A practical guide for KC Digital Drive staff, facilitators, neighborhood associations, and organizations adapting the program. Start with the people and the room. Software comes later.
Who fixes what in Kansas City? for residents. Principles and measures of usefulness for hosts. The workshop manuals below give facilitators a complete learning and problem-solving path.
Help neighbors practice three skills: check an answer, protect private information, and pause when a message creates urgency. Use the free lesson and printable kit. This is a session plan; check confirmed session information before inviting people.
Help a neighbor leave with a specific problem, the responsible office, evidence they can check, and a next step they choose. Use the brief builder or a sheet of paper. A complaint can identify a real failure; listen before asking someone to turn it into a plan.
Build after a clinic reveals a repeated task that a small tool can improve. A clearer form, a printable checklist, or a better referral may be enough. This manual describes a decision process, not an operating local AI service.
This manual covers the published NeighborhoodOS dashboards. Their sources are official Kansas City datasets, and their outputs are dated, privacy-filtered JSON snapshots. Experimental connectors elsewhere in the repository are not the public-site pipeline.
All figures are rough planning ranges in US dollars, reviewed for planning in October 2026. Prices for hardware in particular move quickly. Get real quotes before committing.
From a Problem Brief to a working, safe tool. The full curriculum is in the Level 3 manual.
Guidance for planning a physical space where a neighborhood learns, solves problems, and runs its own AI tools. Written with a historic community campus in mind (such as the LAMP campus in Kansas City), but general enough for a library branch, community center, or church hall.
The data layer starts with sources that are public, linkable, and checkable. Full ingest mechanics are in the Civic Data Ingest Guide.
npm test runs three suites. You should see 35 passed, 5 passed, and 6 passed, each with 0 failed. Both use temporary databases and clean up after themselves.
Measure outcomes, not activity. Three questions decide whether the program is working:
Do we need to be technical to run this? No. The Learn and Solve tracks need a room, a projector, sticky notes, and a good facilitator. Software only enters at Build, and only if a real need shows up.
How to run the room. The session-by-session agenda is in Session Plans; printable handouts are in the facilitator kit.
The handoff report names funding leads. This page links to their official program sources without representing any application as submitted, any deadline as verified, or any award as secured. KCDD leadership owns eligibility checks and applications.
Aggregate: a count or summary across many records (e.g., "42 open streetlight requests in this area") rather than information about one person. Audit log: an append-only record of administrative and safety events. Rows can be added, never edited or deleted. Build: the track where residents and volunteers co-design a small tool. Data steward: the named person responsible for what data is collected, how it's protected, and deleting it on request. Federation: optional exchange of signed, aggregate-only bundles between neighborhood nodes. Hallucination: when an AI model states something false with confidence. Learn: the AI literacy track. Local model: an AI model running on hardware in the hub rather than on a company's servers. Node: one neighborhood's installation of the software, usually one SQLite database file. Ollama: free software for running open AI models locally. Problem Brief: a one-page description of a local problem: what, who has authority, what evidence, and the smallest next move. Provenance: where a piece of information came from; every fact should link back to its source. Red-Light Test: the six questions in the Pilot Rubric that instantly reject a proposal. Safety driver: core/db.js, the code that refuses banned data structures. Solve clinic: a session where residents turn real problems into Problem Briefs. Socrata: the platform behind Kansas City's open data portal. Legistar: the platform many city councils use for agendas, minutes, and votes. Toothbrush test: does anyone use this at least weekly? If not, reconsider it. Trust level: 0-5 scale in the identity system, earned through verification and vouching, used to decide who can vote on what. Wedge: a single, sharp problem a tool focuses on.
NeighborhoodOS is a KC Digital Drive program that combines practical digital-skills learning with civic problem-solving clinics. It begins at the Digital Services & Support Center at LAMP, 1801 Linwood Blvd, Kansas City, MO 64109. Simon L. Paige founded the project and remains site administrator and advisor.
NeighborhoodOS was created by Simon L. Paige in 2026. He has chosen to hand it off so community organizations, starting with KC Digital Drive, can run it without him. Partners are free to use, adapt, and build on everything here: the code, curriculum, playbooks, safety rules, plans, and the NeighborhoodOS name for programs that follow the Safety Doctrine.
A KC Digital Drive program. Starting in Linwood, with a model other neighborhoods can reuse.
Status: October 17, 2026. Availability is separate from adoption, participation, funding, and outcomes.
The site offers Spanish, Chinese (Simplified), Vietnamese, Arabic, French, and Swahili as automatic-translation options. Names appear in each language in the Translate menu on every page.
NeighborhoodOS today is two things: free, practical AI help for Linwood neighbors, and clinics for stuck civic problems. Everything on this page is not part of the current program. It is written down so the ideas aren't lost, and so nobody mistakes them for promises.
The model is designed to be repeated. Each neighborhood runs its own program and, if it wants, its own node.
Planning worksheet for KCDD and a host. It is not an executed agreement or a statement that any partner has committed.
Adoption was confirmed by founder Simon L. Paige on October 17, 2026. KCDD leads program delivery; Simon remains site administrator and advisor. KCDD should name a program lead and a separate data steward before a cohort begins.
NeighborhoodOS helps neighbors understand a problem, find the right source or office, and choose a useful next step together. AI is one tool for drafting and practice. A person decides what matters and what to do.
The order matters. Learn builds trust and shared language. Solve surfaces real needs. Build only happens once a need is real.
Turnout is the most common way community programs fail. The fix is almost always the same: personal invitations from trusted people, not flyers.
A small program needs few people, but the roles must be clear. One person can hold two roles, except program lead and data steward should be different people so there is a check on data decisions.
We design our systems to be bad at surveillance on purpose.
October 2026 delivery note: at LAMP, map these learning objectives onto DSSC’s existing workshops before scheduling. Do not run a duplicate AI-literacy course. The small-business variant uses public business FAQs or checklists, never customer records or payroll. Confirm funded stipends and accommodations in each session listing. These are facilitator plans, not announced dates.
NeighborhoodOS is free, practical AI learning plus clinics for stuck civic problems. It is a KC Digital Drive program, beginning at the LAMP campus. This guide can be adapted by another neighborhood or host.
NeighborhoodOS software is a small, local-first Node.js codebase built on SQLite. It is designed so one neighborhood can run its own node on one machine, and optionally share aggregate results with other neighborhoods.
One page: what data the Linwood program holds, who could force it to be handed over, and how long it's kept.
Free, practical AI help for Linwood neighbors, plus clinics for stuck civic problems.