xamuex

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xamuex

@bluezenx

I know that you can do all things, no purpose of yours can be thwarted.

Katılım Temmuz 2023
101 Takip Edilen52 Takipçiler
xamuex
xamuex@bluezenx·
@barrowjoseph @DenisPeskoff You should monetize this, what you have here is the bible for doing business in any part of the country.
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Joe Barrow
Joe Barrow@barrowjoseph·
New paper: every law in America is technically public. But not really, until now! With @DenisPeskoff at UC Berkeley, we built a corpus of ~every publicly accessibly city and county law, and released a huge chunk of it! 2.2 million laws, you're (probably) covered in it! 🧵
Joe Barrow tweet media
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xamuex
xamuex@bluezenx·
@zanehengsperger Hire Apprentice post high school, put them through a basic aptitude test. Promote it as an option in high school. It will take time that's for sure.
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Zane Hengsperger
Zane Hengsperger@zanehengsperger·
I think every manufacturing company can agree hiring quality factory technicians is insanely hard someone should solve this, we are working on it, but its a huge difficult problem
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xamuex
xamuex@bluezenx·
@FischerKing64 My cousin got an MBA because he was feeling inadequate, he is still inadequate but with a loan.
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FischerKing@FischerKing64·
My guess is 90% of people with ‘MBAs’ are worthless pieces of shit who should be mowing lawns.
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xamuex
xamuex@bluezenx·
@lennysan @evanspiegel No it wont, the Chinese will make a copy of it. They might even put there copy of software on it.
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Lenny Rachitsky
Lenny Rachitsky@lennysan·
There's a very good reason @evanspiegel is investing so heavily in hardware. Everything Snap has built over the last 15 years has been copied. His thesis is that as AI makes writing software easier and easier, hardware will be one of the only remaining durable moats.
signüll@signulll

as an unwilling snap bag holder, i find this deeply disappointing. i’m genuinely curious what evan’s internal reasoning is here. why devote scarce attention, talent, & capital to something like this when the core business still feels so obviously unresolved? like i want to be generous. i just can’t think of anything generous.

