Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Zencoder unveils its next-generation AI coding and unit testing agents today, positioning the San Francisco-based company as a formidable challenger to established players like GitHub Copilot and newcomers like Cursor. The company, founded by former Wrike CEO Andrew Filev, integrates its AI agents directly into popular development environments including Visual Studio Code and JetBrains IDEs, alongside deep integrations with JIRA, GitHub, GitLab, Sentry, and more than 20 other development tools. “We started with the thesis that transformers are powerful computing building blocks, but if you put them in a more agentic environment, you can get much more out of them,” said Filev in an exclusive interview with VentureBeat. “By agentic, I mean two key things: first, giving the AI feedback so it can improve its work, and second, equipping it with tools. Just like human intelligence, AI becomes significantly more capable when it has the right tools at its disposal.” Why developers won’t need to abandon their favorite IDEs for AI assistance Several AI coding assistants have emerged in the past year, but Zencoder’s approach distinguishes itself by operating within existing workflows rather than requiring developers to switch platforms. “Our main competitor is Cursor. Cursor is its own development environment versus we deliver the same very powerful agentic capabilities, but within existing development environments,” Filev told VentureBeat. “For some developers, it doesn’t really matter. But for some developers, they either want or have to stick to their existing environments.” This distinction matters particularly for enterprise developers working in Java and C#, languages for which specialized IDEs like JetBrains’ IntelliJ and Rider offer more robust support than generalized environments. How Zencoder’s AI agents are beating state-of-the-art benchmarks by double-digit margins The company

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. How 3D printing could make better cooling systems A new 3D-printed design could make an integral part of cooling systems like air conditioners or refrigerators smaller and more efficient, according to new research. Heat exchangers are devices that whisk away heat, and they’re everywhere—used in data centers, ships, factories, and buildings. The aim is to pass as much heat as possible from one side of the device to the other. Most use one of a few standard designs that have historically been easiest and cheapest to make.
Energy demand for cooling buildings alone is set to double between now and 2050, and new designs could help efficiently meet the massive demand forecast for the coming decades. Read the full story. —Casey Crownhart
MIT Technology Review Narrated: The quest to figure out farming on Mars If we’re going to live on Mars we’ll need a way to grow food in its arid dirt. Researchers think they know a way. This is our latest story to be turned into a MIT Technology Review Narrated podcast, which we’re publishing each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Thousands of US health agency workers have been laid offExperts warn that patients will die preventable deaths as a result. (Wired $)+ How will the US respond to the measles and bird flu outbreaks? (Reuters)  + US cuts could lead to serious delays in forecasting extreme weather. (Undark)+ The wide-ranging cuts are also likely to lose America money. (The Atlantic $)

2 Donald Trump is set to discuss a proposal to save TikTok  He’s due to meet with aides today to thrash out a new ownership structure. (NYT $)+ Oracle and Blackstone are among the companies in talks to make an offer. (WSJ $)+ The White House is playing the role of investment bank. (The Guardian) 3 X has asked the Supreme Court to exempt its users from law enforcementIt claims to be worried by broad, suspicionless requests. (FT $) 4 Things aren’t looking good for Mexico-based Chinese companies Trump’s tariff plans could imperil an awful lot of deals. (WSJ $)+ The US Chips Act is another probable casualty. (Bloomberg $) 5 US lawmakers want to regulate AI companionsA proposed bill would allow users to sue if they suffer harm from their interactions with a companion bot. (WP $)+ We need to prepare for ‘addictive intelligence.’ (MIT Technology Review) 6 Covid hasn’t gone awayAnd life for the covid-conscious is getting increasingly difficult. (The Atlantic $) 7 Brands are trying to game Reddit to show up in ChatGPT recommendationsCatering to AI search is a whole business model now. (The Information $)+ Your most important customer may be AI. (MIT Technology Review) 8 Nothing could destroy the universeHumans have long been obsessed with nothingness. (New Scientist $) 9 Would you flirt with a chatbot?Tinder wants you to give it a go. (Bloomberg $)+ The AI relationship revolution is already here. (MIT Technology Review)
10 Trading in your Tesla is TikTok’s favorite trendClips of Tesla owners ditching their cars are going viral. (Fast Company $)+ This guy returned his Cybertruck out of fear his daughter would get bullied. (Insider $)+ Sales of new Teslas are slumping too. (NYT $)
Quote of the day “I’d get on in a heartbeat.” —Butch Wilmore, one of the pair of astronauts who was stuck in space for nine months, explains how he’d be willing to fly on the beleaguered Starliner again, the Washington Post reports. The big story Bringing the lofty ideas of pure math down to earth
April 2023—Pradeep Niroula Mathematics has long been presented as a sanctuary from confusion and doubt, a place to go in search of answers. Perhaps part of the mystique comes from the fact that biographies of mathematicians often paint them as otherworldly savants.As a graduate student in physics, I have seen the work that goes into conducting delicate experiments, but the daily grind of mathematical discovery is a ritual altogether foreign to me. And this feeling is only reinforced by popular books on math, which often take the tone of a pastor dispensing sermons to the faithful. Luckily, there are ways to bring it back down to earth. Popular math books seek a fresher take on these old ideas, be it through baking recipes or hot-button political issues. My verdict: Why not? It’s worth a shot. Read the full story.

Gladia, an AI transcription and audio intelligence provider, launched Solaria, a next-gen automatic speech recognition (ASR) model designed to redefine real-time communications for call centers and other voice-first platforms.

Solaria now empowers businesses to enhance and expand their customer service operations with AI-powered voice technology that delivers unmatched language coverage—supporting 40+ languages previously inaccessible with other solutions—without compromising quality or speed.

While outsourcing has long been a cost reduction strategy in the call center industry, businesses now face a new, critical challenge: providing seamless, multilingual support at scale. With 49% of global executives reporting financial losses due to language barriers, the demand for scalable, high-quality multilingual solutions has never been greater.

“We’ve seen in the market a huge surge in voice AI. It’s like voice is part of our life again, and we are introducing a new product called Solaria, which is a model that is real time with advanced capabilities,” said Jean-Louis Queguiner, CEO of Gladia, in an interview with GamesBeat. “And it’s going to be the fastest on the market, and the most accurate in the market, covering 100 languages.”

The product also has features like real time sentiment analysis and real time translation, he said. It handles speech to text translation and transcription. This is important to do in real time for voice agents or call centers, where someone may have to answer a question that comes in with a different language.

Solaria: An enterprise-ready model for global customer experience

Solaria is a speech-to-text (STT) engine built for global scalability. Solaria was designed to meet the demands of today’s contact centers, where both AI automation and human agents need high-accuracy, low-latency, and real-time multilingual support to succeed.

The model achieves industry-leading results in speech recognition, delivering both accuracy and fast processing speed. Recent benchmarks show Solaria has reached an unmatched 94% Word Accuracy Rate (WAR) average in English, Spanish, French and other common languages, while maintaining an ultra-low latency of 270 millisecond, making the conversation feel natural and responsive.

While real-time speech-to-text is often measured by speed alone, accuracy and language coverage are equally crucial for businesses providing seamless services across regions.

Unlike other speech-to-text models that prioritize speed over usability, Solaria balances industry-leading accuracy and speed with unmatched language coverage—100 languages in total, with exclusive support for 42 languages not matched by competitors. For high-population markets and key outsourcing hubs like Bangladesh, India, and The Philippines, native-level accuracy in regional languages is now offered through Solaria.

With native-level transcription, real-time code-switching, and translation across all supported languages, businesses can expand into global markets without constraints.

Designed for enterprise-scale voice automation, Solaria delivers:

Best-in-class accuracy in high-population languages such as Tagalog, Bengali, Punjabi, Tamil, Urdu, Persian, and Marathi.

Ability to adapt the model to industry-specific terminology (like medical or financial jargon) and have it extract critical data, like names, addresses, and numericals.

Adaptive speech processing, ensuring high accuracy in noisy call center environments.

Enterprise-grade data security, in full compliance with GDRP, HIPPA, and SOC 2.

With the addition of Solaria to its product portfolio, Gladia allows businesses to enhance customer service by improving AI-powered voice agents, making IVRs and virtual assistants more reliable across multiple languages, while also optimizing human-assisted workflows with real-time transcriptions and translations to help agents provide more effective assistance.

“Speech is the most natural way to connect with the world—for the first time, automated speech recognition is closing the divide, enabling humans and AI to truly speak the same language,” said Jean-Louis Quéguiner, CEO of Gladia, in a statement. “With Solaria, we have made a breakthrough in AI-powered voice technology that unlocks new opportunities for businesses, driving efficiency and delivering more seamless, impactful customer experiences across diverse languages and markets. Solaria is built for next-generation voice platforms ready to lead this transformation on a global scale.”

