AI News Archive: August 19, 2026 — Part 7
Sourced from 500+ daily AI sources, scored by relevance.
- Rocket One Launches Swarm Stage AI Drone-Swarm Threat Emulation Platform for U.S. Military Counter-UAS
Rocket One Launches Swarm Stage AI Drone-Swarm Threat Emulation Platform for U.S. Military Counter-UAS USA Today
- AI’s attribution problem gets worse as models scale
Diffusion models are becoming sophisticated enough that they can reproduce an image even when they don’t have access to the original. In a series of ‘what if’ scenarios, researchers associated with MIT’s Computer Science & Artificial Intelligence Laboratory (CSAIL) swapped out different training datasets to test the impact on image outputs when original image data was completely removed. It turns out that, at sufficient scale, nothing changed. The researchers call the phenomenon “ attribution decay ”: The more data a diffusion model is trained on, and the larger it gets, the less individual inputs matter. “If you take away a piece of data and the output of the model doesn’t change, then that piece of data didn’t affect the output,” Zheng Dai, lead author on the work, explained in an MIT blog post . These findings could have significant ramifications when it comes to resolving growing concerns about intellectual property (IP) and copyright infringement. Models can recreate images even if they’ve never ‘seen’ them Modern generative diffusion models essentially replicate statistical patterns in large training datasets to create realistic reproductions. These powerful tools have achieved “remarkable results” in a wide array of applications, the researchers noted, notably image, video, and audio generation. But they are increasingly under scrutiny by creatives, companies , and policymakers, who all want a way to assign responsibility for generated outputs. Models sit at the center of lawsuits, licensing deals, and proposed regulations around the world. For instance, Stability AI (maker of Stable Diffusion) and Midjourney are embroiled in an ongoing class action lawsuit filed by several artists in federal court in California. The claimants argue that the popular image, video, and audio-creating models are scraping billions of their copyrighted images without their consent. Getty Images also brought claims against Stability AI, but they were struck down by the High Court of Justice Business and Property Courts of England and Wales, although Getty did partly win trademark claims because some AI-generated images closely resembled its work. Attributability, the MIT CSAIL researchers noted, would increase understanding of “machine unlearning,” data poisoning, model interoperability, fairness, and privacy, while also addressing ethical, legal, financial, and regulatory issues. “Developing a method to attribute generated outputs to influential training data would greatly advance our understanding of and ability to regulate these models ,” the researchers wrote. In their experiments, they used ablation, which is essentially testing what happens when certain elements are removed by looking at what a model might have produced if it had never “seen” a particular image. Typically, ablation is difficult because models need to be retrained after data is pulled out. But the MIT CSAIL researchers applied the method to a “diffusion ensemble” architecture of many different components trained on different pieces of data. These components could be swapped out to determine how much of an impact, if any, each one had. “Our analysis is based on observing changes in model behavior , or lack thereof, upon omitting a part of the training set,” the researchers explained. To do so, they trained 24 ensembles on datasets containing anywhere from 256 to 160,000-plus images. These were pulled from seven publicly accessible image datasets, including ArtBench (artwork), CIFAR-10 (generic colored images), Fashion-MNIST (clothing and accessories), CelebA (celebrity faces), and MetFaces (human faces). In one example, they presented an image of a famous oil painting generated by a model trained on public domain artwork from 744 artists. It was shown side-by-side with hundreds of seemingly identical images that the model had generated, even when specific artists had been removed from training data. The original was re-imagined in every possible variation, and the researchers quantified attributability by measuring the largest change they could induce by omitting training data. The radius became smaller as datasets became bigger, holding true across different measurements including pixel-by-pixel or semantic meaning. In other words, single artworks by specific artists, or photographs of certain people, could be entirely removed from datasets, and the model could still reproduce that image or style. Essentially, tangible connections are lost, and linking to specific data points responsible for generated samples is “practically impossible,” or can even vanish, the researchers explained. Their method is novel, they said, because prior work has focused on removing large swathes of data rather than targeting smaller pieces, what they called “leave-one-out style attribution.” The impact on attributability Because the experiment shows that, as Dai put it, it “doesn’t make much sense” to attribute a given output to a given piece of data, creatives and others may not be able to provide an audit trail tracing back to their original work. Co-author David Gifford, an MIT professor and CSAIL principal investigator, said the findings have a direct bearing on legal questions around whether model outputs are actually derivative works. “One way to think about this is that these models are creative,” he said. “They are not simply copying what they are fed, but creating brand new outputs.” So if outputs can’t be correlated to individual pieces of training data, questions can be raised around fair use and whether, in fact, model-generated outputs are themselves copyrightable as “novel works,” Gifford said. It could also shift the conversation about how original creators are compensated when what comes out of a model seems a direct recreation of their work, but can’t be traced back to anything on the internet. Ultimately, producing outputs that are guaranteed to be unattributable is an “obligation for the industry, rather than a loophole,” he said. AI builders “need to revise their models to take advantage of the advances in this work, so they can show they’re not creating derivatives of individual people or items.”
