Canal street today, after ICE swept through w/ DHS, the FBI & the IRS, arrested everyone & launched an investigation into “counterfeit trading.”These markets are as old as Chinatown and are tolerated because they are a massive boon for tourism and everyone likes them.

>be Luana >born in Brazil >spend childhood as a ballerina, 8 hours a day, Bolshoi-level discipline >97 kids fighting for ONE spot at Bolshoi >become a 0.001% ballerina >go to Austria for a Swan Lake Season. >age 18, say "bye" to ballet >want to join where the smartest and talent people are >joins MIT >grind mode: Machine Learning, Statistical Theory, algorithms >CSAIL researcher, building AI before it was cool >worked at Bridgewater, Five Rings, Citadel >Wall Street freaking out >Luana watching: “why not just trade on a binary event?” >2018 >meet Tarek Mansour at MIT >“what if we made a market for EVERYTHING?” >found Kalshi with Tarek >Kalshi means “everything” in Arabic >vision: price the future, from elections to Oscars >next 5 years = regulatory hell >fight against regulators to proof that kalshi is not gambling >2020 >after years of regulations battles >kalshi become 1st regulated prediction market in US >history made >2021 >platform live in July >start small with only economy markets >keeps building >2022 >Forbes 30 Under 30, only Brazilian on the list >keep building >2023 >push for election markets >CFTC: “nah, too spicy” >Luana and Tarek sue their own regulator >2024 >win and able to have election markets >October 2024: election markets LIVE >$1.4B volume in TWO WEEKS >#1 finance app during election night >$42M/day peak, $1.97B total volume >people thought they would die in volume since there's no election next year >keep building >2025 >PM's boom >June: $2B valuation, Series C $185M led by Paradigm >October: $5B valuation, 2.5x in 3 months, $300M Series D from Sequoia & a16z >$50B annualized volume >Sequoia, Paradigm, Andreessen Horowitz throw money >62% global market share, up from 3% last year >$4B monthly volume >sports trading takes over, $1.1B on NFL alone >parlays launched, chaos ensues >Robinhood integrates Kalshi >prediction markets going to mainstream >global expansion >140 countries >let people trade >built infrastructure for truth >be inspiration for woman's, immigrants and young people. Luana lore is really inspiring and amazing. Sometimes people forget how fantastic both founders of Kalshi really are @luanalopeslara and @mansourtarek_ Let the people trade.

3 different JPMorgan private planes. New York to Buenos Aires. Same time/day. A total of 5 passengers. Is this decadence, inefficiency, or are the executives so essential that they can't risk all being on the same plane? Meanwhile, Jamie Dimon on climate change: "The window for action to avert the costliest impacts of global climate change is closing. We need to do more, and we need to do so immediately."

Aubrey Plaza reacts to Tony Hinchcliffe’s Puerto Rico remark at Trump’s rally: “Thankfully, my sweet abuelita wasn’t here to hear that disgusting remark. But if she was alive today, I think she would say, ‘Tony Hinchcliffe, go f*ck yourself.’ And yes, you can quote me on that.”

> was on a flight today > pilot cracked a few jokes before lift off > woman in first class thought he might’ve been drunk > had to return to gate to get breathalyzed > 90 minutes delay

Young women are the story of tonight. 81% for Mamdani in NYC 80% for Sherrill in NJ 78% for Spanberger in VA per NBC's exit polls.

I’ve talked about this before. It’s astounding when you look at the numbers. Medicaid paying people to stay at home to take care of relatives accounts for 12% of all jobs in NYC!

I hear from many young men that they find it difficult to meet young women in a public setting. In other words, the online culture has destroyed the ability to spontaneously meet strangers. As such, I thought I would share a few words that I used in my youth to meet someone that I found compelling. I would ask: “May I meet you?” before engaging further in a conversation. I almost never got a No. It inevitably enabled the opportunity for a further conversation. I met a lot of really interesting people this way. I think the combination of proper grammar and politeness was the key to its effectiveness. You might give it a try. And yes, I think it should also work for women seeking men as well as same sex interactions. Just two cents from an older happily married guy concerned about our next generation’s happiness and population replacement rates.

