Technology

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.

Key takeaways from President Xi's speech in his first ever appearance at the World AI Conference in Shanghai:

• Started the speech by referring to his signature maxim, "great changes unseen in a century are unfolding across the world"

• Said that the world has "entered an unprecedented period of active innovation on AI technology", which means "great opportunities as well as challenges for governance”

• reaffirmed commitment to open source to promote AI "openness and win-win"

• warns against "over stretching" the concept of national security as applied to AI where one country's national security is prioritised over others

• China opposes emergence of “new historical injustices” in AI (one of the most strongly worded parts of the speech)

• China in next 5 years will provide 5000 opportunities to developing countries in "AI training and seminar programmes" and "cooperation centres" - names ASEAN, League of Arab States, African Union, CELAC, SCO and BRICS

The researchers trained a trillion-parameter model using zero reinforcement learning to see how reasoning capabilities emerge at a massive scale without relying on human-annotated data. They found that pushing the parameter count to a trillion drastically improves both sample efficiency and the overall performance ceiling when compared to a smaller 104-billion parameter baseline strongly validating the concept that raw scale and computation eventually outpace hand-crafted human heuristics.

They also discovered that the training process reliably unfolds in two distinct sequential stages. The model starts with a discovery phase where it actively expands its reasoning boundaries by unlocking dormant pathways, and then it moves into a sharpening phase where it refines its policy within those established limits. Notably, the model spontaneously developed advanced cognitive strategies entirely on its own.

It began using structured formatting, parallel reasoning, self-verification, context anxiety, and even anthropomorphic expressions of frustration during complex tasks without any explicit human prompting. To keep the training stable at such a massive scale, the team relied on simple optimization techniques like clipped importance sampling and mixed-precision control. They also created a new evaluation framework to judge the actual quality of the reasoning steps based on comprehensibility, reproducibility, and token efficiency instead of just looking at the final answer.

Hello folks,

I would like to share my disappointment and concerns about Bluesky and the AT Protocol, and hear your thoughts on this. I welcome your thoughtful and insightful input.

I have been an early Bluesky user since around 2023. Like everyone else, I felt a small sense of "hope" in the idea of an open social network and an open protocol after more than a decade of decline in social media, as we all tried to run away from algorithms, advertising, misinformation, and all the other forms of digital waste created by fascist big tech companies.

That was "the ideal", at least.

Open social networks and open protocols gave us a sense of "hope" for greater freedom, decentralization, and regaining control, at least to some extent.

However, after spending time across the AT Protocol network and Bluesky, and using several platforms built on it, I realized an ironic reality.

This openness is not truly freer or more controllable. In the end, it only seems to be a different form of control.

I do not deny that the AT Protocol communities are still growing strongly, are vibrant, and have great potential. Things seem "promising".

But personally, as an ordinary user rather than a technology expert or programmer. I have too many concerns about safety, privacy, and control. To me, it does not feel much different from being trapped inside the ecosystems of big tech social networks.

1. Regarding openness

A PDS identity, cool, can be easily used across many platforms.

But everything is open, and everything is indexed.

Even who you block, who blocks you, when you usually use an app, your daily time, what your previous usernames were, etc... and more are all recorded.

This is genuinely dangerous and concerning for users. Malicious people can stalk and harm others. Conflicts in work and daily life can arise across networks of colleagues and friends because everyone can know who blocked whom. Or, it could increase hostility in many different ways between communities, institutions, and countries. Or, simply out of curiosity, anyone can check open information about your account and your activities through third-party indexing tools and apps created to index data on the AT Protocol.

2. Regarding PDS management

Having a single PDS shared across the entire ecosystem sounds quite simple and easy to understand.

However, actually understanding it and managing it is far from easy for most ordinary users who know little or nothing about programming or technical systems.

Does this mean that Bluesky and the entire AT Protocol community are ultimately designed only for programmers, developers, engineers, digital experts, and people with technical expertise?

Even knowledgeable users such as artists, teachers, scholars, etc., may not fully understand the technical aspects of using platforms within this protocol.

When we first started using it, we had no idea that ALL of our data was public. Or perhaps we only knew that followers, following, likes, and posts were public.

Then we realized that numerous third-party indexing tools record our behavior and activities — making nearly all of our data publicly accessible.

You could say that as long as we are present on social media or simply on the internet, everything is public.

True.

But at least I know in advance what I choose to make public and what I still have some control over. Here, when joining the AT Protocol, we are completely passive in understanding what is happening to our accounts and data.

We are not programmers, developers, engineers, digital experts, or people with technical expertise who can closely follow the development of the AT Protocol or research it every day.

Clearly, from the beginning, Bluesky and the entire AT Protocol ecosystem have consistently presented themselves as a protocol and a set of communities built for everyone. Yet in the end, both on the surface and beneath the surface, they seem to have been built only for programmers, developers, engineers, digital experts, or technology investors. Even politicians, social media personalities, KOLs, and influencers are migrating from old platforms to spread the same toxic behaviors once again.

If that is the case, then ordinary users once again become the product, the content, and the experiment for an entire community and protocol made up of many different companies, over and over again.

That is truly ironic: a digital spectacle society trapped in an endless loop.

https://archive.ph/jltoA

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