The Scoring Algorithm

We score 700+ articles per batch without a single API call. Here’s the exact system.

How It Works

Every article gets a 0-10 score based on four keyword tiers, source multipliers, trending boosts, and penalty patterns. The whole thing runs in under 1 second on a laptop.

Tier 1: Paradigm Shift Keywords (+2.0 each, max 8 points)

These signal fundamental changes in how AI work gets done:

PARADIGM_SHIFT_SIGNAL = [
    "ai first", "ai-first", "before human", "replaces", "replacing",
    "fundamentally", "paradigm", "game changer", "disrupt",
    "transform", "revolution", "reimagin", "rethink", "new era",
    "without devops", "without engineers", "no-code", "zero-code",
    "autonomous", "self-driving", "self-healing", "self-improving",
    "solo founder", "one-person", "small team", "lean team",
    "without hiring", "replacing team", "ai employee",
    "6 engineers", "10 engineers", "no devops", "no ops",
    "competitor", "competes with", "alternative to", "vs ",
    "openclaw", "defenseclaw", "new player", "just launched",
    "stealth", "backed by", "raised", "funding round",
]

Tier 2: Actionable Keywords (+1.0 each, max 6 points)

Things you can use or implement today:

ACTIONABLE_SIGNAL = [
    "claude", "computer use", "claude code", "anthropic",
    "openai", "gpt-4", "gpt-5", "codex", "gemini",
    "openclaw", "mcp server", "model context protocol",
    "just released", "now available", "launching", "announces",
    "use case", "real-world", "in production", "case study",
    "we built", "how we", "how i", "our experience",
    "framework", "methodology", "playbook",
    "vibe coding", "coding agent", "pr review",
    "scale", "scaling", "growth", "revenue", "customers",
    "startup", "founder", "entrepreneur",
    "pricing", "cost", "cheaper", "faster", "efficient",
]

Tier 3: Infrastructure Keywords (+0.3 each, max 2 points)

Important context but less immediately actionable:

INFRA_SIGNAL = [
    "agent", "agentic", "langchain", "llamaindex", "crewai",
    "llm", "prompt engineer", "rag", "api", "sdk",
    "open source", "hugging face", "mistral", "llama",
    "deploy", "ship", "launch", "release", "production",
    "template", "tutorial", "guide", "how to",
]

Penalties

Research Paper Penalty

RESEARCH_INDICATORS = [
    "arxiv", "we propose", "we present", "ablation study",
    "empirical", "theoretical", "in this paper",
    "markov", "bayesian", "stochastic",
]
# 3+ matches = 0.2x multiplier (80% reduction)
# 2 matches = 0.4x
# 1 match = 0.7x

Skip Patterns (user-trained)

SKIP_PATTERNS = [
    r"\bvllm\b", r"\bconcurrent request",
    r"\bbeginner\b", r"\bintroduction to\b",
    r"\bspeed up.*python\b",
]
# Matched articles score 0.5/10

Source Multipliers

Boosted Sources (user’s preferred)

SOURCE_BOOST = {
    "Vercel": 1.6, "SaaStr": 1.5, "Product Hunt": 1.5,
    "Anthropic": 1.5, "OpenAI": 1.5, "Claude": 1.5,
    "LangChain": 1.4, "Cursor": 1.5, "Latent Space": 1.4,
}

Penalized Sources

SOURCE_PENALTY = {
    "ArXiv": 0.4, "IEEE": 0.5, "ACM": 0.5,
    "KDnuggets": 0.7,
}

Trending Boost

# HN engagement
if points >= 500: score *= 1.8
elif points >= 200: score *= 1.5
elif points >= 100: score *= 1.3

# Tier bonus
tier_bonus = {0: 3.0, 1: 2.0, 2: 1.0, 3: 0.0, 4: 0.0}

Tier 0 (curated newsletters) get +3.0 base bonus because they’re already filtered by smart humans.

Results

From 700+ articles per batch:

• ~200 filtered as noise (score 0)
• ~400 scored low (1-3)
• ~70 scored medium (4-6)
• ~30 scored high (7-10)
• Top 20 shown to you

The whole thing costs $0 to run.