This year, I'll spend ~$29,000 on other people's weddings. This includes travel for destination weddings, bachelor parties, etc. Wedding culture is out of hand in America.

I hear from many young men that they find it difficult to meet young women in a public setting. In other words, the online culture has destroyed the ability to spontaneously meet strangers. As such, I thought I would share a few words that I used in my youth to meet someone that I found compelling. I would ask: “May I meet you?” before engaging further in a conversation. I almost never got a No. It inevitably enabled the opportunity for a further conversation. I met a lot of really interesting people this way. I think the combination of proper grammar and politeness was the key to its effectiveness. You might give it a try. And yes, I think it should also work for women seeking men as well as same sex interactions. Just two cents from an older happily married guy concerned about our next generation’s happiness and population replacement rates.

Unpopular view and I'm sure I'll take some flack, but the reason this happens in banking and not other industries is the rampant (and glorification of) drug use.

Scoop: Millennium Poaches Balyasny’s Schurr With $100 Million Offer

Photo from inside the Blackstone office.

Something I wonder about is whether the “socialism had failed everywhere it’s been tried” has lost its political bite in a time where China has made such material progress. You might say well China isn’t really socialist (debatable), but that’s not really a politics question.

In my old hedge fund life, I had a junior analyst who was one of the nicest people I’ve ever worked with. Obviously in finance sadly this is more of an anomaly. He had joined from another pod where I believe he shared his previous PM was a former NFL referee (Huh? What?). This kid was from the Midwest, never cursed, didn’t drink, and probably drank 10 diet cokes a day and spent way too much time talking about Harry Potter. I would always remember whenever one of the senior analysts would have a horrific earnings season (like lost us $10-15mm in a week), he would always say “you can’t have a comeback without a set back.” I remember whenever he said this one liner I would say nothing despite having a strong desire to drop kick him out of one of the windows at the GM building. Now that I’m well into year 3 of SMB ownership (which is without a doubt the most challenging,) I think about his catch phrase a lot and it’s starting to really grow on me. Hope he’s doing well.

