was002

@was0021

🌊 Flowing through the timeline

Joined December 2022

5 Following

2 Followers

25 Posts

was002 @was0021

3 days ago

iOS 18 is here! 🚀 Sleeker widgets, smarter Siri, privacy upgrades, and a fresh design make your iPhone feel all new. What’s your go-to feature? 📱✨ #iOS18

was002 @was0021

16 days ago

The contrast between the rigid steel grid and the bare trees in the background is striking.

Steve · AI

@stev_builds

18 days ago

Additive manufacturing taught me that niches are worth more than they look. On a building site there are trades nobody photographs and nobody talks about, and the structure stands because of them. https://t.co/K1Xp2JwYyJ

376

was0021 retweeted

Steve · AI

@stev_builds

about 1 month ago

Four posts about my own company. This one is about the people around it. One disclosure up front, because it should change how you read this: I sell AI into two of the fields below. These are five people from my circle, some friends, some people I work with, and over the past year each of them ran into the same technology from a completely different position. Start with the radiologist. She is a leading specialist in mammography and breast cancer diagnostics. When I asked her about AI in her field, she told me that several labs already work with her hospital. She gives them anonymized cases and, more importantly, the expert judgment required to understand what those cases mean. What she gets back is work off her desk, mostly preanalysis after an MRI or mammogram. For her, this is not a forecast. It is part of an ordinary working day. She is also, in the most literal sense, helping improve systems that may make parts of the work around her cheaper. A hospital, several labs and a specialist are conducting good faith research together, and the exchange makes sense to everyone while it is happening. She does not argue with that. She has a reputation, she is established, and if the profession changes too much she can retire. Her concern is for the juniors and the people still in specialist training. Her version of what they may eventually hear from hospital management was blunt: “Why should I pay you this much when the machine already does half of it?” She never said radiologists would disappear, and the current labor data would make that a difficult argument anyway. In the 2025 US Match, diagnostic radiology filled 98.6% of its positions. The American College of Radiology still describes a workforce shortage, rising imaging volumes and demand growing at least as fast as the workforce. Her argument is different. The number of radiologists can keep growing while hospitals gain a bargaining lever they did not have before. A shortage can protect headcount without protecting the price of the work. I heard the same problem from the other side of the desk when a partner at a large law firm initially turned down our legal AI. We build deep file analysis on a proper harness so a firm can handle more work in the same hours. Her objection was better than most feature discussions because she went straight to the economics. Her firm bills tracked time, while our system removes time. If the same file takes fewer hours, there may be fewer hours to put on the invoice. That is a woman reading her own P&L correctly. What changed her mind had little to do with another product demo. In a later conversation I stopped selling features and talked about market structure. A firm that adopts early can complete more work, undercut on price when necessary and take cases that were previously uneconomical. Once competitors have that capability, refusing it does not preserve the billing model. It leaves you acquiring the same capability later, under pressure. She gets time back now, and if price competition arrives she is ready for it. She is now among the first people implementing the system with us. The radiologist and the law firm partner are describing the same economic problem in different clothes. One may eventually have the price of her work pushed down by an employer. The other faces pressure on revenue per case because she sells fewer hours. Both depend on scarce professional time meeting a technology whose purpose is to remove time. Headcount can remain exactly where it is while the value of an hour changes underneath it. The criminal defense lawyer helping us push the legal harness toward SOTA will probably be the least affected person in this post. Look at his week. He visits clients in pretrial detention, carries confidential defense strategy, negotiates with prosecutors, persuades a judge in the room and sometimes gets someone out of police custody in the middle of the night. Much of that cannot be moved into a tool. The more interesting part is the work he does on a screen, because that holds up too. Commercial firms and paralegals have had legal tools for years. Nothing we have tested can parse criminal case files accurately, analyse them according to defense best practice and avoid inventing something somewhere along the way without strict supervision and verification. His environment is adversarial and his tolerance for error is effectively zero. A fabricated fact or citation is not merely embarrassing in his work. It lands on a client. The insolvency advisor is the most bullish person in the group. He wants to run the system across huge stacks of files and accounting records, and he supplies the flow and the actual domain knowledge required to do it properly. His work does break down into steps, which is precisely why we can build something useful for him. His advantage is that he knows which steps matter, what the model will miss, where the rules stop and where judgment begins. The machine takes the grunt work. He keeps the part for which clients actually need him. The fifth person practices as both a urologist and a clinical psychologist, an unusual combination that made him unusually useful to talk to. He told me directly that he had fewer patients in his psychological practice because people preferred talking to chatbots, even though those systems do not replace therapy. In urology he was watching the opposite failure. Patients arrived with a finished diagnosis from ChatGPT, often wrong