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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
No
API Access
Has API
No
Screenshots View All
No images available
Integrations
No details available.
Integrations
No details available.
Pricing Details
$0
Free Trial
Yes
Free Version
Yes
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
No
Free Version
No
Deployment
Web-Based
Yes
On-Premises
Yes
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
Yes
Live Rep (24/7)
Yes
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
Yes
In Person
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
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
No
Entitlement Management
No
Feature Alerts
No
Feature Flag / Toggle
No
Feature Rollout Management
No
KPI Monitoring
No
Kill Switch
No
Multivariate Testing
No
Product Experimentation
No
Whitelist Creation
No
