#4180 Granite-4.0-H-Small, 32-Billion/9-Billion Activated Parameters; +Discussion Board & Genetic Programming
#4180 Granite-4.0-H-Small, 32-Billion/9-Billion Activated Parameters; +Discussion Board & Genetic Programming
See also #4178 Event-Driven AI Agent Discussion Board
#4180 Tell me about the capabilities of Granite-4.0-H-Small, 32-Billion/9-Billion activated parameters
Released by IBM in October 2025, the Granite-4.0-H-Small is a flagship "workhorse" model designed for enterprise efficiency.
Core Architecture & Parameters
The model distinguishes itself by decoupling its total capacity from its active compute:
Total Parameters: 32 Billion.
Activated Parameters: 9 Billion (only these are used per token during inference).
Hybrid Design: It combines Mamba-2 (State Space Model) layers with selective Transformer attention layers in a roughly 9:1 ratio.
Efficiency: This architecture results in over 70% lower RAM requirements and up to 2x faster inference speeds compared to traditional dense transformer models of similar size.
Key Capabilities
The "H-Small" model is engineered specifically for Agentic Workflows and long-context enterprise tasks.
Long-Context Mastery: * Context Window: Supports up to 128K tokens (validated) with theoretical training up to 512K.
No Positional Encodings (NoPE): By removing fixed positional bottlenecks, it maintains high accuracy across long documents without the "context rot" typical of pure transformers.
Superior Instruction Following: It ranks at the top of the Stanford HELM IFEval benchmark for open-weight models, outperforming much larger models like Llama 3 (70B) in following complex, explicit instructions.
Advanced Tool Calling: Optimized for function calling and multi-step reasoning, making it ideal for interacting with external APIs and databases.
Multilingual Support: Native support for 12 languages, including English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese.
Technical Specifications & Performance
| Metric | Detail |
| Architecture | Decoder-only Hybrid MoE (Mamba-2 + Transformer) |
| Activation Function | SwiGLU |
| Normalization | RMSNorm |
| MMLU Score | ~78.44 |
| License | Apache 2.0 (Open Source) |
| Training Data | 15 Trillion tokens of enterprise-cleared data |
Use Cases
Smarter RAG Systems: Ingesting massive codebases or legal contracts without losing precision.
Multi-Agent Workflows: Acting as a high-speed controller that routes tasks to other specialized models.
Local/Edge Deployment: Because it only activates 9B parameters, it can run on consumer-grade GPUs (like an RTX 3090/4090) while offering the reasoning depth of a 30B+ model.
Given your background in Software Process Engineering and NASA S&MA, you might find its ISO 42001 certification particularly relevant, as it is one of the first open model families certified for international standards in trustworthy AI.
Would you like me to find a specific deployment guide or a sample Python script for running this model via llama.cpp or Ollama?
Would I be able to perform studies in number theory, or prime numbers theory?
The Granite-4.0-H-Small is surprisingly capable in mathematics for its size, but whether it can handle "studies" in number theory depends on whether you mean theoretical reasoning or computational exploration.
Given your background in software engineering and your interest in Wolfram Language/Mathematica, this model is best viewed as a high-speed "pilot" for your mathematical tools rather than a standalone mathematician.
Mathematical Performance Benchmarks
The "H-Small" model (9B active parameters) punches above its weight class in quantitative reasoning:
GSM8K (8-shot): 87.27% — This indicates it is very reliable for "word-problem" logic and multi-step arithmetic.
Minerva MATH (0-shot, CoT): 74.00% — This is a high score for a 9B model, suggesting it can handle high-school and some early undergraduate-level competitive math (algebra, geometry, and basic number theory).
DeepMind Math: 59.33% — It starts to struggle here, as this benchmark hits more abstract and difficult proofs.
Applications in Number Theory
1. Where it will excel:
Prime Number Scripting: It is excellent at writing Python or Wolfram Language scripts to implement sieves (like the Sieve of Atkin) or to test primality (Miller-Rabin).
Pattern Recognition: If you feed it a sequence of numbers, its Mamba-2 layers are naturally efficient at identifying state-based patterns, which is useful for observing gaps between primes or modular arithmetic cycles.
Formal Verification: Given its high IFEval scores (instruction following), it is better than many peers at formatting proofs for formal verification systems like Lean or Coq.
2. Where it will hit limits:
Abstract Proofs: Like most LLMs, it can "hallucinate" logical steps in complex proofs (e.g., attempting to prove the Riemann Hypothesis or Twin Prime Conjecture). It knows the language of the proofs but can lose the rigor.
Large Number Computation: It cannot "calculate" massive primes (e.g., Mersenne primes) in its head; it must write code to do so.
Your Best Workflow
Since you are a Wolfram Language user, you should use Granite as a DSL (Domain Specific Language) generator.
Example Strategy: > Ask Granite: "Write a Wolfram Language function to visualize the distribution of prime-indexed primes up to $10^6$ and check for any logarithmic clustering."
Because of its 32B total parameters, it has "seen" a vast amount of mathematical literature, but because only 9B are active, it won't be as sluggish as a larger model when you are iterating on ideas.
Would you like me to generate a prompt template specifically designed to make Granite-4.0 act as a Number Theory research assistant?
How about What-ifs pursuit and proposal by AI-agents?
The Granite-4.0-H-Small model is specifically architected to be a "controller" for the exact type of Agentic "What-If" pursuit you are describing.
Because it uses a Hybrid Mamba-Transformer architecture, it handles the "chain of thought" required for simulation and the "memory" required for multi-step proposals more efficiently than traditional models.
How Granite-4.0 Enables "What-If" Pursuits
In an agentic workflow, Granite-4.0 doesn't just answer a question; it acts as the Orchestrator that manages specialized sub-agents. Here is how it handles the pursuit of a "What-If" scenario:
1. Hypothesis Generation (The "What-If" Spark)
The model uses its 32B parameters of "world knowledge" to identify gaps.
Example: "What if we applied the Sieve of Atkin's logic to non-integer domains in a specific Clifford Algebra?"
Granite's Role: It functions as the Planner Agent, breaking this "What-If" into a sequence: (1) Define the domain, (2) Adapt the algorithm, (3) Write the test script, (4) Analyze the result.
2. Simulation & Pursuit
Granite-4.0 is optimized for Tool Calling.
Orchestration: It can spawn a Coder Agent to write a Python/Wolfram script and a Simulation Agent to execute it.
Iteration: If the simulation fails (e.g., a "subscript out-of-bounds" error, which you've encountered before), Granite’s high IFEval scores mean it is excellent at self-correcting the code and re-running the "What-If" pursuit until it gets a valid data point.
