GoDataAnalytics @GoDataAnalytic

Today we’re introducing Gemini 4 Argon. It delivers frontier performance in complex workflows across real-world software engineering, knowledge work, and cybersecurity defense with an industry-leading 1M token output limit.

Jev for RAG, clearly explained! Hybrid search gives you a shortlist. It does not decide which passages contain evidence, which are merely adjacent, or whether the evidence is strong enough to answer. That missing judgment is where Jev fits. Jev sits between retrieval and generation. It does not replace BM25, embeddings, or the LLM. It evaluates the candidates they produce before those candidates enter the context window. → Retrieve wide Combine dense and keyword search, then merge the results with reciprocal rank fusion. This gives Jev a broad candidate pool, such as the top 20 passages shown in the visual. Retrieval still sets the ceiling. If the right passage is missing from this shortlist, Jev cannot recover it. → Judge every candidate together Send the query as Jev’s state. For each candidate, ask a typed yes-or-no question such as “Does C7 help answer this query?” Jev evaluates every question in one packed request and returns a calibrated probability for each passage. This avoids making a separate model call for every query-passage pair. → Let code apply the threshold Your application compares each probability with a threshold. Candidates above it continue to the LLM. Everything below it is removed before generation. Jev makes the fuzzy judgment. Code remains responsible for the actual decision. → Gate the entire answer The same request can check whether the retained passages make the query answerable. It can also flag signs of prompt injection inside a candidate. If answerability falls below the threshold, the application can skip the LLM and return “not in the documents.” The injection score should remain a filtering signal, not a security boundary. The result is a cleaner division of work. Hybrid search retrieves broadly. Jev reranks, filters, and decides whether sufficient evidence exists. The LLM writes only from the passages that survive. Jev’s value here is not simply moving passages up or down a list. It turns relevance into an explicit probability that your application can inspect, threshold, and act on. To summarise: - Retrieval finds the candidates. - Jev decides what deserves context. - The LLM writes the grounded answer. ---- I also built an open-source project showing how to use Jev as a judge for AI observability with Comet Opik. It evaluates support traces for groundedness, request coverage, action honesty, and helpfulness, then records the results as an auditable experiment. You can explore the project here: github.com/patchy631/jev-… My article on how Jev works is quoted below.

Akshay 🚀 @akshay_pachaar

2 weeks ago

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Lots of discussion out there about our next model(!), so I wanted to give an early look as soon as possible. Introducing Gemini 4 Argon! It shows frontier performance in complex workflows, cyber defense and software engineering. Teams are using it extensively at Google, from coding to quantum computing, great feedback. Here’s a look at the benchmarks:

Took a while, but here it is, a mega write-up on language models for text classification and, of course, Jev. It's basically a visual guide to RNNs, CNNs, transformers, and calibration, with hands-on experiments on accuracy and efficiency.

muse is shopping for me. i'm still not getting clients. gave Explee the link. it finds the buyers and writes the emails. the errand agent was never going to sell. $30 free 👇 explee.com/auto-gtm/x/tw-…

Introducing Claude Sonnet 5.5, the second model in the Claude 5.5 family. It’s a clear upgrade over Sonnet 5, runs more than 30% faster, and costs up to 30% less for most work.

@typesafeai How's RLCD rewarding calibration, and what does RL add over post-hoc scaling?

🚨 لقيت حاجة من Cloudflare تستحق التجربة فعلًا. أطلقوا Security Audit Skill مفتوحة المصدر، وبتخلي وكيل الذكاء الاصطناعي يقدر يعمل تدقيق أمني بشكل منظم، بدل ما تكون العملية مجرد بحث عشوائي عن الثغرات. 🔍 الـSkill بتقسم التدقيق لـ6 مراحل: 🔹 فهم بنية النظام ونقاط الهجوم 🔹 البحث عن الثغرات بشكل شامل 🔹 التحقق من كل ثغرة بشكل مستقل 🔹 تنظيم النتائج 🔹 مراجعة نهائية بواسطة وكلاء مختلفين 🔹 إنشاء تقرير أمني تلقائيًا 📄 لكن أكتر حاجة عجبتني هي فكرة التحقق 👀 الوكيل اللي بيكتشف الثغرة مش هو نفسه اللي بيتأكد إنها حقيقية. وده بيساعد على تقليل الـFalse Positives، بدل ما الوكيل يكتشف مشكلة ويعتبرها ثغرة وخلاص. والأداة مفتوحة المصدر، مجانية، ومرخصة برخصة MIT. ⚡ لو بتبني وكلاء ذكاء اصطناعي أو مهتم بالأمن السيبراني، دي من الحاجات اللي تستاهل التجربة. الرابط 👇

‏عادل | مبرمج @AdelDeveloperX

a month ago

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Locally Laya just beat cloud Jev at live Tetris. Laya System 1 model made Decision time: 40–50 ms. on a 16GB MacBook Air. Jev, running in the cloud, sat above 300 ms. That is enough for 60+ decisions per second.

