Tag: test

Asian Fair Discovery: The Uni-ball Pen and Its Surprising Study | May 18 2026, 18:13

We recently had an Asian fair here – Japanese, Chinese, Korean goods, and street food. We bought a pie and some kind of pen for 6 bucks from Japanese vendors, which they beautifully packaged and asked for a review on Instagram. The pen is just a pen. Compared at home with our existing gel pens – no difference. But I Googled it out of curiosity.

“These inks are not just good-looking — they can even help you learn better. A study conducted at Ritsumeikan University in Japan showed that students who reviewed material from notes written with a black Uni-ball One pen retained information better than those who read notes made with regular black ink.”

I wonder who conducts such ridiculous research and who orders it. No surprises at all. Mitsubishi Pencil (the manufacturer of Uni-Ball) goes to the university, finds Professor Masashi Hattori (服部雅史) from the psychology department at Ritsumeikan University, and he organizes a report about a ‘memory reproduction task’ conducted on high school students: it compared the memorization of handwritten text with pens of different ink density, resulting in the conclusion that text written with dense black ink from the sponsoring company was reproduced more accurately than with regular gel ink.

Some of the co-authors of the report were Mitsubishi Pencil employees. There was no peer-reviewed article, only a conference presentation at the 38th Congress of the Japanese Society of Psychonomics (日本基礎心理学会第38回大会) on December 1, 2019; the results were also presented at the 32nd International Congress of Psychology ICP 2020+.

So, that’s the story with this pen 🙂

Comparing Work Cultures: From Conflict to Courtesy | May 15 2026, 15:12

I reminisce about working on various projects and in different companies up until 2016, comparing it with what I see from the USA (projects in the USA, Europe, and Asia), and I notice one interesting thing – there are no screaming matches during calls, no hysterical outbursts, no unhinged managers yelling at everyone around, and getting upset over every little thing. There are no overt conflicts. Of course, there are still covert games and politics, but if someone is unpleasant to someone else, they do it with a smile and politely (though, overall, not often).

In my past life,” this was a common occurrence that nobody really considered abnormal. Especially if the hysterics were over some genuinely important work issue, and the person was truly passionate about the results.

What I’m really interested in is – what has changed? There are four likely explanations, probably working in conjunction:

1) I moved to the USA

2) times are different

3) people are on meds

4) online etiquette is different.

There’s also an obvious fifth reason – “I have changed,” but it doesn’t quite fit here, because I’m not talking about conflicts involving me or with me, but about observing others’ interactions, which are unlikely to have changed. From my perspective, as I observe, interactions during meetings have become much kinder, but I can’t tell if times have really changed everywhere or if Russian companies are still the same, and I just don’t see it for obvious reasons. Or is it the nuances of online meetings, where yelling at a computer somehow feels odd? We’re talking about major serious companies, not a meeting at a city sports committee.

Anticipating a Special Day: A Dog’s Birthday Tale | May 14 2026, 23:39

Nadya told the dog in the morning that we would be picking up the cat. He came to the kitchen a couple of times and just keeps looking at me incessantly. As if saying, go on, tell me already, I remember that today is a special day. Well, there I am working, and he went off to sleep. Woke up. Looks out the window. Decided to cheer him up 😉

His special day is tomorrow, his birthday.

Advancing Full-Text Search: Testing and Refining with Multi-User Platforms | July 06 2025, 04:35

I have developed expertise in full-text search testing. Essentially, it’s a turnkey multi-user platform that, given roughly 1000 queries and several search engine configurations, can produce reports with graphs, metrics, and conclusions by morning, showing why configuration A performs better than B, and here’s why. It calculates all those NDCG@k, MAP, precision, recall, and about a dozen other metrics. It uses LLM, but only at the final stage, after all the math is done.

So, here’s my question. I’m looking for someone who has faced the same issue in their project, to understand the demand and the ask.

The problem the system solves is defined as follows: there is a functional search for goods, documents – Solr, Coveo, Elasticsearch, Algolia – it doesn’t matter, and there are hypotheses on how to improve it, but there is also the fear that improving one aspect might break another. Well, my thing helps to see this in numbers and graphs, providing a conclusion with justification, including statistical significance and other metrics.

It also acts as a virtual search assessor. For each search result, it can give a rating, assessing how well each document matches the query. This is a very non-trivial task (especially for large documents), involving chunking, embeddings, LLM evaluation of relevant chunks, etc. Non-trivial, but it works.

It also can analyze search queries and break them into groups based on similarity. For instance, such segmentation might show that users sometimes separate the words forming a brand name with a space, and sometimes not. These different variants would be grouped together.

I would like to discuss this with someone who knows more about this topic than I do, someone who has/had such problems and has somehow solved them.

I currently feel like my product is unique in the market. Actually, it’s not even on the market yet. But I really don’t see anything similar out there. Maybe nobody needs it?

I won’t publically post screenshots yet. The picture is merely for attracting attention.

Please share if there might be relevant people in your network.