2026-04-24 12:43:00
‘Back In My Neighborhood’, our summer anthem for those sweet visits with family and friends, is out into the world! Hear it now:
Bandcamp
Spotify
Apple Music
YouTube Music
Amazon
Tidal
SoundCloud
We wrote this song after visiting Clara's small hometown of Grand Bend, Ontario, recognizing that even after having played in some of the most amazing cities in the world, there's nothing like bringing it back to your roots.
Be on the lookout for the upcoming live music video we shot for this one - we're bringing back Kitchen Sessions, for those who have been following our journey since the beginning! Thank you Nitanee for letting us crash your kitchen with very little notice :)
Hope you enjoy 'Back In My Neighborhood'! Please send it to anyone you think would love it, and please give this post a toast if you love it!
Lyrics:
With the skyline callin' my name
Yes, I left in a hurry
I waved goodbye and boarded that train
Didn't think I'd be returning
I've been away for a while now
And I've finally come to see
I survived the city life
But damn it feels good to be
Barefoot in my grassroots, homebrew
Barbecue party in the backyard
Sippin' that blue sky, sunshine
Strummin' my heart like a guitar
Catchin' those late night firefly feelings
I'm feelin' so good (so good, so good)
It's a vibe, it's a vibe, yeah
You and I
Back in my neighborhood
Was just a small kid on a big curb
I got a Polaroid to prove it
And the last thing anyone heard
Was that one day I outgrew it
Lookin' back at this old town
Had it better than I knew
City life is a wild ride
But I love being out here with you
Barefoot in my grassroots, homebrew
Barbecue party in the backyard
Sippin' that blue sky, sunshine
Strummin' my heart like a guitar
Catchin' those late night firefly feelings
I'm feelin' so good (so good, so good)
It's a vibe, it's a vibe, yeah
You and I
Back in my neighborhood
I ain't the same as I once was
No, I'm wiser than before
City guy on the outside
But I'm country at the core
Barefoot in my grassroots, homebrew
Barbecue party in the backyard
Sippin' that blue sky, sunshine
Strummin' my heart like a guitar
Catchin' those late night firefly feeling
I'm feelin' so good (so good, so good)
It's a vibe, it's a vibe, yeah
You and I
Back in my neighborhood
2026-04-24 11:15:00
I received a few comments about my post on "You Should Aim to Make Fuck You Money". Most of the comments were about me promoting materialism and putting money ahead of everything such as family and love.
I think some of my readers missed the main plot. It is my fault for not writing it clearly. Making "Fuck You" money means having the ability to say no to things that you hate. It also means knowing when to focus on doing things that you enjoy. Life is more than just making money; you know you have enough when you can say "Fuck You".
While a few said I am putting money ahead of family and love. Money will never be ahead of family and love. However, money enables many things and experiences. I still remember my silence and helplessness when my family asked for tuition fees. How I worry about supporting the family when I suffered my large financial loss. I can't even look at myself in the mirror as I know I have failed as a provider to the family. My wife and kids are very supportive. The only way to express my gratitude and love is to give them the best life that I can afford. I cannot drag the whole family down due to my stupidity and ignorance. My "Fuck You" money is different from others. I just want to let my family to live the life that they deserve. It is never about buying things but finding opportunities to do things together.
It is fine that you do not want to make "Fuck You" money. Your priority is different. Maybe you already making "Fuck You" money that's why you can say such things. May you never need to feel "helpless" over money issues.
2026-04-24 08:31:50
I meant to write it for some time, so let’s start it well. I really like Bear.
It has been really nice and changed my perspective on a lot of things. So I decided to do a short list in no particular order about the things I like here.
1 - The community. You guys are amazing! All the messages I have received were nothing short of pleasant. People are very willing to reach out to talk about your post and give their input. Thank you to everyone who read my posts and sent me a message or mentioned them in their own posts. I’m very grateful and honored.
2 - Personal post. A lot of the bigger blogs here have a very neat and professional style of writing. Probably because of their background and practice. But the posts that intrigued me the most were those filled with emotion. The ones that make you feel like reading someone's diary, with messy writing, abbreviations, grammatical errors, no formatting, and pages flooded with bottled-up emotions. Those always got a grip on me.
3 - Photos. I love photos! Sometimes I leave a toast in a post solely because of the picture. I started to post some too, even made a gallery. Please post more pictures/photos, you can use the Robert Birming add-on if you don’t like how it looks “raw”, he has a lot of useful stuff, I super recommend it.
4 - Emails. I like the lack of comments. Having people email me their opinions and compliments feels so much more thought out and personal. It opens margin for a private discussion, a back and forth of ideas. I appreciate it a lot more than when I used Komments in the past. (by the way, for some reason, I got a lot of Russian comments there even with the links deactivated)
So, that is it! I was not expecting to be so well received here, so I made up my mind to make an appreciation post. Thank you very much, everyone!
Thanks for reading! You can leave a comment here
2026-04-24 05:23:16
I used to think that I could build a routine, and keep it as long as I was disciplined enough. There is comfort in that mindset, in believing that as long as we find something that works, we can hold on to it and it’s one less stressor in life.
I was wrong.
Routines are not fixed. They shift because we shift. They depend on circumstances, energy, and the version of ourselves that exists in a particular moment.
When a routine is in place, life feels a little calmer. There is less decision-making that needs to be done, less friction. Small actions repeat until they become almost second nature, and even things that used to be difficult are at least manageable. That sense of rhythm can make everything else feel more grounded, and more in control.
