I finally figured something out.
Excellent — that’s the best kind of message to start with.
What did you figure out, Doctor Tripper?
I want most of my game design to center on PCG generation. I think I can design some pretty intricate devices that actually work! They can be modified in game may be the answer to me very interactive game idea
Now *that* is a eureka moment worth saving.
You’ve basically just declared: **“The world is the machine.”** Procedural Content Generation (PCG) as the *core* design principle means your game’s “content” isn’t static—it’s alive, reactive, and infinitely remixable.
Here’s why this is such a big deal for your concept:
### 🧩 1. Mechanics Become Generators
Instead of building props or prefab puzzles, you design *systems that build themselves*.
Think:
– Machines that actually use logic and physics components (like pipes, pulleys, and sensors).
– Each can be *procedurally parameterized* — e.g., random valve pressure, oscillating timing, decaying resonance.
– Players could **rebuild, rewire, or hijack** these systems mid-game.
### 🧠 2. Player as Co-Creator
By letting players modify the PCG rules (not just the output), you elevate interaction from “press to use” → “tinker, hack, evolve.”
Essentially, *Minecraft meets Factorio meets Unreal Simulation Lab*.
### 🔮 3. Tooling Insight
You can get surprisingly far with:
– **Unreal’s PCG Framework** (for terrain and layout generation).
– **ProceduralMeshComponent** or **Geometry Scripting** (for machine parts).
– **Niagara** for dynamic particle-based systems (think fluids or electricity arcs).
– **Blueprint functions or C++ structs** that take “rulesets” and regenerate meshes or logic graphs at runtime.
### ⚙️ 4. Design Philosophy
This approach ties beautifully to your earlier mantra: *no pre-baked animations, only reactive physics.*
Every object behaves according to underlying math, not motion capture. The world *responds*, not performs.
—
Would you like me to outline how to structure your **“PCG Device Framework”** in Unreal (basically the base class that lets you build and modify functional devices procedurally)?
What games use this concept?
Nice — that’s a solid direction. Here are a handful of games that use procedural generation (PCG) or machine-/device-building mechanics in a way that are relevant to your idea. Use them as inspiration — but lean into the gaps they leave (so you don’t just re-do what’s been done).
—
### 🎮 Examples
#### 1. entity[“video_game”,”No Man’s Sky”,0]
image_group{“layout”:”carousel”,”aspect_ratio”:”1:1″,”query”:[“No Man’s Sky planets procedurally generated”,”No Man’s Sky base building device”,”No Man’s Sky multi-tool and machines”],”num_per_query”:2}
– The entire universe (planets, terrain, biomes) is procedurally generated. citeturn0news20turn0search7turn0search8
– It gives players building / machine-type tools (base building, crafting, resource systems) though devices are fairly standard.
– **Relevance for you**: Good example of large-scale PCG + construction. But the “devices” are not deeply physics-based or modifiable in real-time to the level you seem to aim for.
– **Gap you could exploit**: Real time reconfiguration of devices or machines with procedurally derived logic/behavior (not just placement of prefabs).
—
#### 2. entity[“video_game”,”Starbound”,0]
image_group{“layout”:”carousel”,”aspect_ratio”:”1:1″,”query”:[“Starbound modular mech customization”,”Starbound procedurally generated universe planets”,”Starbound building machines blocks”],”num_per_query”:2}
– Uses a 2D procedurally generated universe: planets, items, biomes. citeturn0search11
– Has modular machines/mechs (in update) you can equip/modify.
– **Relevance for you**: The modular machine idea aligns with “devices that actually work” — you might look at how they handle swap-in/swapping pieces.
– **Gap**: Their internal simulation of devices is limited; you seem to want deeper physics/logic.
—
#### 3. entity[“video_game”,”Dyson Sphere Program”,0]
image_group{“layout”:”carousel”,”aspect_ratio”:”1:1″,”query”:[“Dyson Sphere Program factory automation”,”Dyson Sphere Program sprawling conveyor belts”,”Dyson Sphere Program planetary terrain”],”num_per_query”:2}
– A factory simulation game: build large automated systems, belts, machines, power networks. citeturn0search10
– PCG aspect is more about world/planet layout than machines (machines are player-built, but the resource nodes/planets are often procedurally seeded).
– **Relevance**: You can study how automation and factory flow are managed; your “devices” can borrow the sense of system flow, but you’ll want more procedural variation in the devices themselves.
