People on Reddit are calling it something beyond enshittification. One user put it simply: "Google basically just ruined the old ten blue links era." Another said Google is "abusing its status as infrastructure to weasel its AI into consumers' day-to-day." That's the actual audience reaction. The week after Google announced it was replacing its traditional search results with an AI agent that answers queries, executes tasks, and runs background monitoring, DuckDuckGo saw US app installs jump 18.1% week over week on average. It peaked at 30.5% on May 25. On iOS the numbers were sharper, week over week growth hit 33% on average and peaked at 69.9%. The company also said growth held through the Memorial Day weekend, when it usually sees a dip. DuckDuckGo has been stuck at around 2% of the US search market for years. One Google I/O announcement moved its install numbers more than anything DuckDuckGo has done on its own.

The mixture-of-experts trick changed how people think about LLMs. Instead of running every parameter on every token, you activate a small fraction of the network per forward pass and somehow the quality stays competitive while the compute drops. It's the reason models like Mixtral punched above their weight. Everyone in the LLM space understood it immediately. Nobody had done it openly for image generation. Until now. Nucleus-Image is a 17B parameter diffusion transformer that activates roughly 2B parameters per forward pass. It beats Imagen4 on OneIG-Bench, sits at number one on DPG-Bench overall, and matches Qwen-Image on GenEval. It's also a base model. No fine-tuning, reinforcement learning or human preference tuning. What you're seeing in those benchmarks is raw pre-training performance. That's either impressive or a caveat depending on what you need it for, probably both.

There's a quiet assumption baked into how most people think about AI models. Bigger means better. More parameters means more capable. If you want the best results, you run the biggest thing you can afford. Qwen3.6-27B makes that assumption uncomfortable. It's a 27B dense model, fully open source under Apache 2.0, and on agentic coding benchmarks it beats Qwen3.5-397B — a model nearly fifteen times its size — across every major test. That's not a rounding error or a cherry-picked metric. It's a consistent pattern across SWE-Bench, Terminal-Bench, and frontend code generation. This doesn't mean bigger models are dead. It means the gap between what you can run locally and what only clusters could handle a year ago just got a lot narrower.

Andrej Karpathy doesn't make random career moves. He co-founded OpenAI in 2015, left to build Tesla's self-driving program, came back to OpenAI for a year, then left again in 2024 to start an AI education company. Every transition has been deliberate and every one of them has turned out to be worth paying attention to. On Tuesday he posted on X that he's joined Anthropic. "I think the next few years at the frontier of LLMs will be especially formative," he wrote. "I am very excited to join the team here and get back to R&D." The "get back to R&D" part is the signal. Karpathy has spent the last several years teaching, building, and explaining. Now he's going back to the frontier. And the specific place he's going says something about where the most important work in AI actually is right now.

Anthropic has officially taken its first step toward becoming a public company. In a brief announcement on Monday, the company said it had confidentially submitted a draft S-1 registration statement to the U.S. Securities and Exchange Commission for a proposed initial public offering. The filing doesn't reveal a share price, a fundraising target, or even a timeline. For now, it simply gives Anthropic the option to go public once the SEC review process is complete. Just a few years ago, Anthropic was a small group of former OpenAI researchers trying to build an alternative vision for advanced AI. Today, it sits among the handful of companies shaping the industry's future and that's why this filing matters. It's one of the world's most influential AI labs beginning the transition from a privately funded research company to a business that may eventually answer to public shareholders. For most of the AI boom, the biggest bets were made behind closed doors. Venture firms, sovereign wealth funds, and tech giants supplied the capital while the public watched from the outside. Anthropic's filing suggests that era may be starting to change.

I didn’t stop using Perplexity because it’s bad. I stopped because it slowly stopped feeling like mine. At first, it was impressive with fast answers, clean summaries, citations that made Google feel outdated. I used it almost daily. Over time though, it began to feel less like a tool and more like a gate. Features I relied on sat behind prompts to upgrade, and the experience started nudging me in directions I hadn’t chosen. That’s when I started looking sideways instead of paying up. I wasn’t searching for a cheaper alternative. I wanted control search that runs on my machine, uses models I choose, and doesn’t turn every query into someone else’s asset. That curiosity led me to Perplexica. I tried it casually, without expectations. It stuck.

Getting an AI agent to start a task is easy. Getting it to finish one properly is a different story. Most agents fall apart somewhere in the middle. A tool returns unexpected output, the model misreads it, and everything that follows builds on that mistake. By step thirty you are looking at something that has completely lost track of what it was supposed to do. The five AI models here were built with that specific problem in mind. They handle complex multi-step tasks, real browser control, deep research and coding workflows. All open source & self hostable.