Serving more than 700 enterprise customers worldwide, including Attention, Circleback, Method Financial, and VEED.IO, Gladia delivers enterprise-grade service and scalability, backed by dedicated support and infrastructure in the U.S. and Europe, guaranteeing reliable performance for mission-critical applications. Companies looking to scale globally, optimize operational costs, and enhance customer experiences can start building with Gladia’s API today.

As part of the Solaria launch, Gladia has partnered with LiveKit, a leading open-source developer framework for real-time AI voice agents, to power real-time, multilingual translation within AI-driven applications. This gives developers global language capabilities out of the box through seamless integration with Gladia’s API.

Following its $16 million Series A round in 2024 and today’s rollout of Solaria, Gladia has taken another critical step toward establishing itself as a leading end-to-end API audio infrastructure provider—combining speech recognition, generative AI, and voice generation capabilities to help enterprise users and developers tap into the full potential of real-time audio data.

Paris-based Gladia was founded in 2022 by Jean-Louis Queguiner (ex-OVHCloud) and Jonathan Soto (ex-MIT/Sigfox). Gladia’s product has been adopted by over 150,000 users and 700 enterprise clients—including industry leaders like Attention, Circleback, Method Financial, and VEED.IO.

There is a 300 millisecond delay between the moment you start speaking and the moment you receive the first event of voice being activated. It takes 100 milliseconds to do the transcription and so you have near instant results.

To improve the accuracy further, Queguiner said the company needs to train on more data. And it needs to work with the data augmentations to make the data more robust. The company has enterprise pricing in price but has not disclosed it yet. He said it will be among the most affordable solutions in the market.

The company has nearly 40 employees.

A new 3D-printed design could make an integral part of cooling systems like air conditioners or refrigerators smaller and more efficient, according to new research.   Heat exchangers are devices that whisk away heat, and they’re everywhere—used in data centers, ships, factories, and buildings. The aim is to pass as much heat as possible from one side of the device to the other. Most use one of a few standard designs that have historically been easiest and cheapest to make.  “Heat exchangers are at the center of the industrial economy. They’re an essential part of every machine and every system that moves energy,” says William King, a professor at the University of Illinois Urbana-Champaign and one of the authors of the new study. Existing designs tend to favor straight lines, right angles, and round tubes, he adds.   King and his colleagues used 3D printing to design a heat exchanger that includes features to optimize heat movement, like wavy walls and pyramid-shaped bumps, which wouldn’t be possible to make using traditional manufacturing techniques.  
The team had set out to design a system based on a common refrigerant called R-134a, which is commonly used in devices like air conditioners and refrigerators. When cold water lowers the temperature of the refrigerant, it changes from a gas to a liquid on its path through the device. That liquid refrigerant can then go on to other parts of the cooling system, where it’s used to lower the temperature of anything from a room to a rack of servers.  The best way to cool the refrigerant tends to involve building very thin walls between the two sides of the device and maximizing the amount of contact that the water and the refrigerant make with those walls. (Think about how much colder you’d get wearing a thin T-shirt and pants and lying down on ice than simply touching it with your gloved hands.)
To design the best possible heat exchanger, researchers used simulations and developed machine-learning models to help predict the performance of different designs under different conditions. After 36,000 simulations, the researchers landed on the one they decided to develop. Among the key components: small fins that jut out on the side of the device that touches the water, increasing the surface area to maximize heat transfer. The team also designed wavy passageways for the water to pass through—once again helping to maximize surface area. Simulations helped the researchers figure out exactly how curvy the passages should be and where precisely to place the fins. On the side of the devices where the refrigerant passes through, the design includes small pyramid-shaped bumps along the walls. These not only maximize the area for cooling but also help mix the refrigerant as it passes through and prevent liquid from coating the wall (which would slow down the heat transfer). After settling on a design, the researchers used a 3D-printing technique called direct metal laser sintering, in which lasers melt and fuse together a metal powder (in this case, an aluminum alloy), layer by layer. In testing, the researchers found that the heat exchanger created with this technique was able to cool down the refrigerant more efficiently than other designs. The new device was able to achieve a power density of over six megawatts per meter cubed—outperforming one common traditional design, the shell-tube configuration, by between 30% and 50% with the same pumping power. The device’s power density was similar to that of brazed plate heat exchangers, another common design in industry.   Overall, this device doesn’t dramatically outperform the state-of-the-art technology, but the technique of using modeling and 3D printing to produce new heat exchanger designs is promising, says Dennis Nasuta, director of research and development at Optimized Thermal Systems, a consulting firm that works with companies in the HVAC industry on design and research. “It’s worth exploring, and I don’t think that we know yet where we can push it,” Nasuta says. One challenge is that today, additive manufacturing techniques such as laser sintering are slow and expensive compared with traditional manufacturing; they wouldn’t be economical or feasible to rely on for all our consumer cooling devices, he says. For now, this type of approach could be most useful in niche applications like aerospace and high-end automotives, which could more likely bear the cost, he adds.  This particular study was funded by the US Office of Naval Research. Next-generation ships have more electronics aboard than ever, and there’s a growing need for compact and efficient systems to deal with all that extra heat, says Nenad Miljkovic, one of the authors of the study. 

Energy demand for cooling buildings alone is set to double between now and 2050, and new designs could help efficiently meet the massive demand forecast for the coming decades. But challenges including manufacturing costs would need to be overcome to help innovations like the one designed by King and his team make a dent in real devices. Another barrier to adopting these new techniques, Nasuta says, is that current standards don’t demand more efficiency. Other technologies already exist that could help make our devices more efficient, but they’re not used for the same reason.  It will take time for new manufacturing techniques, including 3D printing, to trickle into our devices, Natsua adds: “This isn’t going to be in your AC next year.”

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Imagine if a large team of 200 people could hold a thoughtful real-time conversation in which they efficiently brainstorm ideas, share knowledge, debate alternatives and quickly converge on AI-optimized solutions. Is this possible — and if so, would it amplify their collective intelligence? There is a new generative AI technology, conversational swarm intelligence (or simply hyperchat), that enables teams of potentially any size to engage in real-time conversations and quickly converge on AI-optimized solutions. To put this to the test, I asked the research team at Unanimous AI to bring together 50 random sports fans and task that large group with quickly creating a March Madness bracket through real-time conversational deliberation. Before I tell you how the experiment is going, I need to explain why we can’t just bring 50 people into a Zoom meeting and have them quickly create a bracket together. Research shows that the ideal size for a productive real-time conversation is only 4 to 7 people. In small groups, each individual gets a good amount of airtime to express their views and has low wait time to respond to others. But as group size grows, airtime drops, wait-time rises — and by a dozen people it devolves into a series of monologues. Above 20 people, it’s chaos.  So how can 50 people hold a conversation, or 250, or even 2,500?  Hyperchat works by breaking any large group into a set of parallel subgroups. It then adds an AI agent into each subgroup called a “conversational surrogate” tasked with distilling the human insights within its local group and quickly sharing those insights as natural dialog with other groups. These surrogate agents enable all the subgroups to overlap, weaving

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Another day, another announcement about AI agents. Hailed by various market research reports as the big tech trend in 2025 — especially in the enterprise — it seems we can’t go more than 12 hours or so without the debut of another way to make, orchestrate (link together), or otherwise optimize purpose-built AI tools and workflows designed to handle routine white collar work. Yet Emergence AI, a startup founded by former IBM Research veterans and which late last year debuted its own, cross-platform AI agent orchestration framework, is out with something novel from all the rest: an AI agent creation platform that lets the human user specify what work they are trying to accomplish via text prompts, and then turns it over to AI models to create the agents they believe are necessary to accomplish said work. This new system is literally a no code, natural language, AI-powered multi-agent builder, and it works in real time. Emergence AI describes it as a milestone in recursive intelligence, aims to simplify and accelerate complex data workflows for enterprise users. “Recursive intelligence paves the path for agents to create agents,” said Satya Nitta, co-founder and CEO of Emergence AI. “Our systems allow creativity and intelligence to scale fluidly, without human bottlenecks, but always within human-defined boundaries.” Image of Dr. Satya Nitta, Co-founder and CEO of Emergence AI, during his keynote at the AI Engineer World’s Fair 2024, where he unveiled Emergence’s Orchestrator meta-agent and introduced the open-source web agent, Agent-E. (photo courtesy AI Engineer World’s Fair) The platform is designed to evaluate incoming tasks, check its existing agent registry, and, if necessary, autonomously generate new agents tailored to fulfill specific enterprise needs. It can