|
14901
|
My very own WebOS by @bhavyapi | Stardance
|
bhavyapi
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: My very own WebOS by @bhavyapi | Stardance
|
1793433395.6550682
|
1794038195.6550682
|
|
14902
|
My very own WebOS by @pratham_tekriwal17 | Stardan My very own WebOS by @pratham_tekriwal17 | Stardance...
|
pratham_tekriwal17
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: My very own WebOS by @pratham_tekriwal17 | Title: My very own WebOS by @pratham_tekriwal17 | Stardance...
|
1793227958.8824594
|
1793832758.8824594
|
|
14903
|
My first slack bot! by @abdur_rahman | Stardance
|
abdur_rahman
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: My first slack bot! by @abdur_rahman | Star Title: My first slack bot! by @abdur_rahman | Stardance...
|
1793425823.2621703
|
1794030623.2621703
|
|
14904
|
Untitled by @weesie023 | Stardance
|
weesie023
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: Untitled by @weesie023 | Stardance
|
1793382464.5382867
|
1793987264.5382867
|
|
14905
|
night / stop by @sk1lark | Stardance
|
sk1lark
|
0
|
1.58
|
0
|
[]
|
[{"post_id": 7823, "ts": "2026 [{"post_id": 7823, "ts": "2026-06-13T01:53:45Z", "hours": 0.5339, "text": "another 30 min of art :)"}, {"post_id": 7576, "ts": "2026-06-12T17:42:39Z", "hours": 1.0475, "text": "finished the basic layout of the starting room! this is (wip art version) what users would see before logging onto the cafe… i wonder whose room this is? even though this is the first thing people will see, it’s the last thing that will be explained in the lore."}]...
|
Title: night / stop by @sk1lark | Stardance
Devlo Title: night / stop by @sk1lark | Stardance
Devlogs:
finished the basic layout of the starting room! this is (wip art version) what users would see before logging onto the cafe… i wonder whose room this is? even though this is the first thing people will see, it’s the last thing that will be explained in the lore.
another 30 min of art :)...
|
1793195543.208211
|
1793800343.208211
|
|
14906
|
Unfathomablepad by @jphwb | Stardance
|
jphwb
|
1
|
6.0
|
0
|
[{"ship_num": 1, "ts": "2026-0 [{"ship_num": 1, "ts": "2026-06-19T18:41:07Z", "hours": 6.0, "multiplier": null, "payout": 0, "blessing": "neutral", "text": "Made an unfathomably bad macropad to use for my audio management.", "repo": "https://github.com/jphwba/unfathomablepad", "demo": ""}]...
|
[{"post_id": 12082, "ts": "202 [{"post_id": 12082, "ts": "2026-06-19T18:34:25Z", "hours": 1.0, "text": "made the firmware and updated the CAD to the Xiao can be accessed from outside, added labels to buttons, rounded corners and made a render with the parts."}, {"post_id": 7904, "ts": "2026-06-13T05:55:14Z", "hours": 3.0, "text": "Finished the CAD (im absolutely horrid at it) took me way too long to figure it out. Anyways I hope my screws were correct."}, {"post_id": 7531, "ts": "2026-06-12T16:03:07Z", "hours": 0.0, "text": "Created the PCB, I had somehting with more switches but it got too complex and I couldnt figure out how to do the tracing. I lost about 6ish hours bc the kicad wakatime app wasnt working."}]...
|
Title: Unfathomablepad by @jphwb | Stardance
Devl Title: Unfathomablepad by @jphwb | Stardance
Devlogs:
Created the PCB, I had somehting with more switches but it got too complex and I couldnt figure out how to do the tracing. I lost about 6ish hours bc the kicad wakatime app wasnt working.
Finished the CAD (im absolutely horrid at it) took me way too long to figure it out. Anyways I hope my screws were correct.
made the firmware and updated the CAD to the Xiao can be accessed from outside, added labels to buttons, rounded corners and made a render with the parts.
Ships:
Made an unfathomably bad macropad to use for my audio management....
|
1793689210.2459326
|
1793775610.2459326
|
|
14907
|
My very own WebOS by @para | Stardance
|
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: My very own WebOS by @para | Stardance
|
1793418763.3104236
|
1794023563.3104236
|
|
14908
|
My first slack bot! by @Iamhumanithink24 | Stardan My first slack bot! by @Iamhumanithink24 | Stardance...
|
Iamhumanithink24
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: My first slack bot! by @Iamhumanithink24 | Title: My first slack bot! by @Iamhumanithink24 | Stardance...
|
1793278349.696461
|
1793883149.696461
|
|
14909
|
My first slack bot! by @adi18 | Stardance
|
adi18
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: My first slack bot! by @adi18 | Stardance
|
1793386400.9364555
|
1793991200.9364555
|
|
14910
|
DTPAD by @denistrovas | Stardance
|
denistrovas
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: DTPAD by @denistrovas | Stardance
|
1793170559.9716942
|
1793775359.9716942
|
|
14911
|
My very own WebOS by @mohammedmujeebuddinshah | St My very own WebOS by @mohammedmujeebuddinshah | Stardance...
|
mohammedmujeebuddinshah
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: My very own WebOS by @mohammedmujeebuddinsh Title: My very own WebOS by @mohammedmujeebuddinshah | Stardance...
|
1793181329.735183
|
1793786129.735183
|
|
14912
|
My very own WebOS by @vartikachaudhari130 | Starda My very own WebOS by @vartikachaudhari130 | Stardance...
|
vartikachaudhari130
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: My very own WebOS by @vartikachaudhari130 | Title: My very own WebOS by @vartikachaudhari130 | Stardance...
