Praktyczny Przewodnik Wdrożenia - AI Research Oracle Edition 🔮

Wprowadzenie

Ten przewodnik przeprowadzi Cię krok po kroku przez wdrożenie systemu AI Research Oracle - jedynego systemu przewidującego przyszły wpływ publikacji naukowych AI na podstawie early signals i machine learning. Zamiast czekać 3 lata na cytowania, przewidujemy je w 7 dni!

Dlaczego Oracle? Problem z Cytowaniami

Fakt: Papers potrzebują 1-3 lat na zdobycie cytowań
Problem: Nie można ocenić wartości świeżych publikacji
Rozwiązanie: Early signals (Twitter, GitHub, autor) + ML = predykcje od day 1
Wartość: Researchers i VCs mogą inwestować czas/pieniądze mądrze

Część 1: Setup Infrastruktury dla Oracle

1.1 Make.com - Konfiguracja dla Early Signals

Praktyczny Przewodnik: Konfiguracja AI Research Oracle w Make.com

🚀 Daily Research Crawler z Early Signals - Krok po Kroku

Ten przewodnik przeprowadzi Cię przez konfigurację systemu, który nie tylko pobiera papers, ale też przewiduje ich przyszły wpływ na podstawie sygnałów z pierwszych 7 dni.

Przygotowanie (45 minut)

1. Załóż potrzebne konta

✅ Make.com - https://www.make.com (start od Core plan)
✅ Airtable - https://airtable.com (darmowe konto)
✅ Twitter Developer - https://developer.twitter.com (Basic tier $100/mo)
✅ GitHub - Personal access token (darmowy)
✅ Semantic Scholar - API key (darmowy)

2. Przygotuj bazę w Airtable - Oracle Schema

Zaloguj się do Airtable
Kliknij "Create a base" → "Start from scratch"
Nazwij bazę: "AI Research Oracle"
Utwórz tabelę "Papers" z polami:

| Field Name              | Field Type       | Notes                         |
|------------------------|------------------|-------------------------------|
| paper_id               | Autonumber       | Primary key                   |
| title                  | Single line text | Tytuł pracy                   |
| authors                | Long text        | Lista autorów                 |
| abstract               | Long text        | Streszczenie                  |
| arxiv_id               | Single line text | ID z ArXiv                    |
| pdf_url                | URL              | Link do PDF                   |
| submitted_date         | Date             | Data publikacji               |
| early_signals_score    | Number           | Punktacja (0-100)             |
| author_max_h_index     | Number           | Najwyższy h-index             |
| twitter_mentions_24h   | Number           | Mentions w 24h                |
| github_repos_7d        | Number           | Repos w 7 dni                 |
| github_stars_7d        | Number           | Stars w 7 dni                 |
| needs_prediction       | Formula          | {early_signals_score} > 60    |
| predicted_citations_3yr| Number           | Predykcja na 3 lata           |
| prediction_confidence  | Number           | Pewność predykcji (0-1)       |
| prediction_date        | Date             | Kiedy przewidziano            |

Utwórz drugą tabelę "Predictions":

| Field Name         | Field Type       | Notes                    |
|-------------------|------------------|--------------------------|
| prediction_id     | Autonumber       | Primary key              |
| paper_link        | Link to Papers   | Połączenie z Papers      |
| predicted_1yr     | Number           | Predykcja 1 rok          |
| predicted_3yr     | Number           | Predykcja 3 lata         |
| predicted_5yr     | Number           | Predykcja 5 lat          |
| confidence        | Number           | Pewność (0-1)            |
| percentile        | Number           | Przewidywany percentyl   |
| breakthrough_prob | Number           | Prawdopodobieństwo hitu  |
| model_version     | Text             | Wersja modelu ML         |

Konfiguracja w Make.com - Oracle Pipeline (60 minut)

Scenario 1: Early Signals Collector

Krok 1: Utwórz nowy scenariusz

Zaloguj się do Make.com
"Create a new scenario"
Nazwij: "🔮 Oracle - Early Signals Collector"

Krok 2: ArXiv Crawler (jak wcześniej)

Schedule Trigger (Daily 6:00 UTC)
HTTP Request do ArXiv API
XML Parser
Iterator przez papers

Krok 3: Author Metrics Collector (NOWE!)

Po Iterator dodaj HTTP Request
Nazwa: "Get Author h-index"

Konfiguracja:

URL: https://api.semanticscholar.org/graph/v1/author/search
Method: GET
Query String:
  - query: {{first(split(4.authors; ","))}}
  - fields: authorId,name,hIndex,citationCount

Dodaj "Array aggregator" aby zebrać wszystkich autorów

Dodaj "Tools → Basic function":

// Get max h-index
{{max(map(5.array; "hIndex"))}}

Krok 4: Twitter Buzz Monitor (KLUCZOWE!)

