API

Jammer250

Nope, literally my own desktop app and then applied my set of option criteria as logic for the scanner. I use Tradier API for a real-time data feed. I tried the gamut of option services, from Option Samurai to Market Chameleon and others, but none had the flexibility or combinations I wanted.

AnyHat8807

surely they can build a simple downloadable bit of software which connects to the internet and gives you basic features. Third party API features might stop working if those API change but...maybe open source it or something?

Cool_Sweet3341

This is the part everyone's missing when they're saying oh you're going to go broke. I saw one number that was 26 billion projected in debt for 2026. But 800 million weekly users. So $32 a year per person then you can divide that by 12 and then pad it a little bit. Force the power users to pay up a API. Submission like. I don't know why YouTube keeps saying like oh they're broke they're broke like do the math. 

BuildwithPublic

Having strats that perform in different mkt regimes is really key. Being creative is a must with options- so many opportunities to put on risk. Vol isnt as much a factor with options IMO, like say equities or futures. Execution is another story- it can become extremely frustrating when you aren't getting good fills. This is why using an API to execute is helpful. \-M

skilliard7

>I’d remove Anthropic. They have a great model based on enterprise API usage and are backed by Amazon. What a lot of people miss is that a lot of their "enterprise customers" are re-selling access to Anthropic models at a significant loss, as part of bundled services. This is not sustainable, and will be a significant headwind to Anthropic when these enterprise customers begin to charge customers based on their actual costs.

LBishop28

Yes…. I am aware Anthropic is currently not profitable. 1. They’re backed by Amazon they’re not going anywhere. 2. Their models are the best are replacing human tasks in white collar jobs (admitted by OpenAI in their own research). 3. Their business model is not relying on consumer subscriptions like OpenAI. Claude’s revenue which continues to increase, is based off enterprises using its API.

LBishop28

I’d remove Anthropic. They have a great model based on enterprise API usage and are backed by Amazon. I think Mistral being the EU’s homegrown LLM means it will stay around, whether success stateside or not. Perplexity is likely not going to exist. Cursor probably not either.

iMakeGOODinvestmemts

All the analysts forever writing about OpenAI vs Anthropic vs Google are missing the real story that already happened. 80% of startups pitching Andreessen Horowitz are running on Chinese open-source models. Not OpenAI. Not Anthropic. Chinese models like DeepSeek that cost 214x less per token. The math here breaks everything. DeepSeek trained its model for $5 million. OpenAI spent $500 million per six-month training cycle for GPT-5. That gap translates directly to API pricing where startups pay $0.14 per million tokens versus $30 for GPT-4. For a startup burning through 100 million tokens monthly, that’s $1,400 versus $300,000. The difference between 18 months of runway and 3 months. This tells you the real constraint in AI was never capability. Chinese models are matching GPT-4 on coding benchmarks while costing 2% as much. The constraint was always burn rate, and China solved it first by optimizing for efficiency instead of chasing AGI. The second-order effect gets interesting. When your infrastructure costs drop 98%, you can actually afford to fine-tune models for your specific use case. American startups paying OpenAI’s API rates are stuck with generic models. Chinese open-source users are building specialized variants. Silicon Valley thought the moat was model quality. Turns out the moat was cost structure, and they built it backwards. When a16z partner Anjney Midha says “it’s really China’s game right now” in open-source, he’s not talking about benchmarks. He’s talking about who controls the default foundation layer. Now look at where this goes. American AI labs are optimizing for AGI and superintelligence. Raising billions to chase the theoretical ceiling. China optimized for distribution and adoption. Making AI cheap enough to become infrastructure. All 16 top-ranked open-source models are Chinese. DeepSeek, Qwen, Yi. The models actually being deployed at scale. While OpenAI charges premium rates for exclusive access, Chinese labs are flooding the zone with free alternatives that work. The third-order cascade is what changes everything. Every startup that survives the next funding winter will have optimized around Chinese open-source as default infrastructure. Not as a China strategy. As a survival strategy. That 80% number at a16z only goes one direction. When you’re a seed-stage founder choosing between 18 months of runway or 3 months, economics beats nationalism every time. America is still competing to build the best model. China already won the race to build the one everyone uses.

Clear_Anything1232

So true. It's the same whether you use the AI or them. The API calls directly go to their whatsapp for answers.

NationalOwl9561

Practically speaking, no. When you factor in software/API lag.

BuildwithPublic

Most brokers don’t support auto X% profit/Y% loss exits on options -it’s basically a bracket order for options and very few offer conditional logic like that natively. Most active traders automate it rather your code watches P/L or premium then when the condition hits, it triggers exit instantly. dont have to babysit screens. If you’re looking for that kind of setup, you can build it pretty cleanly on our API. I can show you exactly how traders structure that loop. \-M

uncleBu

Interactive brokers has an API, you should be able to automate that and more. For what you are asking an OCO order would suffice (not that it's automated). Most brokers would have that.

garden_speech

> Fair. They did it with like a 2-3 dollar fee. They sell millions of tickets that more than covers a website, database, and some simple API's Does it? Was anyone profitable at that point? Engineering a scalable system is extremely expensive, APIs are by necessity not "simple" when you are selling millions of tickets. > They calculated the exact amount people will pay before saying fuck that, and gouge to that max I mean if this were true they'd be even more profitable now. But they're still losing money.