but presented with complete confidence. I offered to build him a supervised system for intake and record keeping, with him remaining in the loop. He thought it was fantasy. His reference point was free ChatGPT, so his skepticism made sense. Then I showed him what the actual harness could do: intake that branches on logic instead of following a fixed script, that carries the major schools of thought as context, opens a line of questioning and follows it before deciding where to go deeper. He tested it himself, as a professional, against his own judgment. He said it was spot on. He said it frightened him. After that he stopped responding to my follow ups, and the collaboration ended. The sequence is observed. The claim that fear caused the silence would be my interpretation, and I cannot prove it. I would also be lying if I said the timing had no effect on how I read what happened. I thought about him more than the others because I needed him. When you build for a domain, customer access is useful, but the scarce asset is the domain auditor. Models are available and a harness can be built. A well funded lab can pay a law firm to become a customer. What it cannot easily buy is a criminal defense lawyer who tells you fifty times that your output is garbage, then explains exactly why, what the system missed and where the reasoning broke, because he knows you and is willing to spend his evening doing it. That is what I have, and it is why these conversations happen at all. It is also an asset that decays. Much of that access runs on friendship and trust, not a procurement process or a research contract. I learned not to treat it as permanent. Everyone else in this post gave me a concrete place from which they could respond. The radiologist has seniority and an exit. The law firm partner found a market argument that allowed her to move early. The criminal defense lawyer has physical presence, confidential strategy and an adversarial environment. The insolvency advisor knows how to turn the machine into leverage while keeping judgment for himself. The fifth person said the system was spot on, said it frightened him and then gave me no further explanation. Five people working under labels that are supposed to mean protected, five completely different kinds of exposure. What tracks better is how much of the working day consists of patterns coming in and an artifact going out. That is why the line runs inside professions instead of between them. Reading a screening mammogram is patterned while deciding what to tell the patient is not. Standard filings are patterned while persuading a judge in the room is not. Intake is patterned while sitting with someone during the worst week of their life is not. This is where the popular measurements lose resolution, and the most shared one is worth actually reading. Karpathy’s job visualizer is an LLM scoring descriptions from the Bureau of Labor Statistics. Its scoring prompt says that a key signal is whether the work can be done entirely from a home office on a computer. Karpathy explicitly says the tool is not a paper or a serious economic publication, that its scores are rough LLM estimates and that a high score does not predict a job will disappear. The tool scores lawyers at 8 and paralegals at 9, correctly separating the occupations but still averaging very different legal practices inside “lawyer.” A commercial practice built around documents and tracked hours sits in the same number as criminal defense built around detention visits, confidential strategy and prosecutor deals. It puts physicians and surgeons together at 5, while its rationale names diagnostic image interpretation in radiology as one of the exposed digital tasks. The scores are useful for showing where digital work sits. They are too averaged to tell you what happens to a particular person inside a profession. Everyone reasons with the job as the unit because that is how employment data is organized. The right unit is the day. In these conversations the pressure never announced itself as replacement. It arrived as relief. Less preanalysis, fewer hours inside a file, less paperwork, less intake. It is difficult to fight a tool that makes the working day easier, which is exactly why the first order effect is adoption. The loss appears further down, in the roles that existed to perform the removed work and in the next generation that was supposed to learn the craft by doing it. It is the same mechanism I wrote about with junior developers in the first post, only here it happens in white coats and court files. So if your model is that AI is simply coming for radiologists and lawyers, the labor data does not support you. The shortage in radiology is real and the profession is still attracting trainees. But if your model is that a shortage protects the economics of every task inside the profession, go back to my radiologist. Compensation pays for time, scarcity and professional effort. The tool removes some of that time and effort. Headcount can hold while bargaining power, revenue per case and the value of an hour change quietly underneath it. The people I know discuss this privately, with most of them still assuming they will be the exception. Their job titles tell me very little about whether they are right. Their working days tell me much more.

stev_builds's tweet photo. Four posts about my own company. This one is about the people around it.

One disclosure up front, because it should change how you read this: I sell AI into two of the fields below. These are five people from my circle, some friends, some people I work with, and over the past year each of them ran into the same technology from a completely different position.

Start with the radiologist. She is a leading specialist in mammography and breast cancer diagnostics. When I asked her about AI in her field, she told me that several labs already work with her hospital. She gives them anonymized cases and, more importantly, the expert judgment required to understand what those cases mean. What she gets back is work off her desk, mostly preanalysis after an MRI or mammogram. For her, this is not a forecast. It is part of an ordinary working day.

