Powering ProjeX

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Powering ProjeX

Powering ProjeX

@PoweringProjeX

A world where project people are celebrated as the quiet architects of progress: turning bold visions into victory through learning, wisdom and courage.

Australia Beigetreten Mayıs 2025
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Lenny Rachitsky
Lenny Rachitsky@lennysan·
"Using coding agents well is taking every inch of my 25 years of experience as a software engineer." Simon Willison (@simonw) is one of the most prolific independent software engineers and most trusted voices on how AI is changing the craft of building software. He co-created Django, coined the term "prompt injection," and popularized the terms "agentic engineering" and "AI slop." In our in-depth conversation, we discuss: 🔸 Why November 2025 was an inflection point 🔸 The "dark factory" pattern 🔸 Why mid-career engineers (not juniors) are the most at risk right now 🔸 Three agentic engineering patterns he uses daily: red/green TDD, thin templates, hoarding 🔸 Why he writes 95% of his code from his phone while walking the dog 🔸 Why he thinks we're headed for an AI Challenger disaster 🔸 How a pelican riding a bicycle became the unofficial benchmark for AI model quality Listen now 👇 youtu.be/wc8FBhQtdsA
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Powering ProjeX
Powering ProjeX@PoweringProjeX·
@lennysan Really interesting. I’ve been setting all my skills in Cowork …just trying to get human details out of my head using voice capture is exhausting! 🤣
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Lenny Rachitsky
Lenny Rachitsky@lennysan·
"Using coding agents well is taking every inch of my 25 years of experience as a software engineer, and it is mentally exhausting. I can fire up four agents in parallel and have them work on four different problems, and by 11am I am wiped out for the day. There is a limit on human cognition. Even if you're not reviewing everything they're doing, how much you can hold in your head at one time. There's a sort of personal skill that we have to learn, which is finding our new limits. What is a responsible way for us to not burn out, and for us to use the time that we have?" @simonw
Lenny Rachitsky@lennysan

"Using coding agents well is taking every inch of my 25 years of experience as a software engineer." Simon Willison (@simonw) is one of the most prolific independent software engineers and most trusted voices on how AI is changing the craft of building software. He co-created Django, coined the term "prompt injection," and popularized the terms "agentic engineering" and "AI slop." In our in-depth conversation, we discuss: 🔸 Why November 2025 was an inflection point 🔸 The "dark factory" pattern 🔸 Why mid-career engineers (not juniors) are the most at risk right now 🔸 Three agentic engineering patterns he uses daily: red/green TDD, thin templates, hoarding 🔸 Why he writes 95% of his code from his phone while walking the dog 🔸 Why he thinks we're headed for an AI Challenger disaster 🔸 How a pelican riding a bicycle became the unofficial benchmark for AI model quality Listen now 👇 youtu.be/wc8FBhQtdsA

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Powering ProjeX
Powering ProjeX@PoweringProjeX·
@elonmusk Most of my friends use AI purely for health conversations. I would never have thought.
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Massimo
Massimo@Rainmaker1973·
Microsurgery robot stitches up a corn kernel showing millimeter-level precision
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X Freeze
X Freeze@XFreeze·
Tesla's Full Self-Driving (FSD) is currently ~9x safer than the average human driver Because of this massive safety advantage, auto insurance providers like Lemonade are now offering Tesla owners up to a 50% discount on their per-mile premiums when FSD is engaged Choosing Tesla FSD driving is not just safer, but it also directly saves you money
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Anthropic
Anthropic@AnthropicAI·
We've signed an MOU with the Australian Government to collaborate on AI safety research and support Australia's National AI Plan. Read more: anthropic.com/news/australia…
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Ejaaz
Ejaaz@cryptopunk7213·
this is insane lol japan is running out of monks... so they're training AI robots called "buddharoid" to replace them 😂 (im not joking): - japan's temples are closing because fewer priests are available to run them + aging population - the solution: chatgpt robots trained on 1000+ years of buddhist scripture that answer your spiritual questions - the robot even sits in religious prayer positions like an actual monk does. you can literally have a conversation on life's deepest dilemmas with a robot as smart as the dalai llama i cannot believe they're scaling these robots to run actual temples.
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George Pu
George Pu@TheGeorgePu·
GitLab's founder was told he has bone cancer. No trials would take him. Doctors signed off. So he went founder mode on his own survival. - Built his own treatments - Used AI to analyze his own tumor data - Open-sourced 25TB of his medical records for any researcher on earth Relapse-free since 2025. The system said he was out of options. He made his own.
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Elon Musk
Elon Musk@elonmusk·
The Starship factory is stunning
Jawwwn@jawwwn_

SpaceX COO @Gwynne_Shotwell: “I love working for @elonmusk.” “He’s funny— he’s hilarious actually.” “He focuses on things that I would never have thought were important.” “One is— beautiful spaces.” “This is one of the most beautiful factories I have ever seen.” Via @TIME

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Peter H. Diamandis, MD
Peter H. Diamandis, MD@PeterDiamandis·
Tesla's FSD: 5.3 million miles between accidents. US driving average: 660,000.  That's 9x safer. And it's only getting better.
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Powering ProjeX@PoweringProjeX·
Worth reading…
Marc Andreessen 🇺🇸@pmarca