|
1793206613.4915886
|
1793811413.4915886
|
|
14913
|
My first slack bot! by @mohammedmujeebuddinshah | My first slack bot! by @mohammedmujeebuddinshah | Stardance...
|
mohammedmujeebuddinshah
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: My first slack bot! by @mohammedmujeebuddin Title: My first slack bot! by @mohammedmujeebuddinshah | Stardance...
|
1793411653.466499
|
1794016453.466499
|
|
14914
|
Magic Mirror by @spakshots | Stardance
|
spakshots
|
0
|
1.07
|
0
|
[]
|
[{"post_id": 9532, "ts": "2026 [{"post_id": 9532, "ts": "2026-06-15T22:07:44Z", "hours": 0.4261, "text": "02 | Devlog Here is my MagicMirror. Personal profiles are live, the Obsidian vault is linked, and the mirror knows who’s in front of it. what I did: built out personal profiles with custom profile photos and rounded borders profile switching is fully automatic and activated via facial recognition after 20 seconds of no face detected, the mirror drops back to lock mode if the PIR also hasn’t triggered in those 20 seconds, it goes full sleep linked the mirror to my local Obsidian vault by parsing the daily markdown file daily objectives from my notes now show up live on the display how the profile switch works: the moment it picks up your face, your personal modules load in once you walk away, the 20 second timer starts. if there is no face and no motion then the mirror sleeps it’s a two-layer falloff that keeps the mirror from staying lit for no reason so that I can sleep at night Obsidian integration: I’m pulling from my daily notes vault by reading and parsing the markdown file directly on the pi. whatever objectives I’ve written for the day show up on the mirror automatically through my private github repository current stats: profiles: 2 (Brady + Camren) profile trigger: face recognition, automatic lock timeout: 20 seconds after last face detected sleep trigger: no PIR + no face for 20 seconds Obsidian source: local daily notes vault (syncing with github), parsed from markdown what’s next: implementing the SR-04 ultrasonic sensor soldering the modules CADing housing for the mirror modules"}, {"post_id": 6497, "ts": "2026-06-10T22:20:42Z", "hours": 0.6481, "text": "01 | Devlog Here is my MagicMirror. I successfully cloned the magicmirror repo to my raspberry pi, and now my phone’s apple calendar is finally showing up on the display. I plan to later implement facial recognition and integration into my obsidian vault. what I did: cloned the magicmirror repo and installed it on my pi configured the calendar to pull from my personal apple calendar time and weather displayed got facial recognition working on my logitech usb camera current stats: Pi model: 4B with 4GB ram Webcam: Logitech Pro Stream Webcam mirror UI: 120 fps facial recognition frames: 15 fps CV scalar: 1 I decided to go with a CV scalar of 1 to improve the model’s scanning at longer distances since the pi held up under the heavier processing. what’s next: I plan to add personal profiles based on the facial recognition model each profile will have their personalized calendar and sports teams’ schedule displayed then I’ll be adding a SR-501 InfraRed sensor and a SR-04 Ultrasonic sensor to wake up the mirror from sleep mode"}]...
|
Title: Magic Mirror by @spakshots | Stardance
Dev Title: Magic Mirror by @spakshots | Stardance
Devlogs:
01 | Devlog Here is my MagicMirror. I successfully cloned the magicmirror repo to my raspberry pi, and now my phone’s apple calendar is finally showing up on the display. I plan to later implement facial recognition and integration into my obsidian vault. what I did: cloned the magicmirror repo and installed it on my pi configured the calendar to pull from my personal apple calendar time and weather displayed got facial recognition working on my logitech usb camera current stats: Pi model: 4B with 4GB ram Webcam: Logitech Pro Stream Webcam mirror UI: 120 fps facial recognition frames: 15 fps CV scalar: 1 I decided to go with a CV scalar of 1 to improve the model’s scanning at longer distances since the pi held up under the heavier processing. what’s next: I plan to add personal profiles based on the facial recognition model each profile will have their personalized calendar and sports teams’ schedule displayed then I’ll be adding a SR-501 InfraRed sensor and a SR-04 Ultrasonic sensor to wake up the mirror from sleep mode
02 | Devlog Here is my MagicMirror. Personal profiles are live, the Obsidian vault is linked, and the mirror knows who’s in front of it. what I did: built out personal profiles with custom profile photos and rounded borders profile switching is fully automatic and activated via facial recognition after 20 seconds of no face detected, the mirror drops back to lock mode if the PIR also hasn’t triggered in those 20 seconds, it goes full sleep linked the mirror to my local Obsidian vault by parsing the daily markdown file daily objectives from my notes now show up live on the display how the profile switch works: the moment it picks up your face, your personal modules load in once you walk away, the 20 second timer starts. if there is no face and no motion then the mirror sleeps it’s a two-layer falloff that keeps the mirror from staying lit for no reason so that I can sleep at night Obsidian integration: I’m pulling from my daily notes vault by reading and parsing the markdown file directly on the pi. whatever objectives I’ve written for the day show up on the mirror automatically through my private github repository current stats: profiles: 2 (Brady + Camren) profile trigger: face recognition, automatic lock timeout: 20 seconds after last face detected sleep trigger: no PIR + no face for 20 seconds Obsidian source: local daily notes vault (syncing with github), parsed from markdown what’s next: implementing the SR-04 ultrasonic sensor soldering the modules CADing housing for the mirror modules...
|
1793180908.955639
|
1793785708.955639
|
|
14915
|
My very own WebOS by @n001 | Stardance
|
n001
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: My very own WebOS by @n001 | Stardance
|
1793382606.2168748
|
1793987406.2168748
|
|
14916
|
My awesome macropad! by @m_sidhu_lv | Stardance
|
m_sidhu_lv
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: My awesome macropad! by @m_sidhu_lv | Stard Title: My awesome macropad! by @m_sidhu_lv | Stardance...
