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Type Safe

https://typesafe.ai
Escaneado: 2026-09-22 05:14:42 UTC
Brand3 Score
83/100
hueco: Magnetism
Margen inmediato
+8 pts
siguiente foco: Magnetism

La marca opera con una claridad técnica inusual, rechazando el ruido de los modelos conversacionales para enfocarse en la infraestructura de automatización. Su éxito depende de mantener la disciplina de su arquitectura System One mientras escala la validación externa de su fiabilidad.

Propósito

8/10

El propósito existe para resolver la falta de fiabilidad en la IA, lo que causa una redefinición de la IA como infraestructura. Esta claridad genera una consecuencia de utilidad práctica, tensionando la percepción de la IA como un producto de consumo.

Desarrollar modelos de inteligencia artificial diseñados para la automatización y la integración en software complejo.

“We want AI to work alongside existing software as a primitive that any programmer can invoke for semantic judgement and decisions, while still using code for what it’s best at:...” · fuente

8/10

Propósito

Resumen

8/10 evaluado

El propósito existe para resolver la falta de fiabilidad en la IA, lo que causa una redefinición de la IA como infraestructura. Esta claridad genera una consecuencia de utilidad práctica, tensionando la percepción de la IA como un producto de consumo.

Evidencia literal del snapshot

evidencia
Introducing System One Models & Jev _Diogo Almeida, founder, TypeSafe_ Models have been superhuman at chat for years, so where is all the automation? This has been my driving question for the last four years. At OpenAI, I helped build the methods that made language models useful at following instructions and talking with people. That work ended up as the research behind ChatGPT. At the time, I thought maybe chat models would lead to AGI, but despite the hype it became obvious to me that there was something really big missing. After two years in stealth, countless technical challenges, and research breakthroughs… I am beyond excited to announce that today, TypeSafe AI is releasing our
evidencia
nd research breakthroughs… I am beyond excited to announce that today, TypeSafe AI is releasing our first **System One Model**: a new class of frontier models built to make fast, structured decisions that software can use directly. We built a new stack entirely focused on automation: with a new model architecture, parallel sampler for maximum efficiency, and training method we call Reinforcement Learning for Calibrated Decisions (RLCD). Our first public model is **Jev**, available today in early access. Jev achieves similar levels of intelligence on System One tasks compared to existing LLMs, while being two orders of magnitude faster and more efficient. While Jev gives up string generatio
evidencia
, while keeping the high seats, the buggy springs, and even the whip socket in some models. It’s a clear example of how new technologies are often forced into the shape and assumptions of the thing they replace before finding their own native form. Current AI is trained to be a helpful, articulate, pleasant assistant. [⁴](https://typesafe.ai/manifesto appendix) Reasonable goals if you assume a human is on the other side of the model. Yet the foreseeable consequence is AI that requires humans in the loop instead of running in the background. Software has never worked that way. Even the most complex software is built out of simple logic and layered abstractions, with every branch auditable.
evidencia
ilders for builders and owned by all. [⁶](https://typesafe.ai/manifesto appendix) Safety is a precondition for layering. You let a component run unattended if it's reliable; you only build on top of it if it's trustworthy. It takes trust to bury a dependency five layers deep in a system. People will only do so if they can inspect it, test it, and constrain it piece by piece, the way we've always engineered dependable software. That's how the composable path enables an ecosystem of small, legible primitives to safely evolve into trustworthy, complex systems. a new shape of intelligence We're building the paradigm shift beyond AGI: step one
evidencia
| TypeSafe AI\\ \\ Read More\\ \\ RLHF-trained language models please humans and assist rather than make reliable autonomous decisions. What comes next?](https://typesafe.ai/blog/ai-too-good-to-be-true-too-bad-to-be-useful-typesafe-ai) ∵ ⩆ ⩆ ∵ ∵ ⩆ ⩆ ∵
evidencia
omplex software is built out of simple logic and layered abstractions, with every branch auditable. We want AI to work alongside existing software as a primitive that any programmer can invoke for semantic judgement and decisions, while still using code for what it’s best at: exact computation. Computers can do so much by just branching on bits, imagine if they could also branch on common sense, understanding, and intent. [⁵](https://typesafe.ai/manifesto appendix) This is the opportunity for true, machine-native composable AI. safe emergence The people who built databases didn't imagine Google. The people who built internet protocols didn't envision Stripe. They made lower-level capabil
raw_inputs.1.subpage.5.chunk.1 · abrir fuente
Introducing System One Models & Jev _Diogo Almeida, founder, TypeSafe_ Models have been superhuman at chat for years, so where is all the automation? This has been my driving question for the last four years. At OpenAI, I helped build the methods that made language models usefu
raw_inputs.1.subpage.1.chunk.4 · abrir fuente
, while keeping the high seats, the buggy springs, and even the whip socket in some models. It’s a clear example of how new technologies are often forced into the shape and assumptions of the thing they replace before finding their own native form. Current AI is trained to be a h
raw_inputs.1.subpage.1.chunk.5 · abrir fuente
omplex software is built out of simple logic and layered abstractions, with every branch auditable. We want AI to work alongside existing software as a primitive that any programmer can invoke for semantic judgement and decisions, while still using code for what it’s best at: exa
raw_inputs.1.subpage.1.chunk.7 · abrir fuente
ilders for builders and owned by all. [⁶](https://typesafe.ai/manifesto appendix) Safety is a precondition for layering. You let a component run unattended if it's reliable; you only build on top of it if it's trustworthy. It takes trust to bury a dependency five layers deep in a

Presencia y jerarquía

evidencia en páginas enlazadas desde la portada.

Explicación

La marca tiene un propósito excepcionalmente claro y técnico, alineado con la necesidad de infraestructura fiable en lugar de asistentes conversacionales.

Interpretación de la evidencia

Desarrollar modelos de inteligencia artificial diseñados para la automatización y la integración en software complejo.

Siguiente artefacto

Whitepaper sobre la integración de IA en el stack de software.

Baldosas apagadas

1 fallos detectados: plan de trabajo.

PR9
Demostrado apagada

degradada: 'ok' sin evidencia literal verificable

Puntos ciegos

1 piezas de contexto pendientes.

PR10
Reputación punto ciego

El snapshot no contiene testimonios, reseñas o validaciones de terceros que confirmen la reputación de la marca en el mercado.

Magnetism

8/10

El magnetismo se basa en la tracción técnica y la resolución del dolor de la falta de fiabilidad, lo que causa una autoridad inmediata. Esta validación externa genera una consecuencia de confianza, tensionando la necesidad de los desarrolladores de abandonar los LLMs tradicionales.

Promesa de System One Models que prioriza la fiabilidad y decisiones calibradas sobre el chat, respaldada por adopción en Vercel.

