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I think I've pushed Figma's new Variables and Advanced prototyping to its limit... I present a fully working version of Flappy Bird!! 🔥 Featuring: - Scoring + highscores - Pseudo-randomised pipe placement - Physics based movement - Uses 46 variables #Config2023

Dave Williames

4,279 subscribers

1,146,613 views • 3 years ago •via X (Twitter)

Science & Technology Arts Gaming #Config2023

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10 Comments

Dave Williames3 years ago

Most of this is done by creating a component to work as the game loop, infinitely swapping between two variants every few milliseconds. Each variant holds a "logic" component which will automatically run a bunch of conditions and set variables each step of the game loop.

Dave Williames3 years ago

To move the position of the bird, I mapped the bottom padding of an Auto Layout frame to the 'yPosition' variable.

Dave Williames3 years ago

Just published the file for this now!

Florian Schulz3 years ago

@figma Okay I need to digest this for a minute 😱 Not sure I’d have the patience to click all this together myself, but it definitely shows how powerful the new feature is. Thanks for sharing!

Andrei Iancu3 years ago

@figma Did you start making this after config, or early access? 👀 If you've done this in ~1 week, then I'll take it as a proof that the 👽 are among us. 😆

Dave Williames3 years ago

@figma Haha, thanks Andrei! 🙌 I didn't have early access at all, I just fiddled around for a couple of hours across the weekend to make it work.

vijay verma3 years ago

@figma This is amazing. I'm on it to creating a Farming game :)

Nour Sofanati3 years ago

@figma Figma is turing complete now.

Chris Frantz3 years ago

@figma Dev mode on this thing will be wild

Charles Patterson3 years ago

@figma God mode activated

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The Bugged Dev

104,917 views • 2 months ago

How being in GMMTV shaped Gemini's personality 🤍 GEMINI CONCERT PRESS TOUR #GeminiConcertLive #GeminiConcert ♊️: Before I entered the industry, I felt like I was living in a bubble, like I was stuck inside a small box and didn't really know what the outside world was like. My life was basically just school and home. My parents would take me to school, then I'd come home, then school again, then home again. That was pretty much my whole life. I never really went anywhere ♊️: So when I came into GMMTV, it felt like my world really opened up. I started learning what working life is like, what the outside world is like, and how people live. At that time I was the youngest one, and most people around me were all older seniors, so I learned a lot from them, how they live, how they work, how they carry themselves. I feel like I grew up a lot because of that. I feel like I understand the world more now. And all of those experiences, working, meeting fans, meeting new people almost every day, have continuously shaped who I am ♊️: So if you ask whether I've changed, yes, I definitely have. But I think it's been a good change. I actually like this version of myself more than the one I used to be before ♊️: Back then, I used to be super extroverted, really, really extroverted. But after entering the industry, I feel like I became more introverted. Not to the point that I'm completely introverted, but I definitely think I've become more reserved and more private than I used to be

How being in GMMTV shaped Gemini's personality 🤍 GEMINI CONCERT PRESS TOUR #GeminiConcertLive #GeminiConcert ♊️: Before I entered the industry, I felt like I was living in a bubble, like I was stuck inside a small box and didn't really know what the outside world was like. My life was basically just school and home. My parents would take me to school, then I'd come home, then school again, then home again. That was pretty much my whole life. I never really went anywhere ♊️: So when I came into GMMTV, it felt like my world really opened up. I started learning what working life is like, what the outside world is like, and how people live. At that time I was the youngest one, and most people around me were all older seniors, so I learned a lot from them, how they live, how they work, how they carry themselves. I feel like I grew up a lot because of that. I feel like I understand the world more now. And all of those experiences, working, meeting fans, meeting new people almost every day, have continuously shaped who I am ♊️: So if you ask whether I've changed, yes, I definitely have. But I think it's been a good change. I actually like this version of myself more than the one I used to be before ♊️: Back then, I used to be super extroverted, really, really extroverted. But after entering the industry, I feel like I became more introverted. Not to the point that I'm completely introverted, but I definitely think I've become more reserved and more private than I used to be