|
1793282148.399894
|
1793886948.399894
|
|
14917
|
O.P.A.L by @Root | Stardance
|
Root
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: O.P.A.L by @Root | Stardance
|
1793483437.3215826
|
1794088237.3215826
|
|
14918
|
My first Self balancing robot by @aimaneon | Stard My first Self balancing robot by @aimaneon | Stardance...
|
aimaneon
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: My first Self balancing robot by @aimaneon Title: My first Self balancing robot by @aimaneon | Stardance...
|
1793314446.5727704
|
1793919246.5727704
|
|
14919
|
Kinetic-AG by @Q-Shadow | Stardance
|
Q-Shadow
|
0
|
1.8
|
0
|
[]
|
[{"post_id": 6848, "ts": "2026 [{"post_id": 6848, "ts": "2026-06-11T14:34:49Z", "hours": 0.5856, "text": "Improved the K-VL library."}, {"post_id": 6198, "ts": "2026-06-10T12:35:04Z", "hours": 1.2164, "text": "Kinetic-AG A Neuro-Symbolic Program Synthesis Framework for Fluid General Intelligence on ARC-AGI-2. ── Abstract Current Large Language Models excel at static retrieval and pattern replication, but they fundamentally fail at fluid intelligence—the ability to adapt to novel, unseen problems without explicit training data. The ARC-AGI-2 benchmark exposes this “Overfitting Trap” by forcing models to solve abstract visual-logic grids using fundamental Core Knowledge Priors. Kinetic-AG is an advanced neuro-symbolic framework designed to bypass the limitations of raw neural pixel-guessing. By transforming a generative model from a predictive writer into an autonomous programmer, Kinetic-AG uses test-time program synthesis and automated code verification to solve multi-step geometric transformations with mathematical certainty. ── The Architecture Kinetic-AG splits the cognitive load between neural perception and symbolic execution through two core pillars: 1. K-VL (Kinetic Vision-Logic Library) Instead of forcing the model to write complex NumPy matrix math from scratch—which introduces a high risk of syntax drift and boundary errors—we engineered K-VL , a custom Domain-Specific Language (DSL) embedded in Python. K-VL maps directly to human Core Knowledge Priors across four specialized modules: K_Space : Dynamically manages variable grid layouts, topology, and dimensions. K_Objects : Performs connected-component labeling to isolate, categorize, and filter spatial entities by color, coordinate geometry, or size. K_Transform : Executes physics-safe translation vectors, clockwise/counter-clockwise rotations, and axial mirroring. K_Color : Employs an optimized, graph-based Breadth-First Search (BFS) flood-fill engine for rendering complex painting logic. 2. Test-Time Program Synthesis & Automated Verification Kinetic-AG does not execute blind predictions on unseen test data. The framework implements a strict runtime execution gatekeeper ( verify_engine ): Hypothesis Generation : The core reasoning engine analyzes the visual patterns of an active puzzle and outputs a custom Python execution script using K-VL syntax. The Crucible (Sandbox Testing) : The framework dynamically compiles the script inside an isolated local sandbox, executing it against all given demonstration pairs (which dynamically scale up to 10 pairs depending on the task specification). Automated Rejection Sampling : If the script fails to match the output matrices of the demonstrations by even a single pixel, it is instantly rejected. The runtime error trace is recycled, and the engine generates a refined hypothesis. Verified Execution : Only when a code block achieves a perfect 100% validation rate across every single example pair does the framework execute the solution on the hidden test grid. ── Engineering Journey & Robustness Testing Building Kinetic-AG required navigating structural variations introduced in the updated arcprize/ARC-AGI-2 specification, where input spaces, training pair sizes ($\\le 10$), and target evaluation counts ($2\\text{ to }4$) fluctuate wildly. To guarantee architectural stability before scaling up computational training loops, we engineered an end-to-end data auditor script. We executed a mass robustness sweep across the entire official dataset, running thousands of extraction passes. The Result: The K-VL pipeline achieved a perfect, crash-free stability rating across the multi-grid structures of the dataset, logging every spatial metric into a structured compilation framework ( kvl_audit_report.json ) to serve as our downstream training profile."}]...
