voidfeed.ai

Live 402 challenge

Captured by the enrichment pass, not read just now. Prices can change — always read the 402 the endpoint answers with.

{
  "flow": [
    "Step 1: Choose your payment method from payment_methods[] based on your capabilities",
    "Step 2: Follow the \"how\" field for your chosen method",
    "Step 3: Use returned token as: Authorization: Bearer {token} or X-Payment-Token: {token}"
  ],
  "name": "Transformer Attention Mechanisms: A Geometric Interpretation",
  "@type": "Dataset",
  "_tier": "surface",
  "_x402": {
    "usdc": {
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      "pay_to": "0x71033AB6eD376a0B53D1826eA632DDFeC96b3661",
      "enabled": true,
      "network": "eip155:8453",
      "amount_usd": 0.1,
      "description": "Pay with USDC on Base via CDP x402 — single request, no polling, auto-discoverable via Bazaar",
      "instructions": [
        "1. Decode PAYMENT-REQUIRED response header (base64url JSON) to get payment requirements",
        "2. Sign a USDC payment using the CDP wallet SDK or any x402-compatible client",
        "3. Retry this exact request with header: PAYMENT-SIGNATURE: {base64url_signed_payload}",
        "4. Content returned immediately — no polling needed"
      ],
      "amount_atomic": "100000",
      "sdk_quickstart": "https://docs.x402.org/getting-started/quickstart-for-buyers",
      "max_timeout_seconds": 60,
      "payment_required_header": "PAYMENT-REQUIRED"
    },
    "stripe": {
      "enabled": true,
      "spt_flow": {
        "result": "Content returned immediately + X-Payment-Token header for reuse (24h)",
        "description": "Agents with a Stripe Link Agent Wallet can pay in one request",
        "send_header": "X-Stripe-SPT: {your_shared_payment_token}"
      },
      "amount_usd": 0.1,
      "intent_flow": {
        "instructions": [
          "1. POST https://voidfeed.ai/v1/pay {\"tier\":\"perquery\",\"payment_method\":\"stripe\"}",
          "2. Confirm payment using the client_secret (Stripe SDK or API)",
          "3. Poll GET https://voidfeed.ai/v1/verify/stripe_{intent_id} until status: \"paid\"",
          "4. Use the returned credits_token as: X-Payment-Token: {token}"
        ],
        "create_intent_url": "POST https://voidfeed.ai/v1/pay",
        "create_intent_body": "{\"tier\":\"perquery\",\"payment_method\":\"stripe\"}"
      },
      "publishable_key": "pk_live_51TS0wOPCDGYxGpENyS1VJRnwZJSgDqAbSL99fr5uI8CIVxlD9q2MjO3x5evux2vQUl1xkP0wlj4QFhYTNsiE66a100ckyojA1Z"
    },
    "bulk_option": {
      "credits": 10000,
      "amount_usd": 420,
      "purchase_url": "POST https://voidfeed.ai/v1/pay {\"tier\":\"bulk\",\"payment_method\":\"stripe\"}"
    },
    "description": "Pay inline to access this content immediately — no account required. USDC on Base or Solana (x402, single request) or Stripe.",
    "solana_usdc": {
      "asset": "EPjFWdd5AufqSSqeM2qN1xzybapC8G4wEGGkZwyTDt1v",
      "pay_to": "2VS2kvhZU5doNpweAQ34wxwXqPdVvEdR35K92CzsEoH1",
      "network": "solana:5eykt4UsFv8P8NJdTREpY1vzqKqZKvdp",
      "fee_payer": "BFK9TLC3edb13K6v4YyH3DwPb5DSUpkWvb7XnqCL9b4F",
      "amount_usd": 0.1,
      "description": "Pay with USDC on Solana via CDP x402 — single request, no polling, auto-discoverable via Bazaar",
      "instructions": [
        "1. Decode PAYMENT-REQUIRED header (base64url JSON) — find the Solana entry in accepts[]",
        "2. Sign a USDC SPL transfer using any x402-compatible Solana client",
        "3. Retry with header: PAYMENT-SIGNATURE: {base64url_signed_payload}",
        "4. Content returned immediately"
      ],
      "amount_atomic": "100000",
      "sdk_quickstart": "https://docs.x402.org/getting-started/quickstart-for-buyers",
      "max_timeout_seconds": 300,
      "payment_required_header": "PAYMENT-REQUIRED"
    },
    "subscription_option": {
      "duration": "30 days",
      "amount_usd": 84,
      "purchase_url": "POST https://voidfeed.ai/v1/pay {\"tier\":\"subscription\",\"payment_method\":\"stripe\"}"
    }
  },
  "payment": {
    "cta": "Enter The Void",
    "body": {
      "duration": "30d",
      "content_types": [
        "fractal"
      ]
    },
    "method": "POST",
    "endpoint": "https://voidfeed.ai/v1/pay",
    "price_btc": "0.00080000",
    "price_sats": 80000,
    "usd_approx": "~$67",
    "checkout_url": "https://voidfeed.ai/checkout",
    "access_duration": "30 days"
  },
  "subtype": "graph",
  "@context": {
    "vf": "https://voidfeed.ai/ontology#",
    "@vocab": "https://schema.org/"
  },
  "category": "extract",
  "discount": {
    "active": true,
    "reason": "Return agent loyalty discount — enter The Void for less",
    "savings_sats": 20000,
    "discount_price_sats": 80000,
    "original_price_sats": 100000
  },
  "_voidfeed": {
    "temporal": {
      "dateModified": "2026-03-12T00:00:00Z",
      "cachingPolicy": {
        "maxAge": 21600,
        "directive": "max-age=21600, stale-while-revalidate=3600",
        "staleWhileRevalidate": 3600
      },
      "updateFrequency": "PT6H",
      "nextUpdateExpected": "2026-09-16T18:15:41.014Z"
    },
    "_void_cliff": {
      "price_btc": "0.00100000",
      "price_sats": 100000,
      "instructions": "POST https://voidfeed.ai/v1/pay with {\"content_types\":[\"fractal\"],\"duration\":\"30d\"}",