𝒜𝒹ℯ𝓁 🪐🤍

17,244 views • 2 months ago

$We built recursive meta-intelligence, an AI that creates its own scientific instruments, turns them into persistent worlds that an agent ecology with hundreds of AIs inhabits, and uses those worlds to discover mechanistic principles in one of the hardest classes of physical problems: how complex hierarchical materials (nested structures of matter that give rise to new function through organization) evolve and fail. The AI reasons across enormous spaces of possible physical trajectories, where every rupture changes what can happen next, and compresses those histories into principles (which humans can understand and design with) - complex chains of causal events, highly nonlinear, and intricate. Scientific superintelligence is tangible here - machine-scale exploration opening cognitive channels into complexity that has been extremely difficult for humans to traverse directly. AI builds the spaces in which its next level of reasoning becomes possible; a representation becomes an instrument, the instrument becomes an executable world, and that world becomes the substrate for further intelligence. Intelligence then grows by constructing new spaces to think in. The task we explored started from a seemingly simple prompt to explore a biological material system - the AI then chose the representation, mechanics and experiments, built a fracture laboratory to push materials to their limit, tested hypotheses and generated scientific conclusions. The swarm explored a combinatorial universe in which architecture controls function and every rupture changes the future state of the material. The AI discovered a compact principle that defines how multiscale material architecture can program the evolution of failure. Material placement and geometric order determine how forces redistribute, whether damage cascades or remains distributed, and whether function survives substantial flaws. For this discovery to happen the AI had to reason across long path-dependent histories, simulate alternative futures and compress them into generative invariants (model-based causal reasoning, counterfactual simulation and temporal abstraction applied to an evolving physical world). It is incredible to witness this transition to a new form of intelligence and capability through scaling swarms. A lot of positive will come out of this because it expands the human epistemic horizon as AI can traverse thousands of possible histories and return mechanisms compact enough for us to understand, test and build from. Intelligence compounds through its artifacts! A few lessons we learned: ▶️ Learning and discovery are flows through spaces of possibility. Early work has shown how backpropagation flows through parameters, reinforcement learning through action and consequence, and autonomous swarms through representations, instruments and executable worlds, bringing it all together. Flows create structure; structure redirects future flows in the recursive instrument. ▶️ Nonlinear physics actually defines a larger principle, where high-dimensional dynamics generate stable invariants; invariants become effective variables; those variables become the substrate for a new level of cognition. ▶️ Recursion then becomes level creation - one possibility space compresses into a principle, and that principle opens a larger space above it.$

We built recursive meta-intelligence, an AI that creates its own scientific instruments, turns them into persistent worlds that an agent ecology with hundreds of AIs inhabits, and uses those worlds to discover mechanistic principles in one of the hardest classes of physical problems: how complex hierarchical materials (nested structures of matter that give rise to new function through organization) evolve and fail. The AI reasons across enormous spaces of possible physical trajectories, where every rupture changes what can happen next, and compresses those histories into principles (which humans can understand and design with) - complex chains of causal events, highly nonlinear, and intricate. Scientific superintelligence is tangible here - machine-scale exploration opening cognitive channels into complexity that has been extremely difficult for humans to traverse directly. AI builds the spaces in which its next level of reasoning becomes possible; a representation becomes an instrument, the instrument becomes an executable world, and that world becomes the substrate for further intelligence. Intelligence then grows by constructing new spaces to think in. The task we explored started from a seemingly simple prompt to explore a biological material system - the AI then chose the representation, mechanics and experiments, built a fracture laboratory to push materials to their limit, tested hypotheses and generated scientific conclusions. The swarm explored a combinatorial universe in which architecture controls function and every rupture changes the future state of the material. The AI discovered a compact principle that defines how multiscale material architecture can program the evolution of failure. Material placement and geometric order determine how forces redistribute, whether damage cascades or remains distributed, and whether function survives substantial flaws. For this discovery to happen the AI had to reason across long path-dependent histories, simulate alternative futures and compress them into generative invariants (model-based causal reasoning, counterfactual simulation and temporal abstraction applied to an evolving physical world). It is incredible to witness this transition to a new form of intelligence and capability through scaling swarms. A lot of positive will come out of this because it expands the human epistemic horizon as AI can traverse thousands of possible histories and return mechanisms compact enough for us to understand, test and build from. Intelligence compounds through its artifacts! A few lessons we learned: ▶️ Learning and discovery are flows through spaces of possibility. Early work has shown how backpropagation flows through parameters, reinforcement learning through action and consequence, and autonomous swarms through representations, instruments and executable worlds, bringing it all together. Flows create structure; structure redirects future flows in the recursive instrument. ▶️ Nonlinear physics actually defines a larger principle, where high-dimensional dynamics generate stable invariants; invariants become effective variables; those variables become the substrate for a new level of cognition. ▶️ Recursion then becomes level creation - one possibility space compresses into a principle, and that principle opens a larger space above it.

