Blockchain – promises and pitfalls

Hi there,

With the recent hulabaloo re: Bitcoin, Ethereum etc, I thought I’d spend some time getting myself familiar with these concepts and crypto-currencies.

As some-one who has owned a bit of gold for a long time (seen ups and downs) – due to my inherent distrust in governments being able to control their spending – some of the features of crypto-currencies appeal to me.

The benefits :

  • Cryptocurrencies (eg Bitcoin) can be designed to have a cap / limited amount ever to be issued. This means that they should actually be scarce – you know like the resources on this planet – and through that could be good ‘stores of value’ – basically as a currency should be.
  • As a medium of exchange – with the recent Segwit/Bitcoin cash fork, Bitcoin has a chance to become a good medium of exchange – both for micropayments (e.g via the Lightning network) or currency transfers.
  • Programmable blockchains like Ethereum allow for smart contracts to be implemented on the chain. The ‘ethereum computer’ is actually decentralized network of computers that is ‘Turing complete‘ – meaning that it’s pretty much up to you how complex code you want to write on it.
  • Due to the above programmable nature especially of Ethereum – I think we will see many use cases which will be tried – some will fail, some will  succeed. If you are interested in how blockchain could be applied for example in HR BPO – please contact me here.

The pitfalls :

The main pitfall I would say is still that you have to do your own homework on who / what to trust, but I guess that applies in life generally… The other pitfalls still include a truly easy to use user interface / wallet. However I will investigate those more in detail next.

So yesterday I went to a meetup event called ‘Bitcoin and Cryptocurrency Intro How To Make Money Passively From Home’. The event was hosted in a local Panera Bread, with about 15 people attending. The pitch was for a ‘company’ called BitConnect  where the premise is:

  1. You buy their coin (bitconnect coin or BCC) using Bitcoin(BTC).
  2. The BCC is converted back to USD – and using their HFT (high-frequency trading) algorithms they trade USD vs BTC.
  3. Somehow the Bitconnect team are supposedly able to make daily profits (‘interest’) according the presenter/this chart (no down days):
  4. They state that the high market cap (around 786M USD today, August 18th 2017) is proof of the legitimacy of this platform.

OK, call me a sceptic, here’s why:

  • When I asked about the team behind this, the presenter dodged the question, and no information is available that I can find on the bitconnect.co website. No developers who want to have their names publicly associated with it? Hmm. No early investors? Hmm.
  • From the site “Build trust and reputation in bitcoin and cryptocurrency ecosystem with Open-source platform”. There is no information about what is open source, and this code on Github has one contributor..
  • As Steemit writes – it’s too good to be true, no down days, guaranteed returns, referral schemes etc..
  • You are supposed to send BTC to them – but you are ‘locked in’ for up to 299 days. Guess what will happen to your BTC if they run out of new patsies to pay the old ones..

Bottom-line – there is a ton value that can be created on blockchains – but people – pls do your homework. And if you / or some-one you know is considering bitconnect – caveat emptor…

Cheers,

Oskar

Launch festival April 2017

Hi ya,

A few months ago I got a founder ticket to the Launch Festival in San Francisco for April 7th and 8th. After three whirlwind days in the Bay Area here are the things that stuck out to me. Personally for me / Move Correctly my best meeting was actually with Greg, the CEO of Fit3d – since he provided so much actionable, real advice. It’s just fantastic to exchange ideas with some-one in the same industry. Now here are my notes about Launch:

 

Machine Learning

1. The flywheel for Machine Learning – was articulated by Rob May, the CEO of Talla, is the way ML becomes better the more data there is, makes better algorithms, makes a better product, drives more use and more data … So it’s really about who can build the best/ quickest flywheel for your use case / industry, and in that sense (at least the ML companies want to give that impression) this is the time to make major investments in this area, as the first (successful) movers will have a big advantage.

2. Zorroa – are doing really impressive visual recognition from videos – where they can search inside videos / images just as if you were googling documents. So eg locating any scenes where ‘the Rock’ appears, then narrowing it down if it happens in a bank, and further where there is a Lambo in the scene.. Currently you need to plug into their REST API,  but they mentioned a SAAS app in a few months…

3. Corto – their demo of a chatbot analytics interface to a pharmaceutical genomics data was impressive, with hypergraphs / nodes flowing and a lot of complex words in the presentation, so I have no doubt the tech is solid. Their team is chock-full of smart guys, with eg one of the leading AGI guys – Ben Goertzel as Chief Scientist. What wasn’t clear for me is who will sell their product and what is their value prop?

4. The PAC framework by Rob May again, which essentially states that any company should evaluate how they want to use Machine Learning in these categories:

A) P for Predict, eg. in recruitment which candidates will perform best, in sales which products etc.

B) A for Automate, could be easing workflow, say for example NLP (natural language processing) transcribing recruitment interview notes.

C) C for Classify – say classifying best resumes into different buckets quickly

Now apply these questions across your customers, across Product, across Operations, and you should start to identify good opportunities where to apply ML.

5. Talla is a customer service bot either for IT or HR, that’s been trained to answer IT / HR questions, with a UI either in Slack or Microsoft teams. Their target market is in mid-size companies.

