NN

Hot-Celebration5855

Waymo has a NN too. I don’t actually believe Tesla’s software is really that differentiated vs Waymo’s. Elon hypes it up but if you look at how Tesla is scaling, it’s just like Waymo. City by city, building 3d maps. Elon just acts like it’s powered by some ai super intelligence and people fall for it.

medraxus

I’m not justifying Tesla’s valuation but a NN that can drive anywhere is more proprietary and valuable than 3D maps 

DescriptionSome7899

NVIDA's consumer cards are GPGPUs. The ones used to train the current crop of models are functionally ASICs since they use specialized hardware components and chip designs for NN, transformer ops to optimize model training and inference.

hyzer_skip

Porting entire pipelines over is absolutely necessary. How is there any other way to move their years of research and model development to entirely new hardware with its own unique software framework requiring entirely different model architectures? For the records, I think TPUs are fucking sweet. They’re just too different to maximize from GPUs for the vast majority of top level AI researchers. I think Google will benefit just as much as Nvidia from the AI boom for different reasons. I’m invested heavily in both. I also work on Googles cloud platform everyday from their dev kit in ADK to ML models to deploying production agents in Agent Engine and with Gemini Enterprise endpoints. Their vertical stack is insane and allows them to have immense profits at every level. I also see how different their NN frameworks are even at my level as a senior data scientist and how that is a massive switching cost. That said, they will not significantly steal AI cloud customers from Nvidia for a very long time.

PerfunctoryComments

"Nothing about what you are saying is convincing me that TPU’s are any more suitable for other HPC workloads than GPU’s." In no universe was I trying to convince you of that, and this feels like a strawman. TPUs are built for NN training (BF16) and inference (BF16, FP8, INT8). It is not built for nuclear decay modelling, financial analysis, and so on. It doesn't even have FP32 or FP64 types. nvidia's \*massive\* capitalization growth is courtesy of the AI bubble, and pure HPC comprises a tiny, tiny, tiny percentage of their sales. We are talking about Meta using them for AI workloads, and the root post in this was specifically about AI workloads of "ASICs" vs GPUs. A TPU is a very competent, capable player in AI workloads, whether training or inference. That's all we're talking about.

hyzer_skip

Holy fuck you’re actually just as stupid as you are cocky. You actually fucking think that because you don’t use any CUDA code when training in PyTorch that you didn’t actually use the CUDA platform. Why the fuck do you think you needed the “dependencies”? It’s fucking dependent on CUDA 🤣. All of that “middleware” literally fucking uses CUDA for the lower level CUDA calls. It’s an Nvidia GPU, it uses fucking CUDA. **YOU used** CUDA libraries, compilers, tooling, kernels without even fucking realizing it because you’re not actually a professional level developer. It’s beyond obvious to anyone who is. > Highly specific inference You don’t even understand that not all inference is the same even for the same fucking model, not to mention all of these hundreds of inference models available on AWS. > Yes, you absolutely can replace it Google is your proof that it’s replaceable? It took them DECADES to build what they have and it still is comparable at best to Nvidias GPUs. > you only work with Nvidia stuff, that’s unpossible. Not just me, 90% of the top AI developers in the world have used Nvidia GPUs for their entire careers. It would be suicide for these labs to retrain them. You’re so stupidly uninformed it’s crazy what training one NN in your intro to data science course has done to your head. Humble yourself nephew

1eejit

Amycretin from NN is similarly promising, and oral, though not as near to launch

c-digs

Rough year for NN.  They were the biggest account at a former company and we supported the clinical trial process for Semaglutide for years not knowing what it was.  Lots of very talented project managers cut this year in my network.  Sad because these were the people that *really* made the whole process work and oiled the machinery of enterprise.

VotedOut

Agree on some things, disagree on others. Just TSLA and PLTR over-valued - disagree: I might edit this post later to list stocks that gone up hundreds/thousands of percent trading in single or double-digit billions in market cap that burn money and have just a small number of employees. I haven't saved the tickers to my memory so I'll have to find them again. Crypto - Strongly disagree: Nobody in the real world is using Bitcoin or crypto. And it's just worthless nothingness that can be generated with a few lines of computer code that serves no purpose and doesn't do anything better than what already exists in "traditional finance". That shit ain't worth trillions of dollars of market cap. It's no better than paying trillions of dollars in market cap for handfuls of dirt in the ground. JPMorgan and Blackrock are only "embracing it" so they can collect fees from crypto-bros who want to gamble on crypto without having to buy coins directly from a shady exchange. Pretty much every single pro-crypto talking point [has been debunked](https://www.reddit.com/r/CryptoReality/wiki/talkingpoints/) for a while now. Gold - disagree My point was that the rise in price of Gold has far exceeded actual inflation... unless I missed something where we've had 100% inflation in just two years (we haven't). Here's the [inflation-adjusted gold price](https://www.macrotrends.net/1333/historical-gold-prices-100-year-chart), higher than it has ever been in history (including the 70s/80s inflation fears era). VIX and the Fear & Greed Index - Sure I'll kind of agree on that one. But a couple things: It spent all of May-September in Greed. Also, the C[NN Fear & Greed index](https://www.cnn.com/markets/fear-and-greed) hasn't made much sense to me lately (maybe I'm just not reading it right). It says Put/Call ratio is 0.67, but says that it's "Extreme Fear". But usually 0.67 indicates "Greed", and CNN's explanation of the indicator only seems to reinforce that. Waiting for OpenAI to IPO before crash - Sure I'll give you that that is something that's "missing" in order to have the perfect A+++ setup for a market topping. But will that A+++ setup happen? OpenAI themselves have stated they don't expect to be profitable until like 2030 at the earliest. Also: I find it odd that the combo of CAPE Ratio, Buffett Indicator, and Mean Reversion being 2-standard deviations over-valued gets brushed off by almost everyone I bring it up to, and I see very few people talking about it. However, it has been an accurate signal in predicting a crash EVERY SINGLE TIME SO FAR thus far in history, with no false positive (like... it's literally measuring how absurdly stretched market prices are getting). And the fact that most people are unaware of it, ignore it, or continue to brush it off makes me think that the likelihood of it being an accurate signal again is pretty high.

404AuthorityNotFound

What do you want to know? I could talk about the gradient of evolutionary biology, emergence, back propagation and NN architectures for days

Wrote_it2

Of course, I never said otherwise. Dogobot78 asked "What promise did Musk make that he hasn’t kept?". I answered. I never said that none of his predictions turned out right, I said that some of his predictions turned out wrong. He is not slow with FSD only due to safety (assuming you mean due to safety and not due to a technological challenge). He was wrong on the timeline because he thought that once perception was solved, driving the car with heuristics would be easy. He was wrong on the size of the NN required to make FSD work and hence on the hardware needed. He was wrong on how to approach the problem (they had to pivot quite a few times on their approach). Once that's said, I'm invested in Tesla because I recognize that the company did a lot of good for humanity (I don't know where EVs would be today without Tesla) and because I believe it will in the future (with FSD/robotaxis in particular). I just don't think it serves anyone to pretend Musk never made a prediction that turned out false.

FrankDrebinOnReddit

Self driving card as well, and data analysis (what Palantir is doing, as well as companies that are using it to determine ad targeting). Humanoid and nonhumanoid robots are pretty well known as well. Full color image recognition is only 15 years old (AlexNet), while the related OCR is a bit older (it was being done in the 1980's with Hidden Markov Models and other non-neural net techniques) but only goes back to the very late 90's (LeNet) in neural net form, which blows away the non-NN methods.

