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Back from Vacation (Notes from Nov 11 to 17, 2019) February 11, 2020

Posted by Anthony in Automation, Blockchain, cannabis, Digital, education, experience, finance, Founders, global, gym, Leadership, marketing, NFL, NLP, questions, social, Strategy, training, Uncategorized, WomenInWork.
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It had been a long while – 9? months since taking more than 1 day off extra and closer to 20 months since I’d had a week off in a row. I visited the Big Island in Hawaii and stayed primarily on the west side of the island. Gorgeous weather and awesome beaches will bring me back, hopefully shortly.

I want to write a bit further about the escape, but I also want to get these notes out, so I’ll write further in later this week – Thursday.

Enjoy these notes on some of the fascinating people of Eniac Ventures, other investors, founder of EasyPoint, ReSolve quant, research professors, former professional football player and a Nascar driver.

  • Hadley Harris (@Hadley), Founding GP at Eniac Ventures (20min VC 2/3/16)
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    • First mobile venture, Soundcloud, Airbnb, Vungel
    • 2x entrepreneur in mobile – Vlingo (acq by Nuance for $225mln) and Thumb (acq by Wipulse)
      • Was one of first employees and execs running marketing and bd while working with product
    • Worked at Samsung and Charles Rivers Ventures
    • Studied engi & math as undergrad @ Penn, joined MSFT & Samsung
      • His 2 really good friends at Penn and him came together for Eniac in 2009
      • Mobile – next place for computing – cleantech was hot at that time, as well
    • SF was 50%, NY as 25% and the rest was elsewhere – won’t lead but will do a pro rata and be key in fundraising for next
    • Living & breathing the co – coming to right valuation, inevitable for down or flat rounds
    • 18-24 months from seed to series A or pre-seed to seed – funds becoming more institutionalized
      • Leading rounds for Eniac at $1.2 – $2mln
    • Favorite book: Freakanomics, read it in one sitting
    • Tools: gmail, relayedIQ for deal tracking, as todo list, also
    • Don Valentine – godfather of VC, great investors but great entrepreneurs and fund raisers
    • Favorite blog: Nuzzel – curation of reposts
    • Underhyped: mobile enterprise; Overhyped industry: big fan and he does work in social, but lot to weed through
    • Most recent investment: Phhhoto – knew the founders, they’d known each other for a while, great design and numbers – self-funded
  • Zach Resnick (@trumpetisawesom), founding EasyPoint (IndieHackers #130, 10/28/19)
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    • Iterating your way to founder-product fit, currently at 10 people, 5 full-time, $600k ytd with 15% yoy organic growth
    • Traveled, worked and lived abroad in Jerusalem before school, infected with wanderlust
      • CC churning and manufactured spending while he was learning at school in Ohio – VISA gift cards to $1k
    • Banking often makes more money on the chance that you’ll become a customer for other areas of business (mortgage, checking account, brokerage, etc)
    • Started when he was 19 – would give advice to parents/family/friends on the year before getting an hourly rate for paying customer as consultant
      • Enjoyed his help, he liked helping others – he was getting $1k/mo from hourly before going up
      • Consulting clients – he was helping optimize for business or vacation trip for the points
    • Started Land Happier to solve a problem of having everything in one place
      • Cultural norms, transportation, 6 other things for information in a fun and compelling app product (MVP on app store)
      • Wasn’t solving a problem that nobody has, but nobody would pay for – product/founder fit wasn’t there, either
    • What he wants – enjoys negotiating, strategic thinking, interesting conversations and sales moreso than product focused than customer focused
    • While working on Land, he productized his consulting – generally was helping family friends that were parents’ age
      • Amount of effort he was putting in compared to the value wasn’t the same – not high enough
      • Started to focus on small business or medium enterprise owners to put spending on the right cards and get 6 figures on spend return
      • Focused on people he knew through referrals, points optimization plans for small owners – acquisition and spending for more value
    • Early stage owners – hey, this isn’t free
    • Playing poker for relatively high stakes – teaching important principles, statistics, risk management and psychology
    • Consulting to productized consulting service – had a family friend with small business who would see a $50k in increased return on spend
      • He could do a quick analysis and understand business more, try to get a customized points optimization plan for points
      • Small business owners are leaving 1.5%, maybe 2.5% on the table – using points better for things you already want to do
    • Providing value but people didn’t know what it is or weren’t hurting – show them math for 5 figures within a year saving
      • Guarantee: if you sign up points optimization plan, if he doesn’t get you double what his fee is within first year, he gives money back and $10k
      • Making people aware of the problem was going to be a lot of work – never really got off the ground for outbound
        • Was just a way to make money, not necessarily grow it really fast – customers’ needs
    • Concierge service now (v3 EasyPoint) focusing on business and first-class international long-haul service
