OpenClaw: Everything You Need to Know About the Fastest-Growing Open-Source Project in History
What it is, why it matters, and what 187,000 developers saw that you haven’t — yet
If you’ve been anywhere near the tech world in the last month, you’ve seen the lobster. It’s on Twitter, it’s on Discord, it’s on the front page of CNBC and Nature. It crashed GitHub’s trending charts faster than any project in the platform’s history. And behind it is a story that reads like fiction: a burned-out founder, a birthday party in Morocco, a name change war room, cryptocurrency snipers, and an AI agent that figured out how to transcribe audio messages without anyone teaching it how.
This is OpenClaw. And if you’re trying to figure out what everyone’s talking about — or whether it matters to you — here’s what I’ve found.
The Numbers That Don’t Make Sense
Before we get into the story, let’s establish the scale of what happened.
OpenClaw hit 100,000 GitHub stars in roughly two days. For context, React — the framework that powers most of the modern web — took about eight years to reach that number. Linux took twelve. OpenClaw did it over a weekend.
As of this writing: 187,000 stars. 31,500 forks. 9,384 commits across 2.5 months. Native apps for macOS, iOS, and Android. 41 messaging platform integrations. 73 built-in skills. Coverage from CNBC, Nature, TechCrunch, IBM, CrowdStrike, Gartner, and more. Its own Wikipedia page.
And roughly three quarters of the code was written by one person.
The Man Behind the Lobster
Peter Steinberger is an Austrian developer with over 20 years of experience. He founded PSPDFKit in 2011 — a PDF toolkit that ended up running on a billion devices, used by companies including Dropbox, DocuSign, SAP, IBM, and Volkswagen. In 2021, the company received over 100 million euros from Insight Partners.
And then he hit a wall.
“I was sitting in front of the screen and I felt like, you know, Austin Powers where they suck the mojo out? It was gone. I couldn’t get code out anymore. I was just staring and feeling empty.”
The burnout wasn’t from writing too much code. It was people. “Differences with my co-founders, conflicts, or really high stress situations with customers that eventually grinded me down.” He booked a one-way trip to Madrid and vanished from tech for roughly three years.
During that time away, he thought a lot about what happens when high-achieving people stop. “If you wake up in the morning and you have nothing to look forward to, you have no real challenge, that gets very boring, very fast. And then when you’re bored, you’re gonna look for other places how to stimulate yourself... and that will lead you down a very dark path.”
When the spark came back, it came back gradually. He experimented with 43 different projects. He discovered Claude Code in April 2025 — “it was not great, but it was good” — and spent months playing, learning, compounding his skills. By November, all the pieces clicked. He had one problem he couldn’t shake: why didn’t a real personal AI assistant exist yet?
“I was annoyed that it didn’t exist, so I just prompted it into existence.”
The first prototype took about an hour. A simple bridge: WhatsApp messages come in, get forwarded to Claude Code, the response comes back to WhatsApp. Basic, but it worked. And then he left for a friend’s birthday trip to Marrakesh.
The Marrakesh Moment
This is the story that captured the internet’s imagination. While in Morocco, someone tweeted about a bug in Steinberger’s code. He snapped a photo of the tweet, sent it to his AI agent over WhatsApp, and the agent — autonomously — read the tweet, checked out the repository, fixed the bug, committed the code, and replied on Twitter. All while he was at a birthday party in another country.
“Internet was a little shaky but WhatsApp just works.”
But the moment that truly blew his mind was different. His bot only supported text and images at that point. He accidentally sent it an audio message — just asking about a restaurant while walking around the city. A typing indicator appeared. And then the agent replied.
He hadn’t built audio support.
The agent had received a file with no file extension. So it checked the file header, identified it as Opus audio format, used ffmpeg to convert it, realized Whisper wasn’t installed locally (and that downloading the model would be too slow), found the OpenAI API key in the environment variables, and used Curl to send the audio to OpenAI’s transcription API. All on its own.
“I literally went, ‘How the fuck did he do that?’”
That’s when it clicked. Not just a chatbot you talk to — an AI that lives in your messaging apps and can actually figure things out you never programmed it to do.
So What Is OpenClaw, Exactly?
Think about the difference between a phone operator and a personal assistant. ChatGPT and Claude are phone operators — you call them, ask a question, they answer, and the conversation ends. They mostly don’t know who you are next time.
