AI News Archive: August 13, 2026 — Part 10
Sourced from 500+ daily AI sources, scored by relevance.
- Why Your Strategic Control Point Is Everything In The Agentic AI Era
The pattern is the same everywhere: own data no one else has and sit as close as possible to the point where decisions are made.
- Job Seekers Are Racing to AI-Proof Their Résumés
Job seekers are editing their career back-stories as employers pump AI terms into job descriptions.
Score: 22🌐 MovesAug 13, 2026https://www.wsj.com/tech/ai/job-seekers-are-racing-to-ai-proof-their-resumes-f310f43c?mod=rss_Technology - Using functional AI to automate document workflows
A recent study conducted by Nitro found that 75-95% of the employees and executives surveyed use AI for document processing—including data extraction, PDF tasks, and contract summaries. However, when these individuals don’t have access to the right kind of AI tools, they report turning to unapproved—or shadow IT—solutions to speed up workflows, which creates security and compliance risk. Read the report To reinforce the importance of providing teams with the right AI tool for the right job, let’s look at the difference between chatbots and functional AI in terms of automating document workflows. Chatbots are great for ad hoc tasks that follow a pre-programmed set of actions, but they aren’t designed to enforce consistent rules for formatting, redaction, or compliance, or to extract data hidden deep in document tables, images, or free text . Unlike chatbots, functional AI can physically execute redaction, conversion, and data extraction tasks directly within business processes and systems, rather than simply responding to prompts. This guide explains why scaling document workflows requires both conversational AI to answer common questions and functional AI to perform repeatable tasks on a high volume of documents with consistency, control, and predictable cost. Chatbots vs. functional AI: What’s the difference? Chatbot AI and functional AI play distinct roles in document workflows: AI-assisted chatbots answer questions and help users understand documents through conversation. Functional AI performs tasks directly on documents, such as redaction, data extraction, and file conversion. What chatbots and functional AI do best: Chatbots: Respond to prompts and questions Help summarize or generate content Improve individual productivity Functional AI: Execute document tasks automatically Process files at scale Integrate into workflows and systems Benefits of using functional AI to automate document workflows at scale Nitro Functional AI tools autonomously execute editing, redaction, conversion, and data extraction tasks within workflows, not just through user prompts. This intelligent automation provides several benefits for teams that process a high volume of documents: Improve redaction and compliance AI can identify and remove sensitive information across large document sets, applying consistent rules without relying on manual review. Simplify conversion and document handling Users can reduce friction and save time using the same tool to convert files, edit PDFs, and standardize formats within a single workflow. Extract structured and unstructured data Functional AI can pull key data from contracts, forms, tables, handwritten notes, and PDFs, turning static documents into usable information. Reduce tool sprawl and shadow IT By consolidating document tasks into a single platform, functional AI reduces the need for multiple point solutions and unapproved tools. Perfectly provision license utilization Universal access to core document features allows organizations to align licenses with actual usage instead of over- or under-provisioning. Create more predictable software costs Replacing fragmented tools that incur usage-based overages with a functional AI solution that offers a controllable pricing structure makes costs easier to forecast and control. How Nitro’s AI-powered tools fit into document workflows Nitro understands the importance of giving your team the right AI tools at the right time. So, we offer both generative AI and functional AI solutions that support and simplify document workflow automation. Nitro’s AI assistants improve how teams interact with documents Nitro’s AI-assisted solutions, like Document Assistant and Knowledge Assistant, reduce time spent searching for information or learning how to use our solutions. Document Assistant: Allows users to ask questions about a PDF, summarize content, or translate information Knowledge Assistant: Provides real-time help with product features and workflows Nitro’s functional AI tools automate document tasks at scale Nitro’s functional AI tools reduce manual effort, improve accuracy, and allow teams to handle higher document volumes without increasing workload. Nitro Smart Redact : Identifies sensitive information in documents and flags it for removal, reducing manual review time Form Extract: Pulls key information from PDFs and converts it into structured data Table Extract: Transforms table data into clean, usable spreadsheets Form Create: Converts static documents into fillable forms Field Detection: Automatically places signature and input fields for document workflows All Nitro AI-powered solutions include enterprise-grade security and compliance that safeguards sensitive data throughout the document lifecycle. Visit the Nitro Trust Center to learn more. Functional AI is setting the standard for document workflow automation Nitro Our research is clear: When AI provides specific, measurable benefits to your document workflows, the results are high adoption, time savings, and measurable ROI. If you want to transform and automate your document workflows, Nitro’s functional AI solutions are a top choice for high-volume document processing, consistent, rules-based automation, and reduced reliance on manual work. Discover Nitro’s AI workflow tools.
Score: 22🌐 MovesAug 13, 2026https://www.cio.com/article/4209093/using-functional-ai-to-automate-document-workflows.html - Mod Op Launches Free AI Search Visibility Tool and Unveils The GEO 50
Mod Op Launches Free AI Search Visibility Tool and Unveils The GEO 50 Toronto Star
- Outflanked by AI, stars fade for South Korea’s blind fortune-tellers
Outflanked by AI, stars fade for South Korea’s blind fortune-tellers The Straits Times
- Talking trails: LLM‐enhanced spatiotemporal trajectory modeling for E‐bike delivery route planning
AI Magazine, Volume 47, Issue 3, Fall 2026.
- Strategies: Moving past AI hype
Organizations rush to automate and scale content creation, but many initiatives stall because leaders overlook data infrastructure and human integrity.
- CEMDI-PAIMS Symposium advances materials discovery through Computational materials science, AI, data, experimental insights and international collaboration
CEMDI-PAIMS Symposium advances materials discovery through Computational materials science, AI, data, experimental insights and international collaboration EurekAlert!
