#llms

#llms

Agentic AI has become a hot topic amongst software developers in recent months. As usage of LLMs has become increasingly popular, many developers are switching to agentic AI services to build projects. One point of contention with agentic AI is that productivity is always limited without proper context. Solid context is crucial in streamlining agentic AI tools, as it guards against things like hallucinations and inefficiencies with software development.

We built funnels, tracked clicks and optimized journeys because we could seewhat was happening. We optimized for the "messy middle": customer journeys we can track on the open web. The premise that we can see it all, track it all is obsolete. The customer journey has migrated to closed AI environments, leaving our analytics stacks blind. The causal link between action and result has evaporated.

Marketers believe answer engine optimization (AEO) will significantly reshape their organizations' digital strategy in the next three years, but only 20% have started implementing AEO initiatives. That's according to a survey conducted by Acquia and Researchscape, which also found that 50% of both small businesses (less than 100 employees) and large enterprises (10,000 employees or more) say they are unshare of the share of their traffic is sourced by LLMs.

If your brand isn't being mentioned on credible media outlets, industry lists or podcast transcripts, it's less likely to get pulled into LLM answers. LLMs lean on the same principles as traditional SEO. And earned media remains essential for authority and discoverability, whether it's through an LLM or SEO efforts. In traditional SEO, that authority helps content rank higher. With AI, trusted brand mentions have even greater influence, directly shaping how and where a brand appears in AI-generated results.

The Russian troll farm that in the lead-up to the 2024 US presidential election posted a bizarro video claiming Democratic candidate Kamala Harris was a rhino poacher, is back with hundreds of new fake news websites serving up phony political commentary with an AI assist. In a paper published today, Recorded Future's Insikt Group threat researchers also unveil evidence that the pro-Putin posters known as CopyCop, aka Storm-1516,

In today's dynamic work environment, personalized learning isn't a luxury-it's an expectation. Learners across regions, roles, and functions crave content that feels relevant, specific, and immediately applicable to their day-to-day reality. But traditional personalization strategies-building five versions of every course, rewording every scenario, translating every line-are time-consuming and costly. This is where prompt-powered personalization comes in. By leveraging Large Language Models (LLMs), Learning and Development (L&D) teams can now instantly adapt content for different learner personas using smart prompt templates

Cohere, the Toronto-based startup building large language models for business customers, has long had a lot in common with its hometown hockey team, the Maple Leafs. They are a solid franchise and a big deal in Canada, but they've not made a Stanley Cup Final since 1967. Similarly, Cohere has built a string of solid, if not spectacular, LLMs and has established itself as the AI national champion of Canada.

My name is Mark Kurtz. I was the CTO at a startup called Neural Magic. We were acquired by Red Hat end of last year, and now working under the CTO arm at Red Hat. I'm going to be talking about GenAI at scale. Essentially, what it enables, a quick overview on that, costs, and generally how to reduce the pain. Running through a little bit more of the structure, we'll go through the state of LLMs and real-world deployment trends.

Google's AI Mode is good at finding answers, but what happens when you need to do something with those answers? The thing is, different AI tools excel at different things. Some are built for seamless app integration, others crush data analysis, and a few specialize in understanding your industry context. If you're looking for Google AI Mode alternatives that can handle the heavy lifting in your daily work, these options bring something unique to the table.​​​​​​​​​​​​​​​​ 🎯

MCP gives all three context. MCP stands for Model Context Protocol and was developed and open-sourced by Anthropic to standardize integrations between AI and the tools and data sources that can provide critical information in ways that enable LLMs to understand and take action. Instead of every service building out an integration for every AI agent, MCP defines a protocol where any application can maintain a single MCP server implementation that exposes its functionality,

You might say that the biggest AI startups are led by professionally unreliable narrators, who theorize about the future of their companies - and their industry, and humanity in general - with a variety of clear but sometimes conflicting biases. This can make it somewhat hard, from the outside, to figure out which vision investors are banking on, beyond a general fear of missing out: Mass labor automation? AI-assisted research? Mainstream search-like products that could unseat Google?

AI Overviews offered incorrect information about the game to some players, as well as the crew at Spilt Milk Studios when they tested the responses. For instance, AI Overviews suggested that a player could damage a trinket when they were removing debris from it, which is not true. It also in some cases delivered the correct information, but pointed the user to an incorrect source.

The 5th International Conference on Computing and Communication Networks (ICCCNet-2025) concluded on a high note at Manchester Metropolitan University, solidifying its reputation as a premier platform for global innovation. From August 1-3, 2025, the conference became a crucible for ideas, bringing together brilliant minds from academia, industry, and government to forge the future of technology. The prestigious best paper awards, announced at the close of the event, weren't just accolades; they were a roadmap to a more intelligent, sustainable, and equitable world.

"There are definitely some groups that are using AI to aid with the development of ransomware and malware modules, but as far as Recorded Future can tell, most aren't," says Allan Liska, an analyst for the security firm Recorded Future who specializes in ransomware. "Where we do see more AI being used widely is in initial access."

First things first, you're going to select your LLM. You can go with OpenAI. It's a pretty standard choice for your Hello World. You're going to go to the documentation and you'll see how to actually do a Hello World using OpenAI. Of course, you'll see Python over there. Python is always there. I'm going to count as a win because we're starting to see examples in Java as well.

Large language models (LLMs) are just another fancy compiler. Back in the 50s and 60s, everyone was working in Assembly, and then C showed up, and we didn't stop coding in Assembly because C was suddenly perfect. C isn't perfect, but we stopped doing it because C is good enough, and we're more productive coding in C. And to me, LLMs are a very similar trade-off. They're not perfect yet, but at some point they will be good enough to make us more productive.

The failure to utilize Huawei's chips forced DeepSeek to shift to Nvidia's H20 GPUs for the training of its next-gen LLMs due to instability and performance issues.

OpenAI has released two open weight LLMs, gpt-oss-120b and gpt-oss-20b, which can perform similarly to recent small language models on accessible hardware.

We’re essentially a technology digital team for any business... with particular agents that have particular functions. We just need general ideas on what to do.

The purpose of Meta's copying was to train its LLMs, which are innovative tools that can generate diverse text and perform numerous functions.

Code Feedback MCP Server provides AI with real-time feedback necessary to ensure that code meets quality standards before it is deployed.

The comparisons reveal that GPT-3.5 excels in bug detection with the highest rates on Defects4J and ConDefects, showcasing its powerful mutation generation capabilities.

In this study, we comprehensively assess the capabilities of various LLMs in generating code mutations, emphasizing their effectiveness, usability, and performance across distinct metrics.

LLMs (Large Language Models) have dramatically evolved, accomplishing previously thought impossible tasks. However, challenges remain, and insights from industry leaders are essential to navigate these complexities.

Building LLM-powered applications is comparable to developing modern cloud-native applications, requiring notable support for REST and RPC protocols, where Go excels.

Nowadays, LLMs are everywhere - and it's amazing to see how these models are being used in practical DevOps workflows.