lnofeisone

If this is your hypothesis, I'd go with a vertical bear spread. 140/138, with an expiration of next week, is trading at 0.50. So, if you are right, you get a 4:1 payout, and TSLA only needs to go down to $138. If you bought the 157 ATM Put @8, TSLA would need to go down to 125 to have a similar payout.

This is factually incorrect. 1) Israel didn't attack a sovereign country's embassy. They attacked an annex. Notice how everyone killed was IRGC and the ambassador and embassy staff were safe. A lot of news sources got this wrong, and this has become one of those nuances that got lost for those who aren't reading and tracking this carefully. 2) Let us not forget that Iran set this precedent long ago with this - https://www.reuters.com/world/argentina-court-blames-iran-deadly-1994-bombing-jewish-center-2024-04-12/ Can we agree on this much? 3) Your take on Gaza is also weird and incongruent. Let's do a simple exercise. You claim Israel is an occupier. As an occupier, Israel is obligated to provide food, water, and shelter to the occupied territories, which it did before Hamas and MANY Gazan civilians went on their adventure into israel. However, as an occupier, Israel has some flexibilities, which include setting up checkpoints, restricting population movement, and really anything for the purpose of security. Palestinians have shown time and time again that they are a true security threat. So if Israel is really an occupier, their behavior in Gaza and WB is really justified. Think another way. If there was a terror group that actively lobbed missiles from Puerto Rico and Guam into the continental US, you can bet your portfolio US would not hesitate to deal harshly and there are many existing laws that could be applied to punish those responsible. This is why I think the talking point of "Israel is an occupier" is a really bad one so whoever is giving you your talking points, should take that into account. 4) You want to see real Genocide? Look at how Hamas went into Kibutzes and systemically targetted and killed every family they could find. That's what genocide looks like. If Israel had no ability to stop Hamas, they would go as far as they could. That's Genocide. What Israel is doing is combat. Terrible collateral damage (still well below many comparable urban warfare situations) but it's legal combat. I'll concede that not everything Israel is doing is perfect and by the book but it's much closer to what International laws were designed for.

If you think Vol is underpriced here, you can buy straddles. You can adjust your strategy to backspreads if you have a directional bias.

Thanks for this. I think they are reasonably good with the roll ups and you are right. The spring keeps tightening. Just needs some patience.

Got it. Thanks. I'm looking to double this one to roughly 5% range. I think the challenges will work themselves out and we'll be heading back to the 20+ range.

are you bailing on HQI or rightsizing?

New settlement hot of the press. Sellers no longer have to pay the buyer's agent fee. I know most here would agree that transaction costs in RE are due for a change but I'm thinking through the winners (FSBO, Redfin? for sure) and the losers (Compass) and neutral (Zillow?).

No surprise indeed. Lots of capital got burned up because of these studies. It what makes the market.

I like JCI and I've held for AOS (full position) and LII (starter position). AOS can get volatile but the business is steady.

From what I see, there is now a trend to tap into AWS/GCP to accelerate the journey to LLM and other analytics. Lots of orgs have proprietary data but have no clue how to get it to LLM. As far as the moat for Google/Msft/Aws, I'd argue the moat now got bigger. There are only few start ups that are really thriving here. Anthropic is one. Perplexity is another. Many fold shop before they get to market. The cost to train these models is north of $1M per run. There are a lot of great ideas but few companies with funding to try them so AWS/GCP/Azure have plenty to pick from and fund if they so desire.

I remember when the original Shogun (1980s make?) made its way to Uzbekistan in mid-90s. All of my friends and I were glued to the TV for every episode. It will be the same way for this series as well. The production quality here is very high. I didn't realize it was based on a novel so will get it on Kindle.

1) Go to Wendy's and order 100 of these sandwiches, driving up futures 2) Trigger the algorithm 3) Start selling to people behind you in line for less than the current spot price 4) Orders will be delivered by Wendy's Someone with extra $1k and a sense of humor should totally do this.

I'm also a fan of perlexity. Tried few things that are "hard" and got very impressive answers that saved at least 2-3 weeks of work.

some power generators should be all over this.

I think what you are buying today is an average-priced enterprise with 3 embedded potential catalysts: 1) IFS, 2) Mobile Eye, and 3) Network and Edge group. Getting one of these right will be transformational. Throw in Gov't loan/grant/support and I think the odds are heavily tilted in INTC's favor.

This is what I was curious about too. OZ hinges around investment and holding for deferral purposes. So how would that work if one buys OZ and then sells OZ. Is that the same as selling holdings in OZ triggering tax events? What if OZ itself sells something, does that mean a tax event triggered?

Starters in INTC and COF.

Can you share more please, especially around the tax structure (I always love clunky tax structures)?

