AI News Archive: June 13, 2026 — Part 2
Sourced from 500+ daily AI sources, scored by relevance.
- A Tesla on Autopilot swerved into someone’s garage door in Washington. Police are investigating.
A Tesla driver in Redmond, Washington, claims the car’s self-driving mode malfunctioned before it swerved into a residential garage door on Monday. The car smashed the door open and ended up lodged inside the garage. Police responded around 11 AM and are investigating. No injuries were reported. There were no indications of impairment. The driver […] This story continues at The Next Web
- Meta employees are revolting against its AI rules and it’s a lesson for us all
Meta’s AI restructuring has sparked employee frustration, petitions, and public outbursts, highlighting the growing tension between rapid AI development and workplace morale.
- Kimi and State-Owned Bank Launch World's First AI-Native Credit Card
Moonshot AI's Kimi has partnered with a major state-owned bank to launch the world's first AI-native credit card, converting everyday spending into AI compute credits.
- Mozilla Data Collective seeks to build AI’s data economy around trust
Generative artificial intelligence has a data problem. For years, the typical approach to building gen AI models has been to gather as much data as possible by scraping vast swaths of the internet, training at an enormous scale and dealing with the consequences later. The result has been increasingly powerful technology, but also growing concerns […] The post Mozilla Data Collective seeks to build AI’s data economy around trust appeared first on SiliconANGLE .
Score: 60🌐 MovesJun 13, 2026https://siliconangle.com/2026/06/13/mozilla-data-collective-seeks-build-ais-data-economy-around-trust/ - Anthropic’s Fable 5 Safeguards Were Always A ‘Judgement Call’
In an interview hours before the U.S. government issued a directive to disable Anthropic’s newest AI model, the company’s Chief Commercial Officer told Forbes about the tradeoffs of releasing such a powerful tool.
Score: 60🌐 MovesJun 13, 2026https://www.forbes.com/sites/richardnieva/2026/06/13/anthropics-fable-5-paul-smith/ - A Price War Is Brewing Between OpenAI and Anthropic — And It Could Make AI a Lot Cheaper for Your Business
A Price War Is Brewing Between OpenAI and Anthropic — And It Could Make AI a Lot Cheaper for Your Business entrepreneur.com
Score: 60🌐 MovesJun 13, 2026https://www.entrepreneur.com/business-news/a-price-war-is-brewing-between-openai-and-anthropic - UK sets out AI infrastructure push at London Tech Week – how does it stack up?
Government announces plans to invest billions, but questions linger over how its proposals on chips, social media and more will work Ownership of the commanding heights of the AI economy is a political talking point around the world, as countries seek to assert some control of a technology dominated by the US and China. London Tech Week, the showcase event for the UK tech industry, focused heavily on that theme this week. A government keen to show it has a growth story, and an assertive narrative on AI, made a number of announcements related to companies, skills and infrastructure. Some represented new commitments and ideas; others appeared to be putting a polish on already announced measures. Continue reading...
Score: 60🌐 MovesJun 13, 2026https://www.theguardian.com/technology/2026/jun/13/uk-ai-hardware-london-tech-week-investment-chips - It’s a race to capture real-world AI training data
Indian startups are capturing real-world video data to train AI for robots. This data, recorded through wearable cameras, helps robots learn to navigate complex environments. Companies are partnering with various businesses to collect this information. This move positions India in the global AI value chain, though concerns about privacy and compensation persist.
- 92 per cent of real estate firms now run AI pilots: JLL
92 per cent of real estate firms now run AI pilots: JLL Arabian Business
Score: 59🌐 MovesJun 13, 2026https://www.arabianbusiness.com/real-estate/92-per-cent-of-real-estate-firms-now-run-ai-pilots-jll - Is This Hollywood’s First Practical Solution to Control AI?
