ML

-0x00000000

I really like the photonics & quantum sectors going into the next 10 years. Volumetric photonic processors running analog ML models are far more efficient than GPU based solutions. MIT recently built a proof of concept using refractive index & angle of incidence to map models to volumetric substrates. POET $1000, QBTS $1000

zztopsthetop

The biggest reason for the improvement from 3 to 4 is the huge improvements in resolution and quality of sensor data & the bigger /faster inference chip they have in hw 4, allowing for bigger model architecture & 16 bit float instead of 8 bit float computation. They also went from 1.2 megapixels to 5 megapixels, which greatly improved data quality. You're talking about hardware innovations, those again have nothing to do with the ml engineers at Xai (because those aren't typicaly hardware engineers but ML people). Software innovations (and differences in training methodology) would be visible through version differences on identical hardware.

SoulCycle_

meta revenue growth is from traditional ML applications. The capex bet is for LLM training/applications

LackToesToddlerAnts

Yeah nobody asked you this lmao Love these engagement comments. In what world does “big time player” asked you to replace Windows outside of it being a joke? You are saying this guy knows the costs of operating systems and ignorantly wants an ML engineer to replace it? It’s a cute way to show the reliance of windows OS but Reddit eats this shit up so good job on your engagement

CuteSaggisture

Lol ai has been useful since before 2025. Image recognition being prime example for it. Lol so many ML algorithms being used in wallstreet since god knows when. I have no clue about the recombinant and transformationational creation would look into it.

asdf_lord

They absolutely can create, either recombinant creation or transformational. Both have been observed in academia particularly in mathematics. I have been using ai and ML since 2010 and while it may seem like we've got a logarithmic growth in latent abilities and performance we've not seen that in actual usage. AI is currently still in the early stages of adoption and has only been meaningfully useful since late 2025 for functions outside of chatbots. (Agents, research, and data pipelines).

ouhshuo

cortex AI is better than genie, I'll give you that. but for mature AI/ML, especially mlops either via model serving or containernisation, databricks is way ahead. also snowflake you need a bunch of supporting resources such as dbt right? in contrast, everything can be done in databricks

Gamplato

ML in DB is definitely a good experience. Since our data is Snowflake, we use their end to end ML and it’s super good…but this wasn’t always the case, I hear. The Databricks applied AI is not close to Snowflake. We tried their coding agent recently just to stay up on things. It was actually shocking how immature it felt in comparison to Snowflake.

ShanghaiBebop

Databricks and snowflake is very much converging on the same core things.  Databricks has better AI and classic ML workflows integrated with their platform. snowflake has a better first class warehouse experience OOTB.  Bigquery imo is pretty far behind both of them.  Both DB and SNOW will see significant rev growth acceleration from AI though, since agents will need to query real enterprise data to keep themselves grounded in reality, and is drastically dropping the barrier to querying data. 

Obvious_Inspector779

I mean if they’re good analytics for people who pay for ads sure…I cannot believe this is the major play with the new capex though…I still think they’re cooking something else or maybe just organically increasing the infra for their already ongoing ML/data stuff.

sevopapi

Now add on the large amount of engineer/data/ML job postings for Reddit, which won’t be cheap. This story is just starting.

ActivatingEMP

Yeah I agree on ML but that's not what is getting all the investment, it's these stupid LLMs.

StegersaurusMark

I suggest you look up the statistical results of “active managed funds”. Your ML acct manager was probably blowing smoke to justify his paycheck. I mean he might have believed it, but it was probably still smoke. Then you went and parroted it to your next manager. I can’t offer you any advice. I manage my own accounts. I’m 40. I’ve interviewed a few FA/ account managers and always felt skeeved out by them and walked away. I’m now “using” the Fidelity wealth management but I don’t think I’m getting much more than the basic Monte Carlo sim you can access from any old 401k account these days I’ve been devouring the Rational Reminder podcast over the past year or so. It’s Canadian but still really great IMO as an American. I just tried listening to some Motley Fool podcasts and just don’t feel it.

richierva

I wasn’t doing this ML was.

richierva

ML did. It’s a pretty common strategy.

richierva

I understand the performance of the bonds I don't understand why the underperforming ETFs from the sector rotation plan haven't been addressed. We had a more conservative approach with ML and now this FA doesn't do anything.

BertRenolds

Maybe to being a loser? But I work in software for a living, and reading this post, it's pretty clear they are just using AI slop and calling it ML.

Weak_Emu3659

Is it okay to buy a $25M house? Our household liquid net worth is \~$20M, mostly in S&P 500 holdings with about $5M in unrealized gains. Our annual income averages $10M pre-tax but is highly volatile—ranging from $5M to $25M over the last few years. While we expect this to continue, we’re aware that in tech/ML, things can change quickly.

iHubble

Brain dead take. Meta stole nothing, they just massively improved it and the project got naturally subsumed with scale and resources, as has happened with countlessly many smaller open source projects. If anything, PyTorch is probably the greatest gift to the ML research community of the last 10 years.

Ill_Sun358

Spurs on the ML big homie

SoulCycle_

what about the ML framework used by almost every single MLE in the industry?

pwdbypenguins

Watson was ML

_101010_

So I was talking with a couple people at anthropic. And I guess their belief is that it won’t converge. Basically whoever can get the ML to iteratively improve on itself first takes the bag

pm_me_your_pay_slips

To be clear, the anecdote above includes frontend, product and ML engineers.