Md Ismail Šojal 🕷️ @0x0SojalSec

2 weeks ago

Laya is the open-source Jev that already beats close Jev on latency and calibration. - 10x faster than TypeSafe Jev on the published numbers. - Laya is multilingual, and already ahead of Jev on speed. - 38ms single query vs 400ms Locally. - No per-token bill. - A System-1

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If you're an AI Engineer, here are 5 projects you must build. These projects will force you to make the same decisions production teams make every day. 1️⃣ 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗥𝗔𝗚 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗮𝗻𝗱 𝗶𝗻𝘀𝘁𝗿𝘂𝗺𝗲𝗻𝘁 𝗶𝘁 Don't just wire up retrieval + generation: instrument it. Track retrieval precision, context relevance, and hallucination rate on a held-out eval set. ↳ Tools: LangChain or LlamaIndex, a vector DB, Ragas for eval ↳ YouTube: youtube.com/watch?v=XebKOY… 2️⃣ 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗲 𝗮 𝗦𝗺𝗮𝗹𝗹 𝗢𝗽𝗲𝗻-𝗪𝗲𝗶𝗴𝗵𝘁 𝗠𝗼𝗱𝗲𝗹 𝗳𝗼𝗿 𝗢𝗻𝗲 𝗡𝗮𝗿𝗿𝗼𝘄 𝗧𝗮𝘀𝗸 Take a 7B model, pick one job (classification, extraction, a house style), and fine-tune it with LoRA. Compare it against prompting a frontier model for the same task. ↳ Tools: Hugging Face, PEFT/LoRA, a single GPU (or Colab) ↳ YouTube: youtube.com/watch?v=F29Y_y… 3️⃣ 𝗗𝗲𝘀𝗶𝗴𝗻 𝗮𝗻𝗱 𝗥𝘂𝗻 𝗮𝗻 𝗘𝘃𝗮𝗹 𝗛𝗮𝗿𝗻𝗲𝘀𝘀 𝗳𝗼𝗿 𝗟𝗟𝗠 𝗢𝘂𝘁𝗽𝘂𝘁𝘀 Build a repeatable pipeline that scores model outputs, not vibes, actual metrics: exact match, LLM-as-judge, human-in-the-loop spot checks. ↳ Tools: Python, a judge model, a small labeled dataset ↳ YouTube: youtube.com/watch?v=rS6wZf… 4️⃣ 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺 𝗧𝗵𝗮𝘁 𝗛𝗮𝘀 𝘁𝗼 𝗖𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗲 Two or more agents, one shared goal, real handoffs: not a single agent calling tools in a loop. ↳ Tools: LangGraph, function calling ↳ YouTube: youtube.com/watch?v=1SFZz8… 5️⃣ 𝗦𝗵𝗶𝗽 𝗮 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗪𝗶𝘁𝗵 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀, 𝗡𝗼𝘁 𝗝𝘂𝘀𝘁 𝗮 𝗗𝗲𝗺𝗼 Add input validation, output filtering, rate limiting, and graceful fallback when the model fails or times out. ↳ Tools: Any LLM API, a FastAPI wrapper, slowapi ↳ YouTube: youtube.com/watch?v=tW3hEy… AI Engineering is retrieval, evaluation, fine-tuning, orchestration, and reliability engineering, all applied to models instead of deterministic code. → If you have GPU access, build all five and benchmark them against each other. → If you don't, most of this runs fine on a laptop with a small model or a frontier API. → Even just designing the eval harness on paper will sharpen how you think about AI systems. Pick one. Build it this weekend.

Another insane Jev use case! Jev makes it incredibly cheap to evaluate and classify agent runs at scale. And finally, someone open-sourced a self-improving memory layer that can put that capability to work across agent harnesses. It turns your agent sessions into a compounding knowledge layer, where every successful run can make future agents smarter across: - Codex - Claude Code - Cursor - OpenCode and 20+ more Beacon by @asymptotelabs continuously builds a shared history across your agent harnesses and uses Jev to identify the runs worth learning from. It then turns the best workflows, corrections, and debugging patterns into reusable skills. GitHub repo: github.com/Asymptote-Labs…. (don’t forget to star it ⭐) Most agent runs are messy. They contain exploration, failed commands, dead ends, and one-off fixes that should never become permanent memory. So Beacon preserves the full session history, while Jev helps decide what should be promoted, reviewed, or discarded. The recording below shows this in action. Beacon found 579 sessions across 5 coding-agent harnesses and normalized them into one consistent history. From there, Jev surfaces the lessons worth keeping and makes them available across your agent stack. - A pattern learned in Cursor can carry into OpenCode. - A lesson from Claude Code can improve the next Codex run. Every successful run adds to the shared knowledge layer, making future agents smarter. If you want to dive deeper into Jev, I also wrote a breakdown of how it works. The article is quoted below.

