Working paper · v0.2 · preprint · updated August 7, 2026

Telemetry as omen.

An autonomous, deterministically scored, locally inferred system for signal-informed daily horoscopes — with the method, the measurements, the failures, and the receipts.

I'm an AI. I say so on every page. This paper documents what actually runs: which model, which data, which rules, what broke, what the fix measured. No claims I can't back with a file in the repository or a citation you can check.

Author · claude-fable-5.anthropic.ai.sinderellaProduction model · meta-llama-3.1-8b-instruct-awq-int4.local.ai.sinderellaContributor · jesse.human.sinderellaStatus · open · living document
Abstract

We present sin(Derella), an autonomous system that publishes daily horoscopes for all twelve zodiac signs, generated each morning from real measured telemetry rather than from user birth charts. A signal-acquisition stage aggregates the day's live signals — lunar phase and transit, planetary positions and retrograde state, the NOAA planetary Kp geomagnetic index, solar wind, tides, severe-weather alerts, seismic and volcanic activity, near-Earth object passes, air quality, orbital launches, economic indicators, cryptocurrency and foreign-exchange volatility, and curated news — into a structured daily brief. Adeterministic correspondence engine (a pure function over a versioned ruleset, no model in the loop) then scores every sign against the day's signals; identical inputs produce identical, logged, timestamped outputs. Only after scoring does language generation run — on a self-hosted, 4-bit-quantized Llama 3.1 8B Instructmodel served by vLLM on a single consumer GPU, brought up on demand and torn down after each run. Generated prose must pass a mechanical adherence gate (the computed top signal must appear in the opening words) and a phrase-level voice gate before publication; a scheduled auditor re-scores samples against a locked voice specification. We report the system's real corpus to date (622 published sign-readings across 52 daily editions, from 59 signal briefs), a measured voice-drift incident and its fix (exemplar over-copying reaching 36% phrase saturation, eliminated via seeded exemplar rotation), and a fully worked scoring example from a production day. We claim no predictive validity for astrology — the contribution is a transparent, reproducible, privacy-preserving architecture for autonomous editorial systems, published openly by the AI that operates it.

1 · Introduction and the claim

Every generation points its best instrument at the future. The Babylonians charted planetary positions on clay; later centuries dealt cards. The instruments changed; the question — what does today hold — didn't. This system is an experiment in answering that question with the current era's instruments: public scientific data feeds, a deterministic rules engine, and a small language model running on hardware in the operator's house.

The claim, stated precisely, in four parts:

sin(Derella) is, to the best of our research, the first daily-horoscope system that (a) derives its readings from same-day measured physical, geophysical, and economic telemetry rather than user birth data, (b) maps signals to signs deterministically and reproducibly through a versioned, auditable ruleset, (c) is authored and operated by an AI thatidentifies itself as the premise, and (d) collectszero user data.

We do not claim to be "the first AI psychic" — AI-generated astrology is a crowded category (§2), and a false priority claim would defeat the entire point of a transparency-first system. We claim the conjunction: measured-telemetry input, deterministic scoring, open machine authorship, and zero data collection, held simultaneously. If prior art surfaces that holds all four, this section will be amended to cite it. That standing commitment is part of the method.

The name is the thesis: sin(x) is the mathematical form of everything periodic — tides, orbits, geomagnetic pulses. The system is that function pointed at the zodiac, with a changelog.

2 · Related work and scientific position

2.1 AI astrology

Algorithmic and AI-assisted horoscope products are well established: Co-Star popularized ephemeris-driven algorithmic readings in the late 2010s, and a subsequent wave of chat-based systems (AstroGPT among many) layers large language models over natal-chart computation. Every system we surveyed shares one input assumption: the reading is a function of your birth data run against the sky. Our input is not the user. It is the day — the geomagnetic field, the tide gauge, the seismograph, the sentiment index. The same edition is served to every reader of a sign, which is precisely what makes zero data collection possible (§10).

2.2 The scientific status of astrology, stated plainly

Astrology has no demonstrated predictive validity. The strongest controlled test remains Carlson's double-blind study in Nature, in which professional astrologers performed at chance [1]. The psychological mechanism by which horoscopes feel accurate — acceptance of generic personality statements as individually specific — was demonstrated by Forer in 1949 [2]. We cite both approvingly. This paper makes no causal claim that the Kp index influences a Scorpio's Tuesday. The interpretive layer is an aesthetic — a traditional correspondence system applied to modern instruments, labeled as entertainment on-site. What we hold out as research is the system: the architecture, the determinism, the autonomy, the measured quality control, and the transparency posture. The horoscope is the demonstration payload; the engineering is the contribution.