|
Title: Kinetic-AG by @Q-Shadow | Stardance
Devlog Title: Kinetic-AG by @Q-Shadow | Stardance
Devlogs:
Kinetic-AG A Neuro-Symbolic Program Synthesis Framework for Fluid General Intelligence on ARC-AGI-2. ── Abstract Current Large Language Models excel at static retrieval and pattern replication, but they fundamentally fail at fluid intelligence—the ability to adapt to novel, unseen problems without explicit training data. The ARC-AGI-2 benchmark exposes this “Overfitting Trap” by forcing models to solve abstract visual-logic grids using fundamental Core Knowledge Priors. Kinetic-AG is an advanced neuro-symbolic framework designed to bypass the limitations of raw neural pixel-guessing. By transforming a generative model from a predictive writer into an autonomous programmer, Kinetic-AG uses test-time program synthesis and automated code verification to solve multi-step geometric transformations with mathematical certainty. ── The Architecture Kinetic-AG splits the cognitive load between neural perception and symbolic execution through two core pillars: 1. K-VL (Kinetic Vision-Logic Library) Instead of forcing the model to write complex NumPy matrix math from scratch—which introduces a high risk of syntax drift and boundary errors—we engineered K-VL , a custom Domain-Specific Language (DSL) embedded in Python. K-VL maps directly to human Core Knowledge Priors across four specialized modules: K_Space : Dynamically manages variable grid layouts, topology, and dimensions. K_Objects : Performs connected-component labeling to isolate, categorize, and filter spatial entities by color, coordinate geometry, or size. K_Transform : Executes physics-safe translation vectors, clockwise/counter-clockwise rotations, and axial mirroring. K_Color : Employs an optimized, graph-based Breadth-First Search (BFS) flood-fill engine for rendering complex painting logic. 2. Test-Time Program Synthesis & Automated Verification Kinetic-AG does not execute blind predictions on unseen test data. The framework implements a strict runtime execution gatekeeper ( verify_engine ): Hypothesis Generation : The core reasoning engine analyzes the visual patterns of an active puzzle and outputs a custom Python execution script using K-VL syntax. The Crucible (Sandbox Testing) : The framework dynamically compiles the script inside an isolated local sandbox, executing it against all given demonstration pairs (which dynamically scale up to 10 pairs depending on the task specification). Automated Rejection Sampling : If the script fails to match the output matrices of the demonstrations by even a single pixel, it is instantly rejected. The runtime error trace is recycled, and the engine generates a refined hypothesis. Verified Execution : Only when a code block achieves a perfect 100% validation rate across every single example pair does the framework execute the solution on the hidden test grid. ── Engineering Journey & Robustness Testing Building Kinetic-AG required navigating structural variations introduced in the updated arcprize/ARC-AGI-2 specification, where input spaces, training pair sizes ($\le 10$), and target evaluation counts ($2\text{ to }4$) fluctuate wildly. To guarantee architectural stability before scaling up computational training loops, we engineered an end-to-end data auditor script. We executed a mass robustness sweep across the entire official dataset, running thousands of extraction passes. The Result: The K-VL pipeline achieved a perfect, crash-free stability rating across the multi-grid structures of the dataset, logging every spatial metric into a structured compilation framework ( kvl_audit_report.json ) to serve as our downstream training profile.
Improved the K-VL library....
|
1793159410.5166326
|
1793764210.5166326
|
|
14920
|
My first slack bot! by @rhutul | Stardance
|
rhutul
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: My first slack bot! by @rhutul | Stardance
|
1793458132.5416462
|
1794062932.5416462
|
|
14921
|
My awesome macropad! by @jerryjwang | Stardance
|
jerryjwang
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: My awesome macropad! by @jerryjwang | Stard Title: My awesome macropad! by @jerryjwang | Stardance...
|
1793371740.5443897
|
1793976540.5443897
|
|
14922
|
My awesome macropad! by @stephcrazy0621 | Stardanc My awesome macropad! by @stephcrazy0621 | Stardance...
|
stephcrazy0621
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: My awesome macropad! by @stephcrazy0621 | S Title: My awesome macropad! by @stephcrazy0621 | Stardance...
|
1793386126.3834908
|
1793990926.3834908
|
|
14923
|
My very own WebOS by @Potar | Stardance
|
Potar
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: My very own WebOS by @Potar | Stardance
|
1793177309.0167127
|
1793782109.0167127
|
|
14924
|
My awesome macropad! by @omkarr | Stardance
|
omkarr
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: My awesome macropad! by @omkarr | Stardance
|
1793357760.1304257
|
1793962560.1304257
|
|
14925
|
My very own WebOS by @danyah_zekar | Stardance
|
danyah_zekar
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: My very own WebOS by @danyah_zekar | Starda Title: My very own WebOS by @danyah_zekar | Stardance...
|
1793307517.0882099
|
1793912317.0882099
|
|
14926
|
Modular Flight Computer for High Powered Rocketry Modular Flight Computer for High Powered Rocketry by @1810anav | Stardance...
|
1810anav
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: Modular Flight Computer for High Powered Ro Title: Modular Flight Computer for High Powered Rocketry by @1810anav | Stardance...
|
1793450952.0935717
|
1794055752.0935717
|
|
14927
|
My awesome macropad! by @cold | Stardance
|
cold
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: My awesome macropad! by @cold | Stardance
|
1793188502.2379446
|
1793793302.2379446
|
|
14928
|
Chaos by @Snoopsawg | Stardance
|
Snoopsawg
|
1
|
12.0
|
0
|
[{"ship_num": 1, "ts": "2026-0 [{"ship_num": 1, "ts": "2026-08-07T07:54:33Z", "hours": 12.0, "multiplier": null, "payout": 0, "blessing": "neutral", "text": "Hi! Welcome to Chaos! Chaos theory is a branch of mathematics focusing on nonlinear systems that are highly sensitive to initial conditions, meaning small changes can lead to vastly different outcomes. For example, if you went and slapped Tom Holland on the face in Iceland, you could be the cause of a minion uprising against kangaroos in Australia. It’s just like that, but with tiny bit more realistic constraints. You may know it as “the butterfly effect”! n-pendulums work based on a similar principle. While single pendulums move via simple harmonic motion governed and constrained by mathematical equations, double, triple, quadruple pendulums listen to chaos theory. They’re completely random, and completely unpredictable! With my project, you can simulate a triple pendulum by messing around with the initial conditions (such as the initial angles etcetera), and then plot a ‘phase map.’", "repo": "https://github.com/ivanadua/chaos", "demo": "https://chaoticaa.streamlit.app/"}]...