6. Kylie.ai –  created a customer service bot, integrated with eg ticketing systems like Zendesk. They’d ‘clone’ employee personalities and create a response integrated into existing UI’s eg on Zendesk, Salesforce, SAP etc, which the human customer service agent can review, modify, approve / send.

Cannabis

The Cannabis market is yuuge apparently as it warranted it’s own vertical, next to healthcare, drones, ML etc. Interesting companies included:

  1. Leaf – built a small growing unit looking like fridge, which automates home growing. Sold about 1M of them in advance and are taking orders for 2018..
  2. Alula Hydro, who have created a hydroponic, nutrient delivery system for industrial growers. The 20K industrial growing management system apparently can raise yields from a crappy 1K per pound to 5K-6K per pound.
  3. Baker, who are making a CRM / loyalty / online store SAAS for dispensaries.

 

Miscellaneous notes

  1.  How to get 1000 applicants for a job ad – by Tucker Max from Book in a box
    • Start with the hook – explain the why / the mission of the company, and if some-one doesn’t believe in that clearly they’re not a fit.
    • Sell the role – Talk like you would talk to a friend about the job. Ditch all the standard corporate lingo about ‘mission critical, pro-active go-getter with a nose for global synergies’. 
    • Pleasure and Pain – explain why the company / role is awesome, but clearly also the downsides of the job, no point in trying to sugar-coat stuff.
    • Testimonials / social proof – we use these in any ads (for soda, cars, books etc), why not for job ads?
    • Finally – the actual ‘boring’ details
  2. VR / AR – was actually a bit underwhelming – nothing really that stood out. Yes, it’s cool to get a new, different type of camera or get analytics off VR, but…eh.
  3. In hardware – Megabots was really cool, just in terms of lighting up any 6-year old inside all of us – Robots with fun big weapons (WEABOONS!) and picking up cars 😉

 

Learning Python programming

This is a short post about my experience in learning programming, and Python in particular – how it’s been going, what’s worked and what I’m struggling with. I’ve spent about two hours per day, five days a week, for about 1-2 months now.

As background I’ve worked in IT (consulting, project management, ERP systems, specifically HR&Payroll) for a long time. When I was younger I never really had an inclination to learn programming – I took one course of Java in college, but didn’t like it as the instructor wasn’t very good.

Here are some lessons / nuggets that I’ve found helpful:

  1. Knowing why I’d want learn is motivating for me. I want to be able to hack simpler solutions myself, and so that I can become a better entrepreneur / consultant. Ever since my experiences with Move Correctly , I’ve found that it’s frustrating to have to wait for developers to complete work.  Waiting can be especially taxing if you choose to go with a fixed price project, which by default leaves you less leverage on the project schedule /completion date…. (separate topic..)
  2. I did the Python course on codeacademy – however I felt that many times there was not enough instruction in the course (no videos), so I would bang my head against the wall for sometime an hour / two trying to get some simple function to work.
  3. The Datacamp Intro to Python for DataScience was quite fun, they award you xp based on successful answers/code, however it wasn’t very challenging and I don’t think that I’d learn to write actual code with their approach. They do have other e.g intermediate courses, but to pay 29USD per month – compared to Udemy’s pricing -doesn’t match up.
  4. In December I started taking the “Python for DataScience and Machine Learning bootcamp” and I’m about 40% through it – mostly the data science parts. After going the through the crash-course I’ve learned about Jupyter notebooks and Python libraries such as Numpy, Pandas, Matplotlib, and Seaborn. I like statistics, and it’s cool to be able to extract meaning out of masses of data for sure. However I have a feeling that this post is correct – ‘Data preparation accounts for about 80% of the work of data scientists‘, and I’m not sure that’s for me… Well -this course has the Machine Learning portions coming up, so let’s see. Overall great value, as I picked up the course for 15 USD.
  5. I’m also taking the “Python Mega Course”  and this has been really great, I can highly recommend it, and great value at 10 USD (year end sale). The best portions so far have been:
    1. Learning to write a Windows GUI program (using Tkinter library), with a connection to a SQL database (SQL lite or PostGreSQL).
    2. Learning to write a Python Flask Web app, setting up a Git/Gitbhub profile and deploying the app to Heroku.
    3. Learning the Bokeh library for data visualization on the web, example here. It’s taking a csv file with volcano locations (latitude /longitude) and using the Folium JS library for the viewing in the browser. If you are interested the code looks like this.
  6. Overall I feel I’m now at the stage where I want to start building applications that I’d be interested to see myself – targeting from start of Feb. I’m also at a stage where I can write simple code for myself, but need frequent references to libraries/google/stackoverflow etc.
  7. I will likely also retain a “trainer / programmer” via freelancer.com to help me with the upcoming challenges. A bit like the Thinkful part-time Python bootcamp, but hopefully cheaper :-). I’m planning to try out say 10 sessions/ lessons with another Python programmer to review any questions / issues I’ll have, as well as concepts/tricks etc.

Here are some development projects I’m considering to try out:

  • Simple game using either Kivy or PyGame frameworks
  • Python Django website with user login using social IDs, Paypal/Stripe integration, user data entry etc.
  • An digital health related data visualisation, perhaps with API pulls.

Look for an update on these within one month.. For now if there’s anything you would like me to specifically work on, pls drop me a line here.