Laya the open source version of Jev
The guiding brain in my system was always reinforcement learning, not just an embedding model or an autoregressive LLM.And then in September 2026, a well-funded frontier lab called TypeSafe AI (founded by Diogo Almeida, a co-inventor of ChatGPT at OpenAI) launched Jev. They proposed the exact same non-autoregressive decision concept as if it was a brand-new scientific breakthrough. Except they launched without technical papers, without open weights, and with zero open training datasets.My earlier model used PPO over sequence representations to output turn-by-turn conversion trajectories (probabilities from 0.0 to 1.0) in vertical sales conversations.
- ▪The guiding brain in my system was always reinforcement learning, not just an embedding model or an autoregressive LLM.And then in September 2026, a well-funded frontier lab called TypeSafe AI (founded by Diogo Almeida, a co-inventor of Cha
- ▪They proposed the exact same non-autoregressive decision concept as if it was a brand-new scientific breakthrough.
- ▪Except they launched without technical papers, without open weights, and with zero open training datasets.My earlier model used PPO over sequence representations to output turn-by-turn conversion trajectories (probabilities from 0.0 to 1.0)
3 outlets in our directory ran this story, first to last over 3 days. All of the coverage we found sits in one bucket: centre. That one-sidedness is itself worth noticing.
- ▪ Show HN: Open-Source Alternative to TypeSafe.ai — GitHub
- ▪ Typesafe.ai Jev Open Source Alternative Qwen-2.5-1B-RLCD — Huggingface
Hacker News (Front Page) files mainly under programming. We currently carry 1,852 of its stories. Top-voted stories on Hacker News.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Convaiinnovations |
| Canonical URL | https://laya.convaiinnovations.com/ |
| Publication time | Sat, 19 Sep 2026 10:46:58 +0000 |
| Retrieval time | 2026-09-19T12:08:45.710Z |
| Last seen | 2026-09-19T12:08:45.710Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | XMNse7HB0P7C · 3 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Everyone in AI right now is talking about a new kind of model: an architecture that is not autoregressive, does not generate text, and gives lightning-fast probability predictions over structured schemas.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 discussion).Then in September 2025, I published a second paper (arXiv:2510.01237), formalizing the framework for schema-based decisions…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Convaiinnovations.
Discussion
0 commentsMore from Convaiinnovations
