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Android lockscreen bypass allows Gemini to send SMS without verification
It is apparently a known issue, and a fix is incoming. These things happen from time to time, especially when dealing with such complex software with so...
Rotating Playcube projector is becoming an AI entertainment hub
The Playcube was already an unusual little projector: a palm-sized, Rubik’s Cube-like device with a 360-degree rotating design, built to throw a big image onto a wall, ceiling, or whatever flat surface you have nearby. Now, TCL is adding a new layer of interaction through Google’s Gemini AI.
EU Orders Google to Give Rival AI Apps the Same Android Access as Gemini
The European Commission today formally ordered Google to give third-party AI services the same access to Android device features that Gemini has. Europe's Digital Markets Act requires software makers like Apple and Google to grant equal interoperability to third-party apps and services, and the EC is enforcing it. DMA interoperability rules are why Siri AI won't be available in the European Union when iOS 27 launches.
Google Vids now lets you star in your own AI videos
Google is adding personalized AI avatars to Vids that let users create videos starring a digital version of themselves, alongside Gemini Omni-powered tools for generating and editing videos from prompts and reference images.
Google Gemini Launch Delayed as Tech Falls Short of Internal Goals
Alphabet Inc.’s Google is months behind schedule on delivering Gemini 3.5 Pro, its most powerful flagship AI model, because the company has been taking time to try to improve its capabilities, particularly in coding, according to people familiar with the matter.
Google preps letting you customize Gemini voices by Speed, Energy, and more
After releasing two new options after I/O, Google might soon let you customize Gemini voice across four parameters.
Google continues its renaming streak by turning NotebookLM to Gemini Notebook
Google said users can soon access their notebooks through AI Mode in Search.
NotebookLM is now Gemini Notebook, with 3.5 + Antigravity upgrade coming to AI Pro
Google announced today that NotebookLM is now called “Gemini Notebook” and previewed upcoming features that users can soon expect.
Flex Raises $70 Million to Improve Payments for High Net Worth Business Owners
Business banking platform Flex raised $70 million in a Series B1 round to expand its business finance, payments, private credit, and ERP offerings while doubling its workforce.
Gemini Spark gets Workspace upgrades as Google teases AI Pro access soon
Following the last update two weeks ago, Google is rolling out a series of new features for Gemini Spark...
Chrome on Android is getting a navigation bar redesign to make room for Gemini
Chrome on Android is getting a redesigned navigation bar with a dedicated Gemini shortcut, plus desktop-like multi-tab context.
Waze adds new AI-powered features and customization updates
Some of the new features are powered by Google's Gemini AI assistant, which reflects the the tech giant's broader push to integrate Gemini across its products while also better positioning Waze to compete with rival services such as Apple Maps.
I found a hidden Chrome feature that made my browser the ultimate productivity tool
This is the Chrome upgrade I didn't know I needed
Gemini on Nest Hub redesigns weather and other knowledge answers
The Gemini for Home voice assistant is improving its Nest Hub and Smart Display experience with redesigned visual responses.
Google Home app update makes automation easier, improves cameras, more
The latest batch of Google Home app updates are now rolling out, including new “Suggested Automations” to make it easier...
Google Photos rolling out ‘Video Remix’ powered by Gemini Omni
Google Photos is using Gemini’s “create anything from any input” model to offer a new Video Remix capability for fast and stylized editing.
Google Voice now offers paid tiers with call recording & Gemini for personal accounts
Historically, Google Voice’s paid offerings were meant for enterprise customers, but Google is now letting you upgrade to a paid calling...
I tested Google's new smart speaker with Gemini AI. Here are 3 things to know before you buy.
The new Google Home Speaker with Gemini is smart and capable, but it fails to stand out much from similar rivals.
I am switching from OneNote to Google Keep on Android because of this single feature
Gemini changed the game
Cancel ChatGPT and get lifetime access to Claude, Gemini, and more for $55.30 with this code
No monthly subscription fees, and it'll even let you send one prompt to multiple AI models.
Google Gemini Spark Comes to Mac With Local File Automation
Google today launched Gemini Spark for its macOS desktop app, allowing the AI agent to automate tasks involving local files and desktop workflows. Google announced the update today, adding a dedicated Spark tab to the sidebar of the Gemini app for macOS. The feature allows the AI agent to take action on files stored locally on a user's computer rather than just responding to questions in a chat window.
Gemini Spark, Google's agentic assistant, is now available on Mac
Google's 24/7 agentic assistant, Gemini Spark, comes to Mac alongside other improvements, like real-time tracking and support for more apps.
Gemini Spark Comes To Google's Gemini App For macOS
The Gemini macOS app now has access to Google’s Spark agentic AI assistant.