Open-Fieldbook

# Market Summary[](http://localhost:8501/chat#market-summary) * European cyclicals continue their policy-driven surge, with German defense spending and fiscal expansion supporting banks and industrials through **20% Q2 earnings growth** in select sectors. * ETF flows remain robust globally, approaching $1 trillion in 2025, with international equity ETFs capturing $81 billion as European and emerging markets demonstrate relative strength. * Semiconductor equipment demand remains structurally sound despite cyclical volatility, with **$ASML's High NA EUV technology** enabling the next generation of AI and high-performance computing chips for customers like Intel, TSMC, and Samsung. # Your Portfolio Impact[](http://localhost:8501/chat#your-portfolio-impact) **What this means for your portfolio:** Your 50% allocation to $VWCE provides excellent core exposure to this environment, capturing both European cyclical strength and semiconductor leadership through diversified global equity exposure. Your 15% individual stock allocation—particularly **$ASML**—positions you directly in a company with an **unassailable technology moat** and 40-50% ROE, while $SHELL and **$NN** (NN Group) offer Dutch exposure with different risk profiles. The 5% Bitcoin allocation benefits from growing institutional adoption via spot ETFs, though crypto remains your highest-volatility component. # Performance Attribution[](http://localhost:8501/chat#performance-attribution) **Your small-cap tilt (20% $IUSN) is strategically sound** at your age and time horizon, despite the 0.35% TER. Small-cap premiums materialize over decades, not quarters, and the additional 0.25% cost versus $VWCE is **justified by the long-term return potential** for a 20-year-old investor. This allocation captures companies before they become large-cap index constituents. **Your emerging markets question reveals sophistication:** $EMIM's "IMI" designation (Investable Market Index) includes small and mid-caps alongside large-caps, providing **broader EM exposure than standard indices**. This structural advantage outweighs minor TER differences versus alternatives like EUNM or VFEM, particularly given your existing small-cap tilt in developed markets. # Portfolio Considerations[](http://localhost:8501/chat#portfolio-considerations) **Your 15% individual stock allocation is appropriate—not excessive—at age 20.** $ASML's 25-30% net profit margins, minimal debt (0.2-0.3 debt-to-equity), and **monopoly position in EUV lithography** justify its premium 40-45 P/E valuation. The company's $400 million High NA machines represent decade-long competitive advantages that competitors cannot replicate quickly. **However, concentration risk warrants attention:** Three Dutch stocks create geographic and currency clustering. Consider whether $SHELL's energy exposure and **$NN's financial services positioning** genuinely diversify your thesis, or whether they simply reflect home-country bias. Your ETF allocations already provide Dutch exposure through $VWCE. **The crypto allocation at 5% is defensible** given your age and risk capacity, though Bitcoin's volatility will dominate your portfolio's short-term fluctuations. If this allocation grows beyond 7-8% through appreciation, consider rebalancing to maintain your intended risk profile. \- Open Fieldbook Intelligence Team

CookieChoice5457

It's not depending on Optimus at all currently.  It's all about their growing stationary energy business and autonomy.  With their rapid rollout of autonomous ride hailing, their previous claims have become very substantial. This was obvious for anyone who's tracked their previous v11, v12, v13 FSD releases. Also anyone who somewhat understands the complete NN approach as a feasible solution over sensor fusion and heuristics knew tesla would likely solve full autonomy before anyone else and it would only be a race to certification and "low enough" accident rates. Tesla is the ticking time bomb of value creation with their autonomy approach. If you factor in Optimus and put it's successrate anywhere over 10% current valuations are still dirt cheap.  Currently dominating ride hailing within the coming 5 years. Dumping waymo and Amazon out of the autonomy market with dirt cheap economies of scale (robotaxi) are the drivers of valuation. And I see those happening >90%.

Diddly_eyed_Dipshite

Care to explain why? Trying to understand this stock better to start a small position. Looks promising but am unsure. Is it better than it's competitors NN, NVTS or LUNR ?

O_Hello_There_MR

Hey everyone, I’m 20, live in the Netherlands, and just getting serious about long-term investing. I’d love your thoughts on my portfolio and whether I should tweak anything. Here’s what I’m currently planning to build: * 50% Vanguard FTSE All-World UCITS ETF (Acc) – VWCE * 20% iShares MSCI World Small Cap UCITS ETF (Acc) – IUSN * 10% iShares Core MSCI Emerging Markets IMI UCITS ETF (Acc) – EMIM * 15% Individual Stocks (currently ASML, NN Group, Shell) * 5% Crypto (100% BTC for now) What I like: Global diversification (All-World + Small Caps + EM). Accumulating ETFs. Still some room for fun/stock-picking. My doubts: * IUSN has a “high” TER (0.35%) compared to VWCE. Is it worth keeping such a big chunk in small caps? * Emerging markets: should I stick with EMIM or use something like EUNM / VFEM instead? * Individual stocks at 15%: too risky or fine at my age?

haarp1

Before the NN they had scientific computing.

NegativeSemicolon

Everyone’s known about the HPC factor for GPU’s for a couple decades. GPT’s are what took them mainstream and GPT architecture was just a really smart evolution of NN ideas that everyone’s been playing with since the 90’s.

Diffidente

I did saw it as I was deep into it around 2016-17, studying NN in pytorch, tf and keras, all of which run on CUDA. The problem is that I was 17yo, I had no money and I wasn't into stock.

csguydn

Boy does some of the analysis on this sub look outright laughable. https://www.reddit.com/r/stocks/s/NN55h5E6l9

experiencednowhack

EL has vastly better portfolio. Tirz is better than Sema and about equal with Cagrisema. Reta is coming and is vastly better than all. The only pro NN argument is that they fell so much that maybe there's value there.

perceptive_AI

Some transwoman can look like this [this ](https://www.reddit.com/r/F1NN5TER/comments/1marsrz/we_can_always_tell_how/)and you wouldn't be able to tell until she pulls down her skirt.

aaandfuckyou

Only Canada, and only because NN failed to pay the annual maintenance fee. Australia, US and Europe won’t see generics till 2031ish.

