AIToday
Welcome back
Today's Top AI News
Curated from 200+ sources across AI & machine learning
OpenAI launches Privacy Filter, an open source, on-device data sanitization model that removes personal information from enterprise datasets
In a significant shift toward local-first privacy infrastructure, OpenAI has released Privacy Filter, a specialized open-source model designed to detect and redact personally identifiable information (PII) before it ever reaches a cloud-based server. Launched today on AI code sharing community Hugging Face under a permissive Apache 2.0 license, the tool addresses a growing industry bottleneck: the risk of sensitive data "leaking" into training sets or being exposed during high-throughput inference. By providing a 1.5-billion-parameter model that can run on a standard laptop or directly in a web browser, the company is effectively handing developers a "privacy-by-design" toolkit that functions as a sophisticated, context-aware digital shredder. Though OpenAI was founded with a focus on open source models such as this, the company shifted during the ChatGPT era to providing more proprietary ("closed source") models available only through its website, apps, and API — only to return to op
Google Cloud launches two new AI chips to compete with Nvidia
Google Cloud Launches $750 Million Partner Fund to Accelerate Agentic AI Deployment
General AI
Ex-OpenAI researcher Jerry Tworek launches Core Automation to build the most automated AI lab in the world
New AI lab, familiar face: former OpenAI researcher Jerry Tworek wants to push past the limits of today's AI architectures with a small team and new learning methods. The article Ex-OpenAI researcher Jerry Tworek launches Core Automation to build the most automated AI lab in the world appeared first on The Decoder.
Stock Market Today, April 22: Alphabet Helps Lead U.S. Markets Higher After Unveiling New AI Chips and Partnerships
On April 22, 2026, Alphabet’s fresh AI hardware and alliances helped power a broad tech-led rebound across major U.S. stock benchmarks.
LinkedIn names insider Daniel Shapero as new CEO
Microsoft's LinkedIn named Chief Operating Officer Daniel Shapero as its new CEO, tapping a veteran of the professional networking site to lead it through an era of AI-driven change. Shapero, who joined LinkedIn in 2008 as a general manager for the LinkedIn Research Network, will replace Ryan Roslansky as CEO. The leadership shake-up, effective immediately, comes as LinkedIn looks to deepen its role at the center of an AI-transformed workforce.
Google deepens Thinking Machines Lab ties with new multi-billion-dollar deal
Mira Murati's Thinking Machines Lab has signed a multi-billion-dollar deal with Google Cloud for AI infrastructure powered by Nvidia's latest GB300 chips, TechCrunch has exclusively learned.
Google announces 2 AI chips as competition with Nvidia heats up
Google revealed two AI chips, increasing its competition with Nvidia.
5 AI Models Tried to Scam Me. Some of Them Were Scary Good
The cyber capabilities of AI models have experts rattled. AI’s social skills may be just as dangerous.
Google's new AI tools put film scouting in Street View and promise to cut weeks of satellite analysis to minutes
At Cloud Next, Google unveiled three new AI imaging tools. Creatives can drop AI-generated images into real Street View locations, Google says city planners will be able to analyze satellite imagery in minutes instead of weeks, and developers get new models that can identify objects like bridges and power lines. The article Google's new AI tools put film scouting in Street View and promise to cut weeks of satellite analysis to minutes appeared first on The Decoder.
At $5 billion startup Checkr new employees build an app using AI during onboarding—even the new CFO
Checkr hires ZipRecruiter veteran Tim Yarbrough as its new CFO.
AI Tools Are Helping Mediocre North Korean Hackers Steal Millions
One group of hackers used AI for everything from vibe coding their malware to creating fake company websites—and stole as much as $12 million in three months.
AI Overviews are coming to your Gmail at work
The AI Overviews will offer instant summaries pulled from across multiple emails.
Europe has the talent and dunding to win at AI. First, it needs to break free from the Magnificent Seven
European innovation is battling big tech — and bigger walls.
Geo Content Writer: a backlog-first system for AI visibility content
Article URL: https://github.com/dageno-agents/geo-content-writer Comments URL: https://news.ycombinator.com/item?id=47859983 Points: 3 # Comments: 0
AI was ruining my college philosophy classes. So I assigned a new kind of essay
Article URL: https://globeopinion.substack.com/p/ai-was-ruining-my-college-philosophy Comments URL: https://news.ycombinator.com/item?id=47856742 Points: 1 # Comments: 0
AI news from 200+ sources
Get Started FreeModels & Gen AI
OpenAI launches workspace agents that turn ChatGPT from a chatbot into a team automation platform
OpenAI is rolling out workspace agents in ChatGPT, an evolution of custom GPTs. Powered by Codex, the agents automate complex team workflows and keep running even when no one is watching. Existing custom GPTs will stick around for now, with a migration path coming later. The article OpenAI launches workspace agents that turn ChatGPT from a chatbot into a team automation platform appeared first on The Decoder.
Alphabet’s New AI Chips And Agents Test Investor View On Valuation
Google, part of Alphabet (NasdaqGS:GOOGL), introduced its new TPU 8t and TPU 8i AI chips at Cloud Next, alongside a unified Gemini Enterprise Agent Platform. The company also launched a US$750 million fund aimed at speeding up enterprise adoption of AI agents. Google Cloud revealed new partnerships with Deloitte, Salesforce, Merck, Ulta Beauty, Oracle, and others to bring agentic AI tools into real world business operations. Alphabet, through Google Cloud, is pushing deeper into the AI...