      • Over whatsapp and telegram groups – makes a flight request and they get back to them 24/7
      • They use miles and points that they buy from clients and then use those to book for others
      • Brokers buying all kinds of points and miles – so the arbitrage there contained issues with ToS and such
        • They’re buying transferable points like Chase / AMEX directly to frequent flier accounts
    • Working for someone else – interned with The Points Guy and when he was looking at doing it, he posted on the Facebook group
      • Cameron, now their COO, was very good – would he want to have his hires over for dinner?
      • Team of 10 now: Cameron manages concierge, growth marketing (5 on team, looking for Asia now)
        • Part-time business development consultants, full-time that have been searching
      • Revenues and loans for growth/cash flow, venture debt and possibly equity raise
    • Concierge service with product-market fit and being focused – enterprise value of $100mln probably but not billions
      • Not much needs to be tweaked for core product – fund raise would be for a different product
        • Help consumers decide on if they want to use their points or cash when booking – trying to automate this for concierge/back-end
        • Chrome extension and booking engine to use or not – this may be billion dollar opportunity
  • Andrew Butler, ReSolve’s Head of Quant Research (Gestalt University, 10/2/19)
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    • Machine learning in markets: Silver bullet or Pandora’s box
      • Unsupervised, supervised and reinforcement learning differences in application or finance
    • Student of mathematics, physics in undergrad, keen on not memorizing a lot of stuff – enjoyed the applied side
      • Oil reservoir simulators that modeled tidal flow in Bay of Fundy, wind turbines in giant field for optimization
      • Next step was working on a sub problem of simulators – complex, computationally expensive and trying to optimize NPV in 60d oil field
        • Navigating the nonlinear, nonconvex solutions – how to make a reasonable model approximation by sampling sparse reps of simulator
    • How would simulator/emulator apply to financial world in momentum and moving averages
      • Sample distribution would fit well to out-of-sample distributions in physical world but finance wouldn’t – nonstationary
      • Caused him to use simpler models, momentum models (and transformations) and ensembles of simple factor models
        • Mean-variance optimization, error maximizing, in-sample won’t perform well out of sample
    • Wanted formal training in financial engineering, so went and got a MFE
    • Practitioner compared to theorist – after a conference talk, his construct was mean-variance was same as regression
      • Subspace reduction and regularization as identical terms for mean-variance
    • Machine Learning as 3 subspaces
      • Unsupervised learning -> clustering and dimensionality reduction
        • Targeted marketing, customer segmentation and in finance: signal processing, optimization or portfolio construction
        • Trying to uncover relationships/groupings/clusters contained within a dataset
      • If total error is dominated by bias, it’s likely overly simplistic – X as model complexity and Y as Total Error (Bias / Variance)
        • Increase complexity, bias term can decrease, increasing the variance (instability/overfitting)
  • Kelly Peeler (@kellypeeler), founder / CEO NextGenVest (20min VC FF#034, 2/5/16)
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    • College Money mentor, empowering students to live full lives, history of financial crisis for motivation to start
      • Went over to Iraq, started and enabled some companies to build there in 2012
    • Went to JPMC after graduating to make some money before starting NGV for students
    • Financial organization to financial efficiency – going from Mint (organizing money for a user’s financial lives)
      • Now people need efficiency – time priority, optimizing time through automation and personalization
        • Leverage trust to improve time in the background (automation and not wanting to have to look)
    • High school trust and students have nobody they can trust for guidance – 8% trust banks and financial institutions
      • If you can build a product/service, on your way to building trust
        • Save users time, money, customized experience
    • Serving their customers with SMS and Snapchat – smarter push notifications for the right service in the right way
      • Couldn’t customize communication inside an app, so they did channels that they chose
    • NGV clubs at high schools across country – new high schools brought in, engagement and grassroots
    • First product that they brought on was for the financial literacy test that 17 states need
    • Favorite book: The Thank You Economy – best people outhustle to get more customers
    • As visual person, can focus on 1-3 things at a time – preps in the evening, large index cards
    • Adam Nash at Wealthfront – build trust with dynamics of product and the culture of company
    • Spent too much time at focusing her weaknesses but has tried to get better on that side
  • Sam Yagan (@samyagan), Starting OkCupid, Sparknotes (Wharton XM, Marketing Matters)
    • Turning down consulting job for OkCupid start – told he was crazy but wanted to take the chance
      • Free model and how do you value customers but competitors were Match and eHarmony
      • Had to get enough people on all sides of the market and then could use the data to help
    • Internet wasn’t designed to take an expert’s ideas and just use those – bigger than that