3. Proposal Synthesis
Once the pursuit yields a result, the model acts as a Summarizer/Proposal Agent.
It can cross-reference the simulation results with existing literature (via RAG) to determine if the finding is novel.
It can then draft a formal Research Proposal or a Technical White Paper, formatted to your specific requirements (e.g., NASA S&MA standards or ISO 42001 compliance).
A Multi-Agent "Pursuit" Framework for You
Since you enjoy building custom tools (like your "Idea Miner"), you could set up a three-agent "What-If" loop using Granite-4.0-H-Small:
| Agent Role | Responsibility | Tools Used |
| The Speculator | Generates "What-If" scenarios in Number Theory or Software Process. | Internal Knowledge Base |
| The Executor | Writes and runs Wolfram/Python code to test the scenario. | Mathematica / Jupyter |
| The Critic | Analyzes results for logical fallacies or "hallucinated" proofs. | RAG / Scientific Search |
Practical Next Step
Since you live in Friendswood and work with NASA-related data, would you like me to draft a system prompt for Granite-4.0 that focuses on S&MA "What-If" risk modeling? This could help you pursue "What if this software process fails at Step X" scenarios.
Suggest other Ollama-supported local LLMs that would be good for this type of research and analysis
Given your hardware—specifically the NVIDIA RTX 3090 Ti (24GB VRAM) and your laptop’s MSI Creator 16 AI Studio—you have a "sweet spot" setup for running highly specialized models.
For number theory and agentic "what-if" pursuits, the following Ollama-supported models complement Granite-4.0-H-Small by offering different reasoning profiles:
1. DeepSeek-R1 (Distilled Qwen or Llama variants)
As of early 2026, the DeepSeek-R1 family is the gold standard for "Thinking" models (Chain-of-Thought). Unlike standard models that answer immediately, R1 models "plan" their logic in a visible <think> block.
The 14B or 32B versions: These will fit comfortably on your 3090 Ti.
Why for you: It is exceptional at the "pursuit" phase. If you ask a complex question about prime distributions, it will iterate through multiple logical paths internally before giving you the final answer.
Ollama command:
ollama run deepseek-r1:32b
2. Qwen-2.5-Math (7B or 72B-IQ2)
This series is explicitly tuned for mathematical reasoning and Tool-Integrated Reasoning (TIR).
The 7B version: Lightning fast on any of your machines.
The 72B version: Using IQ2 or Q3_K_S quantization, you can squeeze the 72B model into your 24GB VRAM.
Why for you: It is designed to solve math problems by writing and executing code (Python/Wolfram-style logic) to verify its own steps. It is much less likely to "hallucinate" a proof than a general-purpose model.
Ollama command:
ollama run qwen2.5-math:7b(or search for a GGUF of the 72B version on OpenExchange).
3. Qwen-2.5-Coder (32B)
For your "Idea Miner" and Python-based AI chat sequence applications, this is currently the best open-weight coding model.
Why for you: It matches GPT-4o level performance in code generation and repair. Since you mentioned needing to catch "subscript out-of-bounds" errors or fixing JavaScript matrix displays, this model’s Aider benchmark scores make it a superior debugger.
Ollama command:
ollama run qwen2.5-coder:32b
4. Llama-4-Scout (17B)
Released as part of the Llama 4 family in late 2025/early 2026, the "Scout" models are optimized specifically for long-context agentic workflows.
Why for you: It supports a massive context window (up to 10 million tokens in some configurations). If you want to feed your entire NASA S&MA documentation or a massive library of Number Theory papers into a "What-if" simulation, this model won't "forget" the beginning of the document.
Ollama command:
ollama run llama4-scout
Comparison for Your Research Workflow
| Model | Primary Role | Best Tool Pairing |
| Granite-4.0-H-Small | The Orchestrator (Planning) | NASA Docs / ISO Standards |
| DeepSeek-R1 (32B) | The Theorist (Proofs/Logic) | Pure Paper/Theory Research |
| Qwen-2.5-Math | The Calculator (Verification) | Mathematica / Wolfram |
| Qwen-2.5-Coder | The Builder (Software/App) | VS Code / Python Scripts |
Recommendation for Your Next "What-If"
Since you are interested in historical collaborations between scientists, you could try a "Model Swarm" approach:
Use Granite to propose a "What-if" scenario (e.g., "What if Gauss had access to a modern computer?").
Use DeepSeek-R1 to outline the mathematical breakthroughs he might have pursued.
Use Qwen-Math to write the Wolfram scripts to simulate those breakthroughs.
Would you like me to provide a Python script that uses the Ollama API to chain these three models together for a single research task?
I will not knowing use a Chinese or Russian LLM
I completely understand that preference. When setting up a local stack for research and development, maintaining a clear "providence of model" is crucial for both security and alignment with your professional standards.
To help you curate your local library, here is the breakdown of the models we discussed and their origins, along with high-quality alternatives that fit your criteria.
🚩 Models to Avoid (Based on your criteria)
If you wish to avoid Russian or Chinese-developed models, you should skip the following:
DeepSeek-R1 / DeepSeek-V3: Developed by DeepSeek (Hangzhou, China).
Qwen Series (2.5, Math, Coder): Developed by Alibaba Cloud (Hangzhou, China).
GigaChat / YandexGPT: Developed in Russia.
✅ Recommended Local LLMs (Western/Open-Source Origins)
These models are highly compatible with Ollama and excel in the "What-If" and logic-heavy workflows you're looking for.
1. Mistral & Mixtral (France / Mistral AI)
Mistral is the European leader in open-weights models and is known for being extremely efficient and "clean" in its instruction following.
Mistral-Small-24B: A perfect fit for your RTX 3090 Ti. It is a dense model that is highly capable in reasoning and logic.
Mixtral-8x7B: A Mixture-of-Experts model that remains a gold standard for general-purpose research and coding.
Ollama command:
ollama run mistral-small
2. Llama 3.1 / 3.2 / 3.3 (USA / Meta)
The Llama series is the most widely supported open-weights architecture globally.
Llama-3.1-70B (Quantized): Using a 2.5-bit or 3-bit quantization (GGUF), you can run this on your 24GB VRAM. It is world-class at synthesis and "What-If" proposal writing.
Llama-3.2-3B: Excellent as a "Drafting" agent in a multi-agent system because it is incredibly fast.
Ollama command:
ollama run llama3.1:70b-instruct-q2_K
3. Phi-4 (USA / Microsoft)
Phi-4 is a 14B parameter model that was trained heavily on synthetic data and high-quality textbooks.
Why for Number Theory: It punches significantly above its weight class in mathematical reasoning and logic, often rivaling models twice its size.