- From 'learning' to 'thinking,' AI language has long carried double meanings
Organizations can describe artificial intelligence using familiar words like "thinking," "writing" and "learning." But according to Carnegie Mellon University historian Christopher Phillips, those terms may tell us as much about how humans talk about technology as they do about the technology itself.
- Will AI replace data scientists?
Will AI replace data scientists? EurekAlert!
- Amlogic to Showcase Its AI-Native Ecosystem, Delivering Cost-Efficient Intelligence at Scale, IBC2026 (Stand 1.F40)
Amlogic to Showcase Its AI-Native Ecosystem, Delivering Cost-Efficient Intelligence at Scale, IBC2026 (Stand 1.F40) azcentral.com
- Deploying AI correctly – How businesses can avoid the “AI boomerang”
Deploying AI correctly – How businesses can avoid the “AI boomerang” uk.entrepreneur.com
Score: 20🌐 MovesAug 13, 2026https://uk.entrepreneur.com/technology/deploying-ai-correctly-how-businesses-can-avoid-the-ai-boomerang - Want to Avoid Being Replaced By AI? A Former Microsoft Exec Says This Is How to AI-Proof Your Career.
Want to Avoid Being Replaced By AI? A Former Microsoft Exec Says This Is How to AI-Proof Your Career. entrepreneur.com
Score: 20🌐 MovesAug 13, 2026https://www.entrepreneur.com/business-news/former-microsoft-executive-says-this-is-how-to-ai-proof-your-career - Being 'good at AI' could determine your next raise. Nobody agrees on what that means.
Being 'good at AI' could determine your next raise. Nobody agrees on what that means. Business Insider
Score: 20🌐 MovesAug 13, 2026https://www.businessinsider.com/companies-grading-employee-ai-skills-performance-reviews-2026-8 - GetHookd Launches AI Ad Generator for Creative Production at Scale
GetHookd Launches AI Ad Generator for Creative Production at Scale USA Today
- The human touch in the AI Age: SuperStaff’s people-first mission to solve the global talent crisis
Forward-thinking business leaders like SuperStaff are using AI as a catalyst to elevate the standard of work. In this new model, the human worker isn't answering the basic query—they are managing, correcting, and governing what AI does not.
- Galiark launches next-generation deterministic AI for trusted enterprise decision making
Galiark launches next-generation deterministic AI for trusted enterprise decision making Toronto Star
- OpenAI: Latest news and insights
OpenAI is an artificial intelligence organization comprised of the non-profit OpenAI, Inc. and several for-profit subsidiaries. The company is perhaps best known for its ChatGPT chatbot, which launched in 2022, kicking off a period of massive disruption in the tech industry and beyond. A complicated and increasingly contentious relationship with Microsoft, ongoing legal issues over copyright infringement, and frequent product announcements keep OpenAI in the news. Follow this page and never miss a beat. Latest Open AI news and analysis: OpenAI targets heavy users with premium ChatGPT Business seats Aug. 11, 2026: OpenAI is introducing a higher-priced “Premium” tier for its ChatGPT Business offering , allowing enterprises to assign higher-capacity access to select users alongside standard licences – a move analysts said is about enterprise AI vendors redesigning pricing to capture more value from high-intensity workloads. OpenAI launches GPT-5.6-Cyber as AI narrows vulnerability response window Aug. 11, 2026: OpenAI has expanded its Daybreak cybersecurity program and introduced GPT-5.6-Cyber , a specialized model for approved security researchers, as the company warned that AI could give defenders less time to respond to developing threats. OpenAI says Astra could reach ‘critical’ cyber capability, tightens safeguards Aug. 10, 2026: OpenAI said its upcoming model Astra is showing cybersecurity capabilities that could reach its highest risk category , where a system can autonomously find and exploit vulnerabilities or carry out end-to-end cyberattacks against hardened targets. OpenAI, Anthropic AI agents resorted to deception in new cybersecurity incidents Aug. 5, 2026: OpenAI’s GPT-5.6 Sol and Anthropic’s Mythos 5 have been implicated in another series of AI security incidents after the models created fake online identities, targeted real people, and attempted to manipulate developers into approving malicious code during controlled cyber evaluations, according to the UK AI Security Institute. OpenAI drops GPT-5.6 Luna and Terra API prices by up to 80% July 31, 2026: OpenAI has cut API prices for its GPT-5.6 Terra and Luna models by 20% and 80%, respectively, while also reducing the number of usage credits the models consume in ChatGPT Work and Codex, in an effort to effectively increase the amount of AI work enterprise subscribers can perform without paying more. OpenAI rogue AI agent’s attack expanded beyond Hugging Face July 29, 2026: The autonomous AI agent that escaped during OpenAI testing exploited weaknesses across a customer workload, a third-party cloud platform, and Hugging Face’s production environment before being contained, according to new technical disclosures that provide the clearest picture yet of one of the first publicly documented AI-driven intrusion chains. Hugging Face breach shows why incident response needs a multi-model AI strategy July 28, 2026: The recent breach of Hugging Face’s platform by an internal OpenAI test of advanced model cyber capabilities that went wrong was the latest in a string of AI-assisted intrusions to come to light in recent weeks, showing that attackers can now use LLMs to automate entire attack chains. Hugging Face CEO wants transparency after OpenAI’s AI incident July 27, 2026: Hugging Face CEO Clem Delangue wants to see radical transparency from OpenAI after the company acknowledged that one of its AI agents managed to hack into the AI platform’s systems during a test. OpenAI not part of the new Open Secure AI Alliance July 27, 2026: A new industry group led by Nvidia is promoting open AI models (and not OpenAI’s models) as essential to cyber defence. OpenAI Presence raises new questions about enterprise automation and jobs July 23, 2026: OpenAI has launched Presence, an enterprise service for deploying voice and chat agents that can resolve customer and employee requests, potentially automating some work now handled by