Fixing Fleet Movement: The Multi-Hop Breakthrough

The day began with a curious issue — fleets would stop at their first waypoint instead of continuing to their final destination. If I ordered a fleet to travel across five star systems, it would jump to the first and then just… stop.

There were two culprits:

1. The Zig simulation backend was missing multi-hop continuation logic.

2. The has_planned_route flag wasn’t being properly set during deserialization.

I fixed both by adding continuation logic to the Zig fleet arrival handler and correcting route flag inference from the route array. Fleets now correctly travel across all waypoints to reach distant colonies.

Event Manager Revival: “Salvage the Past” Returns

Next, I discovered that the “Salvage the Past” event — which provides narrative context and a small resource bonus at game start — wasn’t firing.
The cause? The Event Manager was still trying to load from a deprecated JSON file instead of the new TOML configuration system. After migrating all configs months ago, this subsystem had simply never been updated.

I replaced the hardcoded loader with a TOML-based one through the Config Manager, added automatic format conversion (snake_case → camelCase), and fixed an autoload order issue that caused Event Manager to initialize too early. The fix was simple but pivotal: moving Config Manager earlier in the autoload sequence. Events now trigger properly again.

Research System Overhaul: Persistence, Costs, and Effects

The research system was next — and it was a tangle. Research progress wasn’t persisting correctly after saving and loading, and techs were completing at incorrect cost values. Digging in revealed:

• A serialization key mismatch (active_tech vs. active_tech_id)

• Corrupted deserialization from nested research dictionaries

• Over-accumulating progress past 100%

• Flat serialization structures incompatible with the Godot side

I rebuilt the serialization/deserialization layer and restructured ResearchState, SimEmpire, and TurnSimulator to restore consistent persistence.

But research completion costs were still wrong — some techs completed at arbitrary thresholds. The problem was broken tier-matching logic: Zig was inferring tech costs from tiers instead of fetching them from the TOML configuration. After replacing it with proper ID-based lookups, research now respects real costs.

Finally, I discovered that tech effects weren’t being applied when research completed. The Zig backend had a “simplified” completeTech() function that unlocked techs but didn’t apply effects or send notifications. I implemented a temporary workaround in the TurnSimulationService, post-processing newly completed techs and calling the full Godot-side applyTechnologyEffects() method.

This fix is documented for a proper long-term solution in TODO_TECH_EFFECTS.md, which estimates a 15–20 hour implementation.

Planning for Parity: Building a Structured Roadmap

After the bug marathon, I shifted to long-term planning. The Zig backend currently includes 10 simplified systems — construction, combat, AI, events, and more. To bring full parity with Godot, I created a detailed roadmap across five new documents:

1. Zig Implementation Plan — 13-week roadmap with system-by-system breakdowns, totaling 360–450 estimated hours.

2. Zig Parity Roadmap — Executive summary showing 8 full systems, 10 simplified, and a move from 65% tolerance to near 0% parity.

3. Parity Test Examples — Deterministic test specifications for construction, combat, AI, and events using exact RNG seeding.

4. Zig Parity Progress — Session tracking, milestone checklists, and velocity metrics.

5. Session Workflow Guide — Practical start/during/end checklists for ongoing development.

Phase Five Complete: Construction System Implemented

With the roadmap ready, I dove into Phase Five — Construction.

This system enables both turn-based building construction and production-based shipbuilding in Zig. I created:

• SimConstruction.zig (with ConstructionOrder and ConstructionQueue)

• Extended SimGalaxy for build and ship queues

• Added processing to TurnSimulator

Each construction order tracks 13 fields — progress, turns, costs, and more — while queues handle FIFO operations. The logic now supports both building completion and ship production (logged for now, pending full ship creation parity).

The system clocked in at 8 hours — 33% ahead of the 12–16 hour estimate.
Current test results:

• 339 Zig unit tests: ✅ Passing

• 739/740 GUT integration tests: ✅ Passing

Zig parity stands at 1/7 phases complete (14%).

Construction Bugs & Fixes

The new construction system exposed three deeper issues:

1. Disappearing Orders — Planets lacked system_id after deserialization. Fixed by setting it during StarSystem.from_dict().

2. “Unknown Building” Labels — Type strings weren’t deserializing properly. Implemented a temporary queue-preservation workaround.

3. Wrong Building Creation — Empty item types caused incorrect building spawns. Fixed via state preservation until full parity logic is in place.

Smarter Documentation: Resume Management Automation

Finally, I overhauled documentation. The massive RESUME.md file had grown to over 2,000 lines, bloating context windows. I:

• Archived sessions 1–59 to a separate file

• Reduced RESUME.md to 400 lines (80% reduction)

• Created two new Claude agents:

  • Resume Writer Agent — auto-archives when context exceeds 150K tokens or on “done for today”

  • Resume Reader Agent — summarizes state at session start

Now, context is lean, searchable, and structured for long-term development.