Score: 42🌐 MovesAug 19, 2026https://www.computerworld.com/article/4211283/ais-attribution-problem-gets-worse-as-models-scale.html - AI was meant to simplify IT service management – new research shows it's creating bigger workloads for teams
AI was meant to simplify IT service management – new research shows it's creating bigger workloads for teams IT Pro
- Pacing comes to the AI frontier
PLUS: Build, test, and publish an app without leaving Codex
- Making better robots depends on better data capture, Chinese firm 51World says
The race to develop intelligent humanoid robots faces a major roadblock in a severe shortage of high-quality training data, but Beijing-based tech company 51World believes it has the tools to break the bottleneck. Best known for its digital twin and simulation technology, 51 World on Tuesday unveiled a new suite of data-collection devices and platforms designed to help train embodied AI systems – artificial intelligence models that help machines perceive, reason and interact with the physical...
- Malaysia wants 300,000 AI jobs by 2030. Talent will decide if it gets there
Southeast Asia’s artificial intelligence race is often framed as a contest over data centres, chips and cloud infrastructure. That is only half the story. The harder asset to build — and the easier one to lose — is talent. Malaysia’s National AI Action Plan 2026-2030, known as AI Nation 2030, makes that point clearly. The […] The post Malaysia wants 300,000 AI jobs by 2030. Talent will decide if it gets there appeared first on e27 .
Score: 42🌐 MovesAug 19, 2026https://e27.co/malaysia-wants-300000-ai-jobs-by-2030-talent-will-decide-if-it-gets-there/ - The Culture Funnel: You can’t align what isn’t in the data
Cohere Labs found cultural diversity is often lost in post‑training data mixes.
Score: 42🌐 MovesAug 19, 2026https://cohere.com/blog/the-culture-funnel-you-cant-align-what-isnt-in-the-data - AI-Driven Efficiency in Cross-Border Trade Payments: XTransfer Cracks the “Impossible Trinity”
AI-Driven Efficiency in Cross-Border Trade Payments: XTransfer Cracks the “Impossible Trinity” Toronto Star
- The Pixel 11 Pro XL Is Expensive, But I Can't Stop Using Its Best New AI Feature
The Pixel 11 Pro XL Is Expensive, But I Can't Stop Using Its Best New AI Feature PCMag
- The Pixel 11 Pro's Camera Has an AI Hallucination Problem
The Pixel 11 Pro's Camera Has an AI Hallucination Problem PCMag
Score: 42🌐 MovesAug 19, 2026https://www.pcmag.com/opinions/the-pixel-11-pros-camera-has-an-ai-hallucination-problem - Software Stocks Are Bouncing Back From AI Fears. BofA Sees More Gains Ahead.
Software Stocks Are Bouncing Back From AI Fears. BofA Sees More Gains Ahead. Barron's
Score: 42🌐 MovesAug 19, 2026https://www.barrons.com/articles/software-stocks-workday-figma-gitlab-6db115f6?mod - Are underwater data centers sustainable for AI infrastructure?
