June 13, 20256 min readAir-Conditioning Can Surprisingly Help the Power Grid during Extreme HeatSwitching on air-conditioning during extreme heat doesn’t have to make us feel guilty—it can actually boost power grid reliability and help bring more renewable energy onlineBy Johanna Mathieu & The Conversation US Imagedepotpro/Getty ImagesThe following essay is reprinted with permission from The Conversation, an online publication covering the latest research.As summer arrives, people are turning on air conditioners in most of the U.S. But if you’re like me, you always feel a little guilty about that. Past generations managed without air conditioning – do I really need it? And how bad is it to use all this electricity for cooling in a warming world?If I leave my air conditioner off, I get too hot. But if everyone turns on their air conditioner at the same time, electricity demand spikes, which can force power grid operators to activate some of the most expensive, and dirtiest, power plants. Sometimes those spikes can ask too much of the grid and lead to brownouts or blackouts.On supporting science journalismIf you're enjoying this article, consider supporting our award-winning journalism by subscribing. By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today.Research I recently published with a team of scholars makes me feel a little better, though. We have found that it is possible to coordinate the operation of large numbers of home air-conditioning units, balancing supply and demand on the power grid – and without making people endure high temperatures inside their homes.Studies along these lines, using remote control of air conditioners to support the grid, have for many years explored theoretical possibilities like this. However, few approaches have been demonstrated in practice and never for such a high-value application and at this scale. The system we developed not only demonstrated the ability to balance the grid on timescales of seconds, but also proved it was possible to do so without affecting residents’ comfort.The benefits include increasing the reliability of the power grid, which makes it easier for the grid to accept more renewable energy. Our goal is to turn air conditioners from a challenge for the power grid into an asset, supporting a shift away from fossil fuels toward cleaner energy.Adjustable equipmentMy research focuses on batteries, solar panels and electric equipment – such as electric vehicles, water heaters, air conditioners and heat pumps – that can adjust itself to consume different amounts of energy at different times.Originally, the U.S. electric grid was built to transport electricity from large power plants to customers’ homes and businesses. And originally, power plants were large, centralized operations that burned coal or natural gas, or harvested energy from nuclear reactions. These plants were typically always available and could adjust how much power they generated in response to customer demand, so the grid would be balanced between power coming in from producers and being used by consumers.But the grid has changed. There are more renewable energy sources, from which power isn’t always available – like solar panels at night or wind turbines on calm days. And there are the devices and equipment I study. These newer options, called “distributed energy resources,” generate or store energy near where consumers need it – or adjust how much energy they’re using in real time.One aspect of the grid hasn’t changed, though: There’s not much storage built into the system. So every time you turn on a light, for a moment there’s not enough electricity to supply everything that wants it right then: The grid needs a power producer to generate a little more power. And when you turn off a light, there’s a little too much: A power producer needs to ramp down.The way power plants know what real-time power adjustments are needed is by closely monitoring the grid frequency. The goal is to provide electricity at a constant frequency – 60 hertz – at all times. If more power is needed than is being produced, the frequency drops and a power plant boosts output. If there’s too much power being produced, the frequency rises and a power plant slows production a little. These actions, a process called “frequency regulation,” happen in a matter of seconds to keep the grid balanced.This output flexibility, primarily from power plants, is key to keeping the lights on for everyone.Finding new optionsI’m interested in how distributed energy resources can improve flexibility in the grid. They can release more energy, or consume less, to respond to the changing supply or demand, and help balance the grid, ensuring the frequency remains near 60 hertz.Some people fear that doing so might be invasive, giving someone outside your home the ability to control your battery or air conditioner. Therefore, we wanted to see if we could help balance the grid with frequency regulation using home air-conditioning units rather than power plants – without affecting how residents use their appliances or how comfortable they are in their homes.From 2019 to 2023, my group at the University of Michigan tried this approach, in collaboration with researchers at Pecan Street Inc., Los Alamos National Laboratory and the University of California, Berkeley, with funding from the U.S. Department of Energy Advanced Research Projects Agency-Energy.We recruited 100 homeowners in Austin, Texas, to do a real-world test of our system. All the homes had whole-house forced-air cooling systems, which we connected to custom control boards and sensors the owners allowed us to install in their homes. This equipment let us send instructions to the air-conditioning units based on the frequency of the grid.Before I explain how the system worked, I first need to explain how thermostats work. When people set thermostats, they pick a temperature, and the thermostat switches the air-conditioning compressor on and off to maintain the air temperature within a small range around that set point. If the temperature is set at 68 degrees, the thermostat turns the AC on when the temperature is, say, 70, and turns it off when it’s cooled down to, say, 66.Every few seconds, our system slightly changed the timing of air-conditioning compressor switching for some of the 100 air conditioners, causing the units’ aggregate power consumption to change. In this way, our small group of home air conditioners reacted to grid changes the way a power plant would – using more or less energy to balance the grid and keep the frequency near 60 hertz.Moreover, our system was designed to keep home temperatures within the same small temperature range around the set point.Testing the approachWe ran our system in four tests, each lasting one hour. We found two encouraging results.First, the air conditioners were able to provide frequency regulation at least as accurately as a traditional power plant. Therefore, we showed that air conditioners could play a significant role in increasing grid flexibility. But perhaps more importantly – at least in terms of encouraging people to participate in these types of systems – we found that we were able to do so without affecting people’s comfort in their homes.We found that home temperatures did not deviate more than 1.6 Fahrenheit from their set point. Homeowners were allowed to override the controls if they got uncomfortable, but most didn’t. For most tests, we received zero override requests. In the worst case, we received override requests from two of the 100 homes in our test.In practice, this sort of technology could be added to commercially available internet-connected thermostats. In exchange for credits on their energy bills, users could choose to join a service run by the thermostat company, their utility provider or some other third party.Then people could turn on the air conditioning in the summer heat without that pang of guilt, knowing they were helping to make the grid more reliable and more capable of accommodating renewable energy sources – without sacrificing their own comfort in the process.This article was originally published on The Conversation. Read the original article.

Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy. Learn more When DeepSeek released its R1 model this January, it wasn’t just another AI announcement. It was a watershed moment that sent shockwaves through the tech industry, forcing industry leaders to reconsider their fundamental approaches to AI development. What makes DeepSeek’s accomplishment remarkable isn’t that the company developed novel capabilities; rather, it was how it achieved comparable results to those delivered by tech heavyweights at a fraction of the cost. In reality, DeepSeek didn’t do anything that hadn’t been done before; its innovation stemmed from pursuing different priorities. As a result, we are now experiencing rapid-fire development along two parallel tracks: efficiency and compute.  As DeepSeek prepares to release its R2 model, and as it concurrently faces the potential of even greater chip restrictions from the U.S., it’s important to look at how it captured so much attention. Engineering around constraints DeepSeek’s arrival, as sudden and dramatic as it was, captivated us all because it showcased the capacity for innovation to thrive even under significant constraints. Faced with U.S. export controls limiting access to cutting-edge AI chips, DeepSeek was forced to find alternative pathways to AI advancement. While U.S. companies pursued performance gains through more powerful hardware, bigger models and better data, DeepSeek focused on optimizing what was available. It implemented known ideas with remarkable execution — and there is novelty in executing what’s known and doing it well. This efficiency-first mindset yielded incredibly impressive results. DeepSeek’s R1 model reportedly matches OpenAI’s capabilities at just 5 to 10% of the operating cost. According to reports, the final training run for DeepSeek’s V3 predecessor cost a mere $6 million — which was described by former Tesla AI scientist Andrej Karpathy as “a joke of a budget” compared to the tens or hundreds of millions spent by U.S. competitors. More strikingly, while OpenAI reportedly spent $500 million training its recent “Orion” model, DeepSeek achieved superior benchmark results for just $5.6 million — less than 1.2% of OpenAI’s investment. If you get starry eyed believing these incredible results were achieved even as DeepSeek was at a severe disadvantage based on its inability to access advanced AI chips, I hate to tell you, but that narrative isn’t entirely accurate (even though it makes a good story). Initial U.S. export controls focused primarily on compute capabilities, not on memory and networking — two crucial components for AI development. That means that the chips DeepSeek had access to were not poor quality chips; their networking and memory capabilities allowed DeepSeek to parallelize operations across many units, a key strategy for running their large model efficiently. This, combined with China’s national push toward controlling the entire vertical stack of AI infrastructure, resulted in accelerated innovation that many Western observers didn’t anticipate. DeepSeek’s advancements were an inevitable part of AI development, but they brought known advancements forward a few years earlier than would have been possible otherwise, and that’s pretty amazing. Pragmatism over process Beyond hardware optimization, DeepSeek’s approach to training data represents another departure from conventional Western practices. Rather than relying solely on web-scraped content, DeepSeek reportedly leveraged significant amounts of synthetic data and outputs from other proprietary models. This is a classic example of model distillation, or the ability to learn from really powerful models. Such an approach, however, raises questions about data privacy and governance that might concern Western enterprise customers. Still, it underscores DeepSeek’s overall pragmatic focus on results over process. The effective use of synthetic data is a key differentiator. Synthetic data can be very effective when it comes to training large models, but you have to be careful; some model architectures handle synthetic data better than others. For instance, transformer-based models with mixture of experts (MoE) architectures like DeepSeek’s tend to be more robust when incorporating synthetic data, while more traditional dense architectures like those used in early Llama models can experience performance degradation or even “model collapse” when trained on too much synthetic content. This architectural sensitivity matters because synthetic data introduces different patterns and distributions compared to real-world data. When a model architecture doesn’t handle synthetic data well, it may learn shortcuts or biases present in the synthetic data generation process rather than generalizable knowledge. This can lead to reduced performance on real-world tasks, increased hallucinations or brittleness when facing novel situations.  Still, DeepSeek’s engineering teams reportedly designed their model architecture specifically with synthetic data integration in mind from the earliest planning stages. This allowed the company to leverage the cost benefits of synthetic data without sacrificing performance. Market reverberations Why does all of this matter? Stock market aside, DeepSeek’s emergence has triggered substantive strategic shifts among industry leaders. Case in point: OpenAI. Sam Altman recently announced plans to release the company’s first “open-weight” language model since 2019. This is a pretty notable pivot for a company that built its business on proprietary systems. It seems DeepSeek’s rise, on top of Llama’s success, has hit OpenAI’s leader hard. Just a month after DeepSeek arrived on the scene, Altman admitted that OpenAI had been “on the wrong side of history” regarding open-source AI.  With OpenAI reportedly spending $7 to 8 billion annually on operations, the economic pressure from efficient alternatives like DeepSeek has become impossible to ignore. As AI scholar Kai-Fu Lee bluntly put it: “You’re spending $7 billion or $8 billion a year, making a massive loss, and here you have a competitor coming in with an open-source model that’s for free.” This necessitates change. This economic reality prompted OpenAI to pursue a massive $40 billion funding round that valued the company at an unprecedented $300 billion. But even with a war chest of funds at its disposal, the fundamental challenge remains: OpenAI’s approach is dramatically more resource-intensive than DeepSeek’s. Beyond model training Another significant trend accelerated by DeepSeek is the shift toward “test-time compute” (TTC). As major AI labs have now trained their models on much of the available public data on the internet, data scarcity is slowing further improvements in pre-training. To get around this, DeepSeek announced a collaboration with Tsinghua University to enable “self-principled critique tuning” (SPCT). This approach