-UndeadBulwark

I'm not sure about Intel but AMD is doing fine. AMD has diversified so aggressively over the past decade that framing them as a distant second to Nvidia misunderstands what the company actually is now. They're in consoles, desktops, laptops, tablets, phones via Samsung's Exynos licensing, servers, and routers. Ten years ago they were close to folding. That turnaround is not a small thing. On the software side, ROCm has closed the gap with CUDA faster than almost anyone expected. A year ago it was genuinely painful to work with. Now with ROCm 7.x, Windows support has arrived, PyTorch and most major ML frameworks treat it as a first-class option, and the 7.1.1 release delivered up to 5x performance gains over 6.4.4 across key AI models. It's not at full CUDA parity yet in every workload, but it's no longer a footnote. UDNA, which merges the RDNA and CDNA lines into a single unified architecture, is where things get genuinely interesting, and that's still ahead of us. On Nvidia's position in AI more broadly: this cycle has a pattern. One company moves in early, captures the market, prices rise, and the market diversifies to reduce the dependency. The current shift toward local inference first, cloud escalation second, with models like Gemma 4 running on-device and Google pushing AI into phones at the hardware level, represents exactly that kind of structural change. Nvidia's dominance is built on centralized cloud compute demand. If the architecture of deployment moves away from that, the moat shrinks. AMD has been methodical and capital-conservative. Nvidia has been running at full throttle on the assumption that the demand curve only goes one way. That kind of overextension is exactly the setup for a Zen 2 moment.

dintclempsey

...Huh? Where was I disrespectful? Jesus. Cloud is one of the fastest areas of growth for Google atm, driven by... enterprise AI and ML offerings. This isn't about your banker friends using Gemini. (Although now the largest operating system in the history of the world comes with it embedded everywhere.)

Glum-Ostrich-4250

The llm models themselves aren’t fully reliable tho. From what I’ve seen is the ai boom inside the tech industry has led a lot of companies to develop different types of ai for different things like ML algorithms for fraud detection. It’s not just slapping an LLM to something and calling it a day

demaraje

Get off your high horse with this patronizing shit. I'm a ML Engineer. I know how this works. I use it and build it on a daily basis. You're literally talking about SDD like it's some godsend. It fucks up A LOT. Talking about Opus 4.6 Yes, good prompting and planning makes a huge difference. It still fucks up. Do a simple experiment. Write 10 changes by hand and save the git patch. Then write a simple prompt/agentic flow/ReAct flow to produce them from a simple description. One flow for all. Calculate the semantic difference to what you wrote. Then you'll have an idea of how well it performs.

drankleanonce

take your money and go all in on cavs ML tonight your welcome

Master_Enthusiasm410

The bear case about big tech building custom silicon (Google TPUs, Amazon Trainium, Microsoft Maia) is real but consistently underestimated how long CUDA lock-in buys NVDA time. Every ML engineer in the world has years of CUDA code they'd have to rewrite. That's not a moat, that's a fortress. The China H200 approval is the wildcard - if Chinese firms get access, NVDA's revenue ceiling just moved up significantly. Jensen on Air Force One is the most Silicon Valley sentence ever written.

Flat_Editor_2737

Well said. The circular deal narrative is easy for the media to present, and rational enough to believable. It's not wholly absurd to consider as a risk but the performance obligations and 'seeding the market' piece aren't new. Hyperscalers have been doing this for awhile. Ive been in tech infrastructure sales for 14+ years working for companies you would absolutely recognize. Ive sold everything from data center hardware, to cloud infra, to big data saas, to ML, to AI, to Agents. It impresses me how confident the narrative is that AI ROI is playing pin the tail on the donkey in the dark. This might have been true in 2023. I'm now on the professional service consulting and integration side now. In the last 6-12 months the models I'm seeing companies walk in with for use case analysis and the way they are quantifying TCO/ROI is getting orders of magnitude better on a month over month basis. Companies operate on a spectrum of competence, but the financial models at the ones who will likely be successful are iterative already. That knowledge will eventually be more ubiquitous and you'll see the same thing you saw with hyperscale infra - a smaller first mover demographic followed by a larger adoption wave as the risk starts to normalize. . We are still early, IMO.

SkyFew229

iren is ran by retards, NBIS is run by one of the strongest AI/ML teams ever

tristis_veritas

$2.52T in total 2026 * AI Infrastructure: $1.37 trillion * AI Services: $589 billion * AI Software: $452 billion * AI Cybersecurity: $51 billion * Data Science / ML Platforms: $31 billion * AI Models: $26 billion https://www.gartner.com/en/newsroom/press-releases/2026-1-15-gartner-says-worldwide-ai-spending-will-total-2-point-5-trillion-dollars-in-2026

tristis_veritas

$2.52T in total 2026 * AI Infrastructure: $1.37 trillion * AI Services: $589 billion * AI Software: $452 billion * AI Cybersecurity: $51 billion * Data Science / ML Platforms: $31 billion * AI Models: $26 billion

Chumphy

Oh yeah totally, I was just curious. I was asking because not only do they have sensors and cameras that go into ML tech, It’s also a company that is used for the opposite of AI things. Actual movies, cameras for live events, human tech so to speak. They are both complimentary and opposite of AI stuff. 