Active_Status_2267

Fair. They did it with like a 2-3 dollar fee. They sell millions of tickets that more than covers a website, database, and some simple API's They calculated the exact amount people will pay before saying fuck that, and gouge to that max Its the same thing with pizza delivery fees that aren't tips Most of the money is spent paying artists and venues to have a monopoly over the tickets, so you have to get fucked by them, or don't go at all

skilliard7

The unprofitability exists downstream. There are all kinds of services like Copilot offering access to Anthropic models for artificially cheap to the end user, but at massive losses to the provider. With a lot of services, you can pay $20 a month and use up $500+ worth of Anthropic API costs. This is obviously not sustainable, and once these services raise their prices, demand for Anthropic products will fall.

AccountOfMyAncestors

Where can I affordably get access to stock market data, including options chain data, like an API, that I can connect ChatGPT or Gemini to. So I can have AI autonomously pull data out to analyze for my gambling queries?

liketrefiddy

The way the money trail works is Consumer/business pay OpenAI, Anthropic, perplexity, cursor, etc, to use the AI programs directly or the API. However these private companies are selling these services at a steep loss. The private AI companies don't have their own infrastructure to service the compute to run AI, so they pay the hyperscalers/neoclouds with largely VC/PE money from equity financing instead of revenue. There's literally 1000s of these private AI companies building/training models and trying to sell them (in the US alone). I havnt heard/seen 1 of these companies being profitable on their stand alone revenue. The issue isn't with the hyperscalers per se...its the private AI companies with an unprofitable business model.

reaper527

really hope firestocks gets fixed/updated soon. you don't realize how useful some of those browser plugins are until they break. (the provider they use to pull their data from changed their API, which broke the plugin)

MitsotakiShogun

> the question is, are their profitability meets the market expectations That I cannot answer, and deliberate tried to avoid it in my post and comments, and don't even want to given I'm an "almost blind VT" investor. But great opportunity for the consultants I joked at the expense of to get back at me! > it will becomes a commodity market. In a commodity market, the company with the lowest price wins and the profit margins collapse to zero. Yes, I absolutely agree, and I totally forgot to mention commoditization through open-weights models (and other ways). I do think most of the profits are in cloud providers offering these models, and that's also why I think AI startups *might* survive this, since they're building their own infrastructure, and why I conditioned that upon competition slowing down (fifth point). Regarding... > Personally, I don’t think bigger general language model is the way to go, but highly specialized, accurate small models that able to run on a local machine is the future. *Are you influenced by a certain Nvidia paper that was made to sell more GPUs to those who would otherwise have gone with a cloud model?* Jokes aside, yes, I think a lot of tasks can be delegated to small, fine-tuned models that are part of wider systems and may perform better than large generic models. In my job we have plenty of <10B fine-tuned models deployed (one of them for a website with ~100M monthly visits!), and based on the research metrics, they perform better (quality/acceptance rate, inference cost) than their lower-grade cloud model counterparts (Haiku, Flash, Mini, ...). Not to mention all the other (often older) products that are still using some model built on fastText, OpenNMT, sklearn, or similar. The part I'm not sure about is whether it's easier/faster/cheaper to do the required engineering and research work (especially factoring in development costs and project success rate), or if the time is better spent on architecture research and quick experimentation with generic API. Maybe in "the bitter lesson" sense? And one final thing: even smaller models can benefit from cloud deployments, at least for now. Maybe the RTX 7070 Ti Super will be a power efficiency monster with 48 GB of VRAM, or the M8 chips from Apple with have 10x the prefill performance of M3/M4, but right now even running the 30B-A3B Qwen models can be several times faster on 1-2 generation old big cloud machines than on expensive current-gen local hardware, not to mention throughput, electricity, etc.

