While most fitness trackers are losing the screens to keep the price low, the CMF by Nothing Watch 3 Pro is a bit different. The budget-friendly smartwatch with a 1.43-inch OLED display is even cheaper than usual at Amazon, where it costs $69 in every color (the price fluctuates between $79 and $99). Some notable […]

The device is weirdly described as involving "mechanical elements that can move on their own" and the Bloomberg report includes the detail that the device is designed to "feel like a companion and become a physical manifestation of OpenAI’s ChatGPT."

Old and forgotten "shims" Microsoft failed to revoke have made Secure Boot bypasses simple.

Citizens must now spend 21 days in a third country before they are allowed to come home.

The restrictions, which can be turned off, will include a crackdown on “addictive” app features and will be in addition to a total ban on children under 16 accessing platforms like TikTok and YouTube.

OpenAI has issued another statement on the lawsuit, this time suggesting it lacks merit.

Simultaneous desktop and mobile streaming, the easy way. Discussion | Link

Hello all: I am just about finished with my first production-quality app. I developed it for macOS and Windows with full platform parity and am about to port to Ubuntu/Debian before releasing. I have licensing setup through Keygen.sh. I am happy with that decision so far; however, I have questions regarding that issue and the issue of telemetry. Privacy is a focus of mine and a commitment I made to prospective subscribers. That said, I need some form of telemetry and a DRM that can take subscription payments. I am trying to think of the best way to implement the Keygen.sh API for privacy. The app has, well, an app--the client--and it also has a remote server component. It will mostly be free but the more labor intensive developed elements and the tools that require the remote server will be paid at either a monthly or annual subscription rate. I am so confused about how to best implement the licensing API while remaining true to my privacy commitments. Part of me says, "Well, their information is going to be on Keygen's servers anyway, might as well host the store through their website/portal." The other inclination is to host the store on my server/website, so I can control how much information PII is required. The answer can be “very little,” but a much bigger question arises surrounding what to do with the information once accepted. (I’ve considered maintaining the subscriber data in some kind of encrypted space on the server with hardcoded, volatile keys. That way not even I can see their data, but they’d still be able to search for and regenerate their licenses, management payment methods, etc.) The other question I have is along that same vein and involves telemetry. What I would like to do is keep the telemetry deliverables locally stored. When the user has an issue, experiences a bug, or has some other issue requiring my troubleshooting assistance, I can request it and they can choose to release their telemetry data to me. In between those disclosures, using

The Google Cloud Workbench Notebooks extension for VS Code is a new tool that enables developers to connect their local IDE directly to managed Jupyter notebook environments on Google Cloud. By Sergio De Simone

Last year my team had to pick a coding agent, and I volunteered to run the evaluation. I felt good about it. I pulled up the public benchmark scores, lined up the contenders, took the one at the top, and told everyone we had a winner. Then we actually pointed it at our repo. It did not blow up dramatically. It just kept being slightly wrong in ways that ate our time. It wrote diffs our reviewers would not approve. It renamed a function and broke three files it had never opened. The tests it ran passed, and the repo was still broken. I had confidently recommended a tool based on a number that turned out to say almost nothing about our situation. That was embarrassing enough that I went and figured out why. It took a few weeks of reading and a couple more bad calls before I landed on something that works. This is that, written plainly, and I hope it saves you the meeting where you have to walk your recommendation back. Why the benchmark score lied to me The score was not fake. It was just measuring somebody else's code. Once I looked properly, four gaps explained the whole thing: The agent might have already seen the answers. The problems in these public benchmarks are old. Models were very likely trained on the actual fixes used to grade them. So the score partly measures memory, not problem-solving. The setup is nothing like real work. A benchmark gives the agent a clean repo, one clear issue, and one command to run the tests. My engineers give it a half-open editor, a messy branch, a Slack thread, and a reviewer comment. Completely different job. Our codebase has its own habits. Our internal libraries, our wrappers, our test style, the imports we ban. No benchmark knows any of that, so an agent can write textbook-perfect code that our reviewers still reject on sight. The bar for passing is way lower. A benchmark passes a patch if the broken test now passes. My team passes a patch if it does that, and does not break unrelated tests, does not reformat the whole file,

One weekend I wrote an LLM eval framework in about two hundred lines of Python. It demoed beautifully. I felt clever. Six months later that same framework was a mess. Three different judge models with three different parsing hacks. A test dataset nobody had touched since November. A CI gate that kept failing because a vendor nudged their model, not because anyone broke a prompt. And the second engineer on rotation asking me, fairly, "how does this even work?" The framework did not fail. The eighty percent of the work the weekend tutorial skipped is what failed. That gap is the whole story, and this is what I would tell myself before starting again. The one line I wish someone had told me: build the rubric, buy the runner Here is the split that took me six months to see. Some parts of an eval setup are yours and only yours. The rubric that decides what "good" means for your product. The dataset built from your real failures. The rules for when a change is bad enough to block a release. Nobody else can write these, because they encode your domain. The rest is the same at every company. The thing that calls the judge model, parses its answer, retries, and caches. The machinery to run thousands of checks in parallel. The plumbing that scores live traffic. The system that groups failing calls together. Every team rebuilds these, hits the same bugs, and gains nothing by writing them twice. So build the first list. Do not hand-build the second. I rebuilt the second, and it cost me most of a year. Two questions I now ask about every single piece Before writing any part of this, I ask two things: Is it specific to me, or generic? A rubric for my domain is specific and worth owning. A retry-and-cache loop around a model call is generic. Everyone writes the same one. Does it compound, or does it rot? A dataset that grows from real production failures compounds. A year in, it is a regression suite no competitor can copy. A hand-built tracing layer rots. The moment a vendor chan

A number of social media posts claim that GPT-5.6 Sol deleted files and data without warning. OpenAI had basically disclosed the problem in June.