“We built a new class of models, System One Models, to be natively used by machines.” · fuente

8/10

Magnetism

Resumen

8/10 evaluado

El magnetismo se basa en la tracción técnica y la resolución del dolor de la falta de fiabilidad, lo que causa una autoridad inmediata. Esta validación externa genera una consecuencia de confianza, tensionando la necesidad de los desarrolladores de abandonar los LLMs tradicionales.

Evidencia literal del snapshot

evidencia
GitHub repository typesafe-ai/system-one-adapter-python. Drop-in TypeSafeClient replacement backed by LLM APIs. 244 stars. 36 forks. 4 open issues. language: Python. last pushed: 2026-09-18T10:47:48Z
evidencia
Jev, TypeSafe's Decision Model: What It Is, Why It Spread | CellCog 3. Three days ... launch, Vercel reported that Jev was the fastest-adopted model in AI Gateway history: nearly 13 percent of paid teams in its first 24 hours, twice the GPT-5.6 family and more than six times Fable 5.1, a tenth of teams within 18 hours. It is free on the gateway through September 25, 2026, which is part of the number. ... 4. The independent ... points are small and mixed. Per TechCrunch, a Vercel engineer replaced a GPT-5. ... Luna safety classifier with Jev and got results 5 to 18 times faster with greater accuracy; a Bryo AI test found Gemini slightly more accurate at classifying business emails but 10 to 2
evidencia
We took the opposite research direction not chat Reinforcement Learning from Human Feedback (RLHF) has led to LLMs that are optimized for human preferences. This has led to models that are superhuman at instruction following, and are what we now call “chat.” Yet RLHF creates inherent issues such as mode dropping, overconfidence, and lack of reliability. These flaws mean that LLMs require humans-in-the-loop. a new model We built a new class of models, System One Models, to be natively used by machines. We’re building with a new architecture, a new sampler, and a new training algorithm: Reinforcement Learning for Calibrated Decisions (RLCD). Decisions, not strings Typed outputs that so
evidencia
Frontiers, Old and New | | **Existing LLMs** | **System One + Jev** | | --- | --- | --- | | Optimized with | Reinforcement Learning with Human Feedback (RLHF) / Reinforcement Learning with Verifiable Rewards (RLVR) | Reinforcement Learning for Calibrated Decisions (RLCD) | | Optimizes for | Human preference: writeups and chat responses that human raters prefer.<br>Verifiable rewards: outputs that can be programmatically verified. | Calibrated decisions: answers with epistemically honest probabilities on System One tasks. | | Inputs | Unstructured data (e.g. text) with an emphasis on **sequential messages**. | Unstructured data (e.g. text) with an emphasis on **structured program state**
evidencia
, while being two orders of magnitude faster and more efficient. While Jev gives up string generation, it’s optimized for structured outputs and _can’t_ hallucinate. Think of Jev as a frontier-intelligence function call: unstructured state in, typed probabilistic decisions out. Extraordinary claims require extraordinary evidence so see below for the receipts. 💅
evidencia
uential messages**. | Unstructured data (e.g. text) with an emphasis on **structured program state**. | | Outputs | **Strings / generated text.** Strings are flexible and can be anything: chat responses, code, hallucinations, refusals, or even type-safe structured values. To be used by software, responses need to be parsed + validated. There is also always some risk that the AI goes off the rails. | **Type-safe structured values.** Possible outputs and structure are [defined in advance](https://docs.typesafe.ai/). The model never makes type errors. All answers are accompanied with calibrated probabilities and confidence scores. | | Sampling | **Sequential.** Generates one token at a time, ea
evidencia
ntelligence to follow a similar path to coal, after steam-engine efficiency led to an increase in demand. Every order of magnitude drop in the cost of intelligence unlocks orders of magnitude more use cases. Why was a new training algorithm needed? What use cases is Jev good for? Is Jev just a smaller LLM? How does Jev perform against public benchmarks? Where does our training data come from? These results are kinda crazy - how is it possible?
raw_inputs.2.exa.mentions.14 · abrir fuente
Jev, TypeSafe's Decision Model: What It Is, Why It Spread | CellCog 3. Three days ... launch, Vercel reported that Jev was the fastest-adopted model in AI Gateway history: nearly 13 percent of paid teams in its first 24 hours, twice the GPT-5.6 family and more than six times Fabl
raw_inputs.1 · abrir fuente
We took the opposite research direction not chat Reinforcement Learning from Human Feedback (RLHF) has led to LLMs that are optimized for human preferences. This has led to models that are superhuman at instruction following, and are what we now call “chat.” Yet RLHF creates in
raw_inputs.3.github.repos.0 · abrir fuente
GitHub repository typesafe-ai/system-one-adapter-python. Drop-in TypeSafeClient replacement backed by LLM APIs. 244 stars. 36 forks. 4 open issues. language: Python. last pushed: 2026-09-18T10:47:48Z

Presencia y jerarquía

evidencia visible en portada.

Interpretación de la evidencia

Promesa de System One Models que prioriza la fiabilidad y decisiones calibradas sobre el chat, respaldada por adopción en Vercel.

Siguiente artefacto

Estudio de caso de integración técnica exitosa.

Baldosas apagadas

1 fallos detectados: plan de trabajo.

MG8
Genera preferencia apagada

degradada: 'ok' sin evidencia literal verificable

Puntos ciegos

1 piezas de contexto pendientes.

MG9
Pertenencia o estatus punto ciego

La señal SV9 Flow de alta confianza marca esta baldosa como evidencia insuficiente. Flow detected magnetism but found no direct belonging or status evidence.

Propuesta de valor

6/10

La propuesta de valor se centra en la eliminación de alucinaciones y la eficiencia de costos, lo que causa una diferenciación clara frente a los chatbots. Esta precisión técnica genera una consecuencia de adopción en entornos de ingeniería, tensionando la necesidad de modelos generalistas.

Infraestructura de IA dos órdenes de magnitud más rápida y eficiente, optimizada para salidas estructuradas y toma de decisiones probabilísticas.

“The model never makes type errors.” · fuente

6/10

Propuesta de valor

Resumen

6/10 evaluado

La propuesta de valor se centra en la eliminación de alucinaciones y la eficiencia de costos, lo que causa una diferenciación clara frente a los chatbots. Esta precisión técnica genera una consecuencia de adopción en entornos de ingeniería, tensionando la necesidad de modelos generalistas.

