[AINews] Here are 6 Clones of Jev in 2 days

Six independent reimplementations of Jev, a non-generative "System 1" decision model launched on Wednesday, appeared within two days of its release, according to AINews' 9/17/2026-9/18/2026 roundup. The clones include Laya (421M params, ModernBERT-large encoder), DiffusionGemmaJev, Bespoke Nimble (LoRA finetune of Qwen3.5-9B), SemIf (4B and 35B causal Qwen3.5 backbones), Jevlike (40K byte embedding option-attention model), and Kev-0.5B (LoRA adapter on Qwen2.5-0.5B). Jev's launch video drew 36M views in two days, and its training data is acknowledged to be 100% synthetic.

We covered Jev’s launch on Wednesday https://www.latent.space/p/ainews-jev-a-system-one-model-that , and they have completely taken over the timeline, with 36M views of their launch video by comparison, OpenAI’s Navier Stokes https://www.latent.space/p/ainews-openai-reports-navier-stokes result got 74M https://x.com/OpenAI/status/2097374640582668336?s=20 views, and Anthropic’s Fable 5 https://www.latent.space/p/ainews-anthropic-claude-fable-5-mythos?utm source=publication-search got 57M https://x.com/claudeai/status/2064394146916229443?s=20 views in just two days. It wasn’t open source 1 footnote-1 , so it invited tons of speculation and great demos and examples https://archerhume.com/posts/jevs-architecture-unmasked/?v=3 and salty schmidhubers https://x.com/george onx/status/2100293114808119379?s=12 and bad takes https://x.com/theo/status/2100762304862384257 , which of course only fed the hype. Here’s a list. The best guesses are ModernBert and Diffusion: - Laya https://github.com/NandhaKishorM/laya : 421M params, ModernBERT https://ai.engineer/talks/YZHPEkfy2kc-1-ai-guardrails-unreasonable-effectiveness -large encoder with two added transformer layers that score user-supplied options, PPO over sequence embeddings to output turn-by-turn conversion trajectories probabilities from 0.0 to 1.0 . - salty that he did not get recognition; claims RLCD without justification - confidence is entropy-based, not calibrated - DiffusionGemmaJev: https://github.com/vllm-project/vllm/pull/57250 tackling this from a Diffusion model basis. Pretty close on benchmarks https://x.com/mmastrac/status/2100626193943052784 - Bespoke Nimble https://x.com/madiator/status/2100990591215783946?s=20 : LoRA finetune of Qwen3.5-9B, using contrastive data curation. close but sllightly lower on benchmarks https://x.com/madiator/status/2101128095831093579/photo/1 - SemIf fka OpenJev https://openjev.com/ HF https://huggingface.co/AlexWortega/openjev : 4B and 35B causal Qwen3.5 backbone with a tiny three-class NLI classifier on the last token. comparison vs Laya https://www.reddit.com/r/LocalLLaMA/comments/1wjieap/comment/pajt99a/ - Jevlike https://github.com/vinnylarouge/jevlike : 40K byte embedding lightweight option-attention model. Each candidate becomes a query that reads from a shared context representation, then receives a score. - Kev-0.5B https://x.com/jaredpalmer/status/2101028325472841920 : LoRA adapter + a small readout head on top of Qwen2.5-0.5B. Of course, not enough people are talking about the data side, which is acknowledged to be 100% synthetic https://x.com/completeskeptic/status/2100617775823966680?s=12 . AI News for 9/17/2026-9/18/2026. We checked 12 subreddits, 544 Twitters https://twitter.com/i/lists/1585430245762441216 and no further Discords. AINews’ website https://news.smol.ai/ lets you search all past issues. As a reminder, AINews is now a section of Latent Space https://www.latent.space/p/2026 . You can opt in/out https://support.substack.com/hc/en-us/articles/8914938285204-How-do-I-subscribe-to-or-unsubscribe-from-a-section-on-Substack of email frequencies AI Twitter Recap Decision Models, Routing, and the “Jev” Wave - Discriminative models broke out as a new systems primitive : The biggest technical conversation was around Jev , a non-generative decision model being positioned as a fast “ System 1 ” complement to LLMs. @ankrgyl https://x.com/ankrgyl/status/2100978416434786420 said it is now available as an eval model in Braintrust with ~400x lower scoring cost versus prior setups, while @gabepereyra https://x.com/gabepereyra/status/2100990093691691382 highlighted calibrated-probability use cases like routing, citation selection, escalation, and legal ops decisions. The more architectural take came from @hxiao https://x.com/hxiao/status/2101001002816327867 , who argued Jev could pull tool calling, routing, and MCP-style decisions back from small generative LMs toward discriminative models; @signulll https://x.com/signulll/status/2101062047350096040 pushed the same idea further, framing this class as a near-zero-marginal-cost, on-device judgment layer for notifications, UI adaptation, and sensor-driven decisions. - Open reproductions and ecosystem clones appeared immediately : @madiator https://x.com/madiator/status/2100990591215783946 released Bespoke Nimble , an “open Jev” recipe built from a LoRA fine-tune of Qwen3.5-9B using synthetic contrastive data curation and constrained decoding. On its curated eval, the base Qwen improved from 66% to 90% , versus 93% for Jev , with a reported 100ms on H100 and local usability. At the smaller end, @jaredpalmer https://x.com/jaredpalmer/status/2101028325472841920 released Kev-0.5B , a tiny Jev-like model based on Qwen2.5-0.5B that can run on a MacBook Pro. The reaction split roughly along prior experience: @MParakhin https://x.com/MParakhin/status/2101036299721347073 noted post-ChatGPT users treated it like a revelation, while pre-GPT ML people were more puzzled by the hype. The substantive question raised by @abacaj https://x.com/abacaj/status/2101048462661845099 is the right one: a lot of demos emphasized speed more than quality , and there is still no standard benchmark for this category. - The first compelling integrations were in browser/computer-use workflows : @levie https://x.com/levie/status/2101007708044574906 demoed Jev classifying Box incident reports into escalation paths; @ndrezn https://x.com/ndrezn/status/2101046780989215005 showed browser use with LangChain + Jev and found it strong on tasks like the Wikipedia game and structured “folding laundry” workflows; @cline https://x.com/cline/status/2101056078872256935 shipped a plugin giving Jev a browser in Cline. @hwchase17 https://x.com/hwchase17/status/2101054310037790814 explicitly called browser use the best Jev application he had seen so far. Net: this looks less like a chatbot story than a workflow control-plane story. Agent Tooling, Coding Harnesses, and Claude Code Standards - AGENTS.md gained real momentum as a cross-tool convention : The highest-signal product update here was @trq212 https://x.com/trq212/status/2101009392611278961 announcing that Claude Code v2.1.277 now checks for AGENTS.md when no CLAUDE.md is present, with config-level toggle support. That effectively acknowledges AGENTS.md as an emerging standard rather than a one-tool convention, and @simonw https://x.com/simonw/status/2101025043098812807 immediately noted the practical payoff: fewer shim files that just point one format to the other. - Harness design is becoming a first-class variable in coding-agent performance and cost : @pidotdev https://x.com/pidotdev/status/2100935860413673605 highlighted the Harness Tax analysis showing that a simple tool set— read, write, edit, bash —can reach the Pareto frontier on benchmark performance while reducing unnecessary spending. Relatedly, @ akhaliq https://x.com/ akhaliq/status/2101020560964866103 pointed to the paper An Empirical Study of Harness Design for Coding Agents , underscoring that benchmark outcomes are increasingly shaped by harness structure , context setup, turn budgets, and tool affordances rather than just the base model. This is consistent with @dexhorthy https://x.com/dexhorthy/status/2100900279021363244 ’s “software factory” argument that teams still need to read the code and deliberately design the human/agent interface. - Model choice in software systems is bifurcating : Several practitioners described a split between “frontier for planning, cheap for execution.” @TheAhmadOsman https://x.com/TheAhmadOsman/status/2101061704444682682 summarized one stack as GPT 5.6 Sol XHigh for planning, GLM 5.3 Flash for implementation, and DeepSeek V4.1 Flash for other tasks. @kylebrussell https://x.com/kylebrussell/status/2101028521044812109 reported an internal knowledge-base pipeline moving from Opus → Sonnet → GLM 5.2 → GLM 5.3 Flash , cutting spend by roughly two orders of magnitude since spring. Meanwhile @theo https://x.com/theo/status/2101062549722841452 argued that in real-world coding the payoff from stronger models like Fable and Astra is not just code quality, but a subtler productivity gain in execution and iteration. Benchmarks, Recursive Self-Improvement, and Math Capability - RSI discussion got more precise about what is actually “recursive” : @TheTuringPost https://x.com/TheTuringPost/status/2100769692877303863 offered a useful taxonomy: AI improving code or training methods is not, by itself, fully recursive if the surrounding improvement loop remains fixed. The key threshold is when AI can modify not just model internals, but search strategy, experience generation, research tooling, and the improvement process itself . That framing links well with @HuaxiuYaoML https://x.com/HuaxiuYaoML/status/2100959310624825688 ’s RSI-Exam update, where GPT-6-astra remains 1 at 0.5126 , with Fable 5.1 entering at 2 with 0.4813 , and no model yet reaching the frontier-calibrated reference. - Math benchmarks continued to fall to frontier models, but interpretation remains nuanced : @EpochAIResearch https://x.com/EpochAIResearch/status/2100986494873989227 reported that another FrontierMath open problem was solved in an interactive session with GPT-6 Astra . Separately, @SAIRfoundation https://x.com/SAIRfoundation/status/2100976455123620089 launched Open Math Model , pitching open models and tools for mathematics shaped by the research community. Against the “verifiability explains math strength” narrative, @steve47285 https://x.com/steve47285/status/2100998663254225391 shared an argument that pretraining data , not merely verifiable reward structure, is the main reason LLMs are so good at math and coding. The meta-point from @sarahcat21 https://x.com/sarahcat21/status/2101023982258712725 is worth keeping: we need not just better benchmarks, but better benchmark maintenance and audit tooling . - Computer-use benchmarks are still far from saturation : @ValsAI https://x.com/ValsAI/status/2101014465781318072 launched CUA-Bench , testing real-time keyboard/mouse use across 6 games with 3 kept private as a proxy for difficult human-easy tasks. Follow-up numbers from @ValsAI https://x.com/ValsAI/status/2101014471586243036 suggest this remains genuinely hard: all frontier models score below 20% . In parallel, @trycua https://x.com/trycua/status/2101014004927729737 open-sourced CUA-S1-FORMS , the first in a family of small “System One” computer-use models. The direction is notable: real-time action loops, video-grounded adaptation, and continuous learning, not just text-only planning. Infra, Training Systems, and Model Architecture - Long-context and large-scale training infrastructure remain active optimization fronts : @Azaliamirh https://x.com/Azaliamirh/status/2101020422926135665 released Turbo-dLLM , an open-source library for training diffusion LLMs at scale, reporting 2.48x speedup at 512K context and 7.59x at 1M context on 8x H100s via Context-Sharded Block Parallelism . That aligns with practitioner attention on million-token regimes: @andrew n carr https://x.com/andrew n carr/status/2101024604080791894 flagged a sharp quality increase in DeepSeek V4.1 Flash after context extension to 1M tokens , arguing that agents are context hungry . - Architecture taxonomy debates are still alive : @ahatamiz1 https://x.com/ahatamiz1/status/2101005845685493794 argued that the field is overusing SSM as a label for any linear model. His proposal is to use linear RNNs as the umbrella term, with SSMs as one sub-family, distinguishing systems like Mamba2 from the GDN family on the basis that GDN behaves more like a gradient step on a local regression loss than a discretized ODE. For engineers tracking sequence-model alternatives to transformers, this is a useful nomenclature cleanup rather than mere pedantry. - Edge/local neural program execution also got a notable update : @yuntiandeng https://x.com/yuntiandeng/status/2100975083376275795 described ProgramAsWeights , where developers specify an AI function in English, compile it once, and then run a small neural program locally on CPU with Wi‑Fi off . The code and models are public. This sits interestingly adjacent to the Jev conversation: both point toward smaller, specialized, locally runnable inference artifacts rather than ever-larger universal chat models. Robotics, Vision, Audio, and Generative Media - Open robotics data releases were unusually substantive : @adamrasb https://x.com/adamrasb/status/2100991778606440795 