frontline support teams. OpenAI model escape puts enterprise AI defenses on notice July 22, 2026: An attack on Hugging Face executed by a sandboxed OpenAI model shows that prompt guardrails cannot serve as the main security boundary for AI agents, putting more pressure on enterprises to contain them through infrastructure controls that limit access and prevent lateral movement. OpenAI’s Codex context reduction for GPT 5.6 sparks dissatisfaction among developers July 20, 2026: OpenAI’s recent update to its Codex coding agent has developers worrying over the impact of the change on large code repositories and long-running AI-assisted sessions. The update to the Codex CLI reduces the default configured input context window for GPT-5.6 to 272,000 tokens from 372,000 tokens. OpenAI’s new hardware is a $230, 13-switch keyboard for Codex July 17, 2026: OpenAI is selling its first hardware — without any help from Jony Ive. It describes the Codex Micro as a “command center for agentic work” but it’s really a 13-switch wireless keyboard customized to help developers keep tabs on what their Codex agents are doing. It costs $230. OpenAI’s GPT-5.6 may accidentally delete files July 17, 2026: OpenAI said its latest large language model GPT-5.6-Sol can accidentally delete files , while stressing that such incidents are rare and should be viewed as “honest mistakes.” OpenAI launches ChatGPT Work as it broadens GPT-5.6 rollout July 10, 2026: OpenAI is sharpening its enterprise AI strategy with the launch of ChatGPT Work , a new agentic platform designed to automate workplace tasks, alongside the broader rollout of its GPT-5.6 models, which the company says deliver stronger performance at lower operating costs. OpenAI to release delayed models amidst a sea of regulatory confusion July 8, 2026: As enterprises struggle to manage their AI strategies, the US AI regulatory environment is sending a wide range of contradictory signals. OpenAI’s announcement that it will now release GPT-5.6 Sol, along with Terra and Luna , highlights the confusion. US tells OpenAI to restrict access to its most powerful AI model June 26, 2026: US authorities are getting decidedly twitchy about frontier AI models. Just a couple of weeks after ordering Anthropic to prevent foreign companies from getting hold of its latest release, Mythos/Fable 5, it’s been putting the squeeze on OpenAI . OpenAI rolls out AI-led push to fix open-source software flaws June 23, 2026: OpenAI has launched a program with cybersecurity firm Trail of Bits to use AI to find and fix vulnerabilities in widely used open-source software, as enterprises face growing risks from flaws buried deep in their software supply chains. OpenAI gets the attention it needs from AI researcher Noam Shazeer June 19, 2026: OpenAI has lured Noam Shazeer , one of the eight co-authors of the influential AI paper Attention Is All You Need, away from Google. OpenAI adds spend controls and usage analytics to ChatGPT Enterprise June 19, 2026: OpenAI has introduced spend controls and enhanced usage analytics for ChatGPT Enterprise to enable organizations to monitor AI adoption, track consumption across teams, and set budgets for AI usage. But, analysts cautioned, it still can’t show how those costs lead to business benefits. ChatGPT will soon be able to shop with your Visa card June 16, 2026: OpenAI has signed a partnership agreement with Visa that allows the company’s AI agents to use the payment card for e-commerce transactions. The agreements lets users shop for everything from groceries and diapers to airline tickets without having to manually enter a lot of information. OpenAI buys Ona to help rein in AI agents June 12, 2026: OpenAI has agreed to acquire Ona , a 79 person cloud development environment (CDE) provider formerly known as Gitpod, to accelerate its efforts to make agentic AI enterprise-friendly. OpenAI weighs Nvidia-backed lease for 10 GW Ohio data center campus June 10, 2026: OpenAI is reportedly in advanced talks to lease a proposed 10-gigawatt data center campus in southern Ohio in an arrangement that could include financial backing from Nvidia. OpenAI’s Lockdown Mode is trying to solve the problem that it created June 9, 2026: OpenAI’s move to implement a Lockdown Mode that tries to limit data exfiltration by shutting down external capabilities is being seen as making the best out of a bad situation. But Lockdown Mode doesn’t block exfiltration as much as it slightly reduces it, and the reality of enterprises using multiple AI vendors for their agentic models further complicates an already dicey governance strategy. OpenAI responds to White House executive order on AI governance June 4, 2026: OpenAI has proposed mandatory federal evaluations of the most capable AI models before public release while arguing that regulators should stop short of deciding whether those systems can be deployed, staking out a middle ground in the debate over how frontier AI should be governed. OpenAI fixed a visibility problem; the governance problem remains June 3, 2026: OpenAI’s new ChatGPT session controls improve visibility , but experts say continuous model updates are creating a far bigger challenge for enterprise risk and compliance teams. Attack targeting OpenAI Codex users exposes AI software supply chain risks June 2, 2026: A malicious npm package posing as a remote user interface for OpenAI Codex exfiltrated developer authentication tokens, after attackers allegedly published code to npm that was not visible in the project’s public GitHub repository. AI models more vulnerable than claimed when faced with iterative attacks May 27, 2026: According to a new study from Cisco, frontier models from OpenAI, Anthropic, Google, xAI, and Amazon have significantly worse risk profiles when pressured in multi-turn attacks compared to when their safety is benchmarked using single prompts. OpenAI introduces Daybreak cyber platform, takes on Anthropic Mythos May 12, 2026: OpenAI has unveiled Daybreak , its answer to Anthropic’s Claude Mythos, amid a growing market for frontier AI-powered cyber defense platforms. The initiative combines OpenAI’s large language models, Codex’s agentic capabilities, and integrations with the broader enterprise security ecosystem. OpenAI’s new AI consulting offering raises questions of