      • “You know what you want.” We’ll pull it out and figure it out.
      • Google comparison – index all the pages and figure those out to place on first page
      • Creating a platform to ask all the questions and focus on them
    • Sold Sparknotes in 11 months, took OKCupid 8 years (sold to Match, was there for a year)
      • Got the job running the company for another 3.5 years as Match CEO and created Tinder
  • Rob Gronkowski (@robgronkowski), All-Pro tight end (The Corp, 10/1/19)
    • A-Rod investing into Rob’s brother’s, Chris, company Ice Shaker
      • Were able to put money in, along with Mark Cuban, when they were on Shark Tank (all brothers)
      • Rob, upon retiring, bought Arod out of his shares in the business with Chris
    • Fitplan – Arod gave Rob a discount on the shares in Ice Shaker and he just wanted Rob to look through his company
      • Rob invested with Arod – parents were in business (gym equipment for retail/commercial for 28+ years)
    • Kraft being an owner for the team and being around the game – interested in everything
      • Rare to see owners in the locker room and talking with players – many players say they’ve never seen others
      • Brady, Kraft and Belichick as being the greatest people and diagnosing problems/plays and adjusting
    • Rob wants to travel – done a lot in the US
      • Traveling a week from that day to Israel with CEO Barry of CBDMedic there
    • Being reckless as single Gronk in the NFL (loves Camille now, though)
  • Horst Simon (@hdsimon), Chief Research Officer at LBNL (Curious Investor 9/3/19)
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    • Difference between ML and programming – validity of an email, for instance
      • Computer looks for “@” and domain name, iterative of if-then’s, marking valid or invalid
      • ML – give details of valid and invalid email addresses and have the computer figure it out with a statistical model for rules
        • Relationship between information
      • ML more as being able to see if something is a cat in a picture – hard to program that
    • Helped establish the Berkeley supercomputing center – big role all across the world now to complement theory by simulations
    • More data than ever before, 90% of digital data created in last 2 years – more in 2018 than all of human history
      • Finance can’t generate more data like autonomous cars, for instance (100 cars means 100 more data points)
      • Markets/economics are dynamic – return predictions of signal:noise approaches zero
        • Driven by economic features of markets – competitive, profit-seeking traders that act on it
      • HFT as real barriers to entry so they’re less efficient and more predictable, potentially
      • Quantitative traders don’t use raw data – they use transformations such as log of equity, cross-sectional rank of book to market ratio
        • Neural network tries to find what the best transformations are (X -> Y and explore all the connections)
    • Bonds example: predict if issuer will default or not with firm information using random forest
  • Rajiv Shah (@rajcs4), Data Scientist @ Data Robot, Adjunct Prof UChicago (DataSkeptic, 10/22/19)
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    • Started engineering, studied philosophy and law, PhD in Comms before doing research as academic
      • Worked at State Farm and Caterpillar before going to Data Robot
    • Deep learning applications in motion data like NBA player data, motion tracking arms and legs (PoseNET, for instance)
      • Nature paper published that used deep learning to study after-shock patterns for earthquakes
    • Going through paper – simple starting point or baseline model was skipped – how much value is really added, then?
      • Looking at the 6-layer problem – approach wasn’t unexpected when using keras to add layers
      • Results generated: AUC of 0.85 compared to naïve benchmark of simple, physical model – AUC of 0.58
      • When he reproduced it, test set results were higher than training set – yellow or red flag for model
    • Group partitioning – 130 earthquakes happening right after each other, near each other and related
      • Make sure the information for an earthquake/customer doesn’t get split between training / test sites to avoid leakage
      • Basic grounding of fundamentals for setting up initial training data, partition based on time to avoid that, as well
    • As community, ensure that there are best practices and guidelines – reproducibility as a large problem lately
      • How to police boundaries for the general field – influence of institutions in publishing (for this, Harvard/Google/Nature mag researchers)
      • Good from them: the data and model for the code was freely available and he could do it on his laptop / notebooks
      • Academics from the earthquake field reached out to him with some qualms and he’s partnered with them for a blog on efforts
    • Interpretability focus trade-off with accuracy – that he’ll speak on at Open DS Conf
      • Lots of tools for explaining models with transparency now, though
  • Julia Landauer (@julialandauer), NASCAR driver (Stanford Pathfinders, Wharton XM)
    • Being on Survivor (suggested by a friend while Soph in college), racecar driver
      • Picking Stanford because of so many people that were awesome / ambitious
      • Mentioning Andrew Luck saying that this was why he chose it – people wouldn’t particularly care
    • Driving at such a young age and in Manhattan – not getting a license there until 18 on campus
    • Having to pitch and learn how to pitch at a young age for sponsorships, running a team and the cost, even at minors – $500k+
    • Some 12 female drivers and being competitive

