The 3day Rotation: Casing Star Magnitude in Pyramid-Based Internet Attention-Switch Marketing

Great — I expanded the blogpost roughly +300% and integrated concrete, repo-powered implementation ideas from pacobaco/astronomyparty (how the project detects and notifies astronomy events — useful metaphors and code modules you can repurpose for “star magnitude” ranking, timing, and notification in marketing). I cite the repo and its README where I reference specific files or capabilities. 

Casing Star Magnitude in Pyramid-Based Internet Attention-Switch Marketing

(Expanded, applied to pacobaco/astronomyparty inspired tooling and timing orientation)

In the fractured light of today’s feeds, attention behaves like a night sky: fleeting meteors, bright nova-like trends, and slow-burning constellations of conversation. To orchestrate marketing that actually guides users (rather than shouting into the void), treat content and signals as stars with magnitudes and apply a pyramid-shaped attention switch — but powered by event detection, ranking, timezone-aware scheduling, and targeted notifications. The open-source project astronomyparty — an “astronomy event manager” that ranks and notifies astronomy events — contains concrete concepts and scripts we can re-use as the backbone for this system. 

Executive summary — what we build (one-paragraph overview)

Create a three-layered Pyramid Attention Switch (Spark → Channel → Anchor) whose “stars” are measurable social signals. Use an events engine to detect candidate stars, a ranking engine to compute “magnitudes,” a timezone-aware scheduler to orient timing, and a delivery engine to trigger micro-bursts (SMS, push, short-form video, or story drops). This system blends real-time trending detection with planned cadence so that a spark hits at the right moment, curiosity content trails the spark, and anchor content converts and retains. The pacobaco/astronomyparty repo shows the same operational pieces — event detection, ranking, scheduling/notifications — but for astronomy events, which we adapt for marketing. 

The conceptual mapping: astronomy → attention

• Astronomical event = a content signal (tweet, short video, headline, microtrend).
• Magnitude = quantitative visibility score (engagement velocity, share rate, amplification).
• Conjunctions / rankconjunctions = co-occurring signals (topics that amplify each other).
• Timezone scheduling (timezone.csv) = orientation of timing for release across audiences.

These mappings let us leverage code patterns from an event manager (detection → rank → notify) and shape them into the pyramid workflow. The repo includes scripts and resources aligned to each of these roles, which we re-purpose. 

Detailed pyramid + repo-driven implementation

Top: Spark Attention (High-magnitude hooks)

Goal: deliver a single sharp signal that interrupts scroll — an eye-catching piece that becomes the apex of the pyramid.

What to detect / create: trending keywords, celebrity mentions, sudden spikes in query volume, or sudden changes in competitor messaging.

Repo analog / implementation hints: use an event detector (same pattern as astronomyparty.py / rankevents.py) to ingest feed streams (Twitter/X, TikTok trending, subreddit surge, RSS), compute a live velocity metric and flag candidate “superstars.” rankevents.py patterns are directly applicable: detect spikes, compute score, and output candidates for immediate consideration. 

Timing orientation: release the hook at the highest concurrent online windows for your audience segments (derived from timezone.csv mapping). For global audiences, push localized hooks staggered by time zone so each region sees the apex during its prime scroll windows. The repo’s timezone.csv file is a ready resource for mapping users to local release times. 

Delivery options: short video (≤10s), bold stat graphic, single-sentence provocation, or influencer micro-endorsement. Use a push notification / SMS micro-burst to VIP segments for amplified reaction (see Anchor delivery for templates).

Middle: Channel Curiosity (Sustained engagement)

Goal: convert that first glance into a path — a chain of micro content that builds a narrative and deepens interest.

What to produce: explanation threads, follow-up clips, AMA/polls, interactive quizzes, and small-group experiences. Arrange content to form a narrative constellation: individual pieces are dimmer, but together they direct users toward the base.