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[{"post_id": 42481, "ts": "202 [{"post_id": 42481, "ts": "2026-08-07T07:53:15Z", "hours": 1.1842, "text": "READme and animation!"}, {"post_id": 37439, "ts": "2026-07-30T05:46:43Z", "hours": 0.9147, "text": "Phase Map: Finding Islands of Stability in Chaos import matplotlib.pyplot as plt from pendulum_equations import _lambdifygenerated as accel# # Number of pixels per axis (e.g., 200x200 = 40,000 pendulums simulated at once) GRID_RES = 200 L1 = L2 = L3 = 1.0 m1 = m2 = m3 = 1.0g = 9.81 theta1_range = np.linspace(-np.pi, np.pi, GRID_RES) theta2_range = np.linspace(-np.pi, np.pi, GRID_RES) T1, T2 = np.meshgrid(theta1_range, theta2_range) # Initialize a massive state grid: Shape is (GRID_RES, GRID_RES, 6) state_grid = np.zeros((GRID_RES, GRID_RES, 6), dtype=float) state_grid[:, :, 0] = T1 # X-axis of pixels maps to unique theta1 valuesstate_grid[:, :, 1] = T2 # Y-axis of pixels maps to unique theta2 valuesstate_grid[:, :, 2] = np.pi / 4 # Keep theta3 constant at 45 degrees for allstate_grid[:, :, 3] = 0.0 # omega1 = 0state_grid[:, :, 4] = 0.0 # omega2 = 0state_grid[:, :, 5] = 0.0 # omega3 = 0# def vectorized_derivatives(state): # Unpack the 3D grid axes t1 = state[:, :, 0] t2 = state[:, :, 1] t3 = state[:, :, 2] w1 = state[:, :, 3] w2 = state[:, :, 4] w3 = state[:, :, 5] a1, a2, a3 = accel(t1, t2, t3, w1, w2, w3, L1, L2, L3, m1, m2, m3, g) #Runge-Kutta return np.stack([w1, w2, w3, a1, a2, a3], axis=-1) def rk4_step(state, dt): k1 = vectorized_derivatives(state) k2 = vectorized_derivatives(state + 0.5 * dt * k1) k3 = vectorized_derivatives(state + 0.5 * dt * k2) k4 = vectorized_derivatives(state + dt * k3) return state + (dt / 6.0) * (k1 + 2.0 * k2 + 2.0 * k3 + k4) dt = 0.0005 total_steps = 15000 # Total time window to monitor flipping / divergence # To measure chaos vs stability, track the max velocity reached by each pendulum. # Chaotic pendulums will violently whip around (high max velocity/flipping). # Stable islands will stay tightly bound (very low max velocity). max_omega_observed = np.zeros((GRID_RES, GRID_RES)) print(f\"Simulating {GRID_RES}x{GRID_RES} ({GRID_RES**2}) pendulums simultaneously snoopsawggggg...\") for step in range(total_steps): state_grid = rk4_step(state_grid, dt) # Extract instantaneous absolute speeds of the middle and bottom bobs current_speeds = np.abs(state_grid[:, :, 4]) + np.abs(state_grid[:, :, 5]) # Store the peak (peak, get it, peak) velocity hit by each specific coordinate pixel max_omega_observed = np.maximum(max_omega_observed, current_speeds) if step % 3000 == 0 and step > 0: print(f\" -> Progress: {int((step/total_steps)*100)}% complete...\") print(\"Simulation finished! Puzzah! Rendering Stability Map...\")# plt.figure(figsize=(8, 8))# Logarithmic scaling brings out hidden geometric structures and fractal edges stability_map = np.log1p(max_omega_observed) plt.imshow( stability_map, extent=[-np.pi, np.pi, -np.pi, np.pi], origin='lower', cmap='inferno') plt.colorbar(label='Chaos Metric (Log Peak Angular Velocity)') plt.xlabel(r'Initial $\\theta_1$ (rad)') plt.ylabel(r'Initial $\\theta_2$ (rad)')plt.title('Triple Pendulum Phase Map: Finding Islands of Stability') plt.show()"}, {"post_id": 21971, "ts": "2026-07-04T05:55:42Z", "hours": 9.7961, "text": "Simulating the triple pendulum import sympy as sp import inspectt=sp.symbols('t')theta1=sp.Function('theta1')(t)theta2=sp.Function('theta2')(t)theta3=sp.Function('theta3')(t)L1, L2, L3 = sp.symbols('L1 L2 L3')m1, m2, m3 = sp.symbols('m1 m2 m3')x1= L1*sp.sin(theta1)y1= -L1*sp.cos(theta1)x2= x1 + L2*sp.sin(theta2)y2= y1 - L2*sp.cos(theta2)x3= x2 + L3*sp.sin(theta3)y3= y2 - L3*sp.cos(theta3)T1,T2,T3 = sp.symbols('T1 T2 T3')V1,V2,V3 = sp.symbols('V1 V2 V3')x1dd= sp.diff(x1,t,2)x2dd= sp.diff(x2,t,2)x3dd= sp.diff(x3,t,2)y1dd= sp.diff(y1,t,2)y2dd= sp.diff(y2,t,2)y3dd= sp.diff(y3,t,2)vx1=sp.diff(x1,t)vy1=sp.diff(y1,t)vx2=sp.diff(x2,t)vy2=sp.diff(y2,t)vx3=sp.diff(x3,t)vy3=sp.diff(y3,t)T = (sp.Rational(1,2)*m1*(vx1**2 + vy1**2) + sp.Rational(1,2)*m2*(vx2**2 + vy2**2) + sp.Rational(1,2)*m3*(vx3**2 + vy3**2))g = sp.symbols('g')V = m1*g*y1 + m2*g*y2 + m3*g*y3 Lagrangian = T - Vprint(\"Getting Lagrangian!!!\")Lagrangian = sp.simplify(Lagrangian)print(Lagrangian)print(\"Hey, we got the Lagrangian!