FMPAPI

FMP API offers a reliable endpoint to track all the 13F Institutional Investors transactions [https://site.financialmodelingprep.com/datasets/form-13f](https://site.financialmodelingprep.com/datasets/form-13f)

ecnecn

I read it this way: MSAI is using Amazon AWS Services for over 2 years... last year they begang to use the AWS Tools (AI/ML Learning plattforms connected to the warehouses cams and robots). This entire talk is related to the implementation of the testing environment. Furthermore Luke was a maintance engineer - not a manager or anyone that could establish a partnernship. He helped them set up AWS Tool so MSAI could test their infrared AI readers through the warehouse stream API... so no real partnership, just cooperation to create some test environment in AWS Services... nothing more nothing less.

qaswexort

It actually has lost nothing vs ChatGPT, except ChatGPT has brought it more paid conversions via API, and ChatGPT helped it beat its anti-trust case

ZacTheBlob

Right, just like deepseek showed that you can train a model for a few million instead of billions. Until we found out that they just trained everything on OpenAI's API and that they have over $1.6B of Nvidia hardware. The CEO of a company trying to sell something making unverified claims about their own products. It **must** be unbiased and true! CEOs lie about their business to generate hype all the time, this is nothing new 😂. If Pinchai said that they trained and deployed gemini 3.0 on one singular TPU, would you blindly throw your money at them? You still missed the fact that google's inventory is still primarily GPUs and that CoWoS capacity is barely enough for their own workload. Where do you think Amazon, Microsoft and Meta will get the CoWoS allocation for their TPUs? The only threat to Nvidia's business model is if, for some reason, compute demand stops growing or grows slower than CoWoS capacity. That is not happening anytime soon.

Ma4r

I mean once you have the API , making the client for it is typically not that much work. I think the premise makes sense, Bloomberg have too much packaged for retail and small shops, so something more granular could have some demand. But supply chain data is definitely niche though, probably need some industry connections that you already know is looking for this kind of things

averysmallbeing

>not selling anything, just sanity-checking an idea >The goal is for this to be a subscription based API that costs <$100/mo 🙄🖕

AKdemy

An API is just a means to an end. The terminal is so much more than simple data requests. Also, I don't think many retail users would want to work with the Bloomberg API, even if it were free. For your goal, the data and quality you have is what matters. - What's your sources? - Why focus on such a tiny niche? It's probably the least valuable data for someone working with options. Since you want to mimic BBG, what's the terminal equivalent of a volatility index based on downstream news pulses plus price movements? What is that even supposed to measure, and how would it be helpful?

skilliard7

I disagree about thin apps being the most likely to fail. Thin apps actually have the highest success rate according to a MIT study. There is a really high return on capital on taking an API like OpenAI or anthropic and building your product on top of that. The value added is in the integrations, not spending tens of billions to try and build the best LLM.

Fineous40

I charge WSB users to run the API for their AI boyfriends.

joeyat

Microsoft own 28% (was 50% last week I think) of OpenAI and use the ChatGPT models in CoPilot.. MS have been shoving CoPilot down the throats of every business on the planet (even more than they are to Windows consumer desktops), Office 365 is littered with CoPilot, would not be surprised if they don’t make a lot more money off OpenAI’s IP than OpenAI do.. and they have the income at least to attempt to float all this hardware. They also offer ChatGPT models API access through Azure AI Foundry, and sell those to any number of businesses to operate all those AI customer service chatbots that are on every single website… again, only a fraction of the revenue Microsoft makes from those LLM services will end up returning to OpenAI itself in licensing fees.. and ChatGPT’s website and apps all predominately run on Azure, so any revenue OAI do collect will probably go back to Microsoft to pay for that compute and infrastructure. However this setup has just changed as MS and OpenAI reformatted their deal.. OAI is no longer a non-profit and they will branch out to other compute providers and attempt to fund by cashing in on their hype with an IPO.. so is there is a massive bubble, but there is at least some money going into the system to pay for all this via Microsoft.. not enough! .. but, the circular money machine machine these mega corps are operating is a bit more lucrative than it first appears.

Fineous40

I am going to sell WSB users the API for it.

Gonxoxo

Have you used the API post TD Ameritrade merger?

PrimeGGWP

Dude. OpenAI literally needs only to pull of some fb ads / google ads type of shit revenue and they are gucci but they are waiting to grow harder and then step by step .. they get rev share by promoting products they get money by offering ads to companies and they will have a giant user base who pay $10/m at least. Big MRR What do they have atm, 800 million active weekly users? (with free plan ofc) API Calls will make $$$ too Amazon didn't make any profits a long time either ..

Clojiroo

First of all, dismissing ChatGPT to a “Chabot” is woefully ignorant and cynicism masquerading as wisdom. That “chat” has many features and it can do actual work. It is a massive productivity booster and the paid level versions are worth every penny. Second OpenAI has a wide variety of other paid features, and goes way beyond the ChatGPT site/app. Their tech underpins GitHub Copilot, maybe the most important AI tech (that companies pay a lot of money for) in software. It’s also embedded in iOS, which Apple is paying for. It’s a set of deployable private models for Azure, which businesses run to power features. And you pay to run that cloud infrastructure. OpenAI also supports an independently charges for API usage and agent building. Thousands of businesses are paying money to OpenAI right now to run their own business.

