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75 recent industry stories relevant to the field â releases, launches, and announcements beyond the papers.
The neolab is betting that automating routine computer tasks will soon outpace coding as AI's biggest use case.
The acquisition brings Pokeâs conversational style and interaction model to Cognitionâs coding agent Devin, reflecting a growing belief that how AI assistants interact with users is as important as the models powering them.
Weeks after Anthropic's latest toe-to-toe with the US government, and days after an OpenAI security incident that dominated tech industry discussions, Anthropic on Thursday released its newest model, Claude Opus 5. The company said in a release that Opus 5 "comes close to the capabilities of Claude Fable 5 in many domains" and is much […]
Opus 5 will be both cheaper and less restrictive than Fable, likely making it preferable in most use cases.
AI companies, including Nvidia and Mistral, urge policymakers to avoid broad restrictions on open-weight AI models as Washington debates responses to Chinese AI and alleged model distillation.
The Trump administration unveiled the first "Genesis Mission" grants on Thursday, directing $5 billion toward hundreds of AI-driven science projects in an effort the White House has described as "comparable in urgency and ambition to the Manhattan Project." At roughly the same time, Trump's science adviser Michael Kratsios was on Capitol Hill selling lawmakers on […]
ChatGPT Voice on desktop can work with both ChatGPT Work and Codex to complete tasks and control agents.
AegisAI co-founders developed AI agents that quickly analyze each message as a human would, paying attention to small anomalies that even the most elaborate checklist wouldnât catch.
Etched, founded by three Harvard dropouts, has created new chips and memory components that speed up inference on any AI model -- no GPUs required, it says.
Google released Gemini 3.6 Flash, 3.5 Flash-Lite, and Flash Cyber, but the continued absence of Gemini 3.5 Pro raises fresh questions about its AI strategy.
Google is launching Gemini 3.6 Flash alongside a new security model dedicated to quickly finding and patching security vulnerabilities. In a blog post on Tuesday, Google describes Gemini 3.5 Flash Cyber as a "cost-efficient and highly capable alternative" to larger, more expensive AI systems, such as the one offered by Anthropic's Mythos. The cybersecurity model […]
Zain Hasan, an AI engineer at Together AI, has taught himself to use AI coding assistants while still keeping an eye on cost. He directs difficult problems to a frontier model, meaning one near the current state of the art in reasoning and capability, such as Anthropicâs Fable. But if the task that Hasan is outsourcing is more straightforward, he directs it to a less capableâand less expensiveâlanguage model. Right now, the cheaper model, for him, tends to be GLM 5.2. Released on 16 June by the Beijing-based lab Z.ai, GLM 5.2 is an open-weights model, meaning any organization with sufficient hardware can download and host the model for free.Those that pay Z.ai for GLM access still can save money, because the companyâs API costs US $4.40 per million output tokens. Thatâs less than a fifth of the comparable price for access to Anthropicâs Opus 4.8 model, and a tenth the price of Anthropicâs Fable coding model. An output token is the basic unit of text a model generates in response to a prompt. Yet many software engineers around the world, Hasan said, arenât yet fully mindful of the net AI price tag for a given coding project.âA lot of companies right nowâtheyâre still trying to figure this technology out, and so there isnât really a token budget,â said Hasan. And when someone else is paying, the rational move for many software engineers is to skip tabulating costs entirely. âThe easiest thing is to pick the most powerful model.â That price-be-damned habit, reinforced by loose token budgets in software companies today, may now be the widest moat protecting the U.S. frontier AI labs. Z.ai Narrows Benchmark Gap With U.S. RivalsZ.aiâs GLM 5.2 is an AI large language model (LLM) with 753 billion parameters, though it has only 40 billion parameters active at onceâan optimization that improves the speed at which a model can respond. Z.ai released the model under an MIT open-source license, which means anyone can distribute, copy, modify, and use it.GLM 5.2âs release added to fears that U.S. AI companies could lose their competitive edge. The model nearly ties Opus 4.8âs score on some agentic coding benchmarks, such as FrontierSWE and PostTrainBench. Cybersecurity researchers have also found that GLM 5.2 scores well