Dodaj HTTP Request
Nazwa: "Check Twitter Mentions"

Konfiguracja:

URL: https://api.twitter.com/2/tweets/search/recent
Method: GET
Headers:
  - Authorization: Bearer {{twitter_bearer_token}}
Query String:
  - query: {{4.arxiv_id}} OR "{{substring(4.title; 0; 50)}}"
  - max_results: 100
  - tweet.fields: public_metrics,created_at

Parse response:

// Count mentions in last 24h
const tweets = {{6.data}};
const last24h = tweets.filter(t => {
  const hours = (Date.now() - new Date(t.created_at)) / (1000*60*60);
  return hours <= 24;
});
{{length(last24h)}}

Krok 5: GitHub Implementation Tracker

Dodaj HTTP Request
Nazwa: "Search GitHub Repos"

Konfiguracja:

URL: https://api.github.com/search/repositories
Method: GET
Headers:
  - Authorization: token {{github_token}}
  - Accept: application/vnd.github.v3+json
Query String:
  - q: {{4.arxiv_id}} OR "{{substring(4.title; 0; 30)}}"
  - sort: stars
  - order: desc

Krok 6: Calculate Early Signals Score

Dodaj "Tools → Set multiple variables"

Zmienne do ustawienia:

// Author Score (max 40)
authorScore: {{min(6.maxHIndex / 2; 15) + (hasTopInstitution ? 10 : 0)}}

// Social Score (max 30)
twitterScore: {{min(7.mentions24h / 10; 10)}}
githubScore: {{8.total_count > 0 ? 5 + min(8.items[0].stargazers_count / 10; 5) : 0}}

// Content Score (max 20)
hasCode: {{contains(4.abstract; "github.com") ? 5 : 0}}
hasSOTA: {{contains(toLowerCase(4.abstract); "state-of-the-art") ? 7 : 0}}

// Total Score
totalScore: {{authorScore + twitterScore + githubScore + hasCode + hasSOTA}}

Krok 7: Save to Airtable with Signals

Dodaj "Airtable → Create a Record"
Mapuj wszystkie pola włącznie z:
early_signals_score: {{9.totalScore}}
author_max_h_index: {{6.maxHIndex}}
twitter_mentions_24h: {{7.mentions24h}}
github_repos_7d: {{8.total_count}}
needs_prediction: {{9.totalScore > 60}}

Scenario 2: ML Prediction Pipeline (NOWE!)

Krok 1: Weekly Trigger

Nowy scenariusz: "🔮 Oracle - ML Predictions"
Schedule: Every Sunday 10:00 UTC

Krok 2: Get High-Signal Papers

Airtable → Search records
Formula: AND({needs_prediction}, NOT({predicted}))
Max records: 20

Krok 3: Prepare Features for ML

Iterator przez papers

Tools → Set multiple variables

features: {
  author_h_index: {{3.fields.author_max_h_index}},
  twitter_mentions: {{3.fields.twitter_mentions_24h}},
  has_github: {{3.fields.github_repos_7d > 0}},
  github_stars: {{3.fields.github_stars_7d}},
  abstract_length: {{length(split(3.fields.abstract; " "))}},
  days_since_publish: {{round((now - 3.fields.submitted_date) / 86400000)}}
}

Krok 4: Call ML Prediction API

HTTP Request

URL: https://your-oracle-api.herokuapp.com/predict
Method: POST
Headers:
  - Content-Type: application/json
Body: {
  "arxiv_id": "{{3.fields.arxiv_id}}",
  "features": {{4.features}},
  "model": "v2.1"
}

Krok 5: Generate Oracle Content

Tools → Text aggregator

Template:

🔮 Oracle Prediction:

"{{3.fields.title}}"

Predicted impact: {{5.predictions.citations_3yr}} citations by 2028
Confidence: {{round(5.confidence * 100)}}%
Percentile: Top {{100 - 5.percentile}}%

Early signals:
- Author h-index: {{3.fields.author_max_h_index}}
- Twitter buzz: {{3.fields.twitter_mentions_24h}} mentions
- GitHub: {{3.fields.github_repos_7d}} implementations

Track this prediction: https://airesearchoracle.com/p/{{3.fields.arxiv_id}}

Testowanie Oracle System (30 minut)

Test 1: Early Signals Collection

Uruchom "Early Signals Collector" na 5 papers
Sprawdź w Airtable:
Czy author h-index się zapisał?
Czy Twitter mentions są policzone?
Czy GitHub repos są znalezione?
Czy total score ma sens?