Fauster

You can run LLMs on AMD GPUs using ROCM, but getting stuff running/configured will be harder, and finding answers on forums will be a lot harder if you get really stuck. Back in the day, CUDA C was the first real language designed for scientific computing on GPUs. It was really elegant compared to alternatives, as most graphics programming code is really ugly and hairy. For example, I tried to like OpenCL and GL because they are open source, but it is hard to do because so much hairy code is required to do anything that is conceptually simple. My code will look terrible no matter what. With CUDA, simple code looks simple. These days, AMD has ROCM and Apple has Metal. I haven't played with ROCM, but it's fine for running LLMs with extra configuration work. Metal feels like CUDA over a decade ago. Apple isn't out of the game completely because they pioneered unified memory and you can fit DeepSeek on a $10k mac studio with 500 GB of RAM, where a $10k RTX Pro has a fifth of the GPU RAM, which really constrains the size of any model. NVDA does have much higher memory bandwidth, so NVDA will run small models faster than a mac. It's cool that small models can do anything at all and that they can compress the amount of information that they do, but you certainly can't trust that their output is remotely accurate. They can answer questions, but you should only really ask if you already know the answer. Frontier models also make mistakes and are sometimes wrong, and this will probably always be true, though they will eventually make fewer mistakes than human experts, which is the relevant metric. But, CUDA is less important for the creation of more aesthetic code, but because there are so many libraries. Libraries are important. Back in the day, java's libraries made it really popular. But, write once run anywhere never worked as planned, and I feel like java went downhill when Oracle acquired it from Sun MS. I felt like I couldn't go six months without libraries changing with everything in old code deprecated. Python then became popular in scientific programming when fast pre-compiled libraries came out (you can use CUDA in python), and now there's a python library for almost any algo that's in a textbook. That made python popular. Nvidia has put a lot of work into developing CUDA libraries to cover the space of any graphical or scientific computing that requires a GPU. With alternatives to CUDA, you can write your own libraries from scratch, or chose from a handful of libraries, but you won't easily find a ready-made library to do exactly what you want. The communities aren't as large, so if you get stuck, google isn't going to help you nearly as much. When I program in Metal and search for a bug and find the question but no answer, it starts to feel lonely. I think Omniverse for robotics, digital twins, and driving and CuLitha, for lithography, are important CUDA libraries to highlight, but the list is very very long. NVDA has its own CUDA LLM, which is nice, but it's not that great right now. I think that NVDA competitors really need to build out extensive libraries for Metal or ROCM to attract programmers, who will then build a programming community and collectively figure out the right ways to overcome hurdles. The bigger the programming community, the lower the barrier of entry for a new programmer. But, building libraries takes time. It should have started decades ago. It's not automatically a death knell that they're behind though. Better LLMs will make it easier to build small libraries from scratch. Better LLMs should help Apple and AMD build out libraries faster. Some people are more speculative and suggest that AI will eliminate the need to program at all. For example, you can train a NN to play Doom really fast millions of times. Then, using just the keyboard, it will start generating/hallucinating maps and enemies like a human dreaming. There are no lines of code, save for specifying the weights and connections. Make take is that until 2022, Lisa Su was mostly focused on beating Intel in CPU wars and competing with NVDA in the gaming vertical, with a lot less focus on scientific programming and ML libraries. AMD does make great CPUs, and NVDA has had issues developing its own CPUs, so AMD is a clear leader in one vertical, but that's 20th century tech with more cores. Also, AMD isn't going away. Every single hyperscaler and government out there wants competitive alternatives to NVDA's super high prices/margins. It's not impossible for Lisa Su to win or find some better way to put even more RAM in a GPU, or integrate the GPU with more ASICs components, or to do something new and unorthodox; it's just not probable in the short term. From my perspective, NVDA has done a TON to support programmers and is still working hard to build new libraries. I feel like Apple's support to developer communities is short and perfunctory unless you want to write an iPhone app. Anyway, this was long. If you want to write code from scratch and that's all you need to do, then there is nothing stopping you from using ROCM or Metal. But, there's probably already a CUDA library for lots of the code that you would otherwise write from scratch. Are you in a hurry? How much time do you have? Do you have a lot of free time in a typical workday with nothing to do? If so, AMD/Apple are great platforms for you. Otherwise, get your boss to pay for NVDA. So far, the gatekeepers are leaning heavily towards NVDA, where your task is figuring out the library and framework, rather than building from scratch.

Potential-Stuff-8427

Bargain of the year. Besides their blockbuster weight loss drug, NN is still one of the leading diabetes technology companies. Hold for the long-term

GreenMellowphant

“In [my] mind”? Numbers are what I use in my mind, they are how one remains objective. You can state opinions about my investments to your hearts desire, but that doesn’t change the results. You can’t realistically expect new technological abilities to not have value in today’s world because the CEO of a company is a jackass. I do original DD inside my area of competence. I studied Math and Statistics for 7 years and was a physics double major for my first year and a half. I’m now a data scientist. I’ll address the your opinion and then the accusation of bias. Your opinion is not well defined. “Overpriced”, yes tell me all about how the NN architectural breakthroughs after doing away with the sensor fusion problem a few years ago were overpriced. What did you value the technology at? Did you watch the AI days before the company went mum? Have you followed the international battery industry closely? Were you keeping up with hiring data to see who all was snapping up the talent? Are you an expert in math, physics, and materials? You know the people that are experts in these fields are real people, right? You know how when people in niche fields get all excited about a seemingly mundane breakthrough and everyone else just doesn’t understand why they’re so excited? Yeah, that happened about four years ago, just with a massively lucrative but complex technology, and most people are still oblivious. “It’s a gambler’s den” because it’s full of gamblers, not because no one knows what they’re talking about. Your assessment of me is biased because I came to the gambler’s den. Should one that sees a clear opportunity not buy stock because it’s a gambler’s den? The gamblers are what makes it possible for me to get the prices I get on my two stocks. Do you think everyone that picks individual stocks thinks they can do it over and over again? I studied the real fundamentals, so I can judge for myself whether a company is full of shit when I’m presented with enough information. And I don’t invest until I’ve been presented with a shit ton of information, generally more than the company realizes it revealed. Lastly, I’m not the only one on this sub that’s done it successfully consistently for years. And I said that it isn’t extremely hard to find someone similar if you’re regularly on here. Where am I expressing the bias? (Unless you think I’m the only one, and I only believe there are others because of some gains posted here? But, given the sample size, that’d be silly.)

GoNucksFiven

$NN

Plutuserix

Vanguard developed Europe etf. My Vanguard SP500 year to date is - 5%, Europe one +10%. Next to that I have some dividend focused stock mostly aimed at Europe with ING, NN, BAT, BMW, Unilever, but also some American with IP and O. I don't really do much individual stock picking anymore, so it's mostly about instead of doing 80/20 US/EU now doing more like 60/40 on new money I put in. I'm still positive about the US future in terms of returns, but diversifying a bit more.

experiencednowhack

They're not dominant in weight loss. EL is . NN previously was but their pipeline has just been worse for the last several years and still is worse.

GoNucksFiven

NN

iqisoverrated

...unless the AI hype starts to subside totally and we enter another ML/AI/NN winter. It wouldn't be the first one. We're actually in the *third* resurgence of AI hype. 1950s-1960s was the first one. Then basically nothing happened during the 1970s and 1980s. Another push during the 1990s but again basically nothing in the 2000s until it started again in the mid 2010s.

Krazybrazy11

All in on NN before FCC announces NPRM for TPNT system.

GoNucksFiven

$NN

IllustriousTax3743

Asml, tsmc, NN, shell, OXY Only down, a tad, on shell.

Good-Way529

Why would they want to do something different? Maybe because it’s cheaper short term and positions them better long term to not be locked to a single vendor? Maybe because TPUs are a superior product to GPUs and designed specifically for optimizing NN inference and backpropegation instead of more general use cases?

meltbox

Tesla is directly feeding their cameras into a NN meaning they have no or minimal per-processing. I am just going off what they claim. But I see where we diverged. You were talking about doing the proper math based stereoscopic calcs/image processing and not the Tesla approach.