Technical Summary

Yesterday’s session generated 13 commits and over 1,000 lines of code changes:

• Fleet multi-hop fix — 34 lines

• Event system TOML migration — 63 lines

• Autoload dependency fix — 1 line

• Research persistence & cost fixes — 128 lines

• Tech effect workaround — 53 lines

• Planning documents — 2,000+ lines (5 new files)

• Construction system — 454 lines

• Construction bug fixes — 108 lines

• Resume refactor — 1,500 archived lines + 2 Claude agents

Current Status

✅ Fleets now travel through multi-hop routes.
✅ Events trigger properly at game start.
✅ Research completes at correct costs and applies tech effects.
✅ Construction system is fully implemented in Zig.
✅ One of seven parity phases complete (14%).
✅ Documentation system streamlined and automated.

This marks a shift from “fixing bugs in the Zig backend” to “systematically implementing full system parity.” The new roadmap provides the structure to reach 100% Zig simulation parity over the next 13 weeks.

Next up: Phase Nine — Combat Resolution, estimated at 24–32 hours.

You can follow ongoing updates and technical breakdowns at mrphilgames.com.
Thanks for reading — and for joining me on this journey to build Stellar Throne from the ground up.

— MrPhil

Hey everyone, Mister Phil here with another devlog update!

Today marks a major milestone for Stellar Throne, my sci-fi 4X strategy game. For those just joining: Stellar Throne runs on a dual-engine architecture, using Godot for the UI client and Zig for the high-performance simulation backend.

Yesterday’s work was all about one huge goal — finally enabling the Zig simulation backend in production and fixing the critical serialization bugs that had been blocking deployment for days.

🧩 The Context: A Fully Functional Engine That Couldn’t Run

At the start of the day, I had a complete Zig simulation engine — all tests passing, a 52× performance improvement validated, and everything ready to go.

Except… I couldn’t actually use it.

The bridge between Godot and Zig — serialization — was broken. The game state couldn’t properly flow between the two, leaving the new engine stranded behind a wall of data mismatches.

⚙️ Fixing the Turn Processing Hang

The first issue was a classic async race condition. The game was using await to wait for a “turn completed” signal, but the listener was being connected after the signal was emitted.

Result: the code waited forever for an event that had already happened.

Fix: I stored the signal reference before calling the turn coordination function. That ensured the listener was ready before any signals could fire. I also added yield calls in the turn sequencer to give the engine a frame to process signals between phases.

✅ Result: Turn processing is now stable and reliable.

🔄 The Real Challenge: Serialization Across Engines

The real battle was with serialization mismatches.

Godot properties used snake_case (star_system_id, fleet_name), while Zig structs used camelCase (starSystemId, fleetName). Every deserialization failed because of these naming conflicts.

I added manual field mapping in Zig — tedious, error-prone, and partial. Empires loaded correctly, but fleets, planets, and star systems still broke.

The breakthrough.
I implemented bidirectional camelCase serialization across all four major data structures:

• SimEmpire

• SimFleet

• SimPlanet

• SimStarSystem

Each gained custom toJSON() and fromJSON() methods that output camelCase and accept both camelCase and snake_case.

All test fixtures were also updated to camelCase, covering five scenarios:

• Edge Case Deficit

• Edge Case Empty Empire

• Fleet Upkeep

• Single Turn Minimal

• Ten Turn Economy

🧠 Type System Fixes & Data Integrity

Once serialization worked, I enabled the Zig backend in production. Immediately, Godot’s type system rebelled.

Files like Building.gd, Empire.gd, and Fleet.gd had overly strict type annotations that didn’t match the Zig output. I went through each file and refined type declarations to handle the new data correctly.

Then came the fleet disappearance bug — fleets vanished after turn processing. Turns out:

1. The location field was structured incorrectly.

2. fleet_name sometimes wasn’t serialized at all.

Both fixed.

And finally, empire colors were lost during serialization. The culprit?
Godot’s Color type can appear as RGB, RGBA, hex, or even name strings. Zig wasn’t handling that variety.

Solution: standardize on RGBA objects — {r, g, b, a} — for full round-trip fidelity between engines.

📈 The Numbers

8 commits in total.
The Zig backend is now fully active in production.
Game state round-trips cleanly. Colors, fleets, and names all persist. Integration tests are green across the board.

⚡ The Results

The game now runs 52× faster with the Zig simulation engine enabled by default.
Large galaxies that once took seconds now resolve in milliseconds. The performance leap is live — and dramatic.