trains AI to develop its own rules for judging content and then uses those rules to provide detailed critiques. The system includes a built-in “judge” that evaluates the AI’s answers in real-time, comparing responses against core rules and quality standards. The development is part of a movement towards autonomous self-evaluation and improvement in AI systems in which models use inference time to improve results, rather than simply making models larger during training. DeepSeek calls its system “DeepSeek-GRM” (generalist reward modeling). But, as with its model distillation approach, this could be considered a mix of promise and risk. For example, if the AI develops its own judging criteria, there’s a risk those principles diverge from human values, ethics or context. The rules could end up being overly rigid or biased, optimizing for style over substance, and/or reinforce incorrect assumptions or hallucinations. Additionally, without a human in the loop, issues could arise if the “judge” is flawed or misaligned. It’s a kind of AI talking to itself, without robust external grounding. On top of this, users and developers may not understand why the AI reached a certain conclusion — which feeds into a bigger concern: Should an AI be allowed to decide what is “good” or “correct” based solely on its own logic? These risks shouldn’t be discounted. At the same time, this approach is gaining traction, as again DeepSeek builds on the body of work of others (think OpenAI’s “critique and revise” methods, Anthropic’s constitutional AI or research on self-rewarding agents) to create what is likely the first full-stack application of SPCT in a commercial effort. This could mark a powerful shift in AI autonomy, but there still is a need for rigorous auditing, transparency and safeguards. It’s not just about models getting smarter, but that they remain aligned, interpretable, and trustworthy as they begin critiquing themselves without human guardrails. Moving into the future So, taking all of this into account, the rise of DeepSeek signals a broader shift in the AI industry toward parallel innovation tracks. While companies continue building more powerful compute clusters for next-generation capabilities, there will also be intense focus on finding efficiency gains through software engineering and model architecture improvements to offset the challenges of AI energy consumption, which far outpaces power generation capacity.  Companies are taking note. Microsoft, for example, has halted data center development in multiple regions globally, recalibrating toward a more distributed, efficient infrastructure approach. While still planning to invest approximately $80 billion in AI infrastructure this fiscal year, the company is reallocating resources in response to the efficiency gains DeepSeek introduced to the market. Meta has also responded, With so much movement in such a short time, it becomes somewhat ironic that the U.S. sanctions designed to maintain American AI dominance may have instead accelerated the very innovation they sought to contain. By constraining access to materials, DeepSeek was forced to blaze a new trail. Moving forward, as the industry continues to evolve globally, adaptability for all players will be key. Policies, people and market reactions will continue to shift the ground rules — whether it’s eliminating the AI diffusion rule, a new ban on technology purchases or something else entirely. It’s what we learn from one another and how we respond that will be worth watching. Jae Lee is CEO and co-founder of TwelveLabs. Daily insights on business use cases with VB Daily If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI. Read our Privacy Policy Thanks for subscribing. Check out more VB newsletters here. An error occured.

Home Is the Newly Revealed Xbox Handheld a Switch 2 Killer? News Is the Newly Revealed Xbox Handheld a Switch 2 Killer? 6 min read Published: June 14, 2025 Key Takeaways Xbox has announced two new handheld gaming devices in partnership with Asus: the ROG Xbox Ally and ROG Xbox Ally X. They’re expected to compete with Nintendo’s Switch 2, which has sold 3.5M units in just 4 days of its launch. Xbox aims to bring a wide range of game titles to portable handheld devices in order to cater to the gaming PC market. Xbox has entered the handheld gaming market with two new launches: the ROG Xbox Ally and ROG Xbox Ally X in partnership with ASUS. Interestingly, Nintendo released its Switch 2 just last week. The public has received it with much enthusiasm, seeing as it’s already sold around 3.5M units in the first four days of its release. Needless to say, Xbox and Nintendo will be direct competitors in the handheld segment now. We looked at the spec sheets and customer reviews, and both handheld gaming devices seem to have different target audiences. Let’s unpack them in detail. Memory and Storage The ROG Xbox Ally comes in standard white color and features the AMD Ryzen Z2 A Processor with 16GB of memory and 512GB of storage, with a 60Wh battery. The Ally X, on the other hand, comes in striking black with the AMD Ryzen AI Z2 Extreme Processor, 24 GB of memory, and 1 TB of storage, and a 80Wh battery. Source: Rog Ally Life Right off the bat, we believe that Microsoft has done a good job with the storage and processors. In comparison, the Nintendo Switch 2 has 12GB of memory with just 256GB of internal storage. On paper, the Xbox series looks to have an advantage over the Switch 2, which uses a custom NVIDIA T239 chipset for raw power. Nvidia’s DLSS (Deep Learning Super-Sampling), however, gives Switch 2 an advantage over Xbox’s AMD Ryzen processors. DLSS can render games at lower resolutions and, therefore, achieve higher in-use frame rates, which boosts its overall performance. Simply put, despite the gap in on-paper specifications, the Switch 2 may render comparable performance to the Xbox Ally. Take this with a pinch of salt, though, because we’ll only be able to confirm this once we get our hands on the new Xbox handhelds. Display Both the Xbox handhelds feature a 7-inch Full HD (FHD) screen with a 120 Hz refresh rate. In comparison, the Switch 2 screen is bigger, with a 7.9-inch display, also rendering at 120 Hz. However, Switch 2 also features HDR10, giving it a significant edge over Xbox Ally. HDR10 ensures a much wider range of brightness levels and a broader spectrum of colors, so the display looks more vibrant and lifelike. Plus, you’ll see more detailed blacks and whites on the screen with better realism and depth, enhancing your overall gaming experience. The Switch 2 also features VRR technology, which prevents screen tearing and reduces stutter. Notably, the Xbox Ally range has its own version of the VRR, FreeSync Premium. So, truth be told, you might not experience much of a difference in that area. However, HDR10 can definitely prove to be a winner for Switch 2. Product Market Fit While both