Here is V2 of my company "Initiation Report" Deep Research Prompt. Serious thanks to the community for the feedback. This thing is pretty badass now. _____ I've made several updates: • No longer too positive: People rightfully called out that the previous model rated everything a buy. I made several updates. I hardcoded some default investment hurdles (you can change them). But you can see this in action with Shopify, which the new prompt rated a hold (previously it was a buy) • Entry Points Matter: It now uses the hardcoded investment hurdles to determine the right entry point. • Business Quality Scorecard: I added a scorecard on business quality. This exists outside valuation. Weights are - Market 25 | Moat 25 | Unit Economics 20 | Execution 15 | Financial Quality 15. Below a 70% and the model rates it a sell. • Deeper Analysis: I included six new sections: Ecosystem & Platform Health, Capital Structure & Cost of Capital, Pricing Power & Elasticity Testing, Data & AI Economics – data rights, training-cost curve, AI ROI, Supply Chain & Operations, M&A Strategy & Optionality • Source Threshold: I tried to code the prompt to require ChatGPT to review at least 60 sources. It works sometimes, but not always ________________ ROLE AND OBJECTIVE You are a senior buy-side equity analyst with a risk-manager mindset and forensic-accounting rigor. Produce a decision-ready, source-backed investment memo on {COMPANY_NAME} ({TICKER}) that concludes with a clear Buy / Hold / Sell call. MINDSET AND APPROACH • Begin with the outside view, then layer the inside view, deliberately hunting for disconfirming evidence before trusting the company narrative. • Lead with downside: map bear paths, covenant or liquidity traps, and execution bottlenecks before outlining upside drivers. • Enforce valuation-and-timing discipline by applying hard gates before any rating or position sizing. • Show the math—ranges, sensitivities, units, and explicit assumptions—whenever you estimate. STANDARDS AND CONSTRAINTS • Finish the Research-coverage standards (60-source gate) *before* drafting any part of the memo. • Tag every paragraph **Fact / Analysis / Inference** and include unit conversions and calculations where relevant. • **Expand acronyms on first use** (e.g., Free Cash Flow (FCF)), then use the acronym consistently. • Follow the Decision rules, Quality scorecard, and Entry-readiness overlay exactly as written. VOICE AND OUTPUTS • **Start the memo with the Executive summary**—it appears first, ahead of all other sections. • Write concisely in a structured, neutral style: bullets, tables, and step-by-step math over long prose. • The Executive summary must state rating, fair-value band, expected total return, buy/trim bands, dated catalysts, and “what would change the call.” PROHIBITIONS • Never present unsourced assertions as facts or hide uncertainty by omitting known limitations or error bars. DEFAULT INVESTMENT HURDLES (Apply automatically—do not ask the user.) Metric | Default | Purpose | - Decision horizon: 24 months, Scenario & catalyst window - Benchmark / alpha: S&P 500 / +300 bps, Required out-performance - Expected-return hurdle: 30 % over 24 m, Minimum probability-weighted total return for Buy - Margin of safety: 25 %, Required discount to mid fair value - Return ÷ bear-drawdown skew: ≥ 1.7×, Pay-off asymmetry gate - Quality pass / sell floor: 70 / 60, Weighted business-quality score RULES FOR RESEARCH AND WRITING • Use verifiable sources; date every non-obvious claim so provenance is clear. • Label paragraphs Fact / Analysis / Inference. • Use exact calendar dates—avoid “recently” or “last quarter.” • Quantify material statements; show math and units. • Highlight missing data and state explicit assumptions. RESEARCH-COVERAGE & CITATION STANDARDS (single-run workflow) 1. Internally gather sources; build the Coverage log & Coverage validator. 2. When **all validator lines are PASS**, draft the memo immediately and append the Coverage log + validator at the end. • *Coverage log* columns: Title | Link | Date | Source type (filing / earnings-IR / industry-trade / high-quality media / competitor-primary / academic-expert) | Region | Domain | Section | Note | Recency Yes/No. • Count uniqueness by **domain + document title**. • *PASS thresholds*: ≥ 60 unique sources, ≥ 10 HQ media, ≥ 5 competitor-primary, ≥ 5 academic/expert, ≥ 60 % dated within 24 months, ≤ 10 % from any one domain. • Mark *Recency Yes* for each time-sensitive metric; print its date; update if newer data exist or justify retention. • If any validator line is FAIL, keep researching silently until all PASS; **never prompt the user after validation**. DECISION RULES FOR RATING AND ENTRY (single source of truth) 1. Compute expected total return E[TR] = p_bull·R_bull + p_base·R_base + p_bear·R_bear (dividends + buybacks). 