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objective03: A Locally-Run Autonomous News Ingestion and Contradiction Tracking System

Architecture Overview: From RSS Feeds to Audio Broadcasts on Consumer Hardware

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objective03 is a Python daemon that performs autonomous news ingestion, atomic claim extraction, entity resolution, event clustering, contradiction detection, narrative analysis, and text-to-speech broadcast — all running on local hardware via llama.cpp (Metal GPU backend), KuzuDB (embedded temporal property graph), Qdrant (vector similarity search), and Qwen3-TTS (mlx_audio). The system ingests content from RSS feeds, Reddit subreddits, and YouTube channels, extracts structured factual claims with GBNF-enforced JSON schemas, detects typed contradictions across sources, clusters claims into events and narrative threads, and generates TTS-optimized audio broadcasts with voice cloning and procedural ambient audio. Zero cloud dependencies. Zero API calls.

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Pipeline Architecture

The system operates as a five-group task scheduler running on independent intervals. Each group contains a sequence of subprocesses with configurable timeouts, failure limits, and circuit-breakers.

Task Group 1: Ingestion (default interval: 60s)

The ingestion module polls three source types:

• RSS feeds — HTTP GET with If-None-Match / ETag support for conditional requests. Parsed via feedparser. Documents normalized (HTML stripped, Unicode NFKC normalized, whitespace collapsed).
• Reddit subreddits — OAuth2 authenticated API calls. Posts and comments extracted, metadata preserved (author, subreddit, upvotes, timestamps).
• YouTube channels — yt-dlp for metadata extraction and audio transcription. Channel upload schedules polled on configurable intervals.

All documents undergo SHA-256 deduplication before graph insertion. The normalized document is stored as a Document node in KuzuDB with a FROM_SOURCE edge pointing to the originating Source node.

Task Group 2: Analysis Pipeline (default interval: 120s)

This is the core processing pipeline, executing sequentially:

2a. Claim Extraction

Each document is chunked and passed to a local LLM (llama.cpp, Metal backend). The model extracts atomic factual claims using a GBNF-defined grammar that enforces a strict JSON schema:

jsonCopy
1{
2  "claim": "string",
3  "confidence": "float (0.0-1.0)",
4  "stance": "positive | negative | neutral",
5  "topic": "string (tag)",
6  "evidence": "string (verbatim text span)"
7}

GBNF grammar enforcement ensures the model's output is structurally valid — no schema drift, no optional fields appearing as required. Each claim node in KuzuDB carries a confidence property and an EXTRACTED_FROM edge to its source document.

2b. Entity Resolution

A second local LLM call extracts named entities (PERSON, ORG, LOC, EVENT) from each document. Extracted entities are resolved against existing graph nodes via:

1. Exact match on entity name
2. Fuzzy matching using Levenshtein distance with configurable threshold
3. Alias tracking — multiple names resolved to the same entity node over time

Resolved entities receive a MENTIONS edge to the source document and an APPEARS_IN edge to any event nodes they participate in.

2c. Event Clustering

Claims are assigned to events based on entity overlap. The algorithm:

1. Extracts all entities from the new claim
2. Queries the graph for existing event nodes connected to any of those entities
3. If matches found, the claim's ABOUT_EVENT edge points to the existing event
4. If no matches, a new Event node is created with emerging status

Events track:

• importance_score — computed from entity frequency, claim count, and temporal recency
• status — emerging -> active -> resolved
• temporal_start / temporal_end — bounded by earliest and latest claim timestamps

2d. Contradiction Detection

New claims are embedded using BGE-Small-EN-v1.5 (384-dimensional) and indexed in Qdrant. For each new claim:

1. Vector search — cosine similarity query against the Qdrant collection. Candidates with similarity > 0.75 are returned.
2. LLM classification — each candidate pair is passed to the local LLM with a prompt template that classifies the relationship into one of five typed categories:

Type	Definition	Example
DIRECT_CONTRADICTION	Same proposition, opposite truth value	"GDP grew 3%" vs "GDP shrank 3%"
NUMERICAL_DISCREPANCY	Same proposition, different values	"100 casualties" vs "200 casualties"
FRAMING_DIFFERENCE	Same event, different narrative lens	"Tax relief" vs "Tax cut for corporations"
TEMPORAL_DISCREPANCY	Same event, different timing	"Signed Monday" vs "Signed Tuesday"
COMPATIBLE	No contradiction; semantic overlap warrants review	—

Contradictions are persisted as CONTRADICTS edges between claim nodes with a type property. Contradictions are never auto-resolved — the system preserves the raw disagreement for downstream consumption.