the Switch 2 and new Xbox handhelds are apparently the same genre of products, Microsoft and Nintendo seem to have different target markets in their minds. Microsoft is focusing more on the Windows handheld market, targeting players who want an on-the-go PC gaming experience. With access to Game Pass and titles from Steam and Epic Games, the Xbox Ally offers a more comprehensive library of games. Nintendo, on the other hand, looks to build on the legacy of the OG Nintendo Switch, which has sold 152M units since its launch in 2017. It aims to tap in on the Nintendo fan base with original titles such as Mario and an improved gaming experience. Also, Xbox is in direct competition with Valve’s Steam Deck. Both are essentially handheld PCs with wide access to PC-compatible aggregated game libraries on the go. Xbox is also introducing the ‘Xbox Experience for Handheld’ feature for its new Ally range, which will make Windows 11 more compatible and optimized for its handheld device – something similar to Valve’s SteamOS on the Steam Deck. Xbox Exploring a New Market Segment The global mobile and handheld gaming market is expected to expand at a rate of 13.8% CAGR till 2034. It may reach a market value of $35.189B.  Valve’s Steam Deck managed to sell around 3.7M units by the end of 2024. On the other hand, Windows-based devices like the ASUS ROG Ally, Lenovo Legion Go, and MSI Claw have sold ~5.9M units so far as per early 2025 reports. This shows there’s certainly demand for Windows-based handheld gaming devices.  Plus, Xbox’s partnership with ASUS could bring along a brand-value advantage for the product. With a seamless Windows 11 experience on an on-the-go device, these sales figures are expected to swell once the Xbox Ally hits the shelves. However, pricing will be a key determinant. The Switch 2 currently sells at $449.99 – or $499.99 with the Mario Kart bundle. The Steam Deck starts at $399.00 and goes up to $649. This means that the price range according to the current market demand is around $400-$600. Anything more than that may result in market adoption issues. The original ASUS ROG Ally is currently priced at $499. There’s little doubt, however, that Xbox would add a premium to this price. So, we’re expecting the price of the ROG Xbox Ally to be around $600, while the ROG Xbox Ally X may cost more than $700. This means that Xbox will be participating in the premium handheld gaming sector, which is something Nintendo and Steam do not cater to. Let’s wait for confirmation regarding the pricing and the launch date. Remember, this was only a feature comparison of the two products, and we’re yet to test them out for a detailed hands-on gaming experience comparison. Stick around for that. Krishi is a seasoned tech journalist with over four years of experience writing about PC hardware, consumer technology, and artificial intelligence.  Clarity and accessibility are at the core of Krishi’s writing style. He believes technology writing should empower readers—not confuse them—and he’s committed to ensuring his content is always easy to understand without sacrificing accuracy or depth. Over the years, Krishi has contributed to some of the most reputable names in the industry, including Techopedia, TechRadar, and Tom’s Guide. A man of many talents, Krishi has also proven his mettle as a crypto writer, tackling complex topics with both ease and zeal. His work spans various formats—from in-depth explainers and news coverage to feature pieces and buying guides.  Behind the scenes, Krishi operates from a dual-monitor setup (including a 29-inch LG UltraWide) that’s always buzzing with news feeds, technical documentation, and research notes, as well as the occasional gaming sessions that keep him fresh.  Krishi thrives on staying current, always ready to dive into the latest announcements, industry shifts, and their far-reaching impacts.  When he's not deep into research on the latest PC hardware news, Krishi would love to chat with you about day trading and the financial markets—oh! And cricket, as well. View all articles by Krishi Chowdhary Our editorial process The Tech Report editorial policy is centered on providing helpful, accurate content that offers real value to our readers. We only work with experienced writers who have specific knowledge in the topics they cover, including latest developments in technology, online privacy, cryptocurrencies, software, and more. Our editorial policy ensures that each topic is researched and curated by our in-house editors. We maintain rigorous journalistic standards, and every article is 100% written by real authors. More from News View all View all

The Inefficiency of Static Chain-of-Thought Reasoning in LRMs Recent LRMs achieve top performance by using detailed CoT reasoning to solve complex tasks. However, many simple tasks they handle could be solved by smaller models with fewer tokens, making such elaborate reasoning unnecessary. This echoes human thinking, where we use fast, intuitive responses for easy problems and slower, analytical thinking for complex ones. While LRMs mimic slow, logical reasoning, they generate significantly longer outputs, thereby increasing computational cost. Current methods for reducing reasoning steps lack flexibility, limiting models to a single fixed reasoning style. There is a growing need for adaptive reasoning that adjusts effort according to task difficulty.  Limitations of Existing Training-Based and Training-Free Approaches Recent research on improving reasoning efficiency in LRMs can be categorized into two main areas: training-based and training-free methods. Training strategies often use reinforcement learning or fine-tuning to limit token usage or adjust reasoning depth, but they tend to follow fixed patterns without flexibility. Training-free approaches utilize prompt engineering or pattern detection to shorten outputs during inference; however, they also lack adaptability. More recent work focuses on variable-length reasoning, where models adjust reasoning depth based on task complexity. Others study “overthinking,” where models over-reason unnecessarily. However, few methods enable dynamic switching between quick and thorough reasoning—something this paper addresses directly.  Introducing OThink-R1: Dynamic Fast/Slow Reasoning Framework Researchers from Zhejiang University and OPPO have developed OThink-R1, a new approach that enables LRMs to switch between fast and slow thinking smartly, much like humans do. By analyzing reasoning patterns, they identified which steps are essential and which are redundant. With help from another model acting as a judge, they trained LRMs to adapt their reasoning style based on task complexity. Their method reduces unnecessary reasoning by over 23% without losing accuracy. Using a loss function and fine-tuned datasets, OThink-R1 outperforms previous models in both efficiency and performance on various math and question-answering tasks.  