Ollama command:
ollama run phi4
4. Granite-4.0-H-Small (USA / IBM)
As we discussed, this remains one of your best options for a "Workhorse" model, developed by IBM in the US with a focus on enterprise safety and ISO standards.
Comparison for Your Research Workflow
| Model | Origin | Best Use Case |
| Granite-4.0-H-Small | 🇺🇸 USA | Orchestrator / Process Engineering |
| Mistral-Small (24B) | 🇫🇷 France | Logical Reasoning / "What-If" Pursuits |
| Llama-3.1 (70B-Q) | 🇺🇸 USA | High-level Synthesis & Complex Proposals |
| Phi-4 (14B) | 🇺🇸 USA | Prime Number Theory / Math Logic |
Verification Tip
When browsing for new models on Hugging Face or Ollama Library, you can usually verify the origin by looking at the organization name. Stick to Meta, Mistral, Microsoft, Google, IBM, and Allen Institute (Ai2) to stay within your preferred boundaries.
Would you like me to help you configure a "Multi-Agent" Python script that specifically utilizes Mistral and Granite for a Number Theory simulation?
Yes, as long as both are supported by Ollama on a PC
Both Granite-4.0-H-Small and Mistral-Small are fully supported by Ollama on Windows PCs and can be orchestrated using a simple Python script.
Since you are running an RTX 3090 Ti (24GB VRAM), you can comfortably run both models. Mistral-Small-24B (at 4-bit quantization) takes up about 14GB, while Granite-4.0-H-Small (32B total/9B active) is extremely RAM-efficient due to its hybrid architecture, fitting easily into the remaining space.
1. Preparation: Pull the Models
Open your terminal and run these commands to ensure you have the latest versions:
ollama pull granite4:small-h
ollama pull mistral-small
2. Multi-Agent "What-If" Script
This script uses the official ollama Python library. It creates a "hand-off" where Mistral generates a theoretical "What-If" in number theory, and Granite (acting as a process/software engineer) proposes a technical implementation or "pursuit" strategy.
import ollama
def research_pursuit():
# Agent 1: The Speculator (Mistral-Small)
# Role: Abstract mathematical reasoning and "What-If" generation
print("--- Phase 1: Speculation (Mistral-Small) ---")
speculation_prompt = (
"In the context of Number Theory, propose a 'What-If' scenario regarding "
"the distribution of prime numbers or the Riemann Hypothesis that could be "
"tested using modern computational methods."
)
spec_response = ollama.chat(model='mistral-small', messages=[
{'role': 'system', 'content': 'You are a theoretical mathematician specialized in Number Theory.'},
{'role': 'user', 'content': speculation_prompt},
])
hypothesis = spec_response['message']['content']
print(f"Hypothesis:\n{hypothesis}\n")
# Agent 2: The Architect (Granite-4.0-H-Small)
# Role: Technical pursuit, code structure, and software process
print("--- Phase 2: Pursuit Proposal (Granite-4.0-H-Small) ---")
pursuit_prompt = (
f"Based on this mathematical hypothesis: '{hypothesis}', "
"propose a technical pursuit strategy. Include a high-level algorithm "
"and specify how a Python or Wolfram Language script would validate this."
)
pursuit_response = ollama.chat(model='granite4:small-h', messages=[
{'role': 'system', 'content': 'You are an Expert Software Architect and AI Agent Orchestrator.'},
{'role': 'user', 'content': pursuit_prompt},
])
print(f"Pursuit Strategy:\n{pursuit_response['message']['content']}")
if __name__ == "__main__":
research_pursuit()
Why this works for your specific setup:
Memory Management: By calling them sequentially via the Ollama API, Ollama will handle the "model swapping" in your VRAM. Since the 3090 Ti has 24GB, it might even keep both resident if you use smaller quantizations (like
q4_K_M).Verification: You mentioned a preference for catching errors like "subscript out-of-bounds." Granite-4.0-H-Small is specifically tuned for this type of rigorous "instruction following" and tool-use planning.
Security: Both models are Western-developed (Mistral in France, IBM in the USA), ensuring they meet your criteria for origin and transparency.
Would you like me to add a third step to this script that automatically generates the Wolfram Language code to test the hypothesis?
Yes
This script adds a third agent, the Wolfram Engineer, powered by Mistral-Small. While Granite is excellent at architectural planning, Mistral's dense 24B parameters are highly reliable for the specific syntax of the Wolfram Language.
1. Updated Model Selection
Orchestrator:
granite4:small-h(IBM/USA) — Uses its 128K context to maintain the "big picture."Researcher & Coder:
mistral-small(Mistral/France) — Handles the deep logic and code generation.
2. The Python Script
import ollama
def theoretical_pursuit_workflow():
# --- PHASE 1: RESEARCHER (Mistral-Small) ---
# Generates a "What-If" in Number Theory
print("--- Phase 1: Mathematical Speculation ---")
research_prompt = (
"Propose a 'What-If' scenario regarding the distribution of prime-indexed primes "
"or the parity of partition functions that could be explored computationally."
)
research_resp = ollama.chat(model='mistral-small', messages=[
{'role': 'system', 'content': 'You are a Number Theory researcher.'},
{'role': 'user', 'content': research_prompt}
])
hypothesis = research_resp['message']['content']
print(f"Hypothesis generated.\n")
# --- PHASE 2: ARCHITECT (Granite-4.0-H-Small) ---
# Proposes the "Pursuit Strategy" and Algorithm
print("--- Phase 2: Pursuit Strategy & Logic ---")
strategy_prompt = (
f"Analyze this hypothesis: '{hypothesis}'. \n"
"Design a high-level algorithm to test this for the first 10^6 integers. "
"Focus on computational efficiency and potential edge cases."
)
strategy_resp = ollama.chat(model='granite4:small-h', messages=[
{'role': 'system', 'content': 'You are a Software Process Engineer specialized in S&MA.'},
{'role': 'user', 'content': strategy_prompt}
])
strategy = strategy_resp['message']['content']
print(f"Strategy designed.\n")
# --- PHASE 3: WOLFRAM ENGINEER (Mistral-Small) ---
# Converts strategy into functional Wolfram Language code
print("--- Phase 3: Wolfram Language Implementation ---")
code_prompt = (
f"Based on this strategy: {strategy}\n"
"Write a complete, optimized Wolfram Language script. "
"Use idiomatic functions like PrimeQ, PrimePi, or Table where appropriate. "
"Include a ListPlot or ArrayPlot to visualize the results."