Gemini Spark rolling out to macOS app for local tasks, automation
As previewed at I/O 2026, Gemini for macOS now supports Gemini Spark. This is the first of two big desktop app updates...
NotebookLM adding Short Video Overviews with Nano Banana 2 Lite
Google today announced Nano Banana 2 Lite and preview availability of Gemini Omni Flash, as well as NotebookLM Short Video Overviews.
Gemini's personalized AI image generation is now free for U.S. users
Google is expanding Gemini’s personalized AI image generation to eligible free users in the U.S., allowing the chatbot to create images based on your interests and data from connected Google apps.
This might be the new best smart speaker
In this week’s Installer: Gemini, Star Fox, and The Last Airbender.
Gemini’s new Google Play Store integration lets you chat to find Android apps, games
As previewed at I/O 2026 last month, the Gemini app on Android now integrates with the Google Play Store...
Gemini begins rolling out for existing Android Automotive vehicles
Some months after its confirmation, Gemini for Android Automotive appears to be finally rolling out to existing vehicles.
How should European scientists respond to the US AI kill switch?
Earlier in June, Europe’s worst fears of technological dependence were confirmed when the White House ordered the AI firm Anthropic to stop non-US citizens using its leading models. This kill-switch moment crystalised anxieties in Brussels that the continent, lagging the US on the creation of leading-edge AI models, is now a technological of vassal of Washington. The shock has specific implications for European scientists and universities. Some governments are now worried that with AI being integrated into labs, Donald Trump now has his finger hovering over an off switch for European science. “Having restrictions on this tool, it can have an impact on our own research,” said France’s research minister, Philippe Baptiste, at the VivaTech conference in Paris on June 18.Baptiste, himself a former academic, said that colleagues in mathematics had recently warned him they were now cut off from leading-edge US models, risking their work. “This model is not available anymore now because of the US restriction, and for the first time this had an impact on mathematics,” a field where large language models (LLMs) are making a significant contribution, said Baptiste. So, should European scientists avoid relying on US models in the future? Or would this be a self-harming overreaction? And how is AI being used in science, anyway? What happened?On June 12, it emerged that Washington, citing cybersecurity concerns, had ordered Anthropic to stop non-US citizens using two models: Claude Mythos 5, and Claude Fable 5, a version of Mythos but with guardrails to prevent abuse. Anthropic said Washington’s concerns were overblown. But forced to follow the order, yet with no way to check who was using the models, the company suddenly shut down access for everyone, US citizens included. There’s still no agreement on how to restore broad access. But last week, Bloomberg reported that select US companies, and the US government, still had access to Mythos despite the ban, but the EU’s cybersecurity agency Enisa had been shut out. Class division of researchersIn the longer term, access outside the US should be restored, said Marcel Salathé, co-director of the AI centre at the Federal Polytechnic School of Lausanne (EPFL), because a permanent restriction would be financially ruinous to US AI firms. “Even Trump will eventually budge to economic pressure,” he said.However, US AI firms might end up operating rather like banks, doing due diligence on users of their leading-edge AI models, he went on.European scientists are unlikely to fall foul of such checks, he said. But having a Chinese postgraduate in their lab, or doing a type of research the US disapproved of, could potentially result in access being denied. “It will create a sort of class division of researchers,” he said. More than frontier modelsHowever, successfully deploying AI in science is about far more than just having access to leading LLMs such as Mythos.Instead, many science-specific tools, most famously Alphafold, which predicts protein folding structures, require “far less” computing power to train than frontier models, well within the capabilities of European supercomputers, according to a recent analysis by the Arq Foundation, a Brussel-based AI think tank. Instead, tools such as Alphafold are possible because certain fields, like protein folding, weather prediction or astronomy, have large, clean, standardised datasets that can be used to train predictive models. Rather than compute, “data is the central constraint for AI for science,” the report says. For example, a less powerful physics model trained on CERN’s data “will be more valuable than the best model without certain data,” said Bengüsu Özcan, author of the report. European advantage?Europe could therefore steal a march by using its scientific data and infrastructure to create models US digital giants cannot. “They might actually match or go even beyond what these big [US] companies are doing,” said Özcan.Indeed, some European start-ups hope to create automated testing labs to create the kind of standardised data that could speed up the discovery of new materials. Materials scientists say there’s currently too little data to train models. AI scientistsHowever, even if the bottleneck for AI in science is often data, rather than compute, being at the mercy of the US for LLM access could still become a problem for European scientists. A new class of “AI scientists” based on leading LLMs is also emerging, Özcan’s report says.Unlike domain-specific tools like Alphafold, these try to automate day-to-day scientific work, by providing an