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Description
Hypertune stands out as a highly adaptable platform that excels in managing feature flags, conducting A/B testing, performing analytics, and configuring applications. It is designed with comprehensive end-to-end type safety, Git-inspired version control, and allows for local, synchronous, in-memory flag evaluations.
You can establish type-safe, tailored inputs such as the current User or Organization to fine-tune feature flag rules, ensuring precise targeting of your desired audience. Furthermore, the platform enables the creation of reusable variables like user segments that can be utilized across various feature flags, facilitating swift debugging for individual users.
With options for A/B testing, percentage-based rollouts, multivariate tests, and machine learning loops, Hypertune allows for an effortless rollout, testing, and optimization of new features. Additionally, you can log analytics events with type-safe custom payloads and create dynamic funnels and charts within the dashboard to assess the influence of every feature release.
Moreover, the SDK can be initialized with just the necessary feature flags, enabling partial evaluation of flag logic on the edge, thus enhancing both performance and security. This combination of capabilities makes Hypertune a versatile choice for developers aiming to innovate and refine their applications effectively.
Description
Jev is TypeSafe AI’s first public System One Model, a class of AI designed to make fast, structured decisions that software can consume directly. Instead of generating arbitrary strings like a traditional large language model, Jev produces predefined type-safe values accompanied by calibrated probabilities and confidence estimates. Its architecture generates outputs in parallel rather than autoregressively producing one token at a time, allowing the model to prioritize speed and computational efficiency. TypeSafe trains Jev using Reinforcement Learning for Calibrated Decisions, an approach intended to optimize for accurate uncertainty estimates and consistent structured outputs. The model can be embedded into conventional software as an intelligent decision layer for classification, scoring, routing, extraction, branching, and other tasks where hand-written rules would be too rigid. Jev can also be used to judge, verify, guardrail, or detect problematic behavior in outputs from other AI systems. TypeSafe reports typical end-to-end response times between 70 and 500 milliseconds and positions the model for applications where low latency is important. The company also emphasizes schema guarantees, meaning Jev’s outputs are constrained to the structures defined by the application rather than requiring developers to parse and validate unrestricted generated text. Jev is aimed at developers and organizations building automation, real-time software, large-scale data workflows, and production systems that require dependable structured AI decisions.
API Access
Has API
API Access
Has API
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Integrations
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Integrations
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Pricing Details
$0
Free Trial
Free Version
Pricing Details
Input: $0.042 / 1M tokens
Input tokens: $0.042 / 1 million tokens ($42 per billion tokens).
Output tokens: FREE (too cheap to meter).
Output tokens: FREE (too cheap to meter).
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Hypertune
Country
United Kingdom
Website
www.hypertune.com
Vendor Details
Company Name
TypeSafe AI
Founded
2024
Country
United States
Website
typesafe.ai/
Product Features
Feature Management
A/B Testing
Entitlement Management
Feature Alerts
Feature Flag / Toggle
Feature Rollout Management
KPI Monitoring
Kill Switch
Multivariate Testing
Product Experimentation
Whitelist Creation