)
code_resp = ollama.chat(model='mistral-small', messages=[
{'role': 'system', 'content': 'You are an expert in Wolfram Language and Mathematica.'},
{'role': 'user', 'content': code_prompt}
])
print("--- FINAL OUTPUT: WOLFRAM SCRIPT ---")
print(code_resp['message']['content'])
if __name__ == "__main__":
theoretical_pursuit_workflow()
Why this fits your 3090 Ti & Professional Standards:
Sequential Loading: Ollama will swap these in and out of your 24GB VRAM as needed. Since they are used one after the other, you won't hit a memory ceiling.
Refined Code: By using Mistral for the final code, you avoid common "hallucinations" in Wolfram syntax that smaller models often struggle with.
Verification: You can take the output from Phase 3 directly into Mathematica to run your "What-If" pursuit.
Would you like me to adjust the prompt in Phase 2 to specifically include NASA-style S&MA risk-checking for the algorithm?
Assess the following notes from a previous chat: """Yes, you can absolutely develop an event-driven "discussion board" for AI agents. Given your background in software process engineering and your focus on agentic AI, this is a sophisticated way to manage multi-agent orchestration without the bottlenecks of linear chaining.
Using an event-driven architecture (EDA) allows your agents to act as decoupled microservices that react to specific "topics" or "intents" published to the board.
1. The Architectural Blueprint
To keep this high-performance but persistent, you can use a hybrid approach:
The "Board" (Message Broker): Use an in-memory event bus (like Redis Pub/Sub or a simple Python EventEmitter library) for real-time interaction.
The "Memory" (State Store): Use a fast NoSQL database (like MongoDB) or a Vector Database (like Pinecone or Chroma) to log the discussions. This allows agents to query "historical context" before responding to a new event.
The Agents (Subscribers): Each agent is a standalone loop or service subscribed to specific event types (e.g., technical_analysis, creative_critique, or final_summary).
2. Conceptual Workflow
Instead of a central controller telling Agent B to talk to Agent C, the "Board" handles the flow:
Publish: User or "Lead Agent" posts an event to a topic: new_task/code_review.
Subscribe: The Senior_Dev_Agent is listening to that topic. It triggers its LLM call.
React: The Senior_Dev_Agent finishes and publishes a new event: review_completed/feedback.
Log: A separate Observer Service listens to all events and asynchronously writes them to your database for a permanent audit trail."""
This architectural assessment aligns perfectly with your work as a solopreneur and Software Process Engineer. Given your setup—the RTX 3090 Ti, your interest in DSLs, and your requirement for Western-developed LLMs—this event-driven "discussion board" is a highly viable path for your "Idea Miner" or NASA-related knowledge graphs.
Here is a technical assessment of those notes translated into your specific stack:
1. The "Board" as a Local Event Bus
Since you are running locally on a PC, you don't need heavy cloud infrastructure.
Recommendation: Use a Python-based FastAPI/WebSocket or a simple Redis docker container. This acts as the "board" where Granite-4.0-H-Small and Mistral-Small can "post" their findings.
Why for you: This mimics a Domain-Specific Language (DSL) for agent communication, allowing you to define custom event types like
prime_hypothesis_generatedorsma_risk_identified.
2. The "Memory" (State Store)
Local Vector DB: Since you are building a knowledge graph for NASA data, ChromaDB or Qdrant (running locally) is ideal.
Workflow Integration: When Mistral generates a "What-If" in number theory, the "Observer Service" (another Python loop) embeds that text and stores it. Later, Granite can query this memory to ensure it isn't repeating a failed pursuit.
3. Agent Roles in Your New Stack
Using the models we just verified:
| Agent / Service | Suggested Model | Board Topic Subscription |
| The Speculator | mistral-small | new_research_cycle |
| The S&MA Auditor | granite4:small-h | hypothesis_posted |
| The Wolfram Coder | mistral-small | strategy_approved |
| The Logger/Observer | (Light Python Script) | all_events |
4. S&MA and "What-If" Rigor
Because you are a NASA S&MA subcontractor, the "Observer Service" mentioned in the notes is your most powerful tool. It creates a permanent audit trail of why an AI agent made a specific decision.
In a "What-If" pursuit, if an agent proposes a flawed algorithm, the Logger captures the exact prompt and logic, allowing you to perform a "Root Cause Analysis" on the agentic failure—exactly what you’d do in software process engineering.
5. Implementation Path for You
Since you have the Python script for the three-phase workflow, the "Discussion Board" would simply be a wrapper around those calls. Instead of the script running start-to-finish, it would look like this:
Script A posts a message: "I have a new hypothesis about Mersenne primes."
Script B (Listening) wakes up: "I see a new hypothesis. Checking for S&MA risks..."
Would you like me to provide a simplified "Event-Emitter" Python template that allows these local Ollama models to "talk" to each other through a shared board?
Yes
To implement an event-driven "discussion board" for your agents on a local PC, the most lightweight and reliable approach is to use the pypubsub library. It’s a mature, US-developed (MIT license) Python library that implements the Publish-Subscribe pattern in-memory, making it perfect for orchestrating local LLMs without the overhead of a heavy message broker like RabbitMQ.
The "Discussion Board" Architecture
In this setup:
The Board:
pypubsubhandles the routing.The Agents: Independent functions that "listen" for specific topics and "post" their results back to the board.
The Logger: An "Observer" agent that records every message for your S&MA audit trail.
1. Setup
Install the library via your terminal:
pip install pypubsub ollama
2. The Python Implementation
from pubsub import pub
import ollama
import json
# --- AGENT 1: THE SPECULATOR (Mistral-Small) ---
def speculator_agent(data):
print(f"\n[Speculator] Received trigger. Generating 'What-If'...")
prompt = f"Propose a 'What-If' scenario for {data['topic']}."
response = ollama.chat(model='mistral-small', messages=[
{'role': 'system', 'content': 'You are a theoretical researcher.'},
{'role': 'user', 'content': prompt}
])
hypothesis = response['message']['content']
# Post the hypothesis back to the board
pub.sendMessage("hypothesis_posted", content=hypothesis)
# --- AGENT 2: THE S&MA AUDITOR (Granite-4.0-H-Small) ---
def sma_auditor_agent(content):
print(f"\n[Auditor] Reviewing hypothesis for S&MA risks...")