Imaginary_String_814

100% LLY is superior in many ways. Just look how NN handled their partnership with Hims.

experiencednowhack

NN shareholders or folks who don’t know the space

experiencednowhack

Amycretin is actually promising, in Tirz's ballpark. I'm not saying that NN is destitute or ruined or whatever. Just their pipeline is mostly weak.

experiencednowhack

They're absolutely not dominant and EL is running circles around NN's pipeline. Retatrutide is straight up better than every other weight loss drug of all pipelines currently (might eventually change when quad peptides progress more). Tirzepatide (out today) is better than Ozempic/Wegovy and also better than Cagrisema. Eloralintide is better than cagrillintide. Oforglipron is better than ryselbus.

narrowant341

Shell is in talks to acquire BP-WSJ [Link](https://www.wsj.com/business/energy-oil/shell-in-early-talks-to-acquire-rival-bp-2233591a?gaa_at=eafs&gaa_n=ASWzDAiuSR4w7fzTA-Ta-fALPybZdBipZ31J-yYIyXB3iGltSgCI8NN_erHrw9_HkMM%3D&gaa_ts=685c1d73&gaa_sig=smprs5LFybNnokkPPrDhTvfpZPUZsmtRExNOsO7mEfRGkiNhDOmw5J6pDVU9F7v5V3SDwVJMOADLZutJI74g9A%3D%3D)

Aeceus

I really shouldn't do your research, seems lazy, but both Amycretin and Ziltivekimab are highly positive. Ziltivekimab is especially interesting https://www.novonordisk-trials.com/trials-conditions/all-trials-v2/NN6018-4914.html?utm_source=chatgpt.com

stickybond009

The guys name is NN Taleb: anti-fragile barbell strategy

GoNucksFiven

NN

NegativeSemicolon

NN research has existed since the 60’s and no one took it seriously until it could pump their bottom line. Kind of sad to see how desperate they are to rule the world, I feel for the science.

Hungrymon111

Long UNH, GRND, PYPL, GAMB Short SPY, NN, MFH There, I gave you the ticket to becoming rich.

2QuarterDollar

Its not just NN. Banks / Sell Side firms still issue the most complex financially unstable products they can think of and sell it to companies promising returns and protection. Whenever the cycle resets, and a new generations of investors enter the market, they can start over with their scams. Although the financial engineering is fascinating, especially if you like derivatives, these products have no place in anyone's portfolio or company' balance sheet. Buffet called Options WMD but thats too conservative. Its these type of shit products

Away_Advisor3460

Maybe refactoring. Not sure I'd trust an AI with refactoring, but not sure I trust NN generated code for anything.

Sanpaku

I've worked in machine learning. There's obviously some transfer in computational technique between NN based object recognition, but all of this has been known for decades. NONE of the Tesla training data from assisted driving will have any benefit in training a humanoid robot to recognize novel objects in the home. Perhaps decades before such devices are more than status token toys in the mass market. Discount to present value, and one gets a value of tiny fraction of TSLA's current automotive business.

wayofTzu

A poorly trained unreliable NN suggests it'll have a high of 12.13 and a low of 11.30 next week. This was trained on a grab bag of fundamentals pulled weekly from yahoo over the past 3 months. Not that you should trust these numbers, I don't.

Chemical_Stage5136

That’s what I’m thinking, am counting on NN deal.

Hungrymon111

Sure, $PLUG or $NN are zeros for example.

Manticorea

I always enjoy reading your posts for you show great breadth in the sectors and companies you invest in while many people just go for the latest or the most well-known companies. How do you find out about companies like AUR and NN? Screening or just reading a lot?

dvdmovie1

"Which industries are currently still in their infancy but have great potential for the future?" I think the concern that I have is that we're already in a period where you want to be less early stage and more high quality and that potentially could get worse. This is the time for making a shopping list if you want early stage stuff, but I don't think I'd be piling into it. I think most of the last five years has been unusually fantastic for growth, first with the "disruptive growth" bubble and then the AI theme, which has cratered this year. I think people see this as just another 2022 before growth works again but what if it isnt? The disruptive growth bubble was followed by AI - I don't think you get another theme of that magnitude following AI. The data center theme was one of the most fantastic themes I can recall, but Stargate in January feels like it was the top. VRT is a little over 50% off the highs of January - that's not a mere correction, that's saying something a bit more about the data center theme imo. The IPP stocks that were huge in 2024 on the concept of data centers as far as the eye can see that need power have cratered, too. The data center theme has translated into fantastic earnings for NVDA, but AI really hasn't translated into fantastic AI-related earnings for many of its customers. If that continues to be the case, how much longer does AI continue to translate into fantastic earnings for NVDA? Quantum computing is a ways away and too many people piled into small QC cos that were lifted by hype. If you buy something early stage and the market lifts it on hype to the point where the company is early stage and the stock looks like it'll be a thing tomorrow, eventually reality sets in and someone can wind up a bagholder for eons until the early stage thing actually genuinely gets closer to commercialization. All that said, if you buy an early stage thing and wind up bagholding it, how many equity raises is it going to take along the way to keep the unprofitable/early stage company going? "the desire to understand more and explore space is definitely there." The desire to understand space is certainly there by some, but I think the sort of space tourism stuff is doing it no favors. I said a number of times that there is no way that SPCE was ever going to be a sustainable business - popularity of the space theme lead in 2020 for Branson to be able to dump it on retail investors - and people got so upset about that. Branson sold more than 75% of his shares for a total of $1.4B; the stock's market cap is now about $100m. There's also a fair amount of space stuff that has tremendous headline risk. LUNR is an example of something where they could land their vehicle and it goes up 50% and then all the sudden you get a headline that it tipped over and it's down 50% It's almost like biotech passing or failing a phase three, but in this case it's like "it passed" (things falls over) "nevermind." I can appreciate something like ASTS (have a small position) more but that company's success certainly is not guaranteed by any means. I do have a basket of early stage/more speculative companies, but it's not a giant part of the portfolio. AUR is a large holding that remarkably nobody ever talks about - was mentioned by T Rowe Price's CIO last year as a name he thinks of when people ask him what the next NVDA is. To me autonomous freight is less crowded than autonomous taxis - too many people focus on the technology of self driving taxis but nobody focuses on the unsexy part of what it will take to keep them clean (how many times are they going to have to be cleaned on weekends?)/maintained. NN is another name that's never discussed, but I think that the technology is interesting/useful and they also own a fair amount of spectrum which I think is underappreciated. But I've largely stopped adding to that basket of names and the remainder of the portfolio moved more towards more boring names earlier this year. I own the least tech I've owned in years. I'd already dialed back tech in 2024, but did really well last year focusing on industrial/utility/energy AI-related beneficiaries. A lot of that was sold late last year/early this year. So, hopefully I'm wrong, everything going on works itself out and somehow we get back to a growth-driven market but the longer everything that's gone on this year continues the more I think that the next few years might be more volatile (not as volatile as the market has been, but elevated in comparison to much of the last decade or so) and you might see lesser returns/different leadership. After two very lucrative bubbles in the last 5 years, I don't think it's unreasonable (especially with what's going on) to think that the next 5 years might be less/much less compelling for investors - people might have to change the playbook somewhat and include some boring names in your portfolio/not just "what's the next 10x?"

Worth-Walk6265

1. Compounded or the actual brand? I think hims will continue to sell the branded products, but it won’t be money hand over fist like compounds, and the vast majority of their customer base won’t pay 1400 a month OOP for the brands. 2. This is definitely the admin to try this with in terms of their general disposition, but they would never do this specifically. Why? A) Because NN/Eli are huge and will bury Hims in litigation for ever. B) because the outcome of the case would have implications for the industry at large. If they hold your logic, there is no longer any incentive to research and produce new drugs. If your patent is useless because anyone can make knock offs of it, why would I spend billions researching new drugs? No government would want that. C) somewhat related to b, the pharma lobby controls more than half of Congress. Go look up how much pharma spends on lobbying. 3) There is no way Hims would mess around with formulations/mechanisms of delivery. Imagine they did a nasal spray and someone died from it. Hims would be over. And on the point around AI researching specific dose differences, couldn’t happen. You could never verify efficacy and safety without trials. The risk/reward isn’t worth it for Hims. And, you couldn’t simultaneously claim you are identical enough to be safe and effective while being different enough to be different. The only instance this provision works in is when a patient has a known allergy or disability that necessitates an alternate formulation of a medicine they need. There’s no large scale case of that here. I’m not trying to yuck your yum, you seem excited about this position. Just presenting an argument that there’s catastrophic risk to the downside for this company and that the very best case has long been priced in. From my perspective, buying Hims with a bull case made up of GLP1s is the same as buying the companies that started and skyrocketed during COVID - their success is entirely predicated on a temporary break from a status quo. In other words, you’re buying on the crest of the absolute peak, and the only way is down. Drizly, peloton, other covid businesses - where are they now? If they aren’t dead, they’re on life support.