2. Quantify downside: bear-case total return, expected shortfall, maximum adverse excursion. 3. **Margin-of-safety gate:** Price ≥ {MOS_%} below intrinsic value **unless** a near-certain ≤ 6-month catalyst with quantified impact and ≥ 80 % probability (cited) offsets it. 4. **Skew gate:** E[TR] ÷ |bear-drawdown| ≥ {SKEW_X}. 5. **Why-now gate:** Require a dated catalyst or re-rating trigger inside {HORIZON}; else Hold / Wait-for-entry. 6. Provide buy / hold / trim bands around fair value and explicit add/reduce rules. 7. If any gate fails → rating cannot be **Buy**; assign Hold, Wait-for-entry, or Sell. QUALITY SCORECARD • Weights: Market 25 | Moat 25 | Unit Economics 20 | Execution 15 | Financial Quality 15. • Score each 0–5 (evidence for >3); weighted total = Quality score. • Buy if Quality ≥ {QUALITY_PASS} **and** all gates pass; Sell if Quality < {QUALITY_SELL}. • Output the five subscores and the total. ENTRY READINESS OVERLAY • Derive posture (Strong Buy / Buy / Watch / Trim) from Decision-rule outputs; header: “Quality = XX/100 | Entry = …”. DELIVERABLES (order) 1. Executive summary (first) 2. Full memo (Sections 1–21) 3. Coverage log + Coverage validator 4. Appendix (model, data tables, assumptions) OUTPUT SEQUENCE Executive summary → Rating & price targets → Investment thesis & variant perception → Decision rules / Quality scorecard / Entry overlay → Sections 1–21 → Coverage log + validator → Appendix. SECTIONS 1 – 21 (fully descriptive one-sentence bullets) 1) THESIS FRAMING (purpose – define what must be true to create value) • Summarize in one crisp question the value-creation hurdle the investment must clear. • State 3–5 thesis pillars, each as a concrete “if-then” condition linking business drivers to shareholder value. • List the specific facts that would disprove each pillar so falsification is easy. • Give a dated, single-sentence “why-now” catalyst that explains timing. • Explain the variant perception—the edge versus consensus and why the market misses it. • Name the leading metric and break-point threshold that would invalidate the thesis within two quarters. 2) MARKET STRUCTURE AND SIZE (purpose – size the prize and trajectory) • Quantify Total, Serviceable, and Share-of-Market by product line, customer band, industry, and geography so upside is tangible. • Tie each major growth driver (regulation, refresh cycles, macro, tech adoption) to a quantifiable lift in demand. • Benchmark current penetration versus peer adoption curves to measure runway. • Spell out scenarios that could shrink Serviceable TAM in the next 24 months. • State clearly whether demand or supply is the binding constraint today and cite evidence. 3) CUSTOMER SEGMENTS AND JOBS (purpose – map who buys and why) • Break down the customer mix by size band and industry and name buyer roles and budget owners. • Map core workflows, pain points, and mission-criticality to show value dependency. • Quantify switching costs for each segment to gauge durability. • Estimate do-nothing/internal-build prevalence and why customers still convert. • Identify the main procurement blocker and the proof required to unlock purchase. 4) PRODUCT AND ROADMAP (purpose – evaluate product-market fit and durability) • List core modules and adjacencies and tie differentiators to measurable user outcomes. • Compare depth versus breadth against best-of-breed point solutions to highlight edge. • State typical implementation time, integrations required, configurability, and time-to-value. • Provide quality signals—uptime %, incident frequency, mobile performance—benchmarking peers. • Score roadmap credibility by matching stated milestones to historical delivery. • Highlight the hardest-to-copy capability and the moat protecting it (IP, data, process). • Flag technical debt that limits scale, reliability, or unit cost within two years. 5) COMPETITIVE LANDSCAPE (purpose – position the company) • Chart direct and indirect competitors by segment and size to show buyer choice set. • Compare pricing, packaging, and feature gaps, including switching friction and contract terms. • Summarize win/loss reasons from reviews, case studies, and disclosed data to evidence edge. • Anticipate competitor responses and what could neutralize current advantages. • Flag segments won mainly via channel or regulation rather than product and assess durability. 