skilliard7

Software engineer as well, background in AI/ML. I bought a ton of Samsung/SK Hynix back in late 2024 because I knew HBM would become the bottleneck due to growing model sizes, growing KV cache with context size. I foresaw companies producing their own AI accelerator chips, and figured HBM was the common bottleneck, not Nvidia. I also had some WDC(pre sandisk split) because I figured data was important to AI training. AI coding tools definitely improved a lot recently to the point where they can no longer be ignored. And this obviously explains the recent compute shortages; AI coding usually requires intense reasoning models, and millions of engineers suddenly using it is going to cause a surge in demand. But I think inference demand growth is going to slow down from here, though. Most large orgs are already pushing devs to write most code with AI, and all employees in general to use AI. Therefore, there needs to be another massive application that LLMs take over in the very near future, or we will end up with substantial oversupply of compute as new datacenters come online. I think what's very telling is that all it took was a 300 MW SpaceX datacenter for all of Anthropic's compute shortages to be magically solved, and SpaceX was able to give it up without impacting Grok's availability. Additionally, OpenAI shows no signs of shortages, offering extremely generous limits on $20 plans. A 300 MW datacenter solves the compute shortage of a leading frontier lab, and yet there are several Gigawatts of capacity planned each year, and compute also gets substantially more efficient with each hardware generation. This suggests that the "compute shortage" is quite marginal and oversupply is nearly imminent.

Intelligent-Love5146

Similar. I’m in Data Science/Data Engineering/ML and also followed a similar pattern - using ChatGPT to debug to using Claude locally to write code and merge PRs out of whole cloth. I’m not wholly convinced on your thesis. Models are becoming more efficient. It’s not clear whether token usage is simply going to increase exponentially. My opinion is that SaaS and Software stocks in general are doomed. Sales Force, Workday, DataDog etc It’s clear you can build highly customized software for basically a few dollars worth of tokens. You then have to figure out deployment and change management but the value add of cookie cutter SaaS is way way lower than it used to be. Agents can do so much now that you might not even need software UI at all for stuff that used to require standalone apps.

Pale-Entertainer-386

Andrew Ng believes that we are still technologically far from achieving AGI. He worries that companies investing in AI today could easily face disappointment, potentially leading to a sharp decline in AI investment and the bursting of the AI bubble. Andrew Ng is one of the most globally recognized leaders, pioneers, and educators in Artificial Intelligence (AI) and Machine Learning (ML). [https://youtu.be/4vzmTKUFtxg?si=g6eTFcjRlpevGHW_](https://youtu.be/4vzmTKUFtxg?si=g6eTFcjRlpevGHW_)

Jagged_Tide

What I found in a ten second google search on (ironically) a Reddit thread: “A lot of python that’s used for ML like using tensorflow or PyTorch is like an API for the underlying c++ code. It’s when you have to really optimize things that’s when you would dive into lower level” I don’t know much about C++, my understanding was it was used primarily for like games and web design but yeah looks like there’s some practical usage for it in ML. That being said, I feel like a highly competent Python coder would almost never need to dive to that level. At least from what I’ve heard they don’t.

Cold_Specialist_3656

ML is everywhere. Massively expensive LLM inference of huge models is not

Cold_Specialist_3656

Basically nothing runs on AI so far. At least nothing that would justify such valuations.  Most ML use is still for vector embeddings, scoring, sentiment analysis. Tech that's been around for ages and takes 1000X less compute than LLM

jbokwxguy

AI in modern terms generally means LLMs and Generative AI (image creation). Not the ML AI that is used in Medical

ScarletHarpy93

What do you mean it's not a sustainable business model and inevitably people will realize it's not price effective to replace workers with ML?

Plastic_Ostrich897

Not too late to but money on Detroit ML @ the half +870

aedes

This all depends on the balance of how long it takes these companies to start making profits from their AI expenses, vs how long investors have the free capital and hope for the future to keep giving them their money.  No one is making profit from selling AI right now. If rates remains low and the fed continues the QE they started a few months ago, and these companies find a way to either significantly reduce training and compute costs, or convince people/companies that something like Claude or better is worth 5-6 figures per employee per year, then we will avoid problems.  If recent geopolitical problems aren’t resolved and prices of power and electronics manufacturing continue to rapidly increase, if ML researchers have truly reached a plateau in what transformers can do, if money becomes less available due to the need to increase rates or some sort of liquidity crisis, or investors just lose faith that profits are coming or get spooked, we will be in trouble.  In either case, the longer it takes for these companies to start turning a profit, the higher the risk of a correction. 

Leveraged_Lots

"I can’t hire AI/ML people fast enough." As a shareholder, if I heard this on an earnings call, that would mean selling immediately. AI might prove amazing, but you won't be spending my money betting on it.

raisedeyebrow4891

That’s too bad you feel this way about AI. I work for a Fortune 500 company. One of the best pharma firms in the world and I can tell you that our AI modernization efforts are in every single facet of our technology, research, manufacturing, and R&D. I can’t hire AI/ML people fast enough. The main SaaS providers are all building out agentic orchestration layers. The business demands it and if we let up the competition is going to eat us alive. I use AI for my own work every day and it has made me infinitely more efficient and able to produce at a level I cannot dream of. So I would be wary of judging the entire industry by your own anecdotal experience. Just go on LinkedIn and search for AI linked roles across all industries and tell me it’s a passing fad

Apocalyptic-Post

How do I parlay 0DTEs and the Timberwolves ML?

