Built a basic authentication handler for this test just so it wouldn't be in the training data of either model. It had deliberately planted bugs. One was a hardcoded secret, another was a wrap-on-0xFFFFFFFF bug as a result of a malloc(length+1).
Qwen 3.6 found both, alongside two other issues I hadn't even considered, and the location of the magic value. GPT-5.4, though, missed the malloc issue (flagging memory exhaustion as the only risk), it missed a separate timing bug (it explicitly said the function was safe), and it hallucinated the location of the magic value. Qwen correctly identified the integer overflow. GPT-5.4 did not.
I then compared basic research between them using SearXNG for web search. For example, the current status of MTP in llama.cpp. Qwen 3.6 27B found the current PR, but flagged a related issue that shows the current implementation can be slower than just using a draft model right now. GPT-5.5 Thinking found the same PR, but didn't flag the downsides.
In a similar comparison, I asked both models how I should get started with ESPHome as a total beginner. ChatGPT suggested an ESP32-S3 and a BME280, which is... just not a good idea. It also talked about the ESP32-P4 not having Wi-Fi, and installing with HA or Docker. Meanwhile, Qwen3.6 27B said regular ESP32, DHT22, and mentioned HA, Docker, and pip as installation methods. While GPT was good, it was just throwing out jargon for a prompt that explicitly requested it for a beginner.
It kind of blew my mind that in all three of these, Qwen landed it better.
I've been writing for nearly a decade, and I can assure you, all of this is human written. I've long been writing about the Linux kernel where it's been relevant to my coverage, and there are articles under my name talking about low-level technical aspects in drivers and kernels from as far back as 2017.
I get that it's hard to know what to trust out there given that Dead Internet Theory is beginning to feel like a reality, but comments like this can be quite upsetting after spending days researching and writing an article like this. I totally get criticism of the article itself, and I'm fine with that, but it feels as if people are too quick to jump on the "must be written by AI" bandwagon. I receive it, my colleagues receive it, and for the people who I know put in so much effort into their work, it can be upsetting to them as well.
As was mentioned in another thread, there were actually a couple of typos in this article when it went live. I cleaned those up once they were pointed out, but AI doesn't make typos. I get it to an extent; hostility and accusations of all kinds have been levied at writers for the years and years I've been in this industry writing long-form content and analysis. But with the proliferation of AI, that hostility has really ramped up over the last couple of years.
You don't have to wonder whether or not it returns value to the tax payer. The Irish government already monitored the pilot program for two years, publishing all of the details and findings. [1]
"The headline finding from this social CBA is that for every €1 of public money invested in the pilot, society received €1.39 in return"
This came about as a mixture of greater economic activity from participants, cultural impacts that saw public-facing artist activities increase, and improvements to wellbeing of participants that reduced their requirement for psychological interventions by the state. The state also predicts that the further roll-out of this program will benefit consumers with lower prices for artistic works, as there will be more supply overall.
The scheme has been quite popular here in Ireland. Given the history of Ireland when it comes to art (both in the sense of spoken and written word, and in other mediums), it makes sense to introduce a scheme like this to safeguard and uplift those who produce art.
Article author here: you'd be surprised! XDA these days has quite a bit of mainstream outreach, and this article has been getting shared on some socials. Even saw it getting passed around on LinkedIn.