overarching tool that coordinates specific models to synthesise existing literature, generate hypotheses or run analysis of data. The problem for Europe, however, is that these tools are sometimes based on US leading models. Google Deepmind’s AI Co-Scientist, for example, is built on Google’s Gemini LLM. Systematic failureThese scientific automation tools are still in their early stages, and still exhibit “systematic failures on scientific tasks,” Özcan’s report says. One of the problems is that the messy, day-to-day process of scientific discovery is largely not documented, meaning LLMs don’t have a good source of data to learn how scientists actually work. Meanwhile, some tasks, such as literature synthesis, don’t typically require access to leading edge models.However, the performance of AI scientific assistants could improve as companies such as Google pour resources into developing them. Rely on US“Unfortunately, for scientific assistants, there is not much we can do right now except rely on US frontier AI,” said Antoine Bosselut, a natural language processing expert based at EPFL. Chinese-built open models, which can run on local computers, lessening the risk of a shutdown, are touted as one alternative to US dependence. There’s also Mistral, once seen as Europe’s main AI hope, but the Paris-based company has seen its recent models fall behind in performance. But the Chinese alternatives are generally not as capable as US frontier AI models, said Bosselut, unless paired with fiddly, custom tools that allows them to act as independent agents. “Most scientists do not have the time,” he said. Don’t jump ship yetAlthough the kill switch moment is seen a warning shot for European scientists, it doesn’t mean labs should abandon US AI tools immediately. “I do not think the right response is simply to abandon US-based models and systems under the banner of sovereignty or independence,” said Mattias Björnmalm, secretary general of the Cesaer university association. “That would be an overreaction.”Related articlesEuropean sovereignty in AI requires ‘ugly trade-offs,’ say experts Robotic lab start-up makes case for €500M EU-funded materials testing facilityAI in science: is it useful? Instead, European researchers need to ask themselves whether they’re becoming dependent on technology that could be turned off in Washington, said Özcan. “They might not see this today, but they should really start asking this question,” she said. European researchers need a plan B, said Salathé. “I would recommend to every researcher, very proactively explore alternatives.”Own toolsIn addition, Björnmalm and others want European research institutions to develop their own scientific AI tools.Instead of signing AI deals with US digital giants, European universities should be using this money to create their own models, which may end up being superior for research, said Sabine Leonelli, an expert on the use of AI in science at the Technical University of Munich. “Universities in Europe seem to be happy to sign commercial contracts with US companies on the assumption [their AI tools] are better,” she said. But US frontier LLMs are typically opaque, and have been developed without independent scientific input, she said. “This is not good enough for science.”Last year, Swiss researchers released Apertus, an LLM designed to be completely transparent, from its development methods to its training data. The idea is that this makes scientific tools built on top of the model far more reproducible than with closed, commercial alternatives.“Europe needs more of this: serious capacity-building that gives researchers, universities and companies credible options,” said Björnmalm.
Google Home Speaker vs. the mainstay Nest Mini: Is this a true upgrade?
The Google Home Speaker is now the easiest and most affordable way to get Gemini into your smart home, but...
Google's Gemini 3.5 Pro release slips to July
Google's next big AI model is coming later than promised, but the company is trying to use the extra time to make it better.
Gemini in Chrome adds ‘Select from screen’ tool as Gemini 3.5 Flash gains computer use
Gemini in Chrome now lets you focus a prompt with a new "Select from screen" tool, while developers can tap into 3.5 Flash computer use...
Google’s New $100 Speaker Makes a Weak Case for Gemini in the Home
The Google Home Speaker is the first new device of its kind to come from Alphabet Inc. in six years. In that time, Google has reshaped itself as an industry leader in artificial intelligence, with its Gemini platform now ubiquitous across consumer hardware like smartphones and web services like Search, Workspace and YouTube.
Google Home Speaker review: A modest update for the Gemini era
Google's new speaker is a modest update, but the question is whether Gemini is a good smart home assistant.
Are AI chatbots like ChatGPT politically biased? We tested them.
The Post tested ChatGPT, Gemini and other chatbots with political questions, and the results show that the AI tools have different political leanings.
Gemini Intelligence makes brief Pixel 10 series appearance on Android 17 QPR1 Beta
Google today is rolling out Android 17 QPR1 Beta 5 and it’s mainly just a bug fixer except for the inclusion of the Gemini Intelligence logo.
Google's Gemini Partially Figures Out A Lengthy Linux Boot Time On Modern ASUS Laptop
Google Antigravity with the Gemini 3.5 Flash model helped a Linux user sort out a situation where his laptop was taking around 36 seconds to boot the kernel, which shouldn't be the case for the high-end laptop with AMD Ryzen 9 processor and 32GB of RAM
Gemini Live can now access your past chats Memory, Connected Apps info
Following the big Neural Expressive redesign and new voices last month, Google has updated Gemini Live with Memory access.
Android 17 update deletes homescreen widgets for some Google Pixel owners
Widgets have long been a useful part of the Android homescreen and, for many, they’ve solidified their spot and functionality...
A Google veteran who founded character.AI is jumping to OpenAI
Noam Shazeer, a co-lead at Gemini and the founder of Character.AI is leaving Google to join OpenAI in the latest AI talent war move.
Gemini for Android using Bubbles to let you multitask & continue chats
After adding Dynamic Color earlier this month, the Gemini overlay on Android is now using Bubbles to help keep track of conversations.
How to turn off AI in your Google Docs
Here's what you need to do to get those pesky "write with Gemini" pop-ups to go away.
Google Releases Free Android Upgrade To Millions: Samsung Galaxy Next
Samsung's Android 17 upgrade is coming to Galaxy phones via One UI 9. But the biggest feature, Gemini Intelligence, raises serious security questions. Here's what to know
Google bets on Gemini to reinvent the smart home speaker
Google is betting generative AI can breathe new life into the smart speaker. The company's new $99.99 Google Home Speaker replaces the rigid commands of the Google Assistant era with more conversational Gemini interactions.
The Gemini-powered Google Home Speaker arrives on June 25 for $100
Google's new smart speaker is more about Gemini than audio quality.
Google’s first smart speaker in six years arrives next week
The Google Home Speaker is designed for Gemini for Home and its more conversational smart home assistant.
Wear OS 7 drops today with Live Updates, Gemini, and more
The launch of Wear OS 7 is upon us as Google officially announces the arrival of its smartwatch platform's latest update, bringing in a handful of
I spent 48 hours comparing Siri AI to Gemini — and Apple really impressed me
Apple’s new Siri AI finally delivers on its promise. After 48 hours of testing, here’s how it compares to Gemini.
Gemini suddenly can’t make calls on Android and Android Auto for some
The transition to Gemini on Android Auto has been a bit rough for a number of reasons, but a current...
Google leaked its next Pixel Drop with Screen Reactions and paid features
Google's next Pixel Drop looks set to add Screen Reactions, Gemini Omni video tools, and AI music creation.
Google ads tease next Pixel Drop with Screen Reactions and Gemini Omni [Video]
We’re due for Google’s next Pixel Drop and, thanks to some early ads, we now know that Screen Reactions and...
Why Apple built a third-party AI system for Siri and then refused to show it at WWDC
Apple's iOS 27 beta contains an Extensions framework for Claude, Gemini, and ChatGPT inside Siri, but the feature was absent from the WWDC 2026 keynote.
Gemini's Daily Brief replaced my morning routine, and I'm shocked how well it works
Gemini's Daily Brief provides helpful nudges and reminders, making mornings easier
Gemini can now control your Google TV’s settings, but only on these models
Announced earlier this year, Google TV is now rolling out a Gemini update that lets you adjust TV settings with...
Chatbots Keep Telling Stories About Lighthouse Keeper 'Elias Thorne'. We Might Know Why
LLMs including ChatGPT, Gemini and Claude are obsessed with telling stories about lighthouse keepers and clockmakers, and one character named 'Elias Thorne' has made his way from chatbots to Amazon books. Researchers are trying to discover why.
Gemini app facing partial outage for some users
As of Wednesday morning (PT), the Gemini app is facing an outage for some users. Over the past few hours,...
Gemini app adding Google Business Profile integration, Business notebooks
Google today announced two features that make the Gemini app a more powerful tool for business owners. This starts with...
Gemini Is Down? Live Updates on Google Workspace's AI Errors
It's not just you. Many Google users are encountering "Something went wrong" issues on Wednesday.
Google Gemini is down — live updates on rapidly growing outage
Gemini users are reporting problems today
Google makes its AI Plus plan cheaper, doubles the cloud storage that comes with it
Previously, the Google AI Plus plan cost $8 a month and came with 200GB storage. Google scored a big victory yesterday as its Gemini model became the...
Google's Gemini 3.5 Live Translate Is Built for Real-Life Conversations
Gemini 3.5 Live Translate doesn't wait for one speaker to finish before generating a response.
Google announces Gemini 3.5 Live Translate for instant voice-to-voice translation
Voice translations preserve speaker's tone, pacing, pitch—with SynthID watermarks for security.
Gemini 3.5 Live Translate rolling out to Google Meet & Translate with new ‘listening mode’
Google today announced Gemini 3.5 Live Translate as its latest model for live speech-to-speech translation.
macOS 27 Golden Gate makes it clear when apps are sneakily running in background
Google Gemini for macOS was recently flagged for not being a very good app citizen on the Mac. Apple’s new...
Apple's New AI Models Contain 'None' of Google's Gemini Assistant
Apple executives have detailed the architecture of the company's new Apple Foundation Models (AFM) and clarified exactly how Google's technology factored into their development.
Gemini could soon get a lot better for multitaskers
Google seems to be working on making Gemini more useful for multitaskers by adding useful new features that could roll out soon.
We can create the future of science right now
Paul Litvak is the founder and Executive Director of the Robyn Dawes Institute and a Visiting Scholar at UC Berkeley. He has a PhD in Behavioral Decision Research from Carnegie Mellon where he studied emotions and the sunk cost bias. Over 15 years in industry, he solved a wide range of challenging technical problems. At Meta he created models optimizing the ad review process. At Google he ran experiments to measure social influence. At Airbnb, he was a product manager leading machine learning teams optimizing search ranking and price suggestions. He also co-founded and led product at Rhythmic Health, a venture-backed biosensing startup, creating an accurate low cost system using a color changing strip and a smart phone to measure salivary lactate. The bottleneck At this point, it is uncontroversial to say that science needs to stop using the PDF article as the unit of knowledge and currency. The unbearable slowness of scientific publishing, the profit motive and margins: I’m not saying anything new. The PDF also sucks because it’s hard to extract structured information, which makes it hard to do evidence synthesis. As a result, we do much less evidence synthesis than is needed. And evidence synthesis ultimately undergirds most policy and medical decision-making. I can see second-by-second real-time odds for any sporting or newsworthy event, but a school board can’t see the best evidence on whether their 8th graders should be taught algebra. As a society, we don’t treat this as an important problem. Again, not controversial. Not only is the problem well understood, but the solution has already been laid out. What we need is AI-assisted living evidence synthesis - (1) an open knowledge graph of atomic claims, (2) claims linked to evidence, (3) assessment and synthesis of each piece of evidence, and (4) continuous updating with new data. What few realize (yet) is that the technical capacity to build this vision for a significant portion of science already exists. Not only that, scientists and startup teams are already building many of these components. I know this because I’ve been surveying the space and talking to many of the builders. There are some missing pieces: for example, evaluations of how well some of the components work. But at this point, most of what’s missing is a fully end-to-end working integration of all of these parts. In the rest of this essay, I’m going to lay out all the parts of a working living evidence layer for science and who is working on them, and propose concrete next steps for building this system. What’s now possible The diagram above outlines the components of a living evidence synthesis platform, including some of the teams working on each component1. Scientific PDFs are processed into claims with associated evidence. The evidence is subjected to a forensic audit, methodological evaluation and robustness and reproducibility checks. Finally it’s given a weight in a continuously updating synthesis. What follows is a description and status of each component and a few of the teams working on them. Document understanding The first thing you need to be able to do is turn an article into structured data. Mostly that means parsing PDFs. There are often multicolumn layouts that confuse non-specialized PDF to text processing libraries. For scientific papers, there is the added complexity of parsing formulas and tables and figures. This is a really hot area - there are startups offering APIs, and it seems like a new open source package gets posted to Github every few weeks. What follows isn’t exhaustive. A package called GROBID was the state of the art for a while, they didn’t update their package for nearly two years until very recently. In the meantime reducto.ai released an AI powered PDF extraction API, PaddleOCR became popular, IBM released a model called Docling, and both Mistral and Gemini created models and libraries. I also know of at least one other well-funded psychology research group working on a paper parser. By contrast, there are few open evals in this space, with no extensive evals for complex table comprehension in particular. Nonetheless, I’m confident this will be a solved problem soon, given the combination of LLM advances and developer interest. Hypothesis level extraction There has been increasing interest in comprehending the extracted text of papers and linking information to evidence for each hypothesis. A lot of work has already been done. Trialstreamer (Marshall et al. 2020) and RobotReviewer LIVE (Marshall et al. 2023) demonstrated automated extraction of trial population, intervention, and outcome at scale on clinical RCTs. PaperQA2 (Skarlinski et al. 2024) and Ai2 ScholarQA (2024) extended this to retrieval-augmented question answering with citation grounding. Elicit, Consensus, and SciSpace operationalized claim-level extraction for end users. OpenEval (Booeshaghi et al. 2026) is the most recent and most ambitious: 1.96 million atomic claims extracted from 16,087 eLife manuscripts using Claude Sonnet 4.5, grouped into ~299,000 results, with LLM evaluations showing 81% agreement with human peer review on a 2,487-paper subset. None of these solutions link claims to test-statistics, as you would need to evaluate randomized controlled trials. This is why I built the evidence.guide API - to extract hypotheses and associated test statistics from behavioral science papers. The best public eval of this kind of extraction I’m aware of comes from the recent SCORE project - they had humans code thousands of psychology papers to extract their claims by hand. It would be extremely helpful to the world if all scientific PDFs were available as structured open data. I’ve been working to make this happen, both directly at Berkeley and through coordination with large entities I can’t yet speak of; as hard as it is to do, I think it’s possible2. Forensic audit A lot of work has been done on forensic audit, but some gaps remain. Of