29 services carry this tag in their source record. Tags come from the provider, not from us — a tag is a claim about what the service does, not proof that it does it well.
Email deliverability & domain security check — validates SPF, DKIM (multi-selector), and DMARC, detects spoofing and misconfigurations, and returns an A–F email security grade with transparent deductions. Anti-spam / ant...
Text-to-image generation on Venice models (Flux, SD3.5, GPT-Image, HunyuanImage, and more), paid per request in USDC on Base via x402, no API key. Charge is catalog unit × variants (1–4). A bare unpaid probe (no model) q...
Convert text into a 768-dimension embedding vector (nomic-embed-text) for semantic search, RAG retrieval, clustering, deduplication or similarity scoring. POST JSON {"input": string}, up to 3000 characters; returns {embe...
OpenAI-compatible text embeddings (text-embedding-3-small by default; 3-large and ada-002 supported), up to 64 inputs or 16k chars per call, returned in the standard OpenAI shape. Identical inputs repeated within 10 minu...
Generate a text embedding vector using OpenAI text-embedding-3-large (3072 dimensions). Higher accuracy than the small model. Ideal for semantic search, RAG, and clustering. No API key needed; pay per call via x402. Text...
Read back values the paying wallet saved with /paid/memory/set: one key, or list your keys by prefix (keys, sizes and expiry, newest first). Another wallet can never see them. No free trial.
CUDA, driver and Ollama compatibility - GPU support, model memory fit and proof inference uses the GPU
Sentence embeddings (all-MiniLM-L6-v2, 384 dimensions, L2-normalized, multilingual input works best in English) for semantic search, clustering and deduplication, or rerank mode: pass query + documents to get documents s...
Embeddings, OpenAI compatible. A 1,024-dimension embedding vector for up to 4,000 characters of text in 100+ languages, from BAAI's BGE-M3. Use for semantic search, RAG retrieval, clustering and deduplication.
Store a piece of text for later similarity recall - a per-wallet semantic index an agent cannot host in-session. Returns an id. Prefer memory-write for structured state you will look up by exact key; this pair is for fuz...
Fix broken JSON (trailing commas, single quotes, unquoted keys). Returns only valid JSON.
Generate sentence embeddings (all-MiniLM-L6-v2, 384-dim, mean-pooled + L2-normalized) for up to 96 texts per call. Drop-in compatible with any all-MiniLM-L6-v2 index. Pairs with /chunk for a complete RAG pipeline.
Generate a text embedding vector using OpenAI text-embedding-3-small (1536 dimensions). Ideal for semantic search, RAG, and clustering. No API key needed; pay per call via x402. Text capped at 32k chars.
Split long text into overlapping chunks for RAG and embeddings, by characters, words or estimated tokens, keeping sentences or paragraphs whole, with each chunk's position and size.
Classify sentiment (positive/negative) with DistilBERT. POST text; returns label and confidence score. Fixed low per-request price.
Text embeddings for semantic search and RAG: one vector per call, 1024 dimensions.
OpenAI-compatible text embeddings over x402 (768-dim, up to 32 inputs per call) — Genesis402 / UnyKorn Operator Network
Search a namespace you indexed: the query is embedded on the same model and matched against your collection. Returns hits with score, text and metadata, plus latency.
Text to speech API: offline speech in 41 voices and 6 languages (English, Spanish, French, Hindi, Italian, Portuguese), MP3, WAV or Ogg audio (Kokoro-82M, CPU) (mlpeek, Tanod). Use when an agent must turn text into spoke...
Split text into overlapping chunks for RAG ingestion - by characters (default) or by exact LLM tokens. Returns the chunks plus offsets. Deterministic, no model needed.
Breached password check: is this password in known data breaches, and how often? Send only the SHA-1 hash (never the password); just the first 5 characters reach Have I Been Pwned (k-anonymity). Up to 50 hashes per call.
Use when you need image embeddings or text embeddings in one 2048-dimension space for retrieval, RAG, semantic search, clustering, or dedup (Qwen3-VL-Embedding-2B, OpenAI-compatible). Batch up to 256 items per call, serv...
OpenAI-compatible chat completion with Qwen3 30B (fast mixture-of-experts model): set the base URL to /v1/llm/qwen3-30b in any OpenAI SDK. Up to 32,000 characters in and 2,048 tokens out per call, a fixed price per call,...
Text chunker for RAG (heading-aware, token-sized chunks): Splits text you send, or the main content of one public page, into heading-aware chunks of at most max_tokens with optional overlap, each with its heading path an...