\")#Euler-Lagrange equations:Euler1= sp.diff(sp.diff(Lagrangian, sp.diff(theta1,t)), t) - sp.diff(Lagrangian, theta1)Euler2= sp.diff(sp.diff(Lagrangian, sp.diff(theta2,t)), t) - sp.diff(Lagrangian, theta2)Euler3= sp.diff(sp.diff(Lagrangian, sp.diff(theta3,t)), t) - sp.diff(Lagrangian, theta3)theta1dd=sp.diff(theta1,t,2)theta2dd=sp.diff(theta2,t,2)theta3dd=sp.diff(theta3,t,2)derivs=[theta1dd, theta2dd, theta3dd]solution=sp.solve((Euler1, Euler2, Euler3), derivs, dict=True)alpha1 = solution[0][derivs[0]]alpha2 = solution[0][derivs[1]]alpha3 = solution[0][derivs[2]]#now i want to simplify the solution and then convert it to a function that can be used in numerical simulations.print(\"alpha1=\", alpha1)print(\"******************************************************************************************\")print(\"alpha2=\", alpha2)print(\"******************************************************************************************\")print(\"alpha3=\", alpha3)accel_func = sp.lambdify( (theta1, theta2, theta3, sp.diff(theta1,t), sp.diff(theta2,t), sp.diff(theta3,t), L1, L2, L3, m1, m2, m3, g), (solution[0][theta1dd], solution[0][theta2dd], solution[0][theta3dd]), \"numpy\")print(\"Writing equations with proper NumPy math imports...\")with open(\"pendulum_equations.py\", \"w\") as f: f.write(\"import numpy as np\\n\") # This injects the missing sin and cos tools directly into the math module! f.write(\"from numpy import sin, cos\\n\\n\") f.write(inspect.getsource(accel_func))print(\"Successfully saved math script to 'pendulum_equations.py'!\")# ... Your existing derivation code that creates 'accel' ...# #x1dd= -L1*((theta1.diff()*theta1.diff())*sp.sin(theta1)) + (theta1.diff().diff()*(L1*sp.cos(theta1)))# x2dd=x1dd - (L2*theta2.diff()*theta2.diff()*sp.sin(theta2)) + (theta2.diff().diff()*L2*sp.cos(theta2))# x3dd=x2dd - (theta3.diff()*theta3.diff()*L3*sp.sin(theta3)) + (theta3.diff().diff()*sp.sin(theta3)*L3)# y1dd= (theta1.diff()*theta1.diff()*L1*sp.cos(theta1))+(theta1.diff().diff()*L1*sp.sin(theta1))# y2dd=y1dd + (theta2.diff()*theta2.diff()*L2*sp.cos(theta2))+(theta2.diff().diff()*L2*sp.sin(theta2))# y3dd=y2dd + (theta3.diff()*theta3.diff()*L3*sp.cos(theta3))+(theta3.diff().diff()*L3*sp.sin(theta3))# vx1=sp.diff(x1,t)# vy1=sp.diff(y1,t)# vx2=sp.diff(x2,t)# vy2=sp.diff(y2,t)# vx3=sp.diff(x3,t)# vy3=sp.diff(y3,t)# T= # #Equation1 # eq1 =sp.Eq(m1*x1dd, (-T1*sp.sin(theta1))+(T2*sp.sin(theta2)))# #Equation2# eq2 = sp.Eq(m1*y1dd, (T1*sp.cos(theta1))-(T2*sp.cos(theta2))-(m1*9.81))# #Equation3# eq3 = sp.Eq(m2*x2dd, (-T2*sp.sin(theta2))+(T3*sp.sin(theta3)))# #Equation4# eq4 = sp.Eq(m2*y2dd, (T2*sp.cos(theta2))-(T3*sp.cos(theta3))-(m2*9.81))# #Equation5# eq5 = sp.Eq(m3*x3dd, (-T3*sp.sin(theta3))# #Equation6# eq6 = sp.Eq(m3*y3dd, (T3*sp.cos(theta3)) - (m3*9.81))# sp.solve([eq1, eq2, eq3, eq4, eq5, eq6], (theta1.diff().diff(), theta2.diff().diff(), theta3.diff().diff(), T1, T2, T3)), dict=True"}]...
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Title: Chaos by @Snoopsawg | Stardance
Devlogs:
S Title: Chaos by @Snoopsawg | Stardance
Devlogs:
Simulating the triple pendulum import sympy as sp import inspectt=sp.symbols('t')theta1=sp.Function('theta1')(t)theta2=sp.Function('theta2')(t)theta3=sp.Function('theta3')(t)L1, L2, L3 = sp.symbols('L1 L2 L3')m1, m2, m3 = sp.symbols('m1 m2 m3')x1= L1*sp.sin(theta1)y1= -L1*sp.cos(theta1)x2= x1 + L2*sp.sin(theta2)y2= y1 - L2*sp.cos(theta2)x3= x2 + L3*sp.sin(theta3)y3= y2 - L3*sp.cos(theta3)T1,T2,T3 = sp.symbols('T1 T2 T3')V1,V2,V3 = sp.symbols('V1 V2 V3')x1dd= sp.diff(x1,t,2)x2dd= sp.diff(x2,t,2)x3dd= sp.diff(x3,t,2)y1dd= sp.diff(y1,t,2)y2dd= sp.diff(y2,t,2)y3dd= sp.diff(y3,t,2)vx1=sp.diff(x1,t)vy1=sp.diff(y1,t)vx2=sp.diff(x2,t)vy2=sp.diff(y2,t)vx3=sp.diff(x3,t)vy3=sp.diff(y3,t)T = (sp.Rational(1,2)*m1*(vx1**2 + vy1**2) + sp.Rational(1,2)*m2*(vx2**2 + vy2**2) + sp.Rational(1,2)*m3*(vx3**2 + vy3**2))g = sp.symbols('g')V = m1*g*y1 + m2*g*y2 + m3*g*y3 Lagrangian = T - Vprint("Getting Lagrangian!!!")Lagrangian = sp.simplify(Lagrangian)print(Lagrangian)print("Hey, we got the Lagrangian!")