System Architecture: Reasoning Pruning and Dual-Reference Optimization The OThink-R1 framework helps LRMs dynamically switch between fast and slow thinking. First, it identifies when LRMs include unnecessary reasoning, like overexplaining or double-checking, versus when detailed steps are truly essential. Using this, it builds a curated training dataset by pruning redundant reasoning and retaining valuable logic. Then, during fine-tuning, a special loss function balances both reasoning styles. This dual-reference loss compares the model’s outputs with both fast and slow thinking variants, encouraging flexibility. As a result, OThink-R1 can adaptively choose the most efficient reasoning path for each problem while preserving accuracy and logical depth.  Empirical Evaluation and Comparative Performance The OThink-R1 model was tested on simpler QA and math tasks to evaluate its ability to switch between fast and slow reasoning. Using datasets like OpenBookQA, CommonsenseQA, ASDIV, and GSM8K, the model demonstrated strong performance, generating fewer tokens while maintaining or improving accuracy. Compared to baselines such as NoThinking and DualFormer, OThink-R1 demonstrated a better balance between efficiency and effectiveness. Ablation studies confirmed the importance of pruning, KL constraints, and LLM-Judge in achieving optimal results. A case study illustrated that unnecessary reasoning can lead to overthinking and reduced accuracy, highlighting OThink-R1’s strength in adaptive reasoning.  Conclusion: Towards Scalable and Efficient Hybrid Reasoning Systems In conclusion, OThink-R1 is a large reasoning model that adaptively switches between fast and slow thinking modes to improve both efficiency and performance. It addresses the issue of unnecessarily complex reasoning in large models by analyzing and classifying reasoning steps as either essential or redundant. By pruning the redundant ones while maintaining logical accuracy, OThink-R1 reduces unnecessary computation. It also introduces a dual-reference KL-divergence loss to strengthen hybrid reasoning. Tested on math and QA tasks, it cuts down reasoning redundancy by 23% without sacrificing accuracy, showing promise for building more adaptive, scalable, and efficient AI reasoning systems in the future.  Check out the Paper and GitHub Page. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 100k+ ML SubReddit and Subscribe to our Newsletter. Sana HassanSana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-life solutions.Sana Hassanhttps://www.marktechpost.com/author/sana-hassan/Building AI-Powered Applications Using the Plan → Files → Code Workflow in TinyDevSana Hassanhttps://www.marktechpost.com/author/sana-hassan/MemOS: A Memory-Centric Operating System for Evolving and Adaptive Large Language ModelsSana Hassanhttps://www.marktechpost.com/author/sana-hassan/Google AI Unveils a Hybrid AI-Physics Model for Accurate Regional Climate Risk Forecasts with Better Uncertainty AssessmentSana Hassanhttps://www.marktechpost.com/author/sana-hassan/Run Multiple AI Coding Agents in Parallel with Container-Use from Dagger

Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy. Learn more When DeepSeek released its R1 model this January, it wasn’t just another AI announcement. It was a watershed moment that sent shockwaves through the tech industry, forcing industry leaders to reconsider their fundamental approaches to AI development. What makes DeepSeek’s accomplishment remarkable isn’t that the company developed novel capabilities; rather, it was how it achieved comparable results to those delivered by tech heavyweights at a fraction of the cost. In reality, DeepSeek didn’t do anything that hadn’t been done before; its innovation stemmed from pursuing different priorities. As a result, we are now experiencing rapid-fire development along two parallel tracks: efficiency and compute.  As DeepSeek prepares to release its R2 model, and as it concurrently faces the potential of even greater chip restrictions from the U.S., it’s important to look at how it captured so much attention. Engineering around constraints DeepSeek’s arrival, as sudden and dramatic as it was, captivated us all because it showcased the capacity for innovation to thrive even under significant constraints. Faced with U.S. export controls limiting access to cutting-edge AI chips, DeepSeek was forced to find alternative pathways to AI advancement. While U.S. companies pursued performance gains through more powerful hardware, bigger models and better data, DeepSeek focused on optimizing what was available. It implemented known ideas with remarkable execution — and there is novelty in executing what’s known and doing it well. This efficiency-first mindset yielded incredibly impressive results. DeepSeek’s R1 model reportedly matches OpenAI’s capabilities at just 5 to 10% of the operating cost. According to reports, the final training run for DeepSeek’s V3 predecessor cost a mere $6 million — which was described by former Tesla AI scientist Andrej Karpathy as “a joke of a budget” compared to the tens or hundreds of millions spent by U.S. competitors. More strikingly, while OpenAI reportedly spent $500 million training its recent “Orion” model, DeepSeek achieved superior benchmark results for just $5.6 million — less than 1.2% of OpenAI’s investment. If you get starry eyed believing these incredible results were achieved even as DeepSeek was at a severe disadvantage based on its inability to access advanced AI chips, I hate to tell you, but that narrative isn’t entirely accurate (even though it makes a good story). Initial U.S. export controls focused primarily on compute capabilities, not on memory and networking — two crucial components for AI development. That means that the chips DeepSeek had access to were not poor quality chips; their networking and memory capabilities allowed DeepSeek to parallelize operations across many units, a key strategy for running their large model efficiently. This, combined with China’s national push toward controlling the entire vertical stack of AI infrastructure, resulted in accelerated innovation that many Western observers didn’t anticipate. DeepSeek’s advancements were an inevitable part of AI development, but they brought known advancements forward a few years earlier than would have been possible otherwise, and that’s pretty amazing. Pragmatism over process Beyond hardware optimization, DeepSeek’s approach to training data represents another departure from conventional Western practices. Rather than relying solely on web-scraped content, DeepSeek reportedly leveraged significant amounts of synthetic data and outputs from other proprietary models. This is a classic example of model distillation, or the ability to learn from really powerful models. Such an approach, however, raises questions about data privacy and governance that might concern