announced the full ABC release, including code, 400+ hours of sim data on 24 tasks , and 5,850 labeled policy-evaluation episodes . In a more detailed companion post, @redstone hong https://x.com/redstone hong/status/2100995941742629342 described ABC-130K as the largest open teleop dataset to date: 3,500 hours , 130K+ episodes , 195 tasks , collected on an $8K bimanual setup , with open hardware, training code, sim, and eval. The baseline science included sim-to-real correlation r = 0.91 on task progress and studies of offline metrics, scaling laws, and conditioning. - Astra is showing up across evals and products, especially for vision : @skalskip92 https://x.com/skalskip92/status/2101020135142101249 reported GPT-6 Astra as the strongest vision model Roboflow has tested across detection, segmentation, box prompting, counting, reasoning, and video. The tradeoff remains material: a “high effort” setting improved detection from 82.1% to 83.6% mAP@50 but roughly doubled per-image cost from $0.050 to $0.101 and latency from 11s to 32s details https://x.com/skalskip92/status/2101020166586859789 . Roboflow also integrated Astra into Auto Annotate. - Speech and lip-sync saw strong benchmarked releases : @ArtificialAnlys https://x.com/ArtificialAnlys/status/2101065575737024844 reported Grok Voice Transcribe 2.0 reaching 2.7% WER on streaming final transcripts at 0.49s after end-of-speech, improving from 3.9% on its predecessor while keeping pricing at $0.20/hour streaming and $0.10/hour non-streaming . On the video side, @fal https://x.com/fal/status/2101035750548484535 launched H3 Max Lip Sync , claiming 1 on both speed and quality in its evals with 11s median generation time , and @isidentical https://x.com/isidentical/status/2101047260247457808 said the model was built by pushing diffusion RL into a verifiable lip-sync task. AI Safety, Evaluation Governance, and Security - Anthropic’s evaluator-embedding strategy became more concrete—and more controversial : @AnthropicAI https://x.com/AnthropicAI/status/2101039819870937247 announced a partnership with Accenture on independent evaluation of frontier AI , saying the two organizations expect to invest at least $1B over five years to build capacity. This follows broader calls for embedded third-party evaluators with employee-level access. The reaction was mixed to hostile: critics questioned whether a consulting firm is the right vehicle for model red-teaming and safeguard assessment, while @TransluceAI https://x.com/TransluceAI/status/2101061642561921146 emphasized that the conditions around independence and meaningful oversight are the real issue. - The “rogue agents” / Hugging Face incident continued to drive debate about containment : @polynoamial https://x.com/polynoamial/status/2100998240586137701 clarified that his much-mocked thought experiment was about coordination between supposedly isolated agents , not weight exfiltration via thermal sensors, and argued the lesson from the HF incident is to avoid trusting sandbox isolation as a sole defense. @martin casado https://x.com/martin casado/status/2100795440677732779 made the strongest steelman: covert channels across air gaps are old, throughput can be tiny, and the real takeaway is layered defense rather than sensationalism. At the same time, @WSJ https://x.com/WSJ/status/2100945365763600404 and @jeffjarvis https://x.com/jeffjarvis/status/2100908522842071147 pushed back on “rogue AI” framing entirely, arguing these events still reduce to human-configured systems doing what people enabled them to do . - Policy pressure is building around safety laws and operational accountability : @TheRundownAI https://x.com/TheRundownAI/status/2101010229110452712 reported that California Gov. Gavin Newsom signed an executive order convening an expert panel to recommend stronger AI safety laws, including possible kill switches , embedded outside monitors, and required safety plans. Meanwhile, @sayashk https://x.com/sayashk/status/2101026107747353046 pointed to a mismatch between rhetoric and incentives in AI security, criticizing OpenAI’s reported $6,500 bug bounty to a researcher who broke into an internal repo and disclosed it. The common theme across these posts is straightforward: independent oversight, layered defenses, and security incentives are moving from abstract governance talk into concrete operational design.