Jets are approximated
Finally, I conducted the experiment and analyzed the results. The curves were fitted during quite a long session with Claude.
One thing I would do differently now: mark the jets with different colors from the very beginning, and do the same for the hole positions. When Claude can infer the whole idea behind the scene, it is surprisingly hard for it to figure out the precise boundaries of objects.
In the PDF, you can find the full derivation of the parabolic trajectory with a correction for the camera tilt.
As a bonus, there is an old but gold joke about a scientist's perception of the whole world.
Finally, I conducted the experiment and analyzed the results. The curves were fitted during quite a long session with Claude.
One thing I would do differently now: mark the jets with different colors from the very beginning, and do the same for the hole positions. When Claude can infer the whole idea behind the scene, it is surprisingly hard for it to figure out the precise boundaries of objects.
In the PDF, you can find the full derivation of the parabolic trajectory with a correction for the camera tilt.
As a bonus, there is an old but gold joke about a scientist's perception of the whole world.
👍1
#friday #shitposting
It is very important not to let your traffic signs degrade.
Drop your favorites in the comments.
It is very important not to let your traffic signs degrade.
Drop your favorites in the comments.
Armenian spinning top Quiz
Let's get dangerous! (c)
I'll try to send (or give, or whatever) an Armenian spinning top to the person who gives the best explanation of the difference between the left and right pictures.
I solemnly swear that both pictures were done with the same camera, at the same place, the same spinning top.
Let's set deadline for explanations as 13.00 MSK, 13 Jul 2000 + 2*13.
Let's get dangerous! (c)
I'll try to send (or give, or whatever) an Armenian spinning top to the person who gives the best explanation of the difference between the left and right pictures.
I solemnly swear that both pictures were done with the same camera, at the same place, the same spinning top.
Let's set deadline for explanations as 13.00 MSK, 13 Jul 2000 + 2*13.
❤2🤔1
Water jets - navigation
A thought here, a physics puzzle there... One by one, I collected quite a nice series of posts about fluid dynamics.
It flows from the law of conservation of energy to a DIY tower printed on a 3D printer.
Enjoy your ride down the torrent!
If you want to start quickly
Main line of the story.
106 - Water jets | tg / web
107 - Pouring-out velocity | tg / web
108 - Not exponential. This time. | tg / web
109 - New school level physics problem | tg / web
258 - The Water Tower | tg / web
272 - Jets are approximated | tg / web
273 - Stream model | tg / web
Full list
The device
106 - Water jets | tg / web
258 - The Water Tower | tg / web
270 - Experiment video and photo | tg / web
Physics of the jet
107 - Pouring-out velocity | tg / web
108 - Not exponential. This time. | tg / web
109 - New school level physics problem | tg / web
Experiment and model
272 - Jets are approximated | tg / web
273 - Stream model | tg / web
A thought here, a physics puzzle there... One by one, I collected quite a nice series of posts about fluid dynamics.
It flows from the law of conservation of energy to a DIY tower printed on a 3D printer.
Enjoy your ride down the torrent!
If you want to start quickly
Main line of the story.
106 - Water jets | tg / web
107 - Pouring-out velocity | tg / web
108 - Not exponential. This time. | tg / web
109 - New school level physics problem | tg / web
258 - The Water Tower | tg / web
272 - Jets are approximated | tg / web
273 - Stream model | tg / web
Full list
The device
106 - Water jets | tg / web
258 - The Water Tower | tg / web
270 - Experiment video and photo | tg / web
Physics of the jet
107 - Pouring-out velocity | tg / web
108 - Not exponential. This time. | tg / web
109 - New school level physics problem | tg / web
Experiment and model
272 - Jets are approximated | tg / web
273 - Stream model | tg / web
👍4❤1
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The troll farm.
My colleagues reminded me about a beautiful platform. It is a nice way to study programming languages and algorithms. Unlike leetcode, where the feedback is basically "your solution didn't pass test 573", in Codingame you can actually see how your program controls some characters, trolls this time.
The idea of the spring contest is simple. There are resources on the map: trees, water, iron, and so on. The map is symmetrical. You have a tent, and your opponent has a tent. Your program can issue simple commands: move your troll, chop, pick up, put down. The goal is to gather as much fruit and wood as possible.
Of course, I decided to participate using agents and a programming language (Rust) in which I have absolutely no experience.
Vibe programming quickly pushed me through the first two leagues and then I got stuck in the Gold league. It happened exactly when Fable became available again. It gave me a serious boost in the rankings, but not enough to beat Boss 5 and reach the next league.
Then ChatGPT 5.6 appeared and I reconstructed a bot from the next league. Unfortunately, Codingame decided to go offline, so I can not share my current position.
To have something entertaining to read during the flight, I rendered a document with small introduction to Rust and a description of bot's logic. It turned out to be really good reading. I thoroughly enjoyed it on the plane and even solved a few simple problems in Rust. I think I will share these funny little programs in the next posts.