🧭 Next Steps

• Monitor gameplay for any hidden serialization edge cases

• Expand integration test coverage

• Begin profiling the Zig engine for further optimization

That’s the devlog update!
Thanks for following along as Stellar Throne crosses another huge milestone.

👉 Visit mrphilgames.com for more updates, devlogs, and behind-the-scenes deep dives.

Hey everyone, MrPhil here with another devlog update!

Today marks a critical production deployment milestone for Stellar Throne, my 4X strategy game set in a post-empire galaxy. For those new here, I’m building Stellar Throne with a dual-engine architecture — using Godot for the UI client and Zig for the high-performance simulation backend.

This week’s focus: validating that the Zig simulation engine isn’t just fast — but production-ready.

⚙️ Context: The Zig Engine Hits 52.7× Performance

After two weeks of development, the Zig simulation engine is complete — delivering a verified 52.7× speedup over the original Godot implementation. Every one of the 1,069 tests passes cleanly.

The question was no longer “Does it work?” but “Can I ship it?” And even more importantly: “Will it stay stable under real-world stress?”

🧪 Parity Testing and Stress Scenarios

To answer that, I built a comprehensive parity testing system — a harness that runs the exact same turn through both the Godot and Zig engines, then compares every field in the game state to ensure they produce identical results.

Test scenarios ranged from:

• 10 empires with 10 systems each

• Up to 100 empires with 250 star systems

That’s when things got interesting — and the bugs started surfacing.

🐛 Three Critical Bugs (and How I Fixed Them)

1. Negative Resource Spirals

Zig’s resource validation could create negative feedback loops if a colony’s food production went below zero — spiraling further negative each turn instead of clamping to zero.
Fix: Added strict validation to prevent negative resource values during calculations.

2. Fleet Upkeep Mismatch

The Godot version used a flat-rate upkeep (10 Energy + 5 Minerals per fleet), but Zig calculated per-ship upkeep — leading to wildly inconsistent costs.
Fix: Standardized both engines to use the flat-rate formula for base upkeep while keeping per-ship maintenance for hull and weapon costs.

3. Maintenance Persistence Loss

Maintenance costs were being overwritten during stat recalculation, resetting values to zero.
Fix: Preserved maintenance data during the recalculation phase.

After fixing these, I expanded the stress tests — pushing edge cases like 200+ fleets per empire, negative production chains, and zero-population planets. That led to 11 parity fixes across both engines — covering resources, fleets, population growth, and infrastructure costs.

🚀 Deployment Roadblocks and Race Conditions

With parity achieved, I turned to production deployment — enabling the Zig backend automatically during game initialization.

Two blockers emerged:

1. Initialization Timing
   The Zig backend tried to serialize game state before empires existed — crashing when the empires array was still empty.
   Fix: Corrected property references (galaxy.galaxy_width → galaxy.width) and restructured initialization order.

2. Async Signal Race Condition
   The system awaited a signal after it was emitted — a classic async race.
   Fix: Stored the signal reference before starting the turn, ensuring the listener was connected in time.

To prevent future issues, I implemented:

• Helper functions in GameScreenCore and GameInitializer

• Debug logging in TurnProcessor

• A 343-line deployment guide detailing rollback, monitoring, and troubleshooting procedures

🧩 Deployment Decision: Disabled by Default

After reviewing everything, I made the cautious call to keep the Zig backend disabled by default for now.
The timing between empire creation and backend initialization is delicate — and I’d rather avoid subtle production bugs.

That said, the system is fully integrated. Manual enablement is available for testers. The performance boost is real, and parity is 100% confirmed.

📈 Final Stats

• 20 commits in one day

• 4,243 new lines of parity infrastructure across 13 files

• 343-line deployment guide completed

• 11 parity bugs fixed

• All 1,069 tests passing

• 52.7× performance improvement validated

The Zig simulation backend is production-ready, complete with fallback to Godot if anything fails.

🧭 Next Steps

1. Verify signal-timing fixes in live game sessions

2. Test deployment in extended campaign scenarios

3. Decide whether to enable Zig automatically or keep manual control

Either way, the hard part’s done — the Zig engine is live-ready and just one config flag away.

Thanks for following along — and as always, you can find more at MrPhilGames.com.

See you in the next devlog!

Hey everyone — MrPhil here with a major development update for Stellar Throne, my sci-fi 4X strategy game.

This week I finished implementing all 21 simulation phases in Zig and brought the parity testing framework online to compare Zig vs. Godot turn-by-turn. The Zig engine is feature-complete at the phase level, but there are still parity differences to resolve before it becomes the default runtime.

🧠 Dual-Engine Architecture (Why Two Engines?)

• Godot handles the UI, visualization, and input.

• Zig runs the high-performance simulation of empires, colonies, economies, and galactic systems.

The goal is to keep Godot flexible while Zig delivers raw simulation speed — both verified through automated parity testing.

⚙️ Track Two Wrap-Up: 21 Phases Implemented

At the start of the week, only a few phases were functional. By the end, all 21 simulation phases were implemented and validated at the unit level.

Highlights:

• Population growth, colony development, infrastructure maintenance

• Resource extraction and production chains

• Fleet movement and logistics

• Natural disasters and random events

• Quest system and narrative progression

• Diplomacy drift, relationship updates, and victory conditions

• Military combat resolution and research progression

• Final integration phase that ties everything together

Each phase includes validation tests to ensure predictable behavior.

🧩 Real Data via TOML (No More Magic Numbers)

I ported the TOML configuration system so the simulation now loads real game data:

• load_gameplay() — balance parameters and constants

• load_resources() — economy definitions and production chains

• load_content() — events, quests, and narrative triggers

This added 800 + lines of Zig, replacing thousands of hard-coded values.
On the Godot side, the Research Manager migrated from JSON to TOML for consistency.

🧪 Test Suite Rehab + Fixes

The Zig test suite had bit-rotted (wrong layout, 91 compile errors).
I:

• Reorganized tests under src/tests

• Wrote a test health monitor script

• Fixed 11 failing tests (RNG distribution, TOML table arrays, event weighting, colony growth, diplomacy drift)

✅ All Zig tests now pass.

🌉 Godot ↔ Zig Bridge + Parity Harness

Built the Turn Simulation Service in Godot:

1. Serialize state to JSON

2. Run a turn in Zig

3. Deserialize results back into Godot

Added:

• Runtime engine toggle (Godot ↔ Zig A/B testing)

• Parity testing framework that runs identical turns in both engines and compares every field across 10–100 empires and 10–250 systems

🔎 What Parity Testing Found (Work in Progress)

• Godot and Zig diverge slightly on population growth and stability formulas.

• Some mismatches come from floating-point drift and minor logic differences.

So, while Zig has feature parity at the phase level, behavioral parity is not yet complete.

⚡ Performance Snapshot

In large galaxy tests, the Zig simulation ran ≈ 52.7× faster than Godot.

📈 Commits at a Glance

✅ Current Status

• ✅ All 21 phases implemented in Zig

• ✅ All Zig tests passing

• ✅ TOML data loader integrated

• ✅ Parity testing framework online

• 🚧 Parity differences remain to be resolved

• 🚧 Zig engine not default yet

🔭 Next Steps

• Fix remaining parity gaps (population, stability, diplomacy)

• Reduce floating-point variance

• Once identical outputs are confirmed, switch to Zig as default for full performance gain

That’s this week’s devlog — thanks for reading!
Follow progress and upcoming milestones at mrphilgames.com.

— MrPhil

When I first started experimenting with AI-assisted game development, I saw something that changed the way I think about making games: you don’t need to be a programmer to bring your ideas to life anymore.

With the right tools and the right guidance, artists, designers, and dreamers can skip years of technical roadblocks and just start building.

ClaudeCraft is my way of opening that door.

1. Because Non-Coders Can Make Games Now

Game development used to feel locked behind walls of code, syntax, and complex tools.
AI is tearing those walls down. With tools like Claude, you can describe what you want, refine it through conversation, and watch your game take shape—no CS degree required.

2. Because I Want to Empower Artists, Designers, and Hobbyists

I’ve met so many creative people with incredible game ideas… who never got the chance to make them because the technical barrier was too high.
ClaudeCraft is designed for them—for you—so you can stop saying “I wish I could make a game” and start saying “I’m making one right now.”

3. Because Teaching Makes Me Better Too

They say the best way to learn something is to teach it.
By building ClaudeCraft, I’m deepening my own understanding of Agentic Game Development—the process of using AI not as a mere assistant, but as a creative collaborator. Every lesson I create sharpens my own skills.

4. Because AI is the Future of Creativity

From art to music to storytelling, AI is becoming part of the creative process everywhere.
I believe the future belongs to those who know how to work with AI instead of against it—and ClaudeCraft is my way of helping people step into that future with confidence.

The Goal

By the end of ClaudeCraft, you won’t just have learned how to make a game with AI—you’ll have actually made one.
And more importantly, you’ll know how to keep going, creating the worlds you’ve always imagined.

If you want to be part of that journey, stay tuned. ClaudeCraft is coming soon.

Waitlist at ClaudeCraft.com