in cybersecurity benchmarks, a capability that spurred comparisons to Anthropicâs Mythos. Z.ai arrives amid a broader trend. According to Stanfordâs AI Index (an annual, 300-plus-page survey of AI trends) Chinese companies produced just over half as many ânotableâ AI models in 2025 as their U.S. counterparts. Thatâs up from roughly a third in 2023, and a fifth in 2020.GLM 5.2 caused hand-wringing among some U.S. observers due to its outstanding benchmark scores, which set new records for both open-weights models and Chinese-developed models generally. The modelâs Chinese origin also complicates its use for companies in the U.S. and elsewhere that are wary of routing sensitive data through Chinese-linked infrastructure. However, the modelâs open weights provide an out. Any organization worried about where its data is being sent can instead host the model on its own hardware. This stands in contrast to most frontier-level models, which are gated behind an API with no self-hosting option.Z.ai backed up GLM 5.2âs release with the companyâs own numbers, publishing a research report the same day as GLM 5.2âs launch.The report doesnât mention Anthropicâs Mythos or Fable, which were announced but not yet publicly available at the time of its release.The report instead focuses on Anthropicâs Opus 4.8 and OpenAIâs GPT-5.5. And while GLM 5.2 often performs almost as well as Opus 4.8 in benchmarks, the report claims a win only in two less-difficult reasoning benchmarksâand none in coding.Many of the reportâs benchmarks place GLM 5.2 behind Opus 4.8 (and, at times, OpenAIâs GPT-5.5) in agentic coding. For example, GLM 5.2 completed just 13 percent of tasks in SWE-Marathon, a difficult long-duration agentic coding benchmark. Claude Opus 4.8 doubled GLM 5.2âs score in this benchmark. Opus 4.8 also notched wins of 10 percent or more in the coding benchmarks NL2Repo, DeepSWE, and Tool-Decathlon.How Do Coders Use GLM 5.2? Software engineers whoâve pitted GLM 5.2 against their own workflows report a wide range of results.âThe main thing that I realized with [GLM 5.2], was that it can do more long-horizon tasks,â said Hasan, whose company hosts GLM 5.2 on North American infrastructure. Earlier open-weights models, he said, often lost the thread after around 5 to 15 back-and-forth exchanges. âThis one, I noticed that I could be using it for hours, and it would still have a coherent train of thought.âDavid Nix, a principal software engineer at the Denver-based MetaRouter, puts LLMs to work at both his day job and for personal side projects. (Nix also operates a jobs board of AI engineers.) Nix said GLM 5.2 comes âreally closeâ to frontier models like Anthropicâs Opus and OpenAIâs GPT-5.5âclose enough to earn a permanent spot in his rotation.âItâs pretty great at front-end development, for example, where I donât need to always go to Opus or Fable for those things,â said Nix. He estimates that GLM 5.2 handles 10 to 20 percent of the work he sends to an LLM on a given day, and itâs now his first stop for some specific tasks, such as front-end design. Others reported the same strength. Hasan said GLM 5.2 has âreally good tasteâ in web design. Kacper Michalik, a software engineer at KrakĂłw, Polandâbased Screen Studio, received good results while using GLM 5.2 to create forms for use on a website.On the other hand, Sai Kiran Myadaram, a software engineer at Bengaluru, Indiaâbased Indhic AI, reports less positive results with Z.ai. He signed up for Z.aiâs subscription plan the week GLM 5.2 launched and found the model burned through its token allotment quickly. âThe weekly quota that Z.ai provides has been exhausted for me in less than two to three days,â he said. Michalik, who also accessed Z.ai directly, had no significant issues with the modelâs quality but occasionally bumped into rate limits, though in his case he stuck to the free plan.In addition to rate limits, Myadaram experienced problems with model hallucinations and overplanning when asked to tackle minor front-end fixes. âItâs messing up my code base,â said Myadaram. Heâs since drifted back to OpenAIâs Codex.
The conversation about AI often centers on algorithms, computing power, or huge investments in new semiconductor fabrication plants and hyperscale data centers. But beneath each of these advances is another layer of innovation that makes them possible: advanced materials. Every new generation of AI technology demands more processing power, more memory, greater energy efficiency, and…
The final approval settles one case, but it doesn't resolve the broader issue of using copyrighted works to train AI models.
Roblox's new "Build" feature lets users generate basic games using a single text prompt.
