Tootfinder

Long post, game design
Crungle is a game designed to be a simple test of general reasoning skills that's difficult to play by rote memory, since there are many possible rule sets, but it should be easy to play if one can understand and extrapolate from rules. The game is not necessarily fair, with the first player often having an advantage or a forced win. The game is entirely deterministic, although a variant determines the rule set randomly.
This is version 0.1, and has not yet been tested at all.
Crungle is a competitive game for two players, each of whom controls a single piece on a 3x3 grid. The cells of the grid are numbered from 1 to 9, starting at the top left and proceeding across each row and then down to the next row, so the top three cells are 1, 2, and 3 from left to right, then the next three are 4, 5, and 6 and the final row is cells 7, 8, and 9.
The two players decide who shall play as purple and who shall play as orange. Purple goes first, starting the rules phase by picking one goal rule from the table of goal rules. Next, orange picks a goal rule. These two goal rules determine the two winning conditions. Then each player, starting with orange, alternate picking a movement rule until four movement rules have been selected. During this process, at most one indirect movement rule may be selected. Finally, purple picks a starting location for orange (1-9), with 5 (the center) not allowed. Then orange picks the starting location for purple, which may not be adjacent to orange's starting position.
Alternatively, the goal rules, movement rules, and starting positions may be determined randomly, or a pre-determined ruleset may be selected.
If the ruleset makes it impossible to win, the players should agree to a draw. Either player could instead "bet" their opponent. If the opponent agrees to the bet, the opponent must demonstrate a series of moves by both players that would result in a win for either player. If they can do this, they win, but if they submit an invalid demonstration or cannot submit a demonstration, the player who "bet" wins.
Now that starting positions, movement rules, and goals have been decided, the play phase proceeds with each player taking a turn, starting with purple, until one player wins by satisfying one of the two goals, or until the players agree to a draw. Note that it's possible for both players to occupy the same space.
During each player's turn, that player identifies one of the four movement rules to use and names the square they move to using that rule, then they move their piece into that square and their turn ends. Neither player may use the same movement rule twice in a row (but it's okay to use the same rule your opponent just did unless another rule disallows that). If the movement rule a player picks moves their opponent's piece, they need to state where their opponent's piece ends up. Pieces that would move off the board instead stay in place; it's okay to select a rule that causes your piece to stay in place because of this rule. However, if a rule says "pick a square" or "move to a square" with some additional criteria, but there are no squares that meet those criteria, then that rule may not be used, and a player who picks that rule must pick a different one instead.
Any player who incorrectly states a destination for either their piece or their opponent's piece, picks an invalid square, or chooses an invalid rule has made a violation, as long as their opponent objects before selecting their next move. A player who makes at least three violations immediately forfeits and their opponent wins by default. However, if a player violates a rule but their opponent does not object before picking their next move, the stated destination(s) of the invalid move still stand, and the violation does not count. If a player objects to a valid move, their objection is ignored, and if they do this at least three times, they forfeit and their opponent wins by default.
Goal rules (each player picks one; either player can win using either chosen rule):
End your turn in the same space as your opponent three turns in a row.
End at least one turn in each of the 9 cells.
End five consecutive turns in the three cells in any single row, ending at least one turn on each of the three.
End five consecutive turns in the three cells in any single column, ending at least one turn on each of the three.
Within the span of 8 consecutive turns, end at least one turn in each of cells 1, 3, 7, and 9 (the four corners of the grid).
Within the span of 8 consecutive turns at least one turn in each of cells 2, 4, 6, and 8 (the central cells on each side).
Within the span of 8 consecutive turns, end at least one turn in the cell directly above your opponent, and end at least one turn in the cell directly below your opponent (in either order).
Within the span of 8 consecutive turns at least one turn in the cell directly to the left of your opponent, and end at least one turn in the cell directly to the right of your opponent (in either order).
End 12 turns in a row without ending any of them in cell 5.
End 8 turns in a row in 8 different cells.
Movement rules (each player picks two; either player may move using any of the four):
Move to any cell on the board that's diagonally adjacent to your current position.
Move to any cell on the board that's orthogonally adjacent to your current position.
Move up one cell. Also move your opponent up one cell.
Move down one cell. Also move your opponent down one cell.
Move left one cell. Also move your opponent left one cell.
Move right one cell. Also move your opponent right one cell.
Move up one cell. Move your opponent down one cell.
Move down one cell. Move your opponent up one cell.
Move left one cell. Move your opponent right one cell.
Move right one cell. Move your opponent left one cell.
Move any pieces that aren't in square 5 clockwise around the edge of the board 1 step (for example, from 1 to 2 or 3 to 6 or 9 to 8).
Move any pieces that aren't in square 5 counter-clockwise around the edge of the board 1 step (for example, from 1 to 4 or 6 to 3 or 7 to 8).
Move to any square reachable from your current position by a knight's move in chess (in other words, a square that's in an adjacent column and two rows up or down, or that's in an adjacent row and two columns left or right).
Stay in the same place.
Swap places with your opponent's piece.
Move back to the position that you started at on your previous turn.
If you are on an odd-numbered square, move to any other odd-numbered square. Otherwise, move to any even-numbered square.
Move to any square in the same column as your current position.
Move to any square in the same row as your current position.
Move to any square in the same column as your opponent's position.
Move to any square in the same row as your opponent's position.
Pick a square that's neither in the same row as your piece nor in the same row as your opponent's piece. Move to that square.
Pick a square that's neither in the same column as your piece nor in the same column as your opponent's piece. Move to that square.
Move to one of the squares orthogonally adjacent to your opponent's piece.
Move to one of the squares diagonally adjacent to your opponent's piece.
Move to the square opposite your current position across the middle square, or stay in place if you're in the middle square.
Pick any square that's closer to your opponent's piece than the square you're in now, measured using straight-line distance between square centers (this includes the square your opponent is in). Move to that square.
Pick any square that's further from your opponent's piece than the square you're in now, measured using straight-line distance between square centers. Move to that square.
If you are on a corner square (1, 3, 7, or 9) move to any other corner square. Otherwise, move to square 5.
If you are on an edge square (2, 4, 6, or 8) move to any other edge square. Otherwise, move to square 5.
Indirect movement rules (may be chosen instead of a direct movement rule; at most one per game):
Move using one of the other three movement rules selected in your game, and in addition, your opponent may not use that rule on their next turn (nor may they select it via an indirect rule like this one).
Select two of the other three movement rules, declare them, and then move as if you had used one and then the other, applying any additional effects of both rules in order.
Move using one of the other three movement rules selected in your game, but if the move would cause your piece to move off the board, instead of staying in place move to square 5 (in the middle).
Pick one of the other three movement rules selected in your game and apply it, but move your opponent's piece instead of your own piece. If that movement rule says to move "your opponent's piece," instead apply that movement to your own piece. References to "your position" and "your opponent's position" are swapped when applying the chosen rule, as are references to "your turn" and "your opponent's turn" and do on.
#Game #GameDesign

