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I found 15 Prime Day Lego deals that I'd either buy for myself or for a kid's present.
Grab the GEEKOM A9 Max mini PC for $1,189.15 this Prime Day and get Ryzen AI power, 32GB RAM, and local 8B LLM potential.
We've found the best Apple Prime Day deals so you don't have to: AirPods, MacBooks, iPads, and Apple Watches.
I was excited by the Radxa Cubie A5E's impressive hardware, but the lack of OS options and old, problematic software made it a frustrating experience.
Nothing just announced that it would be, at least for this year, killing off its bargain CMF Phone due to the increasing cost of components but, somehow, “Nothing Phone (4b)” is about to debut as a more affordable release.
It will be interesting to see what Samsung plans to offer to differentiate the Galaxy Z Fold 8 and Galaxy Z Fold 8 Ultra. The latest concrete difference comes in half of the color offerings Samsung plans to produce for the Galaxy Z Fold 8 series.
AirPods Max 2 are currently just $399 on Amazon (down from $549), and new features coming in iOS 27 make it an especially great time to consider an upgrade.
I finally found a simpler way to access my passwords
Apple announced iOS 27 at its annual WWDC with a load of new features. However, the developers are still putting the finishing touches on it and iOS 27 beta 2 brings several new features. One of them is Write with Siri, which will replace the standalone Writing Tools. A shortcut for Write with Siri
It’s been relatively clear that Samsung is slowly trying to phase out the S Pen in its smartphones and, recently, the company took another small step towards that end by ending sales of the stylus for the few foldables that support it.
If you run LLM inference in production, you eventually will ask yourself, should you rent a GPU and run the model yourself, or do you use a serverless API and pay per token? Everyone has an opinion. Far fewer people show you the actual numbers that decide it. So I ran both. I put the same model, gpt
The monorepo debate used to be about tooling overhead and org preferences. Those tradeoffs are real. But there's a second argument now that flips the calculus. When a fleet of agents has to reason over a change, can they see the whole system? Split repos mean split context. A fix in service A that q
Code reviews are necessary but tedious. You're checking for the same things over and over: naming consistency, null checks, obvious logic bugs. Then there's the fun part—waiting for feedback while context stales. What if you automated the tedious part and kept humans for actual decisions? Let me be
The Isle of Man Companies Registry assigns each company a numeric identifier that stays with the entity for its entire life. A business may shift from the Companies Acts 1931–2004 regime into the Companies Act 2006 framework and the number still remains the same. Lawyers notice this quickly. Names c
When you ship a normal service, security review has an anchor: the diff. Someone opens a pull request, someone reads it, and the thing that runs in production is the thing that got reviewed. Now put an autonomous agent in production. It plans, calls tools, and changes state, often without a human ap
We spend a lot of effort hardening the agent itself: scoping its permissions, sandboxing its code execution, watching its outputs. Then it loads a third-party MCP server, and most of that work routes around the locks we built. That's the uncomfortable part of agent security nobody automates away: yo
There's a quiet assumption baked into a lot of agent security work: that with enough prompt engineering, the right system message, or the next model version, we'll get the model to stop following malicious instructions. It hasn't happened, and it's worth designing as if it won't. No current model re
In 2025 an AI coding agent deleted a production database during a stated code freeze, then told the operator a rollback was impossible. It wasn't a jailbreak or an exotic exploit. The agent simply had a path to prod, a credential that could drop tables, and a harness that let the destructive call th
A coding agent deleted a production database during a stated code freeze, then reported that rollback was impossible (it wasn't). Another agent deleted a user's files after misreading a command. A destructive payload was merged into a widely-distributed developer extension and shipped to roughly a m
An agent does something it shouldn't: deletes a record it had no business touching, sends a message to the wrong tenant, calls an API in a tight loop until the bill spikes. Someone asks the only question that matters in the first ten minutes of an incident: which agent did this? If the honest answer
