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APIs and MCPs are often mentioned in the same breath as ways that systems can exchange information, but they are designed differently and have different purposes. This article hopes to explain the differences and how software developers and users should approach interaction with each. An API is mainly found in software applications, while an MCP
Agentic artificial intelligence (AI) is set to fundamentally reshape the structure of enterprise work and commerce. Rather than simply responding to instructions, these agents actively participate in workflows by planning tasks, creating and using tools, correcting their own errors, and pursuing multistep goals autonomously. The result is faster, more adaptive workflows. The emergence of the
Audio AI has had a breakout year. Automatic speech recognition has gotten dramatically better with models like OpenAI’s Whisper variants, NVIDIA’s Parakeet, and Mistral’s Voxtral. Audio understanding stepped forward with models like NVIDIA’s Audio Flamingo 3. Dialogue-grade text-to-speech arrived via Nari Labs’ Dia-1.6B. And Meta shipped the Perception Encoder Audiovisual (PE-AV), a multimodal encoder capable
Poolside AI released the first two models in its Laguna family: Laguna M.1 and Laguna XS.2. Alongside these, the company is releasing pool — a lightweight terminal-based coding agent and a dual Agent Client Protocol (ACP) client-server — the same environment Poolside uses internally for agent RL training and evaluation, now available as a research
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 »