course, for biology papers that rely on images for evidence, there are a variety of tools (notably Proofig and ImageTwin) to spot anomalies. These are still well short of what sleuths like Elizabeth Bik can do on her own, but these tools are constant companions among fraud analysts. There’s someone working on auditing Excel files for anomalies, and a number of teams are automating numerical checks like GRIM and SPRITE, including Scrutiny project, the INSPECT-SR team as well as statcheck. The regcheck team is building a way to use AI to compare preregistrations to analyses in papers, to ensure there aren’t significant deviations. Nonetheless, there are many other kinds of anomalies to screen for, both public and less publicly known. And there are no formal evals for anomaly detection that I’m aware of. Still, there’s a lot to draw from in this space and I’m pretty certain we will be able to scan papers for most kinds of obvious anomalies in the near future. Methodological review This area has been white hot, though I fear for many of the startups in this space, because this capability may become commoditized. There are at least six different AI peer review companies, including Refine.ink, Reviewer3, ReviewerZero.ai, Q.E.D. Science, Paper Wizard, and Isitcredible. Coarse (a pun on refine) was also recently created as an open source alternative. These systems provide qualitative feedback on the content of papers, spotting methodological weaknesses and mathematical errors. They seem to work pretty well, and many academics report bitterly that they exceed the average quality of typical peer reviewers. But there are few evals here either. What evals exist so far involve using LLM-as-judge (circularity problems abound) or comparing against human reviews of questionable quality. What you’d ideally want is an eval that measures capturing known errors in papers3. Reproducibility and robustness Another active area has been using AI agents to automate computational reproducibility4 and robustness5 checks in papers that report numerical results. For more recent papers where data and code are available, AI agents can see whether they can re-run the analyses and produce the numbers reported in the published paper. In addition to a handful of individual academics who have been experimenting using Claude Code for this, the Institute for Replication is a leading group working on building an end to end system. The evals related to this problem are the most mature, with CORE-Bench (Siegel, Kapoor, Narayanan 2024) and PaperBench (OpenAI 2025) available to benchmark agents on this task. There is also work on getting AI agents to test alternative ways of analyzing the data to ensure the results are robust to small analytic design choices. Synthesis This is the most underdeveloped area where significant investment is required. Although some automated evidence synthesis systems exist — for example, otto-sr is building an AI agent to write systematic reviews — none of these incorporate the full range of paper level signals to weight evidence appropriately. Nor is there anything like an eval or a gold standard for a good systematic review. Arguably Cochrane reviews are the closest we have to gold standard human systematic reviews, though I’ve heard academics in the know complain about their uneven quality. A key question for a synthesis platform is how to weight anomalies and methodological issues in assessing the quality of a piece of evidence. This is an unsolved problem and one I’m very keen to work on. Continuous updating There are many pieces of basic infrastructure available for monitoring for new research and initiating updates. OpenAlex is the current open citation graph. Retraction Watch integrated into Crossref in October 2023. Scite tracks how citations support, contrast, or mention prior claims. The Living Evidence Network demonstrated continuous-update workflows in clinical guidelines. Engineering this is a relatively straightforward task. When you look over this technical architecture and all the progress being made, it’s hard not to be optimistic that a living guide to scientific evidence will be built. The stakeholders are ready The social infrastructure for this is starting to coalesce — it’s not just a pie in the sky academic exercise to imagine this coming into existence. Institutions like the Center for Open Science, the Institute for Replication, the INSPECT-SR, the Living Evidence Network and more are all working on scaling work to improve research quality. Funders are also aligned. The Sloan Foundation has funded living evidence work through COS. Coefficient Giving supports the Institute for Replication and COS. The Astera Institute and the Institute for Progress have shown interest in this space. NIH has established an Office for Replication and Reproducibility. Although there are (very unfortunately) serious headwinds in science funding generally, there is an active group of funders interested in metascience. A brief word about what I’ve been doing at RDI. First, as a Visiting Scholar at Berkeley I’ve been actively figuring out how a non-profit and a public university can conduct and make public the results of large-scale academic article data mining. With some of the money I raised from donors, I commissioned a legal analysis of recent case law and publisher text data mining (TDM) agreements in order to understand whether a massive open data mining of academic articles is possible (with caveats, it is). I’ve also been working to bring together stakeholders in this space, and identify gaps. I’ve also been doing some software development in this space, with more to come. A pilot proposal The assumption undergirding all of this is that an AI, given all this information, would make the right judgment about a scientific claim with lots of conflicting evidence, weighing all the factors appropriately. That’s the hypothesis we need to test. Randomized control trial research is the best place to focus on first. RCTs are used to make many of the important decisions in society - from medical trials to public policy changes. And they use a relatively uniform set of inferential statistics with