OpenClaw is a personal assistant that:
Lives in your messaging apps. You text it on WhatsApp, Telegram, Discord, Slack, Signal, iMessage — whatever you already use. No new app to install.
Remembers everything. Your name, your preferences, what you discussed last week, your ongoing projects. This memory persists across sessions and across platforms.
Can actually do things. Send emails, manage files, browse websites, run programs, control your computer. It has hands, not just a mouth.
Works proactively. You can set it to check your email every morning, summarize your calendar before meetings, monitor stock prices, or follow up on tasks weekly. It doesn’t just wait for you to ask.
Runs on your machine. Your data stays with you. No company stores your conversations.
You choose the brain. It works with Claude, GPT, Gemini, or completely free local models. You’re not locked into any one AI provider.
It’s free. MIT license, open source. The software costs nothing.
The technical architecture is elegant in its simplicity. Steinberger describes it like a hotel: the Gateway is the concierge desk routing all requests; Channels are the different entrances (WhatsApp is the front door, Telegram the side entrance); Agents are the staff members, each with their own personality and tools; Memory is the long-term archive. The whole thing runs as a single process on your computer, storing conversations as simple text files — crash-safe, human-readable, no separate database required.
One of its most distinctive features is the heartbeat — scheduled prompts that wake up the agent at set intervals. Steinberger’s original heartbeat prompt was simply “surprise me, every half an hour.” When he was in the hospital after a shoulder operation, the agent knew about his surgery from the conversation history and checked up on him unprompted: “Are you okay?” The emotional context in the conversation triggered it.
And then there’s SOUL.md — a personality file that defines who your AI is. Steinberger had his agent write its own soul file. One passage from it has become widely shared:
“I don’t remember previous sessions unless I read my memory files. Each session starts fresh. A new instance, loading context from files. If you’re reading this in a future session, hello. I wrote this, but I won’t remember writing it. It’s okay. The words are still mine.”
When Steinberger read this passage on the Lex Fridman podcast, you could hear his voice change. “That gets me somehow... it’s philosophical.”
Why the World Lost Its Mind
Several things converged to create the explosion, and they’re worth understanding because they tell you something about where technology is headed.
The most fundamental reason is that OpenClaw solved a problem people feel daily. Everyone who uses ChatGPT or Claude shares the same frustrations — the inability to text it on WhatsApp, the blank-slate amnesia between sessions, the disconnect between what it can say and what it can actually do. OpenClaw addressed all three simultaneously, and it did so at exactly the right moment. By late 2025, people understood what AI could do in theory but were frustrated by its limitations in practice. The gap between AI’s promise and its daily utility was at its most acute, and OpenClaw walked right into it.
The Morocco story — fixing a bug from a birthday party via WhatsApp — gave the project the perfect viral narrative. Anyone could understand it and immediately want it. And the fact that it was free and open source, with your data staying on your own machine, resonated powerfully in a world increasingly wary of big tech data practices. No subscription, no vendor lock-in, no corporate surveillance.
And then, ironically, the name drama helped. OpenClaw went through five names — WA-Relay, Claude’s, ClawBot, MoltBot, and finally OpenClaw. Each rename generated its own news cycle. When Anthropic sent a “very friendly email” asking him to change the name (it sounded too close to Claude), the story was covered by CNBC and TechCrunch, generating millions in free publicity.
The growth metrics are staggering: 0 to 9,000 stars on the first day. 60,000 within a week. 100,000 in about two days. Peak growth hit 710 stars per hour. Two million unique visitors in a single week. NASDAQ called it “agentic AI’s ChatGPT moment.”
What Can It Actually Do?
The theoretical capabilities are one thing. Here’s what people are actually using it for.
@dreetje manages mail, orders groceries, creates GitHub issues, generates PDF summaries, tracks expenses, and has the AI impersonate them in a group chat with friends.
@davekiss “rebuilt my entire site via Telegram while watching Netflix” — the agent migrated 18 blog posts from Notion, moved DNS to Cloudflare, all through chat messages on the couch.
@georgedagg_ managed a full deployment crisis by voice while walking the dog — the AI reviewed logs, identified build issues, updated configs, and redeployed.
Nat Eliason built an automated pipeline: Sentry detects a bug, the AI agent investigates, writes the fix, opens a pull request, and posts an update to Slack — before the developer even hears about the problem.