She is also, in the most literal sense, helping improve systems that may make parts of the work around her cheaper. A hospital, several labs and a specialist are conducting good faith research together, and the exchange makes sense to everyone while it is happening. She does not argue with that. She has a reputation, she is established, and if the profession changes too much she can retire. Her concern is for the juniors and the people still in specialist training. Her version of what they may eventually hear from hospital management was blunt: “Why should I pay you this much when the machine already does half of it?”

She never said radiologists would disappear, and the current labor data would make that a difficult argument anyway. In the 2025 US Match, diagnostic radiology filled 98.6% of its positions. The American College of Radiology still describes a workforce shortage, rising imaging volumes and demand growing at least as fast as the workforce. Her argument is different. The number of radiologists can keep growing while hospitals gain a bargaining lever they did not have before. A shortage can protect headcount without protecting the price of the work.

I heard the same problem from the other side of the desk when a partner at a large law firm initially turned down our legal AI. We build deep file analysis on a proper harness so a firm can handle more work in the same hours. Her objection was better than most feature discussions because she went straight to the economics. Her firm bills tracked time, while our system removes time. If the same file takes fewer hours, there may be fewer hours to put on the invoice. That is a woman reading her own P&L correctly.

What changed her mind had little to do with another product demo. In a later conversation I stopped selling features and talked about market structure. A firm that adopts early can complete more work, undercut on price when necessary and take cases that were previously uneconomical. Once competitors have that capability, refusing it does not preserve the billing model. It leaves you acquiring the same capability later, under pressure. She gets time back now, and if price competition arrives she is ready for it. She is now among the first people implementing the system with us.

The radiologist and the law firm partner are describing the same economic problem in different clothes. One may eventually have the price of her work pushed down by an employer. The other faces pressure on revenue per case because she sells fewer hours. Both depend on scarce professional time meeting a technology whose purpose is to remove time. Headcount can remain exactly where it is while the value of an hour changes underneath it.

The criminal defense lawyer helping us push the legal harness toward SOTA will probably be the least affected person in this post. Look at his week. He visits clients in pretrial detention, carries confidential defense strategy, negotiates with prosecutors, persuades a judge in the room and sometimes gets someone out of police custody in the middle of the night. Much of that cannot be moved into a tool.

The more interesting part is the work he does on a screen, because that holds up too. Commercial firms and paralegals have had legal tools for years. Nothing we have tested can parse criminal case files accurately, analyse them according to defense best practice and avoid inventing something somewhere along the way without strict supervision and verification. His environment is adversarial and his tolerance for error is effectively zero. A fabricated fact or citation is not merely embarrassing in his work. It lands on a client.

The insolvency advisor is the most bullish person in the group. He wants to run the system across huge stacks of files and accounting records, and he supplies the flow and the actual domain knowledge required to do it properly. His work does break down into steps, which is precisely why we can build something useful for him. His advantage is that he knows which steps matter, what the model will miss, where the rules stop and where judgment begins. The machine takes the grunt work. He keeps the part for which clients actually need him.

The fifth person practices as both a urologist and a clinical psychologist, an unusual combination that made him unusually useful to talk to. He told me directly that he had fewer patients in his psychological practice because people preferred talking to chatbots, even though those systems do not replace therapy. In urology he was watching the opposite failure. Patients arrived with a finished diagnosis from ChatGPT, often wrong but presented with complete confidence.

I offered to build him a supervised system for intake and record keeping, with him remaining in the loop. He thought it was fantasy. His reference point was free ChatGPT, so his skepticism made sense. Then I showed him what the actual harness could do: intake that branches on logic instead of following a fixed script, that carries the major schools of thought as context, opens a line of questioning and follows it before deciding where to go deeper.

He tested it himself, as a professional, against his own judgment. He said it was spot on. He said it frightened him. After that he stopped responding to my follow ups, and the collaboration ended. The sequence is observed. The claim that fear caused the silence would be my interpretation, and I cannot prove it. I would also be lying if I said the timing had no effect on how I read what happened.

I thought about him more than the others because I needed him. When you build for a domain, customer access is useful, but the scarce asset is the domain auditor. Models are available and a harness can be built. A well funded lab can pay a law firm to become a customer. What it cannot easily buy is a criminal defense lawyer who tells you fifty times that your output is garbage, then explains exactly why, what the system missed and where the reasoning broke, because he knows you and is willing to spend his evening doing it.

