Emily

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Emily

Emily

@emilypvp

Nashville, TN Katılım Ocak 2015
23 Takip Edilen2 Takipçiler
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Position Journal
Position Journal@PositionJournal·
$META is implementing a forcing function with all these layoffs. You will integrate your work style and self with AI to improve productivity out of necessity, not optionality, and you no longer have a team to rely on. I feel very sorry for those laid off.
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Portfolio & Accountability Update My public record thread. I do this so I can be a voice of transparency and look back at my own decisions and thoughts. Q2 - Year to Date. YTD: +37.6% SP500: +7.9% Holds: - $NBIS portfolio position grew from 45% to 56%. Unfortunately had some shares called away, before that it was well above 65% - $TOST my only remaining SaaS. If you look at the price, you'd think its a dead stock. But if you look under the hood, they are growing healthily. In this environment look for SaaS that's aggressively growing, nothing less. Sells: - $FRMI sold for about -40% loss on the position. The company vision is solving a real power bottleneck through behind-the-meter solution offering 11GW in Texas. But the house is a mess, CEO fired and halfway through 2026 they still don't have a tenant. Ridiculous. - $DOCS sold for about -12% loss. SaaS with an additional factor of having a regulatory moat. Nope. Still slow growth. Still too much unclarity and still no monetization or path to DoxGPT. In today's world, SaaS needs to be hypercompetitive and there's no room for this. Buys: - $COIN I've been adding every time when <$200 and it is now firmly 15% of my portfolio. Check my pinned post on my conviction. - $RIVN my focus as of late and now 8% of portfolio, buying under $13 (in Robinhood port). Production -> Software -> Robotics is the entire lifecycle that I foresee for them. - $CMC doubled my position and now 5% of portfolio. Data center and infrastructure build out in United States will be massive and CMC focuses on high margin build outs - $ABSI bought more. Insider Director bought 100,000 shares and they have enough runway until 2028 - $CRSR doubled position and now also 5% of port. Huge insider buys, push to profitability, big institutional buys and an upcoming gaming sector tailwind - $HIMS bought more and also 5% port at $20 cost average. These guys have a lot of problems but the product marketing, no matter what way I look at is, is clean and the health trend behind it, is real.
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PORTFOLIO (and transparency) UPDATE✍️ For Q1 2026 Year-to-date: -3% S&P 500: -3.6% Outperformed by 0.7%. This number fluctuates violently because of my position sizing, sometimes I'm green, sometimes I'm worse off. New/Increased Positions: $COIN $TOST $HIMS $CMC $CRSR $FRMI + some 1-2% positions like $RXRX and $ABSI Trimmed/Exited positions: $DOCS trimmed down to fund more Coinbase ----- Holdings Update $NBIS: 45% of account (inc. calls) and effectively holding my portfolio up. My dedicated position. Be warned: too many short-sighted posts and "analyses" about Nebius on X. People, including influential X investors, are focused on nonsensical 2026 price targets, which will be critically wrong, just like their 2025 price targets the year before. If you want to understand the end-game as someone whose been invested in them since 2021 (Yandex days) then you can read about it in my X article. $VTI: 19% of account. Continued steady monthly deposits. Nothing fancy but contributed to me under performing this year (ironically) $COIN: Increased to ~10% of account. Newest add of 2026. I expanded on the bull, bear and current state of Coinbase in my X article. I'm still critical of Brian for being too late on stock trading. But anything <$200 is a well-adjusted risk to reward entry. $TOST: 6% of account. My one of two SaaS bets. Vertically integrated software with a sticky platform and high-switching costs. There are positive signs that the economy (measured as spending power vs debt) is improving, a tailwind for Toast. $DOCS: 3% of account. My other SaaS bet. This one is strengthened by $500M buybacks (12% of MC) and a regulatory moat (FDA/Health) $HIMS: 3% of account. Personalized distribution platform with incredible product marketing. Litigation with NVO turned to partnership and the only overhang now is DOJ, which I'm not worried about. People love to hate on HIMS, and at the prices it was at previously, $70, I wouldn't blame them, but at my entry of $16? I'm totally relaxed. $CMC: 2.6% of account. Steel manufacturing company whose niche is focused on foundations and kickstarting projects. Trades lower P/E compared to Nucor and has a massive backlog of data center build-outs. Also something I covered as a deep-dive separately $FRMI: 1.3% of account, down -37%! High risk, high reward. I give them one full year, until 2027 to execute and deliver their 1GW promised capacity (eventual 11GW!). If nothing, then I'm out