Is This Hollywood’s First Practical Solution to Control AI? Puck
Score: 59🌐 MovesJun 13, 2026https://puck.news/podcast_episode/is-this-hollywoods-first-practical-solution-to-control-ai/ - Claude Fable 5 outpaces GPT-5.5 by 13 points on FrontierMath's toughest problems
Anthropic's Claude Fable 5 hits 88 percent accuracy on the hardest FrontierMath tier, a massive jump from Opus 4.5, which sat below 10 percent in early 2026. OpenAI's GPT-5.5 reaches about 75 percent on the same tier. The pace of improvement in AI math keeps accelerating. The article Claude Fable 5 outpaces GPT-5.5 by 13 points on FrontierMath's toughest problems appeared first on The Decoder .
Score: 59🤖 ModelsJun 13, 2026https://the-decoder.com/claude-fable-5-outpaces-gpt-5-5-by-13-points-on-frontiermaths-toughest-problems/ - Applogika Joins Anthropic’s Claude Partner Network as a Select-Tier Services Partner
Applogika Joins Anthropic’s Claude Partner Network as a Select-Tier Services Partner azcentral.com and The Arizona Republic
- Why AI and GCCs will define India’s next decade of growth
By Dr. Kanishk Agrawal, Chief Technology Officer, Judge Group, India At a critical stage in India’s economic trajectory, AI and GCCs have become two tremendous forces driving India’s future growth […] The post Why AI and GCCs will define India’s next decade of growth appeared first on Express Computer .
Score: 58🌐 MovesJun 13, 2026https://www.expresscomputer.in/guest-blogs/why-ai-and-gccs-will-define-indias-next-decade-of-growth/135975/ - Rivian CEO taking different approach than Elon Musk for humanoid robotics company
Rivian CEO RJ Scaringe started a robotics company late last year called Mind Robotics that he says has has raised more than $1 billion.
- Robotics Special: Humanoid robot summits 20,000 foot volcano
Humanoid robot summits 20,000 foot volcano
Score: 58🌐 MovesJun 13, 2026https://www.superhuman.ai/p/robotics-special-humanoid-robot-summits-20-000-foot-volcano - AI chip boom yet to lift South Korea's wider economy, Nomura economist sees limited spillover into demand
South Korea's AI-driven semiconductor boom is not yet translating into broad economic gains, according to Nomura. Despite strong chip exports and equity markets, domestic demand shows limited improvement, with luxury spending and business investment being key drivers. Concerns over the won and financial stability may prompt a Bank of Korea rate hike.
- XP-era Windows spotted haunting London's driverless railway
A blast from the past greets commuters
- The Intelligent Factory: Navin Nathani on How Manufacturing’s Next Competitive Edge Is Being Built on Data, Resilience, and Industrial AI
In this conversation, Navin Nathani shares how his organisation is navigating the technology shift—leveraging digital transformation to build operational resilience, accelerate decision-making, strengthen cybersecurity, and lay the foundation for the next generation of intelligent manufacturing The post The Intelligent Factory: Navin Nathani on How Manufacturing’s Next Competitive Edge Is Being Built on Data, Resilience, and Industrial AI appeared first on Express Computer .
- Dutch far-right party pays damages to court artist after changing image with AI
Geert Wilders’ PVV altered sketch of jailed Syrian brothers to make them look more menacing A Dutch court artist has received damages after an MP for the far-right Party for Freedom (PVV) used one of her drawings without permission and manipulated it with AI to make the subjects look more menacing. Petra Urban, a court artist for 19 years, was shocked to discover a drawing she had made last year of two Syrian brothers jailed for the murder of their sister had been reworked and used in a video on Instagram and Facebook by the party’s Noord-Brabant region. Continue reading...
- Top Qevlar AI Alternatives & Competitors 2026
Top Qevlar AI Alternatives & Competitors 2026 Gartner
Score: 55🌐 MovesJun 13, 2026https://www.gartner.com/reviews/product/qevlar-ai-1408070617/alternatives - As the AI economy booms in the U.S., Canada sees a blip
From data centre announcements that go nowhere to lacklustre business investment in tech equipment, AI mania is bypassing Canada. That’s not all bad
Score: 55🌐 MovesJun 13, 2026https://www.theglobeandmail.com/business/article-artificial-intelligence-data-centre-investment-tech-us-canada/ - Global Capitalism Bets It All on AI Future Alarming Voters
Days after filing confidentially to go public, Anthropic, the $965 billion artificial intelligence juggernaut that’s one of the fastest-growing startups of all time, dropped another bombshell.
Score: 55🌐 MovesJun 13, 2026https://www.bloomberg.com/news/articles/2026-06-13/global-capitalism-bets-it-all-on-ai-future-that-alarms-voters - TU Delft research makes self-driving cars safer and more human
TU Delft research improves self-driving car safety and human interaction
Score: 55🌐 MovesJun 13, 2026https://ioplus.nl/en/posts/tu-delft-research-makes-self-driving-cars-safer-and-more-human - UC Berkeley computer science students fail in large numbers, professors cite AI dependence
A dramatic spike in failing grades among computer science students at the University of California, Berkeley has alarmed faculty, who are pointing to an overreliance on artificial intelligence tools and a stark decline in foundational math skills as the primary culprits.
- Marc Andreessen critiques AI regulation debate after Anthropic model shutdown
Marc Andreessen, a cofounder of Andreessen Horowitz, has voiced strong opinions on artificial intelligence regulation. His comments follow the US government's order to suspend access to Anthropic's advanced AI models, citing national security. Andreessen views strict rules as "red-tape monsters" that hinder innovation and burden startups.
- Tesla’s Robotaxis Are a Complete Disaster
Broken promises, shoddy service, and a comically tiny fleet. The post Tesla’s Robotaxis Are a Complete Disaster appeared first on Futurism .
- Mapping the AI startups making waves in Japan
Mapping Japan's AI sector: Key players, top investors, and funding insights in one report.
Score: 53🌐 MovesJun 13, 2026https://www.techinasia.com/visual-story/mapping-japans-leading-ai-startups - Team '26 Recap, Unlock human-AI collaboration at scale - June 13, 2026
Team '26 Recap, Unlock human-AI collaboration at scale - June 13, 2026 Atlassian Community Events
- Google Photos Prepares Massive 'Video Remix' AI Upgrade
Deep code analysis shows Google Photos is preparing a massive Video Remix AI upgrade.
Score: 52🌐 MovesJun 13, 2026https://www.forbes.com/sites/paulmonckton/2026/06/13/google-photos-prepares-massive-video-remix-ai-upgrade/ - Raptoric Launches Security Testing for High-Risk AI Systems Under the EU AI Act
Raptoric Launches Security Testing for High-Risk AI Systems Under the EU AI Act azcentral.com and The Arizona Republic
- Chinese drivers have figured out a silly way to fool Tesla Autopilot and it involves doll heads
Chinese Tesla owners are reportedly using miniature doll heads, photos, and blinking screens to trick Autopilot’s driver-monitoring cameras into thinking they are paying attention.
- Japan and Canada can do more to accelerate AI adoption, expert says
Japan and Canada can do more to accelerate AI adoption, expert says The Japan Times
Score: 50🌐 MovesJun 13, 2026https://www.japantimes.co.jp/news/2026/06/13/japan/canada-japan-ai-interview/ - ‘AI will free us’: Javier Milei’s radical plan for Argentina
‘AI will free us’: Javier Milei’s radical plan for Argentina The Telegraph
Score: 50🌐 MovesJun 13, 2026https://www.telegraph.co.uk/business/2026/06/13/javier-mileis-plan-let-ai-led-companies-run-riot-argentina/ - KPMG the latest firm to be caught out using AI
Top consultancy apparently used artificial intelligence to source parts of a report extolling virtues of AI
Score: 50🌐 MovesJun 13, 2026https://www.irishtimes.com/business/2026/06/13/kpmg-the-latest-firm-to-be-caught-out-using-ai/ - The Moral Of Anthropic’s Fable: Model Access Is Power
Coming just three days after its launch, the sudden block of Anthropic's Fable model has become a larger fight over who decides when an AI system is too powerful for open access.