lots of known and available diagnostics. Behavioral science experiments, within the broader realm of RCTs, should be first used as a testbed whose results can be generalized. Because behavioral science is at the vanguard of open science practices, replications abound (there are thousands of them) to serve as ground truth training data. Key Hypothesis Therefore the pilot would test, in behavioral RCTs that have been replicated, whether the quality of evidence for a claim can be used to accurately predict whether that claim will replicate. Secondary Hypotheses Compared to claims that replicated, non-replicated claims demonstrate a greater share of forensic anomalies in their source literatures. Hypothesis level claim and statistic extraction is accurate enough to scale living evidence without onerous human review costs. Replication prediction is more accurate than prediction markets6 or journal prestige. If all the different quality signals we gather do accurately predict which studies will replicate, then we can use that model to score evidence to power the living evidence layer. Why this is informative regardless of outcome If the pilot succeeds, the architecture extends to medical RCTs (where Living Evidence already operates and integration is mostly about claim representation), then to slices of basic biology with stable replication structure. If it fails, the field learns which quality signals are load-bearing and which ones metascience has oversold. Either result is a contribution to knowing what the literature supports. Implications for funders Because this burgeoning ecosystem of builders already exists, a well-informed philanthropic or government funder could play a crucial catalyzing role in bringing this future about. They could play at least three roles: creating open structured datasets, publishing open benchmarks and incentivizing the solving of key technical challenges. First, open archives of papers that the government maintains, like PubMed, could be turned into structured data amenable to large scale metascience and claim aggregation. I know the US government already has an interest in doing this, though some key open questions remain unanswered. How do you determine the best models and systems for accurately extracting information from papers? How can you establish a robust way to allow researchers to flag errors and correct them? And finally, how do you create a legal regime in working with publishers to maximize the scope of available papers? For the latter, a university or a private philanthropy may be better positioned to make structured data publicly available under journal subscription terms or fair use. Philanthropists or government funders could also coordinate to create or commission benchmarks that evaluate whether important problems have been solved. For example, an open benchmark for claim extraction from a range of different scientific article types would be extremely helpful. Ensuring the underlying data are accurate is vital for creating these evaluations. I’ve discussed opportunistically using various human-created datasets for this purpose, but a consistent problem is that errors in human data make it difficult for them to serve as a gold standard. Finally, with structured data and benchmarks available, the government or private philanthropy could use them to incentivize groups to develop machine learning systems that meet these benchmarks. Prizes are one potentially valuable tool for this. For example, you could establish a prize for a system that accurately updates a living meta-analysis for a small set of claims. Prizes are particularly useful as signals of problem importance, and can help create vibrant ecosystems of public and private research—see, for example the role the government played in kickstarting the current work on nuclear fusion. Conclusion The drawbacks of the current scientific publishing system are known. Scientists agree, metascientists agree, philanthropists agree: the published PDF plus citation graph isn’t the right substrate for maintaining a representation of the evidence base in science. The pieces needed to build the alternative either already exist or are rapidly taking shape. The community is forming around exactly this problem, with concrete partnerships and shared infrastructure. A pilot should start on behavioral science RCTs because that’s the slice of empirical science most amenable to legibility, where replication ground truth is richest, and where the failure modes are best documented. What’s been missing is the galvanizing mission to assemble these pieces into something that works. That’s what I’m proposing to build. *** 1 I have a broader field map that I’ll release publicly soon. This is me, building in public! 2 If this is something that you are excited about, please reach out and talk to me. 3 More on this very soon too! 4 This tests whether, given the code and the data, you can get the same statistics as reported in the published paper. 5 This tests whether the results are the same as a paper’s given alternative analytical decisions in conducting the analysis (like outlier omission). Closely related is the idea of a “multiverse” where you come up with many different ways of answering the same underlying research question with the same data, and test whether the results hold in all those alternative methods. There’s been work on the latter as well. 6 Some of the replications, e.g. those from the SCORE project, had paired the experiments with forecasts from prediction markets. So we get to look at this for free.
Gemini 3.5 and Antigravity come to Google NotebookLM
NotebookLM is getting a big upgrade, but it's only for AI Ultra and enterprise accounts right now.
NotebookLM’s Gemini 3.5 upgrade adds a cloud computer and help finding sources
Google’s AI note-taking app is getting “across the board” updates.
NotebookLM's new update will help you build source repository from chat
Google is making Gemini 3.5 the default model in NotebookLM
Gemini app adding new Google Contacts integration
The Gemini app’s latest first-party integration is with Google Contacts to provide "personalized insights and responses based...
