Technology

With only a generic “we’re rebuilding, please bear with us” message to go on, the internet responded as it always does - with confusion, anger, and conspiracy theories. One Reddit theory even suggested it was a deliberate rug pull designed to cripple the free site to push people towards paid products.

Over the past couple of years, website owners the world over have seen their web traffic change. What was initially only people browsing gave way to an ever-increasing number of bots. By 2024, automated traffic had surpassed human traffic, and just last month, Cloudflare announced that bots had reached 57.5% of web page requests.

Bruce says that only 10% of their traffic is from humans browsing the site, with the rest coming from AI bots and automated traffic.

Some of these used the site using legitimate URLs, others were looking for back doors, most likely so they could get to the data before it appeared on the site, or to manipulate the data presented to users.

In case you haven’t guessed it yet, it’s linked to prediction markets. Polymarket runs weekly markets on opening weekends, and names The Numbers as the ultimate source of truth: The ‘Daily Box Office Performance’ figures found on the ‘Box Office’ tab on this movie’s The Numbers page will be used to resolve this market once the values for the 3-day opening weekend are final.

Feels to me like GrapheneOS did exactly what it should, passing the US border test with flying colours!

Funny part about this lawsuit: “With a little planning ahead of time, you can always download the data you need once you get to where you’re going,”

https://archive.ph/nqFsz

OnePlus arrived on the scene in 2014 with brash marketing and a compelling pitch: What if your phone was cheaper and faster? More than a decade later, the market is much different, and so is OnePlus. Confirming months of rumors and speculation, OnePlus has confirmed it’s ending phone releases in North America and Europe.

See also:

Before performing the study, the developers in question expected the AI tools would lead to a 24 percent reduction in the time needed for their assigned tasks. Even after completing those tasks, the developers believed that the AI tools had made them 20 percent faster, on average. In reality, though, the AI-aided tasks ended up being completed 19 percent slower than those completed without AI tools.

By analyzing screen recording data from a subset of the studied developers, the METR researchers found that AI tools tended to reduce the average time those developers spent actively coding, testing/debugging, or “reading/searching for information.” But those time savings were overwhelmed in the end by “time reviewing AI outputs, prompting AI systems, and waiting for AI generations,” as well as “idle/overhead time” where the screen recordings show no activity.

Overall, the developers in the study accepted less than 44 percent of the code generated by AI without modification. A majority of the developers reported needing to make changes to the code generated by their AI companion, and a total of 9 percent of the total task time in the “AI-assisted” portion of the study was taken up by this kind of review.

[...]

On the surface, METR’s results seem to contradict other benchmarks and experiments that demonstrate increases in coding efficiency when AI tools are used. But those often also measure productivity in terms of total lines of code or the number of discrete tasks/code commits/pull requests completed, all of which can be poor proxies for actual coding efficiency.

Many of the existing coding benchmarks also focus on synthetic, algorithmically scorable tasks created specifically for the benchmark test, making it hard to compare those results to those focused on work with pre-existing, real-world code bases. Along those lines, the developers in METR’s study reported in surveys that the overall complexity of the repos they work with (which average 10 years of age and over 1 million lines of code) limited how helpful the AI could be. The AI wasn’t able to utilize “important tacit knowledge or context” about the codebase, the researchers note, while the “high developer familiarity with [the] repositories” aided their very human coding efficiency in these tasks.

Study finds AI tools made open source software developers 19 percent slower