AI systems · Civic strategy · Built locally

I learn AI by
building what I need.

I’m Jose Munoz—an Artificial Intelligence Agent Orchestrator turning curiosity into agents, memory systems, and local infrastructure that actually runs.

See consulting work Explore the builds

Featured venture Political judgment + agent infrastructure

Neural Cache Consulting Jose Munoz · Principal

Accepting select engagements

Campaigns that remember

Battle-tested local politics. Built for the AI era.

Neural Cache helps local candidates, civic groups, and reform campaigns turn a crowded race into a clear research, message, field, and technology operation.

The work combines practical agent systems with my record as a Hudson County Freeholder from 2007–2014, Board Chair in 2014, a public champion and slate backer of Kids First, and a board member of North Hudson Regional Fire & Rescue (NHRFR).

Visit the full site Review engagements

Principal file / JM Hudson County, NJ

JudgmentLocal campaigns CapacityMulti-agent research ContinuityPrivate memory ControlLocal AI options

Engagement examples Scope → work product → sample outcome

What I can do for you

Useful work, ready for the next decision.

Each engagement is scoped around concrete artifacts your campaign can review, edit, and put to work. AI accelerates the desk; principal judgment decides what ships.

Editorial illustration of a community school board ballot

Historical reference / Kids First

Local school-board / ballot reform Civic strategy

Turn a structural reform into a story voters can act on.

Grounded in my role as a public champion of West New York’s elected Board of Education and political sponsor / slate backer for Joan Palermo and Matthew Cheng’s Kids First slate.

Message framework
Slate identity and brand brief
Eight-week field calendar

Sample outcome One plain-language case for reform, carried consistently from the ballot explainer to the door and volunteer calendar.

AI research desk Issue + opposition

A sourced brief before the room starts guessing.

Several specialized agents examine votes, public statements, coverage, and issue history in parallel. I resolve conflicts and edit the final narrative.

Executive research brief
Claim-and-citation matrix
Narrative and vulnerability map

Sample outcome A decision-ready local-race dossier with sources attached and unresolved claims clearly flagged.

Message Lab pack EN / ES

One message system for doors, press, and fast replies.

Build a shared language around the candidate’s case, the contrast, and the hard questions—drafted bilingually and tightened for how people actually speak.

English / Spanish door script
Rapid-response line bank
Contrast frame

Sample outcome Volunteers and spokespeople can answer the same difficult question without drifting into six different campaigns.

Campaign memory system Private continuity

Keep the facts when staff and chat threads change.

A private, agent-queryable knowledge layer retains approved facts, decisions, message language, and source history for the campaign team.

Structured fact library
Decision log
Staff recall workflow

Sample outcome A new staffer can recover what was decided, why it changed, and which source controls.

Local AI ops Sensitive data

Bring the workflow closer to the machines you control.

Set up private inference and task-focused agents for research, internal drafting, and repeatable campaign operations where cloud exposure is a concern.

Private model runtime
Agent workflow map
Access and handoff guide

Sample outcome Sensitive working material can remain on campaign-controlled infrastructure.

Candidate coaching kit Human-edited

Rehearse the hard exchange before it becomes the clip.

Generate a wide question set, then edit it into focused practice built around local facts, likely attacks, bridge language, and the candidate’s natural voice.

Debate and Q&A grid
Hostile-question drill
Rehearsal scorecard

Sample outcome A repeatable prep session with sharper answers and specific follow-up work.

Approach Curiosity with a command line

Jose Munoz Builder / learner / evangelist

The work starts with a question.

What if an agent could remember, work beside me, and run on machines I control?

That question turned Python lessons into working systems. I’ve been exploring how agents think across sessions, how local models become useful tools, and how several specialized AIs can cooperate on one outcome.

I care about practical independence: understanding the stack, owning the runtime, and sharing what works. Along the way, I advocate for the Quai network and the technologies pushing decentralized infrastructure forward.

Selected builds Systems shaped through practice

From prompt to infrastructure.

Four connected experiments in making AI more useful, personal, and local.

Memory system Python · Vector · Graph

mnemo recall --user jose

Mnemo

Open Mnemo →

A per-user memory layer that gives agents continuity across conversations. Semantic recall meets graph relationships, with a dashboard for seeing what the system knows.

Vector recall
Knowledge graph
Memory dashboard

Agent runtime Shell · Local GLM

glm "help me ship this"

Harness / glm

A terminal-first agent runtime and shell copilot for local GLM models. It brings model reasoning into the place where real work already happens: the command line.

Tool execution
Shell context
Local inference

~/harness

$ glm status › model connected › tools ready_

Orchestration Many agents · One goal

delegate → compare → synthesize

Multi-agent workflows

Codex, Claude, Grok, Kimi, and Pi working as a small, specialized team. The experiment is not more chat windows—it’s deliberate roles, handoffs, and verification.

Parallel research
Role handoffs
Cross-checking

Infrastructure Private · Distributed

docker compose up -d

Local AI stack

A self-hosted foundation for experimenting without giving up control: model serving, containerized services, and a private mesh connecting the machines that do the work.

vLLM
Ollama
Tailscale
Docker

Working knowledge The toolkit keeps evolving

Current field kit

Learning the whole path.

Build

Python

Shell

Docker

Run

vLLM

Ollama

Local GLM

Connect

Tailscale

Agents

Tool calling

Remember

Embeddings

Vector search

Knowledge graphs

The next build starts with a conversation.

Follow what I’m learning next.

Notes on agents, local AI, Python, emerging tech, and the occasional philosophical detour.

Find me on X @lukeNukemAI

I learn AI by building what I need.