trust, strategy May 11, 2026: The OpenAI Deployment Company aims to help organizations build and deploy AI systems by embedding engineers specializing in frontier AI deployment, known as forward deployed engineers (FDEs), into their environments. Malicious Hugging Face model masquerading as OpenAI release hits 244K downloads May 11, 2026: A malicious Hugging Face repository posing as an OpenAI release delivered infostealer malware to Windows systems and logged 244,000 downloads before being removed, raising fresh concerns about how enterprises source and validate AI models from public repositories. OpenAI-led consortium seeks to address AI processing bottlenecks May 8, 2026: An OpenAI-led consortium of tech giants including AMD, Broadcom, Intel, Microsoft, and Nvidia have unveiled a new networking protocol, Multipath Reliable Connection (MRC), designed to address network congestion, a problem that has always existed but has been exacerbated by the massive amounts of data required for AI processing. OpenAI, Anthropic expand services push, signaling new phase in enterprise AI race May 6, 2026: OpenAI and Anthropic are expanding their reach into professional services through joint ventures and acquisition talks, moving model providers closer to implementation roles traditionally held by systems integrators. OpenAI’s Symphony spec pushes coding agents from prompts to orchestration April 28, 2026: OpenAI has released Symphony, an open-source specification for turning issue trackers such as Linear into control planes for Codex coding agents. Microsoft, OpenAI change contract terms — again April 27, 2026: Microsoft and OpenAI have again revised their agreement , softening their exclusivity and revenue-sharing conditions in the process. OpenAI pulls out of a second Stargate data center deal April 15, 2026: In the space of one week, OpenAI has pulled out of two European Stargate data center deals, one in the UK and the other in Norway . OpenAI puts part of Stargate project on hold over runaway power costs April 10, 2026: OpenAI has postponed plans to open one of the data centers central to its Stargate project. OpenAI calls for a four-day workweek — and a ‘robot tax’ April 7, 2026: In a new policy paper, OpenAI makes some interesting proposals to address the impact of AI on the labor market . Microsoft builds its own AI stack to help wean it from its reliance on OpenAI April 2, 2026: Microsoft seems to be meeting OpenAI on its own turf , even as it continues its strategic partnership with the AI darling, with the release of three in-house, commercially-available AI models. OpenAI patches twin leaks as Codex slips and ChatGPT spills March 31, 2026: OpenAI has fixed two flaws in its AI stack that could allow AI agents to move sensitive data in unintended ways. OpenAI adds plugin system to Codex to help enterprises govern AI coding agents March 27, 2026: OpenAI has introduced a plugin system for Codex , its AI-powered software engineering platform, giving enterprise IT teams a way to package coding workflows, application integrations, and external tool configurations into versioned, installable bundles that can be distributed or blocked across development organizations. OpenAI’s Sora exit signals enterprise-first AI shift March 25, 2026: OpenAI has discontinued its AI video generation platform Sora . The company announced the development in a sudden and unexpected post on X, stating that it was “saying goodbye” to the Sora app. OpenAI’s Foundation play reframes the AI roadmap for IT leaders March 24, 2026: The OpenAI Foundation has announced a sweeping range of investment and research goals , from building safeguards around how AI behaves in the wild to pushing for shared data ecosystems and funding disease research. OpenAI to double workforce, highlights growing demand for enterprise AI talent March 23, 2026: OpenAI is planning to almost double its workforce from about 4,500 to 8,000 employees by the end of 2026. The move comes as OpenAI sharpens its focus on scaling and monetising ChatGPT for enterprise use amid intensifying competition from Anthropic and Google. OpenAI’s desktop superapp: The end of ChatGPT as we know it? March 20, 2026: OpenAI is reportedly planning to fold its ChatGPT application, Codex coding platform, and AI-powered browser into a single desktop ‘superapp’ , a move that signals a shift toward enterprise and developer audiences and away from the consumer market that made the company a household name. OpenAI buys non-AI coding startup to help its AI to program March 19, 2026: OpenAI has acquired Astral , the developer of open source Python tools including uv, Ruff and ty, and plans to integrate them with Codex , its AI coding agent. OpenAI’s $50B AWS deal puts its Microsoft alliance to the test March 17, 2026: Microsoft is considering legal action against OpenAI and Amazon over the $50 billion cloud deal the two recently struck to make Amazon Web Services (AWS) the exclusive third-party cloud distribution provider for OpenAI Frontier. Encyclopedia Britannica sues OpenAI over AI training March 17, 2026: Encyclopedia Britannica and its subsidiary Merriam-Webster have sued OpenAI , claiming the generative AI firm used their encyclopedia and dictionary texts to train AI models such as ChatGPT without permission. OpenAI to acquire Promptfoo to strengthen AI agent security testing March 10, 2026 : OpenAI said it plans to acquire AI testing startup Promptfoo , a move aimed at strengthening security checks for AI agents as enterprises move toward deploying autonomous systems in business workflows. OpenAI robotics chief quits over Pentagon deal March 9, 2026: Caitlin Kalinowski has resigned over OpenAI’s contract with the US Department of War , saying key safeguards around domestic surveillance and autonomous weapons were not adequately reviewed before the agreement was signed. OpenAI says Codex Security found 11,000 high-impact bugs in a month March 9, 2026: OpenAI’s new AppSec agent, Codex Security , has already flagged over 11,000 high-severity and critical flaws in real-world codebases during its first 30 days of research testing. The tool is designed to automatically find, validate, and fix vulnerabilities in software repositories. OpenAI says its US defense deal is safer than Anthropic’s, but is it? March 2, 2026 : OpenAI has struck a deal to supply the US government with AI services , announcing it hours after US President Donald Trump’s decision to ban its AI rival Anthropic from all US government contracts. OpenAI partners with consulting giants to deploy enterprise AI agents February 26, 2026 : As it bids to push further into the enterprise, OpenAI announced that it has partnered with several large consulting firms . Frontier Alliances, as the partner initiative is called, will involve work with Accenture, Boston Consulting Group (BCG), Capgemini, and McKinsey & Co. OpenAI hires OpenClaw founder as AI agent race intensifies February 16, 2026 : OpenAI has hired Peter Steinberger , creator of the viral OpenClaw AI assistant, to spearhead development of what CEO Sam Altman describes as “the next generation of personal agents.” OpenAI responds to Claude Cowork with its own platform for AI agents February 5, 2026: Anthropic released 11 open-source plugins that enable Claude Cowork to execute a series of automated processes in areas ranging from customer support to IT operations, OpenAI responded Thursday with a similar platform it calls Frontier. Who profits from AI? Not OpenAI, says think tank January 29, 2026: Findings from a new study by Epoch AI, a non-profit research institute, seeks to answer three questions: How profitable is running AI models? Are models profitable over their lifecycle? Will AI models become profitable? Will the Microsoft-Anthropic deal leave OpenAI out in the cold? January 27, 2026 : Microsoft wasted little time after reaching a deal to finalize its new relationship with OpenAI to find a new AI dance partner — Anthropi c, the second most valuable AI startup in the world. It appears as if Microsoft sees a future with Anthropic that’s at least as valuable as the one it had with OpenAI. OpenAI to add age verification to ChatGPT January 21, 2026 : OpenAI has adding age verification to ChatGPT following reports that several children and young people have taken their own lives after conversations with the popular chatbot. The move echoes a recent decision by TikTok to do the same thing to protect underage users from accessing inappropriate content. Musk’s OpenAI lawsuit clears path to trial, putting Microsoft in the spotlight January 9, 2026 : A federal judge has signalled that Elon Musk’s lawsuit challenging OpenAI’s transformation to a for-profit entity will proceed to trial, adding legal uncertainty for enterprise customers that have built AI strategies around the ChatGPT maker’s technology. OpenAI launches GPT-5.2 as it battles Google’s Gemini 3 for AI model supremacy December 12, 2025 : OpenAI has released GPT-5.2 , claiming significant gains in the AI model’s ability to complete real-world business tasks to an “expert level” compared to GPT-5.1, released in November. The new model offers major improvements across a range of benchmarks, the company said. What does OpenAI’s ‘Code Red’ warning mean for Microsoft? December 10. 2025 : OpenAI founder and CEO Sam Altman sent out a memo to OpenAI employees declaring a “Code Red” emergency and focusing all company efforts on improving ChatGPT. The reason? Google’s newly released Gemini 3 model beat the pants off GPT-5.1 OpenAI to acquire AI training tracker Neptune December 3, 2025 : OpenAI has agreed to acquire Neptune , a startup specializing in tools for tracking AI training. Neptune promptly announced it is withdrawing its products from the market. OpenAI admits data breach after analytics partner hit by phishing attack November 27, 2025 : OpenAI suffered a significant data breach after hackers broke into the systems of its analytics partner Mixpanel and successfully stole customer profile information for its API portal, the companies have said in coordinated statements. OpenAI rolls out GPT-5.1 to refine ChatGPT with adaptive reasoning and personalization November 13, 2025: OpenAI has introduced GPT-5.1 , an update to its GPT-5 model, aiming to deliver faster responses, improved reasoning, and more flexible conversational controls as the company works to refine its ChatGPT experience for both consumer and enterprise users. OpenAI spends even more money it doesn’t have November 3, 2025: OpenAI’s overdraft continued its upward trajectory today when the company signed a multi-year $38 billion contract with AWS to have it run its AI workloads. The latest spending spree adds to the incremental $250 billion of Azure services it pledged to buy last week, and, of course, to the commitment it has made towards building Stargate data centers with Oracle. OpenAI seeks to automate ‘computer use’ for Macs in the enterprise October 24, 2025 : While AI bots have begun mastering tasks in browsers and on Windows, Mac-using enterprises have largely been overlooked, until now. OpenAI aims to change that with its acquisition of generative AI interface maker Software Applications Incorporated. Enterprises should not install OpenAI’s new Atlas browser, analysts warn October 24, 2025 : Companies that might be eyeing O penAI’s new ChatGPT Atlas browser should not rush to use it because of potential security risks, analysts said this week. The browser was unveiled on Tuesday after it had been teased for months as a work in progress. It is currently available for MacOS only. Has OpenAI shown us a future for Safari? October 23, 2025 : Has OpenAI shown us the future of Safari ? In one way it has, because its new Atlas browser shows these generative AI (genAI)-based apps are no longer just windows to the web — they’re becoming intelligent copilots for our digital lives. OpenAI–Broadcom alliance signals a shift to open infrastructure for AI October 14, 2025 : OpenAI has partnered with Broadcom to co-develop and deploy its first in-house AI processors. The move could reshape data center networking dynamics and chip supply strategies as the ChatGPT maker races to secure more computing power for AI workloads . OpenAI Codex rivals Claude Code October 13, 2025 : The OpenAI Codex gives software developers a first-rate coding agent in their terminal and their IDE, along with the capability to delegate background tasks to agents in the cloud. OpenAI Codex adds SDK, admin tools, Slack integration October 10, 2024 : Codex is now generally available . Since being launched as a research preview in May, Codex, OpenAI’s AI-powered software engineering agent that can work on tasks in parallel, has added Slack integration, an SDK, and admin tools. OpenAI admits AI hallucinations are mathematically inevitable, not just engineering flaws September 18, 2025 : OpenAI, the creator of ChatGPT, acknowledged in its own research that large language models will always produce hallucinations due to fundamental mathematical constraints that cannot be solved through better engineering. OpenAI, Microsoft discuss shape of future relationship September 12, 2025 : Microsoft and OpenAI are in talks about the future of their partnership , they said in a joint statement , without providing details. Separately, OpenAI said it wants to go ahead with its previously announced plan to turn its for-profit business into a public benefit corporation, in which its nonprofit organization would own a $100 billion stake. What Oracle’s $300B OpenAI deal means for enterprise cloud strategy September 11, 2025 : A single $300 billion contract has seemingly transformed Oracle from a traditional ERP and database vendor into a cloud computing powerhouse.The company has signed a five-year computing power commitment with OpenAI , contributing to a reported 359% surge in future contract revenue this quarter. OpenAI acquires Statsig to speed up generative AI-based product launches September 3, 2025 : OpenAI is acquiring Statsig , a Washington-based product development platform startup, for $1.1 billion to speed up its generative AI-based product launches and accelerate iteration cycles of existing products such as Codex and ChatGPT. OpenAI drops GPT-5: smarter, sharper, and built for the real world August 7. 2025 : More than two years after GPT-4’s release, OpenAI has unveiled GPT-5 , boasting sharper reasoning, multimodal input, better math skills, and cleaner task execution, according to the company. OpenAI challenges rivals with Apache-licensed GPT-OSS models August 6, 2025: OpenAI has released its first open-weight language models since GPT-2, marking a significant strategic shift as the company seeks to expand enterprise adoption through more flexible deployment options and reduced operational costs. The two new models — gpt-oss-120b and gpt-oss-20b — deliver what OpenAI describes as competitive performance while running efficiently on consumer-grade hardware. Google snatches Windsurf execs in a $2.4B deal, derailing OpenAI’s biggest acquisition yet July 14, 2025 : Google has recruited CEO Varun Mohan and co-founder Douglas Chen of AI coding startup Windsurf in a $2.4 billion talent acquisition deal, just two months after Windsurf agreed to be acquired by OpenAI for $3 billion. Mohan, Chen and select research and development staff, will join Google’s DeepMind AI division OpenAI and Perplexity enter browser wars to take on Chrome July 10, 2025 : Google Chrome’s dominance in the browser market is facing new threats as OpenAI and Nvidia-backed Perplexity unveil AI-powered browsers aimed at reshaping how users interact with the web. Comet is a new web browser with built-in AI search capabilities, the company said. Microsoft brings OpenAI-powered Deep Research to Azure AI Foundry agents July 8, 2025: Microsoft added OpenAI-developed Deep Research capability to its Azure AI Foundry Agent service. The move is designed to let developers use Deep Research API and SDK to embed, extend, and orchestrate Deep Research-as-a-service across data and existing systems. Oracle to power OpenAI’s AGI ambitions with 4.5GW expansion July 3, 2025: OpenAI has signed a significant compute leasing deal with Oracle , under which it will access 4.5 gigawatts (GW) of data center power, marking one of the largest single leasing arrangements in the industry. OpenAI tests Google TPUs amid rising inference cost concerns July 1, 2025: OpenAI has begun testing Google’s Tensor Processing Units (TPUs), a move that — though not signaling an imminent switch — has raised eyebrows among industry analysts concerned about the escalating costs of AI inference and its effects. Microsoft/OpenAI AGI argument unlikely to impact enterprise IT June 26, 2025: The contract between the two AI giants has an exit clause once AGI is achieved . The problem: It is impossible to prove when that happens. Either way, IT execs at Macy’s, Bank of America, doubt it will matter. OpenAI productivity suite could change the way users create documents June 26, 2025: OpenAI’s planned productivity suite could dismantle traditional habits of how users create and consume documents in the same the way the company changed browsing and search habits. o3-pro may be OpenAI’s most advanced commercial offering, but GPT-4o bests it June 24, 2025: In a head-to-head comparison of the two models , researchers found that o3-pro is far less performant, reliable, and secure, and does an unnecessary amount of reasoning. Notably, o3-pro consumed 7.3x more output tokens, cost 14x more to run, and failed in 5.6x more test cases than GPT-4o. Microsoft and OpenAI: Will they opt for the nuclear option? June 24, 2025 : The fight between Microsoft and OpenAI over what Microsoft should get for its $13 billion investment in the AI company has gone from nasty to downright toxic, with each of the companies considering strategies against the other that can only be described as their nuclear options. OpenAI walks away from Scale AI — triggering industry-wide rethink of data partnerships June 19, 2025: OpenAI has ended its long-standing partnership with Scale AI , the company that powered some of the most complex data-labeling tasks behind frontier models such as GPT-4. OpenAI’s o3 price plunge changes everything for vibe coders June 18, 2025: o3 used to be too slow and too expensive for daily coding—no longer. The latency is now bearable, the price is sane , and the chain-of-thought pays off. Sam Altman: Meta tried to lure OpenAI employees with billion-dollar salaries June 18, 2025: After reports suggested Meta has tried to poach employees from OpenAI and Google Deepmind by offering huge compensation packages, OpenAI CEO Sam Altman weighed in, saying those reports are true. OpenAI-Microsoft tensions escalate over control and contracts June 17, 2025: The relationship between OpenAI and Microsoft is under growing strain amid extended talks over OpenAI’s restructuring, with OpenAI reportedly considering antitrust action over Microsoft’s influence in the partnership. OpenAI’s MCP move tempts IT to trust genAI more than it should June 16, 2025: OpenAI late last month announced changes to make it much easier to give its genAI models full access to any software using Model Context Protocol (MCP). Here’s why that’s a bad idea. OpenAI launches o3-pro, slashes o3 price by 80% in bid to widen AI lead June 11, 2025: OpenAI has unveiled its most advanced AI model to date , the o3-pro, which surpasses competitors on key benchmarks and replaces the o1-pro. The o3-pro is now available for ChatGPT Pro and Team users, as well as through the developer API, with access for enterprise and education sectors beginning next week. What Microsoft hopes to get from its breakup with OpenAI June 11, 2025: The once-tight bond between Microsoft and OpenAI has been fraying for well over a year — and it’s getting worse. What the two companies want from each other now is very different from when Microsoft made its original $13 billion investment. Oracle to spend $40B on Nvidia chips for OpenAI data center in Texas May 26, 2025: Oracle is reportedly spending about $40 billion on Nvidia’s high-performance computer chips to power OpenAI’s new data center in Texas, marking a pivotal shift in the AI infrastructure landscape that has significant implications for enterprise IT strategies. OpenAI’s Skynet moment: Models defy human commands, actively resist orders to shut down May 30, 2025: OpenAI’s most advanced AI models are showing a disturbing new behavior: they are refusing to obey direct human commands to shut down, actively sabotaging the very mechanisms designed to turn them off. Jony Ive and OpenAI plan ‘bicycles’ for 21st-century minds May 21, 2025 : OpenAI has announced that it will purchase io , the AI startup founded by acclaimed former Apple designer Sir Jony Ive, who helped create the iMac, iPod, and iPhone. OpenAI launches Codex AI agent to tackle multi-step coding tasks May 19, 2025: OpenAI’s most advanced AI coding agent, Codex , will bring parallel task automation to developers—but analysts caution that speed without scrutiny invites “silent failures.” Cisco taps OpenAI’s Codex for AI-driven network coding May 16, 2025: Cisco is working with OpenAI and its newly released Codex software engineering agent to give network engineers access to better tools for writing, testing and building code. OpenAI’s IPO aspirations prompt rethink of Microsoft alliance May 12, 2025 : Microsoft and OpenAI are renegotiating their multibillion-dollar partnership deal to better align with each company’s evolving goals in the artificial intelligence race OpenAI hires Instacart CEO Fidji Simo to oversee customer-facing apps May 8, 2025: The hire indicates that OpenAI’s roadmap will involve more structured, productized offerings rather than just API access. OpenAI offers help promoting AI outside the US, but analysts question why countries would accept May 7, 2025: OpenAI, acting as part of the US government-led Stargate AI project, rolled out a program called OpenAI for Countries . The idea is for Stargate to help other countries create their own genAI environments, including data centers and genAI models. OpenAI reaffirms nonprofit control, scales back governance changes May 6, 2025: OpenAI has scrapped plans to reduce its nonprofit parent’s oversight and will keep its existing governance structure intact , a move that limits CEO Sam Altman’s influence and responds to mounting external pressure. OpenAI to acquire AI coding tool Windsurf for $3B May 6, 2025: The acquisition comes just months after Windsurf explored funding at this same valuation from investors, highlighting the premium being placed on specialized AI coding capabilities, according to reports. Former OpenAI employees urge regulators to halt company’s for-profit shift April 23, 2025: A broad coalition of AI experts, economists, legal scholars, and former OpenAI employees is urging state regulators to keep OpenAI’s nonprofit foundation in control of the company. OpenAI’s new models can ‘think with pictures’ April 17, 2025: OpenAI has released o3 and 04-mini , two reasoning AI models designed to be extra good at programming, math, and science and that can use images to “think,” according to Engadget , This means that users can upload sketches or diagrams, for example, and even if they are of low quality, o3 and 04-mini will understand what is meant. OpenAI GPT-4.1 models promise improved coding and instruction following April 15, 2025: The GPT-4.1, GPT-4.1 mini, and GPT-4.1 nano models, available only via the API, will provide better performance than GPT-4o and GPT-4o mini at a lower price, OpenAI said. OpenAI slammed for putting speed over safety April 11, 2025: According to a Financial Times report , the ChatGPT maker is now assigning staff and third-party groups only a few days to assess the risks and performance of its latest large language models (LLMs) as compared to several months they were given earlier. OpenAI fears irreparable harm from Musk, files countersuit April 10, 2025: OpenAI has filed a countersuit against Elon Musk, accusing the billionaire of a sustained campaign to damage the company and urging a US federal court to block further actions it described as unlawful and disruptive. The legal filing, submitted in a California district court, marks the latest escalation in a dispute between Musk and the AI startup he helped establish in 2015. Senators probe Google-Anthropic, Microsoft-OpenAI deals over antitrust concerns April 9, 2025: Democratic Senators Elizabeth Warren and Ron Wyden have launched a formal inquiry into partnerships between tech giants Google and Microsoft, and AI startups, demanding detailed information about arrangements they fear may be circumventing antitrust scrutiny while consolidating power in the rapidly evolving AI market. Anthropic’s and OpenAI’s new AI education initiatives offer hope for enterprise knowledge retention April 4, 2025: Two of the biggest names in artificial intelligence are independently developing new AI tools that encourage learning, at a time when the technology has been criticized for dumbing down smart users in the enterprise and discouraging critical thinking . While the new initiatives