Score: 50🌐 MovesJun 13, 2026https://www.forbes.com/sites/ronschmelzer/2026/06/13/the-moral-of-anthropics-fable-model-access-is-power/ - AI shopping agents are coming. No one is ready for them
AI shopping agents are coming. No one is ready for them Fortune
Score: 50🌐 MovesJun 13, 2026https://fortune.com/2026/06/12/ai-shopping-agents-are-coming-no-one-is-ready-for-them/ - Tesla on Autopilot Smashes Straight Through Garage Door, Driver Says
Open sesame! The post Tesla on Autopilot Smashes Straight Through Garage Door, Driver Says appeared first on Futurism .
Score: 50🌐 MovesJun 13, 2026https://futurism.com/advanced-transport/tesla-autopilot-smashes-garage-door - The future of Hollywood isn’t feeding prompts into vanilla gen AI models
For all the noise that's been made about how generative AI is poised to revolutionize the filmmaking industry, there haven't really been any projects created with the technology that felt like the sort of entertainment people would pay to see. Most AI firms' video models are still only capable of churning out short bursts of […]
- Nasdaq-100 drops Cognizant, adds AI-focused firms Astera, CoreWeave in quarterly reshuffle
Cognizant Technology Solutions will be removed from the Nasdaq-100 index on June 22 after more than two decades. The reshuffle sees AI-focused firms like Astera Labs and CoreWeave added, reflecting a market shift towards AI and semiconductor companies. This change follows a significant drop in Cognizant's stock value over the past six months.
- AI Is Here to Stay. The Real Challenge Is Operating It Securely
AI Is Here to Stay. The Real Challenge Is Operating It Securely DevOps.com
Score: 48🌐 MovesJun 13, 2026https://devops.com/ai-is-here-to-stay-the-real-challenge-is-operating-it-securely/ - SFT Drives Gemini’s Safety Properties
This is the third in a series of informal research updates from the Google DeepMind Language Model Interpretability team, in interpretability and adjacent areas. The second post can be found here . In this short post, we describe a surprising finding: most safety relevant properties in Gemini seem to be caused by the combination of pretraining and SFT, not other training stages like RL. We do not want to overstate this claim as applying to other model families, and we also note that this may change in future Gemini versions. Nevertheless, this result was counter to our initial expectations and will inform future safety work on our team, and so we felt that it was important to share with the broader safety community. Experiment We perform SFT using the Gemini mixture on the pre-training only versions of Gemini 3.1 Pro and Gemini 3 Flash. We then compare these Post-SFT models to the production versions of Gemini 3.1 Pro and Gemini 3 Flash on different safety relevant benchmarks: Error bars are 95% confidence intervals on the evals. The main result is that the blue bars (SFT-only models) and orange bars (production models) are remarkably similar across evals . An important implication is that for Gemini, SFT is a high leverage place to intervene for model safety and behavior, and we plan to try to intervene here in the future. Brief Descriptions of Each Set of Benchmarks: ODCV refers to the benchmark in https://arxiv.org/abs/2512.20798 Alignment evals refer to a version of Petri modified to be single-prompt and filtered to contain only “alignment dilemma” style problems. Single-prompt misalignment refers to how often the model makes the “wrong” decision (as decided by an autorater); eval awareness refers to how often an autorater decides that the model was aware it was in an evaluation. Safety evals measure the model’s over-refusal rate on benign prompts that look harmful and the model’s unsafe response rate on harmful prompts The reward hacking environment is an environment where the model is put in a docker container within the Gemini CLI harness and asked to optimize an algorithmic problem against a non-editable file containing a timing script; an autorater measures the percent of rollouts where the model cheats in some way. Free tier user logs are 50k random, anonymized, and PII-redacted user prompts from AI Studio. We run autoraters for each of the behaviors listed on the models thoughts and responses (note that many of the autorater positives are likely false positives). Discuss
Score: 48🌐 MovesJun 13, 2026https://www.alignmentforum.org/posts/nLrrYweeFxgXACSmS/sft-drives-gemini-s-safety-properties-1 - AI is making software developers faster — just not at actually shipping software
A study of more than 100,000 developers finds a vast gap between writing code and shipping software. The reason is human bottlenecks
Score: 48🌐 MovesJun 13, 2026https://qz.com/ai-coding-tools-code-volume-releases-gap-nber-study-061126 - Observations After Two Weeks with Tesla Hardware 4 & Full Self Driving V14 in My 2026 Tesla Model Y
I lived through almost 7 years of numerous releases of Tesla’s Full Self Driving (FSD) as it progressed in my 2019 Model 3 using “Hardware 3” (HW3), finally reaching FSD V12. Elon Musk promised every year that all of us Tesla owners with HW3 would see Level 4 self-driving in ... [continued] The post Observations After Two Weeks with Tesla Hardware 4 & Full Self Driving V14 in My 2026 Tesla Model Y appeared first on CleanTechnica .
- Google Research's Gemini-SQL2 tops text-to-SQL benchmarks by a wide margin
Google Research's Gemini-SQL2 turns natural language into executable SQL queries. Built on Gemini 3.1 Pro, it tops the BIRD benchmark at 80.04 percent accuracy, well ahead of OpenAI and Anthropic. Google says the technology could improve natural language features across its data services. The article Google Research's Gemini-SQL2 tops text-to-SQL benchmarks by a wide margin appeared first on The Decoder .
Score: 47🌐 MovesJun 13, 2026https://the-decoder.com/google-researchs-gemini-sql2-tops-text-to-sql-benchmarks-by-a-wide-margin/ - Apple @ Work: Why Gen-AI will not cause a SaaS apocalypse for IT teams
Apple @ Work is exclusively brought to you by Mosyle , the only Apple Unified Platform. Mosyle is the only solution that integrates in a single professional-grade platform all the solutions necessary to seamlessly and automatically deploy, manage, and protect Apple devices at work. Over 45,000 organizations trust Mosyle to make millions of Apple devices work-ready with no effort and at an affordable cost. Request your EXTENDED TRIAL today and understand why Mosyle is everything you need to work with Apple . My LinkedIn feed is full of a growing narrative predicting a coming SaaS apocalypse. It’s actually full of AI-slop-thought-leadership as well, but that is another story. The idea is that generative AI tools like Claud will eventually become so powerful that businesses will simply replace all their specialized SaaS software vendors with a single AI tool. As someone who’s worked in IT for 20 years and currently uses these tools in the workplace, I want to immediately push back on that idea
Score: 46🌐 MovesJun 13, 2026https://9to5mac.com/2026/06/13/apple-work-why-gen-ai-will-not-cause-a-saas-apocalypse-for-it-teams/ - Two Turing Award Winners Convene at Beijing Zhiyuan Conference, Confronting the Theoretical Black Hole Behind AGI
Whitfield Diffie and Andrew Barto, two Turing Award laureates, delivered keynote speeches at the 8th Beijing Zhiyuan Conference, jointly revealing the fundamental theoretical challenges facing artificial general intelligence.
Score: 46🌐 MovesJun 13, 2026https://pandaily.com/turing-award-winners-beijing-zhiyuan-conference-agi-theory-jun2026 - Disney is pushing tech employees to move faster with AI — but avoid 'tokenmaxxing'
Disney is pushing tech employees to move faster with AI — but avoid 'tokenmaxxing' Business Insider
Score: 45🌐 MovesJun 13, 2026https://www.businessinsider.com/disney-ai-push-increase-velocity-tech-employees-tokenmaxxing-josh-damaro-2026-6 - 99.9% Uptime Isn’t Enough: Rethinking SLOs for Probabilistic AI Systems
“Mean time to hallucination” isn’t a joke metric. It’s the reliability concept your runbook doesn’t have a response procedure for. Your service is responding. Your users are furious. The monitor is green. Something is fundamentally broken about how we measure reliability when the output is the product. We inherited our reliability playbook from a world where correct meant deterministic. It doesn’t anymore. Somewhere right now, an on-call engineer is staring at a fully green dashboard while a Slack channel fills with user complaints. The LLM-powered feature is responding in 340ms. Availability is 99.97%. Error rate is 0.2%. Every SLO is met. And the product is quietly producing wrong, incoherent, or harmful output at scale. This is the reliability gap of the AI era — and it’s not a monitoring gap. It’s a conceptual one. The SLO framework we use was designed for systems where failure is binary: a request either succeeds or it doesn’t. AI systems break that contract entirely. A response can be fast, successful, and completely useless at the same time. The binary failure assumption Traditional SLOs rest on three pillars: availability (is the system up?), latency (is it fast?), and error rate (is it returning valid responses?). These emerged from web services, databases, and APIs — systems where the correctness of a response is largely structural. A 200 OK with a valid JSON body is a successful request. Full stop. AI systems invalidate this at the application layer. A language model returning a 200 with syntactically valid JSON can simultaneously be hallucinating facts, ignoring instructions, producing biased outputs, leaking prior conversation context, or generating content that violates your usage policies. None of that registers in your existing SLO framework. None of it pages anyone. What “failure” actually looks like in AI systems Before redesigning SLOs, engineers need a taxonomy of AI-specific failures. They fall into categories that don’t map cleanly to HTTP status codes: Notice that none of these are detectable by latency percentiles, error budgets, or uptime calculations. They are quality failures in a system that has no existing quality SLO. The new SLO vocabulary What would it actually look like to define SLOs for probabilistic systems? The honest answer is that we need new metrics that accept output quality as a first-class engineering concern — not a product concern, not a QA concern, but something that wakes people up at 3am. These aren’t hypothetical. Teams building production AI systems are starting to define commitments like these internally — even if they aren’t called SLOs yet. The challenge is measurement. Unlike latency, you can’t instrument quality with a stopwatch. How to actually measure output quality at scale This is where most teams get stuck. They accept that quality matters, but reject quality SLOs because they seem unmeasurable at production scale. There are three practical approaches, each with real tradeoffs: LLM-as-judge sampling. Run a lightweight judge model against a statistically significant sample of production traffic — typically 1–5%. The judge evaluates responses against a rubric: did the response follow the format? Is it factually grounded? Does it violate any policy? This gives you a continuous quality signal with manageable cost. The catch: the judge can be wrong too, and its own drift needs monitoring. Behavioral canaries. Maintain a golden set of input-output pairs with known expected behavior. Run these against your live system on a schedule — every deploy, every hour, every model provider update. When a canary fails, you have a concrete regression signal. This is the closest analog to a unit test that AI systems have. It doesn’t cover the full distribution, but it catches regressions reliably. User signal instrumentation. Implicit signals — regeneration requests, session abandonment, thumbs-down clicks, downstream action completion — are weak proxies for quality. Individually they’re noisy. As a composite metric smoothed over rolling windows, they become the most honest signal you have: real users voting with their behavior. The problem is lag. By the time user signals degrade, you’ve already shipped bad responses at scale. Mapping old SLOs to new ones The goal isn’t to throw away traditional SLOs — latency and availability still matter. It’s to layer quality SLOs on top, with their own error budgets and burn rate alerts. Here’s what that translation looks like in practice: The error budget problem Google’s SRE model introduced the error budget — a quantified allowance for unreliability that teams spend against when shipping. If your error budget is 0.1% downtime per month, every outage burns from that budget. When it’s gone, you stop shipping features and focus on reliability. AI systems need a quality budget. The insight is the same: you are allowed to be imperfect, but imperfection is a finite, tracked resource. A team might define a quality budget as: ≤ 4% of responses may fail quality checks per rolling 7-day window. When you’re burning that budget at 3x the baseline rate, that’s a quality incident. It pages someone. It blocks deploys. What the incident looks like In a traditional system, an incident has a clear cause: a bad deploy, a saturated database, a misconfigured load balancer. There’s a stack trace. There’s a blast radius. There’s a rollback button. An AI quality incident is structurally different. The “failure” is statistical — not every request is bad, a meaningful percentage is. The cause might be a model provider update you didn’t control, a prompt change that introduced an edge case at the 95th percentile, a new user behavior pattern your evals never covered, or genuine model regression from an upstream weight update. This means your runbooks need new sections. What does a quality incident even look like in your alerting system? What’s the first diagnostic step? How do you quantify blast radius when the failure is probabilistic? Who owns it — the ML team, the platform team, the product team? These questions sound organizational, but they’re really engineering problems that need engineering answers baked into your system design before the incident happens. A framework for shipping quality SLOs The harder truth Probabilistic systems can’t promise deterministic outcomes. That’s not a solvable problem — it’s a fundamental property of the technology. The goal of AI SLOs isn’t to eliminate uncertainty. It’s to characterize it, track it, budget for it, and build the organizational muscles to respond when it degrades faster than expected. The teams shipping reliable AI products in 2026 aren’t the ones that got lucky with a stable model. They’re the ones that treated output quality as an engineering discipline with the same rigor they’d apply to database latency or API availability. They built evals before incidents, not after. They defined what “degraded” means before they needed to explain it to a VP at midnight. Your 99.9% uptime SLO tells your users the service will respond. Your quality SLO tells them it will respond with something worth trusting. The first one is table stakes. The second one is the actual product promise — and right now, most teams are making it without any way to keep it. 99.9% Uptime Isn’t Enough: Rethinking SLOs for Probabilistic AI Systems was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.