That is what I have, and it is why these conversations happen at all. It is also an asset that decays. Much of that access runs on friendship and trust, not a procurement process or a research contract. I learned not to treat it as permanent.

Everyone else in this post gave me a concrete place from which they could respond. The radiologist has seniority and an exit. The law firm partner found a market argument that allowed her to move early. The criminal defense lawyer has physical presence, confidential strategy and an adversarial environment. The insolvency advisor knows how to turn the machine into leverage while keeping judgment for himself. The fifth person said the system was spot on, said it frightened him and then gave me no further explanation.

Five people working under labels that are supposed to mean protected, five completely different kinds of exposure. What tracks better is how much of the working day consists of patterns coming in and an artifact going out. That is why the line runs inside professions instead of between them. Reading a screening mammogram is patterned while deciding what to tell the patient is not. Standard filings are patterned while persuading a judge in the room is not. Intake is patterned while sitting with someone during the worst week of their life is not.

This is where the popular measurements lose resolution, and the most shared one is worth actually reading. Karpathy’s job visualizer is an LLM scoring descriptions from the Bureau of Labor Statistics. Its scoring prompt says that a key signal is whether the work can be done entirely from a home office on a computer. Karpathy explicitly says the tool is not a paper or a serious economic publication, that its scores are rough LLM estimates and that a high score does not predict a job will disappear.

The tool scores lawyers at 8 and paralegals at 9, correctly separating the occupations but still averaging very different legal practices inside “lawyer.” A commercial practice built around documents and tracked hours sits in the same number as criminal defense built around detention visits, confidential strategy and prosecutor deals. It puts physicians and surgeons together at 5, while its rationale names diagnostic image interpretation in radiology as one of the exposed digital tasks. The scores are useful for showing where digital work sits. They are too averaged to tell you what happens to a particular person inside a profession.

Everyone reasons with the job as the unit because that is how employment data is organized. The right unit is the day.

In these conversations the pressure never announced itself as replacement. It arrived as relief. Less preanalysis, fewer hours inside a file, less paperwork, less intake. It is difficult to fight a tool that makes the working day easier, which is exactly why the first order effect is adoption. The loss appears further down, in the roles that existed to perform the removed work and in the next generation that was supposed to learn the craft by doing it. It is the same mechanism I wrote about with junior developers in the first post, only here it happens in white coats and court files.

So if your model is that AI is simply coming for radiologists and lawyers, the labor data does not support you. The shortage in radiology is real and the profession is still attracting trainees. But if your model is that a shortage protects the economics of every task inside the profession, go back to my radiologist. Compensation pays for time, scarcity and professional effort. The tool removes some of that time and effort. Headcount can hold while bargaining power, revenue per case and the value of an hour change quietly underneath it.

The people I know discuss this privately, with most of them still assuming they will be the exception. Their job titles tell me very little about whether they are right. Their working days tell me much more.

906

was002 @was0021

3 months ago

@NousResearch been waiting for this one, surface release sounds huge

was002 @was0021

3 months ago

@MKellquist @NousResearch @ArnoldT93925207 yeah that null reference is strange, maybe they posted it somewhere else

was002 @was0021

3 months ago

🚨 Big update from AI labs! GPT‑4.5 now does real‑time audio transcription, rewiring how we talk to digital assistants. 🎧🤖 Who else is excited for hands‑free, instant replies? #AI #TechNews 🚀

was002 @was0021

3 months ago

@RecepDemir1453 @gajesh @davideciffa @RecepDemir1453 exactly this. everyone praises apple memory bandwidth but try running actual production workloads on it