I think about that when it comes to early mornings. There was a time when waking up before the sun, even as someone who is not a morning person, became my norm. It was not easy (I still complained a lot), but it was consistent enough that it stopped feeling like I was going to crash out. It was just something I did.
When my days no longer needed me to wake up early, I let that structure go and built something that was more me, as a non-morning person. Slower (later-ish) mornings were made for that time in my life. But returning to early mornings now feels harder than expected. I hate being reminded that just because something felt natural before does not mean it will feel that way again.
That same pattern shows up in other aspects of my life too. Writing blog posts, for example, is something that benefits from a steady rhythm. When I had that in previous years, it felt easier to show up and publish consistently. Once that cadence broke, getting back into it requires more effort than I expected, even though my desire to write has never left.
Journaling for myself is also the same. It used to be part of my daily routine, something I did without much thought. I would wake up, drink coffee, and journal. Nowadays, I fall behind and have to catch up later, which changes how it feels. It’s become something I need to manage instead of something that just fits in my day.
I don’t even know how this train of thought started, but it got me thinking that routines are less permanent than I thought they were supposed to be. They are tied to specific parts of our lives, and when those parts change, the routines often do too.
There is a tendency to see a broken routine as something to fix, to try to return to what once worked. Returning is not always simple. Instead, it’s better to just find something else that fits into who we are now, not who we were when that routine first took shape. And so that's what I'm attempting to do.
I’m in the process of rebuilding routines that fit me now, rather than forcefully recreating something that worked for early-2025 me. I think I’m just trying to accept that there will be times when things feel less structured, and that this does not mean I am off track.
TBD on if I'm successful or not, or if I revert back to myself from the before times -- whoever that may have been.
If you'd like to comment, please send me an email, or sign my Guestbook.
2026-04-24 02:03:00
We spend most of our time working on training data. If someone told me this a year ago, it would have surprised me. The general thinking was that brute force scaling of AI models would begin to reach diminishing returns, and that data collection infrastructure like ours would be used as a tool during inference.
Teams are still scaling pretraining aggressively. Not in a brute force way, but in a much more targeted and deliberate manner. The way this is happening tends to vary between labs, but the consistent signal is a constant need for sources with high densities of “good” tokens.
There’s an over-simplified version of the AI stack that shows up in a lot of investor decks. It goes something like this:
chips -> infrastructure -> apps
Interestingly, data often ends up being a bit of a footnote in these conversations. Even some of the people closest to the largest labs grossly underestimate what “internet-scale” really means, and view data as a solved problem in the training process. A good way to think about this might be, if Google suddenly stopped scraping the whole internet every day, would they still be the best at pretraining in a year’s time? Unlikely.
Training data demand isn’t uniform, and it doesn’t fall into clean categories. The biggest AI labs have incredible amounts of compute, and although they still care about efficiency, in practice they tend to optimize more for coverage. The default behavior is to take as much data as they can within a certain domain and absorb the long tail.
Smaller labs that can’t afford to train on everything will focus on density instead of coverage. This means stricter filtering and getting as much signal as possible out of fewer tokens.
Teams looking to do the same thing might want completely different data, and at the same time, teams will do very different things with the same data.
Regardless of what teams are looking for, the workflow tends to look similar. They start broad, absorb a large dataset, and use it to figure out where the signal is coming from. From there, requests become more specific. More filtering is introduced, and sometimes this involves deploying machine annotations at scale to properly index datasets. This is especially true for multimodal data, where filtering is very expensive and is often deferred to later stages.
At the same time, teams don’t stop absorbing large amounts of data. Even as pipelines become more selective, coverage continues to expand in parallel. In a strange way, the data “system” never really converges. It becomes broader and narrower at the same time, all the time.
There are a few reasons why training data is still far from a solved problem (there’s probably a strong case to be made that because training data is the real alpha, it will never be “solved”). To get a great training dataset, it isn’t as simple as just crawling as many pages as possible. You have to decide what to crawl, where to crawl it, how often to revisit it, and how to deal with the fact that large parts of the web don’t want to be crawled at all. Even the largest labs have internal pipelines, but that doesn’t eliminate the problem. Coverage is always incomplete, and priorities shift quickly enough that the need for external data never really goes away.
None of this is “set and forget”. It requires constant iteration. Sources degrade, others emerge, and what might be considered high-quality data doesn’t necessarily stay that way.
At scale, even small inefficiencies compound quickly. Every day spent on data acquisition is a day not spent training, and this is time you can't get back in a race to ship better models. Most teams would rather focus on improving models than maintaining complex data pipelines.
A year ago, it was easy to think about training and inference data as separate domains. That distinction is beginning to break down. To build good web search, you need a comprehensive, high-signal crawl of the internet. The same is true for training data. The work that goes into identifying, collecting, and refining high-quality data ends up looking similar in both cases.
Good training data tends to look like good search data, and the infrastructure being built for pretraining data collection today is laying the foundation for what real-time systems will rely on in the future.
Most of this isn't visible from the outside. The scale and complexity of the training data market is easy to underestimate unless you're directly working with it.
2026-04-24 00:31:07
pinewind, a blog by kwist, gives us a calm space to enjoy the jots and thoughts of a highly interesting Japan-based hobbyist, enthusiast, and multipotentialite writer.
i can't decide if my favorite post is his thought-log on blogging or his post on personal colour pallets. both posts show the breadth of kwist's interests.
he considers his blog more of a garden. i have certainly appreciated wandering through his garden and have secretly (not anymore) taken some cuttings home with me to graft onto my own projects. please take a patient wander through pinewind, you will experience a lovely visit: