Have I Been Trained? vs IntelliCode
Side-by-side comparison to help you choose.
| Feature | Have I Been Trained? | IntelliCode |
|---|---|---|
| Type | Product | Extension |
| UnfragileRank | 18/100 | 40/100 |
| Adoption | 0 | 1 |
| Quality | 0 | 0 |
| Ecosystem |
| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Capabilities | 6 decomposed | 7 decomposed |
| Times Matched | 0 | 0 |
Accepts an image file and performs reverse-lookup queries against indexed snapshots of popular AI art model training datasets (LAION, Stable Diffusion, Midjourney, DALL-E, etc.) using perceptual hashing and semantic embedding matching. The system likely maintains pre-computed hash tables and vector indices of known training data, then compares incoming images against these indices to detect matches or near-duplicates, returning provenance metadata if found.
Unique: Specializes in detecting whether images appear in AI model training datasets by maintaining indexed snapshots of LAION, Stable Diffusion, and other public training corpora, using perceptual hashing to match images even after compression or minor modifications, rather than generic reverse-image search
vs alternatives: More targeted than Google Images reverse search because it specifically indexes AI training datasets rather than the general web, and more comprehensive than individual model documentation because it aggregates multiple training sources in one query
Maintains a unified index across multiple popular generative AI model training datasets (Stable Diffusion, DALL-E, Midjourney, etc.) and exposes a single query interface to check an image against all indexed datasets simultaneously. This likely involves periodic crawling or partnership access to dataset metadata, normalization of dataset schemas, and a federated search architecture that queries multiple indices in parallel and aggregates results.
Unique: Aggregates training dataset indices from multiple competing generative AI models into a single queryable interface, rather than requiring users to check each model's dataset separately or use disparate tools
vs alternatives: Broader coverage than checking individual model documentation or using model-specific tools, and more efficient than manual searches across multiple platforms
Uses perceptual hashing algorithms (likely pHash, dHash, or similar) to match images even when they have been slightly modified (compressed, cropped, color-shifted, watermarked). The system computes a compact hash fingerprint of the query image and compares it against pre-computed hashes of training dataset images, using a configurable similarity threshold to determine matches. This enables detection of images that are visually identical or near-identical to training data despite minor transformations.
Unique: Implements perceptual hashing with configurable tolerance thresholds to detect training dataset images even after compression, cropping, or minor modifications, rather than requiring exact pixel-level matches
vs alternatives: More robust than cryptographic hashing (MD5, SHA) which fails on any modification, and more practical than deep learning-based similarity because it's faster and doesn't require GPU resources
When a match is detected, generates a detailed report showing which dataset(s) contain the image, metadata about the dataset (size, creation date, model association), and links to source documentation or dataset repositories. The system aggregates metadata from multiple sources and formats it into a human-readable report that provides context about how the image entered the training pipeline.
Unique: Aggregates and formats provenance metadata from multiple training dataset sources into a structured report suitable for legal or research purposes, rather than just returning a binary match result
vs alternatives: More actionable than raw dataset indices because it contextualizes matches with model associations and source documentation, and more comprehensive than individual model transparency reports
Accepts multiple images (via file upload, URL list, or API) and processes them in parallel or queued batches against the training dataset indices. The system likely implements job queuing, rate limiting, and asynchronous processing to handle multiple images without blocking, returning results as a consolidated report or per-image breakdown. This enables artists or platforms to audit large collections of images efficiently.
Unique: Implements batch processing with job queuing and asynchronous result delivery to handle multiple image scans efficiently, rather than requiring sequential single-image uploads
vs alternatives: More scalable than manual per-image uploads for large portfolios, and more practical than building custom batch infrastructure for individual artists or small platforms
Periodically crawls, ingests, and updates indices of public training datasets (LAION snapshots, Stable Diffusion dataset releases, etc.) to keep the searchable corpus current. This likely involves automated pipelines that detect new dataset releases, download metadata, compute perceptual hashes for new images, and update the search indices. The system must handle versioning to track which dataset snapshot was used for each match.
Unique: Maintains versioned indices of multiple training dataset snapshots with automated update pipelines, enabling users to understand which dataset version was queried and track how training data evolves over time
vs alternatives: More transparent than static indices because it tracks versions and update dates, and more comprehensive than relying on individual model documentation which may lag behind actual training data releases
Provides IntelliSense completions ranked by a machine learning model trained on patterns from thousands of open-source repositories. The model learns which completions are most contextually relevant based on code patterns, variable names, and surrounding context, surfacing the most probable next token with a star indicator in the VS Code completion menu. This differs from simple frequency-based ranking by incorporating semantic understanding of code context.
