add post; update toc; update breadcrumbs

Author: tmfarrell

_includes/breadcrumb.html

@@ -1,5 +1,5 @@
-<nav class="breadcrumb" style="font-size: 16px; color: #999; font-weight: 300; padding-left: 40px; margin-bottom: 30px">
-  <a href="{{ site.baseurl }}/" style="color: #c37b8e; text-decoration: none; transition: color 0.2s;">Home</a>
-  <span style="margin: 0 6px; color: #ddd;"> › </span>
-  <span style="color: #9c9c9c;">{{ page.title }}</span>
+<nav class="breadcrumb">
+  <a href="{{ site.baseurl }}/" class="breadcrumb-link">Home</a>
+  <span class="breadcrumb-separator"> › </span>
+  <span class="breadcrumb-current">{{ page.title }}</span>
 </nav>

_posts/2016-04-27-failure_and_success.md

@@ -6,8 +6,7 @@ date:   2016-04-27 20:00
 categories: writing
 ---
 
-Came across [this](http://www.nature.com/naturejobs/2010/101118/pdf/nj7322-467a.pdf) today, a discussion
-of the role of failure as a scientist.
+Recently came across a discussion of the important role of failure as a scientist in Nature (link [here](http://www.nature.com/naturejobs/2010/101118/pdf/nj7322-467a.pdf) seems broken though).
 
 This isn't the only example of failure being labelled requisite for success. For instance, MJ in his famous Nike commercial:
 
@@ -17,7 +16,7 @@ And later:
 
 >I can accept failure, everyone fails at something. But I can't accept not trying.
 
-Then there's Thomas J. Watson (IBM):
+Then there's Thomas J. Watson (CEO of IBM in early 20th century):
 
 >Would you like me to give you a formula for success? It's quite simple, really: double your rate of failure.
 

_posts/2018-01-14-generalizing_biology.md

@@ -6,7 +6,7 @@ date:   2018-01-14 20:00
 categories: writing
 ---
 
-### Specificity and Biology
+## Specificity and Biology
 
 Generality has never been medicine or biology's forte. 
 
@@ -20,7 +20,7 @@ Similarly, it seems physicians have always been more concerned with the specific
 specific techniques for specific cases. Studying "interesting" patients, diseases or disorders.
 Finding rare genetic mutations. 
 
-### Generality and Physics/ Math
+## Generality and Physics/ Math
 
 On the other hand, the harder, more formal sciences---math, physics, computer science, statistics---seem 
 to do the opposite. The more general your contribution, the more widely respected. The more general, 
@@ -33,7 +33,7 @@ generalizability is actually proportional to utility. By definition, the more ge
 greater the number of specific facts it applies to. And it's reasonable that the more 
 specific facts a statement applies to, the more useful.<sup id="a1">[1](#f1)</sup>
 
-### Biology vs Physics/ Math
+## Biology vs Physics/ Math
 
 But why this difference between the "hard" sciences and biology? 
 
@@ -44,7 +44,7 @@ though.
 
 The most obvious example is the discovery of the central dogma: DNA \\(\Rightarrow\\) RNA \\(\Rightarrow\\) Protein. The exact reason this was exciting was because it applied (i.e. generalized) to **all organisms on the tree of life**. The central dogma is the very basis for biological life. 
 
-### Towards a Generalized Biology/ Medicine
+## Towards a Generalized Biology/ Medicine
 
 So it is in fact possible to achieve generality in biology. 
 

_posts/2020-06-01-on_newness.md

@@ -6,7 +6,7 @@ date:   2020-06-01 22:25
 categories: writing
 ---
 
-### No News is Good News
+## No News is Good News
 
 Ever hear this quote? 
 
@@ -20,7 +20,7 @@ This is why you may want to read only older books that are "good" because they'v
 
 This is also the reason to **not** follow the news. Much of daily news will be found to be inconsequential, and some of it flat-out wrong. The most critical, important pieces of information will survive and find you through channels you trust.
 
-### New Ideas and Technologies
+## New Ideas and Technologies
 
 To be fair, good and bad are also blanket terms that aren't very helpful when describing something new, since new things are usually unique and harder to categorize. Some aspects may be good, some may be bad, but all of that is not yet understood.
 
