201 karma · joined February 14, 2026
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.
bash echo "==> Installing Node.js (LTS)" curl -fsSL https://deb.nodesource.com/setup_lts.x | sudo -E bash - sudo apt-get install -y nodejs
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.
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.
It is mainly from a bestseller book. You could get hint from this.
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.
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.
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.
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.