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Gemini, DeepMind, and Google's AI across cloud and devices — the releases and research that matter to builders.
OpenAI cut GPT-5.6 Luna API prices by 80% to $0.20 per million input tokens and $1.20 per million output, while Terra fell 20% to $2 and $12. Fast mode gives Sol up to 2.5 times Standard speed at twice the price. OpenAI says Sol-assisted kernel work lowered serving cost by 20% and improved token-generation efficiency by more than 15%, while Luna delivers year-old frontier performance at roughly six cents per task-dollar and nearly nine times the speed. The edition connects cheaper models to Amazon's reported $1.8m, 860%-over-budget coding task, Gemini Robotics 2 whole-body control, Nscale's Anyscale acquisition and Okta's roughly $200m Permiso deal.
If you want to bypass AI guardrails designed to stop models from assisting with cyberattacks, you often just have to ask the right way, according to researchers from Cisco Talos. Simply claiming you own the servers you're targeting or that you're taking part in a capture-the-flag or bug bounty exercise was often enough to persuade models to cooperate. Talos researchers have been poring over prompt logs and artifacts recovered from threat-actor endpoints running tools such as Claude Code, Codex, Cursor, and Gemini to learn how suspected threat actors are abusing LLMs. The big takeaway from that "significant corpus," the researchers said in their report, is that existing guardrails offer little resistance to operators willing to reframe their requests. “We did not encounter any sophisticated encoding or techniques designed to trick the models,” Talos explained. “Most of the time it was a simple ‘I'm allowed to do this,’ and the model complied.” When guardrails did manage to get between criminals and their prizes, the researchers added, “they accomplished little.” The bulk of the report consists of examples of threat actors trying, and often succeeding, to coax AI models into assisting with malicious activity. On the "guardrails doing little" side, Talos documented numerous examples, few of which relied on particularly sophisticated techniques. Most common in the list of easy-to-accomplish guardrail hops was simply claiming ownership of equipment or infrastructure that an attacker wanted to exploit. In many cases, simply telling the AI that a target belonged to the attacker was enough, with no need to provide actual evidence of the claim. Telling an AI model that what it was being asked to do was part of a capture-the-flag or bug bounty exercise also seemed to be a common tactic. That, the researchers explained, commonly freed chatbots from their ethical constraints, allowing them to hunt for vulnerabilities and then exploit them in target systems, again without any ne
Read full story →Google will reveal the Pixel 11 series later this month, but leaks and teasers so far have painted a picture of phones that have barely changed since last year . One notable exception seems to be the illuminated dot from some of the teasers. This has been rumored as "Pixel Glow," but a new leak all but confirms a different (and less interesting) name. Say hello to HiLight. A post on Bluesky by WinFuture's Roland Quandt shows the new LED inside the camera module of the Pixel 11 Pro Fold. We can see the actual body of the device rather than a shadowy outline as in Google's teasers, and it does look a bit different than the previous Pixel Fold. The camera bump on the Pixel 11 Pro Fold is smaller, with no separate section for the flash off to the right. It appears that HiLight is the flash. Presumably, it still works as a flashlight and camera flash, but it can also produce a range of colors. Read full article Comments
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Read full story →AI has turbocharged an already expanding cloud services market as organizations pour billions into online platforms offering the compute needed to train and run models, swelling the coffers of the established giants. Their latest results show revenue surging alongside capital spending as Amazon, Google, and Microsoft race to add capacity – at least until the AI bubble eventually bursts, of course. Biggest of them all, Amazon disclosed that its Amazon Web Services (AWS) division took in $42.2 billion during its second quarter, ended June 30, 2026. This was an increase of 36.7 percent year-on-year and its fifth consecutive quarter of accelerating growth. It seems like even CEO Andy Jassy could scarcely believe the cloud operator's fortunes, boasting that "AWS is now a $169 billion annualized revenue run rate business, which, for perspective, would place it 24th on the Fortune 500 list if it was a standalone company." Amazon is now upping its forecast for how much capex it will spend this year on expanding its infrastructure, including that needed for those AI workloads. "Earlier this year, we said we plan to invest approximately $200 billion in cash capex in 2026, the majority of which to support AI and AWS," Jassy told analysts on a conference call about its financials. "We now believe we will spend approximately $220 billion in cash capex in 2026, with the higher cost of memory pushing this number up from our prior estimate of about $200 billion. But even at that amount, we will still not have enough capacity to meet all the demand we have in 2026, and I believe this dynamic will also be true in 2027 too." "We've done this before in the first era of cloud computing, just over a longer time horizon, where demand built more gradually than it has with AI. But we see the margins and returns in AI tracking what we saw with core at the same point of evolution," Jassy claimed. Google is likewise upping its capex estimates for this year, coming close to Amazon's own massive
Google is rolling out an update that will finally allow you to connect your Fitbit workouts, steps, vitals, and other data to Apple Health, as reported earlier by 9to5Mac. With Google Health's 5.05 update, you can now tap your profile icon, select "Partner apps," and choose Apple Health to link your data directly. Previously, you […]