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Q1 purchases of hedge funds. They piled into $TOST and some made it significant >5% positions
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$RIVN largest institutional holders (mostly) increased their positions. Notably State Street and Geode Capital being down more than 50% on their positions and choosing to average down signals that confidence has not shaken. Mind robotics article will be published tomorrow
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Bought more $RIVN today -rolled leaps June’27 to lower strike ($15) -bought shares -wired cash and ready to deploy more I’ll provide a comprehensive reply as long as you don’t reply with nonsense like it’s a dead stock
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$RIVN The question was "How do they avoid financial woes from burning too much cash?" A legitimate bear case. Answer below 👇
Position Journal@PositionJournal

Always good to hear from you MP As of Q1'26 they had 5.4B cash on hand, and in 2026 they expect to have about $8B from new investments and milestone based payouts. Of that 8B, 1B from VW has already been received and $300M from Uber is also set to clear. They're losing about 700M a quarter (going back to Q1'25) which gives them a few years of runway, depending on if FCF stays steady or not. In my opinion, my invalidation criteria is Q4'26, if as I predicted unit-economics does not flip the automotive loss (-62M in Q1'26) to positive by Q4, then this means either of the following: -BOM and non-BOM COGS reductions did not materialize as they stated in the shareholder letter -Demand came in lower than expected, without R2 demand, all that revolving credit and facilities are running at full throttle and burning cash Any of those spells doom for the company, which is the most valid bear thesis comes in. But, If automotive does flip to positive in Q4'26 (I estimate ~20-25M gross profitability) resulting from pent-up demand ($RIVN scored 85% on customer satisfaction) and from actual savings from BOM/non-BOM COGS, then the street is going to materially re-rate this company higher because you now have: -High demand, matched by high output capacity -Tempered costs and meaningful supply chain improvements -Amazon as 2nd largest shareholder and largest customer likely has pent-up cash they are waiting to deploy to enable the success of their Supply Chain initiative (Logistics as a service) which they couldn't order more because there is no capacity -As production scales, Rivian has the opportunity to sign more deals with other Retailers

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The point of this X account has always been two things: 1) Journal all my positions openly and transparently, so I can look back on what I did right and wrong and improve 2) Put out high quality, evidence-backed content after years of seeing people around me lose money to hype, FOMO and blatant lies. In February I initiated a 5% position in $DOCS, and this week I sold it all for a -12% loss. So now let's do a look-back on why: In February I laid out both a bull and bear case for Doximity and invested based on the bull case. I believed the AI tools like DoxGPT, Scribe, and PeerCheck would drive real clinical value and long term upside from the physician data moat. But unfortunately the two bear cases I had laid out played out instead. First, pharma ad budget delays from MFN drug pricing uncertainty hit harder and lasted longer than hoped and Q4 revenue grew just 5% to $145 million. Full year FY2027 guidance was given only 3% to 5% growth, well below consensus. The shareholder letter and earnings call directly tied this slowdown to soft digital pharma ad demand and policy uncertainty. Second, the saturation limit at 85% physician penetration was a headwind than a strength. Management delivered the slowest growth outlook in years, confirming that ceiling exactly as my bear thesis described. I still see the AI adoption as promising. Over 800,000 active prescribers used workflow tools in Q4 with and prompts per user doubling but adjusted EBITDA margin dropped to 45% from 50%. The company explicitly called this an AI investment year with minimal AI revenue contribution. With all this being said, I just don't have it in me to hold on to SaaS in today's world unless they're growing rapidly.
Position Journal@PositionJournal