Repo analog / implementation hints: use scripts like rankconjunctions.py to compute which content items naturally link (co-mentions, hashtags, or topic co-occurrence). Rankconjunctions-style logic helps select the 3–5 best follow-up pieces that, when published, maximize dwell and re-sharing. You can pipeline these as scheduled jobs (e.g., “publish explainer in 30 min, poll in 2 hours, micro-AMA in 24 hours”) controlled by the same scheduling engine used in astronomyparty.py. 

Timing orientation: cadence is critical. Use a staggered release:

• T0 + 0–10 minutes: frictionless follow (story reply, single-thread tweet, 15s clip).
• T0 + 30–120 minutes: engagement pieces (poll, short thread, UGC prompt).
• T0 + 12–48 hours: deeper explainers, long-form post, or live session invitation.
  Map these to target timezones using timezone.csv so each audience sees the follow-ups at convenient local hours.  

Base: Anchor Conversion (Trust & Action)

Goal: convert momentary interest into durable action — signups, purchases, into-community behaviors.

What to host: testimonials, case studies, product deep dives, FAQ pages, community groups, and scheduled live demos.

Repo analog / implementation hints: the repository’s pattern of notification (it “notifies clients by text message astronomy events”) is your delivery model for anchors: targeted notifications (SMS/push/email) that invite high-engagement users into conversion moments (demo invite, limited-time offer, trial signup). The repo’s filip.py or throwparty.py naming suggests helper scripts for messaging or orchestrating events; adopt similar modular scripts to send conversion invites according to user-segment rules. 

Timing orientation: anchors should appear during lower-tempo windows where users can act (evenings and weekends for consumers, late mornings for B2B). Schedule anchor nudges on repeat cadence (weekly recaps, scheduled live Q&As) to form expectation and trust.

A concrete architecture (components & flow)

1. Ingest / Event Stream
   
• Sources: social APIs, Google Trends, platform analytics, brand monitoring, UGC tags.
• Role: produce raw candidate signals.
3. Event Detector
   
• Similar to rankevents.py: computes spike detection, baseline noise, velocity (Δmentions / minute), and triage label (flash, trending, steady).  
5. Magnitude Scorer
   
• Score = w1 * velocity + w2 * amplification (shares) + w3 * influencer coefficient + w4 * sentiment alignment.
• Output: magnitude numeric field (0–1000). Use thresholds to allocate to pyramid layer.
7. Conjunction / Path Finder
   
• Inspired by rankconjunctions.py: identify complementary topics and the natural sequence of content pieces that form a “constellation.”  
9. Timing Scheduler
   
• Use timezone.csv equivalents to create release windows per geo/segment. Map T0 and follow-ups to local prime times.  
11. Delivery Engine
    
• Micro-bursts (push/SMS/push cards), scheduled posts, influencer seeding, paid amplification bursts. The repo’s SMS-notification pattern shows how to structure targeted alerts for VIPs.  
13. Measurement & Learn
    
• Track conversion funnel: Hook CTR → Engagement depth (time, replies, shares) → Anchor conversion (signup, purchase). Iterate weights in Magnitude Scorer.

How to reuse specific repo files (practical mapping)

Picking the actual filenames from the repo and reinterpreting them for this marketing system:

• astronomyparty.py → Rewritten as attentionparty.py: orchestrates event ingestion, scheduling, and firing of triggers. Use it as the scheduler + control loop.  
• rankevents.py → ranksignals.py: compute velocity and assign magnitudes. Pull social API metrics and compute Δmentions/time and amplification factors.  
• rankconjunctions.py → rankpaths.py: finds the best chain of follow-up content by identifying co-occurring topics and likely narrative flows.  
• throwparty.py → throwcampaign.py: sequence content drops, create short campaigns seeded to influencers and ad buys for amplification.  
• timezone.csv → local-release calendar: authoritative table of timezones and prime local hours for each segment. Use to orient broadcasts.  
• filip.py → could be repurposed as a lightweight notification/messaging helper (send templated SMS or push for anchors).  