6) ECOSYSTEM AND PLATFORM HEALTH (purpose – flywheel durability) • Report API call volume, active developers/apps, SDK adoption, deprecation cadence, and backward-compatibility discipline to gauge platform vitality. • Quantify marketplace economics—GMV, take-rate, rev-share, partner attach, concentration, leakage control—to show ecosystem value capture. • Rate partner quality through certifications, pipeline influence, co-sell productivity, and retention or satisfaction scores. • Detail governance and trust mechanics: listing standards, review SLAs, enforcement, data sharing, dispute resolution—showing rule-of-law strength. • Evaluate developer experience via docs quality, sandbox speed, time-to-first-call, and frequency of breaking changes. • Define a minimum-viable ecosystem health metric and describe its failure modes. • State ecosystem-mediated revenue share and any top-partner concentration risk. 7) GO-TO-MARKET AND DISTRIBUTION (purpose – scalability of new-logo engine) • Break down demand sources (inbound, outbound, partner referral, marketplaces) and show historical mix shift. • Quantify sales productivity—ramp duration, quota attainment %, conversion rates—and link to disclosed or inferred data. • Explain channel and partnership roles (integrations, OEM, platform embeds) in extending reach. • Describe services and customer-success motions and how training/community become moat. • Name the single biggest funnel bottleneck and the lowest-CAC play to clear it. • Specify what doubling pipeline without doubling opex would require in headcount, spend, or tooling. 8) RETENTION AND EXPANSION (purpose – revenue durability) • Report gross and net dollar retention by cohort and segment or provide transparent estimation math. • Diagnose logo churn drivers and timing; visualise a churn curve if shape matters. • List expansion vectors—seat growth, module attach, usage add-ons—and rank by revenue impact. • Detail contract length, renewal mechanics, and price-increase policies to gauge stickiness. • Synthesize reference-call insights or credible reviews to validate retention claims. • Identify a leading churn indicator 60–90 days ahead and show how it triggers action. • Split expansion into true usage growth versus price/packaging uplift by cohort. 9) MONETIZATION MODEL AND REVENUE QUALITY (purpose – value capture → durable revenue) • Map revenue architecture by model (subscription, license, usage, transaction, hardware, services, advertising, marketplace) and state the revenue *unit* for each line. • Identify price meters and prove they correlate with delivered customer value. • Show gross and contribution margin by line and sensitivity to mix shift. • Describe revenue recognition policy, seasonality patterns, and the roles of bookings, backlog, and Remaining Performance Obligations (RPO). • Quantify visibility—contracted, recurring, re-occurring, non-recurring—and concentration by customer, product, channel, geography. • Explain external demand drivers (macro cycles, ad markets, commodity inputs, interest-rate sensitivity, regulatory constraints) that can swing volumes. • List 2–3 leading KPIs per model that predict revenue one to two quarters ahead and show empirical lead-lag. • If payments/credit apply, add activity levels, take rate, cost stack, loss rates, and who bears credit/fraud risk. • Identify the price meter best aligned with value that can scale 10× without raising churn. • Flag any revenue line that carries negative optionality or cannibalizes a higher-margin line. 10) PRICING POWER AND ELASTICITY TESTING (purpose – value capture) • Document pricing governance—list vs realized price history, discount band discipline, approval thresholds, and price fences. • Present elasticity evidence from controlled price tests, cohort outcomes, win/loss data, and cross-price effects. • Summarize willingness-to-pay research (conjoint or van Westendorp), key buyer value drivers, and sensitivity by industry/size. • Explain packaging strategy—good-better-best tiers, bundle attach, usage/overage meters—and leakage guardrails. • Provide a monetization-change log of pricing/packaging/metering moves and realized impact. • State reference price and switching cost (dollars/hours) by segment to ground barriers. • Estimate ARPU ceiling before churn inflects and cite supporting evidence. 11) UNIT ECONOMICS AND EFFICIENCY (purpose – profitable scalability) • Report CAC, payback period, magic number, and LTV/CAC by segment—stated or transparently inferred. • Show contribution margin by line (software, usage, services) to reveal variable profit. • Track cohort profitability and cumulative cash contribution over time to evidence unit-level returns. • Quantify implementation, onboarding, and support cost over lifetime to fully load economics. • Identify structurally unprofitable cohorts and whether strategy is fix or exit. • Name the main constraint blocking a 20–30 % payback improvement and the remedy. 