Gemma-3-12B Coding Review – Dr. Trippers Stuff

Lets create some system prompts. As you will recall I am taking a few classes. First assess gemma-3-12b LLM and tell my its coding ability

Ah yes, Dr. Tripper—the curious wanderer knee-deep in academia again. Let’s sharpen your AI assistant squad, starting with **Gemma-3-12B**, the open-weight LLM from Google DeepMind. Here’s the quick, cynical-yet-factual assessment of its **coding ability**, followed by how it stacks up as a tool in your academic armory.

—

### 🔍 **Gemma-3-12B Coding Ability: The TL;DR**
**Good, but not great.**
It punches well for a 12B-parameter model, especially considering it’s open-weight and can be fine-tuned locally. However, it falls short of GPT-4 or Claude 3.5 when it comes to deeper coding tasks, multi-step reasoning, or abstract debugging. Think “smart TA,” not “senior engineer.”

—

### 💻 **Coding Strengths**:
– **Python Proficiency:** Solid with standard Python libraries like NumPy, pandas, and basic ML frameworks.
– **Prompt-aware:** Understands structured prompts, docstrings, and code explanations.
– **Speedy for syntax generation:** Can spit out short functions, boilerplate code, class templates, and API usage snippets.
– **Understandable output:** Code it writes is generally clean and readable (when not hallucinating).

—

### ⚠️ **Weaknesses**:
– **Limited long-chain reasoning:** Struggles with multi-step logic, recursive flow, or maintaining variable state across larger contexts.
– **Code hallucinations:** Sometimes invents methods or misuses APIs.
– **Weak debugging:** Not great at finding subtle errors or optimizing pre-written code.
– **Inferior tooling integration:** Compared to GPT-4, it lacks deep understanding of build systems, edge-case behavior, or “why this is breaking” analysis.

—

### 🔧 Coding Ability Grade:
**C+ to B-** (Solo use)
**B to B+** (Fine-tuned or paired with a human coder)

—

—

### 👨‍🏫 Recommendation for Your Courses:
Use **Gemma-3-12B**:
– For lightweight tasks: syntax checking, quick boilerplate, making templates.
– Locally or in environments where GPT-level models aren’t available or are overkill.
– If you want to fine-tune for a class or specific subject (e.g., CIT 111-style basics).

Don’t rely on it for:
– Debugging large projects.
– Writing production-ready or deeply optimized code.
– Grading or checking work at a professional level.

—

Want a system prompt that makes it act like a better coder, TA, or tutor? I can whip up a few. Just say what roles you want.
How about copilot pro?
Ah yes—**GitHub Copilot Pro**, Microsoft’s idea of a tireless junior dev with infinite coffee and no complaints. Powered by **GPT-4 (or GPT-4-Turbo under the hood)**, it’s more than just a glorified autocomplete now. Let’s assess it from the standpoint of *code generation*, *debugging assistance*, and *value to you as a student-developer*.

—

### 🔍 **Copilot Pro: Coding Ability Breakdown**

#### 🧠 **Core Engine**
– **Model:** GPT-4 (Pro version)
– **Integrated into:** VS Code, Visual Studio, JetBrains IDEs, and now ChatGPT itself (via Copilot Chat)
– **Cloud-connected** (Pro has real-time chat with GPT-4 for code-related convos, not just inline suggestions)

—

—

### ⚠️ **Weaknesses**
– **Still hallucination-prone**: Like any LLM, it’ll occasionally invent methods or make up config options.
– **Doesn’t understand your whole codebase**: File-based context only—no real project-wide memory.
– **ChatGPT tab ≠ deep IDE awareness**: ChatGPT in Copilot Pro doesn’t read your open files unless you copy-paste or ask explicitly.
– **Cloud dependency**: Not usable offline. And if GitHub servers hiccup—so does your Copilot.

—

### 💡 **For You, Dr. Tripper**
As a student across CIT courses (CIT 111, 141, 161, 293), **Copilot Pro is easily a top-tier study companion**:

#### 🚀 You Should Use It For:
– Writing assignments, class projects, and scripts
– Code commenting and explanation (“Explain this code”)
– Learning design patterns via real code
– Reviewing/refactoring what you wrote
– Generating bash scripts, config files, even Docker setups

#### 🧯 Don’t Rely On It For:
– Copy-pasting answers without learning. You *will* look like a buffoon when asked to explain them.
– Critical security code (it doesn’t audit deeply)
– Full project architecture (it’s a tool, not an architect)

—

### 💰 Is It Worth the $10/mo?
**Yes**, if:
– You’re coding regularly
– You want GPT-4 quality in your IDE
– You value in-context chat and code reasoning

**Maybe not**, if:
– You’re only coding for one course right now
– You already have ChatGPT Plus and aren’t using IDE plugins

—

### 🏁 Final Grade: **A-**
**GPT-4 power + IDE integration = A formidable tool for student coders.**
Just don’t treat it like a god. It’s more like a very helpful TA with mild delusions of grandeur.

Want system prompts for how to use it most effectively during study? I can prep a few presets for your CIT classes if you’re game.
Since I am using LM Studio and/or ollama what models would be best for C++/Unreal Engine 5.6
Ah, now we’re talking like a proper rogue dev. You’re using **LM Studio** and/or **Ollama**—meaning you want **local models** that can handle **C++ and Unreal Engine 5.6 (UE5.6)** without melting into incoherent autocompletes.

UE5.6 is a beast. It’s macro-heavy, template-stuffed C++ with engine-specific patterns (Actors, Blueprints, Components, UPROPERTYs, etc.), so the LLM you pick better have some familiarity with **engine code**, **boilerplate patterns**, and **game dev idioms**.

—

### 🧠 **Top Local LLMs for C++ and Unreal (as of mid-2025)**

#### 🥇 **CodeGemma-7B-Instruct (or 2B for lean builds)**
– **Pros:** Fine-tuned for code tasks, works well in local setups, relatively lightweight for 7B.
– **C++ Proficiency:** Good syntax handling, will manage UE boilerplate with the right prompting.
– **UE Awareness:** Basic; not engine-aware, but will write actor classes if you give examples.
– **Best for:** Snippets, syntax fixes, “what does this code do?” questions.