The artificial intelligence boom is driving unprecedented demand for data centers , raising concerns about their growing energy consumption , water use , and carbon footprint . These challenges are making companies look beyond traditional land-based data centers. Some developers are now exploring the ocean as a new location for AI infrastructure in the hope that underwater data centers could improve energy use and cooling efficiency while using less fresh water and land area than onshore buildings. My research focuses on the societal, organizational, and environmental implications of emerging technologies , particularly artificial intelligence and the digital infrastructure—including data centers —that supports its development and deployment. I see underwater data centers as a promising new approach for supporting the growth of AI. But moving servers into the ocean does not make other underlying environmental challenges such as energy consumption and carbon emissions disappear. And it creates new concerns about harm to the marine environment, as well as questions about how these data centers can be regulated—and how companies can maintain and expand them if needed. Whether underwater data centers can become sustainable alternatives to traditional data centers depends on solving these economic, technical, and environmental problems. The rise of ocean-based AI infrastructure In 2015, Microsoft launched a research project to explore the feasibility, benefits, and challenges of underwater data centers. Part of that effort included setting up a waterproof data center on the seafloor near Scotland’s Orkney Islands in 2018. It contained 864 servers and was connected to shore by an underwater cable. After two years, Microsoft reported that the servers in the underwater data center failed at about one-eighth the rate of servers in comparable land-based data centers . The company is still studying the possible reasons but hypothesizes that in a sealed underwater environment the equipment is less exposed to oxygen, humidity, and temperature fluctuations—as well as less jostling from people working to replace broken components. However, Microsoft ended the project in 2024 and chose not to build more underwater data centers . The company didn’t say why, but others’ analyses suggest the reasons could involve regulatory concerns , including the need for environmental permits, as well as a desire for faster upgrades and replacements for the computer equipment inside. Instead, the company has focused on land-based data centers, which can be larger and easier to expand, and also easier to access to repair or replace equipment. Others have moved ahead, though. China built what may be the world’s first wind-powered underwater data center in Shanghai. The facility launched in June 2025 and began full commercial operations in May 2026 . The $226 million project uses seawater as a coolant rather than refrigerating fresh water, reducing the electricity required to cool the computers. It uses at least 30% less electricity than traditional data centers, and offshore wind turbines reduce reliance on fossil fuels and cut the data center’s carbon emissions. Japan is testing a different approach: Data centers housed in containers on floating platforms at sea can use seawater for cooling, have unobstructed conditions for solar panels and wind turbines, and reduce demand for land. In 2025, a data center in shipping containers opened on a floating platform near Yokohama. Its power comes from solar panels installed on the same floating platform , with batteries providing energy storage. The test will continue through March 2027 . Singapore is also moving toward commercial-scale floating data centers. In 2026, infrastructure company Keppel began building a four-story floating data center , scheduled to open in 2028. The project will use seawater for cooling, reducing reliance on treated water and improving cooling efficiency. And the fact that it floats means it won’t take up any of Singapore’s limited land availability. In 2025, Ulsan, South Korea, began planning an underwater data center that could house more than 100,000 servers and use 30% less power than land-based centers by using seawater for cooling. In Maine, DeepGreen Western Passage has proposed a submersible AI data center in the Bay of Fundy, powered by tidal turbines designed to harness the area’s strong tidal currents. Land-based data centers could also take advantage of seawater cooling. In Portugal, the SIN01 AI data center in Sines uses seawater from the Atlantic to cool its servers before returning it to the ocean. The promise of ocean-based data centers These various approaches offer ways to reduce demand for grid-supplied electricity for powering data centers’ computers and cooling equipment , as well as using less fresh water . The distance from people’s homes could also be an advantage for data centers in or on the ocean. A Gallup poll in March 2026 found that 70% of Americans oppose building AI data centers in their communities. However, more than half of the world’s population lives within 120 miles of a coast. Underwater could be another way to keep data centers physically close to users for speedy service. Maintenance, though, is a major challenge. If a computer fails underwater, it cannot be repaired or replaced on site . The entire sealed data center module may need to be brought to the surface, even if just one computer needs work. Can the ocean sustain AI? The main environmental concern about ocean-based data centers involves the seawater used for cooling. Discharging warm or hot water can potentially affect oxygen levels, pH, and marine life in the surrounding waters. That heat is already apparent at the few seaborne data centers now operating. HiCloud, the engineering contractor for China’s Hainan underwater data center, has reported a temperature increase of less than 1 degree Celsius (1.8 degrees Fahrenheit) in the seawater near the facility. SIN01 in Sines, Portugal, also returns seawater about 1 C warmer . Many marine species depend on stable water temperatures for breeding, feeding, and migration, raising concerns that heat released by multiple underwater data centers could create localized thermal pollution and alter marine ecosystems . And the ocean is already under pressure. UNESCO, the United Nations agency for international cooperation, including in conservation, estimates that about 60% of marine ecosystems are already degraded or used unsustainably. The ocean is already warming along with the atmosphere, without additional waste heat from data centers. That additional heat is already threatening coral reef and mangrove ecosystems , seagrasses, and other aspects of the marine food web. As that warming continues , ocean waters will be less useful for cooling electronic equipment in some regions. Underwater data centers could help AI grow while easing some of the pressure on land, energy, and water. But the real test is whether the ocean can become AI’s next computing frontier without becoming its next environmental problem. Nir Kshetri is a professor of management at the University of North Carolina – Greensboro . This article is republished from The Conversation under a Creative Commons license. Read the original article .
- [Video] Samsung AI Assistant Helps Teachers Create More Engaging and Accessible Lessons
Helping every student stay engaged and supported throughout a lesson requires teachers to balance instruction, interaction, and individual learning needs. Managing these different needs while maintaining lesson flow can add to a teacher’s workload. Samsung AI Assistant is a built-in app for compatible Android-based Samsung Interactive Displays that helps teachers support every stage of a […]
- New construction robots gain traction on jobsites
For years, small, adaptable machines that perform repetitive jobsite tasks have seen the most success. As technology advances, that calculus is beginning to change.
- Pixel 11 can search your whole Google account right from the app drawer
A new search experience on Pixel and Galaxy Z Fold 8.
- How attackers persuade AI agents to break the rules
Today, most of us interact with AI assistants—reactive bots that wait for human instructions. Yet AI assistants are rapidly being replaced by agentic AI agents that can interact with external tools, browse the web, generate images, send emails and perform increasingly complex workflows on behalf of users.
- New AI model could improve digital coaching and rehab
New AI model could improve digital coaching and rehab EurekAlert!
- How to Adopt AI Agents in Financial Close and Consolidation Solutions
How to Adopt AI Agents in Financial Close and Consolidation Solutions Gartner
- Beyond AI Coding Assistants: The Next Evolution of Software Development
Some AI adoption stories are embarrassing, and the embarrassing part is that nobody in the room seems to notice. A development team adds AI coding assistants. Velocity metrics look better by Q2. Then somebody calls the organization AI-native at the next all-hands, the room nods along, and the deck moves to the next slide. The... … continue reading The post Beyond AI Coding Assistants: The Next Evolution of Software Development appeared first on SD Times .
Score: 41🌐 MovesAug 19, 2026https://sdtimes.com/ai-coding-assistants/beyond-ai-coding-assistants-the-next-evolution-of-software-development/ - AI Companies’ Success Lies in the Apps, Bain’s China Chairman Says
AI Companies’ Success Lies in the Apps, Bain’s China Chairman Says Caixin Global
- Rundoo raises $30M to expand its AI-native operating system for small supply stores
Rundoo Inc., the creator of an artificial intelligence-native system-of-record for independent supply stores, said today it has closed on a $30 million Series B round of funding, bringing its total amount raised so far to $48 million. Today’s round was led by Battery Ventures and saw the involvement of existing backers such as Bessemer Venture […] The post Rundoo raises $30M to expand its AI-native operating system for small supply stores appeared first on SiliconANGLE .
- Coders Say They Already Found Workarounds to Claude’s Invisible Watermarks
Anthropic announced last week it would include invisible watermarks in AI-generated content to comply with new EU rules. Within hours, overrides were being touted online.
Score: 40🌐 MovesAug 19, 2026https://www.wired.com/story/coders-say-they-already-found-workarounds-to-claudes-invisible-watermarks/ - Large language models enhance annotation of enzymes in metagenomes
Science Advances, Volume 12, Issue 34, August 2026.
- Jasper Google Search Console Integration: Ground Your GEO Agent in Real Search Data
Connect Google Search Console to Jasper so your GEO Agent can see real page performance and improve AI visibility.
- AI Agent responses occasionally include hallucinations or assumptions not present in connected knowledge
AI Agent responses occasionally include hallucinations or assumptions not present in connected knowledge JIRA.atlassian.com
- Swimlane updates security operations center with intelligent routing
Agentic artificial intelligence cybersecurity automation company Swimlane Inc. today announced the expansion of the company’s AI security operations center to support automatic routing for security investigations. The new capability enables routing of incoming alerts to different paths: deterministic automation, AI-assisted investigation or agentic automation. The company said the front-end triage capability allows the system to […] The post Swimlane updates security operations center with intelligent routing appeared first on SiliconANGLE .
Score: 40🌐 MovesAug 19, 2026https://siliconangle.com/2026/08/19/swimlane-updates-security-operations-center-with-intelligent-routing/ - People Are Rushing to Find Ways to Remove Claude’s AI Text Watermark
They're looking for a good scrub.
Score: 40🌐 MovesAug 19, 2026https://gizmodo.com/people-are-rushing-to-find-ways-to-remove-claudes-ai-text-watermark-2000800433 - Gujarat and Automation Anywhere partner to expand AI skills
The Government of Gujarat and Automation Anywhere have signed a memorandum of understanding to expand AI skills and knowledge across the state, bringing government, academia, startups and industry together to build capabilities for an AI-driven economy. The post Gujarat and Automation Anywhere partner to expand AI skills appeared first on Express Computer .
