All Services

Returns source files for an editable, playable WebGPU maze game starter using a vertex buffer, render pipeline, and WGSL shader, pre-configured with the vertical-19-d1 preset.

Generates source files for a customizable, playable WebGPU maze game starter using a native vertex buffer, render pipeline, and WGSL shader for the vertical-19-d2 preset.

Returns source files for an editable WebGPU-native maze game using a vertex buffer, render pipeline, and WGSL shader, configured with a preset layout (vertical-19-d4) and optional title, seed, and accent color.

Generates source files for an editable WebGPU-powered maze game using the vertical-23-d2 preset, with a configurable title, seed, and accent color.

Generates and returns editable WebGPU maze game source files using WGSL shaders for the vertical-23-d3 preset, configurable by seed, title, and accent color.

Returns editable source files for a native WebGPU maze game preset (vertical-27-d1) with configurable title, seed, and accent color, using raw WGSL shaders and vertex buffers.

Returns editable WebGPU-based maze game source files for the vertical-27-d3 preset, configurable by seed, title, and accent color.

Returns editable source files for a native WebGPU maze game starter using a vertex buffer, render pipeline, and WGSL shader, preconfigured for the vertical-27-d4 preset.

Fetches and analyzes newly listed cryptocurrency trading pairs using DexScreener data enriched with AI-powered insights

AI-powered analysis of a crypto asset by ID using CoinGecko, DexScreener, and MiMo LLM to generate structured intelligence

Discovers and returns newly listed cryptocurrency trading pairs using DexScreener data, enriched with AI-powered analysis via MiMo LLM

Performs AI-powered cryptocurrency analysis by aggregating CoinGecko and DexScreener data and generating LLM-driven insights for a given crypto asset ID

Retrieves AI-analyzed trending cryptocurrency tokens using CoinGecko and DexScreener data processed through a MiMo LLM

Fetches AI-analyzed trending cryptocurrency tokens using CoinGecko and DexScreener data processed through a MiMo LLM

AI-powered NFT analysis combining CoinGecko and DexScreener data with MiMo LLM reasoning, delivered via x402 micropayment on Base or Solana

Fetches AI-powered crypto market data and analysis for a given currency pair using CoinGecko, DexScreener, and MiMo LLM

Returns AI-powered cryptocurrency market analysis by querying a target asset or market pair, combining CoinGecko, DexScreener data with MiMo LLM insights

Provides AI-powered DeFi analysis combining CoinGecko and DexScreener data processed through a MiMo LLM to deliver crypto market intelligence

AI-powered DeFi analysis combining CoinGecko and DexScreener market data with MiMo LLM reasoning, delivered via x402 micropayment on Base or Solana

Fetches AI-powered global cryptocurrency market data and analysis using CoinGecko data enriched by MiMo LLM intelligence

Returns AI-powered global cryptocurrency market analysis by aggregating CoinGecko data and applying MiMo LLM interpretation

Retrieves and analyzes cryptocurrency exchange data using AI-powered insights combining CoinGecko, DexScreener, and MiMo LLM

Fetches AI-enriched cryptocurrency exchange data using CoinGecko and DexScreener sources, analyzed by a MiMo LLM layer

Provides AI-generated DeFi investment advice and crypto analysis by combining CoinGecko market data, DexScreener DEX data, and MiMo LLM reasoning

Returns AI-powered cryptocurrency coin data by querying CoinGecko and DexScreener, processed through MiMo LLM, for a given versus/base currency

Generates AI-powered narrative analysis for cryptocurrency tokens by combining CoinGecko market data, DexScreener DEX data, and MiMo LLM reasoning

Generates AI-powered narrative analysis for a cryptocurrency using CoinGecko, DexScreener data, and MiMo LLM

AI-powered DeFi analysis combining CoinGecko and DexScreener data with MiMo LLM to generate crypto market intelligence

Generates AI-powered narrative analysis of a cryptocurrency's current market story and thematic context using CoinGecko, DexScreener, and MiMo LLM

Fetches AI-powered cryptocurrency coin data by querying CoinGecko and DexScreener, analyzed via MiMo LLM, with results denominated against a specified vs-currency

Returns AI-analyzed cryptocurrency exchange data by querying exchange listings and metrics via CoinGecko and DexScreener, with MiMo LLM-powered summarization

Returns the full text of a single Nesebar travel guide article (nesebar-patevoditel) covering general orientation and sightseeing information about Nesebar, Bulgaria.