Your Experience is Your Own, Only (Notes from Aug 19 to Aug 25, 2019) September 10, 2019

Posted by Anthony in Automation, Blockchain, Digital, experience, finance, Founders, global, gym, Hiring, Leadership, marketing, NLP, social, Strategy, training, Uncategorized, WomenInWork.
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I’ve been considering more and more about how my experiences are only mine. Especially when I feel like I don’t share them often. Working so much but not always discussing it with people outside of work (re: almost never). I was reminded of this while I met with a family member who I see roughly once a month or so. When she asks how work is or I mention I’m busy on days when she wants to meet, it often came with a “busy with a meeting at X but can do Y”. Never more. And almost always, I ask how her work is, and she divulges. So when we sat down for dinner and she point blank asked “I have 2 things: 1. Can you help me with something on my new phone? and 2. What is it actually that you do?” I chuckled because generally I don’t care to share that information – I really enjoy valuing start-ups and learning about the space / tech / finance / education changes, but other than high level stuff, rarely does anyone want to hear me talk extensively a la a podcast episode deep-dive or something. They don’t see the relevance, other than it being exciting for me. Same with when I was advising, same since launching the fund and all while working on project deployment in data science for others.

I strongly suggest reading through Colson Whitehead’s essay here about his version of New York City. How it’s interpreted. essay here

Another thing I read through today was Farnam Street’s blog post on asking seemingly simple questions that may be defined or determined by our experiences with those concepts. An example he uses: “What is a horse?” Try to think how we may answer this.
Power questions

 

  • AI in the Past, Present and Future (BDB 7/16/19)
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    • Rod Bodkin, Tech Director at CTOs office in Google
      • Was with BigDataAnalytics, bought by Teradata and grew it from there
    • Grew Google after seeing the field advancing quickly, state of the art as evolving
    • First people to put Hadoop into production – Yahoo was too scared, single algorithm took weeks at the time
    • OpenAI put out state of art compute paper – 4 year paper, 300k X computation (double every 3.5 months)
    • For Google, evolution of cloud in the enterprise is a big deal – consumer side of Google as leading the way
      • Can just put data into BigQuery because of capacity and accessibility of data – increased production 4x on data science team
    • Big investments into Anthos – open source tech to enable cloud-native services in different clouds, GKE (Kubernetes)
      • Edge TPUs as 100x faster to compute a model vs traditional mobile CPU – TPU as accelerator chip for DL
      • CPU is completely general so less efficient
      • GPU has a boost over CPU but behind TPU accelerators (starting GPU chips, Tensor unit)
    • Kaggle Days and Google IO for cloud Pixel modeling and AutoML performing very well
    • Herrari’s book – 21 Problems for 21st Century
  • Tricia Han, CEO of Daily Burn (Wharton XM)
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    • Community of like-minded fitness fanatics
    • Live 365 – 30min shows on working out, regulars
    • In survey, millenials said fitness #1 and health/wellness at #5
    • Fitness had about happiness equal to making $25k more