What to Expect From WWDC 2026: Gemini-Powered Siri, iOS 27, macOS 27 and More
Apple's 2026 Worldwide Developers Conference is just days away, and it's going to be an interesting event because it will give us a look at Apple's AI plans. We'll see how Apple is going to compete with OpenAI, Anthropic, and Google in the months to come with an AI version of Siri and new AI features for its apps. Subscribe to the MacRumors YouTube channel for more videos.
Voting rights group urge Florida Supreme Court to put new congressional lines on hold
Plaintiffs say corrective action can still take place before qualification begins on Monday.
Run Google's Gemini LLMs right on your Mac with the new AI Edge Gallery
Google has released its AI Edge Gallery app on the Mac for the first time, allowing AI fans to run its Gemini large language models (LLMs) locally, without the need for an internet connection.
Apple's Overhauled Siri Will Reportedly Run on Nvidia's Blackwell Chips
Apple will rely on Google's fleet of Nvidia chips to power its overhauled version of Siri when it launches in September, according to a new report from The Information. Last week, the outlet reported that Apple plans to highlight the on-device AI capabilities of its devices at WWDC next week, but queries that require cloud-based processing will still fall back on one of Google's large Gemini models, as per an agreement between the two companies.
Report details Apple’s plan to use Nvidia chips for the Gemini-powered Siri
A new report from The Information shares fresh details on how the new Gemini-powered Siri will work under the hood. Here are the details.
Google delivering on Gemini promises means Apple Intelligence can do the same
Tests to determine whether Google’s agentic AI system Gemini Spark can deliver on the promises made on stage at last...
Gemini Spark is the most impressive and terrifying AI experience I’ve had yet
It’s a remarkable piece of technology. But the future sure is creepy.
Gemini’s new AI agent is about as good as Google’s demo
It’s imperfect, but surprisingly capable.
Google Home Speaker gets June release date from one retailer
Following the Pixel 10a and Fitbit Air, Google’s new Gemini-powered Home Speaker might launch on June 25, 2026.
What are you actually doing with Gemini?
Further reflecting on Google’s practical focus for Gemini and AI in general at I/O 2026 last week, I still find...
Meet Google's New Screenless Gemini Smart Glasses
Discover Google's new audio-only smart glasses, offering all-day battery life, cross-platform compatibility, and advanced Gemini AI features.
Cancel your Claude sub and get lifetime access to ChatGPT, Gemini, Claude, and more for $60
No monthly fees, and you can even send one prompt to multiple models
I put Google's 24/7 AI assistant Gemini Spark to work, and it's actually pretty useful
Gemini Spark helps automate everyday tasks, from inbox summaries to local event planning, but it’s unclear why Google made it a separate product.
Google's Agentic AI Tool Gemini Spark Is Now Available
Gemini Spark is currently only available to Google AI Ultra subscribers in the US, meaning you’ll need to spend at least $100 per month to access it.
Google’s Gemini Spark is ready to run your digital errands while your phone is off
You can now give Gemini Spark a task, turn off everything, and it still gets the job done.
Gemini Spark rolls out to Google AI Ultra in the US: How it works
As previewed last week, Gemini Spark is now available in the US for Google AI Ultra subscribers. Spark is Google’s 24/7 personal agent...
Google adjusts Gemini’s new usage limits in response to complaints
At I/O 2026, the Gemini app switched to compute-based usage limits. In response to “feedback about hitting limits too quickly,” Google...
Apple reportedly trying to distill Google's multi-trillion-parameter Gemini AI to run on iPhone
As Apple tries to shrink Gemini for the iPhone, a cloud component is probably inevitable.
New details on Apple-Google AI deal revealed, including Nvidia chips: report
Apple’s big unveiling of iOS 27, the new Siri, and other WWDC reveals are little more than one week away....
Google Home can now use what cameras see as automation starters, Android widget improved
The latest Google Home update lets you tap into Gemini's camera scene understanding for automations, while there are...
Capgemini says AI widens client spending pool
Capgemini on Thursday said artificial intelligence was opening access to client spending beyond traditional information technology budgets, as the French technology services group sought to ease investor concerns that the technology could disrupt its business model.
Google is bringing Gemini to smart glasses, and they come with one big advantage
During the Google I/O 2026 keynote, the company announced a major expansion of the Android XR platform, unveiling a new line of "Intelligent Eyewear." Built in collaboration with Samsung and...
Google I/O 2026: tech giant launches autonomous AI era, transforming search at cost to core business
Google unveils Gemini agents and a generative search overhaul in a direct push against OpenAI and Perplexity, risking ad revenue, publisher ties, privacy and margins as it tries to preserve its internet dominance
Gemini user hits 5-hour usage cap after a single prompt, Google responds
"Yikes!" is exactly the right reaction for this situation.