As AI agents like Claude and Cursor integrate into enterprise workflows, organizations face critical security challenges around safe resource access. The Model Context Protocol (MCP) is establishing communication standards, while OAuth 2.1 and token exchange mechanisms provide authentication frameworks to balance AI capabilities with enterprise security requirements for sensitive corporate data.

We’re entering a new era in marketing—one powered by AI agents, not just analysts. The rise of tools like Clay, Karrot.ai, 6sense, and Mutiny is reshaping how go-to-market (GTM) teams operate, making room for a new kind of operator: the GTM engineer. This hybrid role blends technical fluency with growth strategy, leveraging APIs, automation, and AI to orchestrate hyper-personalized, scalable campaigns. No longer just marketers, today’s GTM teams are builders—connecting data, deploying agents, and fine-tuning workflows in real time to meet buyers where they are. This shift isn’t just evolution—it’s a replatforming of the entire GTM function.

When Bigger Isn’t Always Better: How Context Length Can Break Your LLM ​Longer context windows are the new bragging rights in LLMs — now stretching into the millions of tokens. But can models really handle the first and the 10,000th token equally well?

This paper challenges the LLM-dominant narrative and makes the case that small language models (SLMs) are not only sufficient for many agentic AI tasks—they’re often better. ​🧠 As agentic AI systems become more common—handling repetitive, task-specific operations—giant models may be overkill. The authors argue that: ​SLMs are faster, cheaper, and easier to deploy ​Most agentic tasks don't require broad general intelligence ​SLMs can be specialized and scaled with greater control ​Heterogeneous agents (using both LLMs and SLMs) offer the best of both worlds ​They even propose an LLM-to-SLM conversion framework, paving the way for more efficient agent design.

In this article, Médéric Hurier tests three versions of Google's Gemini 2.5 models—Flash, Pro, and Deep Think—by challenging them to create a complex, multi-scene interactive birthday experience for his daughter. The experiment revealed an exponential gap in capability, with the advanced Gemini Deep Think model delivering a delightful, polished, and fully functional result that surpassed the other models and captivated his daughter.

Demetrios chats with Beyang Liu about Sourcegraph’s AMP, exploring how AI coding agents are reshaping development—from IDEs to natural language commands—boosting productivity, cutting costs, and redefining how developers work with code.

Stack Overflow is adapting to the AI era by licensing its trusted Q&A corpus, expanding into discussions and enterprise tools, and reinforcing its role as a reliable source as developer trust in AI output declines.

From enterprise search to agentic workflows, the ability to reason across text, images, video, audio, and structured data is no longer a futuristic ideal: It’s the new baseline. AI solutions have come a long way in that journey, but until we embrace the need for rethinking how we deal with data, let go of patchwork solutions, and give it a holistic approach, we will keep slowing down our own progress.