Final post of the series unpacking $DOCS. I've narrowed down the bull and bear thesis and I look forward to thoughts/comments. BEAR🐻 Doximity management blames short-term revenue weakness on pharma clients delaying budgets and deals due to uncertainty from Most Favored Nation agreements. Most Favored Nation (MFN) policy requires drug makers to sell medicines in the US at prices matching the lowest offered in other developed countries to "ensure Americans pay prices aligned with the lowest in other developed nations, ending decades of overpayment and delivering immediate relief." - President Trump By early 2026, the administration announced 16 deals with major pharmaceutical manufacturers to provide substantial price relief on numerous products. Companies commit to MFN pricing to avoid tariffs and increase US manufacturing investments. Ok, let's answer in plain english: So what? MFN policies lower pharma revenues by forcing US drug prices to match the lowest in other developed countries. Pharma companies respond by signing agreements to avoid tariffs, which creates short-term budget uncertainty and delays in marketing spend. For Doximity, this means reduced upfront commitments from top pharma clients, as seen in their Q3 2025 earnings where 16 of 20 major pharma firms delayed deals amid MFN negotiations. Long-term, lower pharma profits could cut overall ad budgets but favor cost-effective digital platforms like Doximity over traditional channels. Pharma might optimize spend toward targeted physician networks to maintain sales amid price squeezes. Final note, growing revenue in a segment where you already capture 85% of physicians, is going to be a non-starter. Too difficult to grow revenue when you’re this dominant already You need to materially evolve your product offering or launch into a new segment, which then gives us our bull case. BULL🐂 Doximity divides its AI components into DoxGPT, Doximity Scribe, and PeerCheck. DoxGPT serves as the core clinical AI assistant. Physicians use it for evidence-based answers to questions, drug references, guideline access, full journal PDFs, drafting letters, and patient education materials. "Doximity GPT is a powerful AI tool that excels in clinical support. It understands clinical queries, provides contextual responses, and summarizes relevant information." Over 300,000 prescribers used it in recent quarters, often preferring it over competitors. Doximity Scribe acts as the administrative documentation tool. This ambient AI generates real-time notes from patient visits or calls, capturing key details while integrating with tools like Dialer. "Scribe is a HIPAA-compliant, AI-powered clinical documentation tool that automatically generates notes during patient visits." PeerCheck provides the trust and validation layer. Over 10,000 physician experts review and verify AI outputs, embedding peer-reviewed accuracy directly into DoxGPT responses. This AI implementation is so compelling for me that I decided to initiate a position. The data that $DOCS has is a goldmine for Frontier AI labs like Anthropic or OpenAI and a prime acquisition target even if revenue doesn't grow at a fast pace. And if an acquisition doesn’t work out, then there is big upside to commercializing DoxGPT to regular users This isn't financial advice, do your own research.

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$COIN A piece from @dorvonlevi that uses a real-world example on agentic value. A similar workflow example I laid out in my Coinbase article but from an e-commerce perspective Agentic workflows will dominate almost every interaction with customers whether you see it or not Get ready.
Dor@dorvonlevi