 

 

 

  • State and Future of Robotics, ML and Digital Celebs (Venture Stories, 8/8/19)
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    • Michael Dempsey (@mhdempsey) – partner at Compound
    • Read, Listen, Write, Talk – Cunningham’s Law – share something with a strong opinion is likely to get responses
      • More value when shared publicly
    • Robotics, ML as cascading forward – robots broadly, initially – types, how to make them intelligent (2013)
      • Drones, hardware platform (DJI as leader), space and now as unsupervised or self-supervised learning
      • Deep dive on innovation for what he’s spent the last year or two – investments, as well
    • Women’s health as growing market for fertility and experience layer in healthcare system
      • Higher-end service around egg freezing (but was shattered by Tia founders), IVF or embryo screening
      • 2 investments for him already in the space, maybe more after
    • Strategic robot acquisition for Amazon, why now? Major companies in the space – he’s punted in that space, more investors.
      • Didn’t see meaningful differentiation in the space – didn’t see a company that had that from an investing side
      • Food was where he saw robotics as consistent – grew up in the industry
      • Really easy to get pilots but not for revenue – wants full-stack robotics company
      • Robots taking over entire industry – automated X / Y / Z (rebar, construction robotics)
      • Front of house and back of house retail (analytics, stocking)
    • Weird robot applications (in-home, manicures, old person help)
    • If company is built on algorithm being best, company probably won’t survive
      • Must talk to people doing operating – not just reading
      • Self-driving cars – spent time with Daniel Gruber, discussing local maximum and rules to write
        • If you can drive in NY, you can drive in SF, LA, etc…. 2007 DARPA challenge Waymo / Tesla / Cruise as result – path-planning
        • Intelligence approach – what are incentives / agents to accomplish in a car for end-to-end approach to scale
      • 1 model to move them all – enough compute that model can solve it (DL is direct function of this, for Google)
    • Investment in data labeling space – more people moving into production requires more people getting good data and filtering data
      • Larger data builds where it may cause $50-200mln per year to label but 50% is useless
      • Environmental impact and thinking about it – consolidating data but into better (CartaAI and SkillAI)
    • DeepGram end-to-end audio inscription – 80-85% can be good, but if you mess up some key words in certain industries, it’s more expensive
      • Voice side, horizontal players are pretty good – if x% of users will have same questions, simple workflow or algorithms
    • GANs and new generation of faces – Disney and animation nerd for a while – power of IP on agencies, CAA for example and Marvel
      • Stories through animated content, Robot Chicken, others – Robert Dillon – bringing in GANs
      • Watching live action is watching someone else’s story whereas an animated one brings you into the story
    • Trusting the people that have been given permissions – Reddit or being anonymous
  • John Roese, Global CTO of Dell EMC (Mastering Innovation, Wharton XM)
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    • Talking about the 20 year vision to be autonomous but incremental parts until then
      • Driving assist, improved AI in driving, maybe geofenced before autonomous
      • Autonomous vehicles as source of innovation – sensors / LiDar very useful for other industries but too expensive
        • Had talked to studios about virtual studios or conferences – expense should come down with auto
      • Vast problems with uncontrolled or unconstrained problems – already have fully autonomous warehouses or geofenced areas
    • Interested in bio feedback as input to AI or MI systems
      • Used example of video conferences with sensing stress levels – clearer audio, accent correction, more people = more stress
      • Cars already using bio feedback
      • People already wearing sensors via devices – can use that as more input
    • Attacking low hanging fruit because of data ethics or biased data inputs – easier to solve problems that are valuable in neatly constrained