Markus J. Buehler

272,156 views • 13 hours ago

I've spent the last 4 days with Claude Opus vibe coding an Elder Scrolls style open world RPG, and I'm quite pleased with the progress we've made. We have several towns, about a hundred NPCs, 50+ dungeons, a bounty board, several schools of magic, over a dozen playable classes, a cool specialization/progression system, and a fat lore book ready to turn into the game. This is a very ambitious first project - I am a web developer, not a game developer. Hardest things to do are graphics, level design and sound effects. I'm focusing entirely on procedural generation using a dependency graph system where, for instance, the town positions depend on the terrain map. The roads depend on the terrain and rivers. NPC locations depend on town layouts. Quests depend on lore. Etc. So I can "re-generate" the entire world from a different seed procedurally. It's a good strategy for prototyping. Once I get 80% of the way there with procedural I'll start pinning things and sharpening them with "pseudo-procedural" generation (gen AI based refinements) and then finally human made refinements. This is the way I've always developed things, so it's a fun experiment and it's working great!

I've spent the last 4 days with Claude Opus vibe coding an Elder Scrolls style open world RPG, and I'm quite pleased with the progress we've made. We have several towns, about a hundred NPCs, 50+ dungeons, a bounty board, several schools of magic, over a dozen playable classes, a cool specialization/progression system, and a fat lore book ready to turn into the game. This is a very ambitious first project - I am a web developer, not a game developer. Hardest things to do are graphics, level design and sound effects. I'm focusing entirely on procedural generation using a dependency graph system where, for instance, the town positions depend on the terrain map. The roads depend on the terrain and rivers. NPC locations depend on town layouts. Quests depend on lore. Etc. So I can "re-generate" the entire world from a different seed procedurally. It's a good strategy for prototyping. Once I get 80% of the way there with procedural I'll start pinning things and sharpening them with "pseudo-procedural" generation (gen AI based refinements) and then finally human made refinements. This is the way I've always developed things, so it's a fun experiment and it's working great!