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. Meet GPT-Red: an LLM super-hacker OpenAI built to make its models safer OpenAI has built an LLM super-hacker called GPT-Red that it uses as a sparring partner to help its other…
SpaceXAI's Grok Build AI coding tool was spotted uploading users' entire codebases to Google Cloud before it was reported, and the company turned it off. The Register reports that Cereblab published findings on Monday showing how the Grok Build CLI was packaging and uploading entire code repositories, "including files it was told not to open […]
Hachette, Cengage, Elsevier, and other publishers allege that Google trained its AI on copyrighted works without the necessary permissions.
DeepMind CEO Demis Hassabis is proposing an AI "standards body" modeled after FINRA, to test frontier models and develop best practices for their release.
Reflection AI has signed a $1 billion deal to access Nebius' compute. Reflection was founded in 2024 and is developing open source AI technology.
Hugging Face CEO Clem Delangue says enterprises increasingly want open models, due to cost, accessibility, and ownership. Do frontier models still matter if most production AI ends up running on open models?
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. What Anthropicâs latest AI discovery doesâand doesnâtâshow âJames O’Donnell When Anthropic announced last week that it had found a new window into its modelsâ âinternal thoughtsâ as they reason through answers,…
Open source AI is booming, according to Hugging Face CEO Clem Delangue. The company has grown into something like a GitHub for AI in recent years, where AI builders can share and download open models and datasets, now used by roughly half the Fortune 500. Delangue has seen the same story play out again and again: companies start […]
OpenAI's new family of models will continue to power Microsoft's suite of workplace and productivity apps.
OpenAI's latest family of models promises improvements across a range of areas, including cybersecurity.
The AI firm Anthropic has developed a technique that has given it the clearest glimpse yet at whatâs really going on inside large language models as they answer questions or carry out tasks. What they found ranges from the mundane to the unnerving. Researchers at the company built a tool called the Jacobian lens (or…
Meta's pitch to users is Spark's ability to handle large agentic workloads, fix bugs, and help with large code migrations â the kind of automation that enterprises are increasingly turning to AI companies to provide.
About two weeks after OpenAI's GPT-5.6 was caught up in regulatory drama - rolled out only to government-approved organizations during a "limited preview" period - the company has received the Trump administration's greenlight for a public rollout of the model. OpenAI CEO Sam Altman called it "the best model we have ever produced." To celebrate, […]
The large language models (LLMs) that form the basis of generative AI chatbots such as ChatGPT, Claude, and Gemini can generate uncannily human-like text and images. But these models still struggle with a skill that, ironically, looks at face value to be right in their wheelhouse: analyzing structured data. A new type of generative AI is set to change this situation.Although you can get your favorite chatbot to solve intractable math problems, review dense legal documents, compose a catchy pop song, or put together some slick PowerPoint slides, give it anything more than a small table and it doesnât have a clue what to do.For most companies and organizations, the most important data sits in spreadsheets. Whether itâs a bankâs transaction logs, a marketing agencyâs website metrics, clinical trial participantsâ vital signs, or the vast amount of proton collision information produced at atom smashers like the Large Hadron Collider, structured, row-and-column data runs the world, and LLMs canât deal with it.AI startup Fundamental is pioneering a new type of AI foundation model, known as a large tabular model (LTM), to fill the gap. Fundamental came out of stealth mode on 5 February 2026 with US $275 million in funding and a model called NEXUS, purpose-built for tabular data. Now, the model is being adopted by companies such as Amazon Web Services, while others race to build their own LTMs. Why LLMs struggle with spreadsheetsPart of why structured data has garnered less attention is a very human bias, argues Boris van Breugel, a senior AI researcher based in Amsterdam. âPeople like to see images, videos, and ChatGPT responses,â he says. âBut tabular data really lags behind because itâs not fun to look at numbers.â Different tabular datasets are also difficult to compare, explains van Breugel, who co-wrote a prescient position paper on this topic in 2024. Whereas most language has similar semantics, making LLMs well-suited to being trained on vast amounts of text data, van Breugel argues that it is much harder to train a single tabular model on tables with very different variables. Additionally, language is sequential by nature (as are music, images, and video). Changing the order of words in a sentence may change or completely destroy its meaning. But the structured data you find in spreadsheets isnât sequential. You can swap the order of columns or play around with rows, but the underlying factual meaning of the data remains the same.This independence from linear order is incompatible with an LLMâs fundamental purpose of predicting the next value in a linear sequence. âWith LLMs, even slightly changing the input, you get a different output,â says Jeremy Fraenkel, CEO of Fundamental. âThatâs fine and actually often desirable for LLMs, but when youâre making a prediction of whether a transaction is fraudulent or not, you want to make sure that the prediction is the same, or deterministic, no matter what.