Western enterprise customers. Still, it underscores DeepSeek’s overall pragmatic focus on results over process. The effective use of synthetic data is a key differentiator. Synthetic data can be very effective when it comes to training large models, but you have to be careful; some model architectures handle synthetic data better than others. For instance, transformer-based models with mixture of experts (MoE) architectures like DeepSeek’s tend to be more robust when incorporating synthetic data, while more traditional dense architectures like those used in early Llama models can experience performance degradation or even “model collapse” when trained on too much synthetic content. This architectural sensitivity matters because synthetic data introduces different patterns and distributions compared to real-world data. When a model architecture doesn’t handle synthetic data well, it may learn shortcuts or biases present in the synthetic data generation process rather than generalizable knowledge. This can lead to reduced performance on real-world tasks, increased hallucinations or brittleness when facing novel situations.  Still, DeepSeek’s engineering teams reportedly designed their model architecture specifically with synthetic data integration in mind from the earliest planning stages. This allowed the company to leverage the cost benefits of synthetic data without sacrificing performance. Market reverberations Why does all of this matter? Stock market aside, DeepSeek’s emergence has triggered substantive strategic shifts among industry leaders. Case in point: OpenAI. Sam Altman recently announced plans to release the company’s first “open-weight” language model since 2019. This is a pretty notable pivot for a company that built its business on proprietary systems. It seems DeepSeek’s rise, on top of Llama’s success, has hit OpenAI’s leader hard. Just a month after DeepSeek arrived on the scene, Altman admitted that OpenAI had been “on the wrong side of history” regarding open-source AI.  With OpenAI reportedly spending $7 to 8 billion annually on operations, the economic pressure from efficient alternatives like DeepSeek has become impossible to ignore. As AI scholar Kai-Fu Lee bluntly put it: “You’re spending $7 billion or $8 billion a year, making a massive loss, and here you have a competitor coming in with an open-source model that’s for free.” This necessitates change. This economic reality prompted OpenAI to pursue a massive $40 billion funding round that valued the company at an unprecedented $300 billion. But even with a war chest of funds at its disposal, the fundamental challenge remains: OpenAI’s approach is dramatically more resource-intensive than DeepSeek’s. Beyond model training Another significant trend accelerated by DeepSeek is the shift toward “test-time compute” (TTC). As major AI labs have now trained their models on much of the available public data on the internet, data scarcity is slowing further improvements in pre-training. To get around this, DeepSeek announced a collaboration with Tsinghua University to enable “self-principled critique tuning” (SPCT). This approach trains AI to develop its own rules for judging content and then uses those rules to provide detailed critiques. The system includes a built-in “judge” that evaluates the AI’s answers in real-time, comparing responses against core rules and quality standards. The development is part of a movement towards autonomous self-evaluation and improvement in AI systems in which models use inference time to improve results, rather than simply making models larger during training. DeepSeek calls its system “DeepSeek-GRM” (generalist reward modeling). But, as with its model distillation approach, this could be considered a mix of promise and risk. For example, if the AI develops its own judging criteria, there’s a risk those principles diverge from human values, ethics or context. The rules could end up being overly rigid or biased, optimizing for style over substance, and/or reinforce incorrect assumptions or hallucinations. Additionally, without a human in the loop, issues could arise if the “judge” is flawed or misaligned. It’s a kind of AI talking to itself, without robust external grounding. On top of this, users and developers may not understand why the AI reached a certain conclusion — which feeds into a bigger concern: Should an AI be allowed to decide what is “good” or “correct” based solely on its own logic? These risks shouldn’t be discounted. At the same time, this approach is gaining traction, as again DeepSeek builds on the body of work of others (think OpenAI’s “critique and revise” methods, Anthropic’s constitutional AI or research on self-rewarding agents) to create what is likely the first full-stack application of SPCT in a commercial effort. This could mark a powerful shift in AI autonomy, but there still is a need for rigorous auditing, transparency and safeguards. It’s not just about models getting smarter, but that they remain aligned, interpretable, and trustworthy as they begin critiquing themselves without human guardrails. Moving into the future So, taking all of this into account, the rise of DeepSeek signals a broader shift in the AI industry toward parallel innovation tracks. While companies continue building more powerful compute clusters for next-generation capabilities, there will also be intense focus on finding efficiency gains through software engineering and model architecture improvements to offset the challenges of AI energy consumption, which far outpaces power generation capacity.  Companies are taking note. Microsoft, for example, has halted data center development in multiple regions globally, recalibrating toward a more distributed, efficient infrastructure approach. While still planning to invest approximately $80 billion in AI infrastructure this fiscal year, the company is reallocating resources in response to the efficiency gains DeepSeek introduced to the market. Meta has also responded, With so much movement in such a short time, it becomes somewhat ironic that the U.S. sanctions designed to maintain American AI dominance may have instead accelerated the very innovation they sought to contain. By constraining access to materials, DeepSeek was forced to blaze a new trail. Moving forward, as the industry continues to evolve globally, adaptability for all players will be key. Policies, people and market reactions will continue to shift the ground rules — whether it’s eliminating the AI diffusion rule, a new ban on technology purchases or something else entirely. It’s what we learn from one another and how we respond that will be worth watching. Jae Lee is CEO and co-founder of TwelveLabs. Daily insights on business use cases with VB Daily If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI. Read our Privacy Policy Thanks for subscribing. Check out more VB newsletters here. An error occured.