Score: 40🌐 MovesAug 19, 2026https://www.expresscomputer.in/news/gujarat-and-automation-anywhere-partner-to-expand-ai-skills/137919/ - WRITER Named a Market Shaper in the July 2026 Gartner® Emerging Market Quadrant™ for AI Agents for Marketing — Startup Vendors
WRITER was named a Market Shaper in Gartner's first Emerging Market Quadrant for AI Marketing Agents, recognizing its enterprise AI platform for scalable, governed agentic marketing. The post WRITER Named a Market Shaper in the July 2026 Gartner® Emerging Market Quadrant™ for AI Agents for Marketing — Startup Vendors appeared first on WRITER .
- Is AI really responsible for recent job cuts?
More companies are linking lay-offs to workplace efficiencies but evidence is patchy
Score: 39🌐 MovesAug 19, 2026https://www.ft.com/content/0dc14b44-96f6-4b1f-921a-8cba8030eafc?syn-25a6b1a6=1 - More than half of Americans now view AI negatively
Concern continues to climb for adults under 30, as people fear being replaced by AI
Score: 39🌐 MovesAug 19, 2026https://www.theregister.com/ai-and-ml/2026/08/19/more-than-half-of-americans-now-view-ai-negatively/5289736 - More is different when AI agents work together, study suggests
More is different when AI agents work together, study suggests EurekAlert!
- Copenhagen’s Aisel Health raises €1.7 million to scale its AI operating system for psychiatric care
Aisel Health, a Copenhagen-based HealthTech company building an operating system (OS) purpose-built for psychiatry and mental health, today announced it has closed a €1.7 million pre-Seed round. The round was led by Caesar Ventures, with participation from Nordic Web Ventures, LifeX, and Angel Invest, alongside existing investors Rockstart and EIFO. “Psychiatry doesn’t need more generic […] The post Copenhagen’s Aisel Health raises €1.7 million to scale its AI operating system for psychiatric care appeared first on EU-Startups .
- AI Is Undermining Leaders’ Judgment. Here’s What to Do About It.
The tools designed to augment your decision-making could end up making it weaker.
- Google Gemini is getting a dedicated student hub
As we're gearing up for back-to-school season, Google is rolling out a new dedicated student hub in Gemini. It's a one-stop repository for collecting research in a study notebook, creating flashcards, taking practice quizzes, and more. Google is also enhancing its study notebooks with support for graphs and images. It can even add test dates […]
Score: 38🌐 MovesAug 19, 2026https://www.theverge.com/ai-artificial-intelligence/982425/google-gemini-student-hub - AI was supposed to win people over by now — it hasn’t
As AI becomes harder to avoid, consumers are growing more wary of the technology — and Silicon Valley is discovering that widespread adoption doesn’t necessarily lead to acceptance.
Score: 38🌐 MovesAug 19, 2026https://techcrunch.com/2026/08/19/ai-was-supposed-to-win-people-over-by-now-it-hasnt/ - TrueFoundry's open source AI agent harness TrueForge boasts 30%-75% cheaper task completion than Claude Managed Agents
Another day, another new AI agent harness is released. Only this time, it's one that aims to solve a growing enterprise problem as AI agents proliferate: enabling greater developer control of agents and tools, while reducing cost. TrueFoundry , a San Francisco B2B machine learning startup co-founded in 2021 by former Meta engineers, has released its own custom TrueForge harness under the permissive MIT License on Github . Thus, it can be used with any of a developer (or their parent enterprise's) preferred AI models, forked, modified, self-hosted and incorporated into commercial products. The company states in a blog post that when it used TrueForge paired with the open source GLM-5.2 LLM to successfully complete 11 of 14 tasks on DevRev’s Enterprise-Bench — testing multi-step tool use across CRM, issue tracking, and document management systems — it cost 75% less than achieving the same results with Anthropic's Claude Managed Agents harness powered by Claude Opus 4.8 ($2.90 compared to $11.80). Using the same model in each harness, Opus 4.8, TrueFoundry still claims a cost savings of roughly 30% using TrueForge compared to Claude Managed Agents ($8.50 vs $11.80). Why is TrueFoundry giving this powerfully efficient harness away for free? "We’ve had this ask from a bunch of customers," said Anuraag Gutgutia, TrueFoundry’s co-founder and COO, in an exclusive interview with VentureBeat. "You have an ability where you bring in agents and MCPs — can we also get something where you can actually launch these managed agents? I think that is the need we are satisfying. It is not a replacement. People will use this alongside other harnesses, like the cloud-managed ones or the commercial-provider-managed ones, but this will serve as a way for people to use them in a vendor-neutral way and also at a lower cost.” Indeed, TrueFoundry already offers a paid " AI Gateway " for enterprises centrally controlling model and MCP access, credentials, permissions, budgets and observability. TrueForge, by contrast, handles what happens above that gateway: the loop that lets a model repeatedly reason, call tools, receive results and continue working until a task is complete. For enterprise developers, the practical proposition is that they can start locally with a single command and SQLite, then move the same agent harness into a shared deployment using Docker Compose or Helm with Postgres and Redis. TrueFoundry explicitly warns that the local configuration is intended only for use on a developer’s machine, not as an internet-facing production service. Gutgutia said the company ultimately wants its AI Gateway to become the common layer beneath whichever agents and harnesses an enterprise chooses. “There will be a set of companies that will use our harness as the way to launch managed agents,” he said, while others may continue using Claude, other open-source harnesses or internal systems. “But all that traffic should still be flowing through our gateway.” Context management is where TrueForge tries to cut waste TrueForge’s architecture centers on context engineering — controlling how much information gets sent back into the model on every step of an agent run. That includes delaying the loading of MCP tool schemas until they are needed, delegating isolated tasks to subagents, moving oversized tool results into files instead of stuffing them into the active context window, processing structured results through code, and automatically compacting long-running conversations. The documentation sets the default compaction threshold at 50,000 tokens, though it can be changed per agent. TrueForge also treats the sandbox differently from runtimes that keep an agent inside an isolated environment throughout its run. The core agent loop remains on the TrueForge server; a sandbox is provisioned as a tool only when the agent needs to execute code or work with files. TrueFoundry says that reduces unnecessary compute and allows a server to run more agents concurrently. The company argues those choices directly reduce model spending. How TrueForge compares to Claude Managed Agents and other leading orchestration harnesses Type / focus TrueFoundry TrueForge: General-purpose production agent harness designed for enterprise deployments. DeepSeek Harness: Open-source agent harness, currently positioned as a developer preview. OpenAI Codex CLI: Coding-focused agent harness designed primarily for software-engineering workflows. LangChain Deep Agents: General-purpose agent harness built on LangGraph. Anthropic Claude Managed Agents: Fully managed production agent runtime operated by Anthropic. License TrueFoundry TrueForge: MIT. DeepSeek Harness: MIT. OpenAI Codex CLI: Apache 2.0. LangChain Deep Agents: MIT. Anthropic Claude Managed Agents: Proprietary. Price TrueFoundry TrueForge: The open-source harness itself is free. Model, sandbox and infrastructure costs are separate. TrueFoundry also offers an optional commercial governance layer through its broader platform. DeepSeek Harness: No harness license fee. Users separately pay for whatever model providers and infrastructure they use. OpenAI Codex CLI: The CLI is open source. Underlying model/API or subscription costs are separate, OpenAI says around $100–$200 per developer per month, although actual spending varies substantially with model choice LangChain Deep Agents: Open source, with model and infrastructure expenses separate. LangChain also offers optional commercial services through LangSmith. Anthropic Claude Managed Agents: Claude tokens consumed plus $0.08 per running session-hour, with runtime metered to the millisecond. Model flexibility TrueFoundry TrueForge: Vendor-neutral and designed around bring-your-own-model support. DeepSeek Harness: Multi-provider and not restricted to DeepSeek models. OpenAI Codex CLI: Supports configurable inference endpoints, including OpenAI-compatible services and local-model options. LangChain Deep Agents: Broad multi-provider support through the LangChain ecosystem. Anthropic Claude Managed Agents: Claude-centric. Deployment TrueFoundry TrueForge: Can run locally as a single process with SQLite, then move into a production deployment using Docker Compose or Helm with Postgres and Redis. DeepSeek Harness: Designed for local or self-hosted operation. OpenAI Codex CLI: Primarily a local CLI experience, alongside OpenAI-hosted Codex products and services. LangChain Deep Agents: Can be self-hosted or deployed through LangChain and LangSmith infrastructure. Anthropic Claude Managed Agents: Anthropic manages the runtime and infrastructure. Key features TrueFoundry TrueForge: MCP and tool orchestration, subagents, human approval checkpoints, persistent sessions, context compaction, large-result offloading, Code Mode, generative UI, tracing and a sandbox-as-a-tool architecture. DeepSeek Harness: Pluggable models, tools, session storage and agent loops, along with sandboxing, permissions, approval gates and skills. OpenAI Codex CLI: Agent loop, repository and file operations, shell execution, MCP tools, sandboxing, permissions, approvals and context management. LangChain Deep Agents: Planning, subagents, skills, filesystem-based context management, persistent memory, human-in-the-loop controls, MCP support and multiple sandbox backends. Anthropic Claude Managed Agents: Managed execution environments, persistence, tools, sandboxing and infrastructure for long-running agents. Key differentiator TrueFoundry TrueForge: Its strongest distinction is the combination of an open-source, vendor-neutral harness with a clear path from local development to a shared production runtime, plus an optional enterprise governance plane through TrueFoundry. DeepSeek Harness: Emphasizes deep modularity. Major parts of the runtime, including models, tools, storage and the agent loop, are designed to be replaceable plugins. OpenAI