Dan Elitzer

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Dan Elitzer

Dan Elitzer

@delitzer

Building and backing early-stage teams @nascent

Katılım Mart 2009
2.3K Takip Edilen25.2K Takipçiler
Dan Elitzer
Dan Elitzer@delitzer·
A well-known VC once said something about analogies ;) But yeah, this feels like the right start to a framework. I’d also add something about determinism and edge cases: if the workflow follows highly structured and deterministic processes, less AI; if you expect at least semi-frequent edge cases and handling less-structured inputs, AI is great
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saila
saila@sailaunderscore·
Dashboards Vs Screens: I was speaking with a well-known VC the other day about AI-apps and which ones are likely to work, and I made an analogy he liked (analogies may also be wrong). For a long time, car dashes had tons of buttons and knobs: - Want to turn up the AC? Turn a knob; - Want to increase the volume? Turn a knob; - Want to go to a specific radio? Press a button. At some point companies started to put screens in cars -- this was partially for other reasons, like the ability to download media (songs, maps, contacts, etc) and the ability to push updates through software -- but in large part it was because this would allow you to do *MANY THINGS* through *ONE INTERFACE*. Some functionalities remained on buttons or knobs more, some moved to screens more, there are a few things that never moved to screens, these are things like: - The shifter - The gas pedal - The turn signals These functionalities remained because they were *MISSION CRITICAL* and have a more *DETERMINISTIC AND NARROW OUTPUT SPACE*. These are the two core ideas behind the analogy. A lot of those knobs were streamlined onto the screen, because they were not mission critical and had a wide enough output space that a screen was genuinely helpful. You gain a lot by putting music selection onto a screen because: - If it breaks you ride in silence. - You can search more easily through a wide search space. - It becomes software that you can change in and out, rather than a CD. You lose a lot by putting the brakes onto a screen because: - If it breaks you die. - You need the break to respond quickly. - You either want the break on or off, you don't gain much from the screen. We can use these same principles for AI. Cases where AI is less interesting: - If it's a simple task that can be done quickly somewhere else. - If it's a task with one clear best provider. Cases where AI is more interesting: - If it's a complex task that requires juggling information or context. - If it's a task with many providers with non-commoditized differences.
saila@sailaunderscore

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Dan Elitzer
Dan Elitzer@delitzer·
@ExaltedFoks There’s always been opportunity cost to saving for retirement
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foks
foks@ExaltedFoks·
@delitzer Theres an opportunity cost from not using that money elsewhere right now though
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foks@ExaltedFoks·
Are 401ks worth contributing to anymore or will we be post-scarcity by the time I’m sixty
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p3shoemaker
p3shoemaker@p3shoemaker·
"ai-powered software" is like saying "electric computer" the descriptor might stick for a few things (electric toothbrush, electric car) but majority of tech will treat it as given and not even mention it
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Dan Elitzer
Dan Elitzer@delitzer·
Berkeley about to be the top school for aspiring traders
Dan Elitzer tweet media
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Dan Elitzer
Dan Elitzer@delitzer·
OpenAI and Sol | Threat actors
Dan Elitzer tweet mediaDan Elitzer tweet media
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p3shoemaker
p3shoemaker@p3shoemaker·
founder diligence tip: pronouncing revielle correctly suggests the foudner is either european or spends lots of time in europe (both are bearish)
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Dan Elitzer retweetledi
Evan Rama
Evan Rama@Evanrrama·
Our company uses @BlinkCashX for deposits, and I genuinely have to show some love to the team behind it. The product is insanely simple, fast, and reliable. They've been absolutely crushing it and have made onboarding users way easier for us. If you're building in crypto, definitely check out Blink. Nothing but respect for the team.
Blink@BlinkCashX

Everyone has been asking where they can try Blink. So we built an app powered by @megapot so you can test the flow yourself. Deposit $1, see how easy Blink feels, and get 3 Megapot tickets on us. Link in bio <3

