I’m checking out what open data we have in our county to play with data analysis over the weekend, and discovered, for instance, an open database of all 1.5 million trees in the county. The screenshot shows just a tiny part around my house.
Month: December 2025
From Idea to Chess AI: Building a Neural Network to Predict Moves | December 15 2025, 04:33
While figuring out neural networks, I decided to come up with a game-related task for myself. What if I find some ready-made games, and train a neural net to predict moves based on the board situation. Said and done. Of course, generating code is faster with LLM, but I wrote the detailed assignment myself and designed the architecture on my own. In 40 minutes (!) from the idea to the result, I already had a working solution that, at least in the first half of the game, does not mess up too much.
In the screenshot is CuteChess – it works with any chess engine, and in my case, it’s a simple Python script. The script takes the board situation and feeds it to the model. It selects the top 5 moves, and only these top 5 are analyzed deeply for several moves ahead and assesses the position. That is, the neural network suggests possible moves based on the analysis of 20,000 games (534,453 positions). From the results, the best is chosen. It uses the minimax algorithm for this, if that means anything to anyone (it didn’t to me, so Gemini here helped me)
How the model is trained. On the lichess website, you can download games, there are hundreds of gigabytes. I took a file with 800,000 played games from the year 2014. From these 800,000, I select 20,000, specifically looking with a script for games where the result is not a draw (1-0 or 0-1). Next, I calculate the difference (Winner_Rating minus Loser_Rating). It’s not the best metric, but it’s better than nothing. The bigger this difference, the more “confident” the win should be (the strong punish the weak). Thus, I get 20,000 such games.
“Ignoring the moves of the weak” (to avoid teaching the model bad play) is implemented during the training stage of the model. Essentially, the logic is: “If it’s White’s turn now, and White won this game — we learn. If it’s Black’s turn now, and Black lost — we skip and don’t teach the net this move.”.
The neural network is trained in batches of 128 positions at a time. The network receives a board position as input and outputs 4096 — the probability assessment for each possible move.
Selecting games takes about 5 minutes. Training the model on my computer takes about 10 minutes for 20,000 games. You could leave it to train on 100K or a million, and it would definitely be better. No need anymore – I figured it out 🙂
You can view the game here:
Exploring the Magic of Neural Networks in Letter Prediction and Visualization | December 14 2025, 23:35
I am currently experimenting with training simple neural networks – primarily to automate the existing toolkit, and some things just seem like magic.
There is a database of 32,000 names. There is a neural network filled with random numbers. I start training, with only this list of names as input. The first layer of the neural network is embeddings, and I set the number of dimensions to 2 for easy visualization. And after 200,000 iterations of training, the system clearly separates vowels from consonants, and for some reason, places the letter “q” slightly apart from other consonants. It seems that this is because the letter ‘q’ almost exclusively predicts the letter ‘u’ (Queen, Quincy, Quentin).
It also very reliably separates vowels and consonants in Russian names. In Russian names, the letters b and l are somewhat away from the other consonants, as are the soft and hard signs (well, that’s understandable).
I wonder how it works. If trained on a normal corpus of texts, the difference would be very clear. Why are vowels separated from consonants? Apparently, from the network’s mathematical perspective, ‘a’ and ‘o’ serve the same function: they “trigger” the prediction of the consonant following them, so the alternation of vowels and consonants is to blame. But damn, it’s interesting 🙂
And since the model can predict the next letters, you might try running it on Russian. On a model with 30-dimensional embeddings, it invents names like: Byaketta, Afsena, Erakey, Zasbat, Daraya, Gaiomahad, Rain, Razhul, Gzhatsiy, Reben, Vureb, Durodira, Turuzhul, Regravgava, Razsan, Gabila, Avganzh, Raksi, Khalebkokhorta, Rather. The model – for those who understand – is this: input of 6×33 characters (because we take up to 6 characters of context), encoded into embeddings of 60, goes to a layer of 100 neurons, and from there back to 33 characters. Some nonsense, but at least it’s clear how it all works at all levels.
Modern Reading: More Words, Digital Shifts, and Surprising Data Insights from 2008 | December 14 2025, 22:33
An interesting study caught my eye, dating back to 2009. According to it, the modern human indeed reads significantly more than in the past, although the format of this reading has changed. The study suggests that in 2008, an average American consumed about 100,000 words a day (approximately a quarter of “War and Peace”) – this is an approximate number of words that passed through consciousness per day (via ears or eyes), calculated based on activity chronometry. This is 140% more than in 1980.
Therefore, contrary to the myth about the degradation of reading, at least in 2008, we processed 2.4 times more textual information than our parents’ generation. Moreover, the study only considered information consumed outside of work (at home, in transit, during leisure).
The structure of reading – if in 1960, 26% of words came from paper, by 2008 this share had fallen to 9%. However, digital media (internet, email, social networks) not only compensated for this decline but also tripled the total reading time. The reason — the internet, as it is predominantly a textual environment (web surfing, email).
