Chris Strobl

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Chris Strobl

Chris Strobl

@chrisstrobl

Katılım Eylül 2012
1.3K Takip Edilen9.7K Takipçiler
Adam Barta
Adam Barta@AdamBartas·
First sign that Lovable is dead Pivoting to general assistant is the most "investor-pleasing" move you could do Their app building business is obv going nowhere and investor money is drying up Why should anyone use Lovable instead of the already established ecosystems
Anton Osika – eu/acc@antonosika

Introducing Lovable for more general tasks. Lovable has always been for building apps. Today it also becomes your data scientist, your business analyst, your deck builder, and your marketing assistant. This is a big step toward what Lovable is becoming: a general-purpose co-founder that can do anything. See examples below.

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Chris Strobl
Chris Strobl@chrisstrobl·
In the last 30 days, a lot of people pivoted in the wrong direction. Just look at Lovable. They shouldn't have changed anything... instead, presentations? Idiotic. Gamma is already doing that.
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Chris Strobl
Chris Strobl@chrisstrobl·
Demand for AI managers is unreal
Chris Strobl tweet media
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dionizije
dionizije@dionizije_fa·
@chrisstrobl 2/2 which I think gets missed a lot is - intermediate supply competition. If someone creates a building factory he will buy up all the supply. Think GPUs, the one who discovers hands off RL will push prices up like Sama did with RAM thus removing opportunity from others.
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Chris Strobl
Chris Strobl@chrisstrobl·
Logic 101
TBPN@tbpn

.@travisk says AI will make human labor even more valuable and in-demand than ever before: "Let's say the entire world - everything in our world - was automated, except for plumbers. You had machines making buildings - you would basically have like a thousand buildings a day." "How valuable would those plumbers be?" "Each and every plumber would be like LeBron. Why? Because plumbing would be the long pole in the tent to progress. You can't get those thousand buildings unless you have a plumber." "And by the way, you'd get so much efficiency everywhere else that you'd need millions of plumbers." "Humans [are going to] become more and more valuable because they will be the long pole in the tent to progress - and that progress is going to accelerate and get faster and more robust."

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dionizije
dionizije@dionizije_fa·
@chrisstrobl Limited by resources in this example tho, there are multiple constraints in that reasoning path.
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John Rush
John Rush@johnrushx·
wtf this dude is a genius he completely changed my mind about the future @lexfridman pls bring him on your pod asap for a 8h conversation…I’ll watch it in one shot I guarantee
TBPN@tbpn

.@travisk says AI will make human labor even more valuable and in-demand than ever before: "Let's say the entire world - everything in our world - was automated, except for plumbers. You had machines making buildings - you would basically have like a thousand buildings a day." "How valuable would those plumbers be?" "Each and every plumber would be like LeBron. Why? Because plumbing would be the long pole in the tent to progress. You can't get those thousand buildings unless you have a plumber." "And by the way, you'd get so much efficiency everywhere else that you'd need millions of plumbers." "Humans [are going to] become more and more valuable because they will be the long pole in the tent to progress - and that progress is going to accelerate and get faster and more robust."

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Chris Strobl
Chris Strobl@chrisstrobl·
@gdb So insanely early. Airbnb got successful with 7-star product experience mindset. AGI is a 700 star experience.
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Ryan Carson
Ryan Carson@ryancarson·
Holy shit, just had a feel-the-AGI moment. Symphony was running overnight, working on merging a bunch of PRs, and it encountered some extra issues in the codebase that were related to PR but not directly applicable. It created a backlog issue in Linear and documented it. 😳 What's even more amazing about that is now I can simply drag that issue into the "To Do" column in Linear, and Symphony will automatically pick it up, start working on it, and ship it.
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Chris Strobl
Chris Strobl@chrisstrobl·
Follow Jacob. He is at the cutting edge. Not luck. Skill.
Jacob Klug@Jacobsklug

I've made $4.7M with AI. Today, I recorded myself building an entire @openclaw business in an hour. You'll learn how to: • Find a winning niche using AI • Build an offer people actually want to pay for • To get your first clients • To use AI to productize and fulfill the entire service • The exact prompts I use to orchestrate all of it After building the #1 Lovable agency. This is the entire playbook behind AI native businesses. The full video is live on YouTube. Comment "Playbook" and I'll DM you the link.

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Chris Strobl
Chris Strobl@chrisstrobl·
@christinaqi Patati patata Zuck just doesn’t care about AI. Instagram (e.g., mimetic desires) will grow 100x or 1000x because of AI. He is winning without actually competing.
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ᴅᴀɴɪᴇʟ ᴍɪᴇssʟᴇʀ 🛡️
Ok, I figured out the best way to explain the significance of what Karpathy has done with his autoresearch project. Automation of the scientific method. This is what ML researchers do. They come up with an idea, and then they have to figure out how to test it, which is the experiment design piece. And it's all **super** kludgy and fragile. Tons of wrestling with the different tools and frameworks, getting the code right, all so that you can run an experiment that will take days to run. *Experiment doesn't work. Cool, back to the idea phase. In other words, some massive amount of AI Researcher time IS WASTED. Only a small amount of the time is able to be spent on coming up with ideas. Most of it is managing a shitstack of fragile tech that runs the experiments. Which take forever. Karpathy just automated this. He built and released an *open-source* stack for automating this entire process. You just put what you want to do into a Project.md file and send it off, and it builds all the experiments, all the code, and goes and executes and tells you which ones were successful. And the idea isn't just for a single researcher, but he's already thinking about how you can do like SETI on the whole thing, where you have compute that can take experiments and run them on shared infrastructure. This is the biggest project in all of AI, probably since Claude Code, and it's not close.
Andrej Karpathy@karpathy

I packaged up the "autoresearch" project into a new self-contained minimal repo if people would like to play over the weekend. It's basically nanochat LLM training core stripped down to a single-GPU, one file version of ~630 lines of code, then: - the human iterates on the prompt (.md) - the AI agent iterates on the training code (.py) The goal is to engineer your agents to make the fastest research progress indefinitely and without any of your own involvement. In the image, every dot is a complete LLM training run that lasts exactly 5 minutes. The agent works in an autonomous loop on a git feature branch and accumulates git commits to the training script as it finds better settings (of lower validation loss by the end) of the neural network architecture, the optimizer, all the hyperparameters, etc. You can imagine comparing the research progress of different prompts, different agents, etc. github.com/karpathy/autor… Part code, part sci-fi, and a pinch of psychosis :)

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Greg Brockman
Greg Brockman@gdb·
Benchmarks? Where we’re going, we don’t need benchmarks.
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Anton Osika – eu/acc
Anton Osika – eu/acc@antonosika·
The time it took you to read this, Lovable wrote 500k lines of code.
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andrew chen
andrew chen@andrewchen·
I know I’m not the only one Mon-Fri: zoom zoom email email gsheet gdoc Fri-Sun: ssh tmux vi codex playwright openclaw npm claude git ollama cursor
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Adam.GPT
Adam.GPT@TheRealAdamG·
Don’t sleep on the “visual understanding” capabilities of 5.4
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