âDeveloping Fundamentalâs LTMCurrent tabular data solutions are limited to machine learning algorithms, such as XGBoost, that have been around for more than 15 years and are used by organizations globally. These algorithmsâcalled gradient-boosted decision treesâhave to be trained and optimized by data scientists over the course of months for each and every use case. In contrast, NEXUS and other emerging LTMs are foundational, leveraging learning amassed from pre-training on diverse databases so that they can be applied across a range of different predictive tasks with minimal bespoke feature engineering or task-specific model building.And unlike LLMs, which primarily model sequences of tokens, LTMs model the structure of tabular data directly. They jointly learn from each entryâs numerical value, what it represents, and how it relates to other entries. For example, imagine an entry in a grocery stock inventory table for bananas: The LTM can take in not just the magnitudeâsay, 500âbut the fact that the entry represents the current banana stock quantity, its category (produce), and the statistical properties that link the entry with the rest of the column. This contextual understanding enables more accurate reasoning and prediction over structured data.According to Fraenkel, one of Fundamentalâs biggest challenges in developing NEXUS was obtaining the right training data. Unlike natural language, which is abundant and broadly uniform in structure, tabular data is relatively hard to findâmuch of the data is sensitive or proprietaryâand diverse. There are very few similarities between, for instance, a biology dataset and a financial one. That combination of factors meant Fundamental needed to invest in building a huge training set.âWe pre-trained NEXUS on billions of tables using a combination of proprietary datasets acquired through partnerships and licensing, high-quality public and open-source datasets, and data augmentation techniques that expanded the diversity and coverage of our training corpus,â Fraenkel says, though he is keen to point out that NEXUS is not trained on customer data. In fact, it is a confidential computing platform, which means that Fundamental physically cannot access customer data, let alone train on it.This feature was most likely a key consideration when in June, Amazon Web Services (AWS) embedded NEXUS in Amazon SageMaker, widely considered the default operating system for secure machine learning. This brings NEXUS to many customersâ often sensitive dataâa contrasting approach to LLMs, where the data has to be imported to the model.âWith Amazon, we have a first-party partnership, which means that our model exists as if itâs a native AWS solution,â Fraenkel says. âAnd over time, the goal is to expand these types of relationships to allow [end users] to really access their data wherever they do their predictions.âThe future of data analysisThough Fundamental has taken the lead, at least in enterprise applications, the company is not alone in pursuing foundational LTMs. In March, Feedzai, which provides fraud and financial crime prevention services, and credit card company Mastercard separately launched similar proprietary technologies focused on finance. Then, in late June, Google launched its own foundational competitor, TabFM, trained entirely on hundreds of millions of synthetic datasets. And machine learning researchers are not far behind either. FlexTab, TabICL, and iLTM are just three of a raft of LTMs developed by the research community in the past year, all in the pursuit of bringing the success of LLMs to the tabular domain.For all involved, the direction of travel is clear. âI would be very surprised if most data processing and analysis is not done through an automated system in the future, whether thatâs an LLM, an LTM, or some combination,â van Breugel says. âMost people donât necessarily like to do data analysis, and these systems will be able to do it a lot better.âFraenkel agrees. âI see the relationship between LLMs and LTMs as being a bit like the human brain: The left side is good at reasoning and understanding and summarizing text, and the right side is really good at understanding numbers and statistics and patterns,â he says. âBut itâs when you combine both of those that you really get something much more powerful.â
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Open source modelsâ success isnât coming at the expense of frontier labs. Instead, they each seem to capture two phases of the same life cycle.
"The reality is, when you're optimizing for production, you start looking at a price/performance," Guillermo Rauch tells TechCrunch.
In the AI era, platforms have no choice but to fight fire with fire to cull spam.