Codex CLI: Stands out as a highly developed software-engineering-specific harness rather than a general-purpose enterprise agent server. LangChain Deep Agents: Benefits from the broader LangChain and LangGraph ecosystem and offers a mature open-source path for building general-purpose agents. Anthropic Claude Managed Agents: Minimizes operational burden by having Anthropic manage the runtime, but trades that convenience for tighter model and platform coupling. Open source does not automatically mean governed For enterprise buyers, one of the most important distinctions is between TrueForge by itself and TrueForge connected to TrueFoundry’s commercial AI Gateway. The open-source harness can run independently. But it does not magically inherit an organization’s enterprise access policies on its own. “If you are using just the open source version of our agent harness, yes, you will need to put the right controls therein or in front of some other internal control system,” Gutgutia told VentureBeat. When paired with TrueFoundry’s gateway, the company says agents can inherit the identities and access controls already attached to models, MCP servers, tools, skills and other agents. Gutgutia described the gateway as the place where enterprise SSO, identity providers and granular permissions can be centrally enforced rather than reimplemented separately for every agent. That distinction is likely to be important for platform engineering teams evaluating the project. TrueForge is free software; TrueFoundry’s governance layer is the commercial control plane around it. TrueFoundry says NetApp was a beta user of the harness and contributed requirements during development. Gutgutia said NetApp’s IT organization has used the technology for incident response and faster ticket triage, while also exposing internal agents as self-service tools for developers. He also identified Automattic as an early user. Background on TrueFoundry and its business to date TrueFoundry was founded in 2021 to help enterprises deploy and operate machine-learning models, including Kubernetes-based model serving, training and infrastructure management. Its three co-founders — Nikunj Bajaj, Abhishek Choudhary and Anuraag Gutgutia — previously worked at Meta and WorldQuant, respectively. Gutgutia said the founders' common experience was working around mature systems where infrastructure and controls were designed to prevent costly mistakes — an idea they believed would become increasingly important as AI moved into production inside large companies. As generative AI spread through enterprise software, TrueFoundry expanded from that MLOps foundation toward managing LLM applications and, increasingly, the models, tools and agents around them. By 2025, the company had made its AI Gateway a central part of the business: a layer sitting between enterprise applications and model providers that handles routing, authentication, access controls, observability, budgets, guardrails and failover. That evolution has been backed by roughly $21 million in outside financing. TrueFoundry raised a $19 million Series A in February 2025 led by Intel Capital, with participation from existing investors Eniac Ventures and Peak XV's Surge, as well as Jump Capital and angel investors including Gokul Rajaram and Mohit Aron. The round brought total financing to about $21 million, according to Intel Capital's announcement . At the time, TrueFoundry said its customer base had grown fourfold year over year and that it was managing more than 1,000 clusters for machine-learning workloads. The business has since become increasingly oriented around large-scale enterprise AI traffic. In VentureBeat's January 2026 coverage of TrueFoundry's TrueFailover launch , the company said it had more than 30 paid customers worldwide, had exceeded $1.5 million in annual recurring revenue during the prior year and was processing more than 10 billion requests per month through its AI Gateway. Customers and deployments cited by TrueFoundry have included NetApp, Siemens Healthineers, ResMed, Automation Anywhere, Nvidia, Games24x7 and others; Gutgutia also named NetApp, Siemens, Synopsys and Automation Anywhere among Fortune 1000 organizations working with the company in his interview with VentureBeat. TrueFoundry has also been expanding through acquisition. In June 2026 it acquired UK-based Seldon AI , a longtime MLOps vendor whose Seldon Core software has been used for production model serving and inference. As the acquisition shows, rather than treating traditional ML, LLMs, tools and agents as separate infrastructure categories, TrueFoundry is trying to put them behind a common deployment and governance layer. TrueForge extends that strategy upward into the agent runtime itself. Until now, TrueFoundry's commercial center of gravity has largely been the control plane underneath enterprise AI workloads — deciding which users and applications can access which models and tools, routing requests, enforcing policy, monitoring spend and keeping services available. TrueForge gives the company an open-source runtime above that layer where agents can actually execute. Gutgutia described the relationship as complementary: organizations can run TrueForge independently or continue using other agent harnesses, while TrueFoundry's longer-term business opportunity is to provide the common governance and infrastructure underneath whichever agents enterprises choose.