Akshay 🚀 @akshay_pachaar

2 weeks ago

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Jev feels like a programmable web moment for AI. AI that plugs into application logic. Route a request. Rank candidates. Evaluate an action. Small decisions developers can combine into much bigger systems. Intelligence as a software building block.

Jev Founder, Diogo Amogo, just released a PDF on building a Jev Harness for coding agents this is a blueprint on how to make your coding agents 200× faster and 400× cheaper Send this PDF and the article below to your Claude Code or Codex instance and start shipping 200× faster 👇

darkzodchi @zodchiii

2 weeks ago

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Deploying DiffusionGemma-Jev (djev) just got a lot easier. You can now spin up a Jev API-compatible endpoint on Google Cloud Run using a single command. Performance is solid: ~35-60 ms for single step latency and batch@32 is ~100-123 requests/sec. It's a straightforward way to experiment without needing your own GPU. Runs at roughly $3/hr and drops to $0 when idle. Get the code and instructions here: github.com/taeold/djev-run

PEOPLE ARE BUILDING SOME WILD STUFF WITH JEV 20 mind blowing projects worth checking out: 1. JEV-Ultrafast → browser agent github.com/browser-use/je… 2. Fast-JEV-Compaction → context compression github.com/tamaratran/fas… 3. JSON-Render → generative UI github.com/vercel-labs/js… 4. Typesafe-MCP → use JEV with any client github.com/itsmostafa/typ… 5. JEV-MCP → judgment toolkit github.com/burnigtm/jev-m… 6. Semdecide → CLI classifier github.com/sharziki/semde… 7. JEV-Codex-Router → model routing github.com/0xNatoshi/jev-… 8. Winnow → context garbage collection github.com/GhalebDweikat/… 9. JEV-Review → code review triage github.com/devagrawal09/j… 10. Blink → repo navigator github.com/ellipsis-dev/b… 11. Agent-Desktop → desktop automation github.com/lahfir/agent-d… 12. Typesafe-Mario → plays Super Mario github.com/fhshaik/typesa… 13. JEV-Drone → drone control github.com/RomanSlack/jev… 14. OneVOneJev → browser FPS github.com/emrickgarrett/… 15. JEV-Trader → HFT market making github.com/buberlo/jev-tr… 16. Prism → liquidity signal detection github.com/irfndi/prism-l… 17. Neo4Jev → knowledge graph traversal github.com/jexp/neo4jev 18. JEV-Curate → training data screening github.com/AkashPriyadars… 19. Canny → completion verification github.com/qkal/Canny 20. KillMyIdea → startup idea scoring github.com/monteduro/kill… if you're using JEV, these are worth exploring pick one and start building

Send this Jev prompt to any LLM or AI agent It sets up Jev → analyzes you → finds where you waste time and money → upgrades your AI setup beyond 95% of people... Copy this now. Thank me later:

codila @0xCodila

2 weeks ago

Jev is the "Internet" moment for the AI industry It tells your agents and LLMs what to do next, in milliseconds and at almost zero cost If you set it up correctly, you will have the AI engineer’s stack for 2028 In this article, I show you how x.com/i/article/2077…

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Jev is TypeSafe AI's first System One Model. It is 100x faster and cheaper than frontier LLMs. That opens up a lot of use cases people usually skip because the big models are too slow or too expensive for them. Here are 9 use cases where we think Jev could be used instead of an LLM.

Claude has discovered a previously unknown enzyme system hidden in the DNA of bacteriophages. Beside the enzyme’s gene sits a long array of repeating DNA—a structure that looks somewhat similar to CRISPR. We don’t yet understand what this system does, but only a handful of known systems share its features, and all of them are able to cut, copy, and paste DNA. Historically, the discovery of such programmable systems has helped revolutionize medicine. CRISPR, for instance, is now the foundation of genetic medicines. But it will take much more work to learn what this system does, and whether it can be put to similar use. Read more: anthropic.com/news/claude-di…

جوجل تطرح ما لم يتوقعه أحد! 🔥 ​دليل إرشادي من 8 دقائق فقط يشرح لك بالتفصيل كيفية بناء أول AI Agents خاصة بك من الصفر! 🤯 ​تبسيط مفاهيم الـ Reasoning و الـ Workflows في دقائق معدودة! ​عصر الـ Prompting انتهى.. مرحباً بعصر الـ Agentic AI! 🚀

Introducing Claude Opus 5.5, the first model in our new Claude 5.5 family. It performs at the level of Claude Fable 5.1 for most tasks, and costs 40% less to run than Opus 5.