@@ -30,7 +30,7 @@ The ultimate test of something new is the market. Whether something new survives
 
 Take, for example, the venture capital (VCs) market. 90%+ of startups evenutally fail, so it's extremely hard to pick ones early on that do ultimately win. That's basically proof that new ideas are unlikely to be good ones. 
 
-### Technology and Progress
+## Technology and Progress
 
 Consider that technology hasn’t necessarily made us happier, even though it's made us vastly more productive. For example, farming allowed us to make much more food per capita than ever before; but it's also made us more sedentary, which has had a negative impact on our health.
 
@@ -42,7 +42,7 @@ The bottomline is that Slack is **new**. In some ways it's better and then later
 
 There are countless other examples where later on we've realized unexpected ways a technology has made us worse: cars and climate change, social media and misinformation, automation and underemployment, etc. (Jury's still out on that last one though.)
 
-### Newness and Risk
+## Newness and Risk
 
 These facts about technology should inform our view of things that are new in general. New things always have the risk of side effects, which can often be unforeseen. 
 

_posts/2021-11-29-agile_product_mgmt.md

@@ -6,7 +6,7 @@ date:   2021-11-29 08:00
 categories: writing
 ---
 
-### Product Objectives
+## Objectives
 
 This should be done at the beginning of a product management/ development lifecycle:
 
@@ -15,17 +15,17 @@ This should be done at the beginning of a product management/ development lifecy
     - These key results should also be designed such that they complement each other. For example, if there is one key result tracking a quantity (e.g. increasing # of active users) there should be another key result that tracks quality (e.g. decreasing churn rate).
 - These objectives and key results (OKRs for short) should be refined and revisited each quarter. The most important objectives will not change very frequently while the key results should be updated quarterly.
 
-This is a great reference for how to write good OKRs [https://www.whatmatters.com/faqs/okr-examples-and-how-to-write-them/](https://www.whatmatters.com/faqs/okr-examples-and-how-to-write-them/).
+This is [a great reference](https://www.whatmatters.com/faqs/okr-examples-and-how-to-write-them/) for how to write good OKRs.
 
-### Product Team
+## Team
 
 - Composition:
     - Product Manager
-    - Design Team: 0-1 Designer (depending on if product has a user interface)
+    - Design Team: 0-1 Designer (depending on if product has UX/ UI)
     - Engineering Team: 2-8 Engineers
 - Duration: Ideally a sustained, durable team dedicated to developing a single product throughout its lifecycle.
 
-### Product Development Process
+## Development Process
 
 - This as a four-step cyclic, iterative process that the whole team participates in. The third step in the cycle is commonly referred to in Scrum methodology as a "sprint"[[1]](https://www.amazon.com/Scrum-Doing-Twice-Work-Half-ebook/dp/B00JI54HCU/), while the first two steps are design-focused and commonly referred to as a "design sprint"[[2]](https://www.amazon.com/Sprint-Solve-Problems-Test-Ideas/dp/1442397683).
 - The first two steps in the process can be done at the same time, on the same rhythm as the third step, however there should be an offset where the design team/ sprint is developing designs for future/ upstream features or user stories and the engineering team/ sprint is focused on implementing those features that have already been designed in prior design sprints. The last step of the process, "validation", should ideally be done continuously and is mostly the responsibility of the PM.[[3]](https://www.amazon.com/Lean-Startup-Entrepreneurs-Continuous-Innovation-ebook/dp/B004J4XGN6/)
@@ -34,13 +34,13 @@ This is a great reference for how to write good OKRs [https://www.whatmatters.co
     - Interact with users/ customers to understand their biggest pain points or most desired features.
     - Decide what product features to prioritize that address those needs/ wants, taking into account the business objectives defined above and what value propositions can be offered.
     - This phase should end with a prioritized list of user stories that can be used to design/ build and test prototypes for usability, business viability, feasibility, etc.
-    - _Participants_: PM, designer and at least one engineer
+    - _Participants_: PM, designer and at least one engineer (optional)
 
 
 2. **Design** (Prototype and Test)
     - Take the prioritized list of user stories and produces prototypes to be validated with customers.
     - Based on those results, the most promising user stories should be added and prioritized on the product backlog.
-    - _Participants_: PM, designer and at least one engineer
+    - _Participants_: PM, designer and at least one engineer (optional)
 
 
 3. **Development** (Build and Launch/ Deploy)
@@ -53,9 +53,9 @@ This is a great reference for how to write good OKRs [https://www.whatmatters.co
 4. **Validation** (Test)
     - This phase requires the PM to validate that what was built and released to users had a positive impact on the product objectives.
     - This phase will naturally lead back into the discovery phase if a feature didn't have the desired outcome, or if it did and there are now new objectives the team needs to prioritize and focus on accomplishing.
-    - _Participants_: PM
+    - _Participants_: PM, engineering team (optional)
 
-### Product Backlog
+## Backlog
 
 *The product backlog is important enough that it warrants its own section*
 - The product backlog is a list, ideally prioritized, of user stories (essentially features) grouped into related sets called epics (essentially high-level user stories that describe a larger component with related features) that define the vision for the end product.

_posts/2021-12-14-ml_product_strategy.md

@@ -6,7 +6,7 @@ date:   2021-12-14 20:00
 categories: writing
 ---
 
-### The AI Cold Start Problem
+## The AI Cold Start Problem
 
 Here's the scenario. You want to be fancy and build a product that leverages some of the latest and greatest in AI to satisy and delight your (future) users.
 
@@ -22,7 +22,7 @@ You realize the Wikipedia article could have articulated the problem in a more g
 
 By now you realize you have to build value and start solving your users' problems up front, so you can entice them to your app in return for their data. You realize you'll need to build something like a "marketplace", a "[platform](https://sloanreview.mit.edu/article/the-future-of-platforms/)" or even a regular ol' app.
 
-### Apps Before AI
+## Apps Before AI
 
 Bottom-line: many might think that AI can be leveraged as a core, and potentially killer, feature for a product that satisfies and delights its users. In others words, `AI -> App`.
 

_posts/2022-04-26-product_analytics_from_scratch.md

@@ -7,13 +7,13 @@ categories: writing
 toc: true 
 ---
 
-### Becoming Data-driven
+## Becoming Data-driven
 
 Being a data-driven product team is critical to being competitive in the modern digital product marketplace.[[1]](https://towardsdatascience.com/why-organizations-need-to-be-data-driven-98ade3ca53a)[[2]](https://www.pwc.com/us/en/services/consulting/analytics.html) However, many teams tend to overweight the importance of technology adoption, relative to culture and process change, in efforts to become more data-driven.[[3]](https://hbr.org/2022/02/why-becoming-a-data-driven-organization-is-so-hard)
 
 Thus, product teams should begin their data-driven transformation by firstly buying into and applying current best practices in digital product analytics. And, then only secondarily, adopting modern data tools that facilitate and accelerate that transformation.
 
-### Optimizing the User Journey
+## Optimizing the User Journey
 
 The vision for any product is to delight users and make money in the process. There are many ways to measure how well teams and their products are achieving this vision, however, more than likely these measures will be *lagging indicators*, which are downstream measures of success that are difficult to control and not helpful guides for product development.
 
@@ -24,7 +24,7 @@ A technique commonly used to develop leading indicators in consumer applications
 <img src="/assets/img/user_journey_1.png"
      style="display: block; margin-left: auto; margin-right: auto; width: 50%;" />
 
-### Pirates?!
+## Pirates?!
 
 One popular approach to defining the stage of a user journey is the “AARRR!” framework (also known as the Pirate metrics framework), where each letter in the acronym represents a step in the customer journey: acquisition, activation, retention, referral and revenue.[[4]](https://500hats.typepad.com/500blogs/2007/06/internet-market.html)
 
@@ -35,13 +35,13 @@ Using this framework, we now have a clearer, more mechanistic picture of how we
 
 From here, the would want to develop and operationalize specific measures at each of these stages, which can then be used to gauge how product changes impact performance at each stage. For this, we lean on another 5-step framework.
 
-### Let’s Talk Numbers
+## Let’s Talk Numbers
 
 One well-known 5-step process for developing successful metrics is as follows: define, measure, analyze, improve and control. Called DMAIC for short, this is a Six Sigma process improvement method and was adopted by Amazon to develop metrics across their various business units.[[5]](https://www.amazon.com/Working-Backwards-Insights-Stories-Secrets/dp/1250267595)
 
 Importantly, this is an end-to-end process not only for defining and implementing metrics up front but also for continuously refining the definitions and implementations until they successfully aid product improvement, which in this case means they successfully correlate product improvements with leading indicators and leading with lagging indicators.
 
-#### Define
+**Define**
 
 The purpose of this step is to define how metrics quantify customer behavior at each user journey stage. This step is basically the mock-up design stage of analytics development.
 
@@ -55,7 +55,7 @@ The purpose of this step is to define how metrics quantify customer behavior at
 | Referral | Recommender | Refer 1+ user who visits the site | 1%
 | Revenue | Paying customer | Monthly or yearly paid subscriber | <1%
 
-#### Measure
+**Measure**
 
 This step is performed by the data engineering/ product team, where data engineers develop software that accurately and reliably implements these measurements. A recent trend in this space is to leverage technologies now commonly referred to as the “modern data stack”, rather than developing tools in-house.
 
@@ -68,7 +68,7 @@ For implementing measurements, MDS tools that could be used are: Snowplow[[7]](h
 
 The nice part about using tools like Snowplow is they have built-in functionality for dealing with data quality issues, allowing users to define data schema and validation checks. It automatically saves records that fail these checks so data analysts can go back, analyze and diagnose why these failures might be happening.
 
-#### Analyze
+**Analyze**
 
 This step is all about deeply understanding all factors that influence a metric implementation. To do this, typically a team will implement a dashboard and data visualization layer so they can observe metrics over time and begin to ask questions about them. The technologies commonly used at this layer are Looker[[10]](https://www.looker.com/) or Mode[[11]](https://mode.com/). An example implementation would look something like this:
 
@@ -79,7 +79,7 @@ A common occurrence at this stage is to encounter bugs or issues with the data,
 
 Another aspect to this step is to understand how metrics differ over time between different cohorts of users–for example, age and location demographics–which can be very useful for increasing user retention.[[13]](https://heap.io/topics/how-cohort-analysis-improves-retention-reduces-churn)
 
-#### Improve
+**Improve**
 
 In contrast to the prior step, the purpose of this step is to understand relationships between metrics, rather than examining them in isolation. Specifically, the goal is to understand how leading (or upstream) metrics impact lagging (or downstream) metrics. For example, the data product team might implement a dashboard visualizing both weekly and daily visitors to get a sense whether there is a correlation between the two metrics.
 
@@ -93,13 +93,13 @@ When a company and product team get to a certain level of maturity, another impo
 <img src="/assets/img/improve_2.png"
      style="display: block; margin-left: auto; margin-right: auto; width: 65%;" />
 
-#### Control
+**Control**
 
 In the final step of the DMAIC process, the goal is to demonstrate that the team can control and manipulate specific lagging indicators by changing or experimenting with certain leading metrics. In short, to operationalize product analytics. One interesting part of this step, now possible with MDS tooling, could be to implement so-called “reverse ETL” to export metrics and other data back into upstream product marketing and engagement tools (e.g. MailChimp) for the purposes of increasing user engagement, retention and improving marketing.[[15]](https://hightouch.io/blog/reverse-etl/)
 
 Part of this step can also involve adding additional automation to the data system such that data quality and reliability indicators are tracked clearly in dashboards and operationalized into notifications, where applicable.
 
-### Conclusion
+## Conclusion
 
 To sum up, the highest impact analytics practice product teams can adopt is to map/ define, measure and analyze its users’ journeys. With that, the highest value data product is one that measures and analyzes changes in user journey metrics as they relate to each other, which can ultimately be used to increase retention and revenue. In terms of prioritization, user retention seem most likely to have the highest direct impact on revenue and so should be prioritized for experimentation and control.
 

_posts/2022-08-01-projects_v_products.md

@@ -112,7 +112,7 @@ If the primary concern is _value_, think about cyclic iteration working towards
 <br />
 <br />
 
-## **Side Note:** Product Development and Evolution
+**_Side Note:_ Product Development and Evolution**
 
 Another important aspect about products: there are a lot of similarities between the process of product development and evolution. The space and dynamics of valuable products and businesses–what we call _markets_–closely resemble the competition and dynamics of biological ecosystems.