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If you scroll through job postings right now, you’ll see a pattern. Plenty of roles asking people to train models, fine-tune outputs, build agents and automate workflows. Fewer ones are asking for the kind of judgment that used to sit at the center of how decisions get made. At the […] The post SAS Innovate […]
SAS Innovate 2026: Will people matter as AI scales? Read More »
To regulate software delivery costs and SDLC governance, IBM is launching Bob, an AI platform built to anchor enterprise engineering. Accumulated technical debt, hybrid cloud structures, and rigid compliance requirements clash with the raw speed of coding assistants. Without boundaries, they generate unmanaged liabilities rather than functional progress. Dinesh Nirmal, SVP at IBM Software, explained:
IBM launches AI platform Bob to regulate SDLC costs Read More »
In this tutorial, we build an embodied simulation vision agent that learns to perceive, plan, predict, and replan directly from pixel observations. We create a fully NumPy-rendered grid world in which the agent observes RGB frames rather than symbolic state variables, enabling us to simulate a simplified Vision-Language-Action-style pipeline. We train a lightweight world model
What if a language model had never heard of the internet, smartphones, or even World War II? That’s not a hypothetical — it’s exactly what a team of researchers led by Nick Levine, David Duvenaud, and Alec Radford has built. They call it talkie, and it may be the most historically disciplined large language model
In this tutorial, we build a Reinforcement Learning–driven agent that learns how to retrieve relevant memories from a long-term memory bank. We start by constructing a synthetic memory dataset and generating queries that require the agent to recall specific information. Using OpenAI embeddings, we convert both memories and queries into vector representations, enabling similarity signals
Public web pages are actively hijacking enterprise AI agents via indirect prompt injections, Google researchers warn. Security teams scanning the Common Crawl repository (a massive database of billions of public web pages) have uncovered a growing trend of digital booby traps. Website administrators and malicious actors are embedding hidden instructions within standard HTML. These invisible
Google warns malicious web pages are poisoning AI agents Read More »
In this tutorial, we explore how to build and query a local knowledge base with OpenKB using a free, open model via OpenRouter. We securely retrieve the API key with getpass, set up the environment without hardcoding secrets, and initialize a structured, wiki-style knowledge base from scratch. As we move through the workflow, we add
How to Build a Fully Searchable AI Knowledge Base with OpenKB, OpenRouter, and Llama Read More »
As AI agents move from research demos to production deployments, one question has become impossible to ignore: how do you actually know if an agent is good? Perplexity scores and MMLU leaderboard numbers tell you very little about whether a model can navigate a real website, resolve a GitHub issue, or reliably handle a customer
Top 7 Benchmarks That Actually Matter for Agentic Reasoning in Large Language Models Read More »
In this tutorial, we explore kvcached, a dynamic KV-cache implementation on top of vLLM, to understand how dynamic KV-cache allocation transforms GPU memory usage for large language models. We begin by setting up the environment and deploying lightweight Qwen2.5 models through an OpenAI-compatible API, ensuring a realistic inference workflow. We then design controlled experiments where
Building a production-grade voice AI agent is one of the hardest engineering challenges in applied machine learning today. It is not just about transcription accuracy. You need a system that can hold context across a five-minute conversation, invoke external APIs mid-call without an awkward pause, gracefully recover when a caller corrects themselves, and do all
