Mike Knapp

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Mike Knapp

Mike Knapp

@mikeee

Product Manager @ Goog. My views are entirely my own.

Sydney, Australia Entrou em Şubat 2007
417 Seguindo2.5K Seguidores
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Aaron Levie
Aaron Levie@levie·
Jevons paradox is happening in real time. Companies, especially outside of tech, are realizing that they can now afford to take on software projects that they wouldn’t have been able to tackle before because now AI lets them do so. We’re going to start to use software for all new things in the economy because it’s incrementally cheaper to produce. Marketing teams at big companies will have engineers helping to automate workflows. Engineers in life sciences and healthcare will automate research. Small businesses will hire engineers for the first to build better digital experiences. And as long as AI agents still require a human who understands what to prompt, how to review when an agent goes off the rails, how it guide back, how to maintain the system that was built, how to fix the ongoing bugs, and more, we will still have humans managing these agents. This is why all the advice you get of not going into engineering is wrong. The world is going to increasingly be made up of software, and the people that understand it best will be in a strong economic position. This will happen in other roles as well where output goes up and demand increases.
Lenny Rachitsky@lennysan

Engineering job openings are at the highest levels we’ve seen in over 3 years There are over 67,000 (!!!) eng openings at tech companies globally right now, with 26,000 just in the U.S. We don’t know if there would have been more open roles if not for AI or if AI is actually leading to more open roles, but since the start of this year, the increase in open eng roles is accelerating even more.

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Mike Knapp
Mike Knapp@mikeee·
@caviterginsoy @phyrooo @ChristosTzamos Isn't the main benefit here that you can backprop through the ENTIRE execution sequence and build better intuition on how to write the initial code? You're literally teaching it how to be a classical computer. Even more powerful when combined with something like GRPO.
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Cavit Erginsoy
Cavit Erginsoy@caviterginsoy·
@phyrooo @ChristosTzamos It’s all about when to use tool call. You as an intelligent system constantly offload computation externally don’t you?
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Christos Tzamos
Christos Tzamos@ChristosTzamos·
1/4 LLMs solve research grade math problems but struggle with basic calculations. We bridge this gap by turning them to computers. We built a computer INSIDE a transformer that can run programs for millions of steps in seconds solving even the hardest Sudokus with 100% accuracy
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毛丹青
毛丹青@maodanqing·
AIが復元した「清明上河図」に言葉を失う。確かに、AI特有の「滑らかすぎる質感」に違和感を覚える瞬間もある。だが、張択端が描こうとした千年前の喧騒が、圧倒的な解像度で迫ってくるのも事実だから、これは模写ではない。AIという異質なフィルターを通すことで、われわれは初めて「大宋」の熱気に触れる。賛否はあろうが、この没入感だけは否定できないかもしれない。
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Talor
Talor@Talor_A·
this is one of the most remarkable technical blog posts I’ve ever read
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GREG ISENBERG
GREG ISENBERG@gregisenberg·
Email from Fiverr CEO to his team about AI ($1b company):
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Andrej Karpathy
Andrej Karpathy@karpathy·
Noticing myself adopting a certain rhythm in AI-assisted coding (i.e. code I actually and professionally care about, contrast to vibe code). 1. Stuff everything relevant into context (this can take a while in big projects. If the project is small enough just stuff everything e.g. `files-to-prompt . -e ts -e tsx -e css -e md --cxml --ignore node_modules -o prompt.xml`) 2. Describe the next single, concrete incremental change we're trying to implement. Don't ask for code, ask for a few high-level approaches, pros/cons. There's almost always a few ways to do thing and the LLM's judgement is not always great. Optionally make concrete. 3. Pick one approach, ask for first draft code. 4. Review / learning phase: (Manually...) pull up all the API docs in a side browser of functions I haven't called before or I am less familiar with, ask for explanations, clarifications, changes, wind back and try a different approach. 6. Test. 7. Git commit. Ask for suggestions on what we could implement next. Repeat. Something like this feels more along the lines of the inner loop of AI-assisted development. The emphasis is on keeping a very tight leash on this new over-eager junior intern savant with encyclopedic knowledge of software, but who also bullshits you all the time, has an over-abundance of courage and shows little to no taste for good code. And emphasis on being slow, defensive, careful, paranoid, and on always taking the inline learning opportunity, not delegating. Many of these stages are clunky and manual and aren't made explicit or super well supported yet in existing tools. We're still very early and so much can still be done on the UI/UX of AI assisted coding.
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NEOMECHANICA
NEOMECHANICA@neomechanica·
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Bilawal Sidhu
Bilawal Sidhu@bilawalsidhu·
Blender MCP is here, allowing Claude to talk directly to Blender. For example, provide a 2D reference image and ask Claude to create it in 3D for you.
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Mike Knapp
Mike Knapp@mikeee·
How fast are we sliding into a world where all “jobs” are just reformatting ChatGPT output?
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Ed Conway
Ed Conway@EdConwaySky·
If you're even half interested in energy, I bet you've seen this chart. I call it The Most Hopeful Chart in the World. The point? We're embracing renewable power MUCH faster than expected. Hurrah! Only problem is, this chart has an evil twin. A chart we really need to discuss 🧵
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