I'm interested in this too. I've been using STM32 NUCLEO boards, which are cheap and capable, but even the smallest ones are noticeably larger than this. I'd love to see an STM32 version of this project.
AWS forces an explicit default choice—Allow or Block. Azure defaults to passive "Detection," requiring a manual switch to "Prevention." An AWS engineer, used to making this conscious decision, might miss that Azure requires a separate, critical step to actually turn protection on.
Around last summer (July–August 2025), I desperately needed a sandbox like this. I had multiple disasters with Claude Code and other early AI models. The worst was when Claude Code did a hard git revert to restore a single file, which wiped out ~1000 lines of development work across multiple files.
But now, as of March 2026, at least in my experience, agents have become more reliable. With proper guardrails in claude.md and built-in safety measures, I haven't had a major incident in about 3 months.
That said, layering multiple safeguards is always recommended—your software assets are your assets. I'd still recommend using something like this. But things are changing, bit by bit.
Training students to write a single theme in multiple styles—including intentionally "bad" writing—is "originally" a great educational method. It teaches real composition by helping students understand what works and what doesn't. It builds good criteria in students.
But, the article's focus on writing "worse" for AI detectors misses what is important. Trying to distinguish humans from machines does not develop student capability. In fact, it's a fleeting technique because AI writing styles will vary and improve over time.
Thanks. Your explanation can solve my question. In Japan, the receiver will pay for toll free call around 30 yen per call. Here it is difficult to run free service like Payphone Go considering the cost.
Quick telephony question: how can calls from payphones to (888) 683-6697 be toll-free for the caller? I’m Japanese, so I may be missing something, but I don’t understand the mechanism that makes this free (or low-cost enough) to run as a free service.
From my experience as a software engineer, doubling my productivity hasn’t reduced my workload. My output per hour has gone up, but expectations and requirements have gone up just as fast. Software development is effectively endless work, and AI has mostly compressed timelines rather than reduced total demand.
In my day-to-day coding work, the top 3 coding agents are already good enough for me.
On SWE-bench Verified, mini-SWE-agent + GPT-5.2 Codex is 72.8. I don’t see a comparable GPT-5.3 Codex number there, so I’m using 5.2 as the baseline.
On OpenAI’s GPT-5.4 page (SWE-Bench Pro, Public), the score improves from 55.6 (GPT-5.2) to 57.7 (GPT-5.4), which is about +2.1 points.
It’s a different benchmark, so this is only a rough signal, but I’d expect a similar setup on SWE-bench Verified to improve by a few points, not by a huge jump.
I’m interested in how GPT-5.4 in Codex changes real-world results.
I should admit this is partly my personal preference.
That said, gaming has been a durable market for decades, and there’s a strong cycle where better chips enable better games, which then drives more demand for better chips.
Instead of pouring more money into OpenAI and Anthropic, Nvidia should invest more in expanding production capacity for the RTX 5000 series and future generations. High-end consumer GPU availability is still constrained, especially for the RTX 5090, and street prices remain elevated. Nvidia should come back to the consumer side.
I am Japanese. I want to share a well-known Japanese idea: 人は見た目が9割 ("people are judged 90% by appearance"). It is ironic because it goes against our common sense that substance should matter more than appearance. The intention of this idea is to emphasize the importance of first impressions.
I think the AIDMA model is still relevant. I've seen similar dashboards elsewhere, but FUBAR Daily's design keeps me coming back.
In Japan, my country, this looks a bit different. A lot of electricity still comes from oil- and gas-fired plants. The mechanics differ (gas turbines vs. car engines), but in both cases we’re still relying on combustion. I suppose some countries have the same issue.
Transmitting on AM broadcast frequencies is generally prohibited unless it meets an extremely low-power exemption , even if you have amateur license(I have a Japanese amateur radio license). A practical way to reduce risk is to put a large resistor before the antenna so the radiated power stays within that exemption. You could start with 100 MΩ; if the receiver cannot pick it up, try 10 MΩ, and so on.
Given GPIO frequency limits, reproducing a beautiful sine wave for a 1000 kHz carrier is a real challenge. He should borrow an oscilloscope and measure the output waveform.
With AI coding agents, reverse-engineering a codebase into a spec doc has become much more feasible, including details below the usual spec level. That gives PMs a practical way to understand systems more deeply than before, without having to land production diffs themselves. So to "Why should PMs code?" my take is: sometimes they should, but now there are multiple levels of involvement depending on what understanding is needed.