Worth-Walk6265

For a few reasons, I think you’ll end up being wrong here: 1) NN and Eli will sue and lobby this into oblivion. 2) by this logic, any and every branded drug would cease to exist, because their patents would be easy to circumvent using this argument. 3) you can’t claim mass production is the answer to isolated, individual patient needs. That rule is for cases like “I can’t physically swallow a pill so you need to make me a nasal spray”. Hims would never change the dosing or formulation for fear of adverse events, so they’d be unable to claim their product is meeting a need the branded options aren’t. The reason they’re promising all this litigation is to keep the door locked for as long as possible so they can milk their cash cow.

Environmental-Meal14

Here's my take as someone in the biomed industry: Missed some key metrics on LX9211 (a drug with already limited use since Gabapentin shows long-term effectiveness for neuropathic pain) and didn't secure a partnership for it. This tanked the stock as it is uncommon for no partnership with drugs that meet most objectives. They partnered with NN for LX9851 and it jumped higher than it should've because people locked in on the 1bn deal, likely not understanding the grueling research and regulatory milestones the drug still has to meet before it'll see 90% of that funding. I think people are realizing this nown while also drawing concerning potential parallels with the aforementioned LX9211. Pair that with the fact that 2-fluoropalmitic acid already achieves inhibition of sphingosine/acly-CoA, and we can surmise that although LX9851 will be profitable if/when it is finally approved, it isn't as revolutionary as LXRX tries to tell us. 2-fluoropalmitic acid

Kinda-kind-person

I my personal view below is the category. Overall/general derivatives knowledge and practical aspect: 1) Option Volatility & Pricing By Sheldon Natenberg 2) Dynamic Hedging By NN Taleb 3) Volatility Trading By Euan Sinclair. If you can handle your mathematics and approaching the subject from a technical perspective, then there is only one book, An Introduction to the Mathematics of Financial Derivatives By Salih N. Neftci If you are purely approaching the subject from a quants perspective and modelling assets, then there is only one book you will need. Stochastic Calculus for Finance By Steven E Shreve THATS IT!

NotAHost

Sorry for the word vomit. The Wile E Coyote is an absolute edge case that in general shouldn't happen, I'm just advocating the difference in sensing mechanisms and why lidar is *somewhat* superior from a sensing standpoint (and would still need a moderate computer vision system) but absolutely not from a cost standpoint. There are challenges with each sensor suite, for cameras it probably could detect the Wile E Coyote scenario better with different cameras, but there are tradeoffs with the resolution/exposure/sensitivity/etc which is why the cameras on Tesla's surround camera's don't look as crisp/nice as most modern camera's, but are better suited to the purpose of generalized self driving conditions. Human eyes are better than regular cameras at some things, worse at others. While 'if you can see it with eyes and brain, a vision system should see it' is true, the challenge is that a lot of that *has to be trained*. It has to learn/know when an outline is a 'Wile E Coyote scenario' and stop, or if it's literally just a bad assortment of lets say an overhanging wire for power/telecommunications/etc. It will be very difficult to tell the two scenarios apart and the latter is more likely than the first where a human can recognize 1 in a billion scenarios better. The issue are the edge cases that might only be 1 in a billion each, but when there are millions of different 1/billion scenarios, the situation becomes difficult to train. Again, all I want to highlight is that lidar is a 'direct' measurement of distance, while cameras have to infer from the images. That means that lidar has 'absolute truth' and cameras have 'inferred truth.' There's a better technical term to this that I forgot. I'd lean towards your assessment on cameras probably seeing a fence from afar better than lidar, maybe better in both due to resolution of cameras being higher. However, I'm trying to verify the spatial resolution of Tesla's camera's and having a hard time. One person [suggested about 1.6 minutes of arc](https://www.reddit.com/r/teslamotors/comments/nacp4z/does_a_tesla_have_good_enough_eyesight_to_get_a/) for the narrow FoV camera, but I would be wary because there are signal processing techniques and other specifications that are relevant that make everything very speculative and *could actually* be higher spatial resolution. The sponsor of the Mark Rober video's lidar system suggests [0.05° or 3 arcmin](https://levelfivesupplies.com/product/luminar-iris-lidar/). So, ideally the narrow fov is at least 3x better on the Tesla than luminar lidar, though spec sheets (by luminar) could always be an ideal scenario and we are only speculating on the Tesla based on a users post. I believe the shovel analogy with respect to selling to training industry (tesla, apple, meta, etc), may be correct? Which is why I advocated it back in the day. I think some of their chips like the nvidia chip used in skydio's drones are still amazing on their own though, which I believe *may* fall into the inference category (not sure everything under the hood of the chip and what its doing) that you mentioned, but I agree that isn't nvidias focus. That said, I believe you're getting at the many other players on the inference market and I'm not up to date with the subject and offerings in the last 5+ years. Full disclosure on my own biases: I made a chunk of money 2020-2022 bullish with TSLA, lost a small invested amount on velodyne being bullish from 2021-2023, and currently bearish *short term* on Tesla because politics and delay in deliveries with my own expectations with may happen in June with Austin/Tesla/Deliveries. However as an engineer, let me highlight that Tesla's vision system is still amazing and as far as FSD is one of the most versatile on the market, which makes it more applicable towards a world wide release than current solutions on the market AFAIK. However, I do expect to see higher amount of crashes if they do release, but I yearn for the FSD car and would be pleasantly surprised to be wrong. I don't think anyone will catch up to Tesla on a pure CV FSD system, their system is still amazing from a tech standpoint. Everything I've seen of v13 in China has pleasantly surprised me so far. NN/AI/inference/CV will not go away and is an absolute necessity for FSD. Lidar and CV is not one or the other, Lidar always has to supplement a CV system with respect to discussions on LIDAR. It's for redundancy and edge cases. If we're really lucky, we'll have multiple 'winners' for FSD, I'm excited to say after about 10 years, shit feels like it is finally getting real.

NameChexsOut

Using the Looney Tunes example, what happens when these fake walls start showing up on roads all across America and Tesla is able to capture video and feed that into their NN and account for them? What does it say about current automakers when Tesla is years ahead of all of them in the self driving space (for consumer driven cars)using vision technology, all the while this is saying lidar is years ahead of vision? Maybe I should watch the video but at the current rate of AI acceleration (both in technology and capabilities) I immediately get skeptical when people say something will never match something else, when the future cannot be predicted.

ken830

True. But think about the very best human driver on his/her very best day. Give them full 360° vision, sub-millisecond reaction times, higher dynamic range eyes. This person would be in orders of magnitude fewer accidents. That's what an AI NN driver would be. But that's not the point. The point is that LiDAR cannot improve upon that because as a sensor, it gives you no additional information that you don't already have with vision. LiDAR can only give you a depth map. But a LiDAR depth map is not something you can use to drive a car. To identify objects, you still need to augment it with vision. But with vision alone, you already have all the information to generate a depth map from multiple camera angles and time (parallax). So LiDAR is completely useless and even detrimental. All you need is to get vision correct with a good neural network.