Farseth

I have personally implemented AI at an organization. While it didn't fully replace a role, it took the maintenance of a user knowledge base from being part of 3 people's jobs to 1 in the first 6 months. I can't prove it with math yet, but it also appears to help people onboard faster. Ultimately, the AI is a GPT chat bot via an API call and some proprietary software.

re-thc

What does OpenAI have to do with the AI "bubble" you speak of? OpenAI isn't the stock market or the stock that's going crazy. It's AI in general. A lot of companies are also vested in other AI ventures e.g. Gemini or Anthropic etc. Also OpenAI offers an API for all enterprises to integrate. That's where the real money will be.

Blotter-fyi

Using API as well

popori

I tried doing this but created a webapp instead and used API calls for input/output. Did you do this via web?

DigitalSheikh

You can just buy HIPAA compliant api access to the current flagship models. With most of them, there’s not even a difference between HIPAA compliant and regular API access to begin with. At least in my shop, we don’t have use cases where doing anything else would be more cost efficient or add capability, and I’d suspect most other healthcare oriented places are the same.   There’s a rumor / perception in healthcare that there are legal concerns about using API’s, but if you’re getting API access from one of the main providers there isn’t a problem. That perception too has been going away over the last year as well imo

DrawingLogical

OpenAI hasn't been in the top three APIs on OpenRouter for weeks now: [https://openrouter.ai/rankings](https://openrouter.ai/rankings) Now, granted, this is likely miniscule compared direct OpenAI API access....but still, it's not exactly a vote of confidence. They have overshot a valuation number they will be able to deliver on.

Prytfbyn4369

OpenAI is the first choice for the majority of the enterprises across the world and the majority of services are using their API. OpenAI is already making a lot of money. That said, the expectations are a bit too high and a small correction is imminent, it will happen to Nvidia what happened to Palantir.

PublicCOO

Thanks! We love the API business and are still actively developing in the area, so we’d love to have you give it a try and let us know what you think!

festusssss

I've requested API access numerous times and gotten zero response.

Scannerguy3000

Do you have an API?

Scannerguy3000

I’ve used Fidelity, IBKR, TastyTrade, and Schwab. 1. Schwab has better fills than the other three. 2. Schwab treats me like an adult and gave me the options level I asked for. 3. Schwab has a free API (actually 2, one for personal transactions and one for market data). I don’t know anything about Public. But I’m closing all my other accounts with other brokers and moving everything to Schwab for these reasons.

Silver_Spare_6796

Ahh gotcha yes the API company Isn’t it a huge red flag that the CEO still intended to dilute shareholder at such lower prices in first place ? They did the same with the SCILEX deal at like 45 cents a share or something … it looks terrible optically

entsnack

The signs of a bubble are all the OpenAI API wrappers that are getting millions in funding (just look at the YC launches of this year). They're like the [Pets.com](http://Pets.com) of the dotcom bubble: products you build on a promising platform (world wide web = AI) that are not really worth the hype. OpenAI, NVIDIA, Anthropic, and the others will stand once the bubble burst. But Design/LM/Text/Whatever Arena and the 100 browser-use and coding agent companies are all going to die.

Prytfbyn4369

They can make quick money by adding a ads system in the same way YouTube or Facebook do, but for them is enough selling API subscriptions rather than user subscriptions.

beach-chicken10

Ok so the deal to purchase API using cash and stock is terminated but the purchase of API is alive and well as my above link. The article you linked is written by an actual retard, well AI but checked by one - “no future plans related to the transaction… in other news there is a definitive agreement to acquire API media l” the below is from the article “No further details regarding the reasons for the termination or future plans related to the transaction were provided in the filing. In other recent news, Datavault AI has announced a definitive agreement to acquire API Media, with the deal expected to close in December”

He-Who-Reaches

[Datavault AI ends agreement to acquire API Media Innovations By Investing.com](https://www.investing.com/news/sec-filings/datavault-ai-ends-agreement-to-acquire-api-media-innovations-93CH-4328757)

Momoware

" work still has to get done to provide said data and complete said end products." - This is normal. In the end AI will just be another step in the toolchain. "Normalize" here means that inference API endpoints will be as common as other APIs and developers won't think twice about them being special (as opposed to now, where AI-native development is seen as different and requiring different approaches). Applications where I've personally been part of / experienced / seen real customers pay for: \- Enterprise data analytics - AI is better at navigating really complex enterprise schema (often not labeled correctly not unclean data) - especially helpful if an analyst is not familiar with a specific data warehouse yet. \- Financial analytics - manipulating formulas and formatting in Excel sheets. Saves people a lot of time if they are not high skilled already. Good boilerplate templates \- Image and video gen - right now it's a lot easier for single-discipline creatives to branch out (for instance, Illustrator taking on Animation work) \- AI music - \^ tied into the above. bgm / sound effects for single-person shop. \- Elephant in the room - code gen - productivity gain for developers; enabling cross-functional roles. \- AI meeting notes - pretty much must-have for back-to-back meeting takers now