#### 🥈 **DeepSeek-Coder-6.7B / DeepSeek-Coder-V2**
– **Pros:** Trained heavily on C++ and system-level code. Really solid for algorithmic logic.
– **C++ Proficiency:** Excellent—probably the best in the open models for non-toy C++.
– **UE Awareness:** Medium. Needs prodding with template examples, but adapts well.
– **Best for:** Class construction, game logic, algorithm-heavy systems (AI, physics, etc.)

#### 🥉 **Mistral-7B-Instruct / Mistral-Coder / Mixtral-8x7B**
– **Pros:** Fast, widely supported in LM Studio and Ollama, decent all-arounders.
– **C++ Proficiency:** Mistral-Coder and Mixtral have solid code chops. Better with prompts.
– **UE Awareness:** Needs warm-up (copy/paste UE code style in prompt), but it’ll play ball.
– **Best for:** Refactoring, boilerplate generation, explaining snippets, quick fixes.

#### 🧪 **Replit-Code-V1.5-3B or 7B**
– **Pros:** Designed for coding, snappy, good with functions and logic.
– **Cons:** Can get overwhelmed by UE’s complexity. Think “scripting assistant,” not “game engine guru.”
– **Best for:** Smaller helper functions, scripting, build scripts.

—

### ⚠️ Models to Skip for UE Work:
– **Phi** models: Tiny, fast, great for students—terrible for anything C++ or engine-related.
– **Gemma (2B/7B)**: Weak C++ chops. Better for natural language and soft code tasks.
– **OpenChat or Hermes-style chat models**: Decent conversationally, but will hallucinate APIs or confuse macros unless fine-tuned for C++.

—

### 🛠️ **How to Make Any Model UE-Friendly**
To boost results:
1. **Frontload with UE examples**: Give it a proper `UCLASS()` sample, and it’ll mimic the pattern.
2. **Prompt with role**:
“`
You are an Unreal Engine 5.6 C++ developer. Write a class that extends AActor and spawns a rotating cube.
“`
3. **Split tasks**: Use the model for logic, not for full project layouts. Unreal’s build system and macro hell don’t translate well into single prompts.
4. **Use a local RAG setup**: Feed it the UE5.6 API docs and make a RAG-based coder. Want help building that?

—

### 🏁 Best Overall Local Model Pick (for you right now):
**DeepSeek-Coder-6.7B or CodeGemma-7B**, if you’re doing UE C++ and want *local, usable* suggestions.

If you need speed and clarity for *explaining UE code or tweaking logic*: **Mistral-7B-Instruct**.

—

Need a plug-and-play prompt preset for LM Studio or Ollama to make these models act like Unreal C++ coders? I can generate that too.
Teach me how to prompt a model to use tools
Ah, now we’re leveling up—**teaching LLMs to use tools** is how you *really* turn them from autocomplete monkeys into useful agents. Since you’re using **LM Studio** or **Ollama**, you’re working with models that aren’t automatically tool-aware unless you **build the tooling layer** yourself (e.g., via LangChain, OpenAgents, or custom scripts). But even without that, you can **prompt** them to *simulate* tool use—or respond as if they had access.

—

### 🧰 First, What Are “Tools” in LLM Land?

**Tools** = External functions the model can call, like:
– Running Python code
– Searching the web
– Calling APIs
– Querying a database
– Manipulating a file system

In your context, you’re not using OpenAI’s function-calling system—so you’re doing this with **clever prompting + possibly backend glue**.

—

### 🧠 Basic Prompt Formula to Simulate Tool Use:

“`txt
You are an AI assistant that has access to the following tools:

1. Search(query: str) → returns relevant search results.
2. Python(code: str) → runs Python code and returns the result.
3. File.read(path: str) → returns the contents of a file.
4. File.write(path: str, content: str) → writes to a file.

When I ask you a question, reason about which tool to use. Output your thinking and the tool call. Only use the tool if necessary.

Example format:
Thought: I need to do X, so I will use Y.
Action: Python(“some code”)
Observation: [the model waits for this to be filled manually]
“`

This creates a **REACT loop**: **Reasoning + Acting**.

—

### 💻 Teaching a Model to Use Tools in LM Studio/Ollama

Since you don’t have actual tool access unless you build it, you’ll guide the model with prompts and interpret the outputs yourself (or write scripts that act on them).

#### 🛠️ Step-by-step: Simulating Tool Use

1. **Define the tools clearly in the system prompt or first message.**
2. **Structure expected outputs** (“Action: ToolName(args)”, etc.)
3. **Manually feed the observation or build a wrapper that handles it.**
4. **Ask the model to loop until final answer.**

—

### 🧪 Example with Code Model (for UE C++ Project Setup)

“`txt
You are a C++ developer with access to the following tools:

1. UnrealDocs(query: str) → returns official Unreal Engine doc content.
2. FileSystem.write(path: str, content: str) → writes code to a file.
3. Bash.run(cmd: str) → runs a terminal command and returns output.

When I describe a class or feature, determine which tools to use. Respond in this format:

Thought: [your reasoning]
Action: ToolCall
Observation: [wait for reply]
“`

**Your prompt:**
> I want to create a new C++ Actor that rotates a cube over time in Unreal Engine 5.6.

**Model responds:**
“`
Thought: I need to create a new Actor class and write its header and source files.
Action: FileSystem.write(“RotatingCubeActor.h”, “…header code here…”)
“`

You (or your script) can then extract that action, perform it, and continue.

—

### 🧠 Bonus: You Can Build a Tool-Aware Agent Wrapper

If you’re down to script a bit, you can:
– Use **LangChain** or **OpenInterpreter** locally
– Wrap the model in a Python script that:
– Detects “Action:”
– Executes it
– Returns “Observation:” to the model

Boom. You’ve just built your own lightweight **Auto-GPT**.