Evidencia literal del snapshot

evidencia
robabilities and confidence scores. | | Sampling | **Sequential.** Generates one token at a time, each conditioned on the last. | **Parallel.** Generates all outputs in a single query. Incredibly efficient and hardware-aware. | | Cost | Input tokens: from $0.20 to $10 / MTok.<br>Output tokens: ~5x more expensive than input tokens. | Input tokens: $0.042 / MTok ($42 per billion tokens).<br>Output tokens: FREE (too cheap to meter). | | Speed | **End-to-end response time is** [**3 to 329 seconds**](https://llm-benchmarks.diegoromero.es/) for frontier models. Fast enough for interfacing with humans, but a big bottleneck when integrated in code. | **End-to-end response time is 70ms-500ms** for T
evidencia
uential messages**. | Unstructured data (e.g. text) with an emphasis on **structured program state**. | | Outputs | **Strings / generated text.** Strings are flexible and can be anything: chat responses, code, hallucinations, refusals, or even type-safe structured values. To be used by software, responses need to be parsed + validated. There is also always some risk that the AI goes off the rails. | **Type-safe structured values.** Possible outputs and structure are [defined in advance](https://docs.typesafe.ai/). The model never makes type errors. All answers are accompanied with calibrated probabilities and confidence scores. | | Sampling | **Sequential.** Generates one token at a time, ea
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ns, but a big bottleneck when integrated in code. | **End-to-end response time is 70ms-500ms** for TypeSafe. This can range from 40x-200x faster for the same levels of frontier intelligence for System One shaped queries. | | Confidence | Even if prompted for a confidence estimate, models tend to be overconfident and inconsistent. If a model can do a task 95% of the time but doesn’t say when it’s in the 5%, it can’t automate that task. | Always communicates confidence and uncertainty with every output. Calibrated: higher confidence means higher accuracy. More consistent: returns similar answers for similar inputs. | | Use cases | **Human-in-the-loop tasks (chatbots, copilots, coding agents).*
evidencia
$42 Per Billion input tokens. 238x Lower input price than Claude Fable 5.1
evidencia
, while being two orders of magnitude faster and more efficient. While Jev gives up string generation, it’s optimized for structured outputs and _can’t_ hallucinate. Think of Jev as a frontier-intelligence function call: unstructured state in, typed probabilistic decisions out. Extraordinary claims require extraordinary evidence so see below for the receipts. 💅
evidencia
| TypeSafe AI\\ \\ Read More\\ \\ RLHF-trained language models please humans and assist rather than make reliable autonomous decisions. What comes next?](https://typesafe.ai/blog/ai-too-good-to-be-true-too-bad-to-be-useful-typesafe-ai) ∵ ⩆ ⩆ ∵ ∵ ⩆ ⩆ ∵
evidencia
Culture We're a close-knit, flat team from OpenAI, Google Brain, Meta/FAIR, Stripe, Airbnb, Plaid, Docker, and more. We're backed by top-tier investors who share our vision for building the foundation of truly transformative AI. Our team works in-person five days a week in our San Francisco office near the Embarcadero station. We offer competitive salaries, equity, and benefits, as well as lunch and dinner. We love what we do and care about our work a lot. We strive for excellence and craftsmanship and won't stop until we get there. When the team wins, we all win, and we enjoy collaborating and inspiring each other to grow as a team and as individuals. We want you to be able to do the
raw_inputs.1.subpage.5.chunk.6 · abrir fuente
robabilities and confidence scores. | | Sampling | **Sequential.** Generates one token at a time, each conditioned on the last. | **Parallel.** Generates all outputs in a single query. Incredibly efficient and hardware-aware. | | Cost | Input tokens: from $0.20 to $10 / MTok.<br>
raw_inputs.1.chunk.6 · abrir fuente
$42 Per Billion input tokens. 238x Lower input price than Claude Fable 5.1
raw_inputs.1.subpage.5.chunk.3 · abrir fuente
, while being two orders of magnitude faster and more efficient. While Jev gives up string generation, it’s optimized for structured outputs and _can’t_ hallucinate. Think of Jev as a frontier-intelligence function call: unstructured state in, typed probabilistic decisions out. E

Presencia y jerarquía

evidencia visible en portada.

Explicación

La propuesta de valor es extremadamente técnica y precisa, logrando diferenciar su mecanismo de inferencia estructurada frente a los modelos de lenguaje convencionales mediante métricas de rendimiento y costo contundentes.

Interpretación de la evidencia

Infraestructura de IA dos órdenes de magnitud más rápida y eficiente, optimizada para salidas estructuradas y toma de decisiones probabilísticas.

Siguiente artefacto

Documentación de integración para desarrolladores.

Baldosas apagadas

4 fallos detectados: plan de trabajo.

P1
Detectada apagada

degradada: 'ok' sin evidencia literal verificable

P2
Clara apagada

El uso de terminología técnica como 'frontier-intelligence function call', 'structured program state' y 'probabilistic decisions' requiere conocimiento previo de ingeniería de software y modelos de lenguaje.

P6
Propia apagada

degradada: 'ok' sin evidencia literal verificable

P8
Mecanismo propio apagada

degradada: 'ok' sin evidencia literal verificable

Personalidad / Arquetipo

8/10

La personalidad proyecta una postura humana y artesanal, lo que causa una conexión emocional con los constructores. Esta honestidad radical genera una consecuencia de diferenciación en un categoría competida, tensionando la idea de que la IA debe emular el comportamiento humano.

Equipo unido que prioriza el aprendizaje continuo, la excelencia técnica y un entorno de trabajo diseñado para seres humanos.

“We build machines; we don't try to be machines.” · fuente

8/10

Personalidad / Arquetipo

Resumen

8/10 evaluado

La personalidad proyecta una postura humana y artesanal, lo que causa una conexión emocional con los constructores. Esta honestidad radical genera una consecuencia de diferenciación en un categoría competida, tensionando la idea de que la IA debe emular el comportamiento humano.