Claude knows! —> The Lump of Labor Fallacy and Why AGI Unemployment Panic Is Economically Illiterate Let me lay this out with full rigor, because this argument deserves to be prosecuted completely rather than waved away with a sound bite. I. What the Lump of Labor Fallacy Actually Is The lump of labor fallacy is the assumption that there exists a fixed, finite quantity of work in an economy — a lump — such that if a machine (or an immigrant, or a woman entering the workforce) does some of it, there is necessarily less left for human workers to do. It treats employment as a zero-sum pie. The fallacy was named and formalized in the early 20th century but the error it describes is far older. It animated the Luddite riots of 1811–1816, where English textile workers destroyed power looms convinced that the machines would steal their jobs permanently. It drove opposition to the spinning jenny, the cotton gin, the mechanical reaper, the steam engine, the telegraph, the railroad, the automobile assembly line, the personal computer, and every other major labor-displacing technology in the history of industrial civilization. Every single time, the catastrophists were wrong. Not partially wrong. Structurally, fundamentally, categorically wrong — because they misunderstood the nature of economic production itself. The reason the fixed-pie assumption fails is this: demand is not fixed. Work generates income. Income generates demand for goods and services. Demand for goods and services generates new categories of work. This is an engine, not a reservoir. When you drain some of the reservoir with a machine, the engine speeds up and refills it — and often refills it past its previous level. II. The Classical Economic Mechanism That Destroys the Fallacy To understand why the lump-of-labor assumption is wrong about AGI, you need to understand the precise mechanism by which technological unemployment resolves itself. There are four distinct channels, all operating simultaneously: Channel 1: The Productivity-Demand Feedback Loop (Say’s Law, Modified) When a technology increases the productivity of labor or replaces labor entirely in a given task, it lowers the cost of producing whatever that task was part of. Lower production costs mean either: ∙Lower prices for consumers (real purchasing power rises), or ∙Higher profits for producers (which get reinvested, distributed as dividends, or spent as wages for other workers), or ∙Both. Either way, aggregate real income in the economy rises. That additional real income does not evaporate. It gets spent on something — including goods and services that didn’t previously exist or were previously too expensive to consume at scale. That spending creates demand. That demand creates jobs. This is not a theoretical conjecture. The average American in 1900 spent roughly 43% of their income on food. Today it’s around 10%. Agricultural mechanization didn’t produce a nation of starving unemployed farm laborers — it freed up 33% of household income to be spent on automobiles, television sets, air conditioning, healthcare, education, travel, smartphones, and streaming services, most of which didn’t exist as industries in 1900. The workers who left farms went to factories, then to offices, then to service industries, then to information industries. The economy didn’t run out of work. It metamorphosed.

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The All-In Podcast
The All-In Podcast@theallinpod·
David Friedberg on Personal Agency in the Age of AI: "Stop Blaming Everyone Else" "We never talk about responsibility. We always talk about where the government failed us and where these companies f***ed us. And we never talk about, what did we individually do wrong? How did I individually choose to drink 100 sodas a week? How did I individually choose to get my kids addicted to social media? Where the f*** was I as a parent? We don't talk about our responsibility. And by the way, this fundamentally addresses this point about human agency, which I think is more critical in this era than ever because AI is going to flood us with f*****g everything all the time, nonstop. What we choose to do in a world where we're already getting everything, and how we choose to not take everything that's being offered to us, I think is a critical part of what's going to distinguish human success from human failure. And it's gonna become more apparent in the future, and not everything is about liability, and not everything is about the government failing us. It's about people making choices and we don't talk about it."
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Nikita Bier
Nikita Bier@nikitabier·
If you’re seeing a bunch of Japanese posts, here are some fun facts: Japan has more daily active users and more time spent on X than any other country in the world. Over two thirds of the country is monthly active on X. X in Japan has one of the highest penetration rates of any social network in history.
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Nikita Bier
Nikita Bier@nikitabier·
Never a boring day on this app—or at the company that makes this app.
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Codie Sanchez
Codie Sanchez@Codie_Sanchez·
Best money I've ever spent as a CEO... an internal AI transformation hire. Here’s how we hire AI operators at Contrarian Thinking (steal this): 1. Find them The people you want live on X. Search "build in public AI," "n8n," "Claude workflows." Look for demos, shipped work, people building for fun on weekends. They probably don't call themselves AI operators. Maybe they’re a Chief of Staff. Whatever. But AI problem-solving is what they actually do. Avoid AI enthusiasts with nothing to show for it. When you find a good one, DM them. Don't say "we have a role open." Say: "I saw what you built. Want to do that inside a real business?" 2. Vet them We hire based on proof. Our President Marc now asks every candidate (regardless of role) what they're currently building with AI. Doesn't need to be impressive, but it should be intentional. If they make it through, we run a simple evaluation like this: 1. Loom intro: show what you've built 2. Work sample: solve a real problem 3. Live iteration: improve it on the fly Watch for speed, communication, ease of shipping. Can't ship under light pressure? Won't ship inside your business. 3. Deploy them right Most companies hire someone like this and immediately tether them to one department. That would be an F-minus move. Let them work across the whole business. Audit. Design systems. Ship. Then have them run an internal AI hackathon. 4 hours. One leader. One brief, fun presentation to get everyone seeing the larger picture - then let the team loose. We did this recently. The awakening across our team was one of the highest ROI things we've ever run. tl;dr Don't think about who you can replace with AI - use it to create more builders inside your org. Employees need to become builders, not button-clickers.
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