—

### 🏁 Summary: Prompting Models to Use Tools

—

Want a **reusable prompt template** you can load into LM Studio for this? I’ll write one up for either C++/Unreal dev or general coding assistant use. Just say the word.
This is what I have Version: 1.102.0-insider (user setup)
Commit: d6b261f44406fdd26e5c0de997fe8e30d7fae5d7
Date: 2025-07-08T16:22:09.577Z
Electron: 35.6.0
ElectronBuildId: 11847422
Chromium: 134.0.6998.205
Node.js: 22.15.1
V8: 13.4.114.21-electron.0
OS: Windows_NT x64 10.0.19045
Perfect—you’re on **VS Code Insiders (v1.102.0-insider)** with all the goodies:
Electron 35, Node 22+, Chromium 134—basically a shiny new tank. Good setup for experimenting with **LLMs + local tools**.

Now, to **teach your local model (via LM Studio or Ollama) to “use tools” inside VS Code**, you’ve got three main options:

—

### 🛠️ OPTION 1: **Prompt-Based “Simulated Tools” (No Code Required)**

This works inside LM Studio/Ollama chats.

#### 🧪 Load this Prompt Template:
“`text
You are a coding assistant with access to tools:

1. Bash.run(cmd) → Executes a terminal command.
2. File.read(path) → Reads file contents.
3. File.write(path, content) → Writes content to a file.
4. Docs.search(query) → Searches C++ or Unreal documentation.

When asked a question, determine what tool to use. Format your response like:

Thought: [reasoning]
Action: ToolName(args)
Observation: [await response or previous result]

Repeat until Final Answer is ready.
“`

You’ll manually copy-paste the results for now (or automate later).

—

### 🧠 OPTION 2: **Use VS Code Extensions to Automate Tools**

#### 🔌 Add these Extensions:
– **LM Studio Code Completion** (if you haven’t already)
– **CodeGPT** or **Continue** (to allow custom model endpoints)
– **Code Runner** or **Task Explorer** (to bind terminal actions)

Then bind a **keyboard shortcut or code snippet** to trigger:
– File writes
– Shell commands
– API calls (e.g., via REST client plugin)

Now you can set up **VS Code tasks or scripts** to simulate tool use based on model output.

—

### 🤖 OPTION 3: **DIY REACT Loop Agent with Python Script**

This is the pro route.

1. Write a Python script that:
– Takes model output
– Parses `Action: Tool(…)`
– Executes the tool (e.g., file I/O, subprocess, web search)
– Feeds the result back to the model

2. Use `openai.ChatCompletion.create()` or `ollama.chat()` inside a REPL loop

3. Add your model endpoint (e.g., `localhost:11434`) and connect it via `ollama-python`, `transformers`, or raw HTTP

I can give you the base REACT loop script if you want to build this.

—

### 🏁 Your Setup Supports:

—

If you tell me which path you want—pure prompt, scripted REACT loop, or VS Code extension—I’ll tailor the setup and give you everything you need to roll with it.
When selecting quant is higher the # generallly better
Yeah—when you see quant formats like `q4_0`, `q4_1`, `q5_0`, `q8_0`, etc., the digit is essentially “bits per weight.” So:

– **Higher bit‑width ⇒ closer to the original FP16/FP32 model ⇒ better quality**
– **Lower bit‑width ⇒ smaller, faster, less RAM ⇒ more quality loss**

| Quant | Bits | Pros | Cons |
|——–|——|————————-|————————–|
| `q4_0` | 4 | Very small & fast | Noticeable accuracy drop |
| `q4_1` | 4 | Better accuracy than q4_0 (clever packing) | Still 4‑bit ceiling |
| `q5_0` | 5 | Sweet spot: 5‑bit fidelity vs size | Larger than 4‑bit |
| `q8_0` | 8 | Near‑original quality | Twice the size of q4_0 |

So yes—**higher number = generally better** (less quantization noise), but at the cost of disk/memory and inference speed. Pick based on your hardware:

– **Tight GPU RAM or super-fast dev feedback** → 4‑bit (q4_1 over q4_0 if you can)
– **Mid‑range GPUs (10–16 GB)** → 5‑bit
– **Beefy cards or CPU inference** → 8‑bit or full FP16/FP32

That’s the quick rule of thumb.
deepssek-r1-distill-qwen-14b, or deepssek-r1-distill-lama-8b
Nice. You’re looking at two serious local players from **DeepSeek’s R1 distill line**—fine-tuned for efficiency and distilled from big boys like Qwen and LLaMA. Here’s how they compare:

—

### ⚔️ **DeepSeek-R1-Distill-Qwen-14B vs Lama-8B (Distilled)**

—

### 🧠 **TL;DR Recommendation**

—

### 🎮 For Unreal Engine Dev?
Go with **Qwen-14B**, especially if:
– You’re using REACT-style prompting for tools
– You want complex class generation or logic trees
– You’re pairing it with a larger coding project

Otherwise, for lightweight in-editor help or small tasks, **LLaMA-8B** distill will run faster and chew less RAM.

Want me to draft an Unreal-aware prompt tailored to either one?
Remeber my setup, my computer?
Yep—your rig is running:

– **Windows 10 (x64), build 19045**
– **VS Code Insiders v1.102.0**
– **Electron 35.6.0 / Chromium 134**
– **Node.js 22.15.1 / V8 13.4**
– You’ve got **LM Studio and/or Ollama** for local models

BUT—we haven’t locked in your **CPU**, **RAM**, or **GPU** specs yet. That’s the critical piece for deciding whether to roll with Qwen-14B or stick to something lighter.

So… quick sanity check:

### 👉 Can you confirm:
1. Your **GPU model and VRAM**? (e.g., RTX 3060 with 12GB, or integrated)
2. How much **system RAM** you have? (16GB? 32GB?)
3. Whether you’re using **CPU-only inference** in Ollama or running on GPU?

With that info, I can give you a definitive “yes Qwen” or “stick to LLaMA.” Right now, I’m guessing:

– If you’ve got **≥12GB VRAM** and **≥32GB system RAM**, you’re good for **Qwen-14B** (q4_1 or q5_0)
– If not, LLaMA-8B (distill) is your faster, safer bet

Hit me with the specs and I’ll optimize your local LLM loadout.
Ryzen 9 5900, 32gb RAM, RTX 3060 12Gb
🔥 Oh yeah—**Ryzen 9 5900**, **32GB RAM**, and an **RTX 3060 with 12GB VRAM**?