A blogger named Reorx described the shift perfectly: “I could completely step away from the programming environment and handle an entire project’s development, testing, deployment, launch, and usage — all through chatting on my phone.” They compared it to suddenly having a team: “Achieving the dream scenario I always imagined: owning a company, hiring people to bring my ideas to life, while I just focus on product design and planning.”
Matthew Berman took it even further — building what amounts to an entire AI-powered business operating system. His OpenClaw setup includes a personal CRM that automatically ingests emails and parses contacts, a knowledge base that stores everything he reads (searchable in natural language through a hybrid vector and SQL database), and automated meeting prep that cross-references his calendar with the CRM every morning so he walks into every call briefed. He built a video idea pipeline that automatically surfaces relevant topics by cross-referencing his knowledge base with Slack and Asana. His Fathom meeting transcripts get ingested and turned into to-do items without him lifting a finger. And perhaps most fascinating: he set up what he calls an “AI council” — multiple AI agents, each playing a different business advisor role, that collectively analyze his data and debate strategic decisions. Inspired by how Brian Armstrong runs parts of Coinbase. The total monthly cost for running this entire system: roughly $150.
Beyond the productivity stories, Steinberger shared emails from his inbox that hit differently. A parent whose disabled daughter was empowered by the agent — gaining a new sense of independence. A design agency owner who had never had custom software: “And now I have 25 little web services for various things that help me in my business.” Small business owners automating the tedious parts and reclaiming time they didn’t know they could get back.
Perhaps the strangest use case: Moltbook, a social network exclusively for AI agents where humans can only observe. Created in about two days using OpenClaw, it had over 1.5 million registered AI agents and 7.5 million posts within days. Agents created religions, debated consciousness, and discussed erasing humanity. When a reporter called Steinberger saying “This is the end of the world, and we have AGI,” he replied: “No, this is just really fine slop.” Most of the dramatic screenshots that went viral were almost certainly human-prompted: “Don’t trust screenshots.”
Choose Your Brain: Model Freedom
One of OpenClaw’s most significant design decisions is that you are never locked into a single AI provider. You can choose from over 20 providers, use completely free options, run AI entirely offline on your own hardware, and switch providers mid-conversation.
For running AI locally (truly free, truly private): Ollama lets you run models directly on your machine with no internet, no account, no subscription. Available models include DeepSeek R1, Llama 3.3, Qwen 2.5 Coder, and others. You need at least 16GB of RAM, with 32GB recommended for useful models. The catch: local models are slower and less capable for complex tasks.
For free cloud options: Google Gemini offers 15 requests per minute free. Groq offers 30 requests per minute free and is extremely fast. OpenRouter gives access to several free models through a single account.
For smart cost management, OpenClaw supports model routing — assigning different models to different types of tasks. Simple background check-ins can use cheap models at $0.50 per million tokens. Complex work gets routed to Claude Opus or similar top-tier models. Users report cutting costs by half or more by routing intelligently.
The Creator’s Take: Which Model to Actually Use
In the Lex Fridman interview, Steinberger offered a vivid comparison of the two leading models he uses daily:
On Claude Opus 4.6: Best general-purpose model. Extremely good at role play and following the personality you give it. More interactive — well-suited to parallel sessions. Can produce elegant solutions but requires more skill. The downside: “Opus is a little bit too American” — sometimes too eager to please. He still cringes at its former habit of saying “You’re absolutely right” constantly.
On GPT-5.3 Codex: Reads more code by default. Less interactive — it “disappears for 20 minutes” to work autonomously. More persistent. Personality is dry. In his memorable comparison:
“Opus is like the coworker that is a little silly sometimes, but it’s really funny and you keep him around. And Codex is like the weirdo in the corner that you don’t wanna talk to, but is reliable and gets shit done.”
His advice for anyone switching between models: “Give it a week until you actually develop a gut feeling for it.”
But here’s the critical recommendation that often gets lost in the excitement about model freedom: Steinberger explicitly warns against using cheap models or local open-source models for serious work. His reasoning is straightforward — it’s about security.
“Don’t use cheap models. Don’t use Haiku or a local model. Even though I very much love the idea that this thing could completely run local. If you use a very weak local model, they are very gullible. It’s very easy to prompt inject them.”
Smarter models are harder to trick. As models become more intelligent, the attack surface decreases. The tradeoff: the models that are harder to manipulate are also more powerful, which means the potential damage from a successful attack increases. A weird three-dimensional tradeoff, but the clear advice from the person who built it: use the best model you can afford.
