Quick. Short. Immediate.

Easy. Summarized. Highlighted.

Content is digested for us on social media; then, we digest short impulses.

We only get the gist and can't draw unbiased conclusions.

The alternative?

Consume less, long-form content, and deep dive.

"No such thing as writer's block," says Seth Godin, and he couldn't be more right.

It may not be your best writing, but all you have to do is sit and write. Give it enough time, and something good will come.

I enjoyed learning about Disney's sodium vapor background removal process, which is used in movies such as Mary Poppins (1964), Bedknobs and Broomsticks (1971), and Pete’s Dragon (1977).

This method works much better than green and blue chroma keys, but as the video experiment shows, it's much more challenging to achieve.

This site with Machine Learning Challenges (deep-ml.com) looks really promising to learn about foundational concepts.

"Willpower becomes a habit," says Charless Duhigg, "by choosing a certain behavior ahead of time, and then following that routine when an inflection point arrives."

Routines work the same way the tortoise beat the hare: by steadily working towards the finish line without missing a day, except there's no finish line.

I listed a few ideas I wanted to build in 2024 at the end of last year.

I built a daily word counter and started working on a new folder structure for my Dropbox files. But I haven't put time into developing the others yet.

I'd also like to develop these five half-baked ideas.

• Expensed, a personal expense tracker that turns plain-text notes into charts and infographics and lets me filter and categorize my expenses.
• Sketch Manager, to browse my sketchbooks and see whether each sketch is edited, ready for print, published (and where), and who's bought (or being gifted) a particular piece, with bounding boxes on each scanned page to locate where each sketch is located on the page.
• Sketch Browser, to view sketchbooks with digitized copies of my sketches as they are in the actual sketchbook, with data and references from Sketch Manager.
• Price Tracker, a tool to track the price of online store products, such as CamelCamelCamel.
• Product Recommendations, a site to list my tech gear and things I like to use on a daily basis, with referral links, price history, and descriptions.

You may wonder why some sites help us save by waiting or lead us to buy without the need to.

Online stores like Amazon, Best Buy, or B&H offer so-called affiliate programs, in which websites and social media channels earn a sales commission on eligible purchases they link from their content.

Here's how it works on Amazon.

You add an affiliate tag¹ to your product links, and Amazon tracks purchases made by customers from your links. This way, you may get ~1–10% commission for eligible purchases within twenty-four hours of the link click.² In affiliate marketing jargon, visitors you send to Amazon are referrals.

And that's why many websites display the following notice.

Commissions may be earned from the links below.

I recently bought an Atomos Ninja with the Atomos Connect module and an UltraSync Blue to pair devices via Bluetooth.

I couldn't find an updated list of compatible devices, so I started one.

Timecode Systems, the original creator of the UltraSync One and Blue, was acquired by Atomos, which makes monitor recorders such as the Atomos Ninja or Shogun. The UltraSync Blue is also compatible with all other Timecode Systems devices.

iOS apps

• MAVIS professional camera (video)
• Apogee MetaRecorder (audio)
• Apogee MovieSlate8 (video)

Software

• Softron MovieRecorder and Multicam Logger software

Audio recorders

• Zoom H6essential (with BTA-1)
• Zoom H4essential (with BTA-1)
• Zoom H3VR
• Zoom F3
• Zoom F6
• Zoom F8 and F8n
• Zoom F2-BT
• Tascam Portacapture X6 and X8
• Tascam DR10L PRO

Video cameras

• Nikon Z8
• Nikon Z9
• Fujifilm GFX100S
• Fujifilm X-H2
• Fujifilm X-H2S

Last week, I shared a trick to save by waiting—patience pays off.

On the opposite corner are marketplaces that come up with suggestions we don't need but may find helpful. Sites that make us spend on what we don't need but want.

Amazon is the perfect example, but many other stores, such as IKEA or Zara, fall into the same category. You browse without a specific need in mind in search of potential things to buy. The best in this category are discount sites, such as Chollómetro, which lists discounts from many other sites to find bargains you often don't need.

If you'd like to buy a specific item from Amazon but are not in a hurry, you can set up a price alert and get notified when the price goes down.

Paste the product's Amazon web address at Camelcamelcamel.com¹ to see its price history, choose your fair price, and create a price alert with your email.

It's an easy way to delay purchases and save when you don't mind waiting.

Hi Friends—

I'm working to bring you new episodes with John Pierson, Joel Simon, David Andrés León, and other exciting guests.

Today's episode is a follow-up with Andy Payne on Grasshopper 2's new features, recorded live after Andy's episode was released.

Thanks to everyone who chatted with us during the YouTube premiere.

Let us know your thoughts on the video comments.
Submit your questions at gettingsimple.com/ask.