Building an AI-native @Coinbase means rebuilding everything, especially the hardest parts. We've put a lot of time into redefining compliance, where the stakes are incredibly high, and we have to be extremely thoughtful about implementation. We have invested heavily in rebuilding our compliance ops around AI with that reality as our starting constraint, not an afterthought. Here is an overview of what we've learned and what we built. Most people assume compliance work is mostly checking whether a name appears on a sanctions list. That is the easy 5%. The other 95% is interpretive judgment under uncertainty: a customer claims their wealth came from real estate. Do the property records actually support it? Does the timeline hold? Is the documentation legitimate, or does it feel too polished? You need compliance staff and investigators who understand what “suspicious” actually looks like in context. That's part of why compliance is so hard to automate—and so expensive. The first obvious AI approach is to hand the model the existing procedures and ask it to run them faster. That approach misunderstands what procedures are for. Good procedures are not bad investigations; they are deliberately incomplete investigations. Their job is to create consistency, auditability, and a minimum standard across thousands of cases. They excel at saying what must happen. They are far worse at capturing everything a strong analyst actually notices: which sources they trust, when they widen the search, when a document feels off, when an explanation technically fits but still does not feel earned. Procedures also carry the shape of the old operating model: fragmented systems, time pressure, queue pressure, and the hard limit of how much one human analyst can read, cross-reference, and hold in working memory at once. That is not a flaw in the procedure. It is how you design a process for humans. AI changes the constraint set. Reading, searching, comparing documents, and tracing inconsistencies no longer have to be treated as scarce analyst time. Done carefully, with proper controls and human review, models can explore more context, test more hypotheses, and surface more inconsistencies than any single analyst could reasonably do case by case. So if you simply automate the procedure exactly as written, you may gain efficiency. You will not unlock the full value of AI. You will just make the old bottleneck run faster. The better question is not “Can AI follow the analyst playbook?” It is: once the cost of reading, cross-referencing, and testing hypotheses collapses, what should the investigation become? A second tempting approach: feed it historical Suspicious Activity Reports (SARs) and let it learn from outcomes. This breaks down too. You rarely have the full state of what the analyst actually saw during the investigation. A case that looks straightforward today might only look that way because information surfaced later. A fraud indictment that didn't exist when the original analyst made the call, news articles that hadn't been published yet. Hindsight can contaminate your training data. Also, regulators themselves acknowledge that SAR decisions can be subjective. The architecture has four layers. The first is data: continuously enhancing the coverage, quality, and architecture of the signals the system depends on. The second is classical machine learning models that cluster and classify alerts to determine what type of investigation needs to run. The third is the investigation agent itself: a multi-agent system that orchestrates specialized agents to execute the investigation end to end. The fourth is a safety filter that runs independently of typology, ensuring no risk vector is missed regardless of how the alert is classified. Each layer is independently auditable and learns from the feedback provided by human reviewers. Inside the investigation agent, specialized sub-agents run across the full case surface: alert context, customer and identity signals, access patterns, risk indicators, transaction behavior, source-of-funds, onchain activity, and public adverse media. Each writes its findings into a shared case memory. A coordinator agent reconciles and challenges them. When sub-agents disagree, such as when source-of-funds marks activity as “explained” while adverse media surfaces a recent indictment, the coordinator attempts to resolve these disagreements knowing the common patterns. The narrative agent prepares the final report with all collected evidence and suggested resolution. The last self-validation agent acts as a guardrail: if the system cannot support its conclusion with sufficient confidence or data quality, the case is routed to manual investigation instead of being surfaced as an automated result. Before any of this touched a real customer case, we built what we call a “Golden Set” - historical cases with known right answers. "Known right answers" in compliance is harder than it sounds. It meant re-investigating old cases, getting multiple senior analysts to independently agree on what the right call would have been, then debating the disagreements until consensus. Months of work before we could even start measuring. Here's an important part (for now) - cases currently get BOTH the AI's full investigation AND a senior human review. We didn't reduce scrutiny, in fact, we added more of it until it no longer proves valuable. Cases resolve significantly faster AND get more eyes than they ever did before. Every human correction feeds back into the model as a training signal. It gets better because it's wrong in front of people who know how to fix it. None of this would have shipped without clearing structural blockers most financial institutions are still stuck on. Security and privacy sign-off to send customer data to LLMs at all. Senior compliance officer alignment on AI-assisted human decision making. Model Governance team embedded since December - they observed the entire Golden-Set Evaluation process and are running a formal validation review with our Internal Audit team now. Today this handles roughly 55% of our US fraud case volume with significantly less analyst time per case. Time freed goes to the harder cases AI can't yet handle - and to teaching it. Our internal compliance and quality teams are the ones who are building this system with the engineers, training it, validating it, and continuing to shape how it improves. In the process, they've developed skills that are incredibly valuable: how to design evals, how to think about model bias, how to think about human bias, how to architect human-in-the-loop systems, skills that are becoming among the most valuable at any company. This entire project started ~6 months ago with a whiteboarding session between @galpa42 and I, and was built by an AI-pilled cross-functional and it’s just the first pod - there's a multi-month roadmap,rebuilding compliance from the ground up with AI. Huge thanks to everyone involved and congratulations to @galpa42 for shipping two babies to production this month :) The future of high-stakes work is not AI replacing judgment. It is AI making judgment scalable, auditable, and continuously improvable.

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$RIVN has a capacity problem, not a demand problem.
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“Intellectuals” on X state that in fact you SHOULD be timing the market and when challenged on that dangerous statement, block you.
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$RIVN Should I do a deep-dive on Mind Robotics?
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$NBIS I have my theses laid out pristinely. But still the concentration of my port does give me the jitters every earnings.
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$ABSI AMD is still a top institutional shareholder of ABSI. Absci uses AMD hardware and software to power their AI platform and drug discovery workloads. The potential of AI enabled drug development will be the wake up call no one expected.
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