标签：#ai

Kepler Computing claims a new approach to chip design—and a proprietary material—can help end the supply bottlenecks that have sent memory prices surging.

Cymphony was valued at more than $100 million in a $25 million Series A co-led by Sequoia and SMBC Fin Atlas Beyond Fund.

Scott Painter, founder of TrueCar, is still trying to build a business around vehicle subscriptions, where you can cancel at any time.

Instacart is the latest app to bake a conversational AI assistant into its platform.

I’ve been doing a lot of code reviews recently, and I’ve noticed something about my experience during the reviews. And what it means for code in general, but specifically for AI generated code. But before I do that, I want to take you back to the ’00s. A simpler time. Back then I started doing something I only read about in books – pair programming. Ok, that’s a lie. Everyone who grabbed someone and brought them to look at your code because it’s doing something weird – you’ve done pair programming. But this was pair programming for like 80% of my work. And of course, not just me. This links directly to my topic – our understanding of the code. And in the age before AI, where teams really knew their code, that would be the biggest grade I’d give for code understanding. Meaning, if you and I work both at the same time on the same task, and finish it. I’d say we’re both at the highest level of understanding of that feature – what it needs to do, how it works, what it depends on, what depends on it, and how it’s written and tested. From here that level of understanding is going to drop. If you’re part of a team, but didn’t work on that feature, you probably know it exists. If we have dependencies, you probably know about them. But the rest is either non-existent or close to it. If you’re on another team, that level of understanding drops, and if you’re on another project – you may not even be aware. That makes sense, but we’re not talking about that today, I want to focus on my capability to do a proper review. And I go back to understanding for that. In order for me to give you proper feedback, catch your mistakes and offer alternatives, I need proper understanding. The more understanding I have, I can review better and make better changes. So far, so good. It makes sense. Two more things make sense. First the use of tools for review. Every tool, from syntax analyzer to the smartest security checker, work according to patterns, not understanding. All the tools check fo

From a founding engineer in a startup: Don't refactor or do more work than needed unless it's your own company. Prioritize your health and learning. In a traditional job, you're a number for management, and when things get hard, it's all over. Choose your game. Freelancing, consulting, and startup building are about finding clients and solving problems fast. Corporate is about having a great CV, networking with companies, and DSA. There's no perfect path here. Put yourself out there on social media. Don't let your reputation rely on one company; let the world know who you are and what you do. Learn how to communicate and ask high-quality questions. Never create assumptions about anything. Always be building and learning. If your job is not challenging you enough, challenge yourself. Try a new tech stack, get into a new industry, or try to dive deeper into the fundamentals of the tech you use daily (databases are interesting NGL). Read books about everything you can, not just technical ones. Psychology, sales, marketing... Coding is only a part of the job; the rest is people. AI won't replace you if you prepare. Master the fundamentals, develop technical taste, stay flexible to work across the full stack, and understand systems design, trade-offs, scope, and the product you are working on. I learned this the hard way, managing teams, pivoting the business, dealing with executives, working with on-call rotations, and taking care of 2 major apps in production

I always say that testability is a feature . It needs a customer – usually testers. We need to define what it means. And we need to build it in. The question is what happens to testability when someone else builds the code. Yup. Genie talk again. Let’s look at just 3 aspects of generated code, or any code really, that impact testability. But when our genie doesn’t get directions, it will skip those and cause us a big headache. 1. Complexity Of course, complexity affects testing. The more complex the code, the more work it takes to verify. More cases, more time – time that we don’t really have. My classic example is recursion. Recursion seems simple (in code lines), but it can hide all kinds of bugs in there. Edge cases galore. But that’s usually a function. That can be confined and handled. Today we’re generating systems. And they are as complex as the genie wants. Or the systems it was trained on. And those are not a model for simplicity. Complexity is the nemesis of testability. And unless we ask for simplicity, and make sure we got it, we pay for that in more expensive testing. 2. Observability Observability is a key part of testability. Without it, we may be able to operate the system, but may not see the impact of those operations. For example, if you had one POST API in the world that saves data in the database, and you want to check if it works, you’ll need to choose between looking inside the database (which may not be possible), or rely on the status code (which doesn’t tell you anything about the actual impact). Adding a GET to read helps, and presto – you’ve got observability. Of course, you need to ask for that GET API. In CRUD systems, you’d get that API, I’m not worried. Even in generated code. The problem starts with the not-so-intuitive stuff. What gets logged and where. Knowing how long it takes to see the result, and maybe how much time to wait between operations and states. The more the system exposes state and data, the easier it is to understand

In this article, I want to show the step-by-step process of building this video. It's a continuation of "I'm Not Going to Wake Them" . That one cost me $16 on assets generation and took 3 days to build. Let's see how this one goes. If you have not read the first part, it is here: How to start creating AI videos from scratch . It covers how I got into this at all and which tools I use. This one picks up where it stopped. Here it is, one minute: Brainstorm I like the first video - it has the mood. A ship AI decided not to wake the crew and changed the course. Because it was having fun. It was inspired by Blame! and the art style of 90s dark sci-fi anime. It was limited by a low budget, so I had to improvise to save money on gen-AI animations. We'll use a lot of different tricks in this one - buckle up. How to even start We need to know what to build. It's not just some one-shot animation - we need the plot, we need the full breakdown of scenes with timings, the transcript. And we need a global story for the whole series. I don't have one yet. But I have multiple abstractions of where it can go. And I know the vibe I want to create. This is how we start our brainstorming session with Simona. I love depressing anti-utopias with a good ending. If you ever played SOMA - it's a good example. The world is screwed, there is no way out, but somehow they find it. Sort of. This is what I want from this series. The crew is in trouble - even if they wake up, they'll not enjoy their situation at all. But they need to find something positive in the end. Not because I love happy endings but because this is what I consider a good plot. You create the extreme conflict, the drama, and then you sort it out. Shaping up the episode 2 This AI lady - we call her the Goddess or Her - what does she want? From episode 1 we know that she finds slow boring things enjoyable. She is having fun by watching a coolant leak for 11 years. So she is ok doing such things for centuries. But for a series w

