be bennett
> google fitbit air gets released
> happy that whoop now has competition
> fitbit air has no subscription
> buy fitbit air
> realize whoop has better hardware, bands, battery
> contemplate why he can't have best of both
>> free subscription + best device
> throw a couple billion tokens at reverse engineering the whoop
> successfully do it in a day
> now has full jailbroken whoop without a subscription
Tons of people have messaged me the past few days stating they’re purchasing the Ninja Creami Deluxe…so let’s DO IT AGAIN:
My TOP 7 Ninja Creami Recipes w/ Preparation Instructions
(These actually taste like REAL Ice Cream despite being WAY lower in Calories)
𝗕𝗜𝗥𝗧𝗛𝗗𝗔𝗬 𝗖𝗔𝗞𝗘 𝗪𝗛𝗜𝗥𝗟:
- 360ml Fat Free Fairlife
- 2 servings Sugar Free Cheesecake Jello Pudding Mix
-3 squeezes of SweetLeaf Sweet Drops Vanilla Sweetener
- 1 serving True Nutrition French Vanilla Egg White Protein Powder
- 12g sprinkles
- 2g Xanthan Gum
350 Calories / 45g Protein
𝗖𝗜𝗡𝗡𝗔𝗠𝗢𝗡 𝗧𝗢𝗔𝗦𝗧 𝗖𝗥𝗨𝗡𝗖𝗛 𝗦𝗪𝗜𝗥𝗟:
- 300ml Unsweetened Vanilla Almond Milk
- 120ml Whole Milk from Fairlife
- 68g Trueflavor Cinnamon Toast Swirl Egg White Protein Powder from True Nutrition
- 41g Cinnamon Toast Crunch Cereal
- 2g Xanthan Gum
500 calories / 58g protein
𝗢𝗔𝗧𝗠𝗘𝗔𝗟 𝗖𝗛𝗢𝗖𝗢𝗟𝗔𝗧𝗘 𝗖𝗛𝗜𝗣 𝗖𝗢𝗢𝗞𝗜𝗘:
- 300ml Unsweetened Vanilla Almond Milk
- 120ml Whole Milk from Fairlife
- 76g Oatmeal Cookie Protein Powder from JYM
- 2 Chunky Chocolate Chip Cookies
- 2g Xanthan Gum
550 calories / 57g protein
𝗢𝗥𝗘𝗢 𝗕𝗟𝗜𝗭𝗭𝗔𝗥𝗗 𝗥𝗘𝗣𝗟𝗜𝗖𝗔:
- 240ml Fairlife Skim Milk
- 240ml Almond Milk Unsweetened Vanilla
- 51g True Nutrition Cookies N Cream Egg White Protein
- 30g Oreos Cookies N Creme Instant Pudding Mix
- 4 Oreo Thins
- 2g Xanthan Gum
540 calories / 50g protein
𝗖𝗛𝗢𝗖𝗢𝗟𝗔𝗧𝗘 𝗣𝗘𝗔𝗡𝗨𝗧 𝗕𝗨𝗧𝗧𝗘𝗥 𝗖𝗨𝗣:
- 480ml Unsweetened Vanilla Almond Milk
- 51g Chocolate Peanut Butter Cup Egg White Protein
- 32g 100% Peanuts Natural Peanut Butter
- 10g Zero Sugar Chocolate Pudding Mix
- 2g Xanthan Gum
450 calories / 46g protein
𝗧𝗥𝗜𝗣𝗟𝗘 𝗖𝗛𝗢𝗖𝗢𝗟𝗔𝗧𝗘 𝗟𝗔𝗩𝗔:
- 480ml Fairlife Chocolate Milk
- 51g Chocolate Fudge Brownie Egg White Protein from True Nutrition
- 10g Chocolate Sugar Free Jello Mix
- 2g Xanthan Gum
490 calories / 62g protein
𝗣𝗘𝗔𝗡𝗨𝗧 𝗕𝗨𝗧𝗧𝗘𝗥 𝗕𝗔𝗡𝗔𝗡𝗔 𝗦𝗪𝗜𝗥𝗟:
- 480ml Unsweetened Vanilla Almond Milk
- 51g Chocolate Peanut Butter Cup Egg White Protein
- 13g PB2
- 150g Banana Dannon Light & Fit Greek Yogurt
- 2g Xanthan Gum
380 calories / 56g protein
𝗣𝗥𝗘𝗣𝗔𝗥𝗔𝗧𝗜𝗢𝗡 𝗜𝗡𝗦𝗧𝗥𝗨𝗖𝗧𝗜𝗢𝗡𝗦 👇🏻👇🏻👇🏻
- Put all Creami ingredients (minus the Xanthan gum) in the cup
- Froth it
- Add Xanthan gum
- Froth again
- Freeze for 24+ hours with NO LID
- Thaw out for 15-20 minutes
- Put lid on then run the side & bottom under hot water for 30-40 seconds
- Load in mixing cup with that lid & blade
- Click full and hit spin
- Add 30 more ml of milk after initial spin
- Click full & respin
- Add mix ins
- Click full again and hit mix in (do this even if you did NOT add mix ins)
- Enjoy
One final note:
When in doubt…RESPIN!!!!!!
Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D. I remain deeply passionate about education and plan to resume my work on it in time.
Cinder is an open-source trading terminal for @PhoenixTrade perpetuals on Solana. Level 3 order books, charts, liquidations. Local-first DeFi. Minimal resource footprint.
https://t.co/etlv7gCI89
Any profession where people have decent odds of peaking early (i.e. 20s) can exact a big emotional toll on the unprepared.
Professional athletes, hedge fund guys, etc.
Labor Day? Let the robots do the labor. 😂
@XSquareRobot is already running a human-robot home cleaning service in China, starting at RMB 149 (about $21) per visit.
Now serving hundreds of households and expanding.
The day was always coming and now it is here,
Vietnam is now the second largest economy in Southeast Asia, taking the spot from Thailand which traditionally has held it since the 1970s.
Thailand will likely never be this close to Vietnam ever again,
Vietnam is probably going to become the last developed country from the Global South.
Imagine every pixel on your screen, streamed live directly from a model. No HTML, no layout engine, no code. Just exactly what you want to see.
@eddiejiao_obj, @drewocarr and I built a prototype to see how this could actually work, and set out to make it real. We're calling it Flipbook. (1/5)
I'm lucky enough to have a great doctor and access to excellent Bay Area medical care. I've taken lots of standard screening tests over the years and have tried lots of "health tech" devices and tools.
With all this said, by far the most useful preventative medical advice that I've ever received has come from unleashing coding agents on my genome, having them investigate my specific mutations, and having them recommend specific follow-on tests and treatments.
Population averages are population averages, but we ourselves are not averages. For example, it turns out that I probably have a 30x(!) higher-than-average predisposition to melanoma. Fortunately, there are both specific supplements that help counteract the particular mutations I have, and of course I can significantly dial up my screening frequency. So, this is very useful to know.
I don't know exactly how much the analysis cost, but probably less than $100. Sequencing my genome cost a few hundred dollars.
(One often sees papers and articles claiming that models aren't very good at medical reasoning. These analyses are usually based on employing several-year-old models, which is a kind of ludicrous malpractice. It is true that you still have to carefully monitor the agents' reasoning, and they do on occasion jump to conclusions or skip steps, requiring some nudging and re-steering. But, overall, they are almost literally infinitely better for this kind of work than what one can otherwise obtain today.)
There are still lots of questions about how this will diffuse and get adopted, but it seems very clear that medical practice is about to improve enormously. Exciting times!
Thanks @domcooke for spending months on researching and writing this piece. Einstein once said, "If you can't explain it simply, you don't understand it well enough." By that measure, Dom has blown me away with how deeply he came to understand Hyperliquid and what we're all building together.
When someone asks what "housing all of finance" means, I'm proud to point them to this piece. I hope readers appreciate just how much Dom and his team put into their work. It reflects the thoughtful craft that is in Hyperliquid's DNA. Special thanks to @patrick_oshag for taking a bet on Hyperliquid's story.
Judging by my tl there is a growing gap in understanding of AI capability.
The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code.
But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along.
So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions.
TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.