CuriousAndGolden

I worked with a programmer who was really smart and built a killer day trading app. He was always broke and wanted his paycheck early. I think it’s a combination of chaos, insider trading, and an arms race between whatever ML or AI apps there are out there. It’s like betting on horses. You need to somehow know more than everyone else to consistently win, and that’s really hard to do unless you’re cheating.

C130J_Darkstar

OKLO close to design approval, use this info as you wish. https://www.nrc.gov/docs/ML2608/ML26086A285.pdf

NeedMoarLurk

I work at a biotech and we use a number of "AI" (ML) based tools. They're pretty cool and have come a very long way in the past 2 years. Not sure how they can get you from Ph 1 to Ph2 quicker though, as those stages and progression between them require (positive) clinical data, not AI predictions..

ScarletHarpy93

I think they are going to be built, and then they'll end up like dark fiber from 2000 - 2009: noone can afford to use them because the market will see them as commodities and the ML business case will fail to fill out the demand.

InitiativeGlum2507

I lean long, and I use Schwab. One platform for investing. Another for banking. I set up two brokerage accounts. One is cold storage. I have a little money in ML but that's just to qualify for some benefits. Pro-tip: I gamble like an absolute r\*tard in my IRA and have been wildly successful, without having to deal with tax implications every year.

Ok_Freedom3290

Solid breakdown. It’s interesting that you found RSI more effective than premium size—definitely a lesson in not chasing overcrowded trades. I’ve been experimenting with a similar approach but I focus more on real-time whale movements and 'Z-scores' of the order book. For example, **I'm seeing a Z-score of +1.14 on RIOT right now with a massive relative alpha of 6.79%**. Tracking those huge blocks *before* they hit the tape is where I've found the real edge. I actually ended up building a live terminal for this (ML engine and all) just for my own trading so I could dodge those high-premium traps. If you want to see the data I'm looking at, it's live here: [https://alphasignal.digital](https://alphasignal.digital/)

kinetic_honda

I'm talking about things like evolutionary scale model from Meta which is a protein sequence language model that helps with predicting experimental outcomes using purely sequence alone. Imagine that??? Can you imagine certain efficiency boosts that can be achieved using such tools?? Want to talk about other such machine learning workflows and solutions these tech companies are developing? Or do you want to simplify it to chat bots because that is what you have used? I'm glad to chat further. Have you used other ML models such as phikon for ihc slide scan based predictive models? Something that you can use to train classifiers to identify cell types from scanned tissue images?

AnonymousUser462

Other ML algorithms and especially attention have solved text translation easily since 2013. Voice translation is a bit more of a new game changer, but people on this app are largely there to learn English or have a hobby, not necessarily become fluent to speak on a day to day.

TonguePunchMyPoopBox

Ding ding ding. Nvidia consistently submits across all of MLPerf for several years. Competitors only submit in certain benchmarks when it favors them (not so often). Submitting to MLPerf requires a lot of effort, too. The kind of effort requried to setuo and optimize large scale rujs on major datacenters/clusters. Top answer in this thread talks only about matrix math as though that’s the only thing large scale ML/LLMs are doing. It’s a full stack optimization problem from storage to memory to networking to compute to software orchestration etc. Jensen’s been talking about this for years.

SuffiecientSapian

Man crazy ass games yesterday! Had Raptors and Lakers ML

MaizeandBlue94

I graduated from b-school in 1994 and was offered an interview with Merrill Lynch's treasury office. I flew to NYC and the woman who interviewed me wore the most revealing shirt that she also had strategically unbuttoned just so. I struggled to maintain eye contact and spent much of the interview trying to avoid staring at her breasts.I knew immediately I wouldn't get an offer. It all worked out though as ML treasury was located in the WTC and we all know what happened a few years later.

jhplano

Hi PaPa, First off, I'm a former Nasdaq market maker, held series 7,24,55,63.....now I'd be considered a retail participant. I can answer all your questions: I entered all these trades thru the 'spread' order entry ticket page. I have the highest level of options approval. Yes, this is a Reg T margin account, ML EDGE doesn't offer port margin. So none of those are not true. Yes, some brokers are more conservative and have stricter house rules. Yesterday, the agent said this is a FINRA Rule 4210 deal, not elevated house rules, which he's probably wrong. I've got other accounts at ML that are mid-seven figures but they are not 'tied' to this account. I found out that ML just isn't a good platform for active trading, either equities or options. This is where I whine a little, I had a huge back surgery last Nov, it worked out but I have a new issue that will require a revision procedure. My patience is short, but I feel a little better this week, so I thought I'd get in the pool, stay in the shallow end. This really is making my upset, confused. I elevated the issue up, they'll probably get back to me Monday or Tuesday. I do have access to Brian Moynahan, the CEO, but I hesitate to get him involved, that'd be silly. Thank for getting back to me. After a quick look at some brokers last night, I like TastyTrade and WeBull. I simply need a broker dealer that caters to active trading.