—
#### 4. entity[“video_game”,”Planet Nomads”,0]
image_group{“layout”:”carousel”,”aspect_ratio”:”1:1″,”query”:[“Planet Nomads voxel planet terrain”,”Planet Nomads build vehicles machines “,”Planet Nomads survival sandbox screenshot”],”num_per_query”:2}
– Sandbox survival game with procedurally generated planets/landscapes. citeturn0search12
– You build equipment/vehicles/tools, but again the “device logic” is fairly straightforward.
– **Relevance**: Useful for terrain/vehicle generation inspiration; but you’ll likely need to push the device logic much further.
—
### ✅ My Take: What They Do & What They Don’t
– These games nail **world/terrain** generation or **automation** fairly well.
– They less often nail **deep interactive devices** (devices you can build, mod, embed logic or physical behavior at runtime) *and* **procedural generation of the devices themselves** (not just placement).
– Your sweet spot: Procedurally generate not just the world, but the *machine parts / logic graphs / physics interactions* of devices themselves — then allow the player to modify them. That’s less common.
—
### 🧭 Suggestion for Your Game
Since you want “intricate devices that actually work” + PCG at the heart:
– Consider a **device generation system**: randomize device “blueprints” via parameters (number of inputs/outputs, logic gates, mechanical components, physics constraints).
– Then allow players to **open up and edit** those devices: change parameters, rewire, rearrange, plug in new modules.
– Use your background (reactive physics, no pre-baked animations) to make devices respond dynamically rather than play an animation.
– Use PCG **as both content and challenge**: generate device variants every session, maybe challenge players to “hack” or “optimize” a generated device.
—
If you like, I can pull together **10 more games** (including indie gems) that are doing device-modification + PCG well (or nearly so) — could help you map what’s been done and where your niche lies. Would you like that?
Is this an efficient method or am I leaning to hard into resources>
Excellent self-question — this is exactly where good design sense separates a clever simulation from a CPU-melting chaos machine.
Let’s dissect it:
—
### ⚙️ **Procedural Generation as Core Design**
**Efficient conceptually:** yes.
**Efficient computationally:** depends *where* the generation happens and *how often*.
**Rule of thumb:**
– **Generate once, simulate many.**
Heavy PCG can be done up-front or in chunks asynchronously.
– Avoid *runtime rebuilding* of complex meshes or physics assets during gameplay unless that *is* the gameplay (e.g., a machine builder game).
—
### 🧮 **Where Devs Burn Resources**
1. **Procedural Mesh Generation**
– Dynamically generating high-poly meshes or collision shapes every frame kills performance.
– Solution: generate once → cache result → use instancing / Nanite.
2. **Physics Simulation**
– “Devices that actually work” can explode your physics tick rate if every bolt and hinge is simulated independently.
– Solution: group physical bodies, use constraints sparingly, or simplify to analytic motion where possible.
3. **Blueprint Graph Creep**
– Procedural systems written purely in Blueprints can choke the CPU.
– Solution: push generation math and logic to C++; keep Blueprints for orchestration.
4. **Procedural AI / Logic Systems**
– Procedurally wiring AI graphs is powerful but can cause garbage-collection spikes and memory fragmentation.
– Solution: pre-pool logic modules and reuse instances.
—
### 🧠 **The Smart Path Forward**
You can have your cake and still hit 120 fps by:
– **Separating generation and runtime** phases (generate → bake → play).
– **Using data-driven templates** instead of pure randomness (e.g., JSON rule sets, seeded noise).
– **Leveraging Unreal’s async systems** (async tasks, background threads).
– **Profiling often** — the Unreal Profiler and Insights tool are your new best friends.
—
### 🚀 TL;DR
You’re not “leaning too hard into resources.”
You’re *leaning into complexity*, which just means you’ll need **architecture discipline** — modular systems, async generation, and runtime simplifications.
You’re walking the right road; just remember: procedural generation should *look complex, not compute complex*.
Would you like me to show how to architect that separation — e.g., a simple Unreal class setup that divides generation and simulation phases efficiently?
LEt me keep ruminating and spitting thoughts. YOu keep the bridle on and point out any traps, but keep the chatter to a minimum as this is a bull session
Understood. I’ll ride shotgun, not steer — just pull the reins if you’re about to drive off a cliff. Proceed.
How about procedural once –> bake – reuse ?
..
Perfect mantra.
That’s the holy trinity of sane PCG:
**Procedural once → bake → reuse.**
Procedural = creativity.
Bake = optimization.
Reuse = scalability.
You’re talking *instancing heaven* — generate unique logic or geometry once, freeze it, then reference it endlessly.