Financial-Respond-37

I think you can do this with Alpaca. You can use Alpaca API data to get the options info for certain time frame and then use Alpaca API to get all the data associated with the particular options

Lords3

The bubble only pops if OpenAI can’t prove sticky, high‑margin enterprise revenue; watch margins and retention, not headlines. If/when they file, the tells are: revenue mix (ChatGPT subs vs API vs enterprise), cost of revenue tied to inference/commitments, gross margin trend, cohort retention, and minimum‑commit contracts. A credible path is lowering unit costs (routing to smaller models, caching, distillation), shifting spend to longer‑term compute deals or in‑house silicon, and selling bundles with SLAs, privacy, and audit. Real adoption I’ve seen works when tasks are narrow and workflow‑embedded: support deflection with strict guardrails, code assist for legacy stacks, sales ops Q&A over approved docs. Cost control is prompt templates, token caps, batch jobs, and usage floors. We run Snowflake for warehousing and Azure OpenAI for compliant endpoints, and DreamFactory to generate secure REST APIs from old databases so support bots and agent tools can hit internal records without a rewrite. If OpenAI shows expanding margins and durable enterprise cohorts, the AI trade holds; if not, you get a reset.

theycallmeJTMoney

I’d genuinely be interested to hear your thoughts or experience/examples of value adding (via API or GUI) processes currently being implemented. I see all these concepts and people working connections and creating automated processes but where is the meat and potatoes? What does it replace? The best use case I’ve seen for what you’re describing is reporting and project management, but work still has to get done to provide said data and complete said end products. This is a good faith question.

DiscretePoop

Obsolescence risk is absolutely still an issue especially since the concept of developing IC microarchitecture specifically for AI is relatively new. Nvidia is so well positioned in the market because their CUDA API gave them a headstart by allowing them to leverage existing GPUs for AI. Now, companies (including Nvidia) are developing tensor processing units specifically for AI. At the same time, AI models are being optimized to run faster or with less power. Future AI models optimized for newer processors may not run well on existing hardware. While LLMs and StableDiffusion is impressive, it's not clear that they're even going to be the real money-making engines 5 years from now. Microsoft is spending hundreds of billions on infrastructure with the assumption that current AI models running on current hardware will be what makes trillions in a future market. They're gambling.

Super7vox

My primary concern with OpenAI is that it is a private company. As such, it lacks the mandatory financial reporting and transparency of a public entity. Much of the recent news revolves around its massive hardware expenditures and secondary market stock sales. While OpenAI is undoubtedly a central player in the tech industry, a significant problem remains: we do not know what its primary source of income is. Is this similar to Facebook before its IPO? Perhaps, but even then, Facebook's massive advertising revenue potential was clear. Today, is news of partnerships, like the one with Walmart for customer service, substantial enough to justify its multi-billion dollar valuation? Frankly, it's unclear how OpenAI will become consistently profitable. I don't see corporations rushing to adopt OpenAI for mission-critical automation that *must* be done by AI. While app developers may use its API, and various company departments may experiment with it, these projects still require significant oversight from technical workers. OpenAI may have ample funding to build the largest and most intelligent AI, but without finding widespread, indispensable utility, what is its true value?"

Momoware

AI inference API endpoints are normalized. There's path to profitability in many enterprise and productivity applications. I agree that for personal use there doesn't really exist a solid scenario right now

thrash56

With AWS, one misconfigured API request and they can use up all $38B in a week.

checkArticle36

They're losing 12 billion dollars a quarter. You are only counting the compute not the the several thousand employees it takes to run it, rent, office space. 5 was estimated to be around $1.5-2.7 b. You're falling into an accounting trap, how are they losing 12 billion dollars if it only takes 500 million and their API is profitable given it's the biggest revenue stream. Yes they lose money off the pro version but where is this other money going?

skilliard7

> I think the difference is it costs a lot more to maintain the system than they bring in, I forget the numbers but it’s so strong like $0.36 per search on chat GPT, and a few dollars a search on sora, so the average customer is costing OpenAI a lot of money to provide the service. OpenAI is definitely bleeding money, but the cost per prompt is not that high. if you want to estimate their cost, look at their api pricing. GPT-5 is $10 per 1 million tokens. 100 tokens = ~75 words, which is the approximate length of the average reply. If we assume OpenAI is selling API access with 0% gross margin(to boost adoption), each message costs them about $0.001.