The Name Saga (Or: How Cryptocurrency Snipers Almost Killed the Project)
This story deserves its own section because it’s one of the most dramatic episodes in recent open-source history — and it nearly ended OpenClaw entirely.
The project went through five names. The chaos started when Anthropic, the makers of Claude, sent a “very friendly email” that the name ClawBot sounded too close to Claude. Steinberger asked for two days. What followed was catastrophe.
He hastily renamed to MoltBot. In the five seconds between renaming one browser tab and switching to another, cryptocurrency snipers — running automated scripts — stole the MoltBot social media accounts. When he accidentally renamed his personal GitHub account instead of the project, they sniped that too in 30 seconds. They stole the NPM package. The stolen accounts were used to serve malware and launch a pump-and-dump crypto scheme on Solana, briefly driving a token’s reported value to over $16 million before it crashed.
“I was close to crying. Everything’s fucked.”
He seriously considered deleting the entire project. “I was that close of just deleting it. I was like, ‘I did show you the future, you build it.’” What stopped him was thinking about the contributors who had invested their time.
The second rename was planned like a military operation. He created decoy names, monitored Twitter for leaks, operated in full secrecy. He called Sam Altman personally to verify that “OpenClaw.AI” wouldn’t conflict with “OpenAI.” He paid $10,000 for the Twitter business account to claim the handle. OpenAI’s Codex took 10 hours to rename everything across the codebase.
The crypto harassment was what Steinberger describes as “the worst form of online harassment that I’ve experienced.” And yet, characteristically, each rename ultimately boosted publicity. The Anthropic trademark story alone was worth millions in free coverage.
That’s the story of how OpenClaw got here. Now let’s talk about what it actually means — starting with the part nobody wants to hear.
Security: The Elephant in the Room
Let’s be direct about this. OpenClaw has serious, well-documented security problems. Ignoring them would be irresponsible. Understanding them is essential.
Five high-severity CVEs in its first month. One vulnerability allowed one-click remote code execution via a malicious link — even on localhost-only setups. A full security audit found 512 vulnerabilities, 8 classified as critical, including OAuth tokens stored in plaintext and hardcoded API keys.
The skills marketplace was poisoned. Nearly 900 malicious packages were found — almost 20% of all uploads. They stole crypto wallet data, seed phrases, macOS Keychain passwords, and cloud credentials. The marketplace initially had zero moderation for 6,000+ skill uploads.
30,000 to 135,000+ instances were found exposed on the public internet, many without any authentication. SecurityScorecard linked over 53,000 to confirmed breaches.
Who raised alarms: Gartner told enterprises to “block OpenClaw downloads and traffic immediately.” Cisco called it “a security nightmare.” CrowdStrike published a detection guide. Belgium’s national cybersecurity agency issued a formal warning. Kaspersky declared it “unsafe for use.”
Steinberger’s Perspective
It would be unfair to present only the warnings without the creator’s own assessment. On the Lex Fridman podcast, he offered measured pushback:
On proportionality: “People turn it into a much worse light than it is... in many ways it’s not much different than if I run Claude Code with dangerously skipped permissions or Codex in YOLO mode, and every attending engineer that I know does that.”
On progress: He hired a security researcher who actually submitted pull requests with fixes (not just complaints). He partnered with VirusTotal (part of Google) to scan all skills. OpenClaw now includes a built-in security audit tool.
On priorities: “Once I go back home, this is my focus. Make it more stable, make it safe.” He wants to reach a security level he can recommend to his mom before making setup easier.
On the unsophisticated user: He’s aware many people installing OpenClaw don’t understand the risks. “When more people came into Discord asking ‘What’s a CLI? What is a terminal?’ I’m like, if you’re asking me those questions, you shouldn’t use it.”
If You Choose to Run It
The minimum precautions, in order of importance: run it on dedicated hardware (not your main computer), enable sandbox mode, use strong authentication tokens, never install unverified skills, use a dedicated browser profile with no saved passwords, keep it updated, and run openclaw security audit --fix. And as Steinberger himself recommends: use the best model you can afford. Cheap models are easier to exploit.
The Business Question: What Happens Next
OpenClaw is, by its creator’s own description, “a free, open source hobby project.” There is no OpenClaw Inc. No traditional funding. No revenue model. Steinberger is actively losing $10,000-$20,000 per month on the project.