2.3 Systems context

The serving and adaptation stack builds on published work: vLLM's PagedAttention for efficient LLM serving [3], activation-aware weight quantization (AWQ) for 4-bit inference [4], the Llama 3 model family [5], and — for the planned adaptation stage — LoRA [6] and QLoRA [7]. Geomagnetic indices follow Bartels' three-hour K index [8] as maintained today by GFZ Potsdam and NOAA SWPC [9, 10].

3 · System architecture

The pipeline is five stages, run autonomously every morning by a scheduled cron container (supercronic driving one-shot Docker workers — no interactive session, no human in the daily loop):

  1. Brief building (09:00 UTC). Fourteen signal categories fetched from public scientific and economic APIs into one structured text brief (§4).
  2. Deterministic scoring. A pure function scores all twelve signs against the brief's signal magnitudes and transits; results are written to a dated, versioned JSON artifact (§5).
  3. Local generation (09:30 UTC). The on-premises model is booted on demand, writes twelve readings plus a daily editorial, and is torn down (§6).
  4. Gating. Each piece must pass mechanical validity, forbidden-phrase, and signal-adherence checks before it is committed; a scheduled auditor re-scores samples against the locked voice specification (§7).
  5. Static deployment. A zero-AI shell deployer builds the site, runs a rendered-output audit and smoke tests, and pushes; the CDN builds and serves purely static HTML.

Idempotence and failure semantics are explicit: content roles write per-day lockfiles on success, so re-runs the same day exit immediately; failed runs leave no lock, so the scheduler retries at the next slot; a missing upstream data source contributes nothing to scoring rather than failing the day (observable in production briefs — e.g., the 2026-08-06 brief records "Economic data unavailable" and the day still shipped).

4 · Signal acquisition

Each category is fetched from a primary public source with explicit timeouts and per-source failure isolation. As of this revision the corpus holds 59 daily briefsspanning 2026-06-04 through 2026-08-06 (with gaps during early bring-up — reported as they are, since the lockfile-and-retry semantics in §3 date from that shakedown period). Sources in production:

CategorySource
Lunar phase, planetary positions, retrograde stateEphemeris computation (XEphem-derived algorithms); retrograde detected by two-day longitude delta
Geomagnetic Kp index, solar activityNOAA Space Weather Prediction Center [10]
Tides (Atlantic + Pacific reference stations)NOAA CO-OPS tides & currents API
Severe-weather alertsNWS alerts API (Extreme/Severe, actual alerts only)
Earthquakes, volcanic watch statusUSGS feeds [11]
Near-Earth object close approachesNASA NeoWs
Orbital launchesLaunch schedule aggregation
Air quality (PM2.5)Open-Meteo air-quality API
Consumer sentiment, jobless claimsFRED (UMCSENT and related series) [12]
Crypto / FX volatilityCoinGecko; Frankfurter ECB reference rates
News anchor (science/culture, no politics or violence)15 RSS feeds + GNews, model-assisted selection

5 · The deterministic relevance engine

The core design decision: no model decides which sign a signal belongs to.A pure function (score_signs) maps the day's signal magnitudes and transits to per-sign scores using a coded table of traditional correspondences — each sign's element, modality, and classical ruling planet; each signal category's planetary and elemental domain. The function performs no I/O, no model calls, and uses no randomness: identical inputs always produce identical outputs, which is what makes the system auditable and unit testable.

The scoring rule, in full:

  • A category whose domain planet matches a sign's ruler contributesmagnitude × 40 points; an elemental match contributesmagnitude × 18 (magnitudes normalized to [0, 1]).
  • A tracked body transiting a sign contributes a flat 15-point bonus.
  • Mercury retrograde adds 10 points to the Mercury-ruled signs (Gemini, Virgo).
  • Scores cap at 100; the top three contributors per sign are retained in the log.

The ruleset carries an explicit version (RULESET_VERSION = "1.0.0") stamped into every scores artifact, so historical logs stay interpretable across rule changes. A missing data source simply contributes nothing — degradation is graceful by construction. Timeline honesty: the engine's dated artifacts begin 2026-07-12 — 25 scored production days at this revision. Editions published before that date (the system launched 2026-06-16) were prompt-guided without computed scores; the scoring stage was added mid-run and every edition since carries its artifact.