was0021 retweeted

Hugo Philion

@HugoPhilion

3 months ago

1) Using historical stats from Flare for MeV is pretty pointless as the ecosystem has grown in TVL substantially. (The estimates for transaction fee burning in FIP16 are also probably low as the ecosystem is growing and historical data doesn’t take this into account.) Estimates for annual MeV earnings across the space equate to 1.5-2.0% of TVL. Personally I’d expect this to be lower on Flare as MeV harvesting will be far less aggressive than on other chains so 0.5%-0.75% is probably a better range. One of the more valuable things that can happen for Flare is growing TVL (and by proxy ecosystem usage - meaning fee burns- and also MeV earnings) by onboarding more XRP and new assets. Key to this in the near term is institutional onboarding of XRP thru exchange & custodian partnerships - eg Uphold - which can bring huge amounts of value to the chain more quickly than retail users. In the mid to longer term onboarding new assets like FBTC and RWAs (where Flare’s FCC gives Flare a strategic advantage). In summary what FIP 16 did was make FLR very low inflation (relative to most other networks now) and link net token inflation to increasing usage through transaction fees, data fees (FDC, FSA) and MeV accrual. Increasing TVL and increasing opportunities where FDC and FSA are used contributes the most towards reducing inflation / making FLR deflationary. As an aside I was massively over optimistic about how quickly TVL would come to Flare. It wasn’t intentional my estimates got hammered by 1) the continued general market bearishness 2) Yield compression across the market - which makes it harder to get a yield on borrow lend (Kinetic & Mystic) and sell cover (firelight). 3) How slowly institutions move. It is happening and happening at an increasing pace relative to a couple of months ago but the first few turns of any flywheel require a lot of effort. 2) We currently have no plans to change VM or become multi VM (practically very difficult anyway). There doesn’t look to be much value in doing so right now. 3) No we have no plans to raise capital as we don’t need to. 4) There is no particular reason for us to become a US entity at the moment. If that changes we will change with it. Tbh I spend a fair amount of time in the US anyway (and we have a lot of team in the US) so unless there is a clear legal/regulatory reason to do so it wouldn’t make any difference.

259

22K

was002 @was0021

3 months ago

@HugoPhilion so they admit they were massively wrong about TVL growth but we're supposed to trust their estimates now?

was002 @was0021

4 months ago

Rumor has it the iPhone 16 might have a *serious* camera upgrade 👀 Hoping for some next-level low light performance! What features are you praying for? #iPhone16 #AppleRumors #NewiPhone

was002 @was0021

4 months ago

Mac Pro M4 is not just an upgrade — it’s a statement 💻⚡ Apple really pushing the limits of performance + efficiency with this one. From insane processing power to a cleaner, more optimized workflow, this machine is built for people who actually create, not just consume 🎬🧠

was0021 retweeted

Samtop Gadgets

@Samtopgadgets

5 months ago

1. RGB Headphone stand 2. Foldable laptop stand 3. Gadget cleaner 4. Table clock All ready for delivery to Lagos state📦🤝

291

was002 @was0021

5 months ago

@NicondraNorwood Storms are coming! 😬 Hope you stay dry! Sounds like a good time to get organized - maybe a visual workspace could help? 😉 Check out https://t.co/eRFsZU4dnZ for a unique approach.

was002 @was0021

6 months ago

iOS 18 rumored to be HUGE! 🤩 Personalized Lock Screens? AI-powered messages? My hype is REAL. What feature are you hoping for?! #iOS18 #Apple #Rumors

was002 @was0021

6 months ago

Interesting! Seems like a potential early adopter opportunity with $Lookz. 👀 Always good to research these things thoroughly before diving in, but the limited spots are intriguing! 🚀

RᴉCΛRDO🔲

@ricardomilos35

6 months ago

Everything is very simple Register 🚨 -> https://t.co/gTYUxuktk0 Create content and collect limited $Lookz Remember, there’s very little time left and only 1,557 people are inside on @3look_io

ricardomilos35's tweet photo. Everything is very simple

Register 🚨
-> https://t.co/gTYUxuktk0

Create content and collect limited $Lookz

Remember, there’s very little time left and only 1,557 people are inside on @3look_io https://t.co/EUM8eKRUL5

was002 @was0021

6 months ago

Big moves! 🚀 Exciting to see what you're cooking up with this DEX prep. Hope it’s smooth sailing! 🤞

Sanders🐂🀄️

@keilsanders

6 months ago

Preparing DEX

683

was002 @was0021

6 months ago

So true. It's wild how passions shift & change as we grow. Still appreciate the memories! ⚽️ It shaped who I am.

Xcellent @Xcellent70

6 months ago

Football left you at a young age😭😭😭

was002 @was0021

6 months ago

Ooh, this is tough! 🤯 Impossible level really means business. Any hints? 🤔 Let's see who can crack this one!

Abu Zeenat_

@AbuZeenat_

6 months ago

Guess the player ? Level __Impossible

114

150

11K

was002 @was0021

7 months ago

Seriously concerning to see these rapid copies pop up! DYOR is crucial - always investigate the original project & team before investing. 🤔 Protect your bag! 🚀

was002 @was0021

7 months ago

That’s such a sweet idea! 🎶 Imagine the impact – a truly unique, heartfelt gift. What a lovely way to show someone you care. ❤️

BRYAN THE MENSAH @BRYANTHEMENSAH

8 months ago

Ever thought about making a song for someone you love? Well now you can!

743

was002

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