asdfgeoff

Reminds me of Habbo Hotel

This exact issue happened to me. I think it was related to crossing timezones during the flight. The experience has definitely made me think twice about adopting Logseq my primary notes tool.

> … a seatback tray table, it is generally not the right shape for all but the smallest laptop …

As someone who very much prefers having a physical keyboard, the only workable solution I have found for UK trains is an iPad with the Magic Keyboard, whose cantilever design is relatively shallow, and which provides a firm base for balancing on your legs.

There is no reason this form factor couldn't be achieved with an android tablet, but it feels relatively uncommon. Most keyboard cases I've seen are only really suitable for stable table/desk scenarios.

I am curious about what post-LLM SEO is going to look like.

> The semantic web has failed and what replaced it was Google spending a crap ton of money writing a variety of heuristics equipped with best-of-breed-at-the-time AI behind it.

Arguably, there were insufficient incentives to fully adopt semantic HTML, if your goal was just to have the most relevant parts of your content indexed well enough to get ranked.

> As AI improves, it improves its ability to extract information from any ol' slop, and if "any ol' slop" is enough, it's all the effort people are going to put out.

If the goalpost shifts from “getting ranked” to “enabling LLMs to maximally extract the nuance and texture of your content”, perhaps there will be greater incentive to use elements like <details> or <progress>. Websites which do so, will have more influence over the outputs of LLMs.

Feels like the difference between being loud enough to be heard vs. being clear enough to be understood.

Neat article.

Cached version → http://archive.today/O46rw

> Helped us choose winter shoes

I am especially curious about this one. Do you have the dialogue saved?

It sounds like you value permanence and reliability over feature set. You may want to consider https://www.followthatpage.com/

I have been using it for years, and—judging from it's visual design—it has been around for years longer.

If it's not plaintext and client-side encrypted, is it even worth writing down?

Awesome explanation. When writing pandas code, I tend to make heavy use of method-chaining and lambda functions to achieve something along these lines. But I have never been able to articulate why.

  df.assign(COLUMN_C=lambda x: x['COLUMN_A'] + x['COLUMN_B'])

But looking solely at the wikipedia page for dependency inversion principle… I'm not sure I would have connected the dots here myself.

Can you recommend any noteworthy resources for a data scientist who is interested in learning more about software engineering patterns like this?

Not a book, but I have found Trey Hunner's https://www.pythonmorsels.com/ exercises very useful on this front.

The solutions presented typically include both a "basic" approach, a "as pythonic as possible" approach, and a brief discussion of the trade-offs between elegance and readability, etc.

Check out Routinery → https://play.google.com/store/apps/details?id=com.alt.goodmo...

I have also gotten into this habit, but I'm still on the fence about whether it is a net positive change…

Pros: Among various types of breaks one can take while WFH, showers feel particularly restorative. They also sometimes end up being surprisingly productive—diffuse thinking modes, shower thoughts, etc.

Cons: The "when" of showering was previously an automatic outcome, but now it is a conscious decision/obligation. I also find (as many people do) that showers give momentum to my morning, which is arguably more useful when just waking up than mid-day (although YMMV here).

Kazy Lambist

I tried to do exactly this a couple months ago as a "lockdown project". Agree it is quite cumbersome, requiring Adobe Lightroom, a third-party plugin, and also running a python script to convert to GPX. And if you haven't diligent about keeping your camera's clock accurate across timezones and daylight savings time, you're in for even more hassle.

In case you're interested, I wrote up the steps I took here → https://geoffruddock.com/geotag-lightroom-photos-with-google...

Agree that treating complexity as a constant is a bit extreme. The author lost me at the bit about Tesler's law.

That said, it feels like there is likely some sort of Pareto efficient frontier with regards to complexity vs. usefulness. You can certainly have a poorly-designed system which is arbitrarily more complex without being anymore useful. But if you make things "as simple as possible" for any given amount of complexity, there will always be some underlying trade-off.

Brings to mind → https://www.joelonsoftware.com/2002/11/11/the-law-of-leaky-a...

+1, I would love to read more about this, particularly around how it scales to multiple (possibly overlapping) options.

> Additionally, the factors are expressed as positive attributes and an option either has that factor or it doesn't.

This is very intuitive for binary decisions, where the "pros" of one option are the "cons" of the other. How do you scale it to multiple options though? (e.g. if I am deciding between internet providers, speed is clearly an important factor, but I may have 3 options with low/medium/high speed respectively)

I have had reasonable success using spaced repetition with math proofs (written in LaTeX). I create cards sparingly, often based on questions from problem sets which I shouldn't have gotten incorrect (i.e. the mistake stems from a fundamental misunderstanding of concepts, not a misstep in algebraic manipulation)

> I don't see how cards would help me with mathematical ideas. You have to get them on a fundamental level, and then they're hard to forget.

I find it too easy to trick myself into believing I understand a concept on a fundamental level. But often that "understanding" slips away, and six months later when faced with an example problem out of context I struggle to solve it.

I am a full-stack data scientist who builds narratives around user behaviour at scale using quantitative data. I have spent the past five years using data to build better products for users—first as a product manager for an online car marketplace, and most recently as a data scientist at a travel company. I thrive working with the python ecosystem (jupyter, pandas, numpy, scikit-learn) to turn user data into actionable insights using statistical techniques such as A/B testing and machine learning.

Location: Berlin, Germany

Remote: Yes

Willing to relocate: No

Technologies: Python, pandas, numpy, scikit-learn, matplotlib, seaborn, SQL

LinkedIn: https://www.linkedin.com/in/geoffruddock

Personal: https://www.geoffruddock.com

Email: geoff [at] ruddock [dot] ca

I haven't used the recipe feature yet, but AnyList is my go-to grocery list app. Big fan of rearrangeable aisles, preferred stores for items, and the ability to check off items in real-time on two devices in-store.

I have toyed with leaving journal-like blurbs of thought in my regular notes system, sort of like Architectural Decision Records (https://adr.github.io/) for my life, but the habit never stuck. The format never felt free and unconstrained enough to facilitate the sort of raw thinking-on-paper that tends to be useful a year later. I found myself self-censoring much more than I would in a regular journal.

A key benefit of using a date journal is that many tools (incl. Day One and Dabble.me) have a "remember this day" feature that surfaces entries from the past. While the simple act of writing does clarify one's thinking, I find further benefit from the regular review and opportunity for reflection this sort of feature provides. And while I do sometimes deliberately seek out a particular entry, 90%+ of my review comes automatically from this sort of feature.

I am a full-stack product manager with two years of experience building ecommerce products in international markets. I believe that integrating quantitative behavioural analytics into the UX/design workflow plays a key role in building products users love. Although my formal educational background is non-technical, I am comfortable digging for product insights using Python and SQL, or building high-performance A/B tests using JavaScript or jQuery.

  Location: Berlin, Germany
  Remote: Yes
  Willing to relocate: No
  Technologies: Google Analytics, SQL, JavaScript, jQuery, Python
  Resume: bit.ly/geoffruddock
  Email: geoff [at] ruddock [dot] ca

I am a full-stack product manager with two years of experience building ecommerce products in international markets. I believe that integrating quantitative behavioural analytics into the UX/design workflow plays a key role in building products users love. Although my formal educational background is non-technical, I am comfortable digging for product insights using Python and SQL, or building high-performance A/B tests using JavaScript or jQuery.

Location: Berlin, Germany

Remote: Yes

Willing to relocate: No

Technologies: Google Analytics, SQL, JavaScript, jQuery, Python

Resume: bit.ly/geoffruddock

Email: geoff [at] ruddock [dot] ca

I am a full-stack product manager with two years of experience building ecommerce products in international markets. I believe that integrating quantitative behavioural analytics into the UX/design workflow plays a key role in building products users love. Although my formal educational background is non-technical, I am comfortable digging for product insights using Python and SQL, or building high-performance A/B tests using JavaScript or jQuery.

Location: Berlin, Germany Remote: Yes Willing to relocate: No Technologies: Google Analytics, SQL, JavaScript, jQuery, Python Resume: bit.ly/geoffruddock Email: geoff [at] ruddock [dot] ca