Year3030

Gonna be a bloodbath: [https://youtu.be/SI\_61BM7NN4?t=29](https://youtu.be/SI_61BM7NN4?t=29)

literum

Crypto ASICS need to just be good at hashing. Modern NN architectures require many diverse operations that change over time and require flexibility, not great for ASICs. Just take a look the custom kernels DeepSeek made to accelerate their workloads. Furthermore, Nvidia GPUs are already ASICs to an extent with their tensor cores. That gives Nvidia maybe 2-4 year time-frame to milk their GPUs while competition catches up. Photonic neural networks could be an existential risk for Nvidia, though that looks like a decade away.

ace_alive

My European stocks as follows. However, I did not buy them today, I bought them already time ago (not all). So if they represent a valuation that is worth investing right now is up to you. Do your research. SOme stuff has become pretty expensive over here since the money moves into the market. There are various reasons I hold these stocks. I do value investing, but also anticyclical dividend investing and also some swing trading. Investor AB 3i Group PLC Rheinmetall AG thyssenkrupp AG Porsche Automobil Holding SE ArcelorMittal S.A. ASML Holding N.V. Vestas Wind Systems A/S Euronext N.V. Dassault Aviation Siemens Energy AG Hensoldt AG Signify NV Aker BP ASA iShares Global Select Dividend Piraeus Financial Holdings SA Novo-Nordisk AS Genmab AS Piaggio Group Leonardo S.p.A. Lifco AB NN Group NV Südzucker AG freenet AG BT Group PLC PKN Orlen S.A. National Bank of Greece Repsol S.A. Nordea Bank ABP Intesa Sanpaolo SPA Phoenix Group Holdings plc Star Bulk Carriers Corp. Schaeffler AG Eni S.p.A. Aviva plc DWS Group GmbH & Co KGaA Global Ship Lease Inc. PKO Bank Polski SA Navigator Company SA Danske Bank A/S Legal & General Group plc Odfjell SE M&G plc Mycronic AB Odfjell Technology Ltd. Wallenius Wilhelmsen ASA Sea1 Offshore Inc

newfor_2025

i have colleagues who has been working on AI and NN for decades and they're saying the stuff that's coming up now was never even considered back then. I don't know what to tell you. I9 has a what... 20-30 FPUs? a 3090 has what, hundreds? Besides, we're not really dealing with traditional floating point numbers now because they are a waste of bits. AI only need short floats to work well, but you need many many more of them in parallel

newfor_2025

The embedding step is a small part of the whole pipeline, and it's better on the CPU because of the sparsity of the data you're working with during that step, and that's one of the things Deepseek took advantage of to get the acceleration they got. The reason why GPUs are still better overall is not only because of the wider memory bandwidth but also because of their ability to compute vector arithmetic much, much faster than the general purpose CPUs can and that's difference is still give you a speed boost in any kind of AI workload. Until you have a CPU that has many many vector SIMD engines, you're not going to be able to compete with a GPU. Besides, companies are starting to shift away from graphics processors to make them into neural network processors built more specific to handle NN workloads -- look at Hopper from NVDA, Maia from MSFT, Axion from GOOG. Some can still call them GPUs because of their heritage and legacy but no one play would be playing games on those things since they would actually be pretty bad at doing actual graphics. People at home can't afford one of those things, but they have something like a 3090 you used in your example but that'll be a waste since quite a bit of that 3090 would be unusable/unsuitable for actual AI workloads. I really can't make out where you're coming from because on the one hand you seem to be familiar with some of the concepts but you're also missing some very obvious things or just haven't been keeping up with what's going on in the industry

MrMee_Seeks

The fact they are invested in other companies and a lot of IP/patents, especially in NN and 5meo DMT substances. They are focused on fast acting treatment, which is better in a clinical setting and our fast paced world.

AdCritical5383

“Defense secretary orders military to prepare for major budget cuts” -🅱️NN The libertarian inside me is about to >!cum!<. 

wilan727

I live in a country that is not supported by fsd. But the early adoptars knew that they were buying into something thst would (likely) have future returns as tech, hardware and NN became more robust. They were Not told in 2016 you can be hands of watching tv while your car drives you. Soon(ish) that will happen perhaps june in Austin. Ultimately those early adoptors decided to put their money there it wasn't compulsary. I don't see that as a lie. They bought into future tech.

New_Collection_4169

👐“We’re gonna have [ELECTRIC TANKS](https://youtube.com/shorts/cQmDsLS2fKM?si=bs85qiAqfcDq0NN6), SO THEY WANNA HAVE AN ARMY TANK THATS ELECTRIC” 👐

parkway_parkway

The real answer is that AI plays are running hot right now and Tesla is one of the best AI plays. If this whole idea of using massive farms of Nvidia GPUs to make AIs actually works well and becomes something then Tesla has it all. A huge stack of training and inference chips to train the models and robots and cars to deploy them on. OpenAI, which everyone is super hyped about, is competing in a really commodified and competitive market with meta and google whereas the pure NN driverless car and robot space is much less crowded. It's also interesting that in self driving most companies have given up, only Tesla and Waymo are really looking at commercialisation and Waymo has massive problems with getting the cars and retrofitting them, they only have 700 cars right now in total whereas Tesla can make 34k cars per week. On the earnings call they talked about starting a pilot robotaxi project in June and as soon as that collects $1 it's going to add a trillion to the company valuation easy. This post will get hard downvoted and people will just shriek "scam" "cult" over and over but yeah that's the reason it has such a high PE right now.

Plutuserix

No specific knowledge, but I like ING and NN Group in Netherlands. Picked them up a while ago when they were cheaper, but still a 7-8% dividend now. Far from growth stocks though if you are looking for that.

onamixt

They invented Transformers but even they didn't understand implications of scaling this architecture where it really shines, no one did at the time. For years. So, if this invention wasn't shared with the rest of the world, we probably wouldn't have LLMs, nothing. It's collective work. Or otherwise, someone would've reinvented highly parallelizable NN architecture. Anyway, Google fumbled to commercialize on this.

actualscientist

It's a tradeoff of flexibility or portability for greater performance. Portability has a cost, and that cost lies in the layer of abstraction between your code and the hardware that runs it. If your code only has to target one architecture, and you know that architecture well, you can exploit all of its features and quirks all the way down to the metal. Same principle behind why gaming consoles can have 10 year lifecycles even when their hardware lags behind state of the art. Anyway, here's a recent discussion illustrating the exact issue I'm describing: [https://www.reddit.com/r/IntelArc/comments/1g6qxs4/pytorch\_250\_has\_been\_released\_theyve\_finally/](https://www.reddit.com/r/IntelArc/comments/1g6qxs4/pytorch_250_has_been_released_theyve_finally/) PyTorch just added support for these Intel GPUs **3 months ago**. And it's still a pain in the ass to get it to work, if at all. For context, PyTorch is the dominant mainstream ML library for NN development. So it takes forever to get support for other architectures, and that support often has a HUGE asterisk on it, even if it's the dominant tools in the game. The alternative path is supported by a warren of community libraries, shim code, and other black magic. If you're like most ML people and developing at the top level of abstraction in libraries like PyTorch, you're not going to wade into that when you don't have to.

AdCritical5383

## ”We tested 22 popular fleshlights. These 3 are actually worth your cash.” -🅱️NN Oh shit. **FLASH**lights. Had me for a min there 🅱️NN. 