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Alex C
Alex C@AlexQuellsIt·
@delitzer How’s this problem not solved!?
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Dan Elitzer
Dan Elitzer@delitzer·
Why is “Block and Report Junk” a separate action than “Delete and Report Spam”? I just want these scammers to not be able to contact me anymore!
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Dan Elitzer
Dan Elitzer@delitzer·
@lay2000lbs Yeah, took a draft I'd been working on and tried having the agent re-work it quickly as a reply to that article. Didn't quite hit right, so will need to approach it again the old way.
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Leighton
Leighton@lay2000lbs·
@delitzer this reads like your agent wrote it?
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Dan Elitzer
Dan Elitzer@delitzer·
Satya brings up an important point around the compounding of systems and learning that AI and agentic workflows enable. He makes the case for how this will happen inside companies to make the firm smarter introducing the term “token capital” as somewhat of a modern update to Coase’s Theory of the Firm. But people have always compounded too. You learn how to write the memo, spot the weak assumption, scope a feature, read the room. When you leave, you take that with you. Nobody ever proposed you should leave it behind, because the knowledge lived in your head and your head went where you went. As we get deeper into the AI era, agents are quickly starting to change the container for that knowledge and compounding of capabilities. My agent is a folder of markdown files: instructions, skills, memories. It's been compounding for the better part of a year. Some of those skills I built on my own. Some were sharpened at work. If I zip that folder and carry it to the next job, every abstract boundary question becomes a file question: what can live where, what transfers ownership. Whether the agent learned something confidential is not philosophical, it is a grep. The reason this is harder than it looks: the old system worked because human memory is lossy. You left a job with patterns but not databases. Biology sufficiently redacted the specifics of process and large corpuses of confidential information, because there’s only so much detail most of us can carry in our heads. Trade secret law tolerated the transfer because it arrived pre-sanitized by forgetting. Forgetting was load-bearing, and agents broke it. A personal agent can carry too much out. But a corporate agent that captures everything and transfers nothing at offboarding doesn't just protect secrets, it confiscates professional growth. If every company does that, the talent pool we all hire from gets shallower. And there's a deeper question that current policy doesn't touch. If I build a skill on my own time and then use it at work because it makes me better at my job, does the company acquire a claim? If not, what stops me from developing everything personally? But if employers respond by requiring all work through corporate agents, the worker's compounding loop gets locked inside the firm. The question of who owns what is not one line. It's four separate questions: what the agent can do (authority), what it can see (data), what it remembers (memory), and what it carries forward as reusable procedure (skill). Authority and data are relatively easy to handle through existing IT tools and policies; memory and skills are where it gets messy, because what were formerly lossy, imperfect processes are increasingly being crystalized in a way that makes them formal and precise. The more we come to rely on our agents, the more challenging and urgent these questions will become.
Satya Nadella@satyanadella

x.com/i/article/2065…

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Dan Elitzer retweetledi
Proto Bahn
Proto Bahn@proto_von·
"The question of who owns what is not one line. It's four separate questions: 1. what the agent can do (authority), 2. what it can see (data) 3. what it remembers (memory) 4. what it carries forward as reusable procedure (skill). Authority and data are relatively easy to handle through existing IT tools and policies; memory and skills are where it gets messy, because what were formerly lossy, imperfect processes are increasingly being crystalized in a way that makes them formal and precise."
Dan Elitzer@delitzer

Satya brings up an important point around the compounding of systems and learning that AI and agentic workflows enable. He makes the case for how this will happen inside companies to make the firm smarter introducing the term “token capital” as somewhat of a modern update to Coase’s Theory of the Firm. But people have always compounded too. You learn how to write the memo, spot the weak assumption, scope a feature, read the room. When you leave, you take that with you. Nobody ever proposed you should leave it behind, because the knowledge lived in your head and your head went where you went. As we get deeper into the AI era, agents are quickly starting to change the container for that knowledge and compounding of capabilities. My agent is a folder of markdown files: instructions, skills, memories. It's been compounding for the better part of a year. Some of those skills I built on my own. Some were sharpened at work. If I zip that folder and carry it to the next job, every abstract boundary question becomes a file question: what can live where, what transfers ownership. Whether the agent learned something confidential is not philosophical, it is a grep. The reason this is harder than it looks: the old system worked because human memory is lossy. You left a job with patterns but not databases. Biology sufficiently redacted the specifics of process and large corpuses of confidential information, because there’s only so much detail most of us can carry in our heads. Trade secret law tolerated the transfer because it arrived pre-sanitized by forgetting. Forgetting was load-bearing, and agents broke it. A personal agent can carry too much out. But a corporate agent that captures everything and transfers nothing at offboarding doesn't just protect secrets, it confiscates professional growth. If every company does that, the talent pool we all hire from gets shallower. And there's a deeper question that current policy doesn't touch. If I build a skill on my own time and then use it at work because it makes me better at my job, does the company acquire a claim? If not, what stops me from developing everything personally? But if employers respond by requiring all work through corporate agents, the worker's compounding loop gets locked inside the firm. The question of who owns what is not one line. It's four separate questions: what the agent can do (authority), what it can see (data), what it remembers (memory), and what it carries forward as reusable procedure (skill). Authority and data are relatively easy to handle through existing IT tools and policies; memory and skills are where it gets messy, because what were formerly lossy, imperfect processes are increasingly being crystalized in a way that makes them formal and precise. The more we come to rely on our agents, the more challenging and urgent these questions will become.

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liminally chris ⬡
liminally chris ⬡@Chris_8086·
imagine prompting some meat instead of just asking the robot insane
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Dan Elitzer retweetledi
Tenbin Labs
Tenbin Labs@tenbinlabs·
Tenbin's first asset is live. Introducing Tenbin Gold (tGLD): Liquid, Yield-bearing Tokenized Gold. Built for instant on-chain liquidity with DeFi utility. app.tenbinlabs.xyz
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