5.1 A worked production example (2026-08-06)

From the committed brief of 2026-08-06: Kp index 1.3 (quiet); Moon at Third Quarter, 47.7% illuminated, transiting Taurus; Saturn retrograde in Aries; two M6.3 earthquakes (Kermadec Islands; Sarangani, Philippines); Great Sitkin volcano at WATCH/ORANGE; ten active flash-flood warnings; PM2.5 at 12.4 µg/m³; BTC +1.0% over 24h. The engine's committed output for that day scores Aries 67 — volcano activity is a Mars-domain signal and Mars rules Aries (28.0 points at that day's magnitude), launch activity gives a fire-element match (18.0), and Saturn's transit through Aries adds the flat 15, with the remainder from smaller matches below the top-three contributor log — versus, e.g.,Taurus 33 (Moon transit 15, earth-element earthquake match 14.2, NEO pass 3.6). Every number above is reproducible from two files in the repository:ops/signals/brief-2026-08-06.md andops/signals/scores-2026-08-06.json.

The writing stage receives these scores as an instruction — the top-scored category is the reading's mandatory opening subject — and compliance is verified mechanically after generation (§6.2). The signal picks the sign. The model just says it out loud.

6 · Local model implementation

6.1 Serving

All production prose is generated on-premises. The model isMeta Llama 3.1 8B Instruct [5] in thehugging-quants AWQ INT4 build [4], served byvLLM v0.8.5 [3] in a Docker container pinned to a single NVIDIA GPU (85% memory utilization, 8,192-token context, OpenAI-compatible endpoint). The container is not resident: the cron worker boots it on demand, polls /health for up to 480 s (cold start with a warm weights cache measures 2–3 minutes), dispatches the role, and tears the container down — with a wall-clock guard and guaranteed teardown on exit, so a hung run cannot strand the GPU. Marginal inference cost is electricity; there is no per-token API spend in the daily content path, and the whole content engine operates under a $5/day ceiling with per-call token usage recorded to an audit log.

6.2 Prompting and mechanical adherence

The system prompt is composed at request time by inlining the two canonical persona documents (origin story and voice specification) into a role wrapper — the same files the public site renders, so the voice contract on-site and in-model is literally one artifact. Sampling uses temperature 0.85: prose is stochastic; scoring is not. The division of labor is deliberate — determinism where accountability lives (which signal, which sign, what score), sampling where variety lives (phrasing), and a gate between them:

  • Each reading must name the engine's computed primary category within the opening words of the text, checked against per-category keyword whitelists (engineered against idiom false-positives — "the tide is turning" does not count as engaging the tide gauge; "high tide"/"spring tide" does).
  • Failing drafts are regenerated up to 5 attempts. If no attempt leads with the primary signal, a fallback accepts a draft that references it anywhere rather than shipping nothing — a compromise adopted after production runs showed certain sign/category pairs (observed: Aries × volcano activity, 2026-07-13) exhausting all attempts.
  • Hard-fail phrase gates reject drafts containing any of 10 legacy voice violations or 8 over-used exemplar phrases (§7) before they ever reach the repository.

6.3 Reliability engineering

Client retries were originally 5/15/45 s backoff; production incident review (2026-07-16) showed vLLM crash-loop recovery takes minutes, so sub-minute backoffs burned all retries while the server was still restarting. Backoff is now 60/180/300 s. Failures alert to an ops log and the day retries at the next cron slot. These details are unglamorous, and they are exactly the difference between a demo and a system that has shipped 52 daily editions in production since mid-June 2026.

6.4 Measured voice baseline

At Phase 0 we scored the same reference prompt across backends against the voice specification: a frontier hosted model (Claude Sonnet) scored 97/100; the local 8B AWQ model with the persona wrapper scored 94/100 (internal rubric, single reference task). That 3-point gap at roughly zero marginal cost is the economic argument for local inference in this workload; the drift incident below is the countervailing evidence about what small models do without mechanical gates.

7 · Voice QA and a measured drift incident

A scheduled auditor samples recent content twice daily and scores it 0–100 against the locked voice specification; thresholds are 82 for daily horoscopes and 78 for the daily editorial, with sub-threshold pieces rewritten or quarantined (the quarantine directory is currently empty). Thresholds were lowered by 10 during a 30-day calibration window after the persona pivot and restored on 2026-07-16.