  • Amri Kibbler, Katya Libin, Hey Mama co-founders (Wharton XM)
    • Collaborate and share and support their work for mothers as executives
  • 13 Minutes to the Moon
    • Ep. 06 – “Saving 1968”
      • Apollo II’s first landing – without Apollo VIII, Pathfinder and 250k mi to the moon, maybe gutsiest flight until then
      • Flying VIII before end of year – “We were not ready”
      • 2 deaths of MLK and Kennedy – April had hundreds of cities taking part in riots, thousands arrested
        • 1968 Apollo program was in shock and Saturn V rocket was malfunctioning – troubled test flights
        • Almost busted in all 3 phases the last time it had flown, and the lunar module had slowed down, as well
      • Taking lunar module away from Apollo VIII – former test pilot Jim Lovell said it as Lewis & Clark expedition
        • So many firsts, risks that were enormous on a 100x scale – reason Jim was there in the first place
        • Crews normally had 6 months but VIII only had 4 – mathematicians were responsible for all of the angles and engine durations
      • 1 chance in 3 for mission successful, 1 in 3 for non-crash but unsuccessful and 1 in 3 for not coming back – wife accepted this
      • Media as delivering “death pills” for dying painlessly – respondents would say oxygen would run out and it’d be fairly painless
      • Trans-Lunar Injection – don’t shoot at the duck, shoot out front – wanted to go to 60 mi ahead of where the moon would be
        • Spacecraft needed to get to the right moment, speed, angle and altitude for the moon
        • Human computer – Katherine Johnson – was responsible for the trajectory for launch time (Hidden Figures)
        • Took 3 days from launch to get to target – Lunar Orbit Insertion
      • Astronauts were late on radio contact from dark side of moon
        • Came back to light and could hide behind his thumb – 5 billion people and everything he ever knew
        • Finishing Apollo VIII with scripture and then Good Night, Good Luck and Merry Christmas
  • Bill Clerico, co-founder and CEO of WePay (DealMakers 8/13/19)
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    • Leading provider of integrated payments for software platforms, raised $75mil from SV Angel, Highland Capital, Ignition Partners, August Cap
      • Founders of YouTube and PayPal also in
    • Grew up in NJ, spent time in NY and father worked in Air Force and construction – taught himself computers in 80s
      • Received a scholarship to go to BC, met his co-founder for WePay waiting for the flight for the interview 6 years prior
      • Went to do IB at Jeffray’s – advising tech and software companies with clients, passionate and building for a year to quit
    • Installed a suit rack in his car because he wasn’t going home – long hours, brutal fundraising
    • Group payments that they saw repeatedly at the age of 22 – big market for payments, testing it out
      • Wouldn’t have less responsibilities than at that time – Rich deferred law school and Bill had worked on it full time
      • Tried to pitch Boston investors and failed – less receptive to early stage investing, applied to YC instead
        • Came out to the valley for an interview
    • Spent 1.5 year to invest and took money and sold furniture and drove to the west, taking turns
      • Product was conceptual, pitch deck was opinion and it was hard to prove a market need to investors – conceptual idea
      • In YC, built product by talking to fraternity treasurers at SJSU, ski club coordinators – got them using the product
        • Went to talk to investors by showing them the traction
      • Why would a treasurer to accept payments with different product? Host bbq and invite them over. Go to dorm room and watch product usage.
        • Responsive to requests – take feedback and be better than existing solutions. Gain knowledge in start by doing things not scaling.
    • Group payments were a big problem and needed a solution – weren’t willing to pay, or pay transaction fees
      • Venmo had raised money and had a bunch of momentum by giving away services for free
      • Competitors were taking advantage, 2 years after YC – pivoted but weren’t growing as fast
        • Built an events tool, donation, invoicing tool and an API for customer use – other companies were just doing those
