chore: 🐛 update links

Company Knowledge Management.md

@@ -26,7 +26,7 @@ There are some basic principles and [[values]] that will make maintaining and ev
   - Avoid duplicating knowledge. For each question there is one and only one answer.
 - Link everything together.
   - The documentation should have back links and block references to incentivize small chunks of atomic ideas.
-- When doing presentations, don't present slides, present the content of the [[Company Handbooks |company handbook]].
+- When doing presentations, don't present slides, present the content of the [[Company Handbooks|company handbook]].
 - Information should be easy to add (input) as well as easy to search and find (output) resulting in quick knowledge transfer between different employees.
 - [[Writing]] something in the wrong place is the same as not writing it.
 - Reduce the number of alternatives where information might be stored. GitLab uses [[git]], Basecamp uses Basecamp, ...

Data/Dashboards.md

@@ -12,10 +12,10 @@
   - Purpose and explanation of the data being shown.
   - Caveats and assumptions.
   - Extra Context:
-	- Why this dashboard exists.
-	- Who it's for.
-	- When it was built, and if and when it's set to expire .
-	- What features it's tracking via links to team repositories, project briefs, screenshots, or video walk-throughs.
+    - Why this dashboard exists.
+    - Who it's for.
+    - When it was built, and if and when it's set to expire .
+    - What features it's tracking via links to team repositories, project briefs, screenshots, or video walk-throughs.
   - Take-aways.
   - Metadata (owner, related OKRs, TTL, …).
 - Make them so its easy to go one layer down (X went down in Y location, or for Z new users, etc).
@@ -43,8 +43,8 @@ The value is that now discussions are happening about the data.
 - [They can serve endless needs, but in doing so, rarely do they serve _particular_ needs perfectly](https://win.hyperquery.ai/p/analysis-or-dashboard).
 - Dashboards shouldn't be single-use
   - Ask this:
-	- Can this new dashboard request be added into an existing one?
-	- What are you going to do differently by looking at the Dashboard? Focus on that [[Metrics|metric]] and add it to the main Dashboard
+    - Can this new dashboard request be added into an existing one?
+    - What are you going to do differently by looking at the Dashboard? Focus on that [[Metrics|metric]] and add it to the main Dashboard
   - Beware of the death by 1,000 filters: After a dashboard had gone live, you'll be  flooded with requests for new views, filters, fields, pages, everything
   - Dashboards are decision-making infrastructure, and infrastructure needs to be maintained. Be explicit about which Dashboards are disposable and add a TTL to them.
 - The numbers and charts on a dashboard very rarely have any direct personal meaning to the people using it. There's tons of other work to do, and unless that dashboard is directly tied to your performance or compensation, there are probably more important things to look at. People are more likely to check stock prices when they actually own (and thus benefit from) the stock.