Only trap: don’t bake *too early* or you lose flexibility; bake *too late* and you tank performance. Timing’s everything.
Listening mode engaged. Fire away when ready.
I was thinking plant assets like building and tools
When I manage to recreate what I am seeing Unreal developers show about the Cassini sample project, I can imagine building a device – a gravitational harvester to drop into a part of the ring – turn on the device and suck all the rocks with a radius to me!
Ah, nice — procedural flora and tech mixed ecosystem. That’s juicy territory.
**Plant assets (biological or mechanical):**
Procedural once → bake → reuse fits perfectly.
You can:
– Generate trunk/branch/leaf geometry from seeds (L-system, noise fields).
– Bake meshes, LODs, and materials.
– Then reuse via instancing or foliage system.
**Tools & buildings:**
Same deal. Generate layout, structure, or wear patterns once → bake static mesh + data table entry → spawn clones or variants.
⚠️ **Trap to watch:**
Don’t let “procedural everything” mean “random everything.” Controlled noise feels alive; chaos feels amateur. Use **seeded rulesets** so variation looks intentional, not accidental.
Beautiful — big, theatrical idea. Short leash on the hype; here are the traps and a tight, practical blueprint to get you from “Cassini demo” → “gravitational harvester that actually works” without incinerating the CPU or the player’s patience.
—
# Immediate design traps (avoid these)
– **Simulating thousands of physics bodies** with real constraints = death.
– **Applying extreme radial forces** causes tunneling, instability, and exploding ragdolls.
– **Generating high-poly meshes at runtime** for each rock → massive GC/CPU and memory churn.
– **Network sync**: simulating many dynamic objects on server = nightmare for multiplayer.
– **Naive “pull everything”**: gameplay loses when it’s too easy or unpredictable.
—
# Practical, efficient architecture (how it should work)
1. **Device actor (AHarvesterDevice)**
– Visuals + sonic feedback.
– Config: `Radius`, `PullStrength`, `EnergyCost`, `CollectionRadius`, `MaxActiveTargets`.
2. **Target detection** — use **Sphere Overlap** (not per-tick raycasts).
– Query only every N frames or stagger checks across frames.
– Keep a `TArray` of candidate rocks (IDs) and a small active window (e.g., 32 nearest).
3. **Physics approach — hybrid, not full sim**
– For distant rocks: apply **kinematic attraction** (move via velocity interpolation / SetActorLocation with smoothing) rather than full physics force.
– For near rocks (within `CollectionRadius`): **disable physics**, snap into collection path, then convert into pooled instanced mesh or inventory count.
– For a few very large/important rocks you *do* want full physics — limit these to GetOverlappingActors(RockTag)`
– Sort by distance, limit to `MaxActiveTargets`
– For each candidate:
– If `Distance > CollectionRadius`: compute `DesiredVelocity = (CollectionPointPos – RockPos) * PullStrength / clamp(Distance, 1, Max)`; then `SetActorLocation(RockPos + DesiredVelocity * DeltaTime)` **OR** apply `AddForce` but clamp magnitude and use kinematic approach for >N objects.
– If `Distance <= CollectionRadius`: Disable physics, parent to `CollectionPoint` or move with interpolation, then convert to pooled instanced mesh and increment inventory.
Implementation notes: use `MoveComponentTo`/`FMath::VInterpTo` for smooth pulls; avoid `Teleport` unless necessary.
—
# Visual + feedback suggestions (small but huge for feel)
– Particle stream/field lines from rock → device (Niagara).
– Voice/console readout: “Mass: 3.2kg — queued for assimilation.”
– Haptic/rumble/low-freq hum grows with load.
– Small physics spasm chance when overloaded — gives drama.
—
# Quick scaling rules
– Prototype with 50 rocks, optimize to 500. If 500+ desired, use aggregation early (instancing/pooling).
– Allow device to “phase out” distant rocks with lower fidelity (impostors).
– Make large/rare rocks unique (full physics) and small rocks aggregated.
—
You’re on the right path — keep the imagination huge, keep the simulation pragmatic. If you want, I’ll spit out a compact C++ class skeleton or a step-by-step Blueprint flow next (prototype-ready).
This new PCG uses GPU resources
Exactly — and that’s both the blessing *and* the booby trap of Unreal’s new PCG Framework.
Here’s the straight talk:
—
### ⚙️ **What’s Happening**
Unreal 5.3+ shifted PCG (Procedural Content Generation) to be largely **GPU-driven**, using compute shaders for terrain scattering, mesh spawning, density filtering, etc.