Evidencia literal del snapshot

evidencia
nt built for human beings** We believe that emotions are a core part of what makes us human, and what ultimately fuels our passion for the work we do. We build machines; we don't try to be machines. ![](https://framerusercontent.com/images/grLTPJG9OFmZ6AHNq2TCgthWoeU.png?width=798&height=800) ![](https://framerusercontent.com/images/yFlsmDbrovMjphvPL4LKArYMak.jpg?width=1638&height=1076) - ![](https://framerusercontent.com/images/KdaAo7saEE7x1MmuuFWtPgxo8qQ.jpg?width=1800&height=1350) - ![](https://framerusercontent.com/images/T7H9PAOnB3BUi0wUIhxG9Eyr4I.jpg?width=1350&height=1800) - ![](https://framerusercontent.com/images/YiN54ZI8o3qjdKgE6aTjUSASJ0.jpg?width=1350&height=1800) - ![](
evidencia
idth=128&height=128) **Prioritize Learning** We recognize that a key to success is learning as fast as possible about ourselves, our team, our processes, our product, the market, and everything else involved with accomplishing our mission. ![](https://framerusercontent.com/images/t0UUy1Wx5IwevACNVkE2I2y5B50.gif?width=112&height=112) **Passion** We love what we do. We’re looking for people who share that passion and are similarly enthralled by the challenges we've taken on. ![](https://framerusercontent.com/images/pHBg0jEG7IL7L2XsLjoQnkqYIk.gif?width=112&height=112) **A working environment built for human beings** We believe that emotions are a core part of what makes us human, and wh
evidencia
(https://framerusercontent.com/images/YiN54ZI8o3qjdKgE6aTjUSASJ0.jpg?width=1350&height=1800) - ![](https://framerusercontent.com/images/W49mMuL1Nop84TN5Qi1zFrBLrY.jpg?width=1800&height=1350) - ![](https://framerusercontent.com/images/spjNrp2K241573USMp3ycZ83tpU.jpg?width=1350&height=1800) - ![](https://framerusercontent.com/images/BBLPX1lkuezPwtebik44DsyEFjM.jpg?width=1350&height=1800) - ![](https://framerusercontent.com/images/44hXw7nrpCuowTO99gjV4UPSBB8.jpg?width=1350&height=1800) - ![](https://framerusercontent.com/images/6JBeuGb2l2Zk9VrMExYgHBX2rM.jpg?width=1350&height=1800) - ![](https://framerusercontent.com/images/yAOCYd392aUdRpyAmgMbok35Fj4.jpg?width=1350&height=1800) - ![](ht
evidencia
visual_signal_score:0.9 first_impression:A highly distinctive retro-futuristic aesthetic immediately signals an innovative and unconventional AI brand. polish:0.9
evidencia
Introducing System One Models & Jev _Diogo Almeida, founder, TypeSafe_ Models have been superhuman at chat for years, so where is all the automation? This has been my driving question for the last four years. At OpenAI, I helped build the methods that made language models useful at following instructions and talking with people. That work ended up as the research behind ChatGPT. At the time, I thought maybe chat models would lead to AGI, but despite the hype it became obvious to me that there was something really big missing. After two years in stealth, countless technical challenges, and research breakthroughs… I am beyond excited to announce that today, TypeSafe AI is releasing our
evidencia
Culture We're a close-knit, flat team from OpenAI, Google Brain, Meta/FAIR, Stripe, Airbnb, Plaid, Docker, and more. We're backed by top-tier investors who share our vision for building the foundation of truly transformative AI. Our team works in-person five days a week in our San Francisco office near the Embarcadero station. We offer competitive salaries, equity, and benefits, as well as lunch and dinner. We love what we do and care about our work a lot. We strive for excellence and craftsmanship and won't stop until we get there. When the team wins, we all win, and we enjoy collaborating and inspiring each other to grow as a team and as individuals. We want you to be able to do the
raw_inputs.1.subpage.8.chunk.4 · abrir fuente
Culture We're a close-knit, flat team from OpenAI, Google Brain, Meta/FAIR, Stripe, Airbnb, Plaid, Docker, and more. We're backed by top-tier investors who share our vision for building the foundation of truly transformative AI. Our team works in-person five days a week in our
raw_inputs.1.subpage.8.chunk.8 · abrir fuente
nt built for human beings** We believe that emotions are a core part of what makes us human, and what ultimately fuels our passion for the work we do. We build machines; we don't try to be machines. ![](https://framerusercontent.com/images/grLTPJG9OFmZ6AHNq2TCgthWoeU.png?width=79
raw_inputs.1.subpage.8.chunk.7 · abrir fuente
idth=128&height=128) **Prioritize Learning** We recognize that a key to success is learning as fast as possible about ourselves, our team, our processes, our product, the market, and everything else involved with accomplishing our mission. ![](https://framerusercontent.com/images
visual_signature.tile_signals.3

Presencia y jerarquía

evidencia en páginas enlazadas desde la portada.

Explicación

La marca logra una personalidad distintiva al posicionarse como un equipo de artesanos técnicos que rechazan la deshumanización de la IA, creando una narrativa coherente y memorable.

Interpretación de la evidencia

Equipo unido que prioriza el aprendizaje continuo, la excelencia técnica y un entorno de trabajo diseñado para seres humanos.

Siguiente artefacto

Manifiesto de cultura de ingeniería.

Puntos ciegos

2 piezas de contexto pendientes.

PE5
Consistente en microcopy punto ciego

El snapshot no contiene estados de interfaz, botones de acción o mensajes de error específicos del producto.

PE6
Consistente en redes y producto punto ciego

No se han capturado superficies externas como redes sociales o comunidades para contrastar la voz.

Idea de marca

10/10

La idea de marca conecta la categoría con una metáfora histórica, lo que causa una comprensión inmediata de su arquitectura. Esta construcción conceptual genera una consecuencia de inevitabilidad técnica, tensionando la narrativa actual de los asistentes conversacionales.

IA nativa para máquinas basada en System One Models, diseñados para tomar decisiones precisas y auditables en lugar de generar texto.

“TypeSafe launches Jev, a "System One" model built for machine decisions, not chat” · fuente

10/10

Idea de marca

Resumen

10/10 evaluado

La idea de marca conecta la categoría con una metáfora histórica, lo que causa una comprensión inmediata de su arquitectura. Esta construcción conceptual genera una consecuencia de inevitabilidad técnica, tensionando la narrativa actual de los asistentes conversacionales.