SuffiecientSapian

I had 76ers ML last night but big money on Denver. Man wtf happened to that Nuggets team. You have a multi time mvp, champs 3 years ago and now can’t even make it out the first round against a team missing 3 starters. Fucking embarrassing

RepulsiveDisaster671

#Hope yall took the knicks ML

SuffiecientSapian

Hawks +10000 ML I’m taking that

jhplano

Hello guys, Having trouble with ML EDGE trading zero dte SPX credit spreads. I’m returning to trading after some back issues. Tuesday, I put on a credit put spread in SPX, it worked out, 5 contracts; same on Wednesday, both $10 wide fairly OTM , roughly 10 deltas. Both worked out, 5 contracts each day. Then Wednesday night, I get an email, I’ve become a pattern day trader, this account is roughly $350k, I get an email asking for a deposit of $150,000. I called them, they claimed this was a FINRA rule 4210 violation, in reality it’s a house rule, I don’t know sure seems like it. The agent says I have a naked option, I used the spread order entry page, there was never an exposed naked option. I was operating on the assumption, the max loss was the distance between strikes, $10. Or about $5k. ML is saying to trade SPX zero day options, I’ll to have the full notional value of an SPX value, around $500,000 for each contract, not the difference between strikes, less premium. What to do?

throwaway9gk0k4k569

What was your thinking? About both. MSFT seems fucked for awhile, until they figure out how to properly implement ML, instead of shoving it into everyone's face as hard as possible. They do this with all technology: terrible innovators, pretty good thieves. Google literally invented modern ML, knew it wasn't ready, and held it back on purpose.

brewgeoff

What evidence do you have to support your thesis that ML lied about the outage or even that the outage was intentional? Sounds like you are just mad and jumping to conspiracy.

SuffiecientSapian

Just threw another $200 on lakers ML LeBron is balling out. Insane he’s 41

Big_Habit5918

It’s primarily GCP’s positioning as the driver of academic research. Now I am not sure about how GCP fairs in enterprise but for Academia, GCP works with a lot of leading ML/AI faculty (and for non ML research that requires heavy compute like large scientific datasets) to make compute available. For a lot of my coursework, I have been given up to 100 dollars on GCP credits to utilize for projects. This is of course just an anecdote. GCP has the following educational programs that I can point towards that could explain why they’re popular amongst universities: [Google Cloud Higher Education Programs](https://cloud.google.com/edu?options=research-credits#optionsBlogsocial&utm_campaign=2018-edu-gcp-research-credits) [In order to ensure that more researchers have access to powerful cloud tools, we’re launching Google Cloud Platform (GCP) research credits, a new program aimed to support faculty in qualified regions who want to take advantage of GCP’s compute, analytics, and machine-learning capabilities for research. Higher education researchers can use GCP research credits in a multitude of ways](https://research.google/blog/announcing-the-google-cloud-platform-research-credits-program/)

d_e_g_m

The basic Support and Resistance levels aren't timeframe bound: PDL, PDH, PMH, PML, 5MH Orb, 5ML Orb

Noctis-Banned-793

Yeah, but by ML, mostly machine vision. There is no LLM involved.

suzisatsuma

I've worked in big tech for decades in ML/AI - LLMs will not be how humanity reaches AGI. (if we ever do). They are however powerful tools.

roadwaywarrior

The you have a very basic level of exposure to the product. I used to design it for one of its largest BUs and led the first integrations of ML into its platform. OP’s position is bearish in my opinion. I’ve go over half a million on them

UsnDoto

That's what bloomberg does basically and a bunch of others i don't see how you going to compete. Once again you can have all that sent to a basic ML model. You're essentially working on replacing some stuff that already exist. You son't have to contact to make customers move away from tools they have been using for the past decades and your technology is going to be obsolete in a year or so. My advice, pivot asap.

Valkyrie_Skuld

This was rddts. Same think they did really well and the market was trash Here’s RDDT’s last quarterly earnings (Q4 2025, reported Feb 5, 2026): Headline numbers ∙ EPS: $1.24 v s. $0.94 estimate (beat by ~32%) ∙ Q4 revenue: $726M, +70% YoY ∙ Q4 net income: $252M (35% margin), up $181M YoY ∙ Q4 adj. EBITDA: $327M (45% margin) ∙ DAUq: 121.4M, +19% YoY Full year 2025 ∙ Revenue: $2.2B, +69% YoY ∙ Net income: $530M (24% margin) ∙ Adj. EBITDA: $845M (38% margin) ∙ Free cash flow: $684M, up $468M YoY ∙ Cash & marketable securities: $2.48B Capital return & guidance ∙ Board authorized a $1B share repurchase program ∙ Q1 2026 guide: revenue $595M–$605M (+52%–54% YoY); adj. EBITDA $110M–$220M Drivers ∙ Q4 ad revenue $690M (+75% YoY); active advertiser count grew >75% YoY ∙ 2026 product priorities: improving onboarding, unifying search with Reddit Answers, feed relevancy via ML/AI, and verified profiles