Data/Data Culture.md

@@ -1,16 +1,16 @@
 # Data Culture
 
 - The data team needs to be focus on delivering insights and supporting decisions. The outcome of the data team are *decisions* and a *shared context across the organization* that makes coordination easier.
-  - Your goal as a data professional is to facilitate [[Making Decisions |decision making]].
+  - Your goal as a data professional is to facilitate [[Making Decisions|decision making]].
   - Learning to drive decisions quickly, a bias to action, is a critical competency for an analyst. Every skill you learn – [[communication]], [[writing]], [[experimentation]], [[Metrics|metric design]] – supports this.
   - If analysis is not actionable, it does not really matter. Analysis must drive to action. [Clear results won't spur action themselves](https://www.linkedin.com/posts/eric-weber-060397b7_data-analytics-machinelearning-activity-6675746028144205824-CQxW/). The organization needs to be ready to pivot when something isn't working.
   - [Data's impact is tough to measure — it doesn't always translate to value](https://dfrieds.com/articles/data-science-reality-vs-expectations.html).
   - The Data Team should be building and iterating the [Data Product](https://locallyoptimistic.com/post/run-your-data-team-like-a-product-team/).
 - Data is fundamentally a collaborative design process rather than a tool, an analysis, or even a product. [Data works best when the entire feedback loop from ideation to production is an iterative process](https://pedram.substack.com/p/data-can-learn-from-design).
   - [To get buy in, explain how the business could benefit from better data](https://youtu.be/Mlz1VwxZuDs) (e.g: more and better insights). Start small and show value.
   - Run *[Purpose Meetings](https://www.avo.app/blog/tracking-the-right-product-metrics)* or [Business Metrics Review](https://youtu.be/nlMn572Dabc).
-	- Purpose Meetings are 30 min meetings in which stakeholders, engineers and data align on the goal of a release and what is the best way to evaluate the impact and understand its success. Align on the goal, commit on metrics and design the data.
-	- Business Metrics Review is a 30 to 60 minutes meeting to chat and explore key metrics and teach how to think with data.
+    - Purpose Meetings are 30 min meetings in which stakeholders, engineers and data align on the goal of a release and what is the best way to evaluate the impact and understand its success. Align on the goal, commit on metrics and design the data.
+    - Business Metrics Review is a 30 to 60 minutes meeting to chat and explore key metrics and teach how to think with data.
   - Value of clear goals and expectations. Validate what you think your job is with your manager and stakeholders, repeatedly.
 - [While the output of your team is what you want to maximize, you'll need some indicators that will help guide you day-to-day](https://data-columns.hightouch.io/your-first-60-days-as-a-first-data-hire-weeks-3-4/). Decide what's important to you (test coverage, documentation missing, queries run, models created, ...), and generate some internal reports for yourself.
 - [Data teams should be a part of the business conversations from the beginning](https://cultivating-algos.stitchfix.com/). Get the data team involved early, have open discussions with them about the existing work, and how to prioritize new work against the existing backlog. Don’t accept new work without addressing the existing bottlenecks, and don’t accept new work without requirements. **Organizational [[politics]] matter way more than any data methods or technical knowledge**.
@@ -19,7 +19,7 @@
   - The modern data team needs to have *real organizational power*—it needs to be able to say "no” and mean it. If your data team does not truly have the power to say no to stakeholders, it will get sent on all kinds of wild goose chases, be unproductive, experience employee churn, etc.
   - Data should report to the CEO. Ideally at least with some weekly metrics split into (a) notable trends, (b) watching close, and (c) business as usual.
   - If data is the most precious asset in a company, does it make sense to have only one team responsible for it?
-	- [People talk about data as the new oil but for most companies it’s a lot closer uranium](https://news.ycombinator.com/item?id=27781286). Hard to find people who can to handle or process it correctly, nontrivial security/liabilities if PII is involved, expensive to store and a generally underwhelming return on effort relative to the anticipated utility.
+    - [People talk about data as the new oil but for most companies it’s a lot closer uranium](https://news.ycombinator.com/item?id=27781286). Hard to find people who can to handle or process it correctly, nontrivial security/liabilities if PII is involved, expensive to store and a generally underwhelming return on effort relative to the anticipated utility.
   - [The pain in data teams come from needing to influence PMs/peers with having little control of them. Data teams need to become really great internal marketers/persuaders](https://anchor.fm/census/episodes/The-evolution-of-the-data-industry--data-jobs-w-Avo-CEO-and-Co-founder-Stefania-Olafsdottir-e16hu1l). That said, it shouldn't be the data team job to convince the organization to be data driven. That's not an effective way of spending resources.
 - People problems are orders of magnitude more difficult to solve than data problems.
 - **Integrate data where the decision is made**. E.g: Google showing restaurant scores when you're looking something for dinner.
@@ -42,7 +42,7 @@
 - You won't have the best allocation of resources in a reactive team. Data teams need extra [[slack]]. [Balance user requests with actual needs](https://scientistemily.substack.com/p/product-management-skills-for-data).
 - How can we measure the data team impact?
   - Making a [[Writing a Roadmap|roadmap]] can help you telling if you are hitting milestone deadlines or letting them slip.
-	- Embedded data team members need to help other teams build their roadmap too.
+    - Embedded data team members need to help other teams build their roadmap too.
   - Also, having a changelog ([do releases!](https://betterprogramming.pub/great-data-platforms-use-conventional-commits-51fc22a7417c)) will help show the team impact on the data product across time.
 - [Push for a *centralization of the reporting structure*, but keeping the *work management decentralized*](https://erikbern.com/2021/07/07/the-data-team-a-short-story.html).
 - Unify resources (datasets, entities, definitions, metrics). Have one source of truth for each one and make that clear to everyone. That source of truth needs heavy curation. Poor curation leads to confusion, distrust and…. lots of wasted effort.
@@ -84,9 +84,9 @@
 - [Data ownership is a hard problem](https://www.linkedin.com/posts/chad-sanderson_heres-why-data-ownership-is-an-incredibly-activity-6904107936533114880-gw8n/). Data is fundamentally generated by services (or front-end instrumentation) which is managed by engineers. CDC and other pipelines are built by data engineers. The delineation of ownership responsibilities is very rarely established, with each group wanting to push 'ownership' onto someone else so they can do the jobs they were hired for.
 - [Becoming a data-driven organization is a journey, which unfolds over time and requires critical thinking, human judgement, and experimentation](https://hbr.org/2022/02/why-becoming-a-data-driven-organization-is-so-hard). Fail fast, learn faster.
   - [Path to create a data-driven organization](https://twitter.com/_abhisivasailam/status/1520274838450888704):
-	- 1. Get a well-placed leader with influence to message, model, and demand data-driven execution.
-	- 2. Hire/fire based on data aptitude and usage.
-	- 3. Create mechanisms that force analytical conversations. Sometimes there is no way around spending an afternoon breaking down metrics by different segments until you find The Thing.
+    - 1. Get a well-placed leader with influence to message, model, and demand data-driven execution.
+    - 2. Hire/fire based on data aptitude and usage.
+    - 3. Create mechanisms that force analytical conversations. Sometimes there is no way around spending an afternoon breaking down metrics by different segments until you find The Thing.
 - [Start small. Don't try to wrangle data for the entire company until you have the tools and process down for one team](https://data-columns.hightouch.io/your-first-60-days-as-a-first-data-hire-weeks-3-4/).
   - Difficulty to work with data scales exponentially with size.
   - [Rule of thumb; your first customer as a data person should be growth](https://twitter.com/josh_wills/status/1577699871335010304).

