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Generalist AI has released GEN-1.5, a robot foundation model that learns a new physical task from a single demonstration. Drop 3–12 seconds of sensorimotor data into its 30-second context window, and the robot performs the task. No gradient updates, no fine-tuning, no task-specific programming. Across 10 diverse manipulation tasks, this one-shot in-context prompting averaged 59%
LexisNexis is rolling out a united and frictionless agentic offering, which works with its Legal Intelligence Engine harness. This brings together its mass of legal …
Lexis Rolls Out Legal Intelligence Engine Agentic Capabilities Read More »
Harvey has released Harvey Tenet, its first post-trained model, as a research preview as of today. Tenet is a Kimi K3 base post-trained with Fireworks through asynchronous reinforcement learning on long-horizon legal work. The training corpus combined synthetic data, publicly available legal data, and human expert data. Harvey states no customer data was used. Against
In this tutorial, we implement a document intelligence pipeline with deepDoctection 1.2.x that combines layout detection, table structure recognition, OCR, reading-order reconstruction, annotation linking, and structured export in a single workflow. We configure the analyzer explicitly with DocLayNet-based layout detection, Table Transformer structure recognition, and DocTR OCR, then inspect the resulting Page objects to understand
Building an End-to-End Document Intelligence Pipeline with deepDoctection Read More »
Vercel has released Is Agentic, a public tool that scores how readily AI agents can discover, access, understand, and use a website. Scans are run and scored by Ora, an agent-experience research company from era labs. Vercel operates the interface, report pages, storage, and the grouping that produces the displayed score. Is it deployable? Yes
In this tutorial, we build an in-depth NeMo Guardrails pipeline that demonstrates how layered guardrails can control an LLM-based financial assistant across the full request lifecycle. We combine deterministic PII detection and redaction, LLM-based input and output self-checks, retrieval filtering, account-number masking, topical restrictions, and policy-based tool gating. We also implement stateful multi-turn interactions, detailed
The Developer’s Guide to NeMo Guardrails for Enterprise AI Safety Read More »
Most teams treat ‘which model’ as the important decision. The harness engineering literature keeps pointing somewhere else. In LangChain’s Terminal-Bench experiment, changing only the harness—same model throughout—moved a coding agent from roughly 30th place into the top 5. That result reframes the question. If the harness decides quality, then how you run the loop becomes
LLM Customization services help organizations adapt language-model applications to specific business tasks, knowledge sources, terminology, workflows, and security requirements. The work can include prompt design, retrieval-augmented generation, tool integration, fine-tuning, evaluation, deployment, and continuous optimization. Customization should not begin with the assumption that a new model must be trained. In many cases, clearer instructions and
Anthropic has moved its most cyber-capable model into a product security teams can switch on themselves. As of August 21, 2026, Claude Security scans run on Claude Mythos 5, the Mythos-class model that until now reached only vetted defenders through Project Glasswing. The scan connects to a GitHub repository, traces data flows across files, and
Superwhisper has released the S1 family of models: S1-Voice, S1-Language, and S1-mini. S1-Voice is a cloud speech-to-text model, and S1-Language is a cloud instruction-following model for cleanup and formatting. The one that is quite interesting outside the app is S1-mini, released with open weights on Hugging Face. S1-mini is a 0.6B text normalizer, not a