Warmly,
Nono

Chapters

00:00 · Introduction
00:50 · Grasshopper 2
03:03 · Data types
04:44 · Content Cache component
06:35 · Rhino Compute
07:37 · Object attributes
08:36 · New features
08:51 · Shouts
09:50 · Visual diffing and graphics
10:24 · Figurines
11:33 · Installing Grasshopper 2
12:32 · Andy's day-to-day
13:39 · 3D tools

Generative Ai doesn't make me a better drawer—it draws for me.

A computer program could teach us how to draw, which requires us to put in the effort and time.

You could say that Ai can become a great teacher when it guides you or the worst teacher when it does everything for you.

A teacher shouldn't do things for you but show you by example, leading you to tackle the correct problems and get the right solutions.

You don't get to think about the problem when you obtain answers right away; it's a shortcut.

Hey, you don’t get to decide what spreads—the public does.
—Seth Godin, All Marketers Are Liars

Don't sit to work (only) when you have to get something done.

Sit regularly, with wiggle time and no pressure.

Do your magic when you don't need to get anything done.

Then, enjoy the freedom of having it done when you need it and don't feel like doing it.

That's when it may not feel like work.

I asked ChatGPT 4o to imagine it was a critical journalist writing an op-ed article about Ai's adoption in our daily lives. Here's what it answered.

Artificial intelligence, celebrated for its convenience, poses risks to our privacy and independence. It often functions opaquely, increasing biases and surveillance. Without careful oversight, adopting AI could mean losing human control and ethical standards in favor of machine-driven simplicity. This requires thorough scrutiny.

There are three levels of artificial intelligence, and we default to the third one in our daily lives: using technology developed by others for us.

Thanks to the arms race approach to research and innovations that big tech companies and academic institutions are undergoing, we have access to the key innovations that enable these algorithms to learn from our data and generate such sound output.

But even when the code and datasets are open, it will feel like we are interacting with a black box with unknown biases.

We don't have the time or knowledge to understand those widely available algorithms.

So we'll be left with whatever companies and institutions bring to our hands.

I continue to be fascinated by the effects of time slack on creative output. Too much time makes me do nothing. Too little time stresses me out but forces me to get things done. The in-between is to commit to the practice (in Seth Godin's terms), to systems, not goals, ala James Clear—to engage in your craft daily in search of practice and consistency.

Daily drawing, writing, and weekly publishing are systems—there's no explicit goal. Yet this lets me cherry-pick among dozens of ideas when I want to publish.

I reached for my sketchbook and 005 Micron pen while lying on the couch. Two loose sketches of my right foot in distinct positions count as a daily drawing. I urge myself to comply.¹ Creative friction is everywhere, so the trick is to make your process as frictionless as possible.

Quick is easier than detailed; ink is easier than watercolor.

Of course, optimizing for quick and easy reduces the effectiveness of the habit and can lead to creative cheating. You must exert effort to get better. But some practice, every day, beats the ideal and sporadic training session.

Kevin Kelly celebrated his 68th birthday gifting his children 68 bits of advice he wished he had gotten when he was their age. Every birthday after that, he added more bits of advice for them, and ended publishing a whole book of bits titled Excellent Advice for Living.¹

Recently, he published 101 additional advices. Here are a few I loved.

Whenever you hug someone, be the last to let go.

You can become the world's best in something primarily by caring more about it than anyone else.

Once a month take a different route home, enter your house by a different door, and sit in a different chair at dinner. No ruts.

There should be at least one thing in your life you enjoy despite being no good at ti. This is your playtime, which will keep you young. Never apologize for it.

The patience you need for big things is developed by your patience with the little things.

When you find yourself procrastinating, don't resist. Instead lean into it. Procrastinate 100%. Try to do absolutely nothing for 5 minutes. Make it your job. You'll fail. After 5 minutes, you'll be ready and eager to work.

The most common mistake we make is to do a great job on an unimportant task.

Tol tell a good story, you must reveal a surprise; otherwise it is just a report.

You can read them all here.

Creative friction is anything that gets between you and your creative output.

It can be in your mind — the fear of failing.

It can be physical — you don't have the tools or space.

It can be temporal — you can't put the time.

It can be in your process — you want everything to be perfect.

No matter where it is, reducing what adds friction to your creative process will lead you to quantity, which will often lead you to quality.

Hi Friends—

Andy Payne is an architect and software developer at McNeel, the company behind Rhino and Grasshopper 3D.

I met Andy in the summer of 2016. Autodesk had acquired Monolith (a voxel-based editor) from Andy and Pan earlier that year. I joined them as an intern to build a generator of 3D-printed material gradients and play with a Zmorph 3D printer.