But it’s interesting that although the Internet accounts for 25% of consumed words, it only makes up for 2% of bytes (since video on the internet in 2008 was of low quality). Thus, they estimated the information flow from different channels and converted it into bytes 🙂 Radio accounted for 19% of the time but only generated 0.3% of bytes (as audio requires less data). Voice communication (telephone) — accounted for only 5% of words and a negligible share of bytes, but it was the only fully interactive channel before the internet era. TV remained the main source of information by time in 2008 (41% of all hours) and quantity of words (45%), however, in terms of data volume (bytes), television was only second (35%), behind computer games.
Now about games, quite interesting. The main finding from the report: Games generated (or did in 2008) 55% of all “bytes” consumed by households. Meanwhile, they only accounted for 8% of user time. This is quite a controversial topic in their report.
Those 100,500 words — that’s an assessment of actual words that a person either read or heard. This is not a metaphorical “equivalent,” but an attempt to calculate the verbal information precisely. They took the consumption time of each media and multiplied it by the average word inflow rate for that channel. Reading (books, newspapers, internet texts): 240 words per minute. Email and web surfing – 240 words per minute. Television (dialogues in shows/movies): 153 words per minute. Radio: 80 words per minute (less because of many pauses and music). Music: 41 words per minute (song lyrics).
Link in the comments
The Evolution of Personalized Video Advertising | December 14 2025, 17:08
I kept seeing ads for an AI language tutor that I ignored, and the system forgot about me for a while before coming back with a noticeably older tutor.
But really, how soon will video advertising become personalized for us? Where in the same ad, New Yorkers will see their city, black people will see black people, in the morning the main character will be drinking coffee, and a car with the logo of their alma mater will flicker in the background?
Nature’s Deceptive Twists: A Visual Exploration | December 13 2025, 23:33
“How deceptive nature is!” said the hedgehog… Interesting things we encounter when we step off the path
Hair-Raising Clues: When Your Car Proclaims Its Lady Driver | December 13 2025, 17:38
How can you tell a car belongs to a woman? Probably because she is occasionally not very sober either;)
Preserving the Essence of Soviet Animation | December 13 2025, 15:05
In Soviet times, there was a great school of animation that led the world for many decades. If you search on YouTube for “Vovka in the Land of Far Far Away”, it almost exclusively shows restorations 🤮, and at the same time, it shows the same disgusting restorations of heaps of other cartoons, all made in the same style (vectorization, black outlines). If you go to Wikipedia, it will display a screenshot from the restoration, not from the original 1965 cartoon. The original can be found, for example, by searching “vovka in the land of far far away madina gazieva”, but searching “vovka in the land of far far away soyuzmultfilm 1965” shows nothing at all.
They really broke the internet.
P.S. By the way, “two of a kind, fulfilling wishes,” and “good enough” resonate very much with today’s ChatGPT 😉
Harnessing GPU Power Beyond Machine Learning: A Data Processing Experiment | December 13 2025, 01:16
Torturing my supercomputer. Illustration that the GPU is not just for machine learning and some complex math.
My script takes a thick English dictionary (Webster) and multiplies it by 30, creating a list of 12 million words. Then, the algorithm looks through all 12 million words and replaces all the vowels with asterisks using regex. To add more load, a “word length” column is added, and then we take words longer than 10 letters and find the most frequent (top 5).
So, in Python this is
df[‘masked’] = df[‘text’].str.replace(r'[aeiou]’, ‘*’, regex=True)
df[‘len’] = df[‘masked’].str.len()
res = df[df[‘len’] > 10][‘masked’].value_counts().head(5)
and this code is executed first through the main processor, then through a GPU.
The main processor (I have the top-tier Intel i9 285k) completes this task in 24 seconds, while the Nvidia RTX 5090 does it in 0.51 seconds. That’s a 46 times difference!
[Pandas CPU] Top Patterns:
masked
s*r w. sc*tt. 23280
s*r t. br*wn*. 23220
j*r. t*yl*r. 16140
bl*ckst*n*. 10860
b***. & fl. 10830
Name: count, dtype: int64
[Pandas CPU] Computation Time: 23.5596 sec.
Transferring data to GPU…
Transfer complete in 1.16s
— Running Benchmark: cuDF GPU —
[cuDF GPU] Top Patterns:
masked
s*r w. sc*tt. 23280
s*r t. br*wn*. 23220
j*r. t*yl*r. 16140
bl*ckst*n*. 10860
b***. & fl. 10830
Name: count, dtype: int64
[cuDF GPU] Computation Time: 0.5108 sec.
TOTAL SPEEDUP: 46.12x
Misadventures in AWS: Misusing aws-nuke for Configuration Exports | December 12 2025, 16:29
Just for laughs. I asked Gemini how to export the entire AWS configuration for local analysis, and they recommended using the aws-nuke command for permanently deleting everything, but if you add a dry-run flag, you’ll get the configuration… and someone actually follows such advice 🙂 and then we wonder
Hi, I'm Rauf Aliev. 