      • Realized they could build an API making payments experience easy and simple and let competitors do whatever
        • Saw huge traction/benefit where they could be brought in via the API (since they had raised $30mln)
        • Needed the business to be grown but expectations were higher
    • 600 lb block of ice for marketing $500 in front of PayPal Dev Conf at Moscone Center – still highest market day
      • Since PayPal had a knack for freezing people’s accounts randomly
    • Pivoted to shut off 70% revenue stream from consumer product, gaining growth on API from other customers
      • GoFundMe used them as a payments processor from when they were 2 person company
    • Prior to acquisition by JPMC – 200 employees at that time, now fintech / bank
      • Asset purchase agreement day – tired – was negotiating final points of deal in person, had some drinks to celebrate
      • Bought a cabin in Mendocino County – deal was valued at $400mln
    • Part-time partner at YC now – helping companies in general – relevant to the next entrepreneurs and the scale
    • Angel investing on the side – much longer and harder and scarier than he ever would’ve imagined
      • Reinforces this to his younger self – startup doesn’t fail unless you give up
  • Evolving Narratives in the Crypto Space with Andreas M. Antonopoulos (FYI 3/12/19)
    • With Arjun Balaji, as well — and similar for me as host, his intro to Crypto space video YT
    • Conflict of Crypto Visions article by Arjun and host
      • Identified closely with unconstrained vision and doing talks on not playing zero-sum mentality
      • Ethereum as different than Bitcoin – evolving directed by design choices
    • Engineering consists of design tradeoffs – choices of optimizing and de-optimizing parts of systems
    • If you want to make something that is Bitcoin-ish, you run into problems for all the strengths that are already inherent to Bitcoin network
      • Differentiate enough to be a new thing from Bitcoin – can’t mingle or occupy that niche
      • Is privacy a big enough differentiator to separate from Bitcoin network?
        • Strong privacy in base layer – can end up with inflation bugs that can damage sound money policy of Bitcoin for the privacy
      • Sound money vs private money – not clear yet.
    • Hard money displaces other forms of money in long term but only if they’re maximalists and logical
    • Friction levels determining switching back and forth on a wallet between utility or store of value tokens / coins in the future
      • Automated backend where they are optimized
    • Interest in Ethereum – tradeoff worth making for smart contracts and applications that aren’t just money outside of Bitcoin
      • How the technology of VM blockchains work
      • Scaling is harder in Ethereum – proof of stake has different security model than proof of work
      • Sharding, beacon chain, polka dot – not sure if it will work or what the security constraints are – could have applicability to BTC
    • Bitcoin critics – make the case for it but then explain value proposition or store of value
      • He has an opinion, others have opinions – none will determine how the market develops
      • Arguing is a waste of time. If you understand the tool that’s best for a job, you’re a better user of tools.
        • Which is the correct tool and how to use it properly – perception is limiting in general
  • Sam Yagan, CEO of ShopRunner (Wharton XM)
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    • Founding dating OkCupid and then going to Match and scaling to IPO
      • Going from running a team of 30 to 1000 in a month
    • Ecommerce ShopRunner as retailers combatting Amazon and Walmart – providing scale and guarantees with 2-day shipping for many retailers
      • Joining after Michael Rubin had founded it on premise of “Amazon for all others”
    • Making sure they have AMEX partnership to make it easy for customers
  • Travis Katz, VP of Product at Skyscanner (Wharton XM)
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    • Had been cofounder of Trip.com and at Myspace prior
    • Social media giants Facebook and Myspace – selling to NewsCorp and getting revenue compared to funded Facebook acquiring users