We recorded a podcast conversation in New Orleans in September 2022, where I learned about Andy's latest adventure.

Enjoy this episode on the origins of Grasshopper, Grasshopper 2, Rhino.Compute, teaching, learning to code, generative AI, open-source code and monetization, and Andy's journey.

Thanks to everyone who chatted with us during the YouTube premiere.

Let us know your thoughts on the video comments.
Submit your questions at gettingsimple.com/ask.

Warmly,
Nono

Chapters

00:00 · Introduction
00:35 · Andy Payne
04:11 · Grasshopper origins
07:23 · Andy meets Grasshopper
09:19 · Grasshopper Primer
10:26 · Grasshopper 1.0
13:22 · Grasshopper 2
15:11 · Developing Grasshopper
16:59 · New data types
18:57 · Rhino Compute & Hops
22:32 · Cloud billing
27:05 · Teaching
30:07 · Visual programming
36:23 · Open source & monetization
42:03 · McNeel Forum
50:07 · Connect with Andy
51:57 · Learning to code
58:00 · Generative AI
01:02:09 · The IKEA effect
01:05:38 · Authorship
01:08:56 · AI trade-offs
01:12:58 · Panagiotis Michalatos
01:16:02 · Advice for young people
01:17:08 · Success
01:18:35 · $100 or less
01:20:12 · Outro

I have an Apple M3 Max 14-inch MacBook Pro with 64 GB of Unified Memory (RAM) and 16 cores (12 performance and 4 efficiency).

It's awesome that PyTorch now supports Apple Silicon's Metal Performance Shaders (MPS) backend for GPU acceleration, which makes local inference and training much, much faster. For instance, each denoising step of Stable Diffusion XL takes ~2s with the MPS backend and ~20s on the CPU.

In July 2013, Alex Webb asked whether Grasshopper was initially developed as a teaching tool to show how information flowed through commands.

David Rutten denied this.

[Grasshopper] was developed for Rhino customers as a way to automate tasks without the need to write textual code. We expected that some of our users who were interested in RhinoScript or C# or VB.NET would be interested, but we certainly didn't think that it would be taught (at gunpoint apparently in some universities) to the masses.

Originally, the product was called Explicit History¹, because it was a different approach to Rhino's native (implicit) history feature. Rhino history is recorded while you model and can then be played back, Grasshopper history is defined from scratch while the model is created as an afterthought.

I found this while putting together the episode notes for a conversation with Andy Payne on the Getting Simple podcast, where he shares curiosities of Grasshopper's origins and its transition from Explicit History to the initial Grasshopper release, Grasshopper 1, and Grasshopper 2.

When I switch tasks and embark on projects I haven't worked on for months, my notes on how things work save the day because there's no such thing as easy steps.

I started using ChatGPT Plus¹ seven months ago, and I'm surprised at how much I rely on it for day-to-day tasks. I don't need to Google as much as before, as answers tend to solve my immediate needs.

Here are a few use cases I found extremely useful.

• A custom TypeScript & React GPT for work only allowed to answer with code solutions.
• English-to-Spanish Markdown blog post translation. I only have to apply minor corrections, often because ChatGPT mixes Castilian and American Spanish.
• Write Python and bash scripts, both simple and fairly complex.
• Learning and research. ChatGPT is an avid searcher of related papers and a great explainer of machine learning concepts. (You can even ask it to draft specific neural network architectures!)
• Fix grammar and punctuation.
• Troubleshoot computer errors.

I find it mesmerizing to generate visuals and images with it — I have several parallel side projects on this, which I haven't published yet. But that's a story for another day.

Ethical and safety concerns aside, I find it quite promising that, relatively soon, we'll have models with similar performance to GPT-4 and DALL-E 3 running locally on our devices without a subscription.

I haven't measured how much more efficient as a software developer it's made me. Yet after hundreds of conversations ChatGPT has saved me some "human compute hours" and helped me develop several code solutions faster. In this case, twenty dollars a month is a no-brainer.

"We shape our tools and thereafter our tools shape us." Said John Culkin². Language models are rapidly changing how we interact with machines. The next step could be removing humans from the loop and delegating digital tasks to machines.

Pulling a mini-essay and sketch weekly is not an easy feat.

I've been doing it consistently for years, only delaying on a few special occasions for reasons like not having an internet connection, being in a different timezone, traveling, and some other situations that make a good enough to myself.

I'll keep pushing, and as my initial intention with this project was, I'll try to schedule more than one post per week to give myself a bit of slack to develop ideas more deeply and put more thoughts before I hit send.

Still, this project is for me to explore, and I'll continue to publish even if ideas aren't complete. There's always the following week to correct or expand on it.

See you soon!