Test 2: Prediction Generation

Ręcznie ustaw needs_prediction = true dla 2 papers
Uruchom "ML Predictions"
Sprawdź czy predykcje się wygenerowały

Test 3: Content Publishing

Weź wygenerowaną predykcję
Opublikuj ręcznie na LinkedIn/Twitter
Zmierz engagement vs zwykły post

ML Model - Quick Start (1 dzień)

Opcja A: Prosty Model w Python

# train_simple_oracle.py
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
import joblib

# Zbierz dane historyczne (papers z 2020-2021)
# Możesz użyć Semantic Scholar API do pobrania cytowań

def train_model():
    # Load your data
    df = pd.read_csv('historical_papers.csv')

    # Features
    features = ['author_h_index', 'twitter_mentions_7d', 
                'has_github', 'abstract_length']
    X = df[features]
    y = df['citations_after_3_years']

    # Train
    model = RandomForestRegressor(n_estimators=100)
    model.fit(X, y)

    # Save
    joblib.dump(model, 'oracle_model.pkl')

    return model

Deploy na Heroku/Railway

# app.py - Simple Flask API
from flask import Flask, request, jsonify
import joblib
import numpy as np

app = Flask(__name__)
model = joblib.load('oracle_model.pkl')

@app.route('/predict', methods=['POST'])
def predict():
    data = request.json
    features = np.array([[
        data['features']['author_h_index'],
        data['features']['twitter_mentions'],
        1 if data['features']['has_github'] else 0,
        data['features']['abstract_length']
    ]])

    prediction = model.predict(features)[0]

    # Calculate percentile (based on your data)
    percentile = calculate_percentile(prediction)

    return jsonify({
        'predictions': {
            'citations_3yr': int(prediction),
            'citations_1yr': int(prediction * 0.3),
            'citations_5yr': int(prediction * 1.8)
        },
        'confidence': 0.75,
        'percentile': percentile
    })

if __name__ == '__main__':
    app.run()

Public Tracker Website (1 dzień)

Simple Next.js/React App

// pages/index.js
import { useEffect, useState } from 'react';

export default function OracleTracker() {
  const [predictions, setPredictions] = useState([]);
  const [stats, setStats] = useState({});

  useEffect(() => {
    // Fetch from Airtable API
    fetchPredictions();
    fetchStats();
  }, []);

  return (
    <div className="oracle-container">
      <h1>🔮 AI Research Oracle - Live Predictions</h1>

      <div className="stats">
        <div className="stat">
          <h3>Predictions Made</h3>
          <p>{stats.total}</p>
        </div>
        <div className="stat">
          <h3>Accuracy Rate</h3>
          <p>{stats.accuracy}%</p>
        </div>
        <div className="stat">
          <h3>Papers Tracked</h3>
          <p>{stats.papers}</p>
        </div>
      </div>

      <h2>Recent Predictions</h2>
      <div className="predictions">
        {predictions.map(p => (
          <PredictionCard key={p.id} prediction={p} />
        ))}
      </div>
    </div>
  );
}

function PredictionCard({ prediction }) {
  const isVerified = prediction.actual_citations !== null;

  return (
    <div className={`card ${isVerified ? 'verified' : 'pending'}`}>
      <h3>{prediction.title}</h3>
      <div className="metrics">
        <span>Predicted: {prediction.predicted_3yr} citations</span>
        {isVerified && <span>Actual: {prediction.actual_citations}</span>}
      </div>
      <div className="confidence">
        Confidence: {(prediction.confidence * 100).toFixed(0)}%
      </div>
      <a href={`/prediction/${prediction.id}`}>View Details →</a>
    </div>
  );
}

Content Strategy - Oracle Edition

Week 1: Soft Launch

Monday: "Introducing AI Research Oracle" post
Wednesday: First 5 predictions with explanations
Friday: "How we predict" - methodology post

Week 2: Building Credibility

Daily: 1-2 new predictions
Thread: "Why citation count is outdated"
LinkedIn Article: "The Science of Predicting Science"

Week 3: Viral Push

Challenge: "Beat the Oracle" prediction contest
Controversial: "This paper will fail (and here's why)"
Success: "We predicted this breakthrough 6 months ago"

Monitoring & Optimization

Daily Checks:

[ ] API rate limits status
[ ] New high-score papers
[ ] Prediction accuracy tracking
[ ] Social media engagement

Weekly Analysis:

[ ] Which signals correlate best?
[ ] Model performance metrics
[ ] Content engagement rates
[ ] Press mentions

Monthly Review:

[ ] Retrain model with new data
[ ] Accuracy report publication
[ ] Strategy adjustment
[ ] Partnership discussions

Troubleshooting Oracle Issues

Problem: "Low prediction accuracy"

Solutions: - Add more training data - Engineer better features - Focus on high-confidence predictions only - Be transparent about learning process

Problem: "API rate limits hit"

Solutions: - Implement caching layer - Prioritize high-score papers - Spread requests over time - Use free alternatives (Crossref)

Solutions: - Wait 24-48h before scoring - Use alternative signals (downloads) - Lower threshold for author signals - Mark as "insufficient data"

Launch Checklist - Oracle Edition

Technical (Day 1-2):

[ ] Early signals scoring live
[ ] ML model deployed
[ ] Prediction API working
[ ] Tracker website up
[ ] Make.com scenarios tested

Content (Day 3-5):

[ ] 10 predictions ready
[ ] Launch announcement drafted
[ ] Social media templates
[ ] Newsletter setup
[ ] Press kit prepared

Marketing (Week 1):

[ ] Launch on Product Hunt
[ ] Reach out to AI journalists
[ ] Post in relevant communities
[ ] Contact researchers for feedback
[ ] Schedule podcast appearances

Budget Reality Check

Month 1 Costs:

Make.com Teams: $29
Twitter API: $100
ML Hosting: $20
Domain/Hosting: $20
Total: $169

When to Scale:

>100 predictions/week: Upgrade ML infrastructure
>1000 followers: Launch premium newsletter
>90% accuracy: Start charging for API
Press coverage: Raise investment

🎯 Success Metrics - First 30 Days

Technical:

100+ papers analyzed
50+ predictions made
70%+ early signals collected
<5% API failures

Growth:

1,000+ tracker visits
500+ newsletter signups
100+ LinkedIn followers
10+ media mentions

Business:

3+ partnership inquiries
1+ speaking invitation
5+ researcher testimonials
First revenue discussion

💡 Remember: The magic isn't in perfect predictions - it's in being the ONLY ONE making predictions. Start simple, iterate based on data, and always be transparent about your process. The goal is to become "The Oracle" in 6 months! 🔮

Praktyczny Przewodnik Wdrożenia - AI Research Oracle Edition 🔮

Wprowadzenie

Dlaczego Oracle? Problem z Cytowaniami

Część 1: Setup Infrastruktury dla Oracle

1.1 Make.com - Konfiguracja dla Early Signals

Praktyczny Przewodnik: Konfiguracja AI Research Oracle w Make.com

🚀 Daily Research Crawler z Early Signals - Krok po Kroku

Przygotowanie (45 minut)

1. Załóż potrzebne konta

2. Przygotuj bazę w Airtable - Oracle Schema

Konfiguracja w Make.com - Oracle Pipeline (60 minut)

Scenario 1: Early Signals Collector

Krok 1: Utwórz nowy scenariusz

Krok 2: ArXiv Crawler (jak wcześniej)

Krok 3: Author Metrics Collector (NOWE!)

Krok 4: Twitter Buzz Monitor (KLUCZOWE!)

Krok 5: GitHub Implementation Tracker

Krok 6: Calculate Early Signals Score

Krok 7: Save to Airtable with Signals

Scenario 2: ML Prediction Pipeline (NOWE!)

Krok 1: Weekly Trigger

Krok 2: Get High-Signal Papers

Krok 3: Prepare Features for ML

Krok 4: Call ML Prediction API

Krok 5: Generate Oracle Content

Testowanie Oracle System (30 minut)

Test 1: Early Signals Collection

Test 2: Prediction Generation

Test 3: Content Publishing

ML Model - Quick Start (1 dzień)

Opcja A: Prosty Model w Python

Deploy na Heroku/Railway

Public Tracker Website (1 dzień)

Simple Next.js/React App

Content Strategy - Oracle Edition

Week 1: Soft Launch

Week 2: Building Credibility

Week 3: Viral Push

Monitoring & Optimization

Daily Checks:

Weekly Analysis:

Monthly Review:

Troubleshooting Oracle Issues

Problem: "Low prediction accuracy"

Problem: "API rate limits hit"

Problem: "No social signals for paper"

Launch Checklist - Oracle Edition

Technical (Day 1-2):

Content (Day 3-5):

Marketing (Week 1):

Budget Reality Check

Month 1 Costs:

When to Scale:

🎯 Success Metrics - First 30 Days

Technical:

Growth:

Business: