Jev: System One models for Prod, not God — with Diogo Almeida, CEO, TypeSafe AI

Tickets for AIE NYC now open, and apply for the invite-only AIE CODE . Join us ! We have an unusual relationship with today’s guest: for years since coauthoring the InstructGPT paper , Diogo Almeida had been saying that API-available frontier models have been going down the wrong path, everything from the alignment to refusals to reliability perspectives, that we have dropped every mode other than autoregressive chat-tuned LLMs because of the overwhelming success of ChatGPT. In a launch video now viewed ~40M times (by comparison, GPT4o was 22M , Fable 5 was 15M , Navier Stokes was 74M , and 6 Astra was 137M ), Diogo introduced Jev and it immediately took over the AI timeline — we’ll skip full Jev explainers because your favorite AI influencer/educator has probably already done one. We also collected: * the official patterns and cookbooks you should see first, from Allie * Jev usecases * speed based - games and computer use * the voice + computer use example we discuss at 1h34 mins * voice + browser control * The must not miss Doom demo * Driving cars in games * Excalidraw * virtual try-ons * “Smart Games”/smart NPCs * guided responses in text messages * Jev for coding agents has an official guide * jev for linting * compacting tool calls * reasonable pushback from Theo - Diogo has published a note on the Tyranny of the KV Cache that you should read as a followup after the pod for Jev + coding agents, because of his belief that Cache Rules Everything * Programming Languages built atop Jev (Diogo’s fave) * Jev for analytics replay and user journey review * “dark data” * entity resolution * natural language search * “ smart software ” * a core goal of Jev is to “disappear into the background” - eg as unremarkable as regex * Jev as a judge * Jev memes * Jev vs LLM capabiltiies * blending transformers and classifiers * about the confidence api * Jev vs GLiNER (note difference/pushback , agreed , agreed , agreed ) * Jev on trolley problem * Jev Bush Instead we’ll focus on what we can uniquely offer — a broader philosophical and mission-based understanding of how and why Jev was created , and what you should expect next in terms of future models from TypeSafe ( ReasoningJev ?) and what usecases and ideas you should work on vs the 55th low effort clone of Jev’s API or doing a generic JevBench benchmark - something Diogo has rejected publicly . Why RLCD: Three kinds of RLHF, and why they are ALL the wrong north star Diogo knows a good deal about RLHF, given that he was on the team that pioneered post-training at OpenAI — and traces the three branches to Christiano et al 2017 (the robot backflip demo), Stiennon et al 2020 (learning to summarize) and his baby, Ouyang et al 2022 (InstructGPT). From there on, every innovation from Function Calling to Structured Outputs to Reasoning felt like a hack on top of the string based, sequence to sequence prediction paradigm. As he mentions on the pod, from 2023-2024 he struggled unsuccessfully, due to both personal and organization underestimation, to train a model that accurately addressed what he saw as the core problem with making LLMs the heart of software: reliability . Jev’s core innovation is " Reinforcement Learning for Calibrated Decisions ”, a novel, unpublished technique that optimizes for “answers with epistemically honest probabilities on System One tasks” rather than human rated feedback (RLHF) — which causes hallucinations, sycophancy, and permanent reliance on humans — or programmatically verifiable outputs with rubrics (RLVR) — which solves Navier Stokes but exacerbates jagged intelligence and doesn’t integrate well with other software. We’ve talked about the calibration problem before on the pod, but probably the single best place to understand why RLCD became necessary is Diogo’s AIE talk , which discusses why a generation of training helpful AI assistants for humans has impaired them for training models for composable, programmable AI for automation . At the end he also teases