I’m glad to have never lived in such places that are only accessible by car. Just cities and 'burbs built up before the phenomenon of encapsulated residential developments became dominant. I always walked (or more often biked) to school as a kid and so did my kids (except for K, of course)
I wonder what sort of effect that has on the psyche. Seems like a recipe for people predisposed to xenophobia.

TL;DR: what if nationalism, not anarchy, is futile?
Since I had the pleasure of seeing the "what would anarchists do against a warlord?" argument again in my timeline, I'll present again my extremely simple proposed solution:
Convince the followers of the warlord that they're better off joining you in freedom, then kill or exile the warlord once they're alone or vastly outnumbered.
Remember that even in our own historical moment where nothing close to large-scale free society has existed in living memory, the warlord's promise of "help me oppress others and you'll be richly rewarded" is a lie that many understand is historically a bad bet. Many, many people currently take that bet, for a variety of reasons, and they're enough to coerce through fear an even larger number of others. But although we imagine, just as the medieval peasants might have imagined of monarchy, that such a structure is both the natural order of things and much too strong to possibly fail, in reality it takes an enormous amount of energy, coordination, and luck for these structures to persist! Nations crumble every day, and none has survived more than a couple *hundred* years, compared to pre-nation societies which persisted for *tends of thousands of years* if not more. I'm this bubbling froth of hierarchies, the notion that hierarchy is inevitable is certainly popular, but since there's clearly a bit of an ulterior motive to make (and teach) that claim, I'm not sure we should trust it.
So what I believe could form the preconditions for future anarchist societies to avoid the "warlord problem" is merely: a widespread common sense belief that letting anyone else have authority over you is morally suspect. Given such a belief, a warlord will have a hard time building any following at all, and their opponents will have an easy time getting their supporters to defect. In fact, we're already partway there, relative to the situation a couple hundred years ago. At that time, someone could claim "you need to obey my orders and fight and die for me because the Queen was my mother" and that was actually a quite successful strategy. Nowadays, this strategy is only still working in a few isolated places, and the idea that one could *start a new monarchy* or even resurrect a defunct one seems absurd. So why can't that same transformation from "this is just how the world works" to "haha, how did anyone ever believe *that*? also happen to nationalism in general? I don't see an obvious reason why not.
Now I think one popular counterargument to this is: if you think non-state societies can win out with these tactics, why didn't they work for American tribes in the face of the European colonizers? (Or insert your favorite example of colonialism here.) I think I can imagine a variety of reasons, from the fact that many of those societies didn't try this tactic (and/or were hierarchical themselves), to the impacts of disease weakening those societies pre-contact, to the fact that with much-greater communication and education possibilities it might work better now, to the fact that most of those tribes are *still* around, and a future in which they persist longer than the colonist ideologies actually seems likely to me, despite the fact that so much cultural destruction has taken place. In fact, if the modern day descendants of the colonized tribes sow the seeds of a future society free of colonialism, that's the ultimate demonstration of the futility of hierarchical domination (I just read "Theory of Water" by Leanne Betasamosake Simpson).
I guess the TL;DR on this is: what if nationalism is actually as futile as monarchy, and we're just unfortunately living in the brief period during which it is ascendant?

