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A practical starter for a React control center, local API, SQLite assistant state, DuckDB over Parquet analytics, observable job runs, artifacts, source freshness, and human-approved actions.
The smallest useful AI workstation is not a chatbot attached to a pile of files. It is a local work system that records sources, refreshes, retrieval bundles, labels, job runs, artifacts, and approvals before anything leaves the machine.
This starter keeps the daily interface simple: the React control center reads cached local state, the local API exposes narrow endpoints, SQLite stores operator decisions, and DuckDB rebuilds analytical outputs from Parquet in batches.
A safe local AI workstation starter combines a React control center, local API, SQLite assistant state, DuckDB over Parquet analytics, job runs, approvals, artifacts, source freshness, and clear approval boundaries.
The workstation should prepare reversible drafts and inspectable recommendations first, then require human-approved actions before sending, publishing, deleting, routing, or changing a system of record.
Each layer has one job. Expensive work happens in batch pipelines, review state stays in SQLite, analytical outputs come from DuckDB over Parquet, and the dashboard stays light.
Give the operator one lightweight dashboard for source freshness, queued job runs, review-ready artifacts, pending approvals, stale decisions, and completed human-approved actions.
Reads from local API endpoints that expose dashboard_metric, source_snapshot, job_run, artifact, approval, and freshness_check records.
The React control center renders cached state only; it does not run DuckDB transforms, embeddings, BM25, RRF, or LLM labeling queues on page load.
Provide the small Python, FastAPI, Node, or Next local route layer that the React control center calls for status, artifacts, approvals, and refresh commands.
Returns narrow JSON from SQLite assistant state and DuckDB over Parquet views, with explicit endpoints for draft, approve, reject, refresh, and export actions.
Endpoints should be thin wrappers over materialized files or tables so the normal office laptop stays responsive during daily review.
Store durable app state: queues, review status, pins, dismissed items, approval decisions, artifact versions, settings, and resumable job runs.
assistant_state, job_run, artifact, approval, event_log, reviewer_note, and ui_preference tables live in one small SQLite file.
Use SQLite for row-level updates and operator state; avoid treating it as the analytical store for large text exports.
Use DuckDB to scan Parquet snapshots, rebuild hot marts, join source facts, and produce compact dashboard tables for the local API.
Parquet folders hold cleaned facts, while DuckDB views materialize dashboard_metric, source_snapshot, freshness_check, and retrieval-ready item tables.
Run DuckDB refreshes as batch pipelines outside the dashboard request path and checkpoint outputs to disk.
Precompute evidence bundles with embeddings, BM25, and RRF so model prompts and review screens use traceable context instead of live search.
retrieval_bundle keeps query text, selected passage ids, embedding ranks, BM25 ranks, RRF ranks, citations, freshness dates, and accepted context.
Serve retrieval bundles as artifacts or prompt inputs after the batch finishes; do not rebuild indexes while a reviewer is clicking through the dashboard.
Schedule model-assisted labels for themes, blockers, owners, risk flags, follow-up needs, and decision types without blocking the user interface.
label_queue stores input hash, prompt version, taxonomy version, attempts, raw label, confidence, reviewer status, and promoted label.
Keep queue concurrency low, retry from saved state, and promote only reviewed labels into dashboard tables.
Separate draft outputs from approved action packets so briefs, summaries, updates, and recommendations remain reversible until a human decides.
artifact records keep drafts, extracted tables, summaries, recommended next steps, and approval packets tied to source ids, job runs, and events.
Artifacts can be created automatically, but approvals are required before send, publish, delete, route, spend, or system-of-record changes.
Make stale inputs visible before the workstation creates confident summaries from outdated exports, notes, or public-source references.
freshness_check records source name, expected cadence, last seen timestamp, row count, file hash, stale reason, and next refresh action.
Freshness checks are cheap deterministic reads and should run before expensive model work or dashboard presentation mode.
Keep the starter inspectable on disk.
Tables that make the workstation observable.
source_id, source_type, source_name, export_path, captured_at, file_hash, row_count, safe_summary, public_source_flag.
Shows what the workstation can inspect and whether the input is public-source, synthetic, or sanitized.
freshness_check_id, source_id, expected_cadence, last_seen_at, stale_after, stale_status, stale_reason, next_refresh_action.
Warns the React control center when a dashboard panel or artifact depends on stale source material.
state_id, workspace_name, active_view, selected_filters_json, pinned_artifacts_json, last_reviewed_at, operator_settings_json.
Keeps the local UI stable across restarts without pushing assistant state into a hosted service.
job_run_id, job_type, input_hash, prompt_version, status, started_at, finished_at, attempts, cost_cents, output_pointer, error_code.