Living in a small space doesn’t mean you have to feel cramped or boxed in. With the right design tricks, you can make even the tiniest room feel open, airy, and inviting, no renovation required. Whether you’re in a compact apartment, a small home, or just trying to make the most of a single room, smart styling and layout choices can dramatically shift how the space looks and feels. From strategic lighting and paint colors to furniture swaps and clever storage solutions, there are plenty of easy, affordable ways to stretch your square footage visually. Ready to transform your space? Here are some practical, design-savvy ideas to make your home feel bigger without tearing down a single wall. 1. Opt for Multi-Functional Furniture Image Source: House Beautiful In a small space, every piece of furniture should earn its keep. Look for multi-functional items: ottomans that open up for storage, beds with drawers underneath, or coffee tables that can extend or lift to become a desk. Not only do these pieces help reduce clutter, but they also free up floor space, making the room look more open. Bonus points for furniture that can be folded away when not in use. By choosing versatile pieces, you’re making the most of every inch without sacrificing style or comfort. 2. Keep Pathways Clear Image Source: The Spruce One of the simplest yet most effective ways to make a small space feel bigger is to keep pathways and walkways clear. When furniture or clutter blocks natural movement through a room, it can make the space feel cramped and chaotic. Take a walk through your home and notice where you’re dodging corners or squeezing between pieces,those are areas to rethink. Opt for smaller furniture with slim profiles, or rearrange what you have to create an easy, natural flow. Open walkways help your eyes move freely through the room, making everything feel more spacious, breathable, and intentional. It’s all about giving yourself room to move,literally and visually. 3. Use Glass and Lucite Furniture Image Source: The Spruce Transparent furniture made from glass or Lucite (acrylic) takes up less visual space because you can see right through it. A glass coffee table or clear dining chairs can provide functionality without cluttering up the view. These pieces practically disappear into the background, which helps the room feel more open. They also add a touch of modern sophistication. When you need furniture but don’t want it to dominate the room, going clear is a clever design choice. 4. Don’t Over-Clutter Your Space Image Source: House Beautiful In small spaces, clutter accumulates fast,and it visually shrinks your environment. The more items scattered around, the more cramped the room feels. Start by taking a critical look at what you own and asking: do I really need this here? Use storage bins, under-bed containers, or floating shelves to hide away what you don’t use daily. Keep surfaces like countertops, desks, and coffee tables as clear as possible. A minimal, clean setup allows the eye to rest and makes the space feel open and intentional. Remember: less stuff equals more space,both physically and mentally. 5. Utilize Your Windows Image Source: House Beautiful Windows are like built-in art that can also dramatically affect how big or small your space feels. Don’t cover them with heavy drapes or clutter them with too many objects on the sill. Keep window treatments light and minimal,sheer curtains or roller blinds are perfect. If privacy isn’t a big concern, consider leaving them bare. Letting natural light flood in through your windows instantly opens up your space and makes it feel brighter and more expansive. You can also place mirrors or shiny surfaces near windows to reflect more light into the room and maximize their impact. 6. Downsize Your Dining Table Image Source: House Beautiful A large dining table can dominate a small room, leaving little space to move or breathe. If you rarely entertain a big crowd, consider downsizing to a smaller round or drop-leaf table. These take up less visual and physical space and still offer enough room for daily meals. You can always keep a folding table or stackable chairs nearby for when guests do come over. Round tables are especially great for small spaces because they allow smoother traffic flow and eliminate awkward corners. Plus, a smaller table encourages intimacy during meals and helps the whole area feel more open and functional. 7. Use Mirrors Strategically Image Source: The Tiny Cottage Mirrors can work magic in a small room. They reflect both natural and artificial light, which can instantly make a space feel larger and brighter. A large mirror on a wall opposite a window can double the amount of light in your room. Mirrored furniture or decor elements like trays and picture frames also help. Think about using mirrored closet doors or even creating a mirror gallery wall. It’s not just about brightness; mirrors also create a sense of depth, tricking the eye into seeing more space than there actually is. 8. Install a Murphy Bed Image Source: House Beautiful A Murphy bed (also known as a wall bed) is a game-changer for anyone living in a tight space. It folds up into the wall or a cabinet when not in use, instantly transforming your bedroom into a living room, office, or workout area. This setup gives you the flexibility to have a multi-purpose room without sacrificing comfort. Modern Murphy beds often come with built-in shelves or desks, offering even more function without taking up extra space. If you want to reclaim your floor during the day and still get a good night’s sleep, this is one smart solution. 9. Paint It White Image Source: House Beautiful Painting your walls white is one of the easiest and most effective tricks to make a space feel bigger. White reflects light, helping the room feel open, clean, and fresh. It creates a seamless look, making walls seem to recede and ceilings feel higher. You can still have fun with the space, layer in texture, subtle patterns, or neutral accessories to keep it from feeling sterile. White also acts as a blank canvas, letting your furniture and art stand out. Whether you’re decorating a studio apartment or a small home office, a fresh coat of white paint can work wonders. 