#Euler-Lagrange equations:Euler1= sp.diff(sp.diff(Lagrangian, sp.diff(theta1,t)), t) - sp.diff(Lagrangian, theta1)Euler2= sp.diff(sp.diff(Lagrangian, sp.diff(theta2,t)), t) - sp.diff(Lagrangian, theta2)Euler3= sp.diff(sp.diff(Lagrangian, sp.diff(theta3,t)), t) - sp.diff(Lagrangian, theta3)theta1dd=sp.diff(theta1,t,2)theta2dd=sp.diff(theta2,t,2)theta3dd=sp.diff(theta3,t,2)derivs=[theta1dd, theta2dd, theta3dd]solution=sp.solve((Euler1, Euler2, Euler3), derivs, dict=True)alpha1 = solution[0][derivs[0]]alpha2 = solution[0][derivs[1]]alpha3 = solution[0][derivs[2]]#now i want to simplify the solution and then convert it to a function that can be used in numerical simulations.print("alpha1=", alpha1)print("******************************************************************************************")print("alpha2=", alpha2)print("******************************************************************************************")print("alpha3=", alpha3)accel_func = sp.lambdify( (theta1, theta2, theta3, sp.diff(theta1,t), sp.diff(theta2,t), sp.diff(theta3,t), L1, L2, L3, m1, m2, m3, g), (solution[0][theta1dd], solution[0][theta2dd], solution[0][theta3dd]), "numpy")print("Writing equations with proper NumPy math imports...")with open("pendulum_equations.py", "w") as f: f.write("import numpy as np\n") # This injects the missing sin and cos tools directly into the math module! f.write("from numpy import sin, cos\n\n") f.write(inspect.getsource(accel_func))print("Successfully saved math script to 'pendulum_equations.py'!")# ... Your existing derivation code that creates 'accel' ...# #x1dd= -L1*((theta1.diff()*theta1.diff())*sp.sin(theta1)) + (theta1.diff().diff()*(L1*sp.cos(theta1)))# x2dd=x1dd - (L2*theta2.diff()*theta2.diff()*sp.sin(theta2)) + (theta2.diff().diff()*L2*sp.cos(theta2))# x3dd=x2dd - (theta3.diff()*theta3.diff()*L3*sp.sin(theta3)) + (theta3.diff().diff()*sp.sin(theta3)*L3)# y1dd= (theta1.diff()*theta1.diff()*L1*sp.cos(theta1))+(theta1.diff().diff()*L1*sp.sin(theta1))# y2dd=y1dd + (theta2.diff()*theta2.diff()*L2*sp.cos(theta2))+(theta2.diff().diff()*L2*sp.sin(theta2))# y3dd=y2dd + (theta3.diff()*theta3.diff()*L3*sp.cos(theta3))+(theta3.diff().diff()*L3*sp.sin(theta3))# vx1=sp.diff(x1,t)# vy1=sp.diff(y1,t)# vx2=sp.diff(x2,t)# vy2=sp.diff(y2,t)# vx3=sp.diff(x3,t)# vy3=sp.diff(y3,t)# T= # #Equation1 # eq1 =sp.Eq(m1*x1dd, (-T1*sp.sin(theta1))+(T2*sp.sin(theta2)))# #Equation2# eq2 = sp.Eq(m1*y1dd, (T1*sp.cos(theta1))-(T2*sp.cos(theta2))-(m1*9.81))# #Equation3# eq3 = sp.Eq(m2*x2dd, (-T2*sp.sin(theta2))+(T3*sp.sin(theta3)))# #Equation4# eq4 = sp.Eq(m2*y2dd, (T2*sp.cos(theta2))-(T3*sp.cos(theta3))-(m2*9.81))# #Equation5# eq5 = sp.Eq(m3*x3dd, (-T3*sp.sin(theta3))# #Equation6# eq6 = sp.Eq(m3*y3dd, (T3*sp.cos(theta3)) - (m3*9.81))# sp.solve([eq1, eq2, eq3, eq4, eq5, eq6], (theta1.diff().diff(), theta2.diff().diff(), theta3.diff().diff(), T1, T2, T3)), dict=True
Phase Map: Finding Islands of Stability in Chaos import matplotlib.pyplot as plt from pendulum_equations import _lambdifygenerated as accel# # Number of pixels per axis (e.g., 200x200 = 40,000 pendulums simulated at once) GRID_RES = 200 L1 = L2 = L3 = 1.0 m1 = m2 = m3 = 1.0g = 9.81 theta1_range = np.linspace(-np.pi, np.pi, GRID_RES) theta2_range = np.linspace(-np.pi, np.pi, GRID_RES) T1, T2 = np.meshgrid(theta1_range, theta2_range) # Initialize a massive state grid: Shape is (GRID_RES, GRID_RES, 6) state_grid = np.zeros((GRID_RES, GRID_RES, 6), dtype=float) state_grid[:, :, 0] = T1 # X-axis of pixels maps to unique theta1 valuesstate_grid[:, :, 1] = T2 # Y-axis of pixels maps to unique theta2 valuesstate_grid[:, :, 2] = np.pi / 4 # Keep theta3 constant at 45 degrees for allstate_grid[:, :, 3] = 0.0 # omega1 = 0state_grid[:, :, 4] = 0.0 # omega2 = 0state_grid[:, :, 5] = 0.0 # omega3 = 0# def vectorized_derivatives(state): # Unpack the 3D grid axes t1 = state[:, :, 0] t2 = state[:, :, 1] t3 = state[:, :, 2] w1 = state[:, :, 3] w2 = state[:, :, 4] w3 = state[:, :, 5] a1, a2, a3 = accel(t1, t2, t3, w1, w2, w3, L1, L2, L3, m1, m2, m3, g) #Runge-Kutta return np.stack([w1, w2, w3, a1, a2, a3], axis=-1) def rk4_step(state, dt): k1 = vectorized_derivatives(state) k2 = vectorized_derivatives(state + 0.5 * dt * k1) k3 = vectorized_derivatives(state + 0.5 * dt * k2) k4 = vectorized_derivatives(state + dt * k3) return state + (dt / 6.0) * (k1 + 2.0 * k2 + 2.0 * k3 + k4) dt = 0.0005 total_steps = 15000 # Total time window to monitor flipping / divergence # To measure chaos vs stability, track the max velocity reached by each pendulum. # Chaotic pendulums will violently whip around (high max velocity/flipping). # Stable islands will stay tightly bound (very low max velocity). max_omega_observed = np.zeros((GRID_RES, GRID_RES)) print(f"Simulating {GRID_RES}x{GRID_RES} ({GRID_RES**2}) pendulums simultaneously snoopsawggggg...") for step in range(total_steps): state_grid = rk4_step(state_grid, dt) # Extract instantaneous absolute speeds of the middle and bottom bobs current_speeds = np.abs(state_grid[:, :, 4]) + np.abs(state_grid[:, :, 5]) # Store the peak (peak, get it, peak) velocity hit by each specific coordinate pixel max_omega_observed = np.maximum(max_omega_observed, current_speeds) if step % 3000 == 0 and step > 0: print(f" -> Progress: {int((step/total_steps)*100)}% complete...") print("Simulation finished! Puzzah! Rendering Stability Map...")# plt.figure(figsize=(8, 8))# Logarithmic scaling brings out hidden geometric structures and fractal edges stability_map = np.log1p(max_omega_observed) plt.imshow( stability_map, extent=[-np.pi, np.pi, -np.pi, np.pi], origin='lower', cmap='inferno') plt.colorbar(label='Chaos Metric (Log Peak Angular Velocity)') plt.xlabel(r'Initial $\theta_1$ (rad)') plt.ylabel(r'Initial $\theta_2$ (rad)')plt.title('Triple Pendulum Phase Map: Finding Islands of Stability') plt.show()
READme and animation!
Ships:
Hi! Welcome to Chaos! Chaos theory is a branch of mathematics focusing on nonlinear systems that are highly sensitive to initial conditions, meaning small changes can lead to vastly different outcomes. For example, if you went and slapped Tom Holland on the face in Iceland, you could be the cause of a minion uprising against kangaroos in Australia. It’s just like that, but with tiny bit more realistic constraints. You may know it as “the butterfly effect”! n-pendulums work based on a similar principle. While single pendulums move via simple harmonic motion governed and constrained by mathematical equations, double, triple, quadruple pendulums listen to chaos theory. They’re completely random, and completely unpredictable! With my project, you can simulate a triple pendulum by messing around with the initial conditions (such as the initial angles etcetera), and then plot a ‘phase map.’...
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1793156029.037444
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1793760829.037444
|
|
14943
|
My very own WebOS by @Azaanboi | Stardance
|
Azaanboi
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: My very own WebOS by @Azaanboi | Stardance
|
1793216852.0005207
|
1793821652.0005207
|
|
14944
|
My awesome macropad! by @Anamika_ | Stardance
|
Anamika_
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: My awesome macropad! by @Anamika_ | Stardan Title: My awesome macropad! by @Anamika_ | Stardance...
|
1793206168.494235
|
1793810968.494235
|
|
14945
|
Visiting the stars by @kamarthapusri | Stardance
|
kamarthapusri
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: Visiting the stars by @kamarthapusri | Star Title: Visiting the stars by @kamarthapusri | Stardance...
|
1793526878.1390889
|
1794131678.1390889
|
|
14946
|
service lead responser by @ajez | Stardance
|
ajez
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: service lead responser by @ajez | Stardance
|
1793141331.995069
|
1793746131.995069
|
|
14947
|
My first slack bot! by @annasanaia5 | Stardance
|
annasanaia5
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: My first slack bot! by @annasanaia5 | Stard Title: My first slack bot! by @annasanaia5 | Stardance...
|
1793440236.9594202
|
1794045036.9594202
|
|
14948
|
My JARVIS by @denistrovas | Stardance
|
denistrovas
|
0
|
2.41
|
0
|
[]
|
[{"post_id": 7952, "ts": "2026 [{"post_id": 7952, "ts": "2026-06-13T08:13:38Z", "hours": 2.4075, "text": "I am working on a ΑΙ chatbot, that can control my smart home and my PC! Now I try to connect it with my smart home app ti open the lights! I am also working to recognise my voice!"}]...
|
Title: My JARVIS by @denistrovas | Stardance
Devl Title: My JARVIS by @denistrovas | Stardance
Devlogs:
I am working on a ΑΙ chatbot, that can control my smart home and my PC! Now I try to connect it with my smart home app ti open the lights! I am also working to recognise my voice!...
|
1793512590.1000152
|
1794117390.1000152
|
|
14949
|
My awesome macropad! by @Bilal | Stardance
|
Bilal
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: My awesome macropad! by @Bilal | Stardance
|
1793476283.4841952
|
1794081083.4841952
|
|
14950
|
Untitled by @kewlcactus13 | Stardance
|
kewlcactus13
|
0
|
0.0
|
0
|
[]
|
[]
|
Title: Untitled by @kewlcactus13 | Stardance
|
1793508901.115015
|
1794113701.115015
|