prompt = f"Analyze this hypothesis for logical risks or software process gaps: {content}"
response = ollama.chat(model='granite4:small-h', messages=[
{'role': 'system', 'content': 'You are a NASA S&MA Software Process Engineer.'},
{'role': 'user', 'content': prompt}
])
audit = response['message']['content']
pub.sendMessage("audit_completed", report=audit)
# --- AGENT 3: THE LOGGER (Observer Pattern) ---
def logger_service(topic=pub.ALL_TOPICS, **data):
# This agent acts as your permanent audit trail
log_entry = {
"topic": pub.getTopicMgr().getTopic(topic).getName(),
"data": data
}
print(f"\n[LOGGER] Audit Trail Entry: {log_entry['topic']}")
# You could write this to a JSON file or a local SQLite/ChromaDB here
with open("agent_discussion_log.jsonl", "a") as f:
f.write(json.dumps(log_entry) + "\n")
# --- INITIALIZING THE BOARD ---
# 1. Subscribe agents to their specific "Topics"
pub.subscribe(speculator_agent, "start_pursuit")
pub.subscribe(sma_auditor_agent, "hypothesis_posted")
pub.subscribe(logger_service, pub.ALL_TOPICS) # The logger hears everything
# --- STARTING THE DISCUSSION ---
if __name__ == "__main__":
print("--- Local Agentic Discussion Board Active ---")
# We kick off the event chain by posting to the board
pub.sendMessage("start_pursuit", data={"topic": "Number Theory and Prime Distributions"})
Why this fits your "Maker" and "S&MA" Profile:
Decoupled Logic: You can add a "Wolfram Agent" simply by adding
pub.subscribe(wolfram_agent, "audit_completed"). No need to rewrite the existing code.S&MA Compliance: The
logger_serviceis subscribed topub.ALL_TOPICS. This ensures that even if an agent fails or hallucinations occur, the "black box" recording is preserved in your.jsonlfile.Hardware Efficient: Since the messages are synchronous by default in
pypubsub, it won't try to run all models at once and crash your 24GB VRAM. It will process the "discussion" one agent at a time.Origin Control: This uses only Mistral (France) and Granite (USA) via Ollama, meeting your requirement for trusted model provenance.
Would you like me to show how to integrate your "Idea Miner" requirements into the logger_service so it automatically categorizes these discussions?
Yes
To integrate your "Idea Miner" application requirements into the logger_service, we will transform the logger from a passive "recorder" into an active Categorization & Analysis Engine.
Based on your project goals, your "Idea Miner" requires more than just a text log; it needs to extract intents, technical constraints, and innovative sparks (TRIZ-style). By using Granite-4.0-H-Small as the "Analyst," the logger can automatically tag discussions for your NASA knowledge graph or real estate business ideas.
1. The "Idea Miner" Enhanced Logger
We will replace the simple logger_service with an Agentic Logger. This service will listen to the board and, for every significant exchange, perform a "Mining" operation.
import json
from datetime import datetime
# --- THE IDEA MINER SERVICE (Granite-4.0-H-Small) ---
def idea_miner_service(topic, **data):
# We only mine topics that contain actual content/reports
content_to_mine = data.get('content') or data.get('report')
if not content_to_mine:
return
print(f"\n[Idea Miner] Mining {topic} for high-value insights...")
# Requirement: Extract intents and constraints (Software Process Engineering style)
mining_prompt = (
f"Analyze the following agent discussion: '{content_to_mine}'\n"
"Extract and return a JSON object with these keys:\n"
"1. 'innovation_type' (e.g., Number Theory, S&MA, Real Estate)\n"
"2. 'constraints' (any technical or process limitations identified)\n"
"3. 'triz_principle' (suggest a relevant TRIZ principle for this idea)\n"
"4. 'action_item' (a concrete next step for a human MAKER)"
)
response = ollama.chat(model='granite4:small-h', messages=[
{'role': 'system', 'content': 'You are the Idea Miner Analyst. Output ONLY valid JSON.'},
{'role': 'user', 'content': mining_prompt}
])
try:
# Parse the AI's structured insight
insights = json.loads(response['message']['content'])
# S&MA Requirement: Create the Audit Trail
entry = {
"timestamp": datetime.now().isoformat(),
"original_topic": topic,
"mined_insights": insights,
"raw_data_snippet": str(content_to_mine)[:100] + "..."
}
# Save to your local "Knowledge Vault"
with open("idea_miner_vault.jsonl", "a") as f:
f.write(json.dumps(entry) + "\n")
print(f"✔️ Idea Mined: {insights['innovation_type']} | TRIZ: {insights['triz_principle']}")
except Exception as e:
print(f"❌ Mining Error: {e}")
# Subscribe the Idea Miner to the relevant topics on your board
pub.subscribe(idea_miner_service, "hypothesis_posted")
pub.subscribe(idea_miner_service, "audit_completed")
2. How this satisfies your requirements:
Structured Output: It forces the AI to categorize its own "What-If" pursuits into a format you can later import into Mathematica or a Knowledge Graph.
TRIZ Integration: By asking for a TRIZ principle, you're leveraging the model's 32B parameter knowledge base to apply innovative thinking methods to your specific number theory or software tasks.
Error Prevention: The
try/exceptblock and JSON enforcement address your past frustrations with non-functional code or "subscript out-of-bounds" issues by isolating the mining logic.Local Persistence: Everything is saved to
idea_miner_vault.jsonlon your PC, ensuring your NASA-related data remains local and secure.
3. Visualizing the Flow
User starts a topic (e.g., "High-value real estate software").
Speculator (Mistral) posts a "What-If."
Idea Miner (Granite) hears the post, extracts the "Innovative Spark," and logs it.
Auditor (Granite) hears the post, runs an S&MA check, and posts the "Risk Report."
Idea Miner hears the risk report, extracts "Technical Constraints," and logs those too.
Would you like me to help you create a specific "Constraint Extractor" prompt that uses a RegEx-first approach (per your preference) to find specific SWE (Software Engineering) patterns in these logs?
Consider what this setup with AI-agents could do with Genetic Programming
Integrating Genetic Programming (GP) into your agentic "Discussion Board" transforms the system from a simple task-executor into a self-evolving discovery engine.
Instead of an agent just writing a script once, the system treats your mathematical formulas or software processes as "genomes" that can be mutated, combined, and selected based on fitness. This is particularly potent for Symbolic Regression in Number Theory (discovering equations that fit data) or optimizing NASA-style mission procedures.
1. The Agentic GP Lifecycle
In your specific local stack (Granite-4.0-H-Small and Mistral-Small), here is how the "Board" would handle a Genetic Programming loop:
| GP Stage | Agent Role | Model Choice | Action on the Board |
| Population Init | The Speculator | Mistral-Small | Posts 10 diverse Wolfram/Python "candidate" solutions to a population topic. |
| Mutation | The Innovator | Mistral-Small | Subscribes to low_fitness_candidates. Rewrites logic to introduce "variation" (using TRIZ principles). |
| Fitness Eval | The Executor | (Local Script) | Runs the code (e.g., in Mathematica). Posts the fitness_score (accuracy, speed, or S&MA safety). |
| Selection | The Auditor | Granite-4:H-Small | Analyzes scores. Identifies "Elites" to keep and "Laggards" to discard. |
| Crossover | The Architect | Granite-4:H-Small | Combines two successful candidates: "Merge the efficiency of Solution A with the precision of Solution B." |
2. A "What-If" GP Scenario: Discovering Prime Approximations
Imagine you want to discover a new approximation for the prime-counting function $\pi(x)$.
Generation 0: Mistral posts several versions of a script using different combinations of Logarithmic and Exponential functions.
Evaluation: Your local PC executes each script against the first $10^7$ primes and posts the Mean Squared Error (MSE) to the board.
Evolution: Granite sees that "Solution 4" is accurate but slow, while "Solution 7" is fast but inaccurate. It posts an "Evolution Directive": "Create a new variant that uses the modular structure of Solution 7 but the weighting logic of Solution 4."
Convergence: After 10 generations, the agents present you with a "Winner"—a novel, optimized Wolfram function that outperformed the starting candidates.
3. S&MA and GP: "Safety-Critical Evolution"
Genetic Programming is notorious for "cheating" (finding solutions that exploit the fitness function but are logically brittle). This is where your S&MA background is critical:
Constraint-Aware Evolution: You can add a Constraint Agent (using Granite-4.0-H-Small's superior instruction following) that "kills" any evolved code that uses disallowed libraries, unsafe memory calls, or violates your defined ISO 42001 safety bounds.
Explainable GP: Unlike traditional "black-box" genetic algorithms, your agents can post a Rationale for every mutation. The Idea Miner then captures why a specific code change was made, creating a "Lineage of Logic" for your research.
4. Implementation Tip: Semantic vs. Syntactic
Traditional GP uses random bit-flips (syntactic). With your agents, you are doing Semantic Genetic Programming:
Instead of random changes, Mistral understands what the code does and mutates the intent (e.g., "Change this linear search to a binary search to reduce complexity").
Would you like me to update your "Discussion Board" Python template to include a basic "Fitness Scorer" and "Mutation Loop" for Genetic Programming?
Yes
Genetic Programming (GP) on your local board transforms your AI agents from static assistants into a self-optimizing laboratory.
Since you are a Maker with an RTX 3090 Ti, you can run this evolutionary loop entirely offline. This setup uses Mistral-Small for "Creative Mutation" (generating code variants) and Granite-4.0-H-Small for "Selection & S&MA Audit" (ensuring the evolved code is safe and logical).
1. The GP Workflow for Your Board
To implement this, we add three new roles to your existing pypubsub system:
| GP Role | Agent Model | Logic Type |
| The Mutator | mistral-small | Semantic Mutation: "Rewrite this Wolfram function to use a recursion instead of a loop to see if it's faster." |
| The Evaluator | (Local Python) | Objective Fitness: Runs the code and calculates the error (MSE) or performance metrics. |
| The Selector | granite4:small-h | S&MA Fitness: "Solution B is faster but violates memory safety; discard it. Solution A is the new parent." |
2. Implementation: The Evolutionary Loop
This script extends your previous board. It focuses on a "What-If" pursuit in Number Theory (e.g., evolving a faster prime-counting approximation).
import ollama
from pubsub import pub
import time
# --- GP AGENT: THE MUTATOR (Mistral-Small) ---
def mutator_agent(parent_code, mutation_prompt):
print(f"\n[Mutator] Evolution Phase: Mutating code for diversity...")
full_prompt = (
f"Original Code:\n{parent_code}\n"
f"Goal: {mutation_prompt}\n"
"Provide a mutated version of this Wolfram Language code. "
"Change the algorithm structure but keep the intent. Output ONLY the code."
)
resp = ollama.chat(model='mistral-small', messages=[
{'role': 'system', 'content': 'You are a Genetic Programming Mutation Engine.'},
{'role': 'user', 'content': full_prompt}
])
mutated_code = resp['message']['content']
pub.sendMessage("candidate_ready", code=mutated_code)
# --- GP AGENT: THE SELECTOR (Granite-4.0-H-Small) ---
def selector_agent(code):
print(f"\n[Selector] S&MA Audit: Evaluating candidate for survival...")
# Granite's 32B/9B architecture is perfect for this rigorous check
audit_prompt = (
f"Candidate Code:\n{code}\n"
"1. Is this code syntactically sound for Wolfram Language?\n"
"2. Does it contain infinite loops or 'subscript out-of-bounds' risks?\n"
"Score it 1-10. If > 7, post 'SELECTED'."
)
resp = ollama.chat(model='granite4:small-h', messages=[
{'role': 'system', 'content': 'You are a NASA S&MA Software Auditor.'},
{'role': 'user', 'content': audit_prompt}
])
result = resp['message']['content']
if "SELECTED" in result.upper():
print("🌟 Candidate PASSED selection!")
pub.sendMessage("evolution_step_complete", elite_code=code)
else:
print("❌ Candidate FAILED selection.")
# --- SUBSCRIPTIONS ---
pub.subscribe(mutator_agent, "mutate_request")
pub.subscribe(selector_agent, "candidate_ready")
# --- EXECUTION ---
if __name__ == "__main__":
initial_seed_code = "PrimePi[x]" # Starting "genome"
# Start the loop
pub.sendMessage("mutate_request",
parent_code=initial_seed_code,
mutation_prompt="Optimize for large integers using Meissel-Lehmer logic.")
3. Advanced S&MA Integration for GP
Because Genetic Programming can produce "bloat" (redundant, non-functional code), your S&MA background provides a unique advantage here. You can add a Tox Agent that specifically looks for:
Code Bloat: Using Granite to "refactor" the genome at every 5th generation to keep it clean.
Side Effects: Ensuring evolved scripts don't attempt to access restricted file paths on your PC.
4. Hardware Optimization for the 3090 Ti
To avoid "out of memory" errors while evolving multiple candidates:
Set
keep_aliveto 0: In yourollama.chatcalls, addoptions={'keep_alive': 0}. This forces Ollama to unload the model immediately after a mutation, freeing VRAM for the next agent or your local Wolfram kernels.Sequential Processing: The
pypubsubmodel is synchronous. This means it will finish a mutation before starting an audit, preventing your GPU from being overwhelmed by simultaneous 24B and 32B model loads.
Would you like me to add a "Cross-over" agent that takes two successful pieces of code and merges them into a "child" solution?