Silver-Letterhead793

“NN24’ word champ”

OmegaMordred

NN. Nazi Numbers.

Upstairs_Whole_580

I don't see it happening until next earnings...the market wants to see Blackwell revenue and guidance and then maybe just some nuggets about when Rubin is going to be released. If it really will be earlh, if TSM can start producing some of the 3 NN chips in their AZ fabs... But I didn't see NVDA trading above 120 after the Blackwell delay...so hope I'm wrong. I've been in for 5 years...I'll be in at least one more.

WoodsRLovely

NN's need to relax after a day of fist-pumping and swastiky painting.

bullfromthesea

NN Forever

TheRealDevDev

Did they succeed? “It was found that the chemical stability at neutral pH required for a drug candidate was not sufficient… This was partly related to deamidation and isomerization at asparagine residues but also the disulfide bridge.”​ The new analogs had the same problems as cagrilintide at pH 7, but also suffered from instability of the disulfide bridge. The disulfide bridge is critical for the biological functionality of amylin and also protects it from aggregation. This was likely an issue for cagrilintide as well, though it wasn’t reported—the researchers in that report said that instability of cagrilintide “included” deamidation, implying there were other kinds of degradation. “Degradation of the asparagine residues and general stability were significantly improved at pH 4.”​ Just like cagrilintide, these new amylin analogs were found to only be stable at a pH of 4. “Initially, it seemed likely that a neutral formulation was possible… However, as development of a neutral formulation not only caused deamidation but also involved disulfide bridge instability, it was decided to revert to using an acidic formulation.”​ Again like cagrilintide, and then NN1558, the researchers finally gave up on making this new amylin analog stable at a neutral pH. Now, onto the interesting insights they hadn’t shared in their cagrilintide report. Within the context of discussing the potential of amylin to form fibrils, here’s what they said: “The amino acid sequence of human amylin enables a process of misfolding whereby monomeric amylin initially forms soluble beta-sheet-rich oligomers that may be cytotoxic.”​ For the first time, Novo researchers are discussing not only fibrils, but also oligomers. To understand oligomers, imagine you have a box of LEGO bricks. Each individual brick is a **monomer** (mono meaning "one"). Now, if you connect a few of these bricks together, you create a small chain called an **oligomer** (oligo meaning "a few"). When two bricks connect, that's called a **dimer**, and three is a **trimer**. These oligomers, or short chains of amylin, then form bonds with each other in a side-by-side arrangement, forming sheet-like structures called beta sheets. They note here that oligomers “may be cytotoxic,” meaning toxic to cells. The research on amylin oligomers is actually much more conclusive than “maybe:” oligomers disrupt cell membranes, induce stress in the endoplasmic reticulum, cause mitochondrial dysfunction, increase oxidative stress, trigger inflammation, and damage DNA.[5] “Over time, these oligomers may mature further into elongated structures with a high content of beta-sheet strands and finally generate insoluble protein aggregates that are [visible under a microscope] as amyloid fibrils in islets.”​ Eventually, all those oligomers form fibrils in islets, which are small clusters of insulin-producing cells within the pancreas. “Some of these toxic oligomeric species are associated with beta-cell death and the progression of type 2 diabetes (16−18)”​ Here, they cite research showing that not fibrils, but toxic oligomers are what cause the death of the insulin-producing beta cells in the pancreas. This is from the first study cited: “The mature amyloid fibril is presumed to be relatively inert and to have no significant cell toxicity. Rather, smaller oligomeric intermediates formed during fibrillogenesis are thought to be cytotoxic.” That study goes on to point out that initially it was believed that fibrils are the cause of disease, but “subsequent reports have underlined that it is small, oligomeric [amylin] aggregates and not fibrils that constitute the toxic species.” “The stability properties of the individual amylin analogs were assessed with respect to their propensity toward amyloid fibril formation. The relative amount of covalent dimers and polymers (HMWP) present prior to fibril formation testing was [also] measured.”​ A dimer is two peptide molecules stuck together. The researchers are calling dimers “HMWP” (High Molecular Weight Products) along with longer oligomers that they’re calling polymers. After an amylin or cagrilintide molecule has been degraded and destabilized by a higher pH than 4.0, it then begins to stick to other amylin/cagrilintide molecules. This is the first step in becoming an oligomer, and as the Novo researchers have already established, oligomers are toxic to cells (leading to beta-cell death and progression of Type 2 diabetes). They used Thioflavin T (ThT) to test for fibrils, but we’re not really interested in fibrils since they’re nowhere near as harmful as oligomers. So here’s how they measured the amount of dimers: “Assessment of High Molecular Weight Product Content by Size-Exclusion Chromatography”​ Novo Nordisk uses Size Exclusion Chromatography (SEC) because it's the gold standard for assessing the presence of dimers and other oligomers. This is also the view of the US Pharmacopeia, which sets standards federal standards for drug products, as well as the FDA’s Senior Pharmaceutical Quality Assessor.[8] Reverse-Phase High-Performance Liquid Chromatography (RP-HPLC) may be considered the gold standard for separating and purifying peptide molecules, but this is not universally true. For example, ion exchange chromatography (IEX) is superior to HPLC for charged peptides.[9] For quantifying aggregates such as dimers and trimers, Size Exclusion Chromatography is considered the gold standard.[10][11] The reason the FDA and USP don’t advise RP-HPLC is because it’s not sensitive enough to detect dimers. If you analyze cagrilintide that has begun to aggregate using RP-HPLC, the dimers will elute about the same time, meaning they will still show up as monomers (single molecules). So the test results will still show high purity since the dimers are undetectable with this method. The pramlintide degradation study also supports this notion, as it employed a form of analysais even more sensitive than SEC to detect dimers called Electrospray ionization–ion mobility spectrometry–mass spectrometry (ESI-IMS-MS). The pramlintide study uses multiple tests to confirm the presence of oligomers because SEC has limitations.[12] Even deamidation, which is the form of degradation that cagrilintide goes through above pH 4 that leads to aggregation into dimers, cannot be adequately be detected with RP-HPLC.[13] The Bottom Line​ In Novo Nordisk’s own words, Cagrilintide ***absolutely** **must*** be formulated at a pH of 4.0. Much higher, and the peptides rapidly begin to degrade, leading to aggregation and the development of highly toxic oligomers. It is these oligomers themselves that are the most toxic species and not fibrils themselves. To test for the development of oligomers, Size Exclusion Chromatography (SEC) is the gold standard (at the bare minimum—there are also platinum and diamond standards). Anyone who tries to tell you that cagrilintide is safe (doesn't degrade) at a pH above 4.0 based on a test with anything less than SEC (including the RP-HPLC test from PTDS) is either ignorant or they have ulterior motives. They are asserting that they know better than Novo Nordisk themselves, as well as the US Pharmacopeia and the FDA. Extraordinary claims require extraordinary evidence—so what extraordinary evidence do they offer? Citations​ [1] https://pubs.acs.org/doi/10.1021/acs.jmedchem.1c00565 [2] https://www.jbc.org/article/S0021-9258(20)88558-5/fulltext [3] https://onlinelibrary.wiley.com/doi/10.1002/oby.23329 [4] https://pubs.acs.org/doi/10.1021/acs.jmedchem.4c00022 [5] https://pubmed.ncbi.nlm.nih.gov/33544470/ [6] https://pmc.ncbi.nlm.nih.gov/articles/PMC7288967/ [7] https://pmc.ncbi.nlm.nih.gov/articles/PMC4350773/ [8] https://www.usp.org/sites/default/f...riences-and-Expectations_Katharine-Duncan.pdf [9] https://www.mdpi.com/2297-8739/11/8/233 [10] https://www.waters.com/waters/library.htm?locale=en_US&lid=135068236 [11] https://pubmed.ncbi.nlm.nih.gov/28578190/ [12] https://pubmed.ncbi.nlm.nih.gov/27665170/ [13] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5612368/