Google doesn't pay the Nvidia tax. Its new TPUs explain why
Every frontier AI lab right now is rationing two things: electricity and compute. Most of them buy their compute for model training from the same supplier, at the steep gross margins that have turned Nvidia into one of the most valuable companies in the world. Google does not. On Tuesday night, inside a private gathering at F1 Plaza in Las Vegas, Google previewed its eighth-generation Tensor Processing Units. The pitch: two custom silicon designs shipping later this year, each purpose-built for a different half of the modern AI workload. TPU 8t targets training for frontier models, and TPU 8i targets the low-latency, memory-hungry world of agentic inference and real-time sampling. Amin Vahdat, Google's SVP and chief technologist for AI and infrastructure (pictured above left), used his time onstage to make a point that matters more to enterprise buyers than any individual spec: Google designs every layer of its AI stack end-to-end, and that vertical integration is starting to show up i
Google makes an interesting choice with its new agent-building tool for enterprises
Gemini Enterprise Agent Platform takes an interesting approach: It is geared for IT and technical users.
The Kansas City Royals are betting $3B on downtown in partnership with Hallmark
The Royals will build a $1.9 billion downtown Kansas City ballpark as part of a $3 billion project with Hallmark Cards.
Are you paying an AI ‘swarm tax’? Why single agents often beat complex systems
Enterprise teams building multi-agent AI systems may be paying a compute premium for gains that don't hold up under equal-budget conditions. New Stanford University research finds that single-agent systems match or outperform multi-agent architectures on complex reasoning tasks when both are given the same thinking token budget. However, multi-agent systems come with the added baggage of computational overhead. Because they typically use longer reasoning traces and multiple interactions, it is often unclear whether their reported gains stem from architectural advantages or simply from consuming more resources. To isolate the true driver of performance, researchers at Stanford University compared single-agent systems against multi-agent architectures on complex multi-hop reasoning tasks under equal "thinking token" budgets. Their experiments show that in most cases, single-agent systems match or outperform multi-agent systems when compute is equal. Multi-agent systems gain a competitive
Google and AWS split the AI agent stack between control and execution
The era of enterprises stitching together prompt chains and shadow agents is nearing its end as more options for orchestrating complex multi-agent systems emerge. As organizations move AI agents into production, the question remains: "how will we manage them?" Google and Amazon Web Services offer fundamentally different answers, illustrating a split in the AI stack. Google’s approach is to run agentic management on the system layer, while AWS’s harness method sets up in the execution layer. The debate on how to manage and control gained new energy this past month as competing companies released or updated their agent builder platforms—Anthropic with the new Claude Managed Agents and OpenAI with enhancements to the Agents SDK—giving developer teams options for managing agents. AWS with new capabilities added to Bedrock AgentCore is optimizing for velocity—relying on harnesses to bring agents to product faster—while still offering identity and tool management. Meanwhile, Google’s Gemi
Google unveils two new TPUs designed for the "agentic era"
Google's new generation of Tensor AI chips is actually two chips, one for inference and one for training.
Show HN: AthleteData – AI coach for endurance athletes that messages you first
Im a triathlete and the data for my training lives in 6 apps: Garmin, Strava, WHOOP, Intervals.icu, Wahoo, Withings, Apple Health, sometimes Hevy. Every morning Id eyeball a few of them and make a call on whether to do the planned session. For the past month I have been building a thing that does this for me, and got it to the point where I use it myself every day. It OAuths into whatever platforms you connect, reconciles the activities (tbh harder than it sounds — same ride shows up in Strava, Garmin, and Wahoo with different timestamps and rounding), computes daily load and readiness, and proactively messages you over Telegram or Whatsapp when something matters. Stack is straightforward: Typescript all the way, Postgres, an agent loop running on Claude (via Bedrock) with tool access to all your data + my computed metrics: zones, CTL/ATL/TSB, power/pace curves, anomaly detection on HRV and RHR, etc Two things that were harder than expected: 1. Garmins API only exposes the last 90 day
![[AINews] OpenAI launches GPT-Image-2](https://zmstgxtziqmvvwzllahg.supabase.co/storage/v1/object/public/article-images/latent-space/0cd91719-50be-4c20-921b-4e63319caa15.jpg)
[AINews] OpenAI launches GPT-Image-2
with Cursor getting a $10B contract with xAI and a right to acquire for $60B.
Google turns Chrome into an AI co-worker for the workplace
Google brings Gemini-powered "auto browse" capabilities to Chrome for enterprise users, letting workers automate tasks like research, data entry, and more.
Salesforce’s Agentforce Vibes 2.0 targets a hidden failure: context overload in AI agents
When startup fundraising platform VentureCrowd began deploying AI coding agents, they saw the same gains as other enterprises: they cut the front-end development cycle by 90% in some projects. However, it didn’t come easy or without a lot of trial and error. VentureCrowd’s first challenge revolved around data and context quality, since Diego Mogollon, chief product officer at VentureCrowd, told VentureBeat that “agents reason against whatever data they can access at runtime” and would then be confidently “wrong” because they’re only basing their knowledge on the context given to them. Their other roadblock, like many others, was messy data and unclear processes. Similar to context, Mogollon said coding agents would amplify bad data, so the company had to build a well-structured codebase first. “The challenges are rarely about the coding agents themselves; they are about everything around them,” said Mogollon. “It’s a context problem disguised as an AI problem, and it is the number
Business & Industry
Our newsroom AI policy
How Ars Technica uses, and doesn't use, generative AI.
Robotics
Foxglove launches unified data platform to accelerate physical AI development
Foxglove has launched “Data Search and Curation”, a new set of capabilities that helps robotics teams replace fragmented, manual data workflows with a unified platform to find and curate the mission-critical events, anomalies, and system behavior that matter most across growing volumes of operational data. The company also expanded the Foxglove Data Platform with Bring […]