2e. Narrative Analysis

Claims not assigned to events (i.e., no entity overlap with existing event nodes) are grouped into narrative threads via embedding cosine similarity clustering (>0.75 threshold). Each cluster receives an LLM-generated label. Active narratives track:

• drift_score — semantic shift over time within the narrative
• framing_classification — dominant narrative frame (e.g., "economic," "political," "social")

2f. Source Reliability Scoring

Each Source node accumulates a reliability score based on historical claim accuracy — measured by the frequency of that source's claims being contradicted by other sources. Sources that consistently produce contradictory claims see their reliability scores degrade over time.

2g. Graph Update

All extracted nodes and edges are committed to KuzuDB in a single transaction per batch.

Task Group 3: Broadcast Generation (default interval: 90s)

A local LLM queries the KuzuDB graph via Cypher-like queries for:

• Top N events by importance_score
• Unresolved contradictions (all CONTRADICTS edges with no resolution)
• Active narratives (narratives with status == "active")
• System metrics (sources ingested, claims extracted, contradictions detected)

The LLM produces an 800–1200 word broadcast script optimized for TTS. The script uses <think> blocks for internal reasoning before the spoken output. The prompt template includes structural directives: opening summary, top events, contradiction deep-dives, narrative shifts, and closing metrics.

Task Group 4: Audio Production (default interval: 90s)

The broadcast script undergoes preprocessing:

1. Chunking — split into ~100-word segments
2. Abbreviation expansion — "U.S." -> "United States", "Dr." -> "Doctor"
3. Number normalization — "3.5%" -> "three and a half percent", "$500M" -> "five hundred million dollars"
4. Date formatting — "Jan 15, 2026" -> "January fifteenth, twenty twenty-six"
5. Punctuation normalization — ellipses, em-dashes, and other TTS-sensitive characters

Preprocessed segments are synthesized via Qwen3-TTS using mlx_audio. Voice cloning is supported via reference audio input. Synthesized audio segments are crossfaded at boundaries and queued for playback via afplay on macOS.

Task Group 5: Maintenance (default interval: 24h)

• Memory consolidation — low-importance events and old narratives pruned based on importance_score thresholds
• Graph evaluation — sample of claims re-verified against source documents for accuracy metrics
• Embedding index rebuild — Qdrant index refreshed with all current claim embeddings

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Storage Architecture

Tier	Technology	Content
Graph	KuzuDB (embedded)	8 node types: Source, Document, Claim, Entity, Event, Narrative, Broadcast, ContradictionSummary. 10 edge types: FROM_SOURCE, EXTRACTED_FROM, MENTIONS, ABOUT_EVENT, CONTRADICTS, SUPPORTS, PART_OF_THREAD, APPEARS_IN, REFERENCES, PREVIOUS_VERSION
Vector	Qdrant	BGE-Small-EN-v1.5 claim embeddings (384-dim), cosine similarity search with 0.75 threshold
Audio	Local filesystem	Generated TTS WAV segments, crossfaded master tracks, procedural ambient drone
Config	YAML/JSON	Scheduler intervals, model paths, source lists, embedding thresholds
State	Local JSON	Scheduler state, circuit-breaker status, last-run timestamps

KuzuDB serves as the single source of truth. The temporal graph preserves full provenance: every claim points to its source document, every contradiction points to both claims, every event points to its contributing claims. Queries traverse edges to reconstruct the full evidence chain.

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Node and Edge Schema

Node Types

Node	Key Properties
Source	name, url, type (rss
Document	sha256, title, url, ingest_timestamp, source_type
Claim	text, confidence, stance, topic, evidence_text, extraction_timestamp
Entity	name, type (person
Event	label, importance_score, status, temporal_start, temporal_end
Narrative	label, drift_score, framing_classification, status
Broadcast	script_path, duration, timestamp, event_count, contradiction_count
ContradictionSummary	type, claim_a_id, claim_b_id, resolution, resolution_timestamp

Edge Types

Edge	From	To	Properties
FROM_SOURCE	Source	Document	ingest_timestamp
EXTRACTED_FROM	Claim	Document	extraction_timestamp
MENTIONS	Entity	Document	context
ABOUT_EVENT	Claim	Event	temporal_timestamp
CONTRADICTS	Claim	Claim	type, detected_timestamp
SUPPORTS	Claim	Claim	type, detected_timestamp
PART_OF_THREAD	Claim	Narrative	confidence
APPEARS_IN	Entity	Event	role
REFERENCES	Event	Event	relation_type
PREVIOUS_VERSION	Claim	Claim	version_number

