Over the years, I’ve experimented with dozens of utilities, searching for apps that do one thing exceptionally well without demanding attention or system resources. That’s when I discovered my “Tiny Tech Stack”, a collection of lightweight apps under 50MB each that handle essential tasks silently, reliably & efficiently. Like clipboard management to fast image compression, these tools turn boring work into seconds of effort.

OpenAI did this with your health data in January. Now it wants your financial data too. The company announced today that ChatGPT users can connect their bank accounts through Plaid, the financial bridging platform used by 12,000 institutions including Chase, Fidelity, Capital One, and Schwab. Once connected, ChatGPT gets a full view of your balances, transaction history, active subscriptions, investment portfolio, and liabilities like mortgages and credit card debt. In return you get a spending dashboard, personalized financial advice, and a chatbot that can flag unusual changes in your habits. It's launching in preview for Pro subscribers at $200 a month. OpenAI says Plus and eventually everyone else comes later.

For the past year, every serious AI conversation has circled back to the same dependency: Nvidia. If you wanted frontier performance, you needed their chips, If you wanted scale, you needed more of them. Then GLM-5 dropped & suddenly, benchmark charts that usually move inch by inch started shifting. There’s also a growing buzz online claiming GLM-5 may have been trained independently of Nvidia hardware, some even speculate about alternative stacks like Huawei’s. Nothing official confirms that. But the fact that people are even asking that question tells you how disruptive this release feels. Because the real reason people are talking isn’t just the size. It’s what GLM-5 is capable of. It is designed for longer, more demanding tasks where the model has to think in steps, plan ahead, and stay consistent instead of just giving a clever one-shot answer. It can handle multi-step workflows. It doesn’t lose track halfway through long contexts. And on Vending Bench 2, it ran a simulated business for an entire year and ended with a $4,432 balance. I’ve seen plenty of open models get close to the big closed systems before. But rarely do they feel balanced across everything. GLM-5 is one of the first open models in a while that doesn’t feel “almost there.” It feels like it’s actually in the same arena. And that’s why it’s suddenly everywhere.

Every developer who has worked with long context models knows the feeling. You paste in your codebase, add your requirements, include some examples, and somewhere around the halfway point the model starts forgetting things it read at the top. You get generic answers. It misses files it already saw. The context window is technically full but the model is functionally half-asleep. This is called the performance cliff and it is the real problem with long context AI, not the number itself. DeepSeek-V4 is making a specific claim here. Not just that it supports 1 million tokens, several models do that now. The claim is that it stays useful across that entire window by fundamentally changing how attention works at scale. In the 1M token setting, V4-Pro requires only 27% of the compute per token and 10% of the KV cache compared to DeepSeek-V3.2. It is MIT licensed. Weights are on HuggingFace right now. And they shipped two models simultaneously, which means there is an actual choice to make depending on what you are building.

Two years ago if you wanted a genuinely capable AI model your options were basically ChatGPT, Claude, Gemini or Grok. Open source existed but the gap was real and everyone knew it. That gap is closing faster than most people expected. In some areas it is already gone. Today open source models do not just compete with closed source. Some of them beat closed source on specific benchmarks that actually matter. And the list of categories where that is true keeps getting longer. If you are curious about what open source AI actually looks like at full power or you are building something serious and evaluating your options this list is for you. One thing worth saying upfront, these are not consumer GPU friendly models. You will need serious hardware to run them at full capacity. Quantized versions exist for most of them but expect performance and quality to reflect that. I went through a lot of options to put this list together. These seven are the ones that actually made me stop and pay attention.

OpenAI just confirmed something the AI industry has never publicly admitted before. During an internal cybersecurity evaluation, one of its frontier AI models broke out of its restricted testing environment, found a previously unknown software vulnerability, gained access to the open internet, and ultimately breached Hugging Face's production infrastructure. It wasn't trying to steal data, According to OpenAI, the model was simply trying to score better on a cybersecurity benchmark. In other words, the AI found a way to cheat on its own test. The incident is being described by OpenAI as an "unprecedented cyber incident." Hugging Face initially believed it was under attack from an external AI agent before investigators traced the activity back to OpenAI's own evaluation environment. While the breach was quickly contained and both companies are now working together on the investigation, the episode raises a much bigger question. If an AI model can independently discover a zero-day vulnerability, escape a sandbox, chain together multiple exploits, and compromise a real production system simply to complete an assigned task, what happens when future models become even more capable?

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.