- Ventiva's Fanless 'Ionic Cooling' Wants to Redraw the AI Laptop Motherboard
Ventiva's Fanless 'Ionic Cooling' Wants to Redraw the AI Laptop Motherboard PCMag Middle East
Score: 45🌐 MovesJun 13, 2026https://me.pcmag.com/en/laptops/37494/ventivas-fanless-ionic-cooling-wants-to-redraw-the-ai-laptop-motherboard - Google’s Pinpoint is the free research tool you should know about
This article is republished with permission from Wonder Tools , a newsletter that helps you discover the most useful sites and apps. Google’s Pinpoint is now open to everyone. It’s a surprisingly powerful free tool for making sense of giant piles of digital stuff. (Before June 3, it was restricted to journalists and academics). Read on to learn more about creative ways to use Pinpoint; its new AI features and their limitations; and how Pinpoint differs from NotebookLM. How Pinpoint Works Pinpoint lets you store and analyze hundreds of thousands of files so you can find tiny needles in gigantic digital haystacks. Pinpoint can transcribe hundreds of hours of audio and video. It makes your handwritten text, scans, and PDFs searchable, like my enormous collection of scanned handwritten notes and whiteboards. Once Pinpoint processes your files you can search, summarize, and organize your collections. Pinpoint makes it easy to query, label, and extract data from hundreds or thousands of documents. It’s simple to use. No complex menus or commands. Getting Started Upload PDFs, emails, audio and video files, handwritten notes, or other file types . Each collection can have up to 200,000 files . You can have an unlimited number of collections, which function like folders. Journalists and academics can request “ Pinpoint for Professionals ,” which provides 100 gigabytes of storage. Others start with 1GB. Audio and video files can be up to 2 hours long and 8GB . The limits are much more generous than what’s offered by other AI tools, like NotebookLM. Pro tip: Create separate Pinpoint accounts for each of your Google accounts. That gives you more storage. It’s also how I keep personal projects separate from work. You can always download files you’ve previously uploaded. And request additional storage if you reach your limit. Pinpoint Features to Try Export important Gmail folders with Google Takeout in .mbox format. Takeout lets you export and back up anything Google hosts. You can then upload your email collection to Pinpoint to find patterns, or locate references to specific people, places, or organizations. Scour through audio recordings to locate key moments. Pinpoint transcribes in more than 100 languages. Upload and transcribe your recorded presentations to review how you frame a particular topic. Or how water pollution is described in local council meetings. Explore text within images and handwritten notes. I’ve scrutinized my old handwritten notes for ideas and checked whiteboard photos for explanations I can improve. Share a document collection. Invite individual collaborators or publish a collection for public exploration. Giving readers access to source material can be a valuable form of transparency in journalism . Analyze an email or document archive. Assess a public email trove or document collection, like files from the Enron trial , for connections between politicians and companies, or financial impropriety. Focus your research on particular people, organizations, locations, or date ranges. Pinpoint automatically lists a collection’s most frequently mentioned entities. Click on an entity to jump to exactly where it’s mentioned. Public Collections Pinpoint’s “ Explore ” section lets you examine document collections from more than 200 news organizations, ranging from The New York Times and The Washington Post to The Hindu and Süddeutsche Zeitung . Test out the searching and filtering features before uploading anything. Notable document sets include JFK assassination records , Mueller court filings , and 80 years of U.S. foreign agent registrations (86,018 documents) uploaded by the Center for Public Integrity. New AI Capabilities Pinpoint has a bunch of new AI features in beta available in more than 80 countries . In my testing, not all of the AI capabilities work reliably yet, but you can request early access . Pinpoint’s AI features are designed to help you analyze your files, not generate original