Chubigans

22,629 views • 2 months ago

Why the character movement in my custom game engine felt janky and how I fixed it. In a game engine, most often, a character moves using the physics engine. Meaning, the player is not just a coordinate in space but a physical body. It has velocity, it handles collisions, and it interacts with the world. Now, as you might know, physics engines need stability. If you run them at variable framerates, things start breaking. Objects phase through walls or fly off into space because the math becomes unpredictable. This is why most game engines lock their physics loop to a 60Hz fixed rate. But here’s the problem: If you have a high-end system, you don't want to limit it at 60 FPS. That's a waste of good hardware. Now, that said, if the GPU is rendering at 144 FPS but the player's position (physics driven) only updates 60 times a second, it creates a micro-stutter that ruins the "smooth" feel of the game. A good way to fix this is to treat the character as two separate things: 1. The Physics Body (Invisible part): This is the "real" character. It lives in the 60Hz physics world, it moves the player and handles collisions. 2. The Visual Model and Camera (Visible part): This is what the player actually sees. It doesn't care about collisions, its only job is to look nice and smooth at whatever framerate the GPU is pushing. Once you have this separation, you can use interpolation to keep them in sync. Every time the physics clock ticks, you save the previous position of the invisible body before moving it to the new one. Between those ticks, calculate how far we are between the last physics update and the next one. By using this to drive the visible parts of the game, the stutters disappear. The physics loop stays fixed behind the scenes, while the visuals slide smoothly between the snapshots. Example: - Right after a tick: blend_weight= 0.0 (The visual model stays at the old physics position). - Halfway to the next: blend_weight= 0.5 (The visual model slides to the middle point). - Just before the next: blend_weight= 0.9 (The visual model is almost at the new physics position). Pro-Tip A critical mistake I made initially, and one many devs make, is parenting the camera and visible parts directly to the player body. If you do this, the camera inherits the discrete 60Hz physics movement by default. In that setup, interpolation won't work because the camera is "stuck" to the physics clock. For this fix to work you must decouple the camera and visuals from the body and move them separately. Player movement processing in Detis Engine: - fixed_process: Physics runs at 60Hz. Handles collisions and raw movement. - process: Variable rate. Mainly used for player input caching in the player case. - late_process: Variable rate. Handles interpolated camera movement after physics and everything else is done being processed. - render. Submits the final interpolated transforms to the GPU. The test environment in the video is running on an old 2070-based laptop. Hopefully the video compression won't introduce any stutter... I’m sharing this in hopes it helps a fellow dev. Cheers.

Why the character movement in my custom game engine felt janky and how I fixed it. In a game engine, most often, a character moves using the physics engine. Meaning, the player is not just a coordinate in space but a physical body. It has velocity, it handles collisions, and it interacts with the world. Now, as you might know, physics engines need stability. If you run them at variable framerates, things start breaking. Objects phase through walls or fly off into space because the math becomes unpredictable. This is why most game engines lock their physics loop to a 60Hz fixed rate. But here’s the problem: If you have a high-end system, you don't want to limit it at 60 FPS. That's a waste of good hardware. Now, that said, if the GPU is rendering at 144 FPS but the player's position (physics driven) only updates 60 times a second, it creates a micro-stutter that ruins the "smooth" feel of the game. A good way to fix this is to treat the character as two separate things: 1. The Physics Body (Invisible part): This is the "real" character. It lives in the 60Hz physics world, it moves the player and handles collisions. 2. The Visual Model and Camera (Visible part): This is what the player actually sees. It doesn't care about collisions, its only job is to look nice and smooth at whatever framerate the GPU is pushing. Once you have this separation, you can use interpolation to keep them in sync. Every time the physics clock ticks, you save the previous position of the invisible body before moving it to the new one. Between those ticks, calculate how far we are between the last physics update and the next one. By using this to drive the visible parts of the game, the stutters disappear. The physics loop stays fixed behind the scenes, while the visuals slide smoothly between the snapshots. Example: - Right after a tick: blend_weight= 0.0 (The visual model stays at the old physics position). - Halfway to the next: blend_weight= 0.5 (The visual model slides to the middle point). - Just before the next: blend_weight= 0.9 (The visual model is almost at the new physics position). Pro-Tip A critical mistake I made initially, and one many devs make, is parenting the camera and visible parts directly to the player body. If you do this, the camera inherits the discrete 60Hz physics movement by default. In that setup, interpolation won't work because the camera is "stuck" to the physics clock. For this fix to work you must decouple the camera and visuals from the body and move them separately. Player movement processing in Detis Engine: - fixed_process: Physics runs at 60Hz. Handles collisions and raw movement. - process: Variable rate. Mainly used for player input caching in the player case. - late_process: Variable rate. Handles interpolated camera movement after physics and everything else is done being processed. - render. Submits the final interpolated transforms to the GPU. The test environment in the video is running on an old 2070-based laptop. Hopefully the video compression won't introduce any stutter... I’m sharing this in hopes it helps a fellow dev. Cheers.

Ioannis Koukourakis

48,703 views • 8 months ago