blockchain crypto future payments security startup trading venture capital

The seven deadly paradoxes of cryptoassets

On one hand cryptoassets are losing value but there are still fat margins to be made by providing trading infrastructure (eg exchanges) for people looking for a bit of fun.

The author of this article is taking a longer term view about crypto: Will people in 2030 buy goods, get mortgages or hold their pension pots in bitcoin, ethereum or ripple rather than central bank issued currencies? I doubt it.  Existing private cryptocurrencies do not seriously threaten traditional monies because they are afflicted by multiple internal contradictions. They are hard to scale, are expensive to store, cumbersome to maintain, tricky for holders to liquidate, almost worthless in theory, and boxed in by their anonymity. And if newer cryptocurrencies ever emerge to solve these problems, that’s additional downside news for the value of existing ones.

behaviour people

The Problem With Feedback

The author of the Atlantic article argues: The proliferation of ratings systems doesn’t necessarily produce a better restaurant or hotel experience. Instead, it homogenizes the offerings, as people all go to the same top-rated establishments. Those places garner ever more reviews, bouncing them even farther up the list of results. Rather than a quality check, feedback here becomes a means to bland sameness.

cloud code decisions design patterns startup strategy

Microservices dependencies are difficult to track

After building online mortgages website and backend systems (integrated with top UK banks) using microservices (Azure, NodeJS, Mongo, React) for the new project I picked a less ambitius stack while leveraging teams know how (Azure, NodeJS, MySQL, templated HTML). This article captures my thinking:

  • Fail fast – they let development teams focus on delivering features (to prove or disprove a hypothesis) rather than a complicated microservice architecture
  • It helps you to understand your requirements (UML diagrams and domain models are not perfect first time they need to evolve)
  • Microservices are complicated to develop (e.g. graceful degradation, health checks, retries) and monitor
  • Microservices dependencies are difficult to track
design hacks patterns short startup tools

Early stage startup design resources

The Black Design resources let you conduct a meaningful user observation, engage in product design, and communicate value of your business.

code hacks

SQL query execution order

Since going back from Mongo/NoSQL to MySQL, I keep rediscovering the execution order of SQL query.  

Inside Microsoft® SQL Server™ 2005 T-SQL Querying

(8)  SELECT (9) DISTINCT (11) TOP <top_specification> <select_list>
(1)  FROM <left_table>
(3)       <join_type> JOIN <right_table>
(2)       ON <join_condition>
(4)  WHERE <where_condition>
(5)  GROUP BY <group_by_list>
(7)  HAVING <having_condition>
(10) ORDER BY <order_by_list>

The first noticeable aspect of SQL that is different than other programming languages is the order in which the code is processed. In most programming languages, the code is processed in the order in which it is written. In SQL, the first clause that is processed is the FROM clause, while the SELECT clause, which appears first, is processed almost last.

Each step generates a virtual table that is used as the input to the following step. These virtual tables are not available to the caller (client application or outer query). Only the table generated by the final step is returned to the caller. If a certain clause is not specified in a query, the corresponding step is simply skipped.

Brief Description of Logical Query Processing Phases

Don’t worry too much if the description of the steps doesn’t seem to make much sense for now. These are provided as a reference. Sections that come after the scenario example will cover the steps in much more detail.

  1. FROM: A Cartesian product (cross join) is performed between the first two tables in the FROM clause, and as a result, virtual table VT1 is generated.
  2. ON: The ON filter is applied to VT1. Only rows for which the <join_condition> is TRUE are inserted to VT2.
  3. OUTER (join): If an OUTER JOIN is specified (as opposed to a CROSS JOIN or an INNER JOIN), rows from the preserved table or tables for which a match was not found are added to the rows from VT2 as outer rows, generating VT3. If more than two tables appear in the FROM clause, steps 1 through 3 are applied repeatedly between the result of the last join and the next table in the FROM clause until all tables are processed.
  4. WHERE: The WHERE filter is applied to VT3. Only rows for which the <where_condition> is TRUE are inserted to VT4.
  5. GROUP BY: The rows from VT4 are arranged in groups based on the column list specified in the GROUP BY clause. VT5 is generated.
  6. CUBE | ROLLUP: Supergroups (groups of groups) are added to the rows from VT5, generating VT6.
  7. HAVING: The HAVING filter is applied to VT6. Only groups for which the <having_condition> is TRUE are inserted to VT7.
  8. SELECT: The SELECT list is processed, generating VT8.
  9. DISTINCT: Duplicate rows are removed from VT8. VT9 is generated.
  10. ORDER BY: The rows from VT9 are sorted according to the column list specified in the ORDER BY clause. A cursor is generated (VC10).
  11. TOP: The specified number or percentage of rows is selected from the beginning of VC10. Table VT11 is generated and returned to the caller.

Therefore, (INNER JOIN) ON will filter the data (the data count of VT will be reduced here itself) before applying WHERE clause. The subsequent join conditions will be executed with filtered data which improves performance. After that only the WHERE condition will apply filter conditions.

(Applying conditional statements in ON / WHERE will not make much difference in few cases. This depends how many tables you have joined and number of rows available in each join tables)

conversational UX / AI

What is Conversational UX?

Conversation UX needs to blend people and chatbots delivering dialogue for personalised interaction.

Users want answers and great experience. Conversational UX allows us to escape rigidity of webforms and create UX around specific customer needs.

hacks leadership people process startup strategy

Lessons Learned Running Tech Consultancy

Needless to say that advice here is relevant in 2018 as it was in 2012:

  • Everyone starts on fulltime salary
  • Process is very important
  • Not a fan of remote working setup (I agree 100% unless it gives scale and capability hard to hire locally)
  • Business development ways:
    • Writing blog posts
    • Giving presentations to general tech audiences (more beginners than experts)
    • LinkedIn
    • Referrals
    • Being found on Google
AI venture capital

SFO VC valuation

I spent a week in SFO following my wife who has a Dev team here. At a meetup about ML using AWS SageMaker I heard: One GitHub star is worth 15,000 dollars to your start up valuation.

AI Slack

Platform with 6m ADU launched on Slack giving it instant access to a market as well ability to offer chat UX meaning they had lass to build to enter a market. Slack invested in Butter.


What is AI?

At a San Francisco AI meetup I heard: AI is what you sell to customers and Machine Learning is what you say you do to hire.