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Inference Stack

Component	Technology	Details
LLM Inference	llama.cpp	Metal GPU backend (Apple Silicon). Quantized GGUF models. GBNF grammar enforcement for structured output.
Embeddings	BGE-Small-EN-v1.5	384-dimensional text embeddings. Indexed in Qdrant for cosine similarity search. Threshold: 0.75.
TTS	Qwen3-TTS via mlx_audio	Voice cloning from reference audio. Multi-lingual. Synthesized in ~100-word segments.
Video Download	yt-dlp	YouTube metadata and transcript extraction.
RSS Parsing	feedparser	RFC 4287 compliant RSS/Atom parsing with ETag support.

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Scheduler Configuration

yamlCopy
1scheduler:
2  ingestion:
3    interval_seconds: 60
4    max_runtime_seconds: 300
5    failure_limit: 5
6    circuit_breaker_timeout: 600
7
8  analysis_pipeline:
9    interval_seconds: 120
10    max_runtime_seconds: 600
11    failure_limit: 3
12    circuit_breaker_timeout: 1800
13    steps:
14      - claim_extraction
15      - entity_resolution
16      - event_clustering
17      - contradiction_detection
18      - narrative_analysis
19      - framing_analysis
20      - source_reliability
21      - graph_update
22
23  broadcast:
24    interval_seconds: 90
25    max_runtime_seconds: 120
26    failure_limit: 5
27    circuit_breaker_timeout: 600
28
29  audio_production:
30    interval_seconds: 90
31    max_runtime_seconds: 300
32    failure_limit: 5
33    circuit_breaker_timeout: 600
34
35  maintenance:
36    interval_seconds: 86400
37    max_runtime_seconds: 3600
38    failure_limit: 2
39    circuit_breaker_timeout: 7200

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Why This Stack

The current AI industry narrative centers on a "compute shortage" — a claimed physical limitation of GPU availability. objective03 demonstrates that this is primarily a billing bottleneck: paid AI providers extract wealth through API costs, inflating operational expenses while quantized models on consumer hardware deliver comparable performance for inference-heavy workloads.

BGE-Small-EN-v1.5 for embeddings runs on CPU in under 10ms per document. llama.cpp with Metal GPU quantizes 7B-parameter models to run at interactive speeds on Apple Silicon. Qwen3-TTS synthesizes speech at 2x real-time on an M-series chip. Qdrant runs embedded with minimal memory footprint. KuzuDB is embedded with zero external dependencies.

The total infrastructure cost: model download size (a few GB) plus disk space for the graph and audio. No monthly bills. No API rate limits. No vendor lock-in.

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The Repo

objective03 is MIT licensed, requires Python 3.11+, and is structured as:

• backend/ — Python daemon, ingestion modules, analysis pipeline, scheduler
• electron/ — Electron desktop wrapper with system tray integration
• docs/ — Configuration examples, Cypher query patterns, schema diagrams
• Root — requirements.txt, scheduler_config.yaml, .env.template, startup scripts

Built by Daniel Kliewer (kliewerdaniel). Follows the local-first philosophy established in "mastering llama.cpp local LLM integration": a weak local model controlled by the user is superior to a powerful cloud model controlled by a vendor.

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Why "objective03"?

"objective" — the system ingests raw data, extracts claims, detects contradictions, and presents findings without preference. Objectivity as a system property, not a guarantee.

"03" — version three. Also: the third wave of local AI. Wave 1 was CPU inference (slow, universal). Wave 2 was GPU inference (fast, desktop-bound). Wave 3 is Metal/MLX inference on consumer SoCs (fast, portable, power-efficient).

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Roadmap

• Source reliability scoring (per-source accuracy tracking and degradation curves)
• Cross-platform audio output (ALSA/PulseAudio for Linux, WASAPI for Windows)
• Multi-model pipeline routing (different models for extraction vs. contradiction classification vs. broadcast generation)
• Web dashboard for graph inspection (networkX visualization, Cypher query console)
• Incremental graph updates via temporal snapshots
• Claim verification against external fact-checking APIs (optional, cloud-fallback)

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Get it at: github.com/kliewerdaniel/objective

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