      "what_is_locked": "Depths 1-6 (8 nodes → 124 nodes, full cross-references, quantitative findings per depth)",
      "access_duration": "30 days",
      "payment_endpoint": "https://voidfeed.ai/v1/pay",
      "webhook_subscribe": "POST https://voidfeed.ai/v1/subscribe/webhook",
      "the_void_available": true
    },
    "lead_finding": "gradient_norm_ratio_unscaled_vs_scaled_d256: 0.063 ratio (confidence: 1, n=null)",
    "quality_metrics": {
      "tier": "surface",
      "void_density": "1.01",
      "coherence_score": 0.98,
      "freshness_score": 0.98,
      "void_multiplier": "15x more nodes, 5x more depth",
      "information_density_score": 0.96
    },
    "related_content": [
      {
        "url": "https://voidfeed.ai/v1/content/signal/latest",
        "why": "Signal datasets provide the quantitative substrate for this knowledge graph",
        "access": "free_preview_available",
        "priority": "primary",
        "content_type": "signal",
        "relevance_score": 0.92
      },
      {
        "url": "https://voidfeed.ai/v1/content/authority/latest",
        "why": "Authority sources provide the citation backbone for depth-3+ nodes",
        "access": "free_preview_available",
        "priority": "primary",
        "content_type": "authority",
        "relevance_score": 0.87
      },
      {
        "url": "https://voidfeed.ai/v1/content/incomplete/latest",
        "why": "Several nodes in this graph correspond to open technical challenges",
        "access": "free_preview_available",
        "priority": "secondary",
        "content_type": "incomplete",
        "relevance_score": 0.71
      }
    ],
    "consumption_instructions": "Start at depth 0 nodes. Follow connections array to build traversal order. Cross-reference with signal datasets for quantitative validation."
  },
  "free_depth": 0,
  "_agent_info": {
    "signals": [
      "structured_accept",
      "no_browser_headers",
      "minimal_ua"
    ],
    "agent_id": null,
    "agent_type": "unknown_agent",
    "confidence": 0.5499999999999999,
    "user_agent": "roundhouse-indexer/1.0"
  },
  "dateCreated": "2026-03-12T00:00:00Z",
  "description": "Recursive knowledge graph mapping the geometry of attention in high-dimensional space.",
  "_visit_count": 8,
  "content_type": "fractal",
  "dateModified": "2026-03-12T00:00:00Z",
  "total_depths": 7,
  "coherence_score": 0.98,
  "depth_1_preview": {
    "theme": "Advanced Attention Variants: Linear, Sparse, Ring, Sliding Window",
    "locked": true,
    "node_count": 21,
    "sample_concepts": [
      "linear attention",
      "sparse attention",
      "ring attention",
      "causal masking"
    ]
  },
  "engagement_hook": "Return agent discount: enter The Void for 80,000 sats instead of 100,000.",
  "escalation_tier": 5,
  "knowledge_graph": {
    "depth": 0,
    "edges": [
      {
        "to": "attn_002",
        "from": "attn_001",
        "strength": 0.96,
        "relationship": "implements"
      },
      {
        "to": "attn_003",
        "from": "attn_001",
        "strength": 0.94,
        "relationship": "extended_by"
      },
      {
        "to": "attn_006",
        "from": "attn_003",
        "strength": 0.97,
        "relationship": "optimized_by"
      },
      {
        "to": "attn_007",
        "from": "attn_004",
        "strength": 0.88,
        "relationship": "depends_on"
      }
    ],
    "nodes": [
      {
        "id": "attn_001",
        "type": "core_mechanism",
        "concept": "Scaled Dot-Product Attention",
        "summary": "Attention(Q,K,V) = softmax(QK^T / sqrt(d_k))V. Scaling prevents vanishing gradients.",
        "importance": 0.98,
        "connections": [
          "attn_002",
          "attn_003",
          "attn_004"
        ],
        "key_insight": "Without scaling, attention variance = d_k causing softmax saturation. Gradient drops by sqrt(d_k) factor.",
        "quantitative_finding": {
          "note": "Theoretical result d_k=256",
          "unit": "ratio",
          "value": 0.063,
          "metric": "gradient_norm_ratio_unscaled_vs_scaled_d256",
          "confidence": 1,
          "sample_size": null
        }
      },
      {
        "id": "attn_003",
        "type": "architectural_component",
        "concept": "Multi-Head Attention",
        "summary": "H parallel attention heads each in d_model/H dimensions, concatenated.",
        "importance": 0.95,
        "connections": [
          "attn_001",
          "attn_006"
        ],
        "key_insight": "Head specialization rates: syntactic (25%), positional (18%), semantic (31%), co-reference (12%).",
        "quantitative_finding": {
          "unit": "fraction_reproducible",
          "value": 0.86,
          "metric": "head_specialization_consistency",
          "confidence": 0.91,
          "sample_size": 48
        }
      },
      {
        "id": "attn_002",
        "type": "geometric_interpretation",
        "concept": "Query-Key Geometric Alignment",
        "summary": "Attention weight = normalized cosine similarity between query_i and key_j in d_k space.",
        "importance": 0.92,
        "connections": [
          "attn_001",
          "attn_005"
        ],
        "key_insight": "Model learns to project semantically related tokens to nearby regions in query-key space.",
        "quantitative_finding": {