Example campaign: launch a new feature (end-to-end)

1. Pre-seed (T-48h): create evergreen anchor assets (case study & FAQ). Schedule these in the base to be ready.
2. Spark (T0): the magnitude scorer flags a trending vertical theme that aligns with your feature. Publish a provocative 8s clip + stat, targeted by timezone for Region A (prime hour). Send VIP SMS to top 1% of users inviting them to “see this first.” (Use filip.py-style helper.)  
3. Channel (T0+30m → T0+48h): publish a 3-part thread, run an interactive poll, and seed short explainers to micro-influencers. Use rankconjunctions.py logic to determine which hashtags/topics to attach.  
4. Anchor (T0+24–72h): trigger a targeted push/SMS offering early-trial signups and schedule live demos. Track conversion and feed back into magnitude weights.

Practical templates & nudges

VIP SMS (Spark phase)

“Heads up: you’re invited to a 1st look at [Feature]. See the 8s reveal now — reply YES for an early trial.”

Follow-up push (Channel phase)

“Watch how [Feature] saved 30% time for [Customer]. Join our 20-min demo tomorrow.”

Anchor email

Long-form case study + CTA “Start free 7-day trial →” (timed for evening).

These are simple, but effective when coordinated by an event-driven engine.

KPI suggestions (how to measure star magnitude success)

• Spark CTR (immediate hook success)
• Engagement Path Completion Rate (percent of users who see the hook and then view at least 2 middle-stage pieces)
• Anchor Conversion Rate (percent who convert within 7 days)
• Velocity to Conversion (time between first spark and conversion)
• Repeat Constellation Score (how often a theme returns to trending within X days)

Use these to re-weight the magnitude scorer and improve future scheduling.

Ethics and guardrails

• Avoid exploiting private data without consent. Use aggregated signals where possible.
• Do not spam: cap SMS/push volume per user and respect opt-outs.
• Transparency: label sponsored or partner content clearly.

Why astronomy tooling fits marketing timing & ranking

astronomyparty was built to detect and notify about transient astronomy events — it includes code patterns for detection, ranking, timezone mapping, and notifications. Those exact functional patterns are what attention-driven marketing needs: detect transient attention opportunities, rank them, orient releases by timezone, and notify important audiences. The repo is a practical blueprint; repurposing its modules shortens development time and enforces an event-driven architecture that’s a natural fit for our pyramid Attention Switch model. 

Next steps (practical roadmap)

1. Clone & audit pacobaco/astronomyparty to extract rankevents.py, rankconjunctions.py, and timezone.csv. (These map directly to the detection, path-finding, and timing layers.)  
2. Build ingestion adapters for the social APIs you control (webhooks, polling). Feed them into the rankevents pipeline.
3. Tune scoring weights with two weeks of historical data; A/B test thresholds for “spark” vs “channel.”
4. Design templated messaging (SMS, push, DM, story) and implement a rate-limited delivery engine (respecting opt-outs).
5. Run a pilot campaign: 1 product vertical, 2 geo segments, 1 influencer seed. Measure metrics and iterate.

Sources & inspiration

• pacobaco / astronomyparty — repository (README + scripts for event detection and notification). Useful files: astronomyparty.py, rankevents.py, rankconjunctions.py, throwparty.py, and timezone.csv.  

If you want, I can now:

• Produce a detailed implementation checklist (file-by-file reuse plan) mapping each repo file to marketing code (e.g., how to rewrite rankevents.py into ranksignals.py) — ready to drop into your dev backlog.
• Generate a visual pyramid diagram that labels each repo module on the relevant pyramid layer and shows timing windows per timezone (PNG or SVG).
• Produce a sample pilot config (JSON) for a single campaign (ingestion sources, scoring weights, notification templates, timezone rollout).

Which one should I generate next?