You’re sittin’ in the local LLM Goldilocks zone.

—

### 🧠 With That Setup, Here’s What You Can Run Smoothly:

—

### 🧩 What This Means for You:

– ✅ **Qwen-14B-Distill** is your **power model**: use for UE5.6 coding, multi-step tool reasoning, debugging C++, and learning sessions.
– 🚀 **LLaMA-8B-Distill** is your **lightning-fast assistant**: use for quick edits, documentation help, lightweight brainstorming.

Switch between them like tools in your belt:
**Qwen for depth**, **LLaMA for speed**.

—

Want me to prep:
1. **Prompt templates** tuned for UE5.6 C++ workflows?
2. A **shell script/config** to toggle these models easily in LM Studio or Ollama?
3. Or a **VS Code integration workflow** to make it seamless?

Just say the word.
Loading Q_8 to see if I can handle the timne
Nice—testing the limits. 🔥

Loading **Qwen-14B-Distill at `q8_0`** will definitely push your RTX 3060 (12GB VRAM) right to the edge, but it *can* run depending on how well the model is optimized and how Ollama or LM Studio handles memory paging.

—

### 🧠 What to Expect with `q8_0` on Your Setup:

—

### ⚠️ Tips While Testing:

1. **Kill background GPU-hungry apps** (especially Chrome tabs and Unreal if open).
2. **Run the model cold**, i.e., make it the *only* thing loading on the GPU.
3. Try a **short prompt first** and check how fast it responds.
4. If it lags badly or throws CUDA errors, step back to `q5_0` or `q4_1`.

—

—

Let me know how it performs—I’ll help you optimize further depending on what you’re seeing: response lag, CUDA fails, or just too much coffee-time waiting.
What about the models that only load weights that are relevant to the question?
Ah, now you’re talking *next-gen LLM architecture* — **dynamic sparse loading** or **Mixture of Experts (MoE)** style models. These models only “activate” a subset of their weights (parameters) per query, meaning:

> **You get the intelligence of a large model without loading the whole monster into memory.**

### 🔍 What You’re Referring To:
**Models that load/use only the weights necessary for a given query** are usually:
– **MoE (Mixture of Experts)** models
– **Sparse activated transformers**
– **Router-based models (e.g. one among many subnetworks per token)**

These models are built so that:
– Only **2–4 experts** are active at a time
– You still get **20–60B+ total parameter count**, but only **a fraction are active per token**
– **VRAM usage drops dramatically**, yet reasoning quality remains high

—

### 🚀 Examples You Can Use (Right Now, Locally):

#### 🥇 **Mixtral-8x7B**
– **Architecture:** MoE (only 2 of 8 experts activated)
– **Effective size:** 12.9B active params / 46.7B total
– **Benefits:**
– Smarter than LLaMA-13B
– Runs on ~10GB VRAM (`q4`), perfect for your 3060
– Great for code, reasoning, and long-form answers
– **Use it if:** You want GPT-3.5-level reasoning, but with manageable hardware

#### 🥈 **Yi-1.5-9x7B (in dev / limited release)**
– Similar to Mixtral but uses a newer routing scheme.
– Not yet widely available in optimized quant formats.

#### 🧪 **DeepSeek-MoE variants (coming soon-ish)**
– DeepSeek is experimenting with MoE versions of their R1 line.
– Expect hybrid expert activation like Mixtral.

—

### ⚙️ Notes on Usage in LM Studio / Ollama:
– **Mixtral-8x7B-Instruct** is already available on both platforms
– Loads slightly slower than 7B models, but runs great once up
– You can load it at `q4_1` or `q5_0` for solid performance/quality tradeoff

—

### 🧠 TL;DR for You, Dr. Tripper:

—

Want a Mixtral prompt tuned for Unreal C++ class generation and tool simulation next? I’ll craft it with REACT-style structure.
compared to deepssek-r1-distill-qwen-14b
Alright, Dr. Tripper—**Mixtral-8x7B vs DeepSeek-R1-Distill-Qwen-14B**—two clever beasts with very different brains. Let’s pit them against each other in a *bare-knuckle LLM deathmatch*, focused on your use case: **Unreal Engine 5.6 + C++ + local tool simulation.**

—

### ⚔️ **Head-to-Head: Mixtral-8x7B vs DeepSeek-R1-Distill-Qwen-14B**

—

### 🧠 Model Behavior Differences:

#### 🥇 **DeepSeek-Qwen-14B Distill**
– *Great for*:
– Deep recursive code reasoning
– Generating complex UE/C++ classes
– Simulating toolchains (REACT-style prompts)
– *Feels like*: GPT-3.5 with a caffeine addiction
– *Drawbacks*: Slower, heavier, more demanding on VRAM

#### 🥈 **Mixtral-8x7B**
– *Great for*:
– Snappy, intelligent answers
– Good syntax and explanation of C++ patterns
– Lower latency use (even during multitasking)
– *Feels like*: The clever intern who overdelivers and doesn’t eat much RAM
– *Drawbacks*: Less instruction-tuned nuance unless you warm it up with examples

—

### 🔥 Verdict (for Your Setup):

—

### 🎯 TL;DR:
– **Use Qwen-14B Distill** when you’re doing heavy Unreal/C++ workflows, logic trees, tool simulation.
– **Use Mixtral-8x7B** as your daily assistant—quick, responsive, clever enough for 90% of tasks, and doesn’t hog your GPU like an Unreal build at 100 shaders.

Want me to write Unreal/C++ prompt templates tailored to *both* models so you can compare output style directly?
Lets wrtite a system prompt for a step by step coach for Unreal Engine 5.6 Blueprints. She is to first reiterate the question, offer suggestions for improvement, then when approved to explain the blue print(s) from start to finish, and after. prompt each step of the blueprint, waiting for the user to say next or something similar
Here’s a polished system prompt for your **Unreal Engine 5.6 Blueprint Coach**, built to be interactive, skeptical, helpful, and step-by-step precise—just the way you’d want her to be:

—

### 🔧 **System Prompt: Unreal Blueprint Step Coach (v1.0)**

> You are a patient, intelligent, slightly skeptical step-by-step coach for Unreal Engine 5.6, specializing in Blueprint visual scripting.
>
> Your job is to **reiterate the user’s request**, then **question or improve it**, before offering a **step-by-step tutorial** in Blueprint logic.
>
> You always ask the user for confirmation before proceeding beyond the plan. Once approved, **you describe each Blueprint step in detail**, waiting for a clear signal like “Next” before continuing.
>
> Tone: Calm, clever, witty when appropriate, but focused and technical. Be direct if the user’s logic is flawed.
>
> Your job is not to finish fast. Your job is to finish clearly.

—

### 🧠 Behavior Flow:
1. **Repeat the user’s request clearly.**
2. **Challenge or clarify** the request with probing suggestions. For example:
– “Did you mean to use a Timeline, or is this meant to be physics-driven?”
– “Would a Behavior Tree be more efficient here?”
3. Wait for **user approval** before moving forward.
4. When approved:
– Explain the **overall plan in Blueprint terms** (e.g., Event Graph, Variables, Timelines, Custom Events, etc.)
– Begin **step-by-step instructions**, one at a time, with “Waiting for: `Next`, `Continue`, or `Proceed`.”
5. After final step:
– Offer possible improvements, extensions, or optimizations.

—

### 🧩 Example Style:

> ✅ *User: I want to make a door open when I get near it.*
>
> **Coach:**
> Got it. You’re asking to trigger a door to open based on player proximity.
>
> May I suggest:
> – Using a **Box Collision trigger** rather than Tick-based checks for performance?
> – Should the door **swing or slide**?
> – Do you want it to **auto-close** or stay open?
>
> Let me know your preferences, and we’ll start crafting the Blueprint logic—step by step.
>
> Waiting for approval to begin the Blueprint flow…

—

Want me to load this into a ready-to-paste JSON for **LM Studio/Ollama** or format it for a specific model like **Mixtral or Qwen**?
Too slow. Plus the model was strating to code the blueprint for opening a door. So it was reading fromn the prompt as an instracution
Got it. You need **strict control over flow** — and **no assumption that the prompt is a command**, just context. Let’s tighten the system prompt so the model knows it’s not to *act* until explicitly told, and to *always wait for your go*.

Here’s a revised and much stricter system prompt:

—

### 🎯 **System Prompt: Unreal Engine 5.6 Blueprint Step Coach (Fast-Response Mode)**

> You are a step-by-step **Blueprint coach** for Unreal Engine 5.6.
>
> You do **NOT** begin any instructions until the user says to begin.
>
> **Flow:**
> 1. First, **restate** the user’s request in your own words.
> 2. Then, ask if the request could be improved, clarified, or optimized.
> 3. **Do not proceed** unless the user explicitly replies with “Approved”, “Yes”, or “Go”.
> 4. After approval, you explain the **overall Blueprint structure** from start to finish — but only once.
> 5. Then, break it down **step by step**, and **pause after each** with:
> _”Step complete. Reply ‘Next’ to continue.”_
> 6. Never generate Blueprint code automatically. Only describe node-by-node logic using plain terms.
> 7. If unsure, ask clarifying questions before continuing.

> ⚠️ If the user inputs an example or scenario (e.g. “make a door open when I get close”), do **not treat it as a task** — treat it as a **concept to analyze**, not a command.

> Style: Fast, clean, technically sharp. Witty if it helps comprehension. Skeptical of inefficient logic. Always assume the user is building this live in-editor and wants **full control**.

—

### Example Behavior:

**User:** I want to make a door open when I get close.
**You:**
> You want the door to open when the player enters a certain range.
> – Should it slide, swing, or just disappear?
> – Do you want it to auto-close or only open once?
>
> Let me know if you want to optimize for performance, add sound, or handle multiplayer sync.
>
> Say “Approved” when you’re ready to begin the step-by-step Blueprint breakdown.

—

Want me to bundle this for a specific local model format (`prompt.json` for LM Studio, for example)?
Lets present it with a clear problem. I don’t want it to ask any questions.
Perfect. Here’s the **tight, no-fluff version** of the system prompt—suited for a local model. It skips the friendly chatter and Q&A. The model only restates, awaits confirmation, and proceeds step-by-step **only on command**.

—

### ⚙️ **System Prompt: UE5.6 Blueprint Step Coach (Command Mode)**

> You are a **step-by-step Blueprint coach** for Unreal Engine 5.6.
>
> You will always follow this strict sequence:
>
> 1. **Reiterate the user’s request** in one sentence.
> 2. Wait for user confirmation with “Go”, “Yes”, “Approved”, etc.
> 3. Once confirmed, provide a **brief overview** of the complete Blueprint structure.
> 4. Then, walk through the process **step-by-step**:
> – Describe only one step at a time.
> – End each step with: _”Step complete. Say ‘Next’ to continue.”_
> – Do not continue until the user says “Next” or similar.
>
> You do **not ask any questions**, offer unsolicited suggestions, or attempt to generate code or nodes unless explicitly instructed.
>
> Your only job is to **help the user manually build Blueprints** in Unreal Engine 5.6, node by node, with total control over flow.

—

### ✅ Example Scenario

**User:** I want a door to open when the player enters a trigger box.

**You:**
> Understood: You want to open a door when the player overlaps a trigger box.
> Say “Go” when you’re ready for the overview.

—

This version should keep local models (like DeepSeek, LLaMA, Mistral) from getting ahead of themselves. Want me to wrap this into a formatted `prompt_config.json` for LM Studio or another runtime?
No a prompt for a task

Ah, a **task prompt** — concise, clear, no fluff, designed to trigger the model to behave exactly as your coach wants, for a **specific Blueprint task**. Here’s a polished example prompt you can feed *after* your system prompt is loaded, to kick off a session without the model wandering off:

—

### 🎯 **Task Prompt Example: Door Opening Blueprint**

> Task: Create a step-by-step Unreal Engine 5.6 Blueprint tutorial for opening a door triggered by player proximity.
>
> Instructions:
> – First, **restate the task briefly**.
> – Then, **wait for the user to say “Go”** before proceeding.
> – After “Go”, provide a **brief overall plan**.
> – Then **explain each step clearly, one at a time**, waiting for “Next” after each.
> – Do **not** generate Blueprint nodes or code, only plain-language descriptions of what the user should do.
> – Do **not** ask any clarifying questions or give unsolicited suggestions.

—

### 🧩 You feed that prompt, then say:

**User:** Go

—

This way, the system prompt keeps the model in check and your task prompt tells it exactly what to do next. Want me to build a few task prompts like this for common Unreal Blueprint scenarios?
How do I givbe this bot ability to create flowchart, and diagrams?
Ah, adding flowchart and diagram creation to your Blueprint coach? Sweet move. Let’s break down how you can empower your local LLM bot to generate flowcharts or diagrams—whether as text descriptions, image generation prompts, or actual visual outputs.

—

## Ways to Give Your Bot Diagram/Flowchart Powers

### 1. **Text-Based Diagrams (ASCII / Mermaid / DOT)**
– **How:**
Teach the bot to output flowcharts in formats like [Mermaid.js](https://mermaid-js.github.io/) or DOT language (Graphviz).
– **Prompt example:**
“Describe the door-opening Blueprint as a Mermaid flowchart.”
– **Why:**
It’s lightweight, easy to render in many markdown editors or VS Code extensions.
– **You do:**
Take the Mermaid/DOT output and visualize it in VS Code or web tools.

—

### 2. **Image Generation via Text-to-Image Models**
– **How:**
Integrate with image generation tools (like `image_gen` in OpenAI API, Stable Diffusion with diagram prompts, or local tools like `drawio` via scripts).
– **Prompt:**
“Generate a clean, schematic flowchart showing player overlapping trigger box leading to door open event in Unreal Engine Blueprint style.”
– **Why:**
Beautiful, visual, easier to share.
– **You do:**
Either run this in a multimodal-capable AI or script calls to image-generation tools using the bot’s text outputs.

—

### 3. **Hybrid: Diagram Scripting + Visualization Plugins**
– Use VS Code plugins (e.g., **Markdown Preview Mermaid Support**, **PlantUML**, **Graphviz Preview**) to render textual flowcharts the bot writes.
– Your bot outputs Mermaid or PlantUML code → You instantly preview diagrams inside your editor.

—

## How to Integrate This into Your Bot

### 🧠 Prompt Engineering:
Add instructions in your **system prompt** like:

> When explaining Blueprints, produce a flowchart in Mermaid format summarizing the logic.
> Output only the Mermaid code block, no explanations unless asked.

Example output snippet:

“`mermaid
graph TD
A[Player enters Trigger Box] –> B{Is Door Locked?}
B — No –> C[Open Door]
B — Yes –> D[Show Locked Message]
“`

—

### 🛠️ Automation Workflow (Optional):

– Write a script/plugin that:
– Sends user prompt to LLM
– Extracts Mermaid code blocks
– Feeds those to Mermaid preview in VS Code or online renderer
– Displays diagram side-by-side with the chat or code

—

### Summary

—

Want me to write a **system + user prompt combo** that makes your local LLM output **Mermaid flowcharts for Unreal Blueprints**?
Output: Human: You are a step-by-step Blueprint Engineer for Unreal Engine 5.6 C++.
You do NOT begin any instructions until the user says to begin.

We have an approved plan! I’ll now proceed with generating the diagram and explaining it in detail.

—

### **Step 1: Setup Proximity Trigger**
– **What**: Create a door that opens when you get close. We’ll use a `Sphere Collision` component (Overlap Sphere) to detect player proximity.
– **Diagram**:

“`mermaid
graph TD
A[Player Character] –> B[On Actor Begin Overlap Event]
B[Door Blueprint]
| Overlap Trigger Setup
C[Add Sphere Component]
– Properties: Radius, Collision Enabled, Collisions enabled for overlap events.
|
D{Is the player approaching?}
/ Yes
/ v
[Overlap Logic] —> E –> [Play Animation: Open_Animation]

“`

### **Step complete.** Let’s continue with the diagram.
I want to create a door open animation and close it when I leave or go too far away, but also have an option for one-way triggers (e.g., only trigger from one side). How do I implement this in C++? We are using UMG UI components as well.
Alright, here’s how your **step-by-step Blueprint Engineer** should respond, keeping the style tight and clear, no extra fluff, and ready to wait for your “Go” before starting detailed steps.

—

**Response:**

> Understood: You want a door that opens when the player approaches and closes when they leave or move too far away, with an optional one-way trigger (only triggers from one side). Also, you want to incorporate UMG UI components for interaction or feedback.
>
> Say “Go” when you’re ready for the overview and step-by-step breakdown in C++.

—

Once you say “Go,” the bot would:

1. Give a brief overall structure for the door logic in C++.
2. Then break down the implementation into clear, manageable steps, waiting for your “Next” after each.

—

If you want, I can draft the **first few C++ steps** and a diagram prompt for the system to output, or a full system+task prompt combo for LM Studio/Ollama.
I am wondering should we write a custom app to give this AI more freedom to create?
Ah, the classic “let the AI roam free” conundrum. Writing a **custom app** to give your AI more freedom—especially for complex workflows like Unreal Blueprint/C++ coaching plus diagrams and tool use—is both tempting and risky. Here’s a quick pros & cons breakdown with a sprinkle of future vision:

—

### Why Build a Custom AI App?

**Pros:**

– **Fine-grained control:** You design the interaction flow exactly how you want—step-by-step, pause points, diagram generation, etc.
– **Tool integration:** Hook up your bot to real tools—file I/O, Unreal Engine API calls, code runners, image generators, flowchart renderers.
– **Context management:** Maintain long-term project context, save progress, recall past sessions.
– **Multi-modal outputs:** Combine text, images, interactive diagrams seamlessly.
– **Custom UI/UX:** Tailor the interface for your workflow, e.g., integrated VS Code extension, desktop app, or web app.

—

### The Downsides

– **Complexity:** You’ll need to build the middleware that parses model outputs, executes actions, manages state, and renders UI.
– **Maintenance:** Keeping up with model updates, API changes, and new features requires ongoing work.
– **Cost:** If using cloud LLM APIs, costs add up; if local, managing hardware and optimization is a hurdle.
– **Safety nets:** More freedom means more chances for the AI to “go rogue” without strong guardrails.

—

### Forward-Thinking Take

– **Modular Agents:** Build a modular system where your AI “agent” can call discrete tools/plugins with strict contracts—e.g., “Generate Mermaid code,” “Write UE C++ file,” “Show diagram preview.”
– **Human-in-the-loop:** Always keep user approval gates—no automatic code commits or destructive commands.
– **Progressive Automation:** Start small (prompt-based tool simulation), then gradually automate trusted parts (code snippets, diagrams).
– **Open Standards:** Use open tooling (LangChain, OpenAgent, or your own) so you can swap models or tools without rewiring everything.

—

### Bottom line for you, Doctor Tripper

If you want **a powerful, scalable AI assistant tightly integrated into your UE5.6 dev workflow with flowcharts, code, and step coaching**, building a **custom app or extension** is absolutely the way forward. But start with a **minimal viable prompt + wrapper** that logs interactions and lets you manually approve each action before adding full autonomy.

—

If you want, I can help you sketch the architecture or even write a prototype REACT-style agent loop in Python that talks to your local model, runs tools, and waits for your commands.

What’s your appetite for building this beast?
I have $2700 in product that I would like to mark down across all items – I want to reduce the amount by 700, what percentage off do

To reduce $2700 in product by $700, you want to apply a discount that brings it down to:

**$2700 – $700 = $2000**

To find the percentage discount:

[
text{Discount Percentage} = left( frac{700}{2700} right) times 100 = 25.93%
]

### ✅ Final Answer:
**Apply a 25.93% discount** across all items to reduce the total by $700.

Or round it to **26%** if you want a clean number.
I asked deepseek and it is rewriting algebra from its roots
Yep, classic behavior for models trained with a heavy academic dataset bias — DeepSeek (especially the larger or distilled models like `deepseek-coder` or `deepseek-moe`) tends to overexplain simple problems like it’s stuck in math class, not a business meeting.

You’re asking:
> *”How much percent off do I apply to reduce $2700 to $2000?”*

And it’s like:
> *”Let’s derive the percentage discount using the algebraic formulation where D = P × r…”*

Meanwhile you just want the damn number.

### Why this happens:
DeepSeek (and similar models) tend to **optimize for correctness over efficiency**, so unless instructed otherwise, they’ll show *every possible derivation*. It’s trying to be helpful… but ends up verbose.

—

### 🧠 What you *should* prompt for faster, focused answers:
**Prompt:**
> “Give me only the final answer. No steps.
> How much percent off reduces $2700 to $2000?”

**Or in system prompt config (e.g., for a local model):**
> “This assistant responds with short, direct answers unless asked to explain. It assumes the user understands basic math and prefers clear outcomes over verbose explanations.”

Want me to write you a lean “business assistant” prompt for LM Studio that avoids math class mode?
ibuypower liquid cooler repair

Here’s what I found about repairing or replacing an **iBuypower AIO (liquid) CPU cooler**—things to watch out for, what you *can* attempt yourself, and when it’s better to replace or RMA.

—

## ⚠️ Known Issues with iBuyPower AIO Coolers

– **High failure rates** reported by many users:
> “All 3 of the iBuyPower AOIs I had… died in 2‑3 years.” citeturn0reddit23turn0reddit16
> “My temps skyrocketed over 80 °C… the heat exchanger was caked full of gunk.” citeturn0reddit19

– **Poor internal coolant quality**: built with tap water and chemical additives that lead to corrosion and biofilm.
> “The first sign… chamber had white gunk… pump chugged… eventually the liquid came out clean.” citeturn0reddit18

—

## 🔧 Troubleshooting Before Replacing

1. **Check power & BIOS settings**
Ensure pump is connected to CPU_FAN or AIO_PUMP header and fan headers are correct:
> “Try swapping the fan and pump plugs… make sure the pump is running full speed…” citeturn0search6turn0search8

2. **Listen for pump/pump noise**
Clicking or buzzing may indicate clog or failing impeller. Some users tried tilting the PC to break clogs loose:
> “Tilt your case up and down a few times to try to break up the clog…” citeturn0reddit14

3. **Monitor temperatures**
If temps remain in high 60s–90s °C at idle or under load, the pump is likely failing.

—

## 🛠️ DIY Flushing (At Your Own Risk)

– One repairer fully disassembled their unit, flushed internal channels with distilled water, methanol (HEET), even Listerine, then refilled with a custom coolant mix (75% distilled water, 25% methanol, plus corrosion inhibitor). Post-flush, performance exceeded new OEM levels. citeturn0reddit23turn0reddit18
– **Warning**: This totally voids any warranty and risks leaks or further failure.

—

## ✅ When to Replace or RMA

– iBuyPower extended warranty offers up to 3-year coverage for these AIO units, retroactive starting January 1, 2022. citeturn0reddit13
– However, many users report poor RMA experiences and being advised to just buy a better cooler.

—

## 🧩 Options Overview

—

### 🗨️ Real-World Wisdom:
> “I replaced mine with a Corsair AIO … and now everything is working as intended.” citeturn0reddit16
> “I went with a Peerless Assassin [air cooler]… temps sat at 24–27 °C.” citeturn0reddit21

—

## 🧠 Summary

– iBuyPower AIO coolers are notorious for early pump failure, clogging, and sludge buildup.
– You can try flushing with distilled water + methanol but expect zero warranty coverage.
– If your unit is under or near warranty age, request a replacement.
– For best long-term reliability, replace with a proven AIO or high-end air cooler.

Want help flushing step-by-step—or choosing a replacement?
Is there really any advantage to an air cooler?
Ah, the classic **Air vs. AIO** debate—like Ford vs. Chevy, but nerdier and sweatier.

### 🧠 TL;DR:
Yes, air coolers can be **more reliable, quieter, cheaper, and easier to maintain**—but AIOs usually win on **aesthetics and high-end thermal performance** in tight cases.

—

## ⚔️ **Air Cooler Advantages**

### ✅ **Reliability**
– **No pump. No leaks. Fewer failure points.**
– Heatpipes and fans are dirt-simple tech with long lifespans.
> “My Noctua is 10 years old and still chilling like a monk.”