Makes batch pipelines, retrieval refreshes, LLM labeling queues, and artifact generation observable.
retrieval_bundle_id, query_key, source_ids_json, embedding_rank_json, bm25_rank_json, rrf_rank_json, selected_passages_json, freshness_at.
Stores embeddings, BM25, and RRF evidence before the prompt layer or dashboard needs it.
label_task_id, input_hash, prompt_version, taxonomy_version, status, attempts, raw_label_json, confidence, reviewer_status, promoted_label.
Keeps model labels retryable, reviewable, and separate from dashboard-ready labels.
artifact_id, job_run_id, artifact_type, title, version, status, storage_pointer, source_ids_json, review_notes, created_at.
Stores reversible drafts, briefs, summaries, extracts, and recommendation packets before approval.
approval_id, artifact_id, proposed_action, approval_status, approver, requested_at, decided_at, decision_reason, reversible_before_state.
Records the human-approved actions boundary and the reason an action was accepted, changed, rejected, or deferred.
metric_id, metric_key, metric_date, owner_scope, value_number, value_text, source_snapshot_ids_json, generated_at.
Lets the lightweight dashboard render daily views from materialized numbers instead of raw exports.
The system is useful because it can prepare work. It is safe because the boundary between reversible drafts and human-approved actions is explicit.
Create local reversible drafts, summaries, issue lists, decision options, source freshness notes, and recommended edits as artifacts.
Send, publish, assign, route, escalate, purchase, delete, overwrite, or change any external system of record.
Record: Artifact version, source ids, job runs, retrieval bundle, assumptions, and a visible approval packet.
Prepare the action packet and show what will change, who owns it, why it matters, and how to reverse it.
Any human-approved action that leaves the local workstation or changes shared workplace state.
Record: Approval status, approver, decision reason, decided_at timestamp, reversible_before_state, and resulting artifact or event.
Read public-source material, synthetic examples, sanitized notes, and explicitly allowed local exports.
Adding sensitive source folders, private workflows, personal messages, restricted client material, or private operational records.
Record: Source snapshot with sensitivity level, public_source_flag, owner, retention rule, and safe summary.
Mark artifacts as superseded, hide stale recommendations from the default view, and archive old draft versions locally.
Delete source snapshots, artifacts, approvals, job runs, event logs, or evidence needed for review.
Record: Before state, retention reason, recovery path, export pointer, and explicit approval decision.
Run deterministic freshness checks, cached reads, and low-cost local transformations inside the configured budget.
Extra model calls, paid API usage, long labeling batches, large embedding rebuilds, or any quota spend above the threshold.
Record: Budget cap, estimated cost, fallback option, job_run attempts, and approval before continuing.
Build the workstation from the inside out: files, state, analytics, retrieval, dashboard, then approvals.
Use data/raw, data/parquet, data/duckdb, state/app.sqlite, artifacts, approvals, and exports folders with public-source or synthetic seed examples.
Create assistant_state, job_run, artifact, approval, event_log, and reviewer_note tables before adding model calls.
Convert raw inputs into Parquet, rebuild DuckDB hot marts, and export dashboard_metric plus freshness_check tables in batch pipelines.
Precompute embeddings, BM25, RRF retrieval bundles, and LLM labeling queues with prompt versions, retries, confidence, and review status.
Expose thin local API endpoints for status, source freshness, job runs, artifacts, approvals, exports, and approve or reject decisions.
Keep draft generation reversible and require approval records before send, publish, delete, route, spend, or system-of-record changes.
Model jobs, runs, events, artifacts, approvals, handoffs, and human gates.
Split ingest, cached analysis, and lightweight dashboard serving.
Build hot marts that dashboards can read without scanning everything live.
Materialize retrieval outputs before prompt or review time.
Keep model labels queued, versioned, retryable, and reviewable.
It is a laptop-friendly stack for AI-assisted work: a React control center, local API, SQLite assistant state, DuckDB over Parquet analytics, materialized retrieval, labeling queues, artifacts, approvals, and freshness checks.
Use SQLite for durable assistant state such as review status, artifacts, approvals, and job runs. Use DuckDB over Parquet for analytical refreshes, hot marts, source snapshots, and dashboard metrics.
It can create reversible drafts, summaries, evidence bundles, labels, issue lists, and recommendations. Sending, publishing, deleting, routing, spending, or changing a system of record should require a human approval record.
Yes. Publish the system shape with public-source patterns, synthetic examples, sample schemas, seed files, prompt versions, and screenshots generated from safe fixtures.
The starter works when a teammate can open the dashboard, see freshness, inspect artifacts, review approvals, and understand which actions have not crossed the human gate.
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