OutsideSpirited2198

Welcome back Enron!! **Tl;dr: Today's tech can already do everything we will be able to make money off it with the right configuration, the business community hasn't found out how to do that yet. Investor expectations are way off track with what the ground business reality is, and once everyone figures out how to use it, costs go down faster than our need for GPUs goes up.** I've been building small scale AI workflows for myself and a handful of small customers. Not to replace human jobs, but to really just ask the question "What the hell can we actually do with this stuff that's going to MAKE MONEY?" I'm not convinced it's anywhere near what Big Tech says it is. Tbh, AI could end up being exactly like Cisco and Oracle. They were once proprietary, closed-source behemoths that pretty much everyone dealt with because they are 1) quick to market and 2) locked large organizations (banks, governments, places that move slow to begin with) into contracts. Then, open-source solutions and public cloud killed their dominance. Larry Ellison infamously shrugged off cloud computing in its early days, and now he has to offer his services at a deep discount to even compete because they were late to the party. Should we trust him with $300 billion of your 401k? Today's AI tech is also remarkably easier to migrate providers on than it would have been to migrate from Oracle to a open-source engine like Postgres. And the cost savings can some cases be enormous. We're talking literal API CALLS vs advanced joins, extensions, stored procedures. Ever heard the joke that every AI startup is just a ChatGPT wrapper? ITS TRUE! I predict most companies will likely switch to open-source alternatives like Deepseek, [Z.ai](http://z.ai/), Qwen, Meta itself has models that can are good enough for probably 80% of business use cases. They can also be hosted in-house on the same tech that is on your laptop, and can be trained on proprietary datasets, something more important than how much OpenAI can charge per token on its API. Sorry, I sound like a lunatic but I'm right.

FlyingBishop

> chatgpt loses money on every generation they literally have said it at API prices? you're making shit up.

Low_Membership4915

Does your website offer an API to query a stock's risk, available shares, and breaking news? I'm willing to pay for this service, thank you.

ecnecn

There is no Amazon partnership... MSAI is using AWS Amazon services such as server hosting and AI/ML testing environment (with control of certain warehouse cams and robots through test API) as client. "AWS Partner" is everyone that uses the AWS Services as client. It is a pure client/provider relationship.

poopmee

Yeah don’t use skylit the price is insane. You can get a subscription to the advanced plan on polygon.io for 200 a month. Then just use chatGPT or Claude to code something with the API and you got your own “heatseaker”. Probably won’t look as good or be without flaws but you will save $500 a month lol

Inevitable-Comment-I

Fuck spez for the API changes. 

schultzapps

I built an app for this called WealthTrunk (iOS). I wanted something that could be exported and shared with my wife. The love Personal Capital and it was an inspiration, but I am worried of what the long-term plan for that product is. I imagine that the costs are very high even though they try to funnel you into their advising products. Unfortunately for most small scale developers apps are manual only because there are API costs to connect to live data, which would make the pricing pretty high, and there no market for it. But I am trying to come up with ways where you can manually update your account accounts faster.

Scannerguy3000

I’ll try to find some of my past posts, I’ve broken down my screener many times. You can find it in my history. Let me go try to dig one up. My Excel spreadsheet is a custom build over 10 months, 30 macros, and a python API interface.

Rare-Energy-4357

Losing capital to a fraudulent program understandably necessitates extreme caution, yet your approach of developing a stringent verification checklist is the most effective way to re-engage with legitimate FinTech opportunities. The primary difference between a scam and a trustworthy automated trading platform lies in custody and transparency: your capital must remain in your regulated brokerage account under your exclusive control, with the platform only granted API access for trading execution, making any request to transfer funds directly to their system a major red flag. Furthermore, legitimate services will offer verifiable proof of performance through independent audit links (like Myfxbook), explicitly detailing the Max Drawdown and risks involved, while avoiding any guarantees of profits (such as a fixed 5% monthly return) or the promotion of Multi-Level Marketing (MLM) schemes, which are hallmarks of financial fraud. Your disciplined process of verifying that a system meets these criteria allowing you to safely utilize automated trading while mitigating risk is the essential due diligence for recovery and success.

-Crash_Override-

I mean, tbf, you dont really need to check back in 2 years...as the graph shows above, its already occured in enterprise API usage.

PapaCharlie9

Keep in mind that market makers and wholesalers are for-profit businesses. They have no incentive to share that information with anyone, let alone their competitors or for free. The closest you can come is per-exchange order books, which integrates the net activity of all the market makers involved in a contract series on that exchange. Which means having some kind of license or access to each exchange in question. Since you only want SPX, that narrows it down to CBOE. The level of access you want might only be available to broker-dealers, so that would mean you'd have to find a client facing API or broker platform that provides that data (insofar as they are licensed by the CBOE to republish such data). Notifying /u/Ken385 for further enlightenment.

Reasonable_Roger

Yeah for mobile I use an app called 'relay'. When all the pay for API calls stuff was going on and they killed old apps like RIF, Relay got popular. I think Relay existed before API calls but it wasn't very popular.. Anyway they decided to keep developing with a subscription model. You can pay $1/2/3 per month for API calls and keep using their app. But to me it's totally worth it. The app is very good and there are zero ads. If you make yourself a subreddit and become the mod of it, you can access NSFW subs. It used to be you couldn't access NSFW subs unless you were the mod of any subreddit.. dunno if that's still the case. But ya.. I pay the $2/mo level which is plenty of API calls for the month. Even if you run over you can upgrade and just pay the prorated difference for the remainder of the month. Just remember to downgrade before the next bill cycle. I just fucking *hated* the official reddit app. It's so dogshit. Paying allows me to have an enjoyable reddit experience on my phone, support this dude making the app, and support reddit. Very worth IMO.