Dogs.md

@@ -60,7 +60,7 @@
 - If you find your dog isn't listening perfectly to an old cue, one strategy for dealing with that is to change the cue and work on reinforcing the new cue more carefully. To transfer a cue, give your _new_ cue then immediately follow it with the old cue and reward when the dog performs the behavior.
 - You can speed up a trick training it with toys. Also, you can add tricks in between "drop it" and "get it" to reinforce them.
 - Once the behavior is established, start to reinforce intermittently.
-- For clicker training the main loop is: click, pause, feed. Always feed after clicking! You can charge the clicker while playing [[Dogs#Training Games |training games]].
+- For clicker training the main loop is: click, pause, feed. Always feed after clicking! You can charge the clicker while playing [[Dogs#Training Games|training games]].
 - Develop gradual exposures to new things. Break the challenge into small steps and reward for each step to build confidence. E.g: [reactivity](https://youtu.be/QQ3i6FRyoFs).
 
 ### Training Games

Double Crux.md

@@ -2,7 +2,7 @@
 
 [Double crux](https://www.lesswrong.com/posts/exa5kmvopeRyfJgCy/double-crux-a-strategy-for-resolving-disagreement) is great framework were both parties abstract their arguments by one level and find a falsifiable fact that, if proven true, would cause them to change their beliefs.
 
-- Focus on narrowing the scope of the [[Resolving Disagreement |discussion]]. Find common ground.
+- Focus on narrowing the scope of the [[Resolving Disagreement|discussion]]. Find common ground.
 - Define terms to avoid getting lost in semantic confusions that miss the real point.
 - Ask "What would need to be true for you to change your mind?".
 - Find specific test cases.