avrawat

the first comment is right — "learning AI" is too broad to be useful. the better question is: which part of your work is manual, repetitive, and runs on structured data? that's where AI actually delivers. i track AI upskilling trends across domains professionally — finance is one of the more interesting ones to watch. not because of the big flashy stuff (AI managing your entire portfolio isn't a realistic near-term play for most people), but because of how much manual, structured work exists in day-to-day finance. earnings call summaries, research synthesis, report generation, variance analysis, data reconciliation — a significant chunk of this can be smoothed out with the right AI workflow. i've spoken with a few finance professionals directly on this — including a head of finance and some senior practitioners. the consistent finding: AI is helping every function including finance — automating tasks, increasing speed, removing manual work, reducing errors, improving overall efficiency. and these weren't theoretical observations, they were things people were already doing week to week. the entry bar is also lower than most people assume. you don't need to learn ML or go deep technical. start with prompt engineering — just understanding how to get consistent, useful outputs from an AI tool for your specific workflow. from there, you can build real systems using no-code platforms without writing a single line of code. the constraint isn't technical — it's knowing your use case well enough to automate it. so yes — important. but the right starting question isn't "should i learn AI." it's: what in your current finance role takes the most time and feels most repetitive? I have put together a comprehensive list of AI use cases specifically for finance — dm me if you want the link.

ElevatedAngling

I just have loads of experience dealing with steamed market data and the wild amount of wrong data points. These marks are based off buy sell price spreads which can wildly vary in low vol stocks. Thanks for coming to my ted talk, I’ll teach you how to use ML to beat the market after you get a PhD in data science or math

CEO-of-SOXL

As a bear I am putting all my sqqq position on the Knicks ML. Thank you for your attention to this matter and god speed

Pto2

As a software engineer i know enough about computers and little enough about the mind to conclude that ML is far too simple to emulate something so complex.

zennsunni

I dunno how much clearer I could make my sarcasm. I work in ML, and I'm a motorcyclist. Camera only FSD terrifies me. Teslas have already killed multiple motorcyclists because their self driving sucks at edge cases.

touuuuhhhny

New CTO was 9 year at Meta, good. Including: "[2017-2022] Head of engineering for Video Machine Learning in Facebook App. **Responsible for AI/ML, content recommendations, content understanding,** integrity, publisher/creator success and ecosystem efforts powering Facebook Video, Audio, Music and Gaming. **Scaled video to be 50%+ time spent on Facebook** and a multi billion $$ creator/publisher economy. "

Confident-Web-7118

Hey, thank you, glad the due diligence is helpful and insightful. And apologies for the late reply, just catching up. So, first thing, and just being honest, I can tell you that people severely overestimate the investment ability/ML/quantitative skills at a lot of funds. Many of the people that are great deep value investors and quants that do this, don't actually work at many of those funds. You'd be shocked. And I think there are some great institutional holders for SLS right now, Group One Trading is a great one. You'd be shocked at how lazy people are, and not many people do as extensive DD and building out the machine learning models for every scenario including stress testing impossible scenarios like I have done. Most big funds aren’t running models on tiny bios (this is the first biotech I've ever owned, given it is truly deep value with a gigantic margin of safety, and 99.99% statistical probability of REGAL success, they will jump into SLS at $20+ when it’s after REGAL final analysis readout (or SLS-009 Phase 2B ORR readout) and ride it to buyout. I'm not sure of the exact number but I believe before interim analysis of REGAL on Jan 2025, amount of institutions was 35 to 72 And today, about 14 months later, that number is about 145 to 171+. If you research those institutions, and check their backgrounds for those that own lots of shares/calls, you'll uncover a lot of interesting owners. When looking at institutional ownership, I would focus more on looking at the numbers of shares/calls owned rather than the number of institutions (although that is a positive). Looking at who owns shares/calls is important. Some that stick out when I sorted through a few weeks ago are: 1. Group One Trading (these guys have a similar background to one of my skillsets, really smart machine learning/quant guys), they loaded on millions in call options. Assuming they did similar ML modeling that I did and came to the same conclusions for REGAL success rate being 99.99% 2. Dagco. I researched and they are a small asset manager in Ohio with about $500M in assets. They own almost all blue chips and standard broad based holdings. The only position that sticks out is 577,000 shares of SELLAS, which as of a few days ago, is now over 1M shares. They likely clearly see the asymmetric upside with the margin of safety no REGAL downside

CEO-of-SOXL

Sold all my $NBIS shares and put it on the Knicks ML

ThetaFarmingRegard

i member mainstream media blaming the post Rona 2020-2024 pump on “retailers that received stimulus checks are putting it in the market” mania for 4 straight yrs MF that $600 was spent on the Yankees ML and lost the same day

DrXaos

the core product---Claude Code is excellent. You can dispute me, but to me it's the most impressive and important software product since Netscape 1.0 in 1994. Don't know about the spinoffs. But consider that the core developers at Anthropic write regular text code all the time and know what is good and actually useful. Probably very few personally deal with graphical design or GUI interfaces. I'm a ML developer and I have 0 interest or knowledge about those two either.

psioniclizard

I mean a lot of trading is already algo trading which will be highly optimized already and response fast than agents. I am not saying agents wont be used but agents would generally be too slow and costly for high frequency trading and for long term planning why do you need agent in particular unless you are just calling anything AI like an agent. In fact in the strictest terms agents would be not great for a lot of it. Data ingestion is already done quicker than they can do, ML is better at analysing data (especially when built by a bunch of havard PHDs) and agents are expensive.

