got @typesafeai’s new model Jev to play Super Mario Bros. fast inference + structured outputs makes it surprisingly good for real time use cases. I'm excited to see what can be done with these new models!
Core ConceptsNeuro-symbolic
Neuro-symbolic AI
The manifesto's framing of TypeSafe's aim: neural networks for perception paired with symbolic logic, or smart if-statements.
- Category
- Core Concepts
- Also known as
- Neuro-symbolic
- Related terms
- 3
- Directory entries
- 1
- Docs
- typesafe.ai
- Added
- 2026-09-24
Definition
In the TypeSafe manifesto, neuro-symbolic means letting neural models handle the messy perception side — reading a ticket, a resume, a passage — while symbolic logic in code owns the decisions that must be auditable and repeatable.
Jev fits the neural half as a narrow judgment engine; your workflow, weights, and thresholds are the symbolic half. The split keeps control flow deterministic even when the input is unstructured.
Tagsarchitecturemanifesto
Related terms
Definitions that connect to this one.
Composable AITypeSafe's manifesto position: intelligence as a dependable primitive that software branches on, rather than an assistant that keeps humans in the loop.Core ConceptsSmart if statementTypeSafe's shorthand for what a System One decision is: a condition your code can branch on directly, backed by a model judgment.Core ConceptsAI-powered softwareThe architecture TypeSafe recommends: code owns the workflow and the model appears only where programmable common sense is needed.Core Concepts
From the directory
TypeSafe's case for machine-native composable AI: intelligence as a dependable primitive that software branches on, rather than an assistant that keeps humans in the loop.
Sites & GuidesArticle#official#article#architecture
Official
From the community
Posts from builders shipping with Jev right now.
Jev plays Super Mario Bros.
LLMs generate answers, Jev makes decisions
this is the easiest way to understand Jev: LLMs generate answers. Jev makes decisions. that sounds like a small difference, but it actually changes the entire use case. say you give a normal LLM this: “here’s a user, their account history, payment behavior, support chats, Show more
@CompleteSkeptic
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x
Arbitrary classification as a primitive
Jev is cool not because it re-invented classification, but because it makes ARBITRARY classification into a type-safe programmable primitive. A general purpose zero shot decision model whose native interface is RUNTIME-DEFINED typed decisions, optimized for that exact interface
If you called Yann LeCun an idiot for saying we need to move beyond LLMs and build something new, you are banned from using Jev.
How Jev makes agents faster and cheaper
Jev solved local harness/model routing I use a combination of Claude Code, Codex and Opencode as my local agentic stack and routing to other harnesses was always enforced in the system prompt/rules With a deterministic hook that Claude Code can decide before delegation, Jev Show more
500 emails for 3.5 cents
Yeah Jev by @typesafeai is very cool. It classified 500 emails in seconds. And it costed 3.5 cents.
Headless Chromium agent
Acabo de terminar la implementación de @typesafeai + Chromium Headless para que mis agentes puedan navegar por internet a una buena velocidad! En este ejemplo le pido al agente que entre a la página del término "Café" en Wikipedia y navegue por los hipervínculos hasta terminar Show more
Tremendo esto!! Voy a trabajar en una implementacion propia.