tl2do

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.

This reminds me of Gresham's Law: "bad money drives out good." But here, the result is inverted—efficient replicators drive out the less efficient.

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.

Intriguing, but...

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.

I always add Node.js even for non-JS dev work (many tools need it):

bash echo "==> Installing Node.js (LTS)" curl -fsSL https://deb.nodesource.com/setup_lts.x | sudo -E bash - sudo apt-get install -y nodejs

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.

I couldn’t find it at first, but it is there on SourceForge: https://sourceforge.net/p/opencamera/code/ci/master/tree/app...

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.

Recent SWE-bench Verified scores I’m watching:

Claude 4.5 Opus (high reasoning): 76.8

Gemini 3 Flash (high reasoning): 75.8

MiniMax M2.5 (high reasoning): 75.8

Claude Opus 4.6: 75.6

GPT-5.2 Codex: 72.8

Source: https://www.swebench.com/index.html

By the way, in my experience the agent part of Codex CLI has improved a lot and has become comparable to Claude Code. That is good news for OpenAI.

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.

https://ja.wikipedia.org/wiki/人は見た目が9割

It is mainly from a bestseller book. You could get hint from this.

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.

Nice work.

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.

What matters next is parts supply at affordable prices — like car manufacturers do.

I am looking for an OLED display that fits Mac's aluminium design, but I still have no luck.

The article doesn't address where human oversight is actually necessary. I sometimes use AI for simple spell checking—requiring human review for that would be over-complication. In some more difficult tasks, having AI review AI output works fine for me.

Contrast with Japan, my country, where bike accidents have risen 3 years straight and now make up 20%+ of all traffic incidents. Japan's response: heavier fines. Helsinki's: redesign the system. Big difference in philosophy.

The expected/unexpected distinction assumes you have the capacity to anticipate failures in the first place. But that capacity varies - even among experienced developers, everyone has blind spots shaped by their specific history. What's "obviously expected" to one senior dev is a surprise to another. The article's model is useful, but there's a prerequisite it doesn't address: the ability to expect is itself unevenly distributed.

I haven't dug into this deeply, but to me CRDTs look like a P2P data structure abstracted to the programming language variable level. The article says they shine when you don't want a central server. But most communication libraries already handle authentication and multiple peers well — and if you designate one peer as canonical (via leader election), conflict resolution is solved. I'm curious what use cases make avoiding a central server worth the paradigm shift. That said, it's a choice of approach — some may prefer the CRDT paradig

I admire the de-Googled approach of GrapheneOS. As a lawyer, privacy concerns resonate with me too. I love the rebellious attitude of tech that presents an alternative choice in an overly duopolistic market.

That said, I wouldn't last 8.4 months like the author. Even though he admits to some Google app usage, I'm in too deep — I'd never be able to get out. But if I get the chance, I'd like to try it on a secondary phone. Those solid black icons are one reason. They look cool.

Thanks for the correction — I had no idea it was named after a person. Interesting that in katakana, both "brown" and "Braun" are the same: ブラウン.

I'm not writing this to disregard the PDF author—it's just a personal retrospective.

I'm a 50-year-old Japanese person who watched the original Dragon Ball broadcast on TV around 40 years ago. Back then, there were no LCDs or OLEDs—only CRT ("brown tube") TVs, and the signal was analog. With that kind of analog rendering, it was practically impossible to tell what the "true" colors were. Plus, CRT displays degraded over time, shifting colors toward brown.

The pre-processed raw images in the article actually look like what I remember as the real Dragon Ball colors.

The word "personality" smuggles in biological assumptions. Asking "does this model have personality?" feels unproductive because the term implies something it can't be.

More useful framing: how do these subnetworks produce outputs that observers evaluate as personality-consistent? Personality isn't an internal property - it's a judgment made by people watching behavior.

I've been programming for 20 years and apparently still don't understand what my terminal is doing. Recently I asked Claude Code to generate a small shell script. It came back full of escape codes and I just stared at it like a caveman looking at a smartphone. This article finally explains what's been happening right under my nose.