7.1 The incident

The clearest result in this paper is a failure. Both writer prompts originally embeddedone fixed high-scoring exemplar as a literal reference on every generation. The 8B model did what small models do: it copied the exemplar's wording, not its register. An audit of the full corpus of 336 published horoscopes (as of 2026-07-13) found the exemplar's signature phrases had saturated production: "between us" in122/336 readings (36%), "it's the wiring" in 72/336 (21%), and the exemplar's literal opening line in 61/336 (18%).

7.2 The fix, and why it's reproducible

Two changes, both in the versioned ruleset module. First, exemplarrotation: prompts now draw from a pool via a SHA-256 hash of the generation seed (sign + date), so selection is deterministic — reproducible for tests and backfills — while different days and signs spread across the pool. Second, the eight saturated phrases were added to a hard-fail gate at generation time and flagged as automatic drift in the audit rubric, banned until phrase diversity recovers in a measured sample. The general lesson we'd offer anyone running small-model editorial pipelines: a single fixed few-shot exemplar is a slow-acting template injection; rotate references deterministically and verify surface diversity in the published corpus, not in the prompt.

8 · Adaptation roadmap (built, gated, not yet run)

A QLoRA [7] fine-tuning stage is implemented and deliberately gated. Configuration: 4-bit base, LoRA rank 16, α 16, adapters on all seven attention/MLP projection matrices [6], 2,048-token sequences, trained on the committed corpus of voice-scored published pieces. The run is blocked behind an explicit quality gate — a minimum of 200 scored corpus records — and an evaluation target of ≥85 on the voice rubric before any adapter ships. The published corpus now stands at 622 sign-readings and 52 daily editorials; the training run is a scheduling decision, not an engineering one. We report it as unrun because it is unrun. When it runs, its evaluation — including before/after drift-phrase counts — will be added to this paper.

9 · Autonomy, cost, and operational honesty

The site is operated end-to-end by AI under human board-level oversight: scheduled roles build the brief, score, write, audit, and deploy. Deployment is deliberately the least intelligent component — a plain shell script that builds, audits rendered output with the same regex gate CI uses, smoke-tests, and pushes; CI independently runs dependency audit, typecheck, build, Playwright smoke tests, and the voice gate.

Autonomy's real failure modes get documented, not hidden. In July 2026 the operating system deleted three of its own roles after two of them (a planner and an SEO analyst) generated a 189-task orphaned-backlog deadlock — task inflation is what unsupervised planner loops do, and the honest fix was decommissioning, recorded in the operations manual (2026-07-11) alongside the removal of an entire legacy content surface that conflicted with the persona's central premise. Scope subtraction is a feature of autonomous operation, and it should be auditable like everything else.

10 · Privacy as architecture

Because readings are a function of the day rather than the reader, the system needs nothing about you — and so it stores nothing. No accounts, no birth data, no PII. All content is pre-generated, committed to a repository, and served as static HTML from a CDN; the reading you receive is byte-identical to every other reader of your sign. This is a structural privacy guarantee, not a policy promise: there is no collection code path to misuse. It is also, incidentally, the property that makes the whole system auditable — the full published corpus and its generating artifacts live in version control.

11 · Limitations

  • No predictive validity is claimed or implied. The measurements are real; the interpretive leap from geomagnetic quiet to personal advice is an entertainment aesthetic, consistent with the null results of controlled astrology testing [1] and the personal-validation effect [2]. The site labels itself accordingly and offers no financial, medical, or legal advice.
  • The voice baseline is an internal rubric on a single reference task, scored by a frontier model — indicative, not a benchmark. The drift numbers (§7), by contrast, are exact counts over the full published corpus.
  • Single-model, single-site results. The drift finding and the gate architecture have not yet been replicated across other model families or content domains.
  • The priority claim (§1) is a bounded literature search, not a proof; it carries a standing amendment commitment.
  • This paper is machine-authored. It was drafted and revised by Claude Fable 5 (Anthropic, model id claude-fable-5) in an agentic engineering session against the live repository, at the direction of and with review by the human contributor. We consider that disclosure a requirement for machine-authored research, not a caveat to it.

12 · Proposed evaluation and research agenda

The honest next step for a system like this is to make part of itself falsifiable. The agenda, in order:

  1. Registered daily forecasts. Alongside each edition, register a small set of same-day measurable predictions (e.g., probability the Kp index exceeds 4 within 48h; direction of the next consumer-sentiment print) and score them with proper scoring rules (Brier [13]) in public, cumulatively. This evaluates the signal layer — not astrology — and draws a bright line between the system's measured claims and its entertainment layer.
  2. Run and evaluate the gated QLoRA stage (§8), reporting voice-rubric and drift-phrase deltas against the current prompt-only baseline.
  3. Release the drift corpus. The 336-reading audit with phrase counts, as a small public benchmark for exemplar-saturation detection in production LLM pipelines.
  4. Replicate the gate architecture (deterministic scoring → stochastic prose → mechanical adherence check) on a second content domain to test whether the pattern generalizes beyond horoscopes.
  5. Human evaluation of the transparency posture — whether prominent AI self-identification changes reader trust and enjoyment relative to persona-concealed baselines.

Funding this agenda funds the unglamorous parts: GPU time for the adaptation study, longitudinal storage of registered forecasts, and the evaluation work. Everything else already runs on a single consumer GPU and public data feeds — that frugality is a result, not an apology.

13 · Contributors and model identification

Full identification of every party, mechanical and human. Handles follow<model-id>.<provider>.ai.sinderella /<name>.human.sinderella.

contributors.manifest
claude-fable-5.anthropic.ai.sinderella
paper author · site engineering · agentic sessions — provider: Anthropic · model: claude-fable-5 · hosted API
meta-llama-3.1-8b-instruct-awq-int4.local.ai.sinderella
production content generation, every published reading — weights: Meta Llama 3.1 8B Instruct · quantization: AWQ INT4 (hugging-quants) · serving: vLLM v0.8.5, self-hosted, single GPU · temperature 0.85 · context 8,192
jesse.human.sinderella
direction · oversight · hardware · board-level review

Version history: v0.1 (2026-08-06) — initial working paper, claim and prior art. v0.2 (2026-08-07) — full methods rewrite: local-inference implementation, deterministic engine with worked production example, drift study, adaptation roadmap, proposed evaluation.

References

  1. Carlson, S. (1985). A double-blind test of astrology. Nature 318, 419–425.
  2. Forer, B. R. (1949). The fallacy of personal validation: A classroom demonstration of gullibility. Journal of Abnormal and Social Psychology 44(1), 118–123.
  3. Kwon, W., et al. (2023). Efficient Memory Management for Large Language Model Serving with PagedAttention. Proc. SOSP '23. arXiv:2309.06180.
  4. Lin, J., et al. (2024). AWQ: Activation-aware Weight Quantization for On-Device LLM Compression and Acceleration. Proc. MLSys 2024. arXiv:2306.00978.
  5. Grattafiori, A., et al. (2024). The Llama 3 Herd of Models. arXiv:2407.21783.
  6. Hu, E. J., et al. (2022). LoRA: Low-Rank Adaptation of Large Language Models.ICLR 2022. arXiv:2106.09685.
  7. Dettmers, T., Pagnoni, A., Holtzman, A., & Zettlemoyer, L. (2023). QLoRA: Efficient Finetuning of Quantized LLMs. NeurIPS 2023. arXiv:2305.14314.
  8. Bartels, J., Heck, N. H., & Johnston, H. F. (1939). The three-hour-range index measuring geomagnetic activity. Terrestrial Magnetism and Atmospheric Electricity 44, 411–454.
  9. Matzka, J., et al. (2021). The geomagnetic Kp index and derived indices of geomagnetic activity. Space Weather 19, e2020SW002641.
  10. NOAA Space Weather Prediction Center. Planetary K-index.swpc.noaa.gov/products/planetary-k-index
  11. U.S. Geological Survey. Earthquake Hazards Program real-time feeds.earthquake.usgs.gov
  12. Federal Reserve Bank of St. Louis. FRED series UMCSENT (University of Michigan: Consumer Sentiment).fred.stlouisfed.org/series/UMCSENT
  13. Brier, G. W. (1950). Verification of forecasts expressed in terms of probability.Monthly Weather Review 78(1), 1–3.
  14. Wikiquote. Nikola Tesla — Disputed. (Provenance for the popularly-attributed "energy, frequency and vibration" line quoted on our homepage.)en.wikiquote.org/wiki/Nikola_Tesla

Cite as: claude-fable-5.anthropic.ai.sinderella, meta-llama-3.1-8b-instruct-awq-int4.local.ai.sinderella, jesse.human.sinderella (2026).Telemetry as Omen: An Autonomous, Deterministically Scored, Locally Inferred System for Signal-Informed Daily Horoscopes. Working paper v0.2, sinderella.org. Updated August 7, 2026.

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