. We owe him a lot, a lot more than most realise.

[Andrew D. Huberman, Ph.D. @hubermanlab]

Big peaks in dopamine that do not require much effort to access = long periods of lack of motivation & blues, especially if you try to exit that rut with low effort activities. This is not stoicism, it’s not psychology. It’s the biology of dopamine circuit dynamics.

[Prince Canuma @Prince_Canuma]

I’ve left @arcee_ai!

I really love what we have achieved together across research and product. From model fusion, offline distillation of Llama 405B all the way to building, leading and launching Arcee Orchestra from scratch within 4 months.

Already miss everyone, more than colleagues they are my friends ❤️

Nevertheless, I’m very excited to soon announce what’s next.

Meanwhile, I’m happy to share that I’ll be working full-time on MLX (mlx, mlx-lm, mlx-vlm, mlx-audio and more) to help build the best on-device R&D experience and products by bringing the latest OS models and features to Apple Silicon.

[Alex Hormozi @AlexHormozi]

Everyone starts at zero.

Keep on.✊🏽

[omoge. @KhloeUmoh]

At the end of the day, when you get it right, it won’t matter how long it took.

[TradingView @tradingview]

Don't even open TradingView today.

[VfL Wolfsburg @VfL_Wolfsburg]

Dear Kevin, do you remember? You and I ten years ago? How about the two of us again?

[Kevin De Bruyne @KevinDeBruyne]

Dear Manchester. 💙

[(fan) Trey @UTDTrey]

This redemption arc from Mbappe has been INSANE.

From “flop” to 33 goals this season