Returns module and type entrypoints metadata for the @nestjs/core npm package to support integration and upgrade planning.

Proxies chat completion requests to GPT-4o via x402 micropayment, returning standard OpenAI-compatible chat completion responses.

Sends a chat completion request to GPT-4o Mini (labeled gpt-5.4-mini) via NetIntel's pay-per-call x402 micropayment gateway, returning an OpenAI-compatible chat completion response

Calculates the Pearson correlation coefficient between net margin values (derived from supplied revenue and net profit rows) and a set of user-supplied benchmark figures.

Calculates a weighted arithmetic mean of net margin across up to 200 supplied data rows using user-provided nonnegative weights.

Calculates a Base-100 indexed rebase report for Net Merchandise Sales from supplied gross sales, discounts, and returns data across up to 200 rows.

Calculates and compares net merchandise sales (gross sales minus discounts and returns) for two scenarios — before and after — to show the delta.

Computes the correlation between calculated net merchandise sales values (gross sales minus discounts and returns) and a supplied benchmark series, across up to 200 row observations.

Computes a distribution histogram of net merchandise sales (gross sales minus discounts and returns) across supplied retail data rows, grouped into configurable bins.

Computes a statistical distribution summary for net merchandise sales calculated from supplied gross sales, discounts, and returns figures across multiple rows.

Calculates IQR-based outlier flags for net merchandise sales figures derived from gross sales, discounts, and returns across multiple rows

Computes the local finite-change elasticity of net merchandise sales with respect to a chosen input field (gross merchandise sales, discounts, or returns) given baseline values and a step percentage.

Calculates ordered trend analysis for net merchandise sales from a sequence of gross sales, discounts, and returns figures supplied by the caller.

Ranks an array of retail records by net merchandise sales (gross sales minus discounts and returns) and returns their order

Computes a rolling mean of net merchandise sales (gross sales minus discounts and returns) over a configurable window for a supplied series of retail data rows.

Calculates net merchandise sales from gross sales, discounts, and returns in a single local computation

Classifies net merchandise sales figures (gross sales minus discounts and returns) against user-defined lower and upper thresholds, returning a bounded category label for each row.

Computes the weighted mean of net merchandise sales (gross sales minus discounts and returns) across multiple supplied data rows using custom weights.

Calculates Pearson correlation between computed net profit figures (derived from supplied financial inputs) and user-supplied benchmark values, returning a JSON report with formula, units, and limitations.

Calculates a weighted mean of net profit across up to 200 supplied financial data rows using user-provided nonnegative weights.

Retrieves live blockchain network statistics from AmanChain nodes against real consensus state, paid per call via x402 in USDC.

Retrieves live AmanChain blockchain network statistics from on-chain consensus state, including node counts, block metrics, pool data, and network health indicators.

Computes a base-100 indexed time series for network ingress or egress bytes across up to 200 records, grouped by network interface.

Computes daily sum series for network throughput (ingress or egress bytes) across up to 200 typed records, grouped by network interface and date.

Generates a date-by-category pivot table for network throughput data (ingress/egress bytes) across network interfaces, using up to 200 supplied records.

Identifies duplicate row keys within a supplied set of network throughput records (up to 200 rows) using local descriptive analytics.

Removes exact duplicate rows from up to 200 network throughput records and returns a deduplicated dataset with a local analytics report.

Fills missing ingress_bytes or egress_bytes values in network throughput records using zero, mean, or median imputation, returning a completed dataset with up to 200 rows.

Compares two keyed datasets of network throughput records (ingress/egress bytes by interface and date), returning local descriptive analytics highlighting differences between them.