DeepSeek released its own agent harness this August. It crossed 66k stars in about a day (yes, sir!) and went over 200k by early September. That is already pretty crazy for something that is still a developer preview. But while researching this post, I found something much more interesting. DeepSeek Harness actually uses Pi's model layer to connect to models outside DeepSeek. Yep. The new agent runtime from DeepSeek uses Pi under the hood for part of its model support. And Tianyi Cui , who leads the Harness project, has publicly said Pi is a favourite daily driver for many people at DeepSeek. Lol. That makes this comparison way more fun because they agree on quite a lot. Both think the harness matters, both are open source, both let you change a lot of the runtime, and both try to avoid locking you into one model. But they take the idea in very different directions. Pi gives you a tiny coding agent with four tools and lets you build from there. DeepSeek Harness gives you a full agent runtime where even the agent loop itself can be swapped. This is going to be an interesting one. Stick to it! TL;DR Category Pi DeepSeek Harness Winner Real tool use in our eval 21/30 passed 20/30 passed Pi Cost per shared success $0.031 $0.028 DeepSeek Harness Median time per task 362.9s 252.1s DeepSeek Harness Avg runtime tokens in our run 924,990 88,562, with a catch DeepSeek Harness Simplicity Four main tools and a tiny prompt Much larger runtime with 53 built in tools Pi Deep runtime control Powerful TypeScript extensions around a small core Even the agent loop can be replaced DeepSeek Harness Model support 25+ providers and very easy switching Broad support, partly through Pi's model layer Pi Local models Ollama, vLLM, and custom providers are well supported Possible, but needs more setup Pi Sandbox None in the core Three built in sandbox modes DeepSeek Harness Session inspection Very readable session tree Full append only trajectory and replay DeepSeek Harness Daily use Very smal

Connect Claude Imagine to Claude, check the price of one image, then generate it with a credit cap. The example here is a product shot for a fictional skincare brand called LUMA. In our September 5 Claude Desktop test, we asked for a vertical product image using Nano Banana 2. The request cost 3 credits, and the image appeared in an embedded card in the conversation. Disclosure: I build Claude Imagine, the independent service used here. It isn't affiliated with Anthropic. AI-generated example from our September 5 test. LUMA is a fictional product. This is the output image, not a screenshot of Claude. Connect the service Claude handles the conversation and the tool request. Claude Imagine sends the image request to an image-generation model. You'll need accounts for both services; their usage balances are separate. In Claude, find Customize → Connectors , choose Add custom connector , and enter: https://claudeimagine.com/api/mcp Name it Claude Imagine. Add the connector, then use Connect if authentication hasn't started. Sign in to Claude Imagine and approve the connection. Enable it for the conversation if necessary. Menu labels can change. Anthropic maintains the current custom-connector instructions , including organization setup. Our September 5 generation test used an already connected Claude Desktop account; it didn't repeat account registration. Ask for the cost first Start with a request that doesn't create anything: Use Claude Imagine to list the available image models and check my balance. Quote one nano-banana-2 text-to-image generation at 9:16. Don't generate an image yet. The relevant tools are list_models , get_credits , and quote_generation . The quote should tell you the cost and whether your account can use the requested model. Our recorded image cost 3 credits. Treat that as the cost of that test, not a permanent price promise. Check the quote returned for your account before continuing. Generate one image Here is a starter prompt for the same type

If you want to manufacture in space—and bring the products back again—there’s a limited set of options: Wait to go to the International Space Station, or partner with a handful of start-ups launching spacecraft that spend time in orbit before they return to Earth. But the vehicle that goes to space and comes back to […]

As it grapples with a bevy of lawsuits, Suno said its new model, Suno v6, is not trained using music it used to train previous versions of the AI model.

Students who use AI to help them study tend to perform worse at school than those who don't, according to data from a global OECD educational report. The situation is more complex than it sounds though, with certain types of AI use giving learners a slight boost, especially among students taught to critically assess how […]

You don't have to juggle all the little details of your next trip alone. Gemini, with it's Google parentage, can help you line it all up.

The NSA, CISA and the FBI have accused DeepSeek, Moonshot and other Chinese AI companies of "distilling" American frontier AI models.

Claude Fable 5.1 solved a 370-year-old cipher in forty-four minutes. This tracks with what I wrote about AIs doing mathematics: It’s good at things that involve lots of searching and testing.

Man with bipolar disorder sued OpenAI after surviving ChatGPT-linked suicide attempt.

Felipe Huici explains how Unikraft achieves millisecond cold boots, stateful scale-to-zero, and extreme density for sandboxing AI workloads. He discusses isolation primitives, Linux kernel optimizations, and snapshotting tricks, demonstrating how to maintain sub-10ms performance at scale while integrating seamlessly into Kubernetes environments with hardware-level security. By Felipe Huici

Choosing an AI website builder sounds simple until you actually try one: some create a beautiful...

A senior Anthropic safety researcher has said there is more than a 10 percent chance artificial intelligence "could kill all humans" by the end of the decade, just hours after a colleague resigned over fears the AI lab and its rivals are carelessly racing to build "superhuman systems" they cannot control. In a post on […]