kaycebasques

Can someone who has used both wokwi and this do a compare/contrast? The footer suggests that it's built on top of wokwi-elements

Yes that is their biggest manufacturing hub right? And was the location of the working conditions controversy: https://en.wikipedia.org/wiki/BYD_Brazil_working_conditions_...

I'm a technical writer. Off the top of my head I reckon at least 10 startups have … started up … in this space since 2023.

He really is a remarkable person. An actor plays a high school teacher who is secretly filmed ranting against government corruption. The secretly filmed video goes viral and the teacher is unexpectedly elected president. Reality ends up mirroring fiction: the clip from the show truly does go viral and the actor who plays the teacher actually gets elected president! But then said actor gets thrown into a war against a superpower and really rises to the occasion. What a remarkable life.

How do people hold yuan? I was surprised at the lack of ETFs in this space. There's of course a lot of Chinese equity ETFs. There used to be a pure currency ETF but it was liquidated a couple years back. CBON seems like a good way to get exposure via bonds, but its AUM is quite low.

> “It is in nobody’s interests for there to be worries and instability in the U.S.,” said Jonathan Haskel, an economics professor at Imperial College Business School and former member of the rate-setting committee at the Bank of England. “Other countries hold lots of American assets. Savers in Europe will implicitly be invested in the American stock market. America is in many ways a flagship engine, with the A.I. revolution going on. Nobody in the world wants to see that at risk.”

https://www.nytimes.com/2026/01/13/business/trump-powell-cen...

Good Judgment Project apparently started in 2011 as part of an IARPA tournament. I first learned about through The Economist, which frequently mentions the crowd consensus on forecasts related to Economist articles.

I stumbled on this work while researching cyborgs. There's quite a bit of jargon on this official page. The main idea (as I understand it) is that previously, when you got an amputation above the elbow (for example) the bicep and tricep muscles became dead ends. They would just attach the ends of the muscles wherever feasible. Apparently the act of one muscle like the bicep contracting (agonist) while another related one like the tricep extends (antagonist) is a really important feedback loop in our brains. AAMI essentially restores this feedback loop, making the prosthetic feel like part of the body. The lead researcher is apparently himself a double amputee.

Osseointegration was another example of interesting real-life cyborg technology that I stumbled upon.

Sometimes you get a big price swing but it was on a low volume day. In those cases it's less interesting because it could be just one big buyer pushing the market around. The fact that it was nearly a double digit jump on a single day, and with high volume, is much more newsworthy. It means that a lot of people agreed that silver should be priced 9% higher than it was yesterday. With low volume, the price jump has a higher chance of being a fluke/noise from some uninformed/fringe market participants pricing the asset in silly ways. https://www.investopedia.com/terms/p/pricediscovery.asp

I've only read one Murakami novel but I loved it. Hard-Boiled Wonderland.

My top-rated books this year:

* C++ Crash Course by Josh Lospinoso

* Deng Xiaoping And The Transformation Of China by Ezra Vogel

* Magic: A Very Short Introduction by Owen Davies

* Trigonometry by Hugh Neill

I rated each one 9/10

Trading volume was very high. SLV had 138M today versus 24-78M range over the last month. https://www.investing.com/etfs/ishares-silver-trust-historic...

Better source: https://abc.xyz/investor/news/news-details/2025/Alphabet-Ann...

Is something similar available for GTA3?

> Slightly unfortunate that "Bag of words" is already a different concept

Yes, subconsciously I kept trying to map this article's ideas to word2vec and continuous-bag-of-words.

One thing that's still compelling about all-Mini is that it's feasible to use it client-side. IIRC it's a 70MB download, versus 300MB for EmbeddingGemma (or perhaps it was 700MB?)

Are there any solid models that can be downloaded client-side in less than 100MB?

The proposal to rename the vibecoding tag to something else provides a pretty good sense of the overall sentiment among the site's members: https://lobste.rs/s/gkzmfy/let_s_rename_vibecoding_tag_llms

It's a strong, active community. Much more focused on computing. I'm happy to invite anyone who wants to join. You can find a way to contact me on https://technicalwriting.dev. Please also link me to your website, LinkedIn, etc.

The article says that they were forced out of their jobs. That could mean many things, but it has a different connotation than quitting

> provided confidential mortgage pricing data from Fannie Mae to a principal competitor

It seems like the Fannie Mae data was shared with Freddie Mac. Aren't they both quasi-government organizations? GSEs. So they're both supported by the government but there's a firewall between them to keep some semblance of competition?

Related article: https://www.quantamagazine.org/why-everything-in-the-univers...

From the linked article:

> The widely known example only works because the implementation of the algorithm will exclude the original vector from the possible results!

I saw this issue in the "same topic, different domain" experiment when using EmbeddingGemma with the default task types. But when using custom task types, the vector arithmetic worked as expected. I didn't have to remove the original vector from the results or control for that in any way. So while the criticism is valid for word2vec I'm skeptical that modern embedding models still have this issue.

Very curious to learn whether modern models are still better at some analogies (e.g. male/female) and worse at others, though. Is there any more recent research on that topic? The linked article is from 2019.

> If you know for every feature you release, you need an API doc, an FAQ, usage samples for different workflows or verticals you're targetting, you can represent each of these as f(doc) + f(topic) and find the existing doc set. But then, you can have much more deterministic workflows from just applying structure.

This one sounds promising to me, thanks for the suggestion. We technical writers often build out "docs completeness" spreadsheets where we track how completely each product feature is covered, exactly as you described. E.g. the rows are features, column B is "Reference", column C is "Tutorial" etc. So cell B1 would contain the deeplink to the reference for some particular feature. When we inherit a huge, messy docs set (which is fairly common) it can take a very long time to build out a docs completeness dashboard. I think the embeddings workflow you're suggesting could speed up the initial population of these dashboards a lot.

Is the big US military build up on the coast of Venezuela being affected by this?