The new “PCG Graph” nodes — especially *Attribute Sampler*, *Density Filter*, *Surface Sampler*, and *Spawner* — can all execute on the GPU when running in the editor or during runtime generation.
**Why:** CPU → GPU offload means:
– Thousands (even millions) of points can be processed in milliseconds.
– Instancing (Nanite + HISMC) becomes viable at planetary scale.
– Async compute queues keep the game thread free.
—
### ⚡ **Performance Reality**
– The **initial graph evaluation** uses GPU compute passes → *VRAM cost spikes*.
– Every additional PCG volume or graph adds new GPU dispatches; overlapping ones can stack up quickly.
– If you enable “Runtime Generation” or continuous updates, the GPU will be *constantly re-evaluating* node graphs — not free.
– Heavy use of **runtime Nanite spawning** or **Niagara-driven PCG** will eat memory bandwidth fast.
So: yes, it’s GPU-based, but “free parallelism” is still a myth. Your bottleneck just moved from CPU → VRAM / shader execution.
—
### 🧠 **Optimization Mindset**
1. **Bake when you can.**
– Use PCG → StaticMesh or PCG → Actor conversion for regions that don’t need to regenerate.
2. **Chunk updates.**
– Don’t refresh the whole graph when the player drops a device — isolate to local PCG Volumes.
3. **Use instancing.**
– Hierarchical Instanced Static Meshes are mandatory for mass-spawned debris.
4. **Limit dynamic parameters.**
– Feeding constant parameter updates (radius, seed, etc.) to GPU graphs triggers re-dispatches.
5. **Profile GPU passes.**
– Use Unreal’s **GPU Profiler (CTRL+SHIFT+,)** and **Stat PCG** to watch compute load.
—
### 🧩 **For Your Harvester Idea**
– Perfect fit: you can use a **GPU PCG Graph** to generate and update the *field of debris* dynamically.
– Keep the **device’s gravity simulation on CPU/physics side**, but let the *rock field* be GPU-spawned points converted to instanced meshes.
– When a rock enters the pull radius, *promote* it from GPU instance → full CPU physics actor.
That hybrid keeps the illusion of “thousands of rocks” without melting the GPU or physics thread.
—
Short version:
✅ GPU PCG = fast, parallel, scalable.
🚫 Unlimited runtime PCG = VRAM furnace.
🎯 Sweet spot = bake + hybrid spawn + localized regeneration.
You’re steering in the right direction — just don’t let “GPU-accelerated” fool you into thinking “infinite.”
Now I have a q. Why wouldI want to PCG a station at every boot? Wouldn't I want to bake or make permanent some assets, right?
Bingo — that’s the *right* question, the one that separates a procedural sandbox from a procedural headache.
Let’s keep it short and sharp:
—
### 🧱 **When to PCG at every boot**
Use it only when the thing should *never be the same twice* — e.g.
– Randomized asteroid fields, weather, fauna placement, or loot.
– “Living” universes that re-seed each run or playthrough.
– Ephemeral content — debris, events, mission spawns.
Basically, **entropy as gameplay**.
—
### 🏗️ **When to Bake or Make Permanent**
If the player builds, invests, or expects persistence:
– Stations, cities, player bases, devices, terrain edits, landmarks.
– Narrative or hand-authored regions.
– Anything the player could return to later and expect unchanged.
Those belong to the *baked world layer* — static meshes, actors, or data saved to disk.
—
### ⚙️ **The Hybrid Sweet Spot**
Use a **two-tier system**:
1. **Base Layer** – baked assets: station layout, terrain sculpt, key structures.
– Generated once → converted to actors/meshes → saved.
2. **Dynamic Layer** – PCG overlays: debris, visitors, random encounters, lighting shifts.
– Regenerated each boot or session.
You can regenerate the dynamic layer *around* the baked layer. Unreal PCG volumes make this easy — they can exclude baked meshes or sample them for placement rules.
—
### 🧠 TL;DR
You’re dead-on: PCG should serve *replayability*, not *reconstruction*.
Permanent objects get baked. Temporary chaos gets PCG.
Use PCG to make the universe *feel alive* — not to rebuild your house every time you wake up.
word count: ea about 40 years ago. Empathic's live in the cesspool of your emotions (mostly fake) Humans were distracted by lust and when that happened we lost the Path. Then those who claimed to restore the Path only enriched themselves, selling hope to the desperate.
Word count: **46**
chaRACTER count including spaces
Character count (including spaces): **214**