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FischerKing
FischerKing@FischerKing64·
One of my favorite Steve Jobs stories is that he called the President of Hewlett-Packard in high school for business advice. It’s amazing that this was even possible. We have business and government so centralized now, with so many layers, that you can’t break through this way. HR departments, and now AI reading resumes, are closing doors to people with talent and ambition. And centralization will make it harder and harder for people to challenge the establishment. We are drifting towards USSR stagnation accidentally.
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xamuex
xamuex@bluezenx·
@bsilone This is not new, now do what ever you need to
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Ben Silone
Ben Silone@bsilone·
Building gigawatt scale datacenters in the oceans may be even easier than I had previously thought. Normally for OTEC cold deep sea water and warm surface waters are needed. Because that gigawatt is generating a lot of heat as an end product of compute, along with some additional solar thermal collection, we won’t need warm surface water at all, allowing these datacenters to go almost anywhere with enough depth. Additionally, that gigawatt of heat may be hotter than surface seawater normally used for OTEC, allowing for much shallower piping (200m instead of 1000m) while also being more efficient. The availability of unlimited cold water for the condenser is the key factor we can’t replicate on land. This brings the potential energy generation for ocean based compute up from 10 terawatts to potentially thousands of terawatts, as the locations are not limited to areas with the warmest surface waters. The excess energy needed for hundreds or thousands of people to also live on these structures is minimal compared to that needed for compute.
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xamuex
xamuex@bluezenx·
@CityBureaucrat You're going to need the bottom view to complete the spec
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austin petersmith
The Certifiably Insane Way to Build an AI Agent: 1. choose a category where mistake tolerance is roughly the same as it is in self-driving cars. we chose "email-based scheduling assistant." many people want this product, but they immediately fire him if he screws up an interaction with a prospect, a candidate, or a potential investor 2. you learn that the edge cases are too complex and too frequent to be solvable. ours: managing timezones for people who travel (and change travel plans) constantly. knowing when NOT to respond, when to text the customer on the side to verify something, when to follow up, which sub-calendar to use, when to bend the rules on availability, when we can schedule that one type of call during your commute but not the other type of call. sharing your availabilities without compromising your privacy. and on and on. 3. the product doesn't feel viable, but you don't want to give up. you spend hours in a hot tub in Marin with a friend who makes self-driving cars. you make a plan to do it the way they did: hold the steering wheel. you go home and build a human-in-the-loop platform and hire contractors to serve as a backstop and catch mistakes before they happen (and to help design a map of what a world-class EA would do in every weird scenario). you decide trust is the currency in your category, so it must be the thing you won't compromise on. the product must succeed at any scheduling request, no matter how complicated. 4. you instantly feel an overwhelming market pull. so you keep going, growing that team to 75 people working 24/7 to support the nonstop scheduling needs of your customers. tons of engineering time goes to scaling the human platform instead of building the product. 5. you try to raise a Series A and investors say you are insane. your gross margins are extremely negative. they believe this is a problem worth solving, but they don't believe it is as hard to solve as you say. they want AI, not humans. your competitors put "NO HUMANS IN THE LOOP" on their landing pages to call you out. you keep going. 6. you work day and night building the harness that can meet the quality standard your customers have come to expect. you create a massive synthetic gold dataset. audit it, and clean it, label it. repeat. then, experiments. fine-tuning. RL. ACE. DSPy. sub-agents. sub agents for your sub-agents. rebuild the harness. throw more tokens at the problem. 7. some weeks you make big progress. some weeks your evals climb a single basis point, but that's better than nothing. more experiments. more tokens. john coogan said the hot trend in 2026 will be dogged pursuits. that pushes you to continue the pursuit, doggedly. 8. then, one day, you realize you are scheduling thousands of meetings a day and approaching 50% autopilot with no increase in churn or complaints. you put 150 customers in a full self-driving experiment, and they use the product MORE than they were using it when they had the human backstop. you can really start to let go of the steering wheel. 9. you don't know yet if this was a hill worth climbing, but you are nonetheless stoked that you can see the top. you have created a proprietary map of what to do in a million different situations. nobody else has that map, and the models keep getting better at following maps. your plan was to bet on trust, and your product can be trusted. today was the first day Howie crossed 50% autopilot:
austin petersmith tweet media
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xamuex
xamuex@bluezenx·
Me visiting the Tech section on X
xamuex tweet media
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Surya Ganguli
Surya Ganguli@SuryaGanguli·
Our new paper lead by @vedanglad w/@AToliasLab "Letting the neural code speak..." arxiv.org/abs/2605.12485 We show how to get *monkey* visual neurons to TELL us in *human* language what images make them fire. We do this is an automated verifiable way at scale! How? 1) Build a digital twin of monkey visual areas that can accurately map visual inputs to neural activity. 2) Perform in-silico experiments on this twin to find many complex images that make a model neuron fire. 3) Use a vision-language model to describe these complex images. 4) Verification: use a language-conditioned diffusion model to generate new images, and check they make the monkey digital twin neurons fire a lot. To our knowledge, for the first time, we have a way to convert monkey vision to human language, and from *human* language to sample infinitely many images that make any given *monkey* visual neuron fire, all in an algorithmic fashion. For more exciting details, see @vedanglad's excellent thread!
Vedang Lad@vedanglad

How well can you describe the feature selectivity of a vision neuron … with words? Interpretability has long borrowed from neuroscience — and maybe it can give back too! 🧵

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xamuex
xamuex@bluezenx·
@IBM Happy Birthday!
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IBM
IBM@IBM·
We turn 115 today! 🥰 Say happy birthday 😠
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Charles Curran
Charles Curran@charliebcurran·
I used AI to explain SpaceX to my girlfriend, with fruit.
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xamuex
xamuex@bluezenx·
@satyanadella What applies to individuals applies to companies as well. We are in a recursive loop of extraction, the game is called last man standing.
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Charles Curran
Charles Curran@charliebcurran·
I used AI to explain the Anthropic drama to my girlfriend, with fruit.
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Tim Burchett
Tim Burchett@timburchett·
This rat snake didn’t bite me out of professional courtesy. Hey @RobertKennedyJr you got nothing on me Brother. Snakes are our friends. #SexyLegs
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Andra
Andra@BioavailableNd·
Why do you think this is happening?
Andra tweet media
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