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OpenAI is bulking up before its IPO, landing Transformer co-inventor Noam Shazeer from Google DeepMind and former Trump AI policy official Dean Ball in the same week.

A new system service will roll out this month ahead of big changes starting in September.

TechCrunch spoke to investors to find the hottest startups in the Spring 2026 YC batch. Some of them commanded valuations of over $175 million, VCs said.

Dads are traditionally tough to shop for—let me help with these handpicked gift ideas for fathers with great taste.

Rivian is being sued over the self-driving capabilities of its early vehicles, or lack thereof.

The vulnerability, disclosed 12 months ago, affects multiple manufacturers.

Let your agents read, send, and react to email autonomously Discussion | Link

Plaintiffs in the class action complaint allege Rivian falsely promised for years it would bring hands-free driving to its first-generation R1 vehicles.

On today’s Uncanny Valley, we dive into the dysfunction in Meta’s newly formed AI unit and why it’s been driving already-low employee morale even further into the ground.

Social media growth agents that build your momentum Discussion | Link

The easiest way to save on a streaming service is often to pay for a year upfront, which HBO Max is currently making a lot cheaper. Through July 15, 2026, new and returning subscribers can get 28 percent off any of HBO Max’s yearly plans, bringing the ad-supported tier down to $78.99 ($31 off) per […]

Snap's new smart glasses are probably the most impressive bit of face-computer technology we've seen. They're not VR-headset huge; they don't have a big charging puck; thanks to Snap's many years of AR lens development, they're likely to have a lot of features right out of the box. (Yes, they're $2,195, but that may just […]

Read & listen to native Japanese and Chinese, tap any word Discussion | Link

About 47% of singles look negatively at the use of AI in dating -- but, many dating app users are open to AI helping with profile punch-ups and conversation starters.

AI agents get better the more they run. Every conversation turn, every task completed, every prompt refined adds to a growing body of context that shapes the next output. The compounding effect is real: an agent with 100 turns of memory and a versioned prompt history behaves meaningfully differently from one starting cold. This post walks through using a structured, versioned, API-accessible store as the memory layer for AI agents, with TypeScript examples. Agent messages, system prompts, findings, and instructions are all stored as structured, versioned, API-accessible Objects. Each new turn adds to the record. Each prompt edit is tracked. What Agent Memory Actually Needs The compounding loop only works if the memory layer has the right properties. Most agent frameworks handle working memory well. The gap is episodic and semantic memory: what the agent learned, did, and produced across sessions. Researchers at Elastic recently published a breakdown of agent memory tiers : working memory (in-context), episodic memory (past interactions), semantic memory (knowledge), and procedural memory (learned behaviors). Good persistent agent memory needs four properties: Structured : queryable by type, status, date, or custom field, not just full-text search Versioned : you need to know what the agent wrote at each point in time, not just the latest state API-accessible : any model, any framework, any language should be able to read and write it Human-reviewable : agents make mistakes; a human needs to inspect and correct outputs without touching a database Objects as Agent Outputs When an agent produces output, storing it as a structured Object gives you a queryable record with typed fields, a draft/published workflow so a human can review before promoting to production, a full audit trail of every change, REST API access from any runtime, and a dashboard UI where non-technical team members can inspect, edit, or approve agent outputs. Here's a simple research agent that stores

Instagram carousels can now be up to 20 slides with 20 unique captions.

Expo SDK 56 ships with a custom Kotlin compiler plugin that eliminates reflection from Expo Modules on Android. The result: 70% faster module initialization and a 30% reduction in time to first render. The plugin runs during compilation, so app developers get these performance gains automatically without changing any code. Module authors can unlock even bigger wins with a single annotation. This post walks through how we built it and why this approach succeeded where previous attempts failed. For the Swift side where we now talk to JSI directly, check out our companion post Talking to JSI in Swift . The reflection problem we inherited Before Expo Modules, we had Unimodules. They worked like old React Native bridge modules: you'd sprinkle annotations across methods you wanted to expose, and the runtime would discover everything through reflection. class ClipboardModule ( context : Context ) : ExportedModule ( context ) { override fun getName () = "ExpoClipboard" @ExpoMethod fun getStringAsync ( promise : Promise ) { val clip = clipboardManager . primaryClip ?. getItemAt ( 0 ) promise . resolve ( clip ?. text ?. toString () ?: "" ) } @ExpoMethod fun setStringAsync ( content : String , promise : Promise ) { clipboardManager . setPrimaryClip ( ClipData . newPlainText ( null , content )) promise . resolve ( true ) } } Reflection made sense when we needed metadata about our own code. What methods does this module export? What arguments do they accept? The JVM could answer those questions. But reflection costs time, and on Android that time comes straight out of your startup budget. Every module the runtime introspects adds milliseconds before users see your app. Building the Expo Modules API gave us a chance to fix this. We wanted better ergonomics and less reflection. The Kotlin DSL delivered both in one move, removing most reflection while making modules easier to write. But we couldn't eliminate all of it. Type information for function arguments and Record properties s

I’ve been a software engineer for more than 12 years. And like many developers, I’ve been watching AI improve at an incredible speed. Every new model seems smarter than the one before it. Tasks that used to take hours can now be done in minutes. Problems that required deep research can often be solved with a simple prompt. A few years ago, we used to say: Think of AI as a junior developer. That made sense at the time. But today, I don’t think that’s true anymore. AI still makes mistakes. Sometimes very obvious ones. But it also comes up with solutions that surprise me. Sometimes it finds an approach I wouldn’t have thought of immediately. Sometimes it helps me solve a problem much faster than I could on my own. And honestly, that’s both exciting and a little scary. But the biggest thing AI changed wasn’t how I write software. It changed how I think about my work. For most of my career, I thought I loved writing code. I spent years doing it. At work, on side projects, and whenever I had free time. Then AI became part of my daily workflow. In the last month, I’ve built more projects than I normally would in an entire year. Ideas that had been sitting in my notes for years suddenly became possible. And that’s when I realized something important: I don’t actually love writing code. I love building things. I love taking an idea and turning it into something real. I love creating products, solving problems, and seeing something that only existed in my head become something people can use. Code was simply the tool I used to do that. And now AI is another tool. That’s why I don’t hate it. In many ways, AI has helped me build more than ever before. It helped me revisit old ideas that I never had time to work on. It helped me experiment faster. It even encouraged me to explore areas outside software development, like animation and content creation. And this isn’t just happening to programmers. AI is changing design. It’s changing writing. It’s changing marketing. It’s changin

You don't have to have a Pixel Watch to use Google Wallet. Here's how to set it up on your Galaxy Watch.

AWS is in talks to sell its chips to other data centers. CEO Andy Jassy has said this represents a $50 billion opportunity for the company.