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Learn how SAS 9 programmers can use agentic AI tools such as Claude Code and ChatGPT Codex with SASPy to automate code generation, execution, testing, and debugging while maintaining human oversight, validation, and ownership of results. The post The three components you need to use agentic AI with SAS appeared first on SAS Blogs.
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Customer engagement is entering a new era that’s evolving very quickly. And it’s not defined by more messages or channels – it centers on intelligence, autonomy and trust. As organizations rethink how they connect with customers, a new model is emerging: engagement that predicts, learns and acts with purpose. In […] The post What’s next
Tokenization is the one part of the language modeling stack that almost nobody profiles. Gigatoken, released by Marcel Rød (a PhD student from Stanford) under an MIT license, argues that this was a mistake. The library encodes text at gigabytes per second on a single machine, against baselines that are already multithreaded Rust. The GPT-2
Anthropic has released the Claude Security plugin for Claude Code in beta. The plugin runs a multi-agent vulnerability scan of a repository from inside an existing Claude Code session, then turns the findings you select into patch files that you review and apply yourself. Anthropic emphasized the tool’s versatility upon announcement, highlighting its capability to
Cursor has made Cursor Router generally available for Teams and Enterprise plans. The system is a classifier that inspects each request before a model runs, then dispatches it to the model best suited to that specific task. The cursor team reports frontier-quality performance at 60% savings in online A/B tests, and 30–50% savings for early-access
In this tutorial, we explore EdgeBench as a practical benchmark for evaluating advanced AI agents across diverse task categories, runtime environments, and interaction-time budgets. We begin by downloading the dataset snapshot from Hugging Face, parsing the released task specifications, and examining the benchmark taxonomy, execution settings, internet requirements, judging logic, and scoring metadata. We then
Four open source projects dominate LLM fine-tuning today. Unsloth, Axolotl, TRL, and LLaMA-Factory all wrap the same underlying PyTorch and Hugging Face stack. They diverge on where they spend engineering effort. Unsloth rewrites kernels. Axolotl composes parallelism strategies. TRL defines the trainer APIs the others build on. LLaMA-Factory optimizes for breadth of model coverage and
Poolside has released Laguna S 2.1, a 118B-parameter open-weight model built for agentic coding. It is a Mixture-of-Experts (MoE) model with 8B activated parameters per token. It supports a context window of up to 1M tokens in both thinking and no-thinking modes. The weights are on Hugging Face under an OpenMDW-1.1 license, and the model
Poolside has released Laguna S 2.1, a 118B-parameter open-weight model built for agentic coding. It is a Mixture-of-Experts (MoE) model with 8B activated parameters per token. It supports a context window of up to 1M tokens in both thinking and no-thinking modes. The weights are on Hugging Face under an OpenMDW-1.1 license, and the model
Developers building production agents need higher token efficiency, lower latency, and more reliable performance. Today, Google has released three new Gemini models. The lineup is Gemini 3.6 Flash, Gemini 3.5 Flash-Lite, and Gemini 3.5 Flash Cyber. All three sit in the Flash tier, which Google tunes for speed, cost, and high-volume agentic work rather than