Kylearean

Even before we called it machine learning, it was support vector machines. I briefly worked with one of the early creators of SVMs and other foundational ML methods (Grace Wahba), whose work was instrumental in creating the foundational basis of what we call modern machine learning. Specifically, she showed that learning from data can be framed as an optimization problem balancing fit and simplicity, formalized through smoothing splines and regularization within the framework of Reproducing Kernel Hilbert Space (RKHS). I didn't understand its importance at the time (~1998-2000), because I'm dumb and lack foresight. I blame lead, but it's probably laziness. This work provided the mathematical foundation for kernel methods, explaining how complex, nonlinear relationships can be learned in a principled way while avoiding overfitting. That framework directly underlies many core machine learning algorithms (including support vector machines) and established the now-standard view of machine learning as controlling model complexity through regularization.

Efficient_Design379

Actually not really, you cant really can ML statistics. Just for example watch Welch Labs video about grokking.

AlotEnemiesNoFriends

Yes they do. 90% of the time when people say ML they are building a linear regression. I do agree testing of statistical models has gotten worse and the less statistically inclined folks definitely take shortcuts. I run ML org for a major tech company. I wish I could hire all statisticians or economists. The problem is, they suck at writing production code which is required in tech (but not banks and why these guys always end up at banks).

redeye100

You just described two different kinds of machine learning. Supervised learning vs unsupervised learning (your first paragraph is supervised learning, second is unsupervised). They’re used to solve different problems. The recommendation-type stuff can be either or a mix of both, and what you described is basically the unoptimized version a specific kind of unsupervised learning (look up K-means). There aren’t many strictly non-ML recommend algos, because they’re based on statistics and machine learning is basically statistics

Lucid-bagholder

How is a movie recommendation algorithm “machine learning”? ML is when you give a program an objective and then simulate a ton of iterations until the program learns an optimized path. Movie recs seem like they’d just be a comparison between similar data sets. Take all these viewing patterns and find what people who view this show view in common. That’s just an algorithm. No ai or ML involved. Am I missing something? I’m tired of everything being ai now.

jesusrambo

I refuse to believe people are truly this clueless Netflix is one of the reasons ML progressed into modern AI. They were one of *the* early pioneers in data science.

typo9292

That's ML not AI. If they used any AI there might be something worth watching.

fack-the-suits

Yeah lol it’s pretty wild. I think the UI over the LLM makes it a lot easier for suits and consultants to visualize/understand “what it can do”, which makes it an easier turn around for McKinsey to collect some bs metrics and pitch it to execs, where ML is a more technical concept that’s kind of hard to pitch to nontechnical people. I feel like ML hasn’t had the mania LLMs have had just because ML means you have to hire highly skilled & paid people to do it. Non tech companies & execs don’t really want to do that, they’re just looking to consistently trim fat so the profit margins widen. LLMs (to execs) mean you can just fire everyone and it’ll do it for them, because they saw a video of Sam Altman hyping up his company’s product and McKinsey shows them a slide on how much they’d save if they fired everyone. I really do think McKinsey is driving on a lot of this. They will advise to replace everyone for “AI”, make a buck, then advise on how to bring people back after everything breaks and the AI compute costs 75% the price of an employee except the employee gives you someone to blame.

NoFutureIn21Century

Dude, we've had ML for decades now. Almost nobody cared. Because it was too expensive. Suddenly when AI is 100 times more expensive by using these general models everyone is losing their shit over it.

Candy-Emergency

Software developers can take a couple of ML classes and call themselves a AI/ML engineer and double their salary.