Offline Reinforcement Learning with Generative Trajectory Policies
Xinsong Feng, Leshu Tang, Chenan Wang, Haipeng Chen
https://arxiv.org/abs/2510.11499 https://

Offline Reinforcement Learning with Generative Trajectory Policies
Generative models have emerged as a powerful class of policies for offline reinforcement learning (RL) due to their ability to capture complex, multi-modal behaviors. However, existing methods face a stark trade-off: slow, iterative models like diffusion policies are computationally expensive, while fast, single-step models like consistency policies often suffer from degraded performance. In this paper, we demonstrate that it is possible to bridge this gap. The key to moving beyond the limitati…

TwinTac: A Wide-Range, Highly Sensitive Tactile Sensor with Real-to-Sim Digital Twin Sensor Model
Xiyan Huang, Zhe Xu, Chenxi Xiao
https://arxiv.org/abs/2509.10063 https://

TwinTac: A Wide-Range, Highly Sensitive Tactile Sensor with Real-to-Sim Digital Twin Sensor Model
Robot skill acquisition processes driven by reinforcement learning often rely on simulations to efficiently generate large-scale interaction data. However, the absence of simulation models for tactile sensors has hindered the use of tactile sensing in such skill learning processes, limiting the development of effective policies driven by tactile perception. To bridge this gap, we present TwinTac, a system that combines the design of a physical tactile sensor with its digital twin model. Our har…

Lightweight CNNs for Embedded SAR Ship Target Detection and Classification
Fabian Kresse, Georgios Pilikos, Mario Azcueta, Nicolas Floury
https://arxiv.org/abs/2508.10712 https:…

Lightweight CNNs for Embedded SAR Ship Target Detection and Classification
Synthetic Aperture Radar (SAR) data enables large-scale surveillance of maritime vessels. However, near-real-time monitoring is currently constrained by the need to downlink all raw data, perform image focusing, and subsequently analyze it on the ground. On-board processing to generate higher-level products could reduce the data volume that needs to be downlinked, alleviating bandwidth constraints and minimizing latency. However, traditional image focusing and processing algorithms face challen…

Analysis and Design of Spare Strategy for Large-Scale Satellite Constellation Using Direct Insertion under (r,q) Policy
Seungyeop Han, Zachary Grieser, Shoji Yoshikawa, Takumi Noro, Takumi Suda, Koki Ho
https://arxiv.org/abs/2509.10585

Analysis and Design of Spare Strategy for Large-Scale Satellite Constellation Using Direct Insertion under (r,q) Policy
This paper introduces a Markov chain-based approach for the analysis and optimization of spare-management policies in large-scale satellite constellations. Focusing on the direct strategy, we model spare replenishment as a periodic-review reorder-point/order-quantity policy, where spares are deployed directly to constellation planes. The stochastic behavior of satellite failures and launch vehicle lead times is captured through Markov representations of both failure and replenishment dynamics. …

The A.I. insurance market is in its infancy, but Mr. Kvist says mainstream insurers are lining up to back him.
One of his clients is a job recruiting company that uses A.I. to sift through candidates.
“Which is great, but you can now discriminate at a scale we’ve never seen before,” Mr. Kvist said.
“It’s a breeding ground for class-action lawsuits.”
Mr. Kvist believes the work he is doing now will lay the foundation for more complex A.I. insurance policies to come.

Opinion | The A.I. Prompt That Could End the World
A destructive A.I., like a nuclear bomb, is now a concrete possibility; the question is whether anyone will be reckless enough to build one.

I felt such a deep surge of hope reading this story. Toronto the bad, of all places.
https://www.theguardian.com/world/2025/oct/09/toronto-waterfront-soil-plants-worms

Besides the bruised bananas I am chuffed at today's ride to the store and general adventure ride around town, going places I didn't think I could get to by bike. I rode about 13 miles and a little over an hour. The ebike is great for outrunning traffic or getting up those nasty hills. I still go really slow (PAS 1 or turned off) when on the crowded Oak Leaf Trail.
#mke