My colleagues reminded me about a beautiful platform. It is a nice way to study programming languages and algorithms. Unlike leetcode, where the feedback is basically "your solution didn't pass test 573", in Codingame you can actually see how your program controls some characters, trolls this time.
The idea of the spring contest is simple. There are resources on the map: trees, water, iron, and so on. The map is symmetrical. You have a tent, and your opponent has a tent. Your program can issue simple commands: move your troll, chop, pick up, put down. The goal is to gather as much fruit and wood as possible.
Of course, I decided to participate using agents and a programming language (Rust) in which I have absolutely no experience.
Vibe programming quickly pushed me through the first two leagues and then I got stuck in the Gold league. It happened exactly when Fable became available again. It gave me a serious boost in the rankings, but not enough to beat Boss 5 and reach the next league.
Then ChatGPT 5.6 appeared and I reconstructed a bot from the next league. Unfortunately, Codingame decided to go offline, so I can not share my current position.
To have something entertaining to read during the flight, I rendered a document with small introduction to Rust and a description of bot's logic. It turned out to be really good reading. I thoroughly enjoyed it on the plane and even solved a few simple problems in Rust. I think I will share these funny little programs in the next posts.
👍1🔥1
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🤯4🌚1
Vibe trolling
I have already started talking about Trolls Farm. There are two camps on a symmetric map, and your task is to collect more resources than your opponent. As always in strategy games, you have to balance strategy and tactics. You can plant a lot of trees, collect a lot of fruit, and train a super power troll. But by the time you do that the game may already be ending, and your mighty troll will have no time left to collect resources.
Let us make this a question-and-answer session.
Why vibecoding? Because, unfortunately, I do not have enough time to write all the code myself. At the same time, optimal system management is a very interesting topic to me.
Why spend time on this at all? It is an interesting task at the edge of contemporary agentic programming abilities. It is also a good opportunity to compare OpenAi Sol with Fable.
And what? Fable stopped at 135th place, Sol super max brought me to 6th place in the global rating.
How should you approach a task like this? Give the agent tools for working with the platform: compile the code, submit it to the arena, and read the current position in the leaderboard. You do not need to run an MCP server. All the models can figure out the API and use it. Codex does this faster. Claude usually needs to be explicitly told to investigate the API and write the tools down.
Just run agent and wait? Not quite. It can give you the first prototype, but after that you have to keep pushing the development toward higher places.
What works? Analysis of recent battles. Looking for recurring weak points across a series of games. Numbers, statistics. Idiotically simple, but deep models of the world, like "gain per turn" for resources, which allows to write down a simple expression like tree_gain / round_trip_length and select goal according to this expression.
What doesn't work? Manual watching games and making guesses: "Plant more trees near the camp, use seeds to train powerful trolls!" It gives you +5 wins and +15 loses. Thinking about super complex algorithmic approaches and attempts to implement something, which "automatically finds the best moves just out of the game rules"
Do you feel satisfied? Not quite. Now I understand programming multiagent systems slightly better, but now I want to make a close analysis of the program, main ideas, moving parts. So stay tuned, the case is not closed yet.
I have already started talking about Trolls Farm. There are two camps on a symmetric map, and your task is to collect more resources than your opponent. As always in strategy games, you have to balance strategy and tactics. You can plant a lot of trees, collect a lot of fruit, and train a super power troll. But by the time you do that the game may already be ending, and your mighty troll will have no time left to collect resources.
Let us make this a question-and-answer session.
Why vibecoding? Because, unfortunately, I do not have enough time to write all the code myself. At the same time, optimal system management is a very interesting topic to me.
Why spend time on this at all? It is an interesting task at the edge of contemporary agentic programming abilities. It is also a good opportunity to compare OpenAi Sol with Fable.
And what? Fable stopped at 135th place, Sol super max brought me to 6th place in the global rating.
How should you approach a task like this? Give the agent tools for working with the platform: compile the code, submit it to the arena, and read the current position in the leaderboard. You do not need to run an MCP server. All the models can figure out the API and use it. Codex does this faster. Claude usually needs to be explicitly told to investigate the API and write the tools down.
Just run agent and wait? Not quite. It can give you the first prototype, but after that you have to keep pushing the development toward higher places.
What works? Analysis of recent battles. Looking for recurring weak points across a series of games. Numbers, statistics. Idiotically simple, but deep models of the world, like "gain per turn" for resources, which allows to write down a simple expression like tree_gain / round_trip_length and select goal according to this expression.
What doesn't work? Manual watching games and making guesses: "Plant more trees near the camp, use seeds to train powerful trolls!" It gives you +5 wins and +15 loses. Thinking about super complex algorithmic approaches and attempts to implement something, which "automatically finds the best moves just out of the game rules"
Do you feel satisfied? Not quite. Now I understand programming multiagent systems slightly better, but now I want to make a close analysis of the program, main ideas, moving parts. So stay tuned, the case is not closed yet.
🔥2