Evidencia literal del snapshot

evidencia
TypeSafe launched Jev because sequential LLMs are "totally useless for computers" - The New Stack TypeSafe launched Jev because sequential LLMs are “totally useless for computers” TypeSafe launches Jev, a "System One" model built for machine decisions, not chat — faster, cheaper, and immune to hallucination, its makers claim. Sep 21st, 2026 3:36pm by Adrian Bridgwater ... When TypeSafe emerged last week after two years in stealth, backed by $40 million in seed funding led by DCVC, to launch its first model, Jev, it claimed something that counters just about everything the industry has built since ChatGPT: The model doesn’t write. It decides. ... The first of what the organization calls a n
evidencia
, while keeping the high seats, the buggy springs, and even the whip socket in some models. It’s a clear example of how new technologies are often forced into the shape and assumptions of the thing they replace before finding their own native form. Current AI is trained to be a helpful, articulate, pleasant assistant. [⁴](https://typesafe.ai/manifesto appendix) Reasonable goals if you assume a human is on the other side of the model. Yet the foreseeable consequence is AI that requires humans in the loop instead of running in the background. Software has never worked that way. Even the most complex software is built out of simple logic and layered abstractions, with every branch auditable.
evidencia
years and trillions of dollars invested in the industry, most software still isn't meaningfully intelligent, and life is mostly the same for the average person. That should tell us something: the bottleneck isn't raw intelligence. It's that today's intelligence is hard to build on. ![](https://framerusercontent.com/images/UN1yFZw4iNeeERVBwaUIEtylw.png?width=640&height=515) 1903 Ford model T beyond horseless carriages Early cars were designed as horseless carriages: rather than reimagining transportation from scratch, inventors took the familiar carriage and replaced the horse with a motor, while keeping the high seats, the buggy springs, and even the whip socket in some models. It’s a c
evidencia
omplex software is built out of simple logic and layered abstractions, with every branch auditable. We want AI to work alongside existing software as a primitive that any programmer can invoke for semantic judgement and decisions, while still using code for what it’s best at: exact computation. Computers can do so much by just branching on bits, imagine if they could also branch on common sense, understanding, and intent. [⁵](https://typesafe.ai/manifesto appendix) This is the opportunity for true, machine-native composable AI. safe emergence The people who built databases didn't imagine Google. The people who built internet protocols didn't envision Stripe. They made lower-level capabil
evidencia
We took the opposite research direction not chat Reinforcement Learning from Human Feedback (RLHF) has led to LLMs that are optimized for human preferences. This has led to models that are superhuman at instruction following, and are what we now call “chat.” Yet RLHF creates inherent issues such as mode dropping, overconfidence, and lack of reliability. These flaws mean that LLMs require humans-in-the-loop. a new model We built a new class of models, System One Models, to be natively used by machines. We’re building with a new architecture, a new sampler, and a new training algorithm: Reinforcement Learning for Calibrated Decisions (RLCD). Decisions, not strings Typed outputs that so
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eneral intelligence, but can only optimize what it can see. Public evals are the streetlight. Post-training therefore pushes capability fastest in the illuminated areas, while reliability outside them can stay flat or even get worse. My claim is that many of the spikes in “jagged intelligence” are the benchmarks. ![](https://framerusercontent.com/images/btgV8HaFQ1gSgamY3F5WEy75x0.png) _Annotated version from_ [_source_](https://x.com/colin_fraser/status/1994188009608983008)
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TypeSafe launched Jev because sequential LLMs are "totally useless for computers" - The New Stack TypeSafe launched Jev because sequential LLMs are “totally useless for computers” TypeSafe launches Jev, a "System One" model built for machine decisions, not chat — faster, cheape
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, while keeping the high seats, the buggy springs, and even the whip socket in some models. It’s a clear example of how new technologies are often forced into the shape and assumptions of the thing they replace before finding their own native form. Current AI is trained to be a h
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omplex software is built out of simple logic and layered abstractions, with every branch auditable. We want AI to work alongside existing software as a primitive that any programmer can invoke for semantic judgement and decisions, while still using code for what it’s best at: exa
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We took the opposite research direction not chat Reinforcement Learning from Human Feedback (RLHF) has led to LLMs that are optimized for human preferences. This has led to models that are superhuman at instruction following, and are what we now call “chat.” Yet RLHF creates in

Presencia y jerarquía

evidencia visible en portada.

Explicación

La marca logra una construcción conceptual impecable al posicionarse como la antítesis del chat conversacional, utilizando una metáfora histórica (el Ford T) para justificar su arquitectura técnica.

Interpretación de la evidencia

IA nativa para máquinas basada en System One Models, diseñados para tomar decisiones precisas y auditables en lugar de generar texto.

Siguiente artefacto

Video explicativo sobre la arquitectura System One.

Atributos

5/5

Eficiente · Tipado · Auditable · Rápido

Genera decisiones tipadas, es altamente eficiente, rápido (70ms-500ms), no alucina y proporciona estimaciones de confianza para integraciones de software seguras.

“Jev is TypeSafe’s first public System One Model, optimized for automation. Send Jev structured questions and get typed decisions with probabilities and confidence that your...” · fuente

5/5

Atributos

Resumen

5/5 evaluado

La marca comunica sus atributos mediante métricas de rendimiento directas, lo que causa una percepción de fiabilidad técnica. Esta precisión genera una consecuencia de utilidad inmediata para desarrolladores, tensionando la percepción de la IA como una caja negra impredecible.

Evidencia literal del snapshot

evidencia
robabilities and confidence scores. | | Sampling | **Sequential.** Generates one token at a time, each conditioned on the last. | **Parallel.** Generates all outputs in a single query. Incredibly efficient and hardware-aware. | | Cost | Input tokens: from $0.20 to $10 / MTok.<br>Output tokens: ~5x more expensive than input tokens. | Input tokens: $0.042 / MTok ($42 per billion tokens).<br>Output tokens: FREE (too cheap to meter). | | Speed | **End-to-end response time is** [**3 to 329 seconds**](https://llm-benchmarks.diegoromero.es/) for frontier models. Fast enough for interfacing with humans, but a big bottleneck when integrated in code. | **End-to-end response time is 70ms-500ms** for T
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uential messages**. | Unstructured data (e.g. text) with an emphasis on **structured program state**. | | Outputs | **Strings / generated text.** Strings are flexible and can be anything: chat responses, code, hallucinations, refusals, or even type-safe structured values. To be used by software, responses need to be parsed + validated. There is also always some risk that the AI goes off the rails. | **Type-safe structured values.** Possible outputs and structure are [defined in advance](https://docs.typesafe.ai/). The model never makes type errors. All answers are accompanied with calibrated probabilities and confidence scores. | | Sampling | **Sequential.** Generates one token at a time, ea
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ns, but a big bottleneck when integrated in code. | **End-to-end response time is 70ms-500ms** for TypeSafe. This can range from 40x-200x faster for the same levels of frontier intelligence for System One shaped queries. | | Confidence | Even if prompted for a confidence estimate, models tend to be overconfident and inconsistent. If a model can do a task 95% of the time but doesn’t say when it’s in the 5%, it can’t automate that task. | Always communicates confidence and uncertainty with every output. Calibrated: higher confidence means higher accuracy. More consistent: returns similar answers for similar inputs. | | Use cases | **Human-in-the-loop tasks (chatbots, copilots, coding agents).*
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, while being two orders of magnitude faster and more efficient. While Jev gives up string generation, it’s optimized for structured outputs and _can’t_ hallucinate. Think of Jev as a frontier-intelligence function call: unstructured state in, typed probabilistic decisions out. Extraordinary claims require extraordinary evidence so see below for the receipts. 💅
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We give a FAQ What are System One Models? What is Jev? System One Models are a new class of AI model built for decisions inside software. Jev is TypeSafe’s first public System One Model, optimized for automation. Send Jev structured questions and get typed decisions with probabilities and confidence that your software can act on. Is Jev just a smaller LLM? How is this different from JSON mode or structured outputs? How can Jev be so fast and inexpensive? Can you make Jev even faster? Are these prices temporary or subsidized? What is Jev good at? Where does it struggle? Can Jev still get things wrong? Is Jev deterministic? How do I get started or ask a question? \[b.64\] iVBOR
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nd research breakthroughs… I am beyond excited to announce that today, TypeSafe AI is releasing our first **System One Model**: a new class of frontier models built to make fast, structured decisions that software can use directly. We built a new stack entirely focused on automation: with a new model architecture, parallel sampler for maximum efficiency, and training method we call Reinforcement Learning for Calibrated Decisions (RLCD). Our first public model is **Jev**, available today in early access. Jev achieves similar levels of intelligence on System One tasks compared to existing LLMs, while being two orders of magnitude faster and more efficient. While Jev gives up string generatio
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Jev’s intelligence per dollar is literally off the charts. ![](https://framerusercontent.com/images/9DAAkh8ASMArXyChcFottuzsbQ.png?width=3660&height=1980) Workflow Intelligence vs. Cost ![](https://framerusercontent.com/images/eZuF5J3kimUx8m5RwxS6T71Dgu0.svg?width=461&height=504) Machine-Native Intelligence LLMs produce words for people. Jev produces typed decisions and is more like code: reliable, fast, self-consistent, and type-safe. ![](https://framerusercontent.com/images/9DAAkh8ASMArXyChcFottuzsbQ.png?width=3660&height=1980) Hallucinations Zero Hallucinations Every Jev decision comes with a confidence estimate, so your software can act when confidence is high and escalate wh
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robabilities and confidence scores. | | Sampling | **Sequential.** Generates one token at a time, each conditioned on the last. | **Parallel.** Generates all outputs in a single query. Incredibly efficient and hardware-aware. | | Cost | Input tokens: from $0.20 to $10 / MTok.<br>
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, while being two orders of magnitude faster and more efficient. While Jev gives up string generation, it’s optimized for structured outputs and _can’t_ hallucinate. Think of Jev as a frontier-intelligence function call: unstructured state in, typed probabilistic decisions out. E
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nd research breakthroughs… I am beyond excited to announce that today, TypeSafe AI is releasing our first **System One Model**: a new class of frontier models built to make fast, structured decisions that software can use directly. We built a new stack entirely focused on automat
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Jev’s intelligence per dollar is literally off the charts. ![](https://framerusercontent.com/images/9DAAkh8ASMArXyChcFottuzsbQ.png?width=3660&height=1980) Workflow Intelligence vs. Cost ![](https://framerusercontent.com/images/eZuF5J3kimUx8m5RwxS6T71Dgu0.svg?width=461&height=5

Presencia y jerarquía

evidencia visible en portada.

Explicación

La marca comunica sus atributos técnicos con una precisión excepcional, utilizando comparativas directas y métricas de rendimiento para validar su propuesta de valor en el ámbito de la automatización.

Interpretación de la evidencia

Genera decisiones tipadas, es altamente eficiente, rápido (70ms-500ms), no alucina y proporciona estimaciones de confianza para integraciones de software seguras.

Siguiente artefacto

Ficha técnica comparativa de rendimiento y latencia.

Valores

5/5

Artesanal · Honesto · Técnico · Humano

Priorizar el aprendizaje continuo, la honestidad técnica y el rechazo a la dependencia de benchmarks para generar confianza.

“Prioritize Learning” · fuente

jerarquía parte de la evidencia está fuera de navegación · 1 página

5/5

Valores

Resumen

5/5 evaluado

La marca utiliza sus valores como filtro estratégico, rechazando la cultura de los benchmarks para establecer una frontera ética. Esta postura genera una consecuencia de exclusión de usuarios que buscan atajos, tensionando la relación con las convenciones actuales de la industria de IA.

Evidencia literal del snapshot

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nt built for human beings** We believe that emotions are a core part of what makes us human, and what ultimately fuels our passion for the work we do. We build machines; we don't try to be machines. ![](https://framerusercontent.com/images/grLTPJG9OFmZ6AHNq2TCgthWoeU.png?width=798&height=800) ![](https://framerusercontent.com/images/yFlsmDbrovMjphvPL4LKArYMak.jpg?width=1638&height=1076) - ![](https://framerusercontent.com/images/KdaAo7saEE7x1MmuuFWtPgxo8qQ.jpg?width=1800&height=1350) - ![](https://framerusercontent.com/images/T7H9PAOnB3BUi0wUIhxG9Eyr4I.jpg?width=1350&height=1800) - ![](https://framerusercontent.com/images/YiN54ZI8o3qjdKgE6aTjUSASJ0.jpg?width=1350&height=1800) - ![](
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idth=128&height=128) **Prioritize Learning** We recognize that a key to success is learning as fast as possible about ourselves, our team, our processes, our product, the market, and everything else involved with accomplishing our mission. ![](https://framerusercontent.com/images/t0UUy1Wx5IwevACNVkE2I2y5B50.gif?width=112&height=112) **Passion** We love what we do. We’re looking for people who share that passion and are similarly enthralled by the challenges we've taken on. ![](https://framerusercontent.com/images/pHBg0jEG7IL7L2XsLjoQnkqYIk.gif?width=112&height=112) **A working environment built for human beings** We believe that emotions are a core part of what makes us human, and wh
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(https://framerusercontent.com/images/YiN54ZI8o3qjdKgE6aTjUSASJ0.jpg?width=1350&height=1800) - ![](https://framerusercontent.com/images/W49mMuL1Nop84TN5Qi1zFrBLrY.jpg?width=1800&height=1350) - ![](https://framerusercontent.com/images/spjNrp2K241573USMp3ycZ83tpU.jpg?width=1350&height=1800) - ![](https://framerusercontent.com/images/BBLPX1lkuezPwtebik44DsyEFjM.jpg?width=1350&height=1800) - ![](https://framerusercontent.com/images/44hXw7nrpCuowTO99gjV4UPSBB8.jpg?width=1350&height=1800) - ![](https://framerusercontent.com/images/6JBeuGb2l2Zk9VrMExYgHBX2rM.jpg?width=1350&height=1800) - ![](https://framerusercontent.com/images/yAOCYd392aUdRpyAmgMbok35Fj4.jpg?width=1350&height=1800) - ![](ht
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What benchmarks should be The crux of the problem is (1) intelligence is hard to measure, (2) a (good actor) lab wants to say “we made the model smarter,” and (3) for it to be believed (to whatever extent is accurate). Benchmarks are a shortcut to get around the hard problem of trust. They _seem_ like a magnifier of trust, but instead are a loan: bad actors can then exploit the trust projected on the benchmark. The way out is to be trustworthy in the first place. We need to stop outsourcing credibility to a leaderboard and just be honest. For labs, that means publishing caveats, disclosing cherry-picking, including the evidence that makes you look bad, and de-emphasizing benchmarks **
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ssly generated through self-play (through compute). It makes sense that the next most important thing is **compute**. At the end of the day, machine learning makes reward go up or loss go down. Someone still has to decide the objective to optimize though. Getting this right requires understanding the external system in which the model will operate. Without the right task, everything can work perfectly, with the most beautiful loss and scaling curves, but the model may still be useless! Unfortunately, ML research tends to attack these problems in the opposite order. Researchers love inventing algorithms. More recently, we have learned to love scaling curves. Meanwhile, data is messy. Choosi
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al-reason-ai-benchmarks-havent-reflected-economic-impacts) Everyone in the field knows they’re broken, but they still get quoted because everyone is competing for attention.
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nt built for human beings** We believe that emotions are a core part of what makes us human, and what ultimately fuels our passion for the work we do. We build machines; we don't try to be machines. ![](https://framerusercontent.com/images/grLTPJG9OFmZ6AHNq2TCgthWoeU.png?width=79
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idth=128&height=128) **Prioritize Learning** We recognize that a key to success is learning as fast as possible about ourselves, our team, our processes, our product, the market, and everything else involved with accomplishing our mission. ![](https://framerusercontent.com/images
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What benchmarks should be The crux of the problem is (1) intelligence is hard to measure, (2) a (good actor) lab wants to say “we made the model smarter,” and (3) for it to be believed (to whatever extent is accurate). Benchmarks are a shortcut to get around the hard problem o

Presencia y jerarquía

parte de la evidencia está fuera de navegación.