Feedback Loops.md

@@ -4,12 +4,12 @@
 - There are two types of feedback loops: positive and negative.
   - Positive feedback amplifies system output, resulting in growth or decline.
   - Negative feedback dampers output, stabilizes the system around an equilibrium point.
-- **[[Network Effects |Things are connected]]**. Changing one variable in a [[Systems|system]] will affect other variables in that system and other systems. This is important because it means that designers must not only consider particular elements of a design, but also their relation to the design as a whole and to the greater environment.
+- **[[Network Effects|Things are connected]]**. Changing one variable in a [[Systems|system]] will affect other variables in that system and other systems. This is important because it means that designers must not only consider particular elements of a design, but also their relation to the design as a whole and to the greater environment.
 - All complex [[systems]] are subject to positive and negative feedback loops whereby A causes B, which in turn influences A (and C), and so on – with higher-order effects frequently resulting from continual movement of the loop.
 - Feedback loops vary in their accuracy.
   - Accurate feedback means that it **reliably** and **clearly** tells you when you do something right. If you get the quadratic formula wrong, you can check the right formula and know what was wrong.
   - Inaccurate feedback loop means that the results of the evaluation phase are “**noisy**” and contain significant variance, so the next cycle will need to take that into account. E.g: playing bowls without a coach.
-	- Learning under conditions of noisy data starts with world construction. Imagine a possible future, and repeat this to generate hundreds of possible future worlds. The main skills and resources required are creativity, [[slack]], and [equanimity](https://en.wikipedia.org/wiki/Equanimity). Creativity leads to a higher rate of idea generation and [[slack]] gives us more time to generate ideas. Equanimity is important because it allows us to persevere in the absence of tangible feedback.
+    - Learning under conditions of noisy data starts with world construction. Imagine a possible future, and repeat this to generate hundreds of possible future worlds. The main skills and resources required are creativity, [[slack]], and [equanimity](https://en.wikipedia.org/wiki/Equanimity). Creativity leads to a higher rate of idea generation and [[slack]] gives us more time to generate ideas. Equanimity is important because it allows us to persevere in the absence of tangible feedback.
 - [The shorter and more accurate the feedback loop, the easier it is to learn](https://brianlui.dog/2020/05/10/beware-of-tight-feedback-loops/). The **tighter** your feedback loop, the better your work.
   - Fast and accurate loops might get you on a local maximum or might not work when the underlying system is noisy.
 

Ideas.md

@@ -84,10 +84,10 @@ Instead of building the tool, we can start with a standard protocol and let othe
 
 ### Social Network Improvements
 
-- [[Federated Networks |Federate and open source current networks]] to improve communities.
+- [[Federated Networks|Federate and open source current networks]] to improve communities.
   - [The protocol should evolve differently for each community](https://youtu.be/P-2P3MSZrBM?t=5953). Communities will mix and match protocols (rules, monetization, rewarding, actions, [[governance]], ...) to make the protocol fit their network.
 - We need a social network that does not cause divisiveness and negativity that is currently the natural by-product of optimizing for greater engagement.
-- How can the network [[Incentives|incentivize]] healthy conversations and encourage nuanced [[Resolving Disagreement |discussions]]?
+- How can the network [[Incentives|incentivize]] healthy conversations and encourage nuanced [[Resolving Disagreement|discussions]]?
 
 ### Web3 Recommendation Engine
 

Learning.md

@@ -32,11 +32,11 @@
   - Have an Analogy about it.
   - Visualize it in a Diagram.
   - Gather Examples. Examples are an amazing way to learn things!
-	- Examples are an excellent way to resolve lossy information transfer - they’re a completely different channel of communication than normal. If nothing else, they serve as an error check.
-	- Examples are a great way to transfer tacit knowledge, without necessarily making it legible - this is what it means to build intuition.
+    - Examples are an excellent way to resolve lossy information transfer - they’re a completely different channel of communication than normal. If nothing else, they serve as an error check.
+    - Examples are a great way to transfer tacit knowledge, without necessarily making it legible - this is what it means to build intuition.
   - Come with a Plain-English description of the concept.
   - Then, dive into the Technical side.
-- When discovering a pattern, try to abstract it as much as you can instead of applying it only to a certain area. Once you made this abstraction you will have a new [[Mental Models |mental model]].
+- When discovering a pattern, try to abstract it as much as you can instead of applying it only to a certain area. Once you made this abstraction you will have a new [[Mental Models|mental model]].
 - Learning to program shapes the mind the same way learning a new language does. Each new word, concept or expression helps you model the world.
 - Use [[Spaced Repetition]] and get some [[Sleep]].
 - [Test your knowledge easily and often and iterate](https://youtu.be/Y_B6VADhY84?list=WL). It's the number of iterations, not the number of hours, that drives learning. Shorten the [[Feedback Loops]]. You don't need to know everything to start. Start and you'll learn things along the way (Just In Time /JIT learning).