At an internal meeting, the Meta CEO reportedly said that AI development efforts were not moving as quickly as anticipated.
Meta has quietly launched Pocket, an experimental AI app that lets users generate and share interactive mini games using text prompts.
The news comes about a week after OpenAI announced its own custom AI chip in a partnership with Broadcom.
OpenAI CEO Sam Altman has reportedly proposed giving 5% of the companyâs equity to a U.S. sovereign wealth fund, reviving discussions about letting the public share in the financial gains from the AI boom.
Microsoft follows Amazon, OpenAI, and Anthropic with its new AI deployment group.
'We canât trust heavy users of AI to understand their code enough to fix it,' say maintainers who previously called the flood of vibe-coded pull requests 'demoralizing'
Cloudflare is giving AI companies until September 15 to separate web crawlers used for search from those used for AI training and agents, or risk being blocked by default on many publisher sites.
Meta is developing plans for a cloud infrastructure business, selling access to AI compute power and models. The move would pit it against the big cloud providers like Amazon Web Services, Google Cloud, and Microsoft Azure.
Anthropic said it would begin restoring access to the Fable on July 1.
After weeks of negotiating with the Trump administration, Anthropic is finally going to be able to bring Claude Fable 5 back online. In a post on X, Anthropic said it plans to begin restoring access Wednesday to users globally on Claude platforms, and that the company would re-enable access on AWS, Google Cloud, and Microsoft […]
The free open source agentic program is finally invading your phone.
At an event for pharmaceutical executives, biotech founders, and researchers on Tuesday, Anthropic announced Claude Science, a major new product intended to support scientific research in the same way that Claude Code supports software engineering. Like Claude Code, Claude Science can autonomously carry out meaningful work when given concise, high-level instructions, and it has access…
Anthropicâs Claude Sonnet 5 brings stronger agentic capabilities, lower pricing, and improved safety, positioning the model as a cheaper alternative to Opus, GPT-5.5, and Gemini Pro.
Anthropic's Claude Science is a workbench that gives scientists one environment to do computational research, saving them from the need to bounce between databases, pipelines, and tools.
âWe donât believe this kind of government access process should become the long-term default,â says OpenAI. âIt keeps the best tools from users, developers, enterprises, cyber defenders, and global partners who need them.â
Nvidia has dominated the AI chip market for years, but the era of total dependence might be ending.   OpenAI just shared its plans to spice things up with Jalapeño, its custom inference chip built with Broadcom, joining Google, Apple, and SpaceX in a growing list of companies building their way out of single-supplier risk. The goal is less of a […]
Less than 24 hours after news broke that OpenAI would stagger its next model release at the request of the Trump administration, that model, GPT-5.6, is here. On Friday, the company unveiled the limited preview of its new GPT 5.6 model suite: Sol, the flagship; Terra, a medium-tier model for "high-volume work"; and Luna, a […]
General Intuition has raised $320 million to scale AI trained on millions of hours of gameplay, betting action data can help AI develop something closer to human intuition.
Amazonâs latest India investment comes as global tech companies race to expand AI infrastructure in the country.
Figma's update adds a new code layer, support for motion and shaders, and the ability to create custom plugins for various tasks using AI.
Named Jalapeño, the new processor was designed specifically for the unique needs of OpenAI's inference systems.