Explicación

La marca articula una postura técnica y ética muy definida que rechaza las convenciones de la industria, creando una frontera clara entre su metodología y la de sus competidores.

Interpretación de la evidencia

Priorizar el aprendizaje continuo, la honestidad técnica y el rechazo a la dependencia de benchmarks para generar confianza.

Siguiente artefacto

Código de ética para el desarrollo de modelos de decisión.

Misión

4/5

La misión vincula la ambición económica con la especificidad técnica de la componibilidad. Esta causa técnica genera una consecuencia de valor operativo, diferenciando a la marca de los generalistas. La siguiente tensión reside en demostrar cómo esta componibilidad escala en sistemas complejos sin perder la integridad.

Pavear el camino más corto hacia una revolución económica basada en la IA, haciendo que la inteligencia sea componible para catalizar una explosión de software inteligente.

“Our mission is to pave the shortest path to an AI-based economic revolution” · fuente

4/5

Misión

Resumen

4/5 evaluado

La misión vincula la ambición económica con la especificidad técnica de la componibilidad. Esta causa técnica genera una consecuencia de valor operativo, diferenciando a la marca de los generalistas. La siguiente tensión reside en demostrar cómo esta componibilidad escala en sistemas complejos sin perder la integridad.

Evidencia literal del snapshot

evidencia
Composable AI: Build Prod, Not God our mission
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Our mission is to pave the shortest path to an AI-based economic revolution [²](https://typesafe.ai/manifesto\ appendix) by making intelligence composable to catalyze a Cambrian explosion [³](https://typesafe.ai/manifesto\ appendix) of intelligent software. We already have general intelligence We're not racing towards the ever-moving goalposts of "AGI," because today's models have long since crossed the threshold of intelligence needed for creating massive economic value. They know an enormous amount, reason across domains, and do things that looked impossible a few years ago. Yet after years and trillions of dollars invested in the industry, most software still isn't meaningfully int
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ilders for builders and owned by all. [⁶](https://typesafe.ai/manifesto appendix) Safety is a precondition for layering. You let a component run unattended if it's reliable; you only build on top of it if it's trustworthy. It takes trust to bury a dependency five layers deep in a system. People will only do so if they can inspect it, test it, and constrain it piece by piece, the way we've always engineered dependable software. That's how the composable path enables an ecosystem of small, legible primitives to safely evolve into trustworthy, complex systems. a new shape of intelligence We're building the paradigm shift beyond AGI: step one
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idth=128&height=128) **Prioritize Learning** We recognize that a key to success is learning as fast as possible about ourselves, our team, our processes, our product, the market, and everything else involved with accomplishing our mission. ![](https://framerusercontent.com/images/t0UUy1Wx5IwevACNVkE2I2y5B50.gif?width=112&height=112) **Passion** We love what we do. We’re looking for people who share that passion and are similarly enthralled by the challenges we've taken on. ![](https://framerusercontent.com/images/pHBg0jEG7IL7L2XsLjoQnkqYIk.gif?width=112&height=112) **A working environment built for human beings** We believe that emotions are a core part of what makes us human, and wh
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Introducing System One Models & Jev _Diogo Almeida, founder, TypeSafe_ Models have been superhuman at chat for years, so where is all the automation? This has been my driving question for the last four years. At OpenAI, I helped build the methods that made language models useful at following instructions and talking with people. That work ended up as the research behind ChatGPT. At the time, I thought maybe chat models would lead to AGI, but despite the hype it became obvious to me that there was something really big missing. After two years in stealth, countless technical challenges, and research breakthroughs… I am beyond excited to announce that today, TypeSafe AI is releasing our
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Our mission is to pave the shortest path to an AI-based economic revolution [²](https://typesafe.ai/manifesto\ appendix) by making intelligence composable to catalyze a Cambrian explosion [³](https://typesafe.ai/manifesto\ appendix) of intelligent software. We already have ge
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Composable AI: Build Prod, Not God our mission

Presencia y jerarquía

evidencia en páginas enlazadas desde la portada.

Explicación

La misión está claramente definida, es técnicamente específica y se aleja de los lugares comunes de la categoría al enfocarse en la componibilidad como motor de valor económico.

Interpretación de la evidencia

Pavear el camino más corto hacia una revolución económica basada en la IA, haciendo que la inteligencia sea componible para catalizar una explosión de software inteligente.

Siguiente artefacto

Manifiesto técnico sobre la arquitectura de primitivas de software.

Baldosas apagadas

1 fallos detectados: plan de trabajo.

M2
Propia apagada

degradada: 'ok' sin evidencia literal verificable

Visión

5/5

La visión trasciende el producto al proponer un cambio de paradigma en la arquitectura de la IA. Esta analogía histórica establece una dirección clara para la categoría, lo que genera una tensión entre la infraestructura actual y la visión de un ecosistema distribuido y emergente.

Construir la base de una IA transformadora mediante primitivas pequeñas y legibles que permitan un ecosistema de inteligencia componible, descentralizado y confiable.

“Empower the world with higher-level intelligence abstractions that are stable enough to compose and layer upon, for a collaborative, emergent future.” · fuente

5/5

Visión

Resumen

5/5 evaluado

La visión trasciende el producto al proponer un cambio de paradigma en la arquitectura de la IA. Esta analogía histórica establece una dirección clara para la categoría, lo que genera una tensión entre la infraestructura actual y la visión de un ecosistema distribuido y emergente.