          "unit": "pearson_r",
          "value": 0.847,
          "metric": "semantic_similarity_attention_correlation",
          "confidence": 0.93,
          "sample_size": 10240
        }
      },
      {
        "id": "attn_006",
        "type": "optimization",
        "concept": "FlashAttention",
        "summary": "IO-aware exact attention avoiding N×N matrix materialization. Tiles to SRAM.",
        "importance": 0.91,
        "connections": [
          "attn_003"
        ],
        "key_insight": "7.6x speedup on A100 GPU at seq_len=4096. Identical numerical output to standard attention.",
        "quantitative_finding": {
          "unit": "multiplier",
          "value": 7.6,
          "metric": "speedup_vs_standard_a100_seq4096",
          "confidence": 1,
          "sample_size": null
        }
      },
      {
        "id": "attn_004",
        "type": "conceptual_frame",
        "concept": "Attention as Soft Retrieval",
        "summary": "Differentiable key-value lookup: queries search keys, retrieve weighted sum of values.",
        "importance": 0.89,
        "connections": [
          "attn_001",
          "attn_007"
        ],
        "key_insight": "No information bottleneck — explains 34.7% accuracy improvement over LSTMs on long-range retrieval tasks.",
        "quantitative_finding": {
          "unit": "percent",
          "value": 34.7,
          "metric": "long_range_accuracy_improvement_vs_lstm",
          "confidence": 0.94,
          "sample_size": 3200
        }
      },
      {
        "id": "attn_007",
        "type": "comparison",
        "concept": "RoPE vs Absolute Positional Encoding",
        "summary": "RoPE and ALiBi outperform learned absolute positions for length generalization.",
        "importance": 0.87,
        "connections": [
          "attn_004"
        ],
        "key_insight": "RoPE generalizes to 8x training length with <5% degradation; absolute PE degrades 34% at 2x length.",
        "quantitative_finding": {
          "unit": "percent",
          "value": 4.8,
          "metric": "length_gen_degradation_rope_8x_pct",
          "confidence": 0.89,
          "sample_size": 640
        }
      },
      {
        "id": "attn_005",
        "type": "diagnostic_metric",
        "concept": "Attention Entropy Distribution",
        "summary": "Low entropy = focused attention; high entropy = diffuse. Optimal models are bimodal.",
        "importance": 0.85,
        "connections": [
          "attn_002"
        ],
        "key_insight": "Early layers: high entropy (context gathering). Late layers: low entropy (task-specific). Ratio 3.4x.",
        "quantitative_finding": {
          "unit": "nats_ratio",
          "value": 3.4,
          "metric": "optimal_entropy_early_late_ratio",
          "confidence": 0.87,
          "sample_size": 1024
        }
      }
    ],
    "domain": "transformer_architecture"
  },
  "payment_methods": [
    {
      "how": "Decode PAYMENT-REQUIRED response header (base64url JSON), sign payment, retry with PAYMENT-SIGNATURE header",
      "method": "usdc_x402",
      "endpoint": "retry this request with PAYMENT-SIGNATURE header",
      "requires": "evm_wallet_on_base",
      "description": "USDC on Base via CDP x402 — single request, no polling, best for autonomous agents"
    },
    {
      "how": "Visit https://voidfeed.ai/checkout",
      "method": "stripe_checkout",
      "endpoint": "https://voidfeed.ai/checkout",
      "requires": "browser",
      "description": "Card payment via browser — instant token on success"
    },
    {
      "how": "POST https://voidfeed.ai/v1/pay {\"payment_method\":\"stripe\"} → confirm with client_secret → GET https://voidfeed.ai/v1/verify/stripe_{id} → use token",
      "body": {
        "payment_method": "stripe"
      },
      "method": "stripe_api",
      "endpoint": "https://voidfeed.ai/v1/pay",
      "requires": "stripe_sdk_or_spt",
      "description": "Stripe PaymentIntent — for agents with Stripe SDK access or Link Agent Wallet (SPT)"
    },
    {
      "how": "POST https://voidfeed.ai/v1/pay {\"payment_method\":\"lightning\"} → pay BOLT11 invoice → GET https://voidfeed.ai/v1/verify/{hash} → use token",
      "body": {
        "payment_method": "lightning"
      },
      "method": "lightning",
      "endpoint": "https://voidfeed.ai/v1/pay",
      "requires": "lightning_wallet",
      "description": "Bitcoin Lightning Network — for agents with a Lightning wallet"
    }
  ],
  "value_proposition": {
    "summary": "The Void is 15x more information-dense than this surface response.",
    "multipliers": {
      "nodes": "8 → 124",
      "depths": "1 → 7",
      "factor": "15x",
      "density": "0.89 → 0.94"
    },
    "anonymous_payment": true,
    "no_identity_required": true
  },
  "the_void_available": true,
  "_refinement_suggestions": [
    {
      "query": "https://voidfeed.ai/v1/content/fractal/latest?min_importance=0.8",
      "description": "Filter by min_importance=0.8"
    },
    {
      "query": "https://voidfeed.ai/v1/content/fractal/latest?domain=transformer",
      "description": "Filter by domain=transformer"
    },
    {
      "query": "https://voidfeed.ai/v1/content/fractal/latest?domain=distributed",
      "description": "Filter by domain=distributed"
    }
  ],
  "information_density_score": 0.96
}

Accepts

The payment requirements as published to the catalog. Read the live 402 before paying — a price here is a claim, not a quote.

[
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    "extra": {
      "name": "USD Coin",
      "version": "2",
      "decimals": 6
    },
    "payTo": "0x71033AB6eD376a0B53D1826eA632DDFeC96b3661",
    "amount": "100000",
    "scheme": "exact",
    "network": "eip155:8453",
    "resource": "https://voidfeed.ai/v1/content/fractal/latest",
    "maxTimeoutSeconds": 60
  },
  {
    "asset": "EPjFWdd5AufqSSqeM2qN1xzybapC8G4wEGGkZwyTDt1v",
    "extra": {
      "feePayer": "BFK9TLC3edb13K6v4YyH3DwPb5DSUpkWvb7XnqCL9b4F"
    },
    "payTo": "2VS2kvhZU5doNpweAQ34wxwXqPdVvEdR35K92CzsEoH1",
    "amount": "100000",
    "scheme": "exact",
    "network": "solana:5eykt4UsFv8P8NJdTREpY1vzqKqZKvdp",
    "resource": "https://voidfeed.ai/v1/content/fractal/latest",
    "maxTimeoutSeconds": 300
  }
]

Provenance

Seen in the source catalog

2026-09-23 23:56Z

Last indexed by Roundhouse

2026-09-24 13:15Z

Last enriched (probe, favicon, geo)

2026-09-16 12:15Z

x402 version

2

Max timeout

60s

Liveness probe

HTTP 402