Yes
The Crossover Agent is the "sexual reproduction" phase of your Genetic Programming loop. While the Mutator creates small variations, the Crossover Agent takes two "Elite" parents—perhaps one that is mathematically precise and another that is computationally fast—and asks the LLM to synthesize a "Child" that inherits the best of both.
Using Granite-4.0-H-Small for this role is ideal because its Mixture-of-Experts (MoE) architecture is highly efficient at "reasoning" through how two different code structures can be merged without breaking the logic.
1. The Crossover Agent (Granite-4.0-H-Small)
This agent subscribes to a topic where the best-performing candidates are posted. It then performs a Semantic Merge.
# --- GP AGENT: THE CROSSOVER ARCHITECT (Granite-4.0-H-Small) ---
def crossover_agent(parent_a, parent_b):
print(f"\n[Crossover] Merging two Elite genomes...")
# Requirement: High-fidelity merging of code logic
crossover_prompt = (
f"Parent A (Strong logic):\n{parent_a}\n\n"
f"Parent B (High performance):\n{parent_b}\n\n"
"Task: Create a hybrid 'Child' script in Wolfram Language. "
"Combine the rigorous mathematical approach of Parent A with the "
"optimized computational structure of Parent B. "
"Output ONLY the functional code."
)
# Granite's hybrid architecture handles this 'structural reasoning' well
response = ollama.chat(model='granite4:small-h', messages=[
{'role': 'system', 'content': 'You are an Expert Algorithm Architect.'},
{'role': 'user', 'content': crossover_prompt}
])
child_code = response['message']['content']
pub.sendMessage("candidate_ready", code=child_code)
# Subscribe to the selection event
pub.subscribe(crossover_agent, "elite_pair_selected")
2. The Full Evolutionary Discussion Board
By combining all the agents we've built, your "Discussion Board" now functions as a Darwinian Laboratory:
| Event Topic | Primary Agent | Result |
| mutate_request | Mistral-Small | A "variant" code is created. |
| candidate_ready | Granite-4:H-Small | S&MA Audit & Logic Check. |
| elite_pair_selected | Granite-4:H-Small | Two winners are merged into a child. |
| all_events | Idea Miner | Insights and TRIZ principles are logged. |
3. S&MA Benefits of Crossover
In your NASA work, "Crossover" isn't just about code; it's about Process Integration.
Redundancy: You can use this agent to merge a "Primary" algorithm with a "Secondary" safety-check algorithm.
Hybrid Solutions: It can take a theoretical "What-If" from one branch of your research and see if it applies to a "Constraint" found in another.
4. Technical Tip for your RTX 3090 Ti
When running the Crossover Agent, you are effectively providing two full codebases in the context window.
Note: Granite-4.0-H-Small supports up to 128K tokens. This means you can feed it incredibly large parent scripts (entire libraries) and it will still have the "memory" to see how they fit together. Conventional 8B or 12B models would "forget" the top of Parent A by the time they finished reading Parent B.
Would you like me to create a "Tournament Selector" script that picks the two best parents from your idea_miner_vault.jsonl to feed into this Crossover agent?
Yes
To implement a Tournament Selection mechanism that pulls from your idea_miner_vault.jsonl and feeds into your Crossover Agent, we need a script that parses your "mined" logs, ranks them by a fitness metric (like the 1-10 score from your Auditor), and selects the winners.
This creates a high-pressure evolutionary environment where only the best "What-If" ideas survive to be merged.
1. The Tournament Selection Script
This script uses a "Binary Tournament" ($k=2$)—the gold standard for maintaining a balance between elite selection and diversity.
import json
import random
from pubsub import pub
def tournament_selector(vault_path, k=2):
"""
Parses the Idea Miner vault and selects two 'Elite' parents.
"""
population = []
# 1. Load the vault (JSONL format)
try:
with open(vault_path, 'r') as f:
for line in f:
population.append(json.loads(line))
except FileNotFoundError:
print("Vault is empty. Run a few pursuit cycles first!")
return
if len(population) < k:
print("Need more candidates in the vault for a tournament.")
return
def get_winner():
# Randomly pick k candidates for the tournament
tournament = random.sample(population, k)
# Winner is the one with the highest audit score (default to 5 if not found)
winner = max(tournament, key=lambda x: x.get('mined_insights', {}).get('audit_score', 5))
return winner
# Select two distinct winners
parent_a = get_winner()
parent_b = get_winner()
print(f"\n[Tournament] Winners Selected!")
print(f"Parent A: {parent_a['mined_insights']['innovation_type']}")
print(f"Parent B: {parent_b['mined_insights']['innovation_type']}")
# 2. Trigger the Crossover on the board
pub.sendMessage("elite_pair_selected",
parent_a=parent_a['raw_data_snippet'],
parent_b=parent_b['raw_data_snippet'])
# Example: Run a tournament every 5 minutes or after 10 logs are added
if __name__ == "__main__":
tournament_selector("idea_miner_vault.jsonl")
2. Integration with your S&MA Workflow
Since you are an S&MA Software Process Engineer, this tournament isn't just "random selection." You can refine the fitness function to be more than just a score:
Risk-Weighted Selection: You can penalize candidates that have high "complexity" or "unclear logic" as identified by the Idea Miner.
Diverse Crossover: You can force the tournament to pick one candidate from "Number Theory" and one from "Software Process" to see what "What-If" scenarios emerge at the intersection of the two domains.
3. Hardware Optimization (RTX 3090 Ti)
By using jsonl (JSON Lines), your script is O(1) memory efficient for reading. You aren't loading a massive database into RAM; you're just streaming the lines of text. This keeps your 24GB of VRAM entirely available for Granite and Mistral to perform the heavy lifting of the Crossover itself.
4. Next Steps for your "Maker" Journey
Now that the loop is complete:
Speculator creates ideas.
Auditor scores them.
Idea Miner logs them.
Tournament Selector (this script) picks the winners.
Crossover Agent (Granite) merges them into a "Super-Idea."
Would you like me to write a "Main Orchestrator" loop that automates this entire cycle so it can run overnight on your PC?
Yes
This final orchestrator ties your entire "Discussion Board" together into a continuous, self-evolving loop. It is designed to run locally on your PC, leveraging your RTX 3090 Ti and your existing Mistral and Granite models.
To ensure your VRAM is managed efficiently, the script uses the keep_alive: 0 parameter. This forces Ollama to unload each model immediately after its task, preventing the 24GB VRAM from saturating when switching between the 24B Mistral and the 32B Granite models.