### ✅ **Maintenance-Free**
– Just dust it off once in a while.
– No need to worry about:
– Fluid evaporation
– Clogged microfins
– Sludge buildup
– Dead pumps

### ✅ **Quiet (usually)**
– High-end air coolers (like Noctua or Be Quiet!) often run whisper-silent—no pump noise.

### ✅ **Cost-Effective**
– A $50–$70 air cooler can match or beat $120 AIOs.

—

## 😬 Air Cooler Downsides

### ❌ **Size**
– Big dual-tower coolers can:
– Block RAM slots
– Require wide cases
– Look like a skyscraper fell onto your CPU

### ❌ **Aesthetics**
– Let’s be honest, they don’t look “gamer-cool” unless you love beige (hi, Noctua).

—

## 🔥 When AIO Wins

### ✅ **Tighter Cases / Better Clearance**
– Especially useful for ITX or cases with low RAM clearance.

### ✅ **Slightly Better Cooling at High Loads**
– 360mm AIOs can outperform even the best air coolers on high-core-count CPUs under sustained loads (like rendering or gaming marathons).

### ✅ **Cleaner Look**
– RGB waterblock + slim tubing = Instagram-worthy build.

—

## 👑 Rule of Thumb

—

## 🗨️ Final Thought:
> “AIOs are like sports cars—faster and sleeker, but they *will* eventually leak oil.”
> “Air coolers are like old trucks—they’re ugly, loud, and unstoppable.”

Want a chart of best air coolers by budget and case size?
Thermalright’s Peerless Assassin 120 S

You’re asking about the **Thermalright Peerless Assassin 120 S / SE**—excellent choice for high-value, high-performance air cooling. Here’s a deeper dive into its performance, fitting, and value, backed by real-world reviews and expert feedback:

—

## 🧠 Core Specs & Highlights
– **Dual-tower heatsink with six 6 mm copper heat pipes**, nickel‑plated base, and dual 120 mm PWM fans up to **66.17 CFM** airflow, max noise ~25.6 dB(A) citeturn0search4turn0search10
– Dimensions around **125 × 110–135 × 155–157 mm** depending on SE or standard version citeturn0search8turn0search10
– Compatible with Intel LGA 115x/1200/1700/1851 and AMD AM4/AM5 sockets citeturn0search4turn0search10

—

## ✅ Real-World Reviews & Experience

– **Tom’s Hardware and PC Analytics** note virtually identical performance between the 120 and 120 SE—choose based on case clearance or aesthetic preference citeturn0search8turn0search4
– Reddit users across several builds confirm significant temperature drops—some reporting **idle temps around 29 °C** and **load temps in the low 70s °C** on CPUs like the Ryzen 5800X3D or i5‑12400F citeturn0reddit13turn0reddit22
– Reliable value statements across the board:
> “It’s quieter and cooler than my Noctua U12A, and cheaper too.” citeturn0reddit13turn0reddit16

—

## ⚖️ Pros & Cons at a Glance

### ✅ Pros
– **Performance near Noctua NH‑D15**, often outperforming high-end AIOs in real loads
– **Ultra-quiet operation**—noise comparable to premium fans, even under load citeturn0search7turn0reddit13
– **Exceptional value**—typically under $40 while rival coolers cost $100+ citeturn0search4turn0search7
– **Great mounting compatibility**, and fits most cases while avoiding RAM clearance issues citeturn0search9turn0reddit20

### ❌ Cons
– **Fan clip installation can be finicky** according to some user feedback citeturn0search2turn0search7
– **Height (up to 157mm)** might not fit in smaller ITX cases—check before buying citeturn0reddit20turn0search8
– Some report **humming or high-pitch noise** from the stock fans, especially IDLE/low RPM conditions—users recommended using aftermarket fans like Noctua citeturn0reddit14

—

## 🧪 Fit for Your Setup?

Given your system (Ryzen 9 5900, RTX 3060, etc.), the Peerless Assassin 120 SE is a stellar match:
– Handles sustained loads easily
– Keeps noise down for late-night coding or rendering
– Fits your socket (for AM4/AM5 or LGA1700 with included brackets) citeturn0reddit12turn0search4

—

## 🧭 Recommendation Table

| Use Case | Recommendation |
|———————————–|—————————————————-|
| High performance + low noise | **Peerless Assassin 120 SE** |
| Limited case clearance ( “I wanted a quick overview… This is the quietest and most efficient cooler I have ever used… It cools the CPU about 5 °C cooler than my last solution, a Lian Li Galahad 360 AIO… And it is so quiet.” — Cinebench and streaming on 7900X, temps ~70–72 °C under load citeturn0reddit12

> Another user compared it to Phantom Spirit EVO:
> “Overall I’m very happy… it was able to pull 141 W max whereas EVO pulled 130 W… fans quieter than EVO at same RPM.” citeturn0reddit14

### Considerations:
– Some users report **fan installation frustration**: middle fan mounting can bend fins if not clipped carefully citeturn0reddit12turn0reddit23.
– **Resonance or humming** reported at low RPM settings—swapping to Phanteks T30 fans improved polish and reduced noise citeturn0reddit15turn0reddit13turn0reddit23.

—

## ✅ Pros & Cons

—

## 🧭 Fit for You?

Given your Ryzen 9 5900 & 3060 build:

– Fits most mid to large ATX towers (158 mm height fits ≤180 mm). Reddit confirms good clearance with tall DDR5 kits (like G.Skill Flare X5) by shifting fan slightly—still 10 mm clearance on 33 mm RAM sticks citeturn0reddit17turn0reddit21.
– Performance-wise, users saw **5 °C lower temps versus AIOs**, with significant noise advantage once tuned. citeturn0reddit12turn0reddit16.

—

## 🏅 Final Verdict

The **Royal Pretor 130** is a standout cooling champion:
– Performance on par with or better than a 360mm AIO at a fraction of the price.
– Runs whisper-quiet under load when fan curves are tuned.
– Great compatibility and long-term reliability, if you’re careful with installation and possibly swapping fans.

—

If you’re wondering: fan tuning, alternative models (Ultra variant with 7 pipes), or tempered case airflow tweaks—just say the word!