Imagine a game where the guard doesn't check a flag variable. Instead, the guard listens to what you actually said—"I want to sneak past" versus "I'm going to charge through"—and reacts to the meaning behind your words. That's the promise of semantic ifs: conditional logic driven by natural language understanding instead of rigid boolean checks.
TheoLeeCJ/SemIf is an open-source project that generates semantic if statements using open language models. It's designed to run on consumer hardware—specifically an NVIDIA RTX 3090 with 24GB of VRAM, sitting in your home office. No cloud subscription. No API keys. No data leaving your machine.
For game developers, this opens doors that were previously locked behind expensive inference servers or simplified keyword matching. Dynamic NPCs, adaptive difficulty, and quest generation on the fly—all powered by a local model that understands player intent.
A quick note on independence: SemIf is not affiliated with TypeSafe (the company behind Scala tooling) or Jev. It's a standalone project hosted on GitHub under TheoLeeCJ. The name is a portmanteau of "semantic" and "if," which tells you exactly what it does.
Here are seven ways SemIf can change how you build game logic.
Traditional dialogue systems rely on branching trees. You write every possible player input, map it to a response, and hope the player doesn't go off-script. When they do, you either fall back to a generic line or break immersion entirely.
Semantic ifs flip this. Instead of matching keywords, the system interprets intent. A player types or says "I want to sneak past the guard," and the semantic if evaluates to true, triggering stealth mode. The NPC doesn't need a pre-written response for that exact phrase—it understands the meaning.
Example in practice:
- Player: "Can you help me get past the checkpoint?"
- Semantic if: if (intent == "request_assistance" && context == "stealth_opportunity")
- Result: NPC offers a distraction or points to a side entrance.
Compare this to a keyword system that would need to catch "help," "past," "checkpoint," and "get" separately, then guess at the combination. Semantic ifs handle synonyms, phrasing variations, and implied meaning without requiring you to write hundreds of conditional branches.
Implementation on a 3090: A 7B parameter model like Llama 2 or Mistral runs at over 50 tokens per second with 4-bit quantization. That's fast enough for real-time dialogue in most games. You host the model locally, expose an API endpoint, and your game engine calls it when a player speaks or types.
Key Takeaway: Semantic ifs replace brittle keyword matching with intent recognition, letting NPCs respond to what players mean, not just what they say.
Difficulty settings are usually static: easy, normal, hard. Some games adjust based on performance metrics—deaths, accuracy, time-to-complete. But they miss the emotional state of the player.
Semantic ifs can detect frustration or excitement from player text or voice input. If a player types "This is impossible" or "I keep dying," the semantic if evaluates to true, and the game lowers enemy aggression or spawns a health pack. Conversely, if a player says "This is too easy," the game can ramp up the challenge.
Example:
- Player: "I've died five times to this boss."
- Semantic if: if (sentiment == "frustration" && context == "boss_fight")
- Result: Boss damage reduced by 15%, or a hint system activates.
This isn't just about making games easier. It's about keeping players in flow—the sweet spot between boredom and anxiety. A player who feels heard is less likely to quit.
Technical note: Sentiment analysis via fine-tuned open models can hit 90%+ accuracy on domain-specific datasets. You're not guessing; you're classifying intent and emotion with a model you control.
Key Takeaway: Adaptive difficulty driven by semantic ifs creates personalized experiences that respond to how players feel, not just how they perform.
Procedural quests often feel generic: "Kill 10 rats" or "Collect 5 herbs." They're generated from templates, and players notice the seams. Semantic ifs can trigger quests based on player intent, creating content that feels responsive rather than random.
Example:
- Player: "I'm looking for a challenge."
- Semantic if: if (intent == "seek_challenge" && player_level > 5)
- Result: Generate a quest with higher-level enemies, rare rewards, and a narrative hook tied to the player's current location.
The quest isn't pulled from a static list. It's assembled on the fly, using the player's words as the seed. You can integrate this with Unity or Unreal via local API calls to your model server.
Challenges: Maintaining coherence and avoiding repetition. A model that generates quests needs context—player history, world state, available assets. Semantic ifs help by narrowing the intent space before generation begins. You're not asking the model to invent everything; you're asking it to fill in the blanks for a specific player need.
Key Takeaway: Semantic ifs turn player intent into quest triggers, enabling content that adapts to what players actually want.
Keyword matching fails when players phrase things differently. "Open the door," "Unlock the gate," and "Let me through" all mean the same thing, but a keyword system treats them as separate cases. Semantic ifs use natural language understanding to classify intent, not just words.
Example:
- Player A: "That's a great idea!"
- Player B: "That's a terrible idea."
- Semantic if: if (sentiment == "positive") vs if (sentiment == "negative")
- Result: NPC responds with enthusiasm or defensiveness.
Fine-tuned LLMs exceed 90% accuracy on domain-specific intent classification. That's not perfect, but it's far better than keyword matching, and you can improve it with more training data.
Use cases: - Command interpretation: "Attack the left flank" vs "Retreat to the left flank." - Dialogue branching: "Tell me about the king" vs "Tell me about the queen." - Action triggers: "I'll pay you" vs "I'll fight you."
Key Takeaway: Semantic ifs understand meaning, not just words, making them robust to phrasing variations that break keyword systems.