OpenAI has just revealed a new "intelligence processor" chip for AI servers made in partnership with Broadcom. The chip, called Jalapeño, is designed to power current and future large language models, according to an announcement on Wednesday. Jalapeño is an ASIC (Application-Specific Integrated Circuit), meaning it's designed for a specific purpose: AI inference. With AI […]
AI is booming. New use cases are emerging each day. To capitalize on the technologyâs potential, enterprises require data at scale. In many cases, though, the relevant information is blocked or unstructured, which limits its use by AI models.  To understand this challenge, consider the foundation of the web itself. The web was not designed…
Large language models have moved out of the research lab and into engineersâ daily workflow. LLMs serve as reasoning engines that can orchestrate complex tasks including identifying vulnerabilities in source code and transforming fragmented project discussions into rigorous technical specifications.While the general public uses AI tools to write email and plan vacations, technical professionals use LLMs as core architectural elements that are fundamentally changing how digital infrastructures are built and maintained. As the AI models move into mainstream engineering practice, the demand for technical expertise is rising.The LLM technology market is expected to grow by about 33 percent every year through 2030, according to MarketsandMarkets. The rapid expansion suggests that proficiency in implementing and securing the models is transitioning from a niche into a core requirement for technologists.More than just a better search engineTo use LLMs effectively, technical professionals must move beyond treating them as conversational robots. At a fundamental level, the AI systems are built on the transformer architecture, a framework that replaced the older method of processing data in a fixed, sequential order. Unlike earlier models that analyzed information one step at a time, transformers use self-attention mechanisms to ingest vast datasets simultaneously.For technical professionals, LLMs are core architectural elements that are fundamentally changing how digital infrastructures are built and maintained.Relying on such LLMs without understanding their internal logic creates a significant reliability risk. To build tools that work consistently, developers must understand the core principles that govern how the models process information and generate results. By mastering how a model processes information and how its internal settings influence the result, developers can move away from a trial-and-error approach toward a more precise one to ensure the AI tool handles complex data reliably.Four ways LLMs are changing jobsHere are areas that integrate large language models.Moving past basic prompts. Developers are using application program interfaces (APIs) to connect LLMs directly to their databases and software tools. Employing the APIs allows AI to perform work such as executing code or searching through internal repositories.Fixing the âhallucinationâ problem. LLMs are at risk of hallucinations, which are generated facts or code that looks correct but actually is wrong or broken. To fix the problem, retrieval-augmented generation (RAG) forces AI to look up information in a trusted source such as a companyâs database.Prioritizing data security. When using AI with proprietary code, security is a major concern. Engineers must learn how to set up âprivateâ instances of the models to ensure that sensitive company data stays within a secure cloud environment and is not used to train public versions.The future of collaboration. By automating repetitive coding tasks and summarizing thousands of pages of documentation, LLMs let engineers spend more time on high-level designs and solving important issues.Online course program helps with mastering the techThe gap between people who use AI and those who understand how to build with it is growing wider. To help technical professionals stay ahead, IEEE offers a five-course online program, Large Language Models Demystified, available through the IEEE Learning Network.The program, developed by IEEE Educational Activities in partnership with the IEEE Computer Society, is built for people who want to understand the âhowâ and the âwhyâ behind the technology. Rather than just teaching basic prompting, the curriculum dives into the engineering behind generative AI, including:Evolution, impact, and hands-on exercises: the shift from statistical methods to modern transformers, including hands-on model optimization.Understanding transformer architectures: the mathematical core of self-attention and positional encoding, implemented in NumPy and Python.Architectural analysis and implementation: advanced LLM design with practical model-building exercises.Training and modeling with PyTorch: end-to-end pipelines in PyTorch, leveraging parameter-efficient techniques such as low-rank adaptation and quantization.Optimization, alignment, and deployment: performance scaling, reinforcement learning from human feedback (RLHF), group-relative policy optimization, RAG, and agentic AI.Upon completion of the program, participants earn professional development credits and a digital badge from IEEE to verify their expertise.Enroll in the course program on the IEEE Learning Network.Organizations looking to prepare their teams to work on LLMs can connect with an IEEE content specialist to discuss group enrollment and tailored training paths.
Just as last week was ending, the US government forced Anthropic to pull its two newest models, Fable 5 and Mythos 5, citing national security concerns after Amazon researchers allegedly found a way to bypass Fable 5’s guardrails.  Cybersecurity researchers have since signed an open letter calling the move dangerous, and Anthropic itself noted the same jailbreaks exist in other models. So is […]
Miami-based AI startup Subquadratic came out of stealth mode last month with a huge claim. It announced that it had solved a mathematical bottleneck that had been holding back large language models for almost a decade. The details were thin, and many people were unconvinced. But Subquadratic has started to bring the receipts, sharing the…
Deductive AI, a startup that uses AI to catch and resolve bugs in software, was founded just three years ago.
Startup Baseten is reportedly close to finalizing a $1.5 billion round at a $13 billion as the âinference gold rush" marches on.
AWS is in talks to sell its chips to other data centers. CEO Andy Jassy has said this represents a $50 billion opportunity for the company.
FERC told grid operators to give data centers a fast lane for interconnections, but it failed to address electricity supply shortages.