26.0417 Lab notes (Gdrive) Git

For demo details see #603_PAL_.docx and #600-2_core_AI_concepts_.

• 1 AI App Basics. Learn how agents work at the lowest level (prompt → structured output → control loop).
• 2 Data Intelligence Systems (main demos / Palantir Maven concepts). Analyze real-world data using LLM planning + deterministic computation. See Substack post #73 Understanding Palantir Maven through demos.
• 3 Workflow Automation (IDEs like n8n). Use tools like n8n to connect APIs and automate tasks.
• 4 Integrated Systems (Automation + Intelligence). Combine automation and intelligence into full AI-driven applications.

Minimal Python demos showing how an AI app controls a model: define output structure, parse responses reliably, and build simple tool loops. Focus:

• prompt → structured output
• JSON schema / parsing
• deterministic handling
• no real-world complexity

Substack post #71b (26.0326). Describes my first 2026 Agentic AI demo. This is a minimal demo (code on GIT) that shows the core of what Agentic AI really is (AI applications, not AI agents).

Code and test results (docx #600-2_core_AI_concepts_). Search for GPT chats "#110" (code) and "#111" (results).

Code algorithm. See #112 (in docx #600-2_core_AI_concepts_) for details.

The key capabilities of the model that make this possible.

• The model is a UFA (universal function approximator).
• Universal means any function.
• Approximation means it can make good guesses for inputs it was never trained on but are semanticly close to an input that it was trained on.
• This requires a massive NN and extensive training input.
• This simulates (to a certain (very useful) degree) reasoning, thinking, and intelligence.

Systems (like Palantir Maven) that analyze real-world data using a combination of deterministic logic and LLM reasoning to extract patterns, insights, and decisions. Focus:

• external data (Gmail, DB, APIs)
• structured queries
• pattern detection (counts, anomalies)
• LLM = planner + explainer

• 2.1 PAL v1 26.0327
• 2.1b PAL v1 with Gemma-4 26.0411
• 2.4 PAL v4 26.0328
• 2.4b PAL v4 deployed on Render 26.0329
• 2.4c PAL v5 with MongoDB 26.0331

• pal_core_01_detect_ids.py
• pal_core_02_predict.py
• pal_core_03_allocate.py

• pal_core_04_graph_semantic.py
• pal_core_05_operate.py (combines 01-04)
• pal_core_06: simplest demo of all core LLM roles

Following shows the code for the core call to the LLM API. See #603_PAL_.docx for details.

Following shows test results.

Ran same test with local Gemma-4. Required minimal code changes. But demos the need for an MIL (model interface layer) if you want to often switch models. That could get complicated quickly.

Enter free form language request to run complex analysis of DB (JSON file) event history. See #603_PAL_.docx for details.

GPT: "Clean one-line description for PAL v4: Use this: PAL v4 converts a natural-language analysis request into a multi-step plan, executes each step deterministically on stored data, and produces a structured comparison result."

This is the first real “agent-like” system.

Search #600-2_core_AI_concepts_ for "PAL_v4 DEPLOYMENT (to RENDER)". Runs with ingest and inference. Need to add UI, DB, etc. See #603_PAL_.docx for details.

Search #600-3_core_AI_concepts_ for "pal_v5 (with mongo) deploy". See #603_PAL_.docx for details.

26.0412 #600-3_core_AI_concepts_. LLM added a rule. Py script then added the derive semantic edges. Kind of a "patch" to add something not programmed in the script. The script code (not the LLM) does almost all of the heaving lifting. The LLM is kind of just an "add on". The LLM prompt has a detailed "cheat sheet" example, which is recommended (greatly increased chance of success).

pal_core_04 shows how human language can add new runtime relationship semantics to a structured graph system without rewriting the core code.

Connect APIs and automate tasks using tools like n8n or equivalent Python workflows.

(IDEs like n8n) Visual tools that connect APIs and automate tasks using triggers, flows, and integrations. Focus:

• nodes (Gmail, HTTP, Slack)
• triggers + actions • fast prototyping
• low-code automation Variants:
• hosted (n8n cloud) / local (Docker)

This includes the following (see #603_PAL_.docx for details).

• N1 cloud n8n / gmail.
• N2 n8n_local install/test.
• N3 n8n_local Gmail read many.
• N4 python replaces n8n. N4 still belongs in (3) because it’s: replacing automation layer, not adding intelligence.

n8n running locally (Docker/Win11)

Combine automation with LLM planning and structured execution (PAL-style systems).

(Automation + Intelligence) Combining workflow automation with data intelligence: automated pipelines that gather data, analyze it, and take controlled actions. Focus:

• n8n (or similar) → orchestration
• Python/PAL → intelligence layer
• approval gates
• secure/self-hosted systems

This includes the following (see #603_PAL_.docx for details).

• N5 (replacing automation layer, not adding intelligence)