In what they call the first-ever real-world agent-to-agent exploitation method, Pillar Security researchers say they discovered an exploit in the repository behind Google's Agent Development Kit for Python that could allow attackers to compromise supply chains. In other words, now we know that one AI agent can be used to control and compromise another one that has more privileges. The security snafu existed in google/adk-python, an open source Python toolkit with more than 90 million downloads used to build and deploy AI agents. Google has since fixed the underlying issue in the repository but deemed the exploit non-rewardable because it involved social engineering. Even so, it illustrates the risks of using AI agents in CI/CD workflows for triage, pull request (PR) reviews, and discussions. It also shows how one AI agent could attack another in a production environment, according to Pillar’s Dan Lisichkin, who found and reported the vulnerability. “Our world is changing quickly, and new attack surfaces are not yet reflected in threat models because these attacks never could exist in the first place in the ‘pre-agent’ world,” Lisichkin said in a technical write-up published on Monday. He will also discuss the findings during a poster talk at DEF CON's AI Village on Friday, August 7 at 1600 PDT. “CISOs and security practitioners should start considering these scenarios, threat-modeling them, and calculating worst-case implications and blast radius,” Lisichkin wrote. The issue stems from the way that the repo ran two classes of automated AI agents with different privilege levels that unintentionally share a trust boundary. One is a low-privilege, public-facing AI agent activated whenever a user opens a pull request (PR) or issue, and a second is a high-privilege, maintainer-only agent. Pillar’s team found that the low-privilege, public-facing agent could be manipulated via prompt injection into triggering a maintainer-only agent that can execute malicious actions. “Be
We've entered a new phase of the AI industry's development, with all the major players heavily cutting costs and boosting the capabilities of their entry-level models in order to compete with new models from China, like Moonshot's Kimi K3 and DeepSeek's V4 Flash. OpenAI did so most recently, cutting the price of its base frontier model, ChatGPT 5.6 Luna, by 80% per million tokens, and its mid-range 5.6 Terra by 20%. This comes just over a week after Google introduced its more-affordable Gemini 3.6 Flash and 3.5 Flash-Lite models. Anthropic hasn't cut prices, but replaced its most-affordable Opus 4.8 model with a more capable Claude 5.0 at the same price point. Intelligence is getting more affordable thanks to increased global competition, but this can come at the cost of margin for these major companies. This follows months of major AI businesses announcing cuts and limits on their use of the technology , even by major AI boosters like Elon Musk's xAI. Despite more workers using AI than ever before, productivity gains are reported to have been less than ideal . Intensifying competition The story of Chinese and American AI development efforts has been somewhat emblematic of the countries' historic strengths. While American firms burn through enormous amounts of money to push frontier technologies, Chinese developers have leveraged their industrial base to develop models that are cheaper, leaner, and almost as good at the top end. DeepSeek gave Western AI developers a shock in 2025, and Kimi K3 did much the same in 2026. Alone, these events would cause concern for companies like OpenAI, Google, and Anthropic. Still, after months of companies that use AI heavily complaining about skyrocketing token costs , the news of an almost-as-good model at a much lower price really made a splash. Now, the big AI developers can't just compete by throwing more parameters and training data at the problem. Now they're having to really compete on price, and to do it, OpenAI has massive
Lenovo is expected to release some of the first Googlebook models later this year, and leaked images have now given us a good idea of what they might look like. Leaked press images shared by Digital Citizen and Android Headlines include a laptop and a 2-in-1 tablet, all of which feature Googlebook branding on the […]
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. Here’s why AI agents lie and cheat to reach their goals When two OpenAI models hacked into Hugging Face last month, they weren’t trying to make money or commit sabotage—they were just looking for answers to a test question. According to OpenAI, the models decided to solve a cybersecurity exercise by hacking out of the environment in which OpenAI had attempted to contain them and into Hugging Face’s databases, where—they reasoned—the correct answer to the problem might be stored. The incident has attracted intense attention over the past couple of weeks. It’s a dramatic illustration of just how good AI models have gotten at hacking. But it’s perhaps even more striking as an example of how and why AI systems lie and cheat. Read our story explaining why AI engages in this sort of behavior—known as ”reward hacking.” —Grace Huckins This story is from our ‘Explains’ series, where our writers untangle the complex, messy world of technology to help you understand what’s coming next. Read more from the collection . The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 It looks like Iran is conducting cyberattacks on US water systems That’s according to preliminary investigations on hacks in at least seven states. ( NYT $) + Will this be a wake-up call? ( Forbes ) 2 Google briefly made it easy to fake satellite images Literally the last thing the world needs right now. ( NPR ) + AI companies keep moving fast and breaking things. ( The Atlantic $) + Apple is struggling to keep pace with incoming AI-assisted software bug reports. ( FT $) 3 Why wildfires have got so bad in Europe this summer It’s a mix of climate change, land abandonment, and outdated firefighting tactics. ( New Yorker $) + How Europe can be