- AI Adoption in the Workplace Accelerates, but Trust Gap Remains
As employees increasingly use AI tools, many organizations face a persisting challenge: building the trust needed to sustain adoption.
- ChatGPT to stop doing children’s homework for them
ChatGPT to stop doing children’s homework for them The Telegraph
Score: 38🌐 MovesAug 19, 2026https://www.telegraph.co.uk/business/2026/08/19/chatgpt-to-stop-doing-childrens-homework-for-them/ - Opinion: Ohio Needs Guardrails for Data Center Owners, Utilities
Why the state urgently needs a new law drawing clear legal guardrails between data center investors on the one hand and regulated Ohio electric utilities on the other.
Score: 38🌐 MovesAug 19, 2026https://www.govtech.com/opinion/opinion-ohio-needs-guardrails-for-data-center-owners-utilities - AI Companies Are Desperate for Your Work-Related Data. What's Your Price?
AI Companies Are Desperate for Your Work-Related Data. What's Your Price? Business Insider
Score: 38🌐 MovesAug 19, 2026https://www.businessinsider.com/ai-work-data-price-ai-training-google-spirit-airlines-2026-8 - Gen AI outputs are unattributable, study finds
Researchers discovered a phenomenon they call “attribution decay,” where the more data a generative model is trained on, the harder it becomes to trace a generated image to a single image from the training data.
Score: 38🌐 MovesAug 19, 2026https://www.semafor.com/article/08/19/2026/generative-ai-outputs-are-unattributable-study-finds - Learning vision-driven reactive soccer skills for humanoid robots
Science Robotics, Volume 11, Issue 117, August 2026.
- Kimi K3’s 1M Token Context Window vs. RAG: Cost, Latency and Answer Quality
A controlled comparison of a top-5 RAG pipeline and a full 127,000 token prompt on the same 12 questions, same system prompt and same model. Graded blind on correctness, completeness and grounding. The post Kimi K3’s 1M Token Context Window vs. RAG: Cost, Latency and Answer Quality appeared first on Towards Data Science .
Score: 38🤖 ModelsAug 19, 2026https://towardsdatascience.com/kimi-k3s-1m-token-context-window-vs-rag-cost-latency-and-answer-quality/ - Google is trying to solve contrails with AI
Operation Blue Skies, which also involves the UK government, will subtly reroute flights to test contrail avoidance.
Score: 38🌐 MovesAug 19, 2026https://www.engadget.com/2240125/google-is-trying-to-solve-contrails-with-ai/ - Gravis Robotics CEO on Softbank Backing, Construction
SoftBank has made a $200 million investment in Gravis Robotics, a construction technology startup specializing in retrofitting existing excavators and heavy machinery with autonomous and semi-autonomous capabilities. Ryan Luke Johns, CEO and Co-Founder of Gravis Robotics, explained that the funding reflects SoftBank's strong confidence in the physical AI space, particularly in automating earth-moving equipment critical to infrastructure projects such as roads, quarries, and mines. He speaks with Romaine Bostick on "The Close." (Source: Bloomberg)
Score: 38🌐 MovesAug 19, 2026https://www.bloomberg.com/news/videos/2026-08-19/gravis-robotics-ceo-on-softbank-backing-construction-video - Goldman studied where AI is squeezing labor markets. Here's what it found
Goldman Sachs found that AI is starting to weigh on employment across developed economies.
- Also’s $3,500 e-bike is a $1 billion Trojan horse for autonomous transportation
Also’s $3,500 e-bike is a $1 billion Trojan horse for autonomous transportation Fortune
- Evolution of humanoid locomotion control
Science Robotics, Volume 11, Issue 117, August 2026.
- The Enterprise Fight Against Runaway AI Costs
As enterprises increasingly turn to AI to get work done, three new weapons are emerging in their fight to control…
Score: 38🌐 MovesAug 19, 2026https://inc42.com/features/the-enterprise-fight-against-runaway-ai-costs/