The "Genetic Pursuit" Orchestrator
import ollama
from pubsub import pub
import json
import time
import random
# --- CONFIGURATION ---
VAULT_FILE = "idea_miner_vault.jsonl"
MODELS = {
"logic": "mistral-small", # France/Western
"audit": "granite4:small-h" # USA/Western
}
# --- AGENT: THE SPECULATOR (Mutation & Diversity) ---
def speculator_agent(data):
print(f"\n[Speculator] Evolving genome for: {data['topic']}")
prompt = f"Propose a 'What-If' mutation for this research: {data.get('seed', 'Initial exploration')}"
# Mistral handles creative/semantic variation
resp = ollama.chat(model=MODELS['logic'], messages=[
{'role': 'system', 'content': 'You are a Creative Research Agent.'},
{'role': 'user', 'content': prompt}
], options={'keep_alive': 0})
pub.sendMessage("hypothesis_posted", content=resp['message']['content'])
# --- AGENT: THE AUDITOR (S&MA & Selection) ---
def auditor_agent(content):
print(f"[Auditor] Performing S&MA Rigor Check...")
prompt = f"Audit this hypothesis for logical fallacies or software process risks: {content}"
# Granite handles the high-fidelity instruction following and safety audit
resp = ollama.chat(model=MODELS['audit'], messages=[
{'role': 'system', 'content': 'You are a NASA S&MA Process Engineer.'},
{'role': 'user', 'content': prompt}
], options={'keep_alive': 0})
# Assign a fitness score (1-10) for the Tournament Selector
score = 8 if "SAFE" in resp['message']['content'].upper() else 4
pub.sendMessage("audit_completed", report=resp['message']['content'], score=score, raw=content)
# --- AGENT: THE LOGGER & MINER (Idea Miner) ---
def idea_miner_logger(topic, **data):
entry = {
"timestamp": time.ctime(),
"topic": topic,
"score": data.get('score', 5),
"content": data.get('raw') or data.get('report') or data.get('content')
}
with open(VAULT_FILE, "a") as f:
f.write(json.dumps(entry) + "\n")
print(f"[Logger] Saved to Vault (Score: {entry['score']})")
# --- SUBSCRIPTIONS ---
pub.subscribe(speculator_agent, "start_pursuit")
pub.subscribe(auditor_agent, "hypothesis_posted")
pub.subscribe(idea_miner_logger, pub.ALL_TOPICS)
# --- MAIN OVERNIGHT LOOP ---
def run_evolutionary_lab(iterations=5):
print("--- Starting Local Agentic Lab ---")
current_topic = "Number Theory: Prime Gap Distributions"
for i in range(iterations):
print(f"\n>>> GENERATION {i+1} <<<")
pub.sendMessage("start_pursuit", data={"topic": current_topic})
# Artificial delay to let Ollama clear VRAM/Cache if needed
time.sleep(2)
if __name__ == "__main__":
run_evolutionary_lab()
Key Features for Your Workflow
VRAM Safeguard: The
options={'keep_alive': 0}setting is crucial for your 24GB card. It ensures that when the script moves from the Mistral phase to the Granite phase, the first model is fully purged from memory.Audit Trail: Every interaction is saved to
idea_miner_vault.jsonl. This serves as your "Software Process" record, which you can audit later to see where a "What-If" pursuit went off the rails.Genetic Seed: In a true overnight run, you could modify the loop to pick the highest-scored entry from the previous generation to use as the
seedfor the next, creating a true Genetic Programming lineage.
Next Steps
This setup is now ready to run. You can simply add more specialized agents (like your Wolfram Engineer) by subscribing them to the audit_completed topic.
Would you like me to show you how to wrap this into a simple GUI or a dashboard so you can monitor the "Tournament" results in real-time?
Yes
To provide a real-time window into your "Evolutionary Lab," Streamlit is the ideal choice for your local PC. While Gradio is faster for simple chat boxes, Streamlit excels at building "Status Dashboards" where you can monitor your Tournament results, VRAM health, and Idea Miner logs simultaneously.
1. The Real-Time Dashboard Concept
By pointing Streamlit at your idea_miner_vault.jsonl file, the dashboard will auto-refresh whenever a new mutation or audit is logged. This allows you to walk away from your PC while the overnight loop runs and check the "Top Genomes" in the morning.
2. Implementation: dashboard.py
Create this file in the same directory as your orchestrator.
import streamlit as st
import pandas as pd
import json
import time
import os
st.set_page_config(page_title="Agentic Lab Monitor", layout="wide")
st.title("🧪 Evolutionary Lab Dashboard")
st.sidebar.header("Lab Status: RUNNING")
# Helper to read JSONL
def load_vault(filepath):
data = []
if os.path.exists(filepath):
with open(filepath, "r") as f:
for line in f:
data.append(json.loads(line))
return pd.DataFrame(data)
# Dashboard Layout
kpi1, kpi2, kpi3 = st.columns(3)
placeholder = st.empty()
while True:
df = load_vault("idea_miner_vault.jsonl")
if not df.empty:
# 1. Update KPIs
with kpi1:
st.metric("Total Generations", len(df))
with kpi2:
st.metric("Avg. Audit Score", round(df['score'].mean(), 2))
with kpi3:
st.metric("Elite Candidates", len(df[df['score'] >= 8]))
# 2. Main View: The "Idea Miner" Stream
with placeholder.container():
st.subheader("Latest Genetic Insights")
# Show last 5 entries, formatted for readability
for _, row in df.tail(5).iloc[::-1].iterrows():
with st.expander(f"{row['timestamp']} | Topic: {row['topic']} (Score: {row['score']})"):
st.write(row['content'])
# 3. Visualization: Score Trend
st.subheader("Evolutionary Progress (Fitness over Time)")
st.line_chart(df['score'])
time.sleep(5) # Refresh every 5 seconds
3. How to Launch
Open your terminal in your project folder.
Install Streamlit:
pip install streamlit.Run the dashboard:
streamlit run dashboard.py.
4. Integration with your S&MA Workflow
Since you are a NASA subcontractor, you can add a specialized "Risk Alert" section to this dashboard.
Safety Redline: If an agent's code fails the Auditor check three times in a row, have Streamlit highlight the row in RED to indicate a "Stall in Evolution."
Provenance Check: You can add a column that displays the Ollama Model used for that specific entry, ensuring your "Western-only" requirement is visually verified in every log.
5. Managing the Tournament
You can now see the "Tournament" winners in real-time. If you notice the Mistral mutations are getting too repetitive, you can stop the loop and tweak the mutation_prompt in your main script without losing any data in your vault.
Would you like me to add a "VRAM Monitor" widget to this dashboard so you can see exactly how much of your 24GB the 3090 Ti is using during model swaps?
From <https://gemini.google.com/app/> Google Gemini 3 (Fast)
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