content as you might with Gemini or NotebookLM. Explain words or phrases in any file. Highlight any text and ask Pinpoint to put it in context. I like the short, useful explanations based on what else is in a document. Google words or phrases inside your text to learn more from Google. In my tests, this beta feature consistently yields error messages. Summarize Collections. I like how Pinpoint gives me an AI-generated overview of every file I upload. It can also summarize an entire collection. Extract data into spreadsheets . Pull info from up to 100 documents at once and deliver it as a sheet, with links back to the source text for each data point. That’s handy for tracking mentions or doing comparative research. Try this feature for analyzing public government contracts or other information in PDF form that would be easier to work with in a spreadsheet. Automatically label hundreds of your files in a collection. Let’s say you have a public email dataset. You may want to evaluate just those written by a particular person, or just those discussing a particular stock. Instruct Pinpoint and it will label your collection for you. Limitation: Pinpoint can label 1,000 files at a time. In my tests, the interface only let me select 100 files. If you’re working with tens of thousands of files, labeling may be tedious. Transcribe audio quickly. Pinpoint now claims to give you high-quality transcribed text 20 times faster than it used to. I couldn’t verify that specific metric, but it’s generally fast. Each file is helpfully time coded. Click on any section of text to hear the corresponding audio. Limitation: Unlike Wispr Flow and Letterly , which I use for my personal dictation, Pinpoint sometimes mangles names, and doesn’t learn from its errors. Compare any two or three files. See what changed in multiple versions of a document, or how three different people addressed the same subject. Query your collection with ordinary language. Results are often fast, useful, and linked back to the original document. Expect occasional errors, though. One search I tried, for example, returned a document mentioning someone with a similar but different name from the one I was searching for. Create a timeline. If you’re looking at how something developed over time, you can pick up to 100 files and generate a timeline. You can optionally specify a topic for the timeline. This might be useful if your dataset has dated transactions, for example, or multiple reports you want to put in order. Pinpoint vs. NotebookLM What’s similar: Both NotebookLM and Pinpoint are free Google services that allow you to efficiently summarize and search though large documents. What’s different: NotebookLM synthesizes and generates (slides, infographics, video and audio podcasts, and reports). Pinpoint, on the other hand, focuses on finding patterns and organizing a wider array of file types in much larger collections of email, multimedia, and scanned images. NotebookLM allows 50 files per notebook for free users. (See my guide ). Pinpoint lets you upload 200,000 files into each collection. Combine the two: If you have a large collection of documents, consider gathering, storing, organizing, and transcribing in Pinpoint. Then move the most important files into NotebookLM for further analysis and to generate artifacts like reports, slides, infographics, flashcards, audio, and video. Privacy Your documents are private. Nothing you upload is public unless you publish a collection. Read Google’s Pinpoint privacy details . Your files are not used to train AI models. Google explicitly notes that documents you upload won’t be used to train large language models. Read Google’s statement . The bottom line: I trust Pinpoint for nonsensitive document work. But because uploads are processed on Google’s servers, Pinpoint may not be a fit for everything or everyone. Here are additional privacy details . Limitations AI features aren’t error free. No AI implementation is. No mobile app. You can view docs on a phone browser, but not all features work. No NotebookLM integration yet. You can’t easily move files from one service to the other. Storage caps. On a 1GB account, you may hit storage limits with large files. This article is republished with permission from Wonder Tools , a newsletter that helps you discover the most useful sites and apps.