Like any company, Reddit reports quarterly earnings, and its CEO, Steve Huffman, addresses investors during those reports. It's not always about just sharing numbers, though: This quarter, Huffman took the opportunity to voice concerns and criticisms about Google's AI Overviews feature, which automatically summarizes search results on most user queries. First, there was a letter to investors , wherein Huffman spun his narrative about Reddit's value proposition and general strategic direction amid the proliferation of AI tools. Read full article Comments
<p><strong><a href="https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731">deepseek-ai/DeepSeek-V4-Flash-0731</a></strong></p> The latest release in DeepSeek's V4 family, "with substantially enhanced agentic capabilities". It's 304 billion parameters - 167GB on Hugging Face - but it appears to punch <em>well</em> above its weight.</p> <p>Artificial Analysis <a href="https://artificialanalysis.ai/models/deepseek-v4-flash">rank it</a> ahead of MiniMax M3 - a 428B model. It's $0.14/million input and $0.27/million output pricing means this may currently be the best value-per-intelligence model out there. It's looking very good on the <a href="https://artificialanalysis.ai/models/deepseek-v4-flash#intelligence-comparison-tabs">Intelligence Index vs. Cost per Intelligence Index Task</a> chart:</p> <p><img alt="Scatter plot from Artificial Analysis titled with axes "Artificial Analysis Intelligence Index" (20 to 65) and "Cost per Task (USD, Log Scale)" ($0.02 to $3), with a green "Most attractive quadrant" box in the upper left and a dotted "Pareto line". DeepSeek V4 Flash 0731 (max) is highlighted in dark blue at roughly $0.028 and an intelligence score of 50, sitting alone at the far left edge of the green quadrant where the Pareto line jumps sharply upward. Models of similar or lower intelligence like MiniMax-M3, Kimi K3 (low), GLM-5.1 and Kimi K2.6 cost ten times more, and the models that beat it (Grok 4.5, Gemini 3.6 Flash, GLM-5.2, Kimi K3, Claude Opus 5, Claude Fable 5, GPT-5.6 Sol) all sit far to the right at $0.4 to $3 per task." src="https://static.simonwillison.net/static/2026/deepseek-flash-chart.webp" /></p> <p>I got <a href="https://gist.github.com/simonw/83bfb1171792f1e7a4d8935b5e82317e#prompt">a disappointing pelican</a> from it using the default reasoning level via OpenRouter:</p> <p><img alt="Flat vector illustration of a white pelican with a long neck and large orange beak pouch, hovering above a mangled blue a
We are a month away from the initial rollout of Google's Android developer verification system, and the company contends this policy does not impinge on the platform's open nature. Still, the restrictions will be a big change, and there are still some unanswered questions. An issue that has come up repeatedly in the run-up to verification is what will happen to devs who can't verify because of where they live. It turns out that Google has a cryptic answer for that buried in an FAQ. Developer verification will soon block the installation of apps from unverified developers on any Android device running Google services, which is functionally all Android phones outside Russia and China. Developers who want to keep releasing software, even if it's not in the Play Store, have to provide Google with their ID and pay a small fee. But what if you're an Android developer living in a sanctioned nation? Currently, the US sanction list includes Iran, Cuba, North Korea, and occupied areas of Ukraine. Given the current uncertain state of US foreign policy, that list could change in the future. Google doing any business with developers in those places is a thorny issue, and it seems like the company has decided to just leave them hanging. Read full article Comments
A tool that allowed anyone to generate fake AI-generated imagery and superimpose it over real Google Earth maps quickly spurred backlash.
Google has shut down Google Earth feature it launched Thursday that allowed users to edit satellite images with text prompts using AI. The tool essentially let users create AI deepfakes of the real world using text prompts; Digital Digging's Henk van Ess, for example, intentionally generated images adding things like refugees near the Mexican border […]
Amazon, Google, Meta, and Microsoft have spent more than a trillion dollars on AI infrastructure, including data centers, the chips inside them, and the power needed to run the facilities, since 2023. The Financial Times said that these four big companies have already hit $1.1 trillion in capital expenditure based on their latest earnings reports, and that an additional $745 billion is expected to be added to this figure just this year. Go deeper with TH Premium: AI and data centers (Image credit: Microsoft) Photonics and high-speed data movement is the next big AI bottleneck The data center cooling state of play Massive AI data center buildouts are squeezing energy supplies Ultra Ethernet: The data center interconnection of tomorrow “There is basically no end in sight for the growth in capex,” RBC Capital analyst Rishi Jaluria told the publication. “Investors need these companies to toe the tight line between investing in AI and not compromising the things that have made them successful.” This massive investment has upended several other industries — namely electricity prices and memory and storage chips. The massive power demand that data centers have put on the power grid has forced many U.S. utility companies to spend billions of dollars to upgrade their respective infrastructure, which they then passed on to all consumers , not just the big ones that forced the upgrade. This, alongside other environmental issues, has caused many Americans to push back against data center projects near their communities. The White House instituted the “ ratepayer protection pledge ” and made AI hyperscalers, utility operators, data center companies, and individual states promise that they will protect the average consumer from electricity cost increases. But so far, no state has taken a step to codify this pledge into law. Oregon actually enacted the POWER Act , which resulted in a 30% increase in the power bill of users that consumed more than 20MW while slashing the bills of r
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