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Leon
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Leon
@leon_meier
Building AI agents in place of procurement teams
Berlin Katılım Şubat 2022
248 Takip Edilen368 Takipçiler

yearning for the bug-free apple experience
Anthropic@AnthropicAI
Mythos Preview has already found thousands of high-severity vulnerabilities—including some in every major operating system and web browser.
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Companies got used to a calmer world:
- lean teams
- concentrated supplier bases
- long qualification cycles
- and too much manual work to react quickly.
That is fine when supply is stable.
It is dangerous when energy shocks, route disruptions, and supply interruptions start compounding, which is exactly what markets are now trying to price. [1]
Being prepared now means having alternatives before you need them:
qualified fallback suppliers, better market visibility, and the ability to move fast under pressure.
(4/4)
[1] reuters.com/world/middle-e…
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Post-hormuz brings a new supply chain paradigm, that changes manufacturing, sourcing, and changes what “good procurement” means.
🔙 The old model rewarded whoever bought cheapest.
🔜 The next one will reward whoever can react fastest, when their suppliers declare force majeure.
Most companies are not yet built for this change.
(3/4)
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hear me out: usage-based taxes
1) Citzens and companies pay proportionate to their your use of government services and facilities. automagically attributed with ai (🪄)
2) the more people use a service, the more funding that gov agency receives.
3) The cost to run and improve the agency is split equally among all verified users of that gov facility.
e.g.
- #all defense, grid, trains, parks, public health...
- #uber overuse of roads
- ...
4) No elections for local gov, they just hire the best people for the job from the free market. High-paying prestigious jobs.
5) All citizens sit on on the board of all gov facilities that they're using, where they vote on granular budget allocation, new hires, attribution of gov.facility use, etc.
6) Fed gov keeps all national functions. Same election process. Reviews performance of local gov by law-binding KPIs and OKRs. can hire and fire with executive authority in special scenarios. Can set tax-to-use attribution model.
Rewards:
- Gov innovation and consolidation of functions through competition over a scarce max amount of taxes.
- Tax avoidance only possible through limiting their use of public property
- Crowdsourcing local public works projects by wealthy citizens and companies to.
- Each political partys only advocate for their thesis on optimal tax attribution logic. Hard mathematical models instead of political bs. Performance discussed publicly.
- Infrastructure is maintained obsessively to avoid outages which could result in a direct drop in tax revenue.
Disincentivizes:
- Waste
- Fraud
- Abuse
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That's so stupid, they're seriously walking around with "Stop Skynet" signs.
They've watched too many doom and gloom sci-fi movies.
We still have a lot of educational work to do, friends.
Michaël Trazzi@MichaelTrazzi
On our way to OpenAI!
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cowgirithm is the dumbest thing i heard today
Trung Phan@TrungTPhan
AI is coming to farms. Halter makes AI-powered cow collars. Valued at $2B+, the NZ startup helps farmers: ▫️track GPS location ▫️monitor cow health ▫️draw virtual fences on an app to herd cows via a “cowgirithm” (guide to grazing areas with vibrations and audio cues in collar)
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I often ask myself: 10 years from now, when I look back on what happened in this period in history, what would I have wanted myself to do in retrospect?
Teaching my friends and family how to describe their intent to models and vibe-create anything is probably among the highest ROI things.
Everyone has at least one beautiful idea in their lifetime.
If they had the power to bring that idea to life, how could the world not be more beautiful.
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Leon retweetledi

My ux friend has been getting super into cursor lately. it's about time i encroach on his territory
#pixelpushers
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@dwarkesh_sp Hardware supply chains are as big a blocker for AI rollout as energy, if not bigger.
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EUV machines are the most complicated tools humans make. Their supply chain has over 10,000 individual suppliers, and any one of them not scaling fast enough can bottleneck the entire AI industry.
An EUV tool fires lasers at a tiny tin droplet three times in precise sequence, blasting it hard enough to emit EUV light. That light bounces off 18 multilayer mirrors onto the wafer. Meanwhile, the two platforms inside the machine - one holding the stencil, one holding the chip - are flying back and forth at 9Gs in opposite directions. The successive passes have to land on top of each other to within 3 nanometers. If any part of this is off, yield goes to zero.
Take just one component. The mirrors are mostly supplied by Carl Zeiss, who have probably fewer than a thousand people working on them.
In turn, Carl Zeiss rely on machines from Switzerland to deposit each of the layers, and use a coating process co-developed with a different German company.
None of these companies have woken up. They’re gradually increasing production, but nowhere near the levels necessary for what the labs want by the end of the decade.
@dylan522p predicts production can't scale beyond about 100 EUV machines per year by 2030, no matter how much money gets thrown at the problem. In the medium term this is the key bottleneck on scaling.
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Merz called Germany's nuclear phase-out 'a serious strategic mistake.' Two months later: 'irreversible.' Meanwhile Germany is burning coal and the rest of the world is building reactors. Bring back nuclear everywhere.
gli.st/nzb54xgl
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