The Worst Thing About AI Is That People Can't Shut Up About It A plea from WIRED's top boss: Say less. I tried to get out of this assignment so many times, in so many different ways. Not every package needs an editor's letter, I told them. I was very busy recording a new podcast, getting ready to speak at a tech conference, eating and sleeping, parenting, doodling, revising my to-do list, retying my shoelaces. I was doing my best, I tried to convey to my editor.

Large Language Models (LLMs) generate functionally correct solutions but often fall short in code efficiency, a critical bottleneck for real-world deployment. In this paper, we introduce a novel test-time iterative optimization framework to address this, employing a closed-loop system where LLMs iteratively refine code based on empirical performance feedback from an execution sandbox. We explore three training strategies: Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Group Relative Policy Optimization (GRPO). Experiments on our Venus dataset and the APPS benchmark show that SFT and DPO rapidly saturate in efficiency gains. In contrast, GRPO, using reinforcement learning (RL) with execution feedback, continuously optimizes code performance, significantly boosting both pass@1 (from 47% to 62%) and the likelihood of outperforming human submissions in efficiency (from 31% to 45%). Our work demonstrates effective test-time code efficiency improvement and critically reveals the power of RL in teaching LLMs to truly self-improve code efficiency.

If humanity is obliterated by climate change, how will we even know it's happened? That's the question being answered by Australian scientists, who are building Earth's Black Box – a 32-foot-long steel monolith that captures data about our planet. It'll be filled with hard drives that constantly document climate change, giving an'unbiased account of events' that lead to Earth's demise. In the event of a climate apocalypse, it will provide a document of how humanity failed to avoid the disaster – as long as there's someone or something around to access it. Artist impressions suggest it will have a similar aura to the mysterious monolith in Stanley Kubrick's sci-fi film '2001: A Space Odyssey'.

The reason we can't just wish away or "fix" complexity is that every solution--whether it's a technology or methodology--redistributes complexity in some way. When microservices emerged (a software architecture approach where an application or system is composed of many smaller parts), they seemingly solved many of the maintenance and development challenges posed by monolithic architectures (where the application is one single interlocking system). However, in doing so microservices placed new demands on engineering teams; they require greater maturity in terms of practices and processes. This is one of the reasons why we cautioned people against what we call "microservice envy" in a 2018 edition of the Technology Radar, with CTO Rebecca Parsons writing that microservices would never be recommended for adoption on Technology Radar because "not all organizations are microservices-ready." We noticed there was a tendency to look to adopt microservices simply because it was fashionable.

Before the project has been initiated, the logistics on the shop floor relied mostly on manual processes, forklifts and stackers equipment, without any automation. As the matter of fact, a risk of human injuries or industrial diseases has been quite high, and the company decided to automate the most heavy and dangerous processes. The principal goal was to bring technology that increases both a level of safety and productivity. There was a number of other requirements, an important condition for selection of the right vendor was its global presence, as the automatization and digitalization project in Forvia runs over the world. Complexity of the autonomous technology was another convincing point, including wi-fi connectivity, independent mapping and fleet software, all providing big opportunities for automation.

Back in 1969 I finally caught 2001: A Space Odyssey in a Cinerama theater in Scottsdale, Arizona. At that point, the film had been running in that theater for over a year. I had longed to see it since its release in 1968 (I remember seeing it on the marquee of a theater in downtown Indianapolis), but when we visited relatives in Phoenix the following summer the opportunity finally presented itself. After the crescendo of its end, and the credits that ran to the tune of Johann Strauss' "The Blue Danube," I stepped out of the theater in a fog, completely stunned. From the hype I had heard about the film I was expecting something of an ambitious, up-to-date Destination Moon.

Earlier this week, we released a story on how TikTok has revolutionised the short-video industry through its recommendation system. In just five years, the platform acquired about 1.2 billion monthly active users (as per Q4 2021) and is estimated to reach 1.8 billion users by the end of year. Today, tech giant ByteDance revealed the main structure of'Monolith', TikTok's recommendation system's algorithm. TikTok has undoubtedly taken over the internet by basically reading your mind to get personalised content. TikTok is undoubtedly one of the fastest growing social media services and several researchers have credited the app's success to produce their recommender system algorithm. Now, the secret is out.

The Tomorrow Children, the game once described as a "Marxism simulator," is coming back this September with brand new features and gameplay elements. Originally released as a PlayStation 4 exclusive in 2016, the online social action game spent a year in early access before Sony ultimately shut it down. In 2021, however, Q-Games, the developer who worked on the project alongside Sony's now defunct Japan Studio, obtained the intellectual property rights to the game with the intention of relaunching it. Now, after tweaking and reworking parts of the game, the developer is officially introducing it as The Tomorrow Children: Phoenix Edition. In the game, an experiment to unite all human consciousness went awry and destroyed society.

Nowadays, Machine Learning (ML) is all in rage worldwide. A lot of companies are adopting ML (or AI or Advanced Analytics or Data-Driven Decision Making) in their current business processes. In this organization, a lot of effort is going towards recruiting ML talents, forming teams, identifying the feature scope of the team. Like many tech organizations, these organizations are also producing monoliths applications, e.g., one platform that includes workflow orchestration, model management, feature management, ML application code, etc. When such an organization realizes that they have ten different teams with seven different architectures, they realize that it is neither scalable nor reasonable to be in such a situation.

Monolith Productions, the studio responsible for Middle-earth: Shadow of Mordor and its sequel, is working on a new game in the Wonder Woman franchise. It features an original storyline where players will "become Diana of Themyscira in the fight to unite her Amazon family and the humans from the modern world," Monolith says. Publisher Warner Bros. Games showed off a teaser for the new project during The Game Awards. Wonder Woman is being billed as "a new third-person, open-world action-adventure" game, according to Monolith, and really, that's no surprise. Middle-earth: Shadow of Mordor was a third-person action RPG with the Nemesis System, a clever orc-management mechanic, tacked on top of it.