Scannerguy3000

You can use it for all kinds of things. You can automate trades, like custom code your own application to pull data and send data. Personally I’m not that involved. I track all my options trading in my own spreadsheet, and I started by manually entering trades. Then I made macros so I could import CSV files from the broker. Now I pull all my trade data from Schwab’s API, so I don’t have to enter anything manually. And it updates my greeks and prices on all my positions. I still have to use CSV import for Fidelity, which is why I’m in the process of moving out of there and get everything into Schwab.

Financial-Space-2835

What do you use the API for?

Scannerguy3000

Schwab treats you like an adult if you want high options levels. And they have an API that any individual can use. Great fills also.

kam_L

**Who Provides Dealer/Market Maker Order Book Data?** I'm looking for data providers that publish dealer positioning metrics (dealer long/short exposure) at minutely or near-minutely resolution for SPX options. This would be used for research (so historical) as well as live. Ideally: 1. Minutely (or better) time series of dealer positioning 2. API or file export for Python workflows 3. Historical depth (ideally 2018+), as well as ongoing intraday updates 4. Clear docs I've been having difficulty finding public data sets like this. The closest I’ve found is Cboe DataShop’s Open-Close Volume Summary, but it’s priced for large institutions (meaningful spans >$100k to download; \~$2k/month for end-of-day delivery, not live). I see a bunch of data services that are stating they have "Gamma Exposure of Market Maker Positions", however, upon further probing, it really seems that they don't actually have Market Maker Positioning, and instead have Open Interest that they make assumptions on (assuming Market Makers are long all calls and short all puts). I have been reading into sources talking about how to obtain this data, however, I simply can not find any data providers with this data. Brief background: 25M, technical (physics, stats, cs). Building systemic volatility research stack

Shensmobile

It’s wrong for me as wrong as reported by the API. Lots of my % changes in my widgets are totally off.

optiontrader1138

I was using Kubera and it's API because it does one thing and does it well. Or used to. It's now been two weeks since I reported that their feeds are not working correctly. My emails bounce between one random Indian support rep after another, one of whom is incredibly rude. Can no longer recommend.

Clear_Anything1232

Goog is the only big company that's spending on ai with any level of foresight They literally are building capacity to sell it in various forms Gemini API To Meta and anthropic as tpus As Gemini app On top of that building data centers in cheap ass locations like india

tjinga2u

There's a Python modules that can work with Robinhood API, can link it to a butt plug so next time our savior Jerome Powell speaks you can take it personally 😉

HasFiveVowels

You’re asking chatgpt what the current date is and it doesn’t tell you the right answer? This is not behavior you should expect from an LLM model but ChatGPT conversions start with "the current date is ___". Are you using the API by chance? There’s no way it’s answering this question incorrectly. If it is, I’d be interested to get more context on what you’re doing. This is not typical behavior.

JorginJargin

"I'd compare the fidelity output of AI to what you'd be willing to pay someone to pull the same information/provide a response" I agree but asking an expert shouldn't cost 40B a quarter, and do we really want to put experts out of their jobs, to become entirely reliant on systems under the control of literally a handful of people? AI systems MAY help disseminate information to people and generate productivity but only if its accessible, as in available and free. Most people won't or can't pay for an API key so the promises of "expanding human productivity" are illusory.

Money_Reserve_791

Start small: pick one ticker, one metric, ship a tiny chart. Use yfinance to pull data, Jupyter to test, and DreamFactory to expose a simple API for dashboards. Automate one win, then scale to harder datasets

jglenn9k

There is no grand unifying API. Login to each place you have money or owe money and write down the number. Use SUM(). I have one bank, two stock accounts, three credit cards, and a car loan. Takes like 10 minutes to type in numbers twice a month.

putdownthekitten

Yes! It’s free on the Sheets end (your API of choice may charge an access fee), but Google App Script is VERY powerful. You can set up triggers so you get the data, say, once a day between 2-3a for example. You can link functions to drawings to make button for an entire GUI if you want. You can have tabs talk to each other, you can have seperate files talk to each other. It can talk to your calendar, email, Google Drive. You can set up a free web app that gets fed the data from your sheet. No cost, comes with your google account. Also, AI is very good at using Google App Script, so it can do the heavy lifting now, saving you HOURS of learning how to do simple things, like make a button that moves a value from one cell to another in a completely different file. This is a superpower you need to learn if you want any kind of robust tracking system. Be careful though, cause you know what they say. With Great Power, and all that jazz…