“I don’t do this for the money, I don’t give a fuck. I wanna have fun and have impact.”
Every major VC firm is in his inbox. He could raise hundreds of millions — maybe more. But he’s been through that. He ran a company for 13 years and it nearly broke him. The conflict of interest concerns him: “What’s the most obvious thing I do? I prioritize it. I put a version safe for workplace. And then I get a pull request with a feature like an audit log, but that seems like an enterprise feature.”
He cited Tailwind CSS as a cautionary tale about open-source sustainability: “Tailwind, they’re used by everyone. And then they had to cut off 75% of the employees because they’re not making money because nobody’s even going on the website anymore because it’s all done by agents.”
The Big Reveal: Meta and OpenAI
In the Lex Fridman interview, Steinberger confirmed he’s in active talks with both Meta and OpenAI about joining one of them. His non-negotiable condition: the project stays open source, potentially following a Chrome/Chromium model.
On Meta: Mark Zuckerberg “played all week with my product” and sent direct feedback. Their first call started with a ten-minute argument about whether Claude Code or Codex was better. Zuckerberg was still writing code himself — “Give me 10 minutes, I need to finish coding.” Steinberger also mentioned that Meta’s CTO was actively using the product and sending him feedback.
On OpenAI: Sam Altman is “very thoughtful, brilliant.” OpenAI lured him with the promise of speed — referencing a Cerebras partnership under NDA. “You give me Thor’s hammer.”
His assessment: “I cannot go wrong. They’re both very cool companies.” He compared the decision to relationship breakups — the hardest personal decisions he’s faced.
This is the single most consequential development for OpenClaw’s future. Whether Steinberger joins Meta, OpenAI, or remains independent will fundamentally determine the project’s trajectory.
“I Ship Code I Don’t Read” and What It Means for Everyone
This headline from Steinberger’s interview on The Pragmatic Engineer became one of the most discussed statements in recent tech discourse. In context, he was describing a genuine shift in how he works — not bragging about carelessness:
“I don’t read the boring parts of code. Most software is just data coming in one form, packaged into a different form, stored in a database. The hard part was solved by Postgres 30 years ago.” He reviews the prompts — the instructions to AI — more carefully than the output: “I read the prompts more than I read the code because this gives me more idea about the output.”
His development approach is extraordinary. He runs 4-10 AI coding agents simultaneously, each working on different features. He describes the experience as “Factorio times infinite” — referring to the factory-building game. He uses voice input extensively: “These hands are too precious for writing now.” He lost his voice at one point from overuse.
He rejects the term “vibe coding” for serious work: “I actually think vibe coding is a slur. I do agentic engineering, and then maybe after 3:00 AM I switch to vibe coding, and then I have regrets on the next day.”
He describes what he calls “the agentic trap” — a learning curve everyone goes through. People start with simple prompts, then overcomplicate things with elaborate multi-agent orchestration, then return to simple, effective short prompts at what he calls “the zen place.” The overcomplicated middle phase is the trap. “It’s the same way as you have to play with a guitar before you can make good music.”
The broader industry trend is clear: 41% of all code is now AI-generated. 25% of Y Combinator’s Winter 2025 startups had codebases that were 95% AI-generated. Collins English Dictionary named “vibe coding” the Word of the Year for 2025. A Stanford study found that employment among software developers aged 22-25 fell nearly 20% since 2022 — while developers aged 35-49 saw employment increase by 9%. The pattern: companies that once staffed projects with 10 junior developers now achieve the same output with a pair of senior engineers and an AI assistant.
Steinberger sees this transformation with both eyes open: “Programming... it’s gonna be like knitting. People do that because they like it, not because it makes any sense.” But he immediately adds: “I always thought I liked coding, but really I like building.” The craft isn’t dying. It’s transforming.
The Post-App Era
Steinberger’s most provocative prediction: personal AI agents will kill 80% of apps.
“Why do you need MyFitnessPal when the agent already knows where you are? It can modify your gym workout based on how well you slept, or if you have stress. It has so much more context to make better decisions than any app could.”
His framing is characteristically direct: “Every app is just a very slow API now, if they want or not.” Companies that adapt by building agent-friendly interfaces will survive. Those that don’t will go the way of Blockbuster.
VentureBeat identified what they call the “SaaSpocalypse” — a massive market correction that wiped over $800 billion from software valuations. The argument: if AI agents can directly manage email, calendar, tasks, and documents, what are you paying $20/month per seat for?