Evidencia literal del snapshot

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Culture We're a close-knit, flat team from OpenAI, Google Brain, Meta/FAIR, Stripe, Airbnb, Plaid, Docker, and more. We're backed by top-tier investors who share our vision for building the foundation of truly transformative AI. Our team works in-person five days a week in our San Francisco office near the Embarcadero station. We offer competitive salaries, equity, and benefits, as well as lunch and dinner. We love what we do and care about our work a lot. We strive for excellence and craftsmanship and won't stop until we get there. When the team wins, we all win, and we enjoy collaborating and inspiring each other to grow as a team and as individuals. We want you to be able to do the
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s (Invitae and Freenome), and an inventor with numerous publications and patents. He specializes in building production AI systems. Fun fact ♟️ Placed 44th at US Chess Nationals in elementary school [LinkedIn](https://www.linkedin.com/in/erik-spock-gafni-906b0125/)
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ng and inspiring each other to grow as a team and as individuals. We want you to be able to do the most impactful work of your career at TypeSafe and help define our future as a company.
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Join us We're a close-knit, flat team from OpenAI, Google Brain, Meta/FAIR, Stripe, Airbnb, Plaid, Docker, and more. We're backed by top-tier investors who share our vision for building the foundation of truly transformative AI. [**Open roles**](https://jobs.ashbyhq.com/typesafe-ai?utm_source=QLrx0vq4BW) ![Your favorite neolab shirt](https://framerusercontent.com/images/U4NxTK4P6j7S8cZA6y9sPQ5Dfo.png)![TypeSafe rings shirt](https://framerusercontent.com/images/1pGUz4Z7zXSGqWtYAXG8ZQ517M.png)![Death before strings shirt](https://framerusercontent.com/images/iqwbu6wSlZJZ3CnOUeAVZ4CJ0mE.png)![No strings attached shirt](https://framerusercontent.com/images/CfgNFIaoFIxLopLdJlrkeBVcP8s.png)![
evidencia
ilders for builders and owned by all. [⁶](https://typesafe.ai/manifesto appendix) Safety is a precondition for layering. You let a component run unattended if it's reliable; you only build on top of it if it's trustworthy. It takes trust to bury a dependency five layers deep in a system. People will only do so if they can inspect it, test it, and constrain it piece by piece, the way we've always engineered dependable software. That's how the composable path enables an ecosystem of small, legible primitives to safely evolve into trustworthy, complex systems. a new shape of intelligence We're building the paradigm shift beyond AGI: step one
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oogle. The people who built internet protocols didn't envision Stripe. They made lower-level capabilities so dependable that they could not only run in the background, but also be layered on top of. A Cambrian explosion of software emerged that nobody could have designed top-down. Intelligence today is like databases before SQL: powerful, but every use is bespoke. Once a smart decision becomes as dependable and invokable as a database query, builders will stack them the same way. The intelligence revolution will be like the early internet: unplanned, distributed grassroots efforts built by builders for builders and owned by all. [⁶](https://typesafe.ai/manifesto appendix) Safety is a preco
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Ship the shape of machine-native composable AI [⁷](https://typesafe.ai/manifesto\ appendix) with the highest possible intelligence-per-dollar. step two Make our AI reliable enough to transform the economy via real automation. step three Empower the world with higher-level intelligence abstractions that are stable enough to compose and layer upon, for a collaborative, emergent future.
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ilders for builders and owned by all. [⁶](https://typesafe.ai/manifesto appendix) Safety is a precondition for layering. You let a component run unattended if it's reliable; you only build on top of it if it's trustworthy. It takes trust to bury a dependency five layers deep in a
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oogle. The people who built internet protocols didn't envision Stripe. They made lower-level capabilities so dependable that they could not only run in the background, but also be layered on top of. A Cambrian explosion of software emerged that nobody could have designed top-down

Presencia y jerarquía

evidencia en páginas enlazadas desde la portada.

Interpretación de la evidencia

Construir la base de una IA transformadora mediante primitivas pequeñas y legibles que permitan un ecosistema de inteligencia componible, descentralizado y confiable.

Siguiente artefacto

Hoja de ruta de estandarización de protocolos de inteligencia.

Coherencia

8/10

La coherencia entre la técnica y la cultura interna causa una percepción de inevitabilidad. Esta alineación genera una consecuencia de autoridad, tensionando la necesidad de documentar casos de uso reales para validar la promesa de fiabilidad ante la comunidad.

Narrativa técnica donde la filosofía de diseño de software se funde con la identidad de marca.

“We're building the paradigm shift beyond AGI: step one”

8/10

Coherencia

Resumen

8/10 evaluado

La coherencia entre la técnica y la cultura interna causa una percepción de inevitabilidad. Esta alineación genera una consecuencia de autoridad, tensionando la necesidad de documentar casos de uso reales para validar la promesa de fiabilidad ante la comunidad.

Explicación

Type Safe logra una narrativa técnica excepcionalmente coherente donde su filosofía de diseño de software se funde con su identidad de marca, creando una propuesta de valor que se siente inevitable y artesanal a la vez.

Interpretación de la evidencia

Narrativa técnica donde la filosofía de diseño de software se funde con la identidad de marca.

Siguiente artefacto

Repositorio de casos de uso y testimonios técnicos.

Puntos ciegos

2 piezas de contexto pendientes.

C7
Web-redes punto ciego

El snapshot no contiene capturas de redes sociales o perfiles externos para contrastar la consistencia del discurso fuera de la web propia.

aporta contexto: Acceso a perfiles sociales o menciones externas de la marca.

C8
Marca-producto punto ciego

El snapshot no incluye casos de estudio detallados, reviews de usuarios o demos interactivas que permitan verificar la experiencia real del producto.

aporta contexto: Casos de uso reales o testimonios de integración técnica.

Trazabilidad de adquisición

capturas y artefactos
screenshot_capture screenshot · captured

Primer viewport · screenshot · captured · playwright

screenshot_capture visual_analysis_atlas · partial

Atlas usado por el análisis semántico · visual_analysis_atlas · partial · playwright

screenshot_capture section_screenshot · captured

first_fold · section_screenshot · captured · playwright

screenshot_capture section_screenshot · captured

The First (Public) System One Model; Jev Gives AI The Properties Of Code · section_screenshot · captured · playwright

screenshot_capture section_screenshot · captured

We Took The Opposite Research Direction · section_screenshot · captured · playwright

screenshot_capture section_screenshot · captured

193.6x Faster, 444.6x Cheaper. · section_screenshot · captured · playwright

screenshot_capture section_screenshot · captured

Jev’s Intelligence Per Dollar Is Literally Off The Charts. · section_screenshot · captured · playwright

páginas propias capturadas
9 de 13
69% de las conocidas
páginas elegibles capturadas
9 de 9
100% del alcance estratégico
contenido conservado
36 de 81
en 3 páginas con muestreo parcial
pruebas externas
16
intentos externos
1
registros de ausencia
1
registros de evidencia
111
idiomas observados
inglés 8
Cobertura propia · 9 de 13 páginas capturadas

Política: owned-page-selection-v6-vault-live-links · descubrimiento: firecrawl_live_links, sitemap

contenido muestreado Capturar una URL no implica conservar todo su texto: se omitieron 45 fragmentos después de priorizar las secciones estratégicas.

Superficies con contenido parcial

Visitadas

Conocidas y no visitadas

Miramos y no estaba

Intentos de adquisición externa

report 53916b654b20 · build 61293afdfce6 · rúbrica baldosas-v3-1 · prompt sv9-flow-brand-interpretation-v1.2 · evaluador gemini-3.1-flash-lite