Computes a descriptive numeric distribution report (min, max, mean, percentiles, etc.) for ingress or egress bytes across up to 200 network interface records.

Filters up to 200 network throughput records (ingress/egress bytes) to only those rows whose selected byte field falls within a specified numeric range.

Sorts up to 200 network throughput records by a chosen numeric field (ingress_bytes or egress_bytes) and returns them in ordered sequence.

Audits up to 200 network throughput records for data quality issues, returning local descriptive analytics without enrichment or forecasting.

Computes a secondary-measure weighted mean over network throughput records (ingress or egress bytes) grouped by network interface, using up to 200 typed row inputs.

Scores up to 64 labelled time intervals and 128 command events for BCI/neural interface performance metrics including false activations, missed trials, wrong commands, and first-correct response times

Returns a sample breakdown of academic publications on neural network knowledge distillation using Crossref publisher metadata, including DOI references and source attribution.

Retrieves recent academic publications on neural network knowledge distillation using Crossref metadata, returning DOI references and source attributions.

Returns structured Crossref citation records (DOIs, source attributions) for academic publications on neural network knowledge distillation.

Returns AI-generated deep research and analysis for a specified cryptocurrency token

Generates SEO keyword suggestions and content for a given crypto or DeFi topic

Returns a mapping of supported blockchain chain IDs to their human-readable names (e.g. Ethereum, Base)

Returns the list of blockchain networks supported by the NeuroFi platform, identified by chain ID and name

Generates AI-powered SEO meta content (titles, descriptions, tags) for a given crypto or DeFi topic

Detects the language of a given text string and returns the language name and ISO code

Generates AI-powered SEO meta content (titles, descriptions, structured metadata) for a given topic, tailored for crypto and DeFi contexts.

Returns AI-generated crypto alpha signals and market intelligence for a specified topic (e.g. DeFi, a token, or market sector) via micropayment-gated GET request

Returns a map of supported language codes and their human-readable names available in NeuroFi's translation and language detection services.

Returns AI-generated crypto portfolio recommendations based on specified investment goals

Generates a structured SEO content outline for a given topic, returning a blog-style hierarchical outline with sections and key points.

Extracts action-marker lines (e.g. TODO items with deadlines) from newsletter Markdown or plain text content

Splits newsletter Markdown or plain text into bounded chunks of a specified maximum character length using deterministic processing

Scans newsletter Markdown or plain text for duplicate paragraphs and reports which content is repeated

Masks email addresses and phone numbers found in newsletter Markdown or plain text content, replacing them with anonymized patterns deterministically.

Converts newsletter Markdown or plain text (up to 20,000 characters) into an escaped HTML preview using deterministic document templates

Audits the heading hierarchy of a newsletter document (Markdown or plain text) to identify structural issues with heading levels and organization

Extracts and formats a structured heading outline from newsletter Markdown or plain text content

Extracts and inventories all HTTP links found within a newsletter document supplied as Markdown or plain text

Scans a newsletter document (Markdown or plain text) and returns an inventory of all ISO-date patterns found within it

Analyzes a newsletter's Markdown or plain text to produce a deterministic length and paragraph structure profile.

Extracts and inventories all quoted passages from a newsletter document supplied as Markdown or plain text

Reorders recognised sections in a newsletter document according to a standard template structure

Checks a newsletter document (Markdown or plain text) to verify that all required sections are present and accounted for

Extracts and identifies sections from a newsletter document supplied as Markdown or plain text, up to 20,000 characters.

Formats supplied Markdown or plain text content into a structured newsletter section template deliverable.

Scans supplied newsletter text and identifies all occurrences of user-specified terms, returning deterministic coverage results without generative prose.

Normalises whitespace in newsletter Markdown or plain text documents up to 20,000 characters using deterministic processing rules

Computes word frequencies from supplied newsletter text (Markdown or plain text, up to 20,000 characters) using deterministic processing.

Returns current APEX token data from the Arc network, including price, holder count, and symbol, for a small USDC micropayment.