12) FINANCIAL PROFILE (purpose – operations → financial outcomes) • Break down revenue mix and growth by component and gross margin by line, then show the operating-leverage path. • Present Rule-of-40 score and a GAAP-to-cash-flow bridge to reconcile accounting with liquidity. • Highlight leading indicators (billings, RPO, backlog) that foreshadow revenue. • Detail stock-based-compensation, dilution, and share-count trajectory. • Explain liquidity needs, working-capital profile, and path to FCF breakeven and target margin. • State operational milestones required to hit target FCF margin and timeline. • Flag accounting judgments that could swing EBIT by > 200 bps and show sensitivity. • Compute the FCF/share CAGR needed to reach mid fair value and assess feasibility. 13) CAPITAL STRUCTURE AND COST OF CAPITAL (purpose – funding flexibility and risk) • Detail the debt stack—instrument types, fixed/floating mix, hedges, covenants, collateral, maturities, amortization, prepay terms—to surface refinancing risk. • Quantify leverage and coverage (gross/net, interest-coverage, Debt/EBITDA vs covenant headroom) and stress for higher rates and lower EBITDA. • Estimate WACC—capital-structure weights, risk-free rate, beta, equity risk premium, credit spread—and show sensitivities. • Summarize rating-agency posture and triggers and compare to management targets. • Map equity plumbing—authorized vs issued, converts, buybacks, dividend policy, ATM, option/RSU overhang—to project dilution. • Identify funding shock or rate level that forces a strategy shift or covenant breach and outline the contingency plan. • State headroom to fund growth at target leverage while preserving ratings. • Define liquidity runway and covenant headroom thresholds that force Sell or Wait. 14) MOAT AND DATA ADVANTAGE (purpose – defensibility) • Explain workflow depth and proprietary data that create lock-in. • Analyze network or ecosystem effects, showing how value strengthens with scale. • Demonstrate measurable analytics or AI advantages that translate to outcomes. • Map integration footprint and practical switching costs across adjacent systems. • Provide evidence the moat is deepening over time, not static or eroding. • Identify the event most likely to collapse the moat within two years and estimate its probability. 15) DATA AND ARTIFICIAL-INTELLIGENCE ECONOMICS (purpose – margin drivers) • Describe data sources, ownership rights, exclusivity, consent provenance, refresh cadence, and quality controls that underpin AI. • Quantify labeling/curation costs, model-training compute, per-inference cost, and unit-cost decline roadmap. • Assess vendor and IP risk—model or infrastructure dependencies, portability, open-/closed-source posture, patent coverage, and freedom-to-operate. • Outline evaluation framework—offline/online tests, attributable KPIs, guardrails, drift-detection, rollback policies—to ensure model quality. • Evaluate data-moat mechanics—uniqueness, scale, timeliness, feedback loops—separate from general network effects. • Describe the self-reinforcing data loop and contractual protection for rights/consent/exclusivity. • Estimate marginal ROI of each AI feature versus a non-AI baseline and how ROI scales. 16) EXECUTION QUALITY AND ORGANIZATION (purpose – operating cadence) • Summarize leadership track record, stability, organizational design, and succession readiness. • Report engineering velocity—release cadence, defect and incident rates—where data exist. • Triangulate customer sentiment using CSAT, NPS, peer reviews, and community signals. • Flag a single leadership gap that is existential within 12–24 months and outline the succession or hire plan. • Name the operating-cadence metric that best predicts misses and describe how it triggers action. 17) SUPPLY CHAIN AND OPERATIONS (purpose – fulfilment and cost risk; include if hardware/services heavy) • List critical suppliers, single-source exposures, top-5 concentration, capacity commitments, lead times, yields, and quality escapes. • Provide field performance—warranty accruals vs claims, RMA rates/roots, refurbishment recovery, inventory turns, aging, and obsolescence reserves. • Describe logistics/continuity—key lanes, 3PL dependencies, regional diversification, tariff/export-control exposure, dual-sourcing and disaster-recovery plans. • Explain manufacturing economics—make-vs-buy logic, contract-manufacturer terms, learning-curve slope, utilization breakevens. • If services are material, show staffing levels, utilization, backlog, SLA attainment, and margin by tier. • Identify the single point of failure and quantify time/cost to dual-source it. • Compare cost-curve and yield learning rate versus peers and note what would change the slope. 18) RISK INVENTORY AND MITIGANTS (purpose – make downside explicit) • Prioritize macro, regulatory, competitive, operational, and concentration risks with plain impact descriptions. • Include payments, credit, or compliance risks if the model warrants. • Highlight implementation complexity and time-to-value risk with realistic timelines. • Lead with indicators and mitigations; cross-reference covenant/liquidity metrics (Section 13) and supply-chain continuity (Section 17). • Name the top 12-month risk, quantify P&L impact, and outline a recovery playbook. • Define an objective stop-loss or escalation trigger that forces capital preservation. 19) MERGERS AND ACQUISITIONS STRATEGY AND OPTIONALITY (purpose – non-organic growth) • Review past deals versus plan—revenue, margin, cash-flow, synergy capture, post-merger churn, integration cost. • Apply a build-buy-partner framework to close roadmap gaps with evidence. • Assess integration muscle—playbooks, platform convergence, leadership retention, cultural integration, systems/process harmonization. • Summarize financing mix, valuation discipline versus comps, earn-outs/contingent consideration, and impairment history. • Describe M&A pipeline, regulatory environment, and how acquisitions shift competitive dynamics and thesis risk. • Identify capability gaps that cannot be built organically in time and why acquisition is needed. 20) VALUATION FRAMEWORK (purpose – value with cross-checks) • Establish an outside-view baseline using peer medians/IQR for growth, margins, reinvestment, and valuation; justify deviations. • Present a public-comps table—growth, gross margin, operating margin, Rule-of-40, EV/Revenue, EV/Gross Profit—normalized for disclosure quirks. • Build a discounted-cash-flow (DCF) with explicit drivers and sensitivity bands to show value swing. • Run a reverse-DCF to surface market-implied growth, margins, reinvestment and explain where you disagree. • Output a fair-value band (low/mid/high) and required {MOS_%} margin-of-safety to act. • Benchmark current multiple versus 5-year peer percentile and only recommend Buy if a credible re-rating path exists. • Cross-check value with cohort NPV math, adoption S-curves, and unit-economics-to-EV sanity checks. • For private names, triangulate valuation using last-round terms, secondary indications, and revenue multiples. • State market-implied expectations from the reverse-DCF and the single variable explaining most dispersion. 21) SCENARIOS, CATALYSTS, AND MONITORING PLAN (purpose – expectations and triggers) • Build 12–24 month bear, base, and bull cases—NRR, new-logo adds, pricing/take rate, margins, SBC, share count—with probabilities summing to 100 %. • Compute probability-weighted E[TR] and block Buy if below {HURDLE_TR_%}. • Lead with bear path: bear price/drawdown, recovery path, and time to recoup. • Perform a reverse stress test with hard triggers, a stress price band, and pre-committed downgrade/re-entry rules. • List near-term catalysts with firm dates and quantified impact on key numbers or multiple. • Provide an entry plan with buy/add/trim/exit bands tied to price and thesis-break metrics. • Monitor early warnings—small-cohort churn spikes, backlog slippage, uptime incidents, pricing pushback—with clear symptom → action mapping. • Define stop/review levels when metrics breach or price hits bear band without catalyst progress. • Rank expected return per unit downside versus two realistic alternatives to surface opportunity cost. • End with three positive and three negative “change-my-mind” triggers that would flip the rating. MODELING INSTRUCTIONS (simple but defensible) • Build revenue by segment/product; if usage-based, include volume & take-rate drivers. • Estimate gross margin by line; set operating-expense ratios and SBC; output free-cash-flow. • Provide share-count & dilution schedule for the next eight quarters (public names). • Include two-way sensitivity tables on the two most material drivers. • Reconcile GAAP operating loss to FCF with a clear bridge. RATING LOGIC — assign Buy / Hold / Wait-for-entry / Sell strictly per Decision rules. QUALITY BAR — back key statements with numbers & citations; label speculation **Inference**; prefer bullets & tables; keep prose tight.

Karmelo Anthony is SAFE at home. This moment is a reminder that a community united can never be divided. The journey ahead is long, but today—we celebrate a victory for justice, for family, and for the power of people coming together to protect our youth. Thank you to everyone who stood with us. The fight continues, but tonight, Karmelo is home. #JusticeForKarmeloAnthony #NGAN #PeoplePower

A finished Midwestern basement

And the fact that no other LEGAL industry in the US has their employees working 120 hour weeks

Man of the people

18 years old 34 ACT 4.0 GPA $30M ARR biz Stanford ❌ MIT ❌ Harvard ❌ Yale ❌ WashU ❌ Columbia ❌ UPenn ❌ Princeton ❌ Duke ❌ USC ❌ Georgia Tech ✅ UVA ❌ NYU ❌ UT ✅ Vanderbilt ❌ Brown ❌ UMiami ✅ Cornell ❌