project rollup
jev
300
mentions
across 2 keywords
across 2 keywords
net sentiment
+14%
95 positive
151 neutral
54 negative
volume & sentiment over time
Sep 18–26, 2026 · Daily
1–2 of 2 keywords
mentions
Showing 76–100 of 300 mentions
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@mysticaltech @DataChaz yeah, that’s impressive! have you tried Opus 5.5 or Jev yet? @mysticaltech @DataChaz yeah, that’s impressive! have you tried Opus 5.5 or Jev yet?view source ↗
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まだあんまりキャッチ出来てないけど、Jevとかをゲーム内のCPUとしてのAIとして使い始めたら何か色々よしなに良さそう まだあんまりキャッチ出来てないけど、Jevとかをゲーム内のCPUとしてのAIとして使い始めたら何か色々よしなに良さそうview source ↗
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@BLUECOW009 Jev did better in my tests @BLUECOW009 Jev did better in my testsview source ↗
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Derrière le harness va générer le reste, de la priorisation des tâches et tout. Je penses que Jev peut aider de fou dessus par exemple avec un minimum de coup. L'argent s'aura quoi faire, orchestrer le tout, et manipuler juste tout ça comme une todo app Derrière le harness va générer le reste, de la priorisation des tâches et tout. Je penses que Jev peut aider de fou dessus par exemple avec un minimum de coup. L'argent s'aura quoi faire, orchestrer le tout, et manipuler juste tout ça comme une todo appview source ↗
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@CompleteSkeptic just released this lil interview tool powered by Jev https://t.co/uWv6ci3AKI all the old BYOK apps i wanted to build w/ LLMs are now much more tractable @CompleteSkeptic just released this lil interview tool powered by Jev https://t.co/uWv6ci3AKI all the old BYOK apps i wanted to build w/ LLMs are now much more tractableview source ↗
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@noisyb0y1 Jev is genuinely useful @noisyb0y1 Jev is genuinely usefulview source ↗
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📢 TYPESAFE AI · JEV IS NOW LIVE ON https://t.co/kgZeByAQj2 API 🚀 Responds in about 70–500ms, and costs $0.042 per million input tokens (output free), making it a fit for ticket routing, moderation, risk scoring, and agent branching. 🧮 Worth actually reading "70–500ms" once more directly against the newly confirmed GL… 📢 TYPESAFE AI · JEV IS NOW LIVE ON https://t.co/kgZeByAQj2 API 🚀 Responds in about 70–500ms, and costs $0.042 per million input tokens (output free), making it a fit for ticket routing, moderation, risk scoring, and agent branching. 🧮 Worth actually reading "70–500ms" once more directly against the newly confirmed GLM-5.3-FlashX's own genuinely comparable, considerably fast "200 tokens/sec" generation speed, worth appreciating these two figures describe genuinely different things, latency versus throughput, worth being genuinely curious whether a developer building a genuinely latency-sensitive pipeline should prioritize Jev specifically for its own explicit millisecond-level response guarantee over a comparably fast, but not explicitly latency-benchmarked, generative model. 💭 Worth actually reading "output free" once more as a genuinely specific pricing structure worth checking directly against the newly confirmed four-tier discount system, worth appreciating Jev's own genuinely unique, input-only pricing model sits entirely outside that broader percentage-discount framework applied to this account's own more conventional, text-generating models. 🎯 Worth actually reading "Choice, Score, and Noul questions in parallel" directly against the fuller documentation. 👉 Try now: https://t.co/xSnqFyNE3M 🔗 https://t.co/XdOzfnbUkW @justinsuntron @BAI_AGI #TRONEcostarview source ↗
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Mario's memory has a UI. My demo links observed jumps, landings, enemy motion and outcomes in a knowledge graph. Observations feed later decisions; you can inspect the evidence. See it in action: https://t.co/HJSalb66rN #JEV #LocalAI AI-assisted. Mario's memory has a UI. My demo links observed jumps, landings, enemy motion and outcomes in a knowledge graph. Observations feed later decisions; you can inspect the evidence. See it in action: https://t.co/HJSalb66rN #JEV #LocalAI AI-assisted.view source ↗
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Recent attentions were on the latest frontier models. ScienceBuddy, JEV, Laya and Kev will shine more soon. Recent attentions were on the latest frontier models. ScienceBuddy, JEV, Laya and Kev will shine more soon.view source ↗
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コメント欄みたけど、たしかに jev って マルチモーダル対応じゃないし、、、 コメント欄みたけど、たしかに jev って マルチモーダル対応じゃないし、、、view source ↗
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Interesting use of Jev, seems to me, isnt one fast, cheap decision — it’s the ability to fan out on many decisions in parallel. Eg intent, relevance, progress, routing, tool choice, etc — evaluated in parallel, forming a decision matrix that deterministic harness code resolves into the next action. Interesting use of Jev, seems to me, isnt one fast, cheap decision — it’s the ability to fan out on many decisions in parallel. Eg intent, relevance, progress, routing, tool choice, etc — evaluated in parallel, forming a decision matrix that deterministic harness code resolves into the next action.view source ↗
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@typesafeai This is so annoying , today was supposed to be my big JEV implementation & testing day. 🥲 @typesafeai This is so annoying , today was supposed to be my big JEV implementation & testing day. 🥲view source ↗
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@spaceman_gilda Welcome! This week's goal for everybody in AI -- show how your stuff works with Jev 🤣 @spaceman_gilda Welcome! This week's goal for everybody in AI -- show how your stuff works with Jev 🤣view source ↗
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Looks like Jev from @typesafeai is still the king for now, compared to Kev 4b (now available via @OpenRouter ) since it is a bit too big for my laptop GPU. Kev 4b is certainly getting really close, actually showing better performance on 2 tests. Interestingly Jev via OpenRouter is 4x faster than Jev direct from @types… Looks like Jev from @typesafeai is still the king for now, compared to Kev 4b (now available via @OpenRouter ) since it is a bit too big for my laptop GPU. Kev 4b is certainly getting really close, actually showing better performance on 2 tests. Interestingly Jev via OpenRouter is 4x faster than Jev direct from @typesafeaiview source ↗
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C++ devs: your AI output can be an enum. I built JevT++ with typed choices, explicit abstention and an optional Boost.Asio co_await adapter. Local Laya via ONNX/ggml; CUDA optional. MIT: https://t.co/CvpN54FgN5 #JEV #cpp AI-assisted. C++ devs: your AI output can be an enum. I built JevT++ with typed choices, explicit abstention and an optional Boost.Asio co_await adapter. Local Laya via ONNX/ggml; CUDA optional. MIT: https://t.co/CvpN54FgN5 #JEV #cpp AI-assisted.view source ↗
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An AI can pick the right move for the wrong moment. My JevT++ demo checks model plans against the current game state before execution. Watch the decision graph alongside Mario: https://t.co/HJSalb5yCf #JEV #LocalAI AI-assisted. An AI can pick the right move for the wrong moment. My JevT++ demo checks model plans against the current game state before execution. Watch the decision graph alongside Mario: https://t.co/HJSalb5yCf #JEV #LocalAI AI-assisted.view source ↗
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Ollaya – Ollama for open-source, Jev-style decision modelsI've been following jevbench twice a day for the past week and that's been a lot of fun. Latest update: Rank System Score Public / sealed accuracy Evidence 1 decider-4b v2 64.13 83.5% / 34.7% Evaluator-run, offline 2 Jev 1.13 63.29 86.6% / 36.7% Evaluator-run API 3 JevK5 v0.2 62.04 85.3% / 33.1% Evaluator-run 4 Cygnet … I've been following jevbench twice a day for the past week and that's been a lot of fun. Latest update: Rank System Score Public / sealed accuracy Evidence 1 decider-4b v2 64.13 83.5% / 34.7% Evaluator-run, offline 2 Jev 1.13 63.29 86.6% / 36.7% Evaluator-run API 3 JevK5 v0.2 62.04 85.3% / 33.1% Evaluator-run 4 Cygnet 12B 61.76 87.9% / 33.8% Evaluator-run, offline 5 Hopper 59.43 82.3% / 34.1% Evaluator-run 28 Kev 4B 36.14 66.2% / 22.4% Evaluator-run 41 Laya 421M 30.25 58.4% / 30.8% Evaluator-runview source ↗
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@JoshARosen The state-into-decisions framing is why Jev slots under an existing agent graph instead of demanding a rewrite -- most frameworks bolt a decision model on top, this one routes on what the graph already tracks. https://t.co/fD8thmC2ik @JoshARosen The state-into-decisions framing is why Jev slots under an existing agent graph instead of demanding a rewrite -- most frameworks bolt a decision model on top, this one routes on what the graph already tracks. https://t.co/fD8thmC2ikview source ↗
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Has anyone trained a Jev / Laya type model on finance data yet? Seems like it would be a no brainer to train it on specific setups for stock / crypto trading. It could act as a layer in an LLM powered quant for quick decision making based on patterns. Has anyone trained a Jev / Laya type model on finance data yet? Seems like it would be a no brainer to train it on specific setups for stock / crypto trading. It could act as a layer in an LLM powered quant for quick decision making based on patterns.view source ↗
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dear vibe coders jev is not generative ai dear vibe coders jev is not generative aiview source ↗
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Jev BenchmarksThere are comming more and more Jev alternatives. Is there anywhere a benchmarklist of these jev competitors? There are comming more and more Jev alternatives. Is there anywhere a benchmarklist of these jev competitors?view source ↗
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@shimadaeisuke Loved this, Keisuke. I've featured your post on https://t.co/6rvbCXTB3W, a directory of things people have built with Jev: https://t.co/nJGM0Xe8kk @shimadaeisuke Loved this, Keisuke. I've featured your post on https://t.co/6rvbCXTB3W, a directory of things people have built with Jev: https://t.co/nJGM0Xe8kkview source ↗
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@itIsGokulNair Loved this, Gokul. I've featured your post on https://t.co/6rvbCXTB3W, a directory of things people have built with Jev: https://t.co/nJGM0Xe8kk @itIsGokulNair Loved this, Gokul. I've featured your post on https://t.co/6rvbCXTB3W, a directory of things people have built with Jev: https://t.co/nJGM0Xe8kkview source ↗
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Ollaya – Ollama for open-source, Jev-style decision models https://ollaya.dev/ (https://news.ycombinator.com/item?id=49848269) Ollaya – Ollama for open-source, Jev-style decision models https://ollaya.dev/ (https://news.ycombinator.com/item?id=49848269)view source ↗
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@simplydt @nicochristie I can tell u now jev cannot handle macro lol @simplydt @nicochristie I can tell u now jev cannot handle macro lolview source ↗