Unique: Uses a neural model trained on open-source repository patterns to rank completions by likelihood rather than simple frequency or alphabetical ordering; the star indicator explicitly surfaces the top recommendation, making it discoverable without scrolling
vs alternatives: Faster than Copilot for single-token completions because it leverages lightweight ranking rather than full generative inference, and more transparent than generic IntelliSense because starred recommendations are explicitly marked
Ingests and learns from patterns across thousands of open-source repositories across Python, TypeScript, JavaScript, and Java to build a statistical model of common code patterns, API usage, and naming conventions. This model is baked into the extension and used to contextualize all completion suggestions. The learning happens offline during model training; the extension itself consumes the pre-trained model without further learning from user code.
Unique: Explicitly trained on thousands of public repositories to extract statistical patterns of idiomatic code; this training is transparent (Microsoft publishes which repos are included) and the model is frozen at extension release time, ensuring reproducibility and auditability
vs alternatives: More transparent than proprietary models because training data sources are disclosed; more focused on pattern matching than Copilot, which generates novel code, making it lighter-weight and faster for completion ranking
IntelliCode scores higher at 40/100 vs Have I Been Trained? at 18/100. IntelliCode also has a free tier, making it more accessible.
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Analyzes the immediate code context (variable names, function signatures, imported modules, class scope) to rank completions contextually rather than globally. The model considers what symbols are in scope, what types are expected, and what the surrounding code is doing to adjust the ranking of suggestions. This is implemented by passing a window of surrounding code (typically 50-200 tokens) to the inference model along with the completion request.
Unique: Incorporates local code context (variable names, types, scope) into the ranking model rather than treating each completion request in isolation; this is done by passing a fixed-size context window to the neural model, enabling scope-aware ranking without full semantic analysis
vs alternatives: More accurate than frequency-based ranking because it considers what's in scope; lighter-weight than full type inference because it uses syntactic context and learned patterns rather than building a complete type graph
Integrates ranked completions directly into VS Code's native IntelliSense menu by adding a star (★) indicator next to the top-ranked suggestion. This is implemented as a custom completion item provider that hooks into VS Code's CompletionItemProvider API, allowing IntelliCode to inject its ranked suggestions alongside built-in language server completions. The star is a visual affordance that makes the recommendation discoverable without requiring the user to change their completion workflow.
Unique: Uses VS Code's CompletionItemProvider API to inject ranked suggestions directly into the native IntelliSense menu with a star indicator, avoiding the need for a separate UI panel or modal and keeping the completion workflow unchanged
vs alternatives: More seamless than Copilot's separate suggestion panel because it integrates into the existing IntelliSense menu; more discoverable than silent ranking because the star makes the recommendation explicit
Maintains separate, language-specific neural models trained on repositories in each supported language (Python, TypeScript, JavaScript, Java). Each model is optimized for the syntax, idioms, and common patterns of its language. The extension detects the file language and routes completion requests to the appropriate model. This allows for more accurate recommendations than a single multi-language model because each model learns language-specific patterns.
Unique: Trains and deploys separate neural models per language rather than a single multi-language model, allowing each model to specialize in language-specific syntax, idioms, and conventions; this is more complex to maintain but produces more accurate recommendations than a generalist approach
vs alternatives: More accurate than single-model approaches like Copilot's base model because each language model is optimized for its domain; more maintainable than rule-based systems because patterns are learned rather than hand-coded
Executes the completion ranking model on Microsoft's servers rather than locally on the user's machine. When a completion request is triggered, the extension sends the code context and cursor position to Microsoft's inference service, which runs the model and returns ranked suggestions. This approach allows for larger, more sophisticated models than would be practical to ship with the extension, and enables model updates without requiring users to download new extension versions.
Unique: Offloads model inference to Microsoft's cloud infrastructure rather than running locally, enabling larger models and automatic updates but requiring internet connectivity and accepting privacy tradeoffs of sending code context to external servers
vs alternatives: More sophisticated models than local approaches because server-side inference can use larger, slower models; more convenient than self-hosted solutions because no infrastructure setup is required, but less private than local-only alternatives
Learns and recommends common API and library usage patterns from open-source repositories. When a developer starts typing a method call or API usage, the model ranks suggestions based on how that API is typically used in the training data. For example, if a developer types `requests.get(`, the model will rank common parameters like `url=` and `timeout=` based on frequency in the training corpus. This is implemented by training the model on API call sequences and parameter patterns extracted from the training repositories.
Unique: Extracts and learns API usage patterns (parameter names, method chains, common argument values) from open-source repositories, allowing the model to recommend not just what methods exist but how they are typically used in practice
vs alternatives: More practical than static documentation because it shows real-world usage patterns; more accurate than generic completion because it ranks by actual usage frequency in the training data