RTS games live and die by command interpretation. Players issue orders like "Flank the enemy from the north" or "Hold position until reinforcements arrive." Traditional systems require you to map these to specific unit actions, often with clunky UI.
Semantic ifs can parse complex commands into unit actions. The model understands "flank" as a tactical maneuver, "north" as a direction, and "enemy" as a target. It then translates that into a series of waypoints and engagement rules.
Example:
- Player: "Flank the enemy from the north."
- Semantic if: if (intent == "flank" && direction == "north")
- Result: Units move to northern edge, then engage from the side.
Latency considerations: A 3090 runs 7B models at 50+ tokens per second. For RTS commands, that's fast enough to feel responsive. You're not waiting seconds for a response; you're waiting fractions of a second.
Potential: Voice-controlled gameplay. Instead of clicking through menus, players speak their orders. The model interprets, the game executes.
Key Takeaway: Semantic ifs enable complex command interpretation in RTS games, opening the door to voice-controlled tactical gameplay.
Simulation games—flight sims, farming sims, city builders—thrive on immersion. But NPCs and systems often respond with canned lines or rigid menus. Semantic ifs can make interactions feel natural.
Example:
- Player: "Request landing clearance."
- Semantic if: if (intent == "request_clearance" && context == "approach")
- Result: ATC responds with runway assignment and wind speed.
The model understands the request, checks context (are you approaching the runway?), and generates a response. It's not a pre-recorded line; it's generated on the fly, tailored to the situation.
Hardware requirements: 24GB VRAM handles 13B models with 4-bit quantization. That's enough for context-aware interactions in most simulations. You're not limited to 7B models; you can run larger ones if you're willing to accept slightly slower inference.
Key Takeaway: Semantic ifs enhance immersion by enabling context-aware, natural language interactions in simulation games.
The biggest barrier to AI in games is cost and complexity. Cloud APIs charge per request, and self-hosted solutions often require expensive hardware. SemIf lowers that barrier by running on a single RTX 3090 at home.
Why this matters: - No cloud costs: You pay for the GPU once, then run as many inferences as you want. - Full privacy: Player data never leaves your machine. - Open-source: Modify and extend the project for your game.
For indie developers: You can prototype AI-driven features without a budget. Test semantic ifs in your game, see what works, and iterate. If you decide to scale, you can move to a larger model or a cloud service later.
Community contributions: The project is hosted on GitHub under TheoLeeCJ. You can contribute models, integrations, or documentation. The more developers experiment, the faster the ecosystem grows.
Key Takeaway: SemIf makes AI accessible to indie developers by running on consumer hardware and removing cloud dependency.
Semantic ifs aren't a replacement for traditional logic—they're an addition. You still need boolean checks for health, ammo, and position. But when it comes to understanding player intent, semantic ifs fill a gap that keyword matching and dialogue trees can't.
The seven ways we covered: 1. Dynamic NPC dialogue 2. Adaptive difficulty 3. Quest generation 4. Player intent recognition 5. RTS command interpretation 6. Immersive simulations 7. Accessible prototyping for indie devs
The trend is clear: local LLMs are becoming viable for game development. A 3090 with 24GB VRAM can run 7B to 13B models at usable speeds. Quantization techniques reduce memory usage without destroying performance. Open models like Llama and Mistral are freely available.
SemIf is one project in this space. It's independent, open-source, and designed for developers who want to experiment without relying on cloud services. If you have a 3090 and an interest in game AI, it's worth exploring.
Key Takeaway: Semantic ifs represent a shift in how game logic handles player intent, and local hardware makes it accessible to anyone with a capable GPU.
What is TheoLeeCJ/SemIf?
An open-source project that generates semantic if statements using open language models, designed to run on consumer hardware like an RTX 3090.
How does a semantic if work?
Instead of evaluating a boolean condition (e.g., if (health < 10)), a semantic if evaluates natural language intent (e.g., if (player_intent == "retreat")). The model interprets the input and returns a classification.
Can I run this on my own hardware? Yes. The project targets an RTX 3090 with 24GB VRAM. You can run 7B to 13B models comfortably, and up to 30B with quantization.
Is this affiliated with TypeSafe or Jev? No. SemIf is independent and not affiliated with TypeSafe (the Scala tooling company) or Jev.
What gaming applications does this enable? Dynamic NPC dialogue, adaptive difficulty, quest generation, intent recognition, RTS command parsing, simulation interactions, and accessible prototyping for indie developers.
Which open models does it use? Likely Llama, Mistral, or similar open models, fine-tuned or prompted for semantic classification. The project leverages local inference frameworks like llama.cpp or vLLM.
Is the project actively maintained? Check the GitHub repository for the latest commits and releases. Open-source projects vary in activity, so verify before committing to a production use case.
What are the limitations? Latency depends on model size and quantization. Accuracy isn't perfect—fine-tuned models exceed 90% on domain-specific tasks, but edge cases exist. You'll need to handle fallbacks for misclassifications.
How can I contribute? Visit the GitHub repository, open issues, submit pull requests, or share your experiments. Community contributions drive the project forward.
Does it require internet? No. The model runs locally on your hardware. You only need internet to download the model and dependencies initially.
Ready to bring semantic ifs to your game? Star the TheoLeeCJ/SemIf repository on GitHub, try it on your RTX 3090, and join the community of developers pushing the boundaries of game AI. Share your experiments and contribute to the future of interactive storytelling.