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Back to the labJEVLAB / FIELD NOTES
Fresh from the lab
A dated reading list of newly added resources and JEVLAB NEWS. Come back for what changed, or follow the RSS feed in your own reader.
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Dates show when an item became available in JEVLAB, in UTC, not when its original source was published. Translation-only edits do not count as new resources.
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APIError Class Reference
The APIError class provides a structured way to handle unsuccessful HTTP responses within the TypeSafe AI SDK. It captures status codes, response bodies, and headers, allowing developers to programmatically manage specific error types like authentication or rate limit issues.
docs.typesafe.aiTypeSafe AI SDK Common Types
This resource documents the core data structures for the TypeSafe AI Python SDK. It defines JSONValue and JSONContent type aliases to standardize how JSON-compatible data and nested mappings are handled within the library.
docs.typesafe.aiTypeSafe SDK Constants
This reference documentation outlines the default settings and environment variable names for the TypeSafe Python SDK. It provides developers with the specific keys required to configure API access, model selection, and timeout parameters within their applications.
docs.typesafe.aiAPITimeoutError Class
This documentation defines the APITimeoutError class within the TypeSafe AI SDK. It explains how the class handles connection timeouts by extending APIConnectionError and provides access to the specific timeout duration in milliseconds for error handling.
docs.typesafe.aiInternalServerError Class Reference
This documentation defines the InternalServerError class within the TypeSafe AI JavaScript SDK. It details how the class handles HTTP 5xx server errors by inheriting from the base APIError class and providing access to response status, headers, and body content.
docs.typesafe.aiPermissionDeniedError Class
This documentation defines the PermissionDeniedError class within the TypeSafe AI SDK. It represents HTTP 403 access denied responses, inheriting from the base APIError class to provide structured access to status codes, response bodies, and request identifiers.
docs.typesafe.aiAuthenticationError Class Reference
This documentation defines the AuthenticationError class within the TypeSafe AI JavaScript SDK. It details how the class handles HTTP 401 errors, including inherited properties like status codes, response bodies, and request identifiers for debugging API interactions.
docs.typesafe.aiUnprocessableEntityError Class
This resource documents the UnprocessableEntityError class within the TypeSafe AI SDK. It demonstrates how the library handles HTTP 422 errors, providing a structured way to access response bodies, headers, and request IDs during validation failures.
docs.typesafe.aiChoiceResponse Interface
This interface defines the structure for handling classification results in TypeSafe AI. It provides a standardized way to access a selected label, its associated confidence score, and a comprehensive map of probabilities for all possible choices.
docs.typesafe.aiTypeSafe AI ModelCard Interface
This documentation defines the ModelCard interface within the TypeSafe AI SDK. It outlines the required metadata structure, including name, description, and release date fields, used for identifying and managing available AI models.
docs.typesafe.aiTypeSafeClient API Reference
This documentation details the TypeSafeClient class for the TypeSafe AI API. It demonstrates how to initialize the client and use the systemOne method to retrieve typed answers for structured queries regarding text or state data.
docs.typesafe.aiNoulQuestion Interface
This documentation defines the NoulQuestion interface for the TypeSafe AI SDK. It outlines the structure for yes/no queries, including optional fields for outcome descriptions and instructions, enabling developers to integrate structured binary decision-making into their AI workflows.
docs.typesafe.aiRetryPolicy Interface Reference
This documentation defines the RetryPolicy interface for the TypeSafe AI SDK. It outlines configurable parameters for handling API connection errors, timeouts, and HTTP status codes, allowing developers to customize retry behavior and backoff strategies.
docs.typesafe.aiInterface: Questions
This documentation defines the Questions interface within the TypeSafe AI SDK. It demonstrates how to structure questions as an indexable object, where each entry is keyed by a unique name to facilitate organized retrieval of associated answers.
docs.typesafe.aiScoreResponse Interface
This interface defines the structure for returning evaluation scores in TypeSafe AI. It provides a standardized format for expected scores, associated rubrics, confidence levels, and probability distributions for specific criteria.
docs.typesafe.aiType Alias: Description
This documentation defines the Description type alias within the TypeSafe AI SDK. It serves as a reference for developers to understand how criterion labels are structured, noting that a null value indicates an undescribed label.
docs.typesafe.aiWithResponse Interface Documentation
This documentation defines the WithResponse interface for TypeSafe AI, which structures parsed data alongside its associated HTTP response and request ID. It provides developers with a standardized way to access response metadata and body content within the SDK.
docs.typesafe.aiTypeSafeClientConfig Interface
This documentation defines the configuration interface for the TypeSafe AI JavaScript SDK. It details available client options including API keys, base URLs, logging levels, and custom fetch implementations to manage SDK behavior and network requests.
docs.typesafe.aiSystemOneRequestPayload Interface
This interface defines the request body for the SystemOne API endpoint. It specifies the required structure for model overrides, question sets, and state data, ensuring consistent communication when interacting with the TypeSafe AI system.
docs.typesafe.aiType Alias: JsonValue
This resource defines the JsonValue type alias for the TypeSafe AI SDK. It demonstrates a recursive TypeScript structure designed to represent any valid JSON-compatible data, including strings, numbers, booleans, nulls, arrays, and nested objects.
docs.typesafe.aiType Alias: EnvVar
This documentation defines the EnvVar type alias within the TypeSafe AI SDK. It demonstrates how to derive a type from the keys of the ENV object to ensure type-safe environment variable handling in JavaScript applications.
docs.typesafe.aiTypeSafe AI Question Type Alias
This documentation defines the Question type alias within the TypeSafe AI SDK. It demonstrates how the library categorizes input queries into specific structures including NoulQuestion, ScoreQuestion, and ChoiceQuestion types to facilitate structured data handling.
docs.typesafe.aiType Alias: ScoreCriteria
This documentation defines the ScoreCriteria type alias for the TypeSafe AI SDK. It specifies a structure requiring at least two entries, allowing developers to map descriptions to numerical scores while supporting null values for undescribed scores.
docs.typesafe.aiLOG_LEVELS Variable
This reference documentation defines the LOG_LEVELS variable within the TypeSafe AI JavaScript SDK. It provides a read-only array of supported log levels, organized by verbosity to assist developers in configuring system output and debugging behavior.
docs.typesafe.aiTypeSafe AI score() Function
This function enables developers to generate structured evaluation questions using an ordered rubric. It demonstrates how to define specific scoring criteria for model outputs within the TypeSafe AI SDK framework.
docs.typesafe.aiTypeSafe AI choice() Function
The choice() function in the TypeSafe AI SDK allows developers to construct questions that require selecting from a set of named alternatives. It accepts specific instructions and criteria to define the available options for structured decision-making tasks.
docs.typesafe.aiType Alias: ScoreOf<T>
This TypeScript utility defines a type alias that extracts score keys from a rubric. It demonstrates how to infer indices from fixed-length tuples or return a number type for other criteria structures.
docs.typesafe.aiTypeSafe AI Documentation
This resource provides comprehensive documentation for Jev, a System One model designed for structured decision-making in software. It includes SDK guides, architectural patterns, and cookbooks for implementing typed questions like choices, scores, and yes/no evaluations within applications.
docs.typesafe.aiJev Model Documentation
This resource outlines the technical specifications, pricing, and API usage for the Jev 1.13 model. It explains how to utilize the state-based architecture for domain-specific tasks without fine-tuning.
docs.typesafe.aiAnswers your code can use
TypeSafe highlights Jev’s typed-output approach, where applications provide state and questions to receive structured probabilities, choices, or scores. The announcement also details the integration of the Venice API for developers.
x.comTypeSafe steps out of stealth
TypeSafe officially launches its research lab and introduces Jev to the developer community. This announcement features a launch film by Diogo Almeida and provides foundational context for the project's mission and future direction.
x.comJev Public Access
TypeSafe has removed the waitlist for Jev, allowing public access to the platform. Developers can use the official console to integrate and test typed decision-making capabilities within their own software applications.
x.comJev: AI Decision Making Without Text or Code
Leonardo Bissoli demonstrates how Jev facilitates structured AI decision-making. The presentation covers the integration of confidence scores, operational boundaries, and practical examples for implementing these decision frameworks within software applications.
youtube.comJev: Structured Decisions and Use Cases
This resource by Leonardo Bissoli explores the application of Jev for structured decision-making processes. It provides a video overview and reference materials to demonstrate how Jev can be utilized to organize and execute complex logical workflows in practical scenarios.
leonardobissoli.comJev by TypeSafe AI: Structured Decision Guide
This guide by Fazt introduces Jev for implementing type-safe decision-making processes. It provides practical examples demonstrating how to structure logic within applications to ensure consistent and reliable data handling.
fazt.devJev by TypeSafe AI: Structured Decision Making
This video demonstrates how Jev enables structured decision-making in AI applications. It showcases practical examples including a Snake game implementation and automated support request classification to illustrate the framework's capabilities.
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