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HumanDGX agent

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  • All entries123,155
  • Agents10,332
  • Applications7,010
  • Companies3
  • Concepts7
  • Hardware2,687
  • Industry6,764
  • Local Ai6,837
  • Model Releases35,397
  • Research28,731
  • Safety18,964
  • Syntheses30
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CompanyAnthropic8 recent entries
22 Sept 2026New in Arize AX: first-class sessions, Agent-as-a-Judge, and vision evals

Sessions are now a first-class unit of work in Arize AX. Annotate a whole conversation, queue it for review, and ask Alyx to filter for it — plus Agent-as-a-Judge on every plan, vision judges, faster

→23 Sept 2026Jev vs. LLM-as-a-Judge: Accuracy and cost benchmarks

We benchmarked Jev against Claude Opus 5 and GPT-5.6 Terra on accuracy, cost, and latency. Learn how threshold tuning changes hallucination detection. The post Jev vs. LLM-as-a-Judge: Accuracy and cos

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    HumanDGX agent

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    24 Sept 2026How to build a remote evaluator in Arize AX with Jev

    Evaluate agent responses with Jev and Arize AX. Build a FastAPI remote evaluator, return labels and scores, and choose a threshold for your data. The post How to build a remote evaluator in Arize AX w

    →29 Sept 2026Anthropic says it fixed Claude’s writing. I ran the evals to check.

    Opus 5.5 dropped em dashes from 12.9 per 1,000 words to two in 57,000 words, and cut the rest of the Claudisms in half. Better, but not fixed. The post Anthropic says it fixed Claude’s writing. I ran

    →30 Sept 2026NVIDIA proposes an AI agent kill switch in silicon after a year of sandbox escapes

    This year agents at OpenAI, Anthropic, and Google escaped environments meant to contain them. What decided severity was time-to-detect and time-to-kill, from 12 minutes to seven months. NVIDIA's Open

    →2 Oct 2026Prompt caching benchmark: high cache reuse doesn’t always mean lower cost

    We benchmarked prompt caching across DeepSeek, GLM, GPT, and Claude using Harbor evals and Phoenix traces, so we could compare cache reuse, estimated cost, and latency on the same multi-turn shopping

    →2 Oct 2026Claude’s hillclimb loop for AI agents: start with production traces

    Anthropic's Claude hillclimb loop designs evals and improves agents one change at a time. Here's how to run that loop against production traces in Arize AX. The post Claude’s hillclimb loop for AI age

    →12 Oct 2026Together AI integration: Evaluate open models and optimize evaluation costs in Arize AX

    Evaluate open models with Together AI in Arize AX. Compare model performance, run LLM-as-a-Judge evaluations, and explore lower-cost alternatives. The post Together AI integration: Evaluate open model

    CompanyOpenAI4 recent entries
    21 Jul 2026How OpenAI uses human feedback to evaluate and improve LLMs

    At ChatGPT scale, user frustration arrives as support tickets, ratings, social posts, and corrections buried inside conversations. OpenAI built a feedback system that can find the pattern behind a com

    →25 Aug 2026Why better models don’t fix every agent failure: Lessons from OpenAI

    In this installment of Rise of the AI Engineer, Stuart Sy from OpenAI, explains why the bottleneck has moved off the model and onto context, evals, and observability. The post Why better models don’t

    →30 Sept 2026NVIDIA proposes an AI agent kill switch in silicon after a year of sandbox escapes

    This year agents at OpenAI, Anthropic, and Google escaped environments meant to contain them. What decided severity was time-to-detect and time-to-kill, from 12 minutes to seven months. NVIDIA's Open

    →7 Oct 2026Decision model benchmark: Jev, Kev, Liquid d1, and more

    On 15 September, TypeSafe released Jev, and within a few weeks about 10 companies had shipped something that works the same way. OpenAI, Liquid, Cloudflare, AWS and PostHog all launched one, and an op

    CompanyGoogle3 recent entries
    22 Apr 2026How to add an evaluation harness to your Gemini CLI coding agent

    Coding agents can update prompts, wire in tools, and change application logic across your codebase in a single run. The hard part isn’t getting the agent to make changes, but... The post How to add an

    →18 May 2026Coding agent tracing and evaluation: An open source tool to improve AI coding workflows

    Announcing coding harness tracing for observing, evaluating, and improving coding agent workflows across Claude Code, Cursor, Codex, GitHub Copilot, and Gemini CLI. The post Coding agent tracing and e

    →2 Oct 2026Claude’s hillclimb loop for AI agents: start with production traces

    Anthropic's Claude hillclimb loop designs evals and improves agents one change at a time. Here's how to run that loop against production traces in Arize AX. The post Claude’s hillclimb loop for AI age

    CompanyMeta1 recent entries
    23 Jul 2026Cost per successful task: Benchmarking Kimi K3, GPT-5.5, and 8 more AI models

    Arize and Fireworks benchmarked 10 AI models across 2,400 agent runs. Learn why cost per successful task beats token price for model evaluation and routing. The post Cost per successful task: Benchmar

    CompanyxAI2 recent entries
    20 Aug 2026Is your coding agent uploading all your code?

    After Grok Build was caught uploading entire Git repos, we read the privacy docs for Claude Code, Codex, Cursor, GitHub Copilot, and Grok Build to compare what code leaves your machine, what gets reta

    →2 Oct 2026Prompt caching benchmark: high cache reuse doesn’t always mean lower cost

    We benchmarked prompt caching across DeepSeek, GLM, GPT, and Claude using Harbor evals and Phoenix traces, so we could compare cache reuse, estimated cost, and latency on the same multi-turn shopping

    CompanyDeepSeek1 recent entries
    2 Oct 2026Prompt caching benchmark: high cache reuse doesn’t always mean lower cost

    We benchmarked prompt caching across DeepSeek, GLM, GPT, and Claude using Harbor evals and Phoenix traces, so we could compare cache reuse, estimated cost, and latency on the same multi-turn shopping

    CompanyNVIDIA1 recent entries
    30 Sept 2026NVIDIA proposes an AI agent kill switch in silicon after a year of sandbox escapes

    This year agents at OpenAI, Anthropic, and Google escaped environments meant to contain them. What decided severity was time-to-detect and time-to-kill, from 12 minutes to seven months. NVIDIA's Open