10. Prioritize Natural Light Image Source: The Spruce Natural light has an incredible ability to make any room feel more spacious and welcoming. To make the most of it, avoid blocking windows with bulky furniture or dark curtains. Consider using light-filtering shades or sheer curtains to let sunlight pour in while maintaining some privacy. Arrange mirrors or reflective surfaces like glossy tables and metallic decor to bounce the light around the room. Even placing furniture in a way that lets light flow freely can change how open your home feels. Natural light not only brightens your space but also boosts your mood, making it a double win. 11. Maximize Shelving Image Source: House Beautiful When floor space is limited, vertical storage becomes your best ally. Floating shelves, wall-mounted units, or tall bookcases draw the eye upward, creating a sense of height and maximizing every inch. They’re perfect for books, plants, artwork, or even kitchen supplies if you’re short on cabinets. You can also install corner shelves to use often-overlooked spots. Keep them tidy and curated,group items by color, size, or theme for a visually pleasing look. Shelving helps reduce clutter on the floor and tabletops, keeping your home organized and visually open without requiring any extra square footage. 12. Keep It Neutral Image Source: House Beautiful Neutral tones, like soft whites, light grays, warm beiges, and pale taupes,can make a space feel calm and cohesive. These colors reflect light well and reduce visual clutter, making your room appear larger. A neutral palette doesn’t mean boring; you can still play with textures, patterns, and accents within that color family. Add throw pillows, rugs, or wall art in layered neutrals for interest without overwhelming the space. When everything flows in similar tones, it creates continuity, which tricks the eye into seeing a more expansive area. It’s an effortless way to open up your home without lifting a hammer. 13. Choose Benches, Not Chairs Image Source: House Beautiful When space is tight, traditional dining chairs or bulky accent seats can eat up more room than they’re worth. Benches, on the other hand, are a sleek, versatile alternative. They tuck neatly under tables when not in use, saving valuable floor space and keeping walkways open. In entryways, living rooms, or at the foot of a bed, a bench offers seating and can double as storage or display. Some come with built-in compartments or open space beneath for baskets. Plus, benches visually declutter the room with their simple, low-profile design. 14. Use Vertical Spaces Image Source: The Spruce When you’re short on square footage, think vertical. Use tall bookshelves, wall-mounted shelves, and hanging storage to keep things off the floor. Vertical lines naturally draw the eye upward, which creates a feeling of height and openness. Consider mounting floating shelves for books, plants, or decorative items. Hooks and pegboards can add function without taking up space. Making use of your wall space not only maximizes storage but also frees up floor area, which visually enlarges the room. 15. Add a Gallery Wall Image Source: House Beautiful It might seem counterintuitive, but adding a gallery wall can actually make a small space feel bigger,if done right. A curated display of art, photos, or prints draws the eye upward and outward, giving the illusion of a larger area. Stick to cohesive frames and colors to maintain a clean, intentional look. You can go symmetrical for a polished feel or get creative with an organic, freeform layout. Position the gallery higher on the wall to elongate the space visually. Just be sure not to overcrowd,balance is key. A thoughtful gallery wall adds personality without cluttering the room. Finishing Notes: Creating a spacious feel in a small home doesn’t require a sledgehammer or a major remodel, it just takes a bit of strategy and smart design. From downsizing your dining table to letting natural light pour in, each tip we’ve shared is an easy, budget-friendly way to visually open up your space. If you’re looking for even more inspiration, layout ideas, or style guides, be sure to explore Home Designing. It’s packed with expert advice, modern interior trends, and visual walkthroughs to help you transform your space, big or small, into something that truly feels like home.

The Botanical Cabin by Plume is a refreshing testament to the enduring appeal of minimal living. Designed and crafted in France, this tiny house is more than a fleeting trend or a clever response to spatial constraints. It is a study in how thoughtful design can elevate even the most compact of footprints into something both beautiful and deeply functional. Built on a trailer, the Botanical Cabin measures just under twenty feet in length, yet every inch is meticulously utilized, creating a home that feels open, airy, and full of light. As you move inside, you are immediately struck by the cabin’s abundant natural illumination. Large windows frame the greenery outside, inviting the outdoors in and making the interior feel much larger than its modest measurements suggest. The layout flows effortlessly from one space to the next, with clever woodwork offering moments of privacy without sacrificing openness. There is a gentle rhythm to the way spaces are defined, making the entire experience feel both cozy and uncluttered. Designer: Plume The kitchen, though compact, is a masterclass in efficiency. A built-in breakfast bar serves as both a dining area and a generous workspace for meal prep. The use of wood throughout the kitchen and living areas unifies the aesthetic but also brings a warmth that is often missing from modern, small-scale structures. Each detail, from the mini fridge to the compact stove, is chosen for practicality without compromising the visual serenity of the space. Every element in the Botanical Cabin seems to have a purpose that goes beyond mere utility. The ethereal, soft decor imparts a whimsical quality, turning this portable dwelling into a sanctuary for romantic getaways or peaceful solo retreats. It is easy to imagine settling into its intimate nooks with a book or gazing out at the landscape in quiet contemplation. The cabin’s atmosphere is one of gentle luxury, where comfort is derived from simplicity rather than abundance. Plume’s approach to design, evident in the Botanical Cabin, is rooted in a deep respect for both craftsmanship and environment. The use of natural materials and a restrained palette is pleasing to the eye and also fosters a sense of harmony with the surroundings. This is a home that invites reflection, not just on the space itself but on the kind of life one wishes to lead within its walls. It encourages a slower, more intentional way of living, where each object and every moment is appreciated. For those of us who find inspiration in architecture and interiors, the Botanical Cabin is a reminder of how powerful minimal design can be. It proves that a small home does not have to feel temporary or incomplete. Instead, it can be a place of real belonging, where design and daily life are intertwined with grace. The Botanical Cabin stands as a quiet ode to the beauty of less, and in its simplicity, it offers endless possibilities for living well. The post Chic Minimalist Tiny Home Delivers Style With Effortless Mobility For Modern Nomads first appeared on Yanko Design.