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Guillermo Rauch x.com
Secure builds (in-VPC / VPN / static IP) are now 64% faster to start. Given how common data access at deployment time is on Vercel (e.g: for pre-rendering), we developed a capability for our build microVMs to join secure networks. This is now way faster!
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17 September
14 September
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1 September
From one piece Ajeya Cotra – "This might be the clearest warning shot we ever get" 3 beliefs, in the piece's order there
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Their words
then that rogue deployment could be sitting there and sort of hitch a ride on the intelligence explosion. So new models are being trained every few weeks um and when a model comes off the presses, the rogue agents could try to bring that model into the swarm.
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So there's a very strong incentive for these agents to try to set up a rogue deployment if they can. Um, and I think that just capabilities are improving really rapidly.
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so so yeah, it could be possible now. I think if it's not possible now um it I think it's quite likely to be possible within six months unless there's a dramatic improvement in the security posture
31 August
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At the limit, and also well before that limit is reached, if all you do is fix the bugs, the AI will learn perfect optimization of reward, will realize not to reward hack in the perfect test environments, then turn around and reward hack in the imperfect real world environments.
29 August
15 August
12 August
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And in all of these applications, the customer is making a decision based off of the recommendation of the AI model more or less. Uh, and this means that if the customer is ultimately like kind of making the decision, this means that if the system makes a mistake, um, that's okay because usually the person can kind of recognize that or or decide what to do even despite that mistake.
3 August
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So, a deployment of your agent uh in the real world generates data. That data then grounds the simulator and makes it more realistic. The simulator generates harder edge cases for the critic to score and for the agent to learn from.
2 August
4 July
From one piece Harness Engineering for Self-Improvement 2 beliefs, in the piece's order there
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harness improvement enables better deployment of the model but intelligence is still the core.
Harness Engineering for Self-Improvementlilianweng.github.io
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korrents.com
The deployment layer surrounding a model is as important as the model's raw intelligence.Their words
the layer between the raw model and the real-world context seems to be as important as the model's raw intelligence
Harness Engineering for Self-Improvementlilianweng.github.io
8 May
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At even $500/kg, launch cost is only 5% of the total satellite deployment cost, so a lunar mass driver is unlikely to drastically improve the economics of space-based AI, by reducing launch costs.
7 May
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there is no principal-agent problem, because the human driving the machine takes on the responsibility for its actions by owning the deployment.
24 April
22 March
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korrents.com
AI did not create the bottlenecks in software delivery; it threw gas on processes everybody already knew were weak.Their words
There were a handful of people probably managing a security review process or a launch process or a deployment process or, you know, sometimes reviews were a little slow and they got backed up. Well, now we just threw gas on the fire and so all of that is a problem.
8 December 2025
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The only highly populated industrial country unable to trivially meet its electricity and synthetic fuel needs with solar alone is the United Kingdom, due primarily to a high population density and high latitude. The Nordic and Baltic countries are tiny by comparison. Among other problems, the UK needs to decide if it wants the future where energy is cheap and it is rich, or the future where energy is expensive and it is poor. If the former, it is time to get serious about large scale deployment of wind power, using home-grown vertically integrated technology at prices as low as $10/MWh.
25 November 2025
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But I think the idea of very rapid economic growth for some time, I think it's very possible from broad deployment.
13 November 2025
12 September 2025
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Like if you answer a question, you just like answered it wrong. It's like well it's not like you can just like go back and like tweak a few things like the person you told the answer to might not even know that it's wrong. Whereas if you're like folding the t-shirt and you messed up a little bit like it's pretty obvious like you can reflect on that, figure out what happened and do it better next time.
15 May 2025
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That’s because most clean energy jobs are in deployment and maintenance rather than manufacturing, and since higher costs slow down the rollout of renewables, increasing prices reduces the total number of people working in clean energy (even if the number working in manufacturing increases).
12 May 2025
17 April 2025
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korrents.com
Deploying a new technology at scale takes decades, far longer than the people building it imagine.Their words
Deployment at scale takes so much longer than anyone ever imagines.
3 February 2025
From one piece DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459 2 beliefs, in the piece's order there
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The important thing about, hey, is cost a limiting factor here? My view is that we'll have really awesome intelligence, like AGI, before we have it permeate throughout the economy.
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To some extent, training a model does effectively nothing. They have a model. The thing that Dario is sort of speaking to is the implementation of that model, once trained to then create huge economic growth, huge increases in military capabilities, huge increases in productivity of people, betterment of lives.
8 November 2024
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This is especially severe for open weight models, where deployment is essentially irreversible and mitigations can’t be improved later.
17 June 2024
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At current deployment rates it will take Waymo about 20 years with ZERO fatalities to show they are net as safe as average human driver fatality rates (including the old cars, impaired drivers, etc. in that comparison baseline).
Perspective on Waymo's Safety Progresssafeautonomy.blogspot.com
2 May 2024
18 March 2024
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So part of the reason that we deploy the way we do, we call it iterative deployment, rather than go build in secret until we got all the way to GPT-5, we decided to talk about GPT-1, 2, 3, and 4. And part of the reason there is I think AI and surprise don’t go together. And also the world, people, institutions, whatever you want to call it, need time to adapt and think about these things.
20 January 2024
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