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OpenAI has introduced Codex Security in research preview, positioning it as a project-contextual security capability within the Codex ecosystem. The feature is designed to build threat models for a codebase, validate potential findings in sandboxed environments, and surface proposed patches for human review. The release is significant for teams evaluating AI-assisted application security because it frames security work as more than a static scan. OpenAI's current approach combines codebase context, validation, and reviewer oversight, while keeping the feature tied to its cloud and web product experience rather than presenting it as a standalone open-source security command-line tool. What OpenAI has released According to OpenAI's Codex Security research preview announcement , Codex Security is being introduced as a research-preview feature for customers using Codex web. OpenAI says the tool develops a threat model based on project context, investigates potential issues, validates findings in sandboxed environments, and presents patches for human review. That sequence matters because security tooling can produce findings that require substantial engineering effort to triage. A system that can incorporate project context and test a potential issue before escalating it is intended to make findings more actionable. However, OpenAI's own description still places a human reviewer in the decision loop for proposed changes. OpenAI says Codex Security is rolling out through Codex web for ChatGPT Pro, Enterprise, Business, and Edu customers. It is therefore an enterprise-oriented, cloud-hosted preview capability with controlled availability, not a generally available security product for every developer environment. The currently described workflow includes: Building a threat model using the context of a software project. Investigating and validating potential security findings in sandboxed environments. Surfacing proposed patches for human review. Providing access through Co

Xbox is having yet another tough quarter, as revenue from content and services like its Game Pass subscription dipped 10 percent over the past few months. At the same time, Xbox hardware sales declined 13 percent, according to Microsoft's fourth-quarter earnings report released on Wednesday. The news comes just weeks after Xbox head Asha Sharma […]

Government ban on foreign-made robots may hinder instead of help US robotics.

Comcast, accused of negligence, says lawsuit mischaracterized alleged events.

Curious if you should get Dyson’s new 2026 stick vacuums or stick to the older ones? I tested five models, old and new, to find out for you.

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You correct someone once. Not perfectly, but they get it. Next time, they do not make the same mistake. That is not optimism. That is just how correction works, "with people". I worked with agents on that assumption for a long time before I even noticed I was doing it. The plan that never held Before I had a single written rule anywhere, I would open a new session and ask for a plan first. Resolve the edge cases before touching a line of code, I said. The agent would agree, in whatever way a chat window agrees, and go straight to implementation anyway. I corrected it. Same session, it adjusted. New session, next day, same repo, same everything except the chat history: straight to implementation again. Every single time! So I did what looked reasonable. I wrote the plan myself. I resolved the edge cases myself, the open questions, the gaps the agent skipped past on its way to code. ' Tedious ' is the polite word for it. I was doing the one task I brought the agent in to do, and calling it collaboration. The same recipe, again The second correction arrived the same way. Every repo had its own shape. A recipe, a standard, a way things were supposed to be built here and not there. I would explain it. Full session, good results, the agent following the standard like it understood the standard. New session. Same repo, sometimes the new repo. Explain it again. Word for word, close enough. It was not that the agent forgot how to code. It was that nothing from the last conversation traveled with it into this one. Nothing said in the chat survives it I kept treating this like a training problem. Say it clearer. Say it earlier. Say it with an example next time. None of that was wrong exactly. It was aimed at the wrong layer. The actual mistake was assuming correction compounds the way it does with a person. It does not. A person carries what you told them into the next conversation without being asked to. An agent starts the next session exactly where it started the first one.

Apple's new Upgrade program is here, allowing you to lease select models of iPhones, iPads, Macs, and Watches with a relatively low monthly payment. The company promises you won't pay more than the full price of the device over the course of the one- to three-year lease, and in some cases, you'll pay hundreds of […]