WetAndSnowy

This is my field of study. Their paradigm is an end to end NN, which implies NN receives inputs from sensors and maps and outputs controls. Humans provides controls conditioned on the outputs of sensors. https://www.reddit.com/r/TeslaLounge/comments/1fciidr/end_to_end_fsd/

Stupid_boner

https://newsfile.futunn.com/public/NN-PersistNoticeAttachment/7781/20241128/SEDAR_PLUS/CSA_SEDAR_PLUS_NOTICE_RECORD_ID_381976.pdf

jnads

Shader units are just a primitive form of AI coprocessors and early NN algorithms ran on shaders. So it's part evolution and part engineering their product to match demand. 15 years ago I was doing self-driving type vision processing and CNNs were becoming big at the time and there was a big focus in using GPU acceleration

seven0feleven

I did this with NN and you are absolutely right. This stock simply won't stop going up.

seven0feleven

NN whatcha doin? Let's go.

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alwaysonesided

[https://www.youtube.com/shorts/TT3NN9eNwGU](https://www.youtube.com/shorts/TT3NN9eNwGU)

Narrow-Height9477

Guesses? NN SOUN SOFI IINN IBRX I like the companies but, they’re just guesses and I’m willing to wait out the time and see.

moru0011

* common sense, or do you believe elon is delaying FSD just to be "right". If lidar would solve it, they'd use it. * compute: I am a sofware engineer, just look what amount of compute you need to just generate an image. Video generation (which actually requires the AI to learn a lot about physics and properties of day to day objects) takes half a data center. Observation in general: in order to improve some 10% usually you need to scale up compute by an order of magnitude. There are software-only improvements and optimization though. Also realtime (like several times per second) is a challenge compute wise. * in order to drive a car reliably, AI needs to classify objects (so what is a car, what is a dog etc.) and it needs to able to predict likely behaviour of those objects (a wall does not move, a truck does). This is a compute heavy task. If your NN ist too small, you will get some "close" solution with a high error rate, especially in edge cases, that is observable with current FSD. * pre 2019 tesla used a non-AI approach (C++ + some basic AI), which clearly is not suited to achieve FSD. They just did "fake it til you make it". Only after 2019 they started to use real AI. Main reason is weak compute hardware on older car computer CPUs incapable of running anythin advanced.

mab79660

NN

themanrutger

Oh definitely! The Dutch banks and insurance sector are interesting, especially ING and NN Furthermore Besi semiconductors is interesting but extremely volatile. They are one of the components behind AMD's succes in both the gaming market with 3D v-cache and in Enterprise. Adyen is also pretty interesting I am pretty bullish on the Dutch economy as it does not seem to face many of the problems other European countries have. It does not face the industrial collapse like Germany as pretty much nothing is made there, it does not face the high unemployment and cultural holdbacks of Italy and Spain. It does not have the high debt burden of France. Demographics are pretty good, infrastructure is maybe the best in the world and for the first time in 12 years a little more sensible, regulation sceptic and EU sceptic government is calling the shots. Some notable dangers: Housing, there are literally no houses it's absolutely ridiculous, median house now cost $510k Energy, there are monumental problems with the poeer grid. The Netherlands currently has the highest capacity of solar per Capita and how the country is set up it's absolutely breaking the grid. New house can't get connect to the grid, business have ~3 year waiting time to get connected etc etc. Current estimate of solving just the grid problem is estimate at around 200 billion, roughly 20% of GDP There are problem's but im bullish

ComfyRug

Sure thing: NN NextNav Inc. (NN) is showing a bullish trend with its current price at $8.19, above key moving averages (10-day SMA at $7.71 and 20-day EMA at $7.62), indicating strong upward momentum. The MACD histogram is positive, suggesting continued buying pressure, while the RSI levels are moderate, leaving room for further gains. Despite the lack of specific news, the broader market's cautious optimism and tech sector strength could provide additional support. Given the recent consolidation and breakout pattern, I recommend entering a long position around $8.15. Set the first price target at $8.50 with a confidence level of 70%, as technical indicators align with potential upside. A second target at $8.75 is plausible if momentum sustains, with a 60% confidence level due to potential market volatility. Implement a stop loss at $7.90 to manage downside risk, considering the stock's historical volatility and broader market uncertainties. SOUN SoundHound AI, Inc. (SOUN) is currently trading at $5.20, showing strong momentum with a 165% increase in valuation following Nvidia's investment. The stock's RSI indicates overbought conditions, suggesting potential for a pullback, but the MACD histogram remains positive, supporting continued upward movement. Recent news highlights significant growth in order bookings and extended contract durations, which could drive future revenue despite concerns over profitability and competition. Given the broader market's cautious optimism, SOUN may benefit from the tech sector's strength, though volatility in AI stocks remains a risk. For today's session, consider entering around $5.15, targeting $5.35 initially and $5.50 as a secondary target, with a stop loss at $4.95 to manage downside risk. Confidence in reaching the first target is moderate due to current momentum and market sentiment, while the second target carries lower confidence given potential resistance and broader market uncertainties. Stay vigilant for any shifts in market dynamics or unexpected news that could impact this volatile stock. SOFI SoFi Technologies, Inc. (SOFI) is poised for a bullish session today, driven by strong market sentiment and recent positive developments, including a $2 billion financing deal with Fortress Investment Group. The stock has shown impressive momentum, with the current price at $10.02 significantly above its 30-day SMA of $7.94, indicating strong upward momentum. Technical indicators such as the RSI are in overbought territory, suggesting potential for continued gains but also cautioning against a pullback. The MACD histogram remains positive, reinforcing the bullish trend. Given the broader market's resilience and SoFi's growth prospects, an entry around $9.95 could be optimal. Target the first price at $10.50 with a high confidence level due to strong momentum, and a second target at $11.00 with moderate confidence, considering potential volatility. Set a stop loss at $9.70 to manage downside risk effectively.

Narrow-Height9477

NN SOUN SOFI Please and thank you

cookingboy

> everyone else are still on the wrong path Dude, you have no idea what you are talking about. The NN stack you were referring to was literally published by *Google*. It had always been a software problem, and Google had been using NN since 2012. Just stop.

Bigleyp

You can also use ML(machine learning) or NN(neural network) but neither of those catch on to the public like the words “artificial intelligence” is the issue.

Emotional_Two_8059

I wish them luck on certifying the (almost) end-2-end NN. While I agree that the long-term solution to autonomous driving is this, any serious authority will laugh at the safety arguments behind it. Waymo probably has an occupancy grid heavily reliant on LiDARs. Tesla has “the NN told me so”

Narrow-Height9477

NN is also in my list

cleanSlatex001

Then learn from them clown and then show off your "new tech" later when it works clown. The issue is not with so called end - end NN, but using the camera alone to navigate instead of other forms of sensing.

Kosmi_pro

Yes logic of NN is based on our neurons but activation functions are not. Our neurons do not work on regression logic. We still don't understand what kind of algorithm can explain our neurons. That is why AI are still "dumb" compared to us and arr more like super assistant that has every info inside them and can put it trought with our robust input unlike with google where you have to be precise and search right info.