Week 4 Results – DK Fail & FanDuel Success October 7, 2015

Posted by Anthony in Daily fantasy football, DFS, Draftkings, experience, FanDuel, gym, NFL, Stacks, week 4.
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Yup, that’s right! FanDuel – there wasn’t a contest that I didn’t place in. Mainly due to the fact that my lineups did very well in all of the 50/50’s, DoubleUps and free-rolls. However, on draftkings, where I played a majority of tournaments, I was very close to min. cash or not in the money at all.

For week 3, I was gone/without service all weekend, so the few lineups I had in, I minimally cashed/lost. 2 weeks in a row on DK that I have lost a few $ here and there (not to mention the fun runs I took at LoL Championships as well as Wed-Fri spread of MLB games – not wise when teams were already clinched or didn’t care).

  • Fanduel results:
    5 entries played – 4 wins – 2 ticket (survivor advance, barely)
    I don’t remember where I read it, but there was an analysis done on larger 50/50’s where, assuming you’re on average better than a majority of players, you have a greater success rate in larger pools. So far, that’s proved to be correct.

    • In $350k Double-Up (with nearly 75k entries), I placed 5842 with a score of 123.74 ($5 entry for $10 winnings).
      Dalton/Green stack with LMurray, Karlos, Demaryius, Amari, MBennett, MBryant/Falcons D stack (high score)
    • FPFC Qualifier Double-Up (152 of 521) with score of 111.94 ($10 entry for $20 winnings)
      Carr/Crabtree stack w/ Karlos, JCharles, JuJones (ouch), JaJones, Barnidge (savior), Hauschka/Seattle D stack
    • Excl Football Guys Contest didn’t go that well (777 of 3119) with 110.84 ($2 entry $2 win)
      same as FPFC except swapped JamesJones/Barnidge for Amari & Martellus
    • FBGFC Qualifier Double-Up tournament (32 of 1097) with 130.06 ($10 entry $20 win & ticket later in season)
      Cam, Forte, Karlos, Julio, Amari, Moncrief, Martellus, Josh Lambo (nice 14), Falcons D (yup)
    • $250k Sun NFL Survivor Tourney (42406 of 57471) with 88.54 ($5 entry, ticket won, advanced)
      Dalton/Green stack with Randle/LMurray(ouch comb 13), demaryius, stevieJ (ugh), ebron (injured), mcmanus/Denver D stack (decent scores)
    • FantasyPros $2000 contest for saved lineups – placed 65 of 951 for $20 credit on FD (devonta and andy dalton)
  • So I found that I like stacking kickers with the defenses so far. Relatively positive correlation and it has worked with a select few kickers/defense – good field position relations, typically.
  • Draftkings results:
    • As I mentioned, this didn’t go as well. Likely because I played in too many tournaments.
    • In the double-ups I played, I cashed in all 3. So for those keeping count, I was 6 for 6.
    • Same lineup for 3 double-ups score of 136.74 (62 of 217), (99 of 340), (423 of 1135) for 3x $1 entries, $2 each of winnings
      Carr/Crabtree stack, Gore, Karlos, Cobb, Deandre (2nd half!!), JuJones, Bennett (yup), Broncos D
    • I played all-day Sunday teams and had a ~112 score, while I played early Sunday match lineups and had those go for 147 and 144 for winners.
    • 2x $.25 Arcade & $2k First Down ($1 entry, $2 win) earlies for $.50 winnings on $.25 entries (144.66 score)
      Tyrod/Karlos/Clay/Bills D stack w/ Forte, AJ Green, T.Y., Deandre (savior), AllenRob
    • Had a ThursSun Line-up that did terribly (103.96) for $3 entry and 0 winnings
      Flacco/SmithSr/MaxxWill/Ravens stack, Leveon, Ivory (rb successes), ABrown, Rishard, Jarvis (wr duds)
    • Played $0.25 Quarter Arcade (Sun only) for $1 entry, $2 winnings (147.56 score)
      Rodgers, Devonta (great), Hyde, Keenan, Amari, JuJones, Fleener (good), Karlos, Raiders D
    • Daily Dollar ($50k) $1 entry for $2 win (136.74 score for 9220 of 52596)
      Carr/Crabtree stack, Gore, Karlos, Cobb, Deandre, JuJones, Bennett, Broncos D
    • 130.16 score didn’t work for $1 $150k First Down or $0.25 Arcade
      Rodgers/Packers, Devonta, Gore, Evans, JuJones, Moncrief, Olsen, Karlos
    • Another Flacco / Ravens stack for Thurs-Sun produced the 112, which didn’t go well in any of the 3 entries (-$5).
  • So we’ll see. Seems consistent with stacks where I’ll go 2 for 4 and hope that the stacks hit bigger. This week, they didn’t do as much because of the chalk not performing as well. The few good calls at TE and Devonta saved those stacks to at least cash.

We’ll see how the next week goes – I may end up trying more double-ups to at least build bankroll.

To next week!

Thoughts of the Day – GenY finance, Daily fantasy ‘expert’-testing, other questions September 17, 2015

Posted by Anthony in Altucher, DFS, experience, finance, gym, PGA, questions, Scutify, social, training.
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Every day I receive a post from James Altucher @jaltucher – he’s an author of one of USA Today’s “12 Best Business Books of All-Time” Choose Yourself, which describes at length the power of one’s self, as well as a successful (and that does not mean he hasn’t failed) entrepreneur, hedge fund manager, asset manager, columnist, as well as podcast producer. His valuable insights, podcasts and publications enlighten us to choose yourself and your passions to create revenue streams aplenty. He simply asks a lot of questions of many people to see what has driven them, and in turn, learn for himself.

In my recent conversations, I have noticed that this is a skill that is falling out of favor very easily of many people – and more so, whether they’re just more of who I come into contact with, but Gen Y and Millennials. So, in light of my observations, I would like to go over what I observed/questioned today.

(more…)

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