Next.js Sitemap Not Updating? Here's the Real Fix If your Next.js sitemap is not updating after you publish new content, you're dealing with a cache-coherence bug that almost nobody writes up. It has an exact symptom, a reproducible root cause, and a one-line fix. This is the guide you'll wish you had the moment you notice /sitemap.xml serving fewer entries than your real site. The symptom: your sitemap lags behind your published content The mismatch is impossible to miss once you look. On our own site, /lab lists 11 published posts, yet /sitemap.xml shows only 7. Same database, same deploy, two different answers. If that gap sounds familiar, you're in the right place. You might have checked your afterChange hook, verified that revalidateTag('posts') fires, and even confirmed that the tagged data refreshes — only to find the sitemap still frozen. That's because the problem lives between two cache layers, not inside the data fetch. Why revalidateTag doesn't fix a stale Next.js sitemap The answer lies in what sitemap.ts actually is. According to the Next.js Metadata Files: sitemap.xml documentation, it's a special Route Handler. And like any Route Handler, Next.js caches its rendered output by default. Here's what happened in our own repository (this bug is documented in a comment at the top of app/(frontend)/sitemap.ts because it cost real indexation time): We read content from Payload using unstable_cache , tagged with the collection slug posts . An afterChange hook called revalidateTag('posts') whenever a post was published. That call did work — it invalidated the inner unstable_cache data entry. But the route's statically-rendered outer XML output was never re-run. The frozen route output kept serving the old XML built from the old data, long after the inner cache was refreshed. Two cache layers. Tag-based revalidation busted the inner one, but the outer route handler cache was never told to re-execute. That's the missing piece. The one-line fix: route-level ISR o

I put together a project to document an architectural discipline I call " Lateral Isolation ". The core idea is simple: preventing direct communication between peer services by requiring all interactions to pass through a controlled boundary. I am not claiming this is a brand-new pattern—it is essentially Information Hiding and the Acyclic Dependencies Principle applied strictly at the service level. However, I wanted to provide more than just theory. My GitHub repository includes runnable code and an ArchUnit test that physically proves the isolation holds and prevents the inevitable "just this once" dependency sprawl. The Trade-offs (The "Tax") I have explicitly documented the costs because architectural rules are never free: Latency: Enforcing this means accepting a 5–10 ms latency tax per hop. Centralization: A shared boundary introduces centralization risks. Because it is not meant to be a blanket rule, I also included a framework for deciding when to enforce it versus when to skip it. Looking for Critique I am looking for this community to poke holes in the logic. Where does my "decision rule" fall apart? I would appreciate any blunt feedback or edge cases I might have missed. You can check out the runnable demos and the full logic here: https://github.com/vijayagopalsb/isolation-tax

Internships are one of the most valuable experiences for any undergraduate, and I am grateful to have completed mine. This journey allowed me to bridge the gap between academic knowledge and real-world software development while improving both my technical and professional skills. From my very first day, I was introduced to a collaborative development environment where teamwork, communication, and problem-solving played a major role. I had the opportunity to work on real projects, understand industry workflows, and learn how professional software products are built and maintained. Throughout my internship, I gained hands-on experience with modern web technologies, version control using Git, API integration, debugging, and deploying applications. I also learned the importance of writing clean, maintainable code and following industry best practices. Working alongside experienced developers helped me improve my coding standards and exposed me to new tools and frameworks. One of the biggest lessons I learned was that software development is not only about writing code. It involves understanding user requirements, collaborating with team members, managing deadlines, and continuously learning new technologies. Every challenge I encountered became an opportunity to grow and improve my skills. Beyond technical knowledge, this internship strengthened my confidence, communication, time management, and ability to work effectively in a professional team. The guidance and support from my mentors played a significant role in my growth throughout this journey. Looking back, this internship has been a milestone in my career. It has given me practical experience, valuable industry exposure, and a clearer vision of the software engineering field. I am excited to apply these lessons in my future projects and continue growing as a developer. I would like to express my sincere gratitude to my mentors, teammates, and the organization for providing me with this incredible opportunity. Th

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In June 2024, a small company called Zanskar purchased a geothermal power plant in New Mexico that was failing fast. The water coming from the underground reservoir was getting colder by the day, making the plant uneconomical to run. Now, two years later, that plant is running at full capacity again, thanks to a new…

Musk defends Grok, says Minnesota's nudifying app ban is unconstitutional.

To the horror of commenters across the internet, the Ferrari Luce appears to be a sales success.

Waymo robotaxis are now using freeways in Phoenix with more cities to follow in the coming days.

We played spot the difference and noticed this intriguing difference in Google's teaser for the Pixel 11 Pro.

Companies are making sure we have a surplus of options for building qubits.

Tens of thousands of AI-generated songs are uploaded to music streaming platforms every day.