GameKing505

What’s with all the spreadsheet answers? How are you keeping them updated to reflect the latest prices or your buying/selling activity?? Is there some way to pipe in that data via API? I feel like I’m taking crazy pills but using excel for this task seems ridiculous.

diagnosedADHD

No real product or service, no hardware sales. Microsoft: Windows, Office, LinkedIn, Xbox (Activision), azure cloud, etc, etc OpenAI: Chat interface, API chat interface

SheriffBartholomew

Meaning the API is more useful? I've been using ChatGPT and Claude at work for 3 years already, and Gemini isn't even an option. I've created a ChatGPT API utility in my free time just to test it out a couple years ago and it was easy. From my experience the other AI systems have a massive headstart over Google, and lots of enterprise integration already. I'm not sure what "more user-friendly" means in the context of your statement. Can you clarify? I'm sure Google will iron out the bugs and other issues with their AI. They certainly have the resources. That, or they'll pull the plug completely like all of their other useful projects.

Clear_Anything1232

Open ai has 6B tokens per minute on its api Gemini is already processing 7B tokens per minute on their API. Their corporate clients are slurping it up. Goog at 29 pe is still a steal

Crazykirsch

>OpenAI is investing hundreds of Billions in improving its product. Anyone who relies or utilizes OpenAI products daily could not say this with a straight face. 5.0 has been an unmitigated disaster to the point they were forced to offer the legacy models after massive outcry when the 5.0 launch removed them. On the Sora side while 2.0 video capability is impressive they're years into public access and cannot provide a functional UI with most of the basic HTML issues like ghosting and a completely broken library/trash. Meanwhile Stable Diffusion continues to narrow the gaps in capability and user-friendly GUIs and API's are flourishing.

kerwrawr

Ok the actual correct answer is API access. No SME is going to be willing or able to build and train their own LLM, so they hook up to chatgpt via API to do their AI processing for them.

serendipity777321

Competion drives down token API prices

shasta747

Good god, hope this trade works out for you regard. So what priced in before were DAU/ARPU metrics PLUS its data being used by LLMs, now if Google doesn't rollback API changes, you think OpenAI and others to start paying RDDT for data? That's the only way it can get back to the hype it had IMO.

ChainOfThot

They don't care though. They'll sell it via API and make all their money there

thunderboxer

Hey what catalysts are you eying for Reddit? It seems almost certain that they will be able to charge more for API access. There’s an ongoing lawsuit between reddit and perplexity, claiming perplexity scraped Reddit data without permission. I’m following the case because I think it will set a precedent for LLM’s and their API access. If I could buy stock in pushshift I totally would. I feel like Reddit may enable pushshift data for commercial use at a price, and a high one.

Pvt_Twinkietoes

lol. They're probably using Google's API

VanillaLifestyle

Depends on the company, by for the model labs and cloud providers, most of the money is expected to come from API services to other companies. I believe it's already where they make most of their money. Think of all the random back office corporate tasks that could be performed by an LLM with a few guardrails, or at least accelerate a competent human. If you run a sales team, you can use it to analyze all the sales calls, categorize themes, flag bad employees (Gong.io sells the SaaS product but it probably runs on LLM tokens from a Cloud supplier). If you run a tech support team, you can do similar. Auto-draft or respond to tickets, categorize feedback, etc. Service now does this (and their office is literally across the road from NVIDIA).

sillychillly

I think API usage is the real money maker for LLMs. Once companies establish technical debt, it’ll be hard to move off the LLMs, especially off the jump when LLMs are more finicky and companies are afraid of slight changes

GoZukkYourself

Alphabet CEO Sundar Pichai: "Our first party models, like Gemini, now process 7 billion tokens per minute, via direct API use by our customers."

Flying_Spaghetti_

Sorry I am not just making API calls from one thing to another. There is a lot of extra logic going on. Its kind of complicated but it works great for the niche we are in and saves us a lot of time on the easy but time consuming stuff that you just want out of the way. I get what you are saying. I don't think the actions AI makes today are what's worth their valuation. Its the future value that people are investing in. I'm just saying its far from worthless today and it absolutely can save enough time to reduce team sizes. Not replace teams, but make teams smaller. That's big $$ when your biggest expenses are salaries.

chaosdemonhu

I mean I wouldn’t call making API calls “custom code.” It’s boiler plate. Everyone calls the same APIs the same way because the contract is defined by the API. Especially if it’s a big company’s API like Google. It’s the business logic before and after the API call on both ends that drives the value and does the work and AI has not been doing a great job of making my life easier or simpler there and it being in that part of the code base has actually made my productivity worse in many cases. If I gave it some API docs, example requests and responses yeah it can scaffold out the code that will handle that piece for me and that saves me time on menial coding but for me it’s not such a time saver that it’s worth the valuation of these AI companies. Also idk what’s taking you days to write out an API call especially in a script? If it’s even half well documented you should be able to crank out a script and have it tested in a day imo