MilesDelta

TLDR for everyone Lifting this reply for visibility I wrote: " Note we are all just Monke's, life is short, call your mother, or your family, stuff is getting wierd, tell your freinds they matter, go on that trip you've been wanting to, call that girl, apologize for that thing you should have a while ago. I want you to understand, I appreciate you all for giving a 29 year old guy with 8 disabilites a voice, most people just see a guy with a cane, and severe autsim, they don't listen, they are rude to me, this is what it is like for many people world wide. This has been one of the first times, I feel seen in my life, So to everyone on this chain, thank you for helping a broken man heal, I lived on 25K for two years up until March, I am climbing out, but when I say dominos or wendy's rocks, it was because it was all I could afford, I worked construction cleanup, I scrapped, I felt the pain of not having anything to eat and going to bed hungry. I don't think any of you will appreciate how much the opportunity to do something like this means to a guy like me, WSB mods I love you, WSB community I love you. I am just a sh\*tposter, and this is part of my healing. On a serious note, if you are struggling, it will get better, I was an Idiot, and did not ask for help, I sufferd, you don't have to, ask for help, you are not alone, I care, other people care, If you are struggling, message someone, Message me if you feel like you have no one, to those people who need to hear it, you matter. Anyways, back to shitposting. Gonna engage seriously bc you bring up good points Issue with darkpools is you don't see the whole trade, you see parts of an options trade, but the block could be a hedge or a larger position. TLDR, you can't tell if they are buying spreads, condors, or hedging an equity position etc. Darkpools are useless because you do not know why people are buying or selling. You are not god. Nah, I got cheap puts, I did not want to deal with long dated theta decay, so I bought deep OTM, and I am looking for changes in higher order greeks like GAMMA and vega that move a lot with very large sudden price movements. I am buying insurance, it is cheap, does not matter if I lose it, and I can keep buying it till it strikes like a hand of poker. This is ratio betting. Disagree, America is a flash in the pan that pushed the british stirling aside in the 50's, there have been dominant powers before, and there will be dominat powers after. Iran is the oldest culture in the world full stop, so have no idea what you are talking about there bud. As for AI, I disagree, I build ML for a living, and I built something that is 20% ahead of Opus 4.6, and has an OHMocr Benchmark of 99% versus Google's 84%, I did this with a brain injury because I was poor, I had no options, and my cat has to eat, and so do I. Respectfully you don't know what the hell you are talking about when it comes to AI, like most people: TLDR on AI: LLMS are a word soup that guess what you want, they are not smart, the human brain is the most efficent learning computer that exists, with very little in terms of energy, and food, it can learn and become quite smart or quite regarded. LLMS on the other hand took trillions, guzzle so much power, and then, are still stupider than people, they halluciante all the time. This is because they only understand 40% of what you say, the rest is swhaili. You use AI as a blanket term, stop, AI econompasses many things, LLMS, ML, NLP, computer visions, so which of these do you mean? My assesment of AI as a whole is this: It is great at production work, so if you do not think and are the brain trust of the person who just threw a bunch of words they did not understand at me, yeah you are cooked. BUT: Humans who think, and do valuable jobs and are creative are fine. Think why are these AI companies selling a narriative so hard, THEY ARE BAG HOLDERS OF BILLIONS IN CAPEX, there models are flawed, they encourage you to keep using them, ANTROPIC IS THROTLING and raising prices. Altman and OPEN AI are cooked btw, like DIDDY cooked. If you are gonna use a model and not here bc ai is bad bad not good, Use Antrhopic, use a privacy browers, VPN, and do not. DO NOT PUT ANY INFORMATION IN IT THAT YOU WOULD NOT TEL ANYONE YOU DON'T know. Anyways autistic rant over. Respectfully my brother in christ, you have not got a clue what you are talking about. you are on my lawn, sit down, smell the grass, enjoy the flowers, It aint that serious, bro we are just a bunch of Monke's who are confused, I am confused, you are confused, get a banana, chill TF out, and go tell your parents or family or freinds they matter. Life is short, love you brother, I hope you get the most out of it. Miles "

MilesDelta

Gonna engage seriously bc you bring up good points Issue with darkpools is you don't see the whole trade, you see parts of an options trade, but the block could be a hedge or a larger position. TLDR, you can't tell if they are buying spreads, condors, or hedging an equity position etc. Darkpools are useless because you do not know why people are buying or selling. You are not god. Nah, I got cheap puts, I did not want to deal with long dated theta decay, so I bought deep OTM, and I am looking for changes in higher order greeks like GAMMA and vega that move a lot with very large sudden price movements. I am buying insurance, it is cheap, does not matter if I lose it, and I can keep buying it till it strikes like a hand of poker. This is ratio betting. Disagree, America is a flash in the pan that pushed the british stirling aside in the 50's, there have been dominant powers before, and there will be dominat powers after. Iran is the oldest culture in the world full stop, so have no idea what you are talking about there bud. As for AI, I disagree, I build ML for a living, and I built something that is 20% ahead of Opus 4.6, and has an OHMocr Benchmark of 99% versus Google's 84%, I did this with a brain injury because I was poor, I had no options, and my cat has to eat, and so do I. Respectfully you don't know what the hell you are talking about when it comes to AI, like most people: TLDR on AI: LLMS are a word soup that guess what you want, they are not smart, the human brain is the most efficent learning computer that exists, with very little in terms of energy, and food, it can learn and become quite smart or quite regarded. LLMS on the other hand took trillions, guzzle so much power, and then, are still stupider than people, they halluciante all the time. This is because they only understand 40% of what you say, the rest is swhaili. You use AI as a blanket term, stop, AI econompasses many things, LLMS, ML, NLP, computer visions, so which of these do you mean? My assesment of AI as a whole is this: It is great at production work, so if you do not think and are the brain trust of the person who just threw a bunch of words they did not understand at me, yeah you are cooked. BUT: Humans who think, and do valuable jobs and are creative are fine. Think why are these AI companies selling a narriative so hard, THEY ARE BAG HOLDERS OF BILLIONS IN CAPEX, there models are flawed, they encourage you to keep using them, ANTROPIC IS THROTLING and raising prices. Altman and OPEN AI are cooked btw, like DIDDY cooked. If you are gonna use a model and not here bc ai is bad bad not good, Use Antrhopic, use a privacy browers, VPN, and do not. DO NOT PUT ANY INFORMATION IN IT THAT YOU WOULD NOT TEL ANYONE YOU DON'T know. Anyways autistic rant over. Respectfully my brother in christ, you have not got a clue what you are talking about. you are on my lawn, sit down, smell the grass, enjoy the flowers, It aint that serious, bro we are just a bunch of Monke's who are confused, I am confused, you are confused, get a banana, chill TF out, and go tell your parents or family or freinds they matter. Life is short, love you brother, I hope you get the most out of it. Miles

_echo_home_

The constraints on the ML industry are energy and mechanical supply at this point. Equipment supply chains have been sucked dry. I think you're in a really good spot with some patience - I develop energy infrastructure, and there's going to be a reckoning soon.

No_Minute2664

Is your background in ML or math?

fudge_mokey

To be fair, you also have to give a lot of personal data to Intuit or whatever software you're using to file. Still a third party at the end of the day. And honestly, they might both be using your data to train ML models lol