Lobstersdev.to · 19 September 2026 · not counted as a party

From our March 2025 arXiv paper on RL conversion trajectories to building a sub-40ms open-weight System 1 decision engine with RLCD. Everyone in AI right now is talking about a new kind of model: an architecture that is not auto-regressive, does not generate text, and gives lightning fast probability predictions over a structured JSON schema. Seeing the hype online feels both validating and deeply frustrating. I worked on this literally one year back in March 2025. I spent months of hard work, sweat, and sleepless nights building it, published an arXiv paper (arXiv:2503.23303), released the model weights on Hugging Face (sales-conversion-model-reinf-learning), published the open dataset (saas-sales-conversations), built a PyPi package, and posted the whole approach on Reddit (r/LocalLLaMA post). Then in September 2025, I published a second paper (arXiv:2510.01237), laying out the exact framework for schema-based decisions guided by reinforcement learning. For anyone curious, the guiding brain in my system was always reinforcement learning, not just an embedding model or an a

Vercel changelogvercel.com · 18 September 2026 · 2 posts

Within 24 hours of launching on AI Gateway, Jev from TypeSafe AI reached more than twice as many paid teams as any previous model launch, making it the fastest-adopted model in gateway history.Jev passed every other comparison model in its first twelve hours and continued to widen its lead for the rest of the day. By hour 24, nearly 13% of paid teams were using it. That's 2x the GPT-5.6 family and more than 6x Fable 5.1's share.Jev’s launch shows how quickly a specialized model can find a place in production. Its first-day adoption was unmatched among recent launches; the next test is whether that early adoption lasts. Copy link to headingAbout JevJev was introduced on September 15 as a probabilistic decision model designed to support structured decision-making within software. An application sends it context and a set of questions. Jev evaluates those questions in parallel and returns typed choices, scores, or true-or-false answers, along with probabilities.Unlike the text produced by a general-purpose language model, Jev’s answers come in a format the code can use directly. Developers can use it to:choose an agent’s next tool

Arize blogarize.com · 18 September 2026

This week the AI community was in uproar about Jev from TypeSafe, not just a new model but a new kind of model: one that classifies, scores, and routes but cannot write a sentence. The reason for the fuss is simple: it’s radically faster and cheaper than using an LLM to perform the same task, up to 200x faster and 400x cheaper if TypeSafe’s numbers are to be trusted. In one small independent test, a general-purpose model spent about 910 output tokens reasoning its way to each yes-or-no answer while Jev spent 85, and it doesn’t even bill for them. That’s potentially a really big deal. An enormous share of LLM-powered components in AI applications today are being asked to make decisions: pass or fail, route A or route B, which of 5 labels to pick. In particular, that’s something that LLM-as-a-judge evaluations are doing all the time, so it really made our ears perk up at Arize AI. This post is about how we got here, what this new kind of model buys you, what you lose, and what choices you should be making about your application’s architecture as a result. What is TypeSafe’s Jev, and how does it work? Jev is TypeSafe AI’s first S

TechCrunch startupstechcrunch.com · 18 September 2026

ChatGPT broke Diogo Almeida’s heart. Almeida was an OpenAI researcher who helped build the chatbot and then invent reinforcement learning from human feedback (RLHF), the model-training technique perhaps most responsible for our current age of AI. But despite its capabilities, he was disappointed. “We have lightning in a bottle, and yet it is not useful,” Almeida told TechCrunch. “I’ve been battling that problem since then. It took me a while to come to the conclusion: The problem is we are optimizing for human language … We have been super good at human language for four years, but it’s not useful for automation because computers speak a different language.” Two years ago, Almeida left OpenAI to start TypeSafe AI, a startup trying to fix that problem. This week, the company released a new transformer-based model, Jev, that is not a large language model (LLM). It doesn’t output text, but instead produces probabilities, or what the company calls “calibrated decisions.” Eschewing language does a few things: It makes the model incredibly cheap and fast, and because users define the outputs in advance, it

Hugging Face bloghuggingface.co · 3 September 2026

This guide is a fully public, inexpensive recipe for making a small model substantially better at structured-output compliance. We fine-tune LFM2.5-350M with Group Relative Policy Optimization (GRPO) using the TRL library and evaluate it on the IFStruct benchmark. The full run takes around 500 samples and 100 training steps, small enough for a free-tier Colab or Kaggle GPU, and is available on GitHub. The results show that even a light fine-tuning procedure improves performance from 22.6% to 29.7% on the IFStruct benchmark. Structured output is one of the most common real-world tasks for LLMs, yet most benchmarks fold it into broader reasoning or extraction scores rather than measuring it on its own. Whether a model reliably returns valid, parseable output in the requested format and shape — schema compliance — is often what decides whether it can be wired into a downstream system at all. Note that the training pipeline described here is not the one used to train the RL model described in the IFStruct blog. This notebook doesn't aim to recreate the IFStruct benchmark score, but to show how task-specific fine-t