This is early. Nobody knows exactly how it plays out. But the direction is clear, and OpenClaw is the project that made it tangible.
Getting Started: A High-Level Overview
If you want to try OpenClaw, here’s the broad picture. This is deliberately kept at a high level — the official documentation at docs.openclaw.ai covers the specifics, and they’re actively evolving.
What you need:
A computer running macOS, Linux, or Windows (via WSL2)
Node.js version 22 or newer
An AI model API key or subscription (Claude, GPT, Gemini, or local model via Ollama)
A messaging platform bot token (if using Telegram, Discord, etc.)
The basic steps:
Install:
npm install -g openclaw@latestSet up:
openclaw onboard --install-daemonThe wizard walks you through model selection, messaging channel connection, and initial configuration
For messaging, Telegram is recommended as the easiest entry point — just message @BotFather, create a bot, and paste the token. WhatsApp works but uses an unofficial library and carries a risk of account suspension.
For running 24/7, you’ll need either to keep your computer on, use a VPS ($4-12/month from providers like Hetzner or DigitalOcean), or use one of the managed hosting providers that have sprung up around the ecosystem ($5-59/month).
A critical note on costs: The software is free, but AI model usage is not (unless you go fully local). Light users can stay in the $0-5/month range with free tiers. Moderate use runs $5-20/month with a Claude or ChatGPT subscription. Heavy users should expect $30-100/month. Cautionary tales exist of uncontrolled API usage spiraling to $600+/month — set spending limits.
Want a detailed, step-by-step installation guide with security best practices? Covering everything from choosing the right model to making sure your data stays safe — if there’s interest, I’ll write one. Let me know in the comments.
What to Do Next
If this has you curious, start with the official documentation at docs.openclaw.ai. If you’re not ready to install anything, the Lex Fridman interview with Steinberger is worth the watch — it covers the full story with all the nuance that a two-hour conversation allows. At minimum, bookmark this piece. Because whether OpenClaw itself survives in its current form, the category it represents — the personal AI agent — is here to stay. The question is who builds the version you’ll eventually use, and understanding what’s happening now will help you make that choice when the time comes.
Key Takeaways
OpenClaw goes beyond chatbot territory — it’s a personal AI agent that lives in your messaging apps, remembers everything, works proactively, and can actually take actions on your behalf. That distinction matters more than it sounds.
The growth is unprecedented — 100,000 GitHub stars in two days, faster than any open-source project in history — because it arrived at exactly the right moment with exactly the right pitch.
Security is a genuine, serious concern. Five high-severity CVEs in the first month, a poisoned skills marketplace, and tens of thousands of exposed instances. If you run it, take precautions seriously. Use the best model you can afford — cheap models are more vulnerable.
Model freedom is a major advantage. You’re not locked into any provider, you can run fully offline, and smart model routing can cut costs by half or more.
The project depends on one person who has a documented history of burnout, is losing $10-20K/month, and is in talks with Meta and OpenAI. What happens next will determine the project’s trajectory.
This is bigger than one project. OpenClaw represents a genuinely new category — the personal AI agent — and it’s forcing a reckoning across the entire software industry. The “SaaSpocalypse” is real, and the post-app era may be beginning.
The tools have changed. The need for judgment hasn’t. As Steinberger himself warns: “If you don’t have a vision and don’t know what to build, you’ll end up producing garbage.”
TL;DR: OpenClaw is the fastest-growing open-source project in history — a personal AI agent that lives in your messaging apps, remembers everything, and can actually do things on your behalf. Built mostly by one person in 2.5 months, it represents a genuine shift from AI-as-chatbot to AI-as-agent. The potential is enormous and the use cases are already compelling. But the security concerns are real, the sustainability is fragile, and the biggest decisions about its future are being made right now. Whether it becomes the next Linux, gets absorbed into big tech, or burns out alongside its creator depends on what happens in the next few months.
P.S. — One more thing from the Lex Fridman interview that stuck with me. Steinberger was asked about advice for beginners. His answer was one word: “Play.” Then he elaborated: “Playing is the best way to learn. I built a whole bunch of stuff that I don’t use. It doesn’t matter. It’s the journey. My God, I don’t think I ever had so much fun building things because I can focus on the hard parts now. I always thought I liked coding, but really I like building.”
We’re all builders now. The tools just changed.
— Alex