You can join the newsletter here.

Researchers develop algorithms — the so-called neural networks and machine learning models.

Software engineers train and deploy those models and make interfaces for them.

Users interact with the artificial intelligence through machine-learning-powered applications.

Last week, I tested Luma for a podcast release. Luma is a platform for people to register for your events (for free or with paid tickets). Although I started using the platform to schedule my live streams a few months ago, this was the first time I used it for the podcast.

I was gathering interest for an incoming podcast release that would take place on YouTube in the form of a scheduled premiere, as YouTube calls uploaded videos scheduled to stream for the first time. During the video premiere, your audience can participate in the chat as the video plays, which, in our case, allowed listeners to ask questions to our latest guest, Andy Payne. After the video "premieres," it will be available on YouTube like any other on-demand video you upload. The gist is that, during the premiere, everyone watches the video simultaneously as if it were a live event.

Luma will remind your attendees when the event occurs in their timezone one day before and one hour before, sending them the link and instructions you set up.

Upon registration to an event, attendees have to enter their name and email, which allows you to invite them to similar events in the future or to reach out to them directly, for instance, when new content related to the episode they signed up for is available, i.e., the audio version of the podcast on Spotify and Apple Podcasts.

You can change the location link of the event so that attendees will receive it when the event is about to happen. If you change your mind before the event, you can direct users to a different platform (say, Twitch or Riverside). If that were to happen and you've directed users to YouTube, you can do nothing.

I shared the Luma link on a LinkedIn post, a tweet, and this newsletter and got twenty-two sign-ups from people I don't know, which isn't bad.

People can subscribe to you on Luma to get notified about future events you schedule. (Here's my Luma profile.) It's a great alternative to newsletters to capture interest in specific events. People may not be willing to sign up for your newsletter, but they might for the events they want to attend.

I met Andy Payne in the summer of 2016. Autodesk had acquired Monolith¹ (a voxel-based editor) from Andy and Pan earlier that year, and I joined them as an intern to build a generator of 3D-printed material gradients and play with a Zmorph 3D printer.

I interned at Autodesk’s Boston Seaport office with Jose Luis García del Castillo and Keith Alfaro. Jose Luis and I would bike to Central Square, ride the Red Line T to South Station, and hop onto the Silver Line Bus to Drydock Avenue. By the end of the summer, I open-sourced Voxel2GCode²—a set of workflows to transform geometric objects and voxel-based models into 3D-printable G-code instructions.

Andy is an architect and software developer at McNeel, the company behind Rhino and Grasshopper 3D. We recorded a podcast conversation in New Orleans in September 2022, where I learned about Andy's new adventure.

Enjoy this episode on the origins of Grasshopper, Grasshopper 2, Rhino.Compute, teaching, learning to code, generative AI, open-source code and monetization, and Andy's journey.

We'll premiere the episode on YouTube on Wednesday, March 27, at 2 PM ET, which means you'll be able to chat with Andy and me during the episode.

You can RSVP here.

I export videos from Descript, which has embedded subtitles, and Descript doesn't have a way to export subtitles by chapter markers; it only exports them for an entire composition.

Here's a command that extracts the embedded subtitles from a given video—and supports any format supported by FFmpeg, such as MP4, MOV, or MKV.

ffmpeg -i video.mp4 -map 0:s:0 subtitles.srt

Here's what each part of the command does.

• -i video.mp4 - the input file.
• map 0:s:0: - maps the first subtitle track found in the video. (You can change the last digit to extract a different track, e.g., 0:s:1 for the second subtitle track.)
• subtitles.srt - the output file name and format, e.g, SRT or VTT.

If you found this useful, let me know!

In Live 116, I conducted a live work session learning how to fine-tune Stable Diffusion models with Low-Rank Adaptation (LoRA).

If this interests you, please let me know on Twitter or, even better, on the Discord community.

Thanks for watching.

See you next time!

Chapters

01:07 · Introduction
01:21 · Today
02:19 · Fine-Tune with LoRA
04:09 · Image Diffusion Slides
06:43 · Fine-Tune with Lora
13:31 · Stable Diffusion & DALL-E
22:27 · Fine-Tuning with Lora
01:34:20 · Outro

I check whether my Tuesday post is ready.

As of lately, the answer is usually, No, the post is not ready. I then browse my archives or write something completely from scratch.

Why? I decided to do so, and I've experienced how it pays off in the long run.

It's craft, not art.

"A pro views her work as craft, not art." Says Steven Pressfield in The War of Art.

It's professional, not amateur.

"Professionals stick to the schedule; amateurs let life get in the way." Says James Clear in Atomic Habits.

Good Mondays, I enjoy the freedom of having completed my "homework" in advance.