There is some fun history here - and this is my electrical engineer side. X Fab looks to produce analog devices. Those generally tend to run larger than digital chips. The sizing they give you corresponds to the actual sizing. 3nm stuff is marketing fluff, and those chips are much bigger than 3nm. So it's not apples to apples, but yes, the analog world is much larger. In the analog world, 1um is not terribly far behind "cutting edge." Take a look at at ADI's roadmap and zoom in on the sizing. https://www.analog.com/media/en/news-marketing-collateral/solutions-bulletins-brochures/adis-resilient-hybrid-manufacturing-network.pdf The bit on history: When semiconductors were first produced the sizing was describing the size of the transistor gate length. Over time because of the chip architecture innovation, "nm" took on a marketing tilt. The 3nm doesn't mean a 3nm transistor gate length. All it means is that 3nm process makes smaller stuff than a 7nm process of the same company. The latter point is important because it means that 7nm process of one company can be bigger or smaller than 7nm process of another company.

I think there is a lot of truth to what Einhorn is saying and we see it play out with multiple favorites on this board - JOE, BTI, FRPH, CPNG. One of my holdings - VET. Small companies where value is "easier" to recognize are appreciating just fine without shareholder yield. Companies where things are complex really do need that extra push. The value is not dead, it just requires a little bit more thinking, recognition, and patience.

This. Demand for NatGas is notoriously hard to model. It's subject to weather and, for the longest time, it was centered around local markets. This is why LNG imports were hyper-profitable. Now that LNG transportation infra is becoming more robust, prices across the world will harmonize better, i.e., there won't be a $20 difference between NA and Asian NatGas but there will still be a difference to account for shipping cost/time. Supply is a bit easier to model as you can assume companies will pump out as long as their profit is above or at $0.

Depends on the industry you are in. I've supported model development for several CFO shops. We usually had ranges, and then scenarios planned, i.e., if this happens (e.g., your workforce quits or demands higher pay), then the range gets adjusted like so and so. The complexity of models goes up with more business lines, geographic diversity, etc. At some point, models become more directional or you have a forest of sub-models that average out to be generally correct. Really hard to model discontinuities like rapid interest rate hikes, COVID, Ukraine/Russia war, etc.

You have to deconstruct the problem. Usually LLMs are trained with question-answer pairings (take a look at SQuAD). It's what data you use and how you decompose it into Q-A pairs. Court cases tend to have a lot of reasons AND (this the core value of court data) you have a quantitative measurement of truth. For example, Party A sues Party B and asks for $1,000. If Party A gets $1000, you can label their argument as correct and Party B as incorrect (I'm grossly oversimplifying here). Party A gets $500, you can label it as half correct, etc. You can refine your vector database with assigned probabilities and use that to do dynamic Q-A build-outs. This works really well for general public use cases such as landlord disputes, small claims, etc. Doesn't work as well for big one-of-a-kind cases that will have major discontinuity (e.g., Roe v. Wade, Plessy v Ferguson -- Brown vs. Board of Ed). I would also hire a real lawyer if I have serious $$$ on the line. ChatGPT is trained with a lot of data, some of which can be questioned all day long. Court data is FAR better. It gets even better once you take into account metadata (e.g., timelines, law firms, etc.). Legal language is unique, though decisions have a bit of narrative, and can be somewhat arbitrary. General language models like ChatGPT or Gemini will not do well (due to hallucination for example) but if you decompose it into sub-models your results vastly improve. You can even get substantial result improvements with basic fine-tuning. If you can, play around with BloombergGPT. Few Gov't agencies are also building out their own LLMs, and the results are substantially better than ChatGPT/BARD, which is expected as those models are specialized (i.e., you'll get HORRIBLE answers if you ask something general but amazing results that are specific to the domain of the model).

Judges and clerks who wrote decisions are your quality control. Lots of work has already been done by them, and incorporating them into your learning loop is essentially free.

Great idea but it exists already - it's called Retrieval-Augmented Generation. There is also a heavy shift from general closed models (think chatGPT) to more open (so web + research articles) to more domain specific (e.g., specific area of law, specific area of tax code, etc.). You also don't need a team of paralegals and lawyers if you are trying to help the "average Joe." Just need court data (verdicts, etc.) which is generally easy to get. Where you need a team of lawyers and actual law firms is some of those private settlements. I know of one law firm that has experimented with that but with mixed results. I have theories on why they had mixed results and happy to share them for a substantial fee (in case someone on this board is from that firm). Broadly speaking, however, AI is driving a lot of investments at the corporate level. Many of my clients are finding money for pilots, prototypes, and near-production-ready solution testing. Where a lot of them hit the pause button is when they realize that they need to revamp the entirety of their data ecosystems and project estimates go 10-100x but the willingness to experiment hasn't abated.