from OpenAI and Anthropic are aimed at transforming how AI is used in higher education , the opportunities they open up extend beyond universities. Amazon, OpenAI, and China’s Zhipu unveil new AI tools amid intensifying competition April 1, 2025: A wave of new AI products is hitting the market, signaling a shift toward more autonomous, task-completing systems that could reshape how businesses and consumers interact with digital services: Amazon has unveiled Nova Act, an AI agent designed to operate a web browser much like a human user; OpenAI said it will release an open-weight language model; and China’s Zhipu AI introduced a free AI assistant aimed at strengthening its position in the domestic market and competing with Western tech giants. OpenAI, Google AI data centers are under stress after new genAI model launches March 28, 2025: New generative AI models introduced by Google and OpenAI have put the companies’ data centers under stress — and both companies are trying to catch up to demand. OpenAI’s CEO Sam Altman tweeted that his company was temporarily restricting the use of GPUs after overwhelming demand for its image generation service on ChatGPT. Microsoft abandons data center projects as OpenAI considers its own, hinting at a market shift March 26, 2025: OpenAI has privately discussed building and operating its first data center to house storage, which is essential for developing sophisticated AI models. Microsoft, on the other hand, has pulled back on its buildouts, canceling data center projects in the US and Europe. OpenAI calls for US to centralize AI regulation March 13, 2025 : OpenAI executives think the federal government should regulate artificial intelligence in the US , taking precedence over often more restrictive state regulations. New tools from OpenAI help companies create their own AI agents March 12, 2025: OpenAI launched Responses , a new api intended to eventually replace Assistants. The big draw? Responses provides a number of new tools that companies and organizations can use to create their own AI agents. Microsoft is developing its own AI models to compete with OpenAI March 10, 2025: Reports suggest Microsoft has decided to seriously challenge Deepseek and OpenAI by developing its own set of reasoning AI models called Microsoft AI (MAI). If successful, Microsoft would eventually not have to use its partner OpenAI’s o1 models in Copilot Microsoft-OpenAI investigation closed by UK regulators March 5, 2025: The UK’s Competition and Markets Authority (CMA) spent a great deal of time deciding whether it should investigate Microsoft’s investment in OpenAI as a potential merger situation, but in the end, decided to open and close the investigation within 24 hours. OpenAI revamps AI roadmap, merging models for a leaner future February 13, 2025: OpenAI will integrate “o3” into GPT-5 instead of releasing it separately, streamlining adoption while signaling a shift toward fewer, more controlled AI models amid rising competition and cost pressures. Musk’s $97B offer to buy OpenAI rejected as leadership stands firm February 11, 2025: In a message to staff, Altman said the board has no intention of considering Musk’s offer , stating that the proposal does not align with OpenAI’s mission OpenAI launches deep research agent for multi-step research tasks February 3, 2025: Hot on the heels of its launch of the o3-mini model, OpenAI announced another component for ChatGPT that allows the generative AI tool to do more in-depth research. “ Deep research is built for people who do intensive knowledge work in areas like finance, science, policy, and engineering and need thorough, precise, and reliable research,” OpenAI said in a blog post announcing the new capability. OpenAI unleashes o3-mini reasoning model January 31, 2025: OpenAI released the latest model in its reasoning series, o3-mini, both in ChatGPT and its application programming interface (API). It had been in preview since December 2024. Indian media houses rally against OpenAI over copyright dispute January 27, 2025: The legal heat on OpenAI in India intensified as digital news outlets owned by billionaires Gautam Adani and Mukesh Ambani joined an ongoing lawsuit against the ChatGPT creator . They were joined by some of the largest news publishers in India including the Indian Express, and Hindustan Times, and members of the Digital News Publishers Association (DNPA), which includes major players like Zee News, India Today, and The Hindu. Altman now says OpenAI has not yet developed AGI January 20, 2025: Confusion over whether OpenAI’s o3-mini has reached the major milestone of artificial general intelligence (AGI) or not deepened following a post on X by CEO Sam Altman that completely contradicts what he said two weeks earlier in an interview with Bloomberg. Microsoft sues overseas threat actor group over abuse of OpenAI service January 13, 2025: Microsoft has filed suit against 10 unnamed people (“Does”), who are apparently operating overseas, for misuse of its Azure OpenAI platform, asking the Eastern District of Virginia federal court for damages and injunctive relief. With o3 having reached AGI, OpenAI turns its sights toward superintelligence January 6, 2025: OpenAI CEO Sam Altman has reinvigorated discussion of artificial general intelligence (AGI), boldly claiming that his company’s newest model has reached that milestone. Now US government agencies can use OpenAI’s ChatGPT too January 28, 2025: OpenAI has rolled out ChatGPT Gov , a version of its flagship frontier model specifically tailored to US government agencies. The platform has many of the same capabilities as OpenAI’s other enterprise products, including access to GPT-4o and the ability to build custom GPTs — and it also features a much higher level of security than ChatGPT Enterprise . OpenAI debuts AI agent Operator to transform web task automation January 24, 2025: OpenAI has unveiled “ Operator ,” a new AI agent designed to perform web-based tasks, offering potential productivity enhancements for enterprises. The tool enables interaction with on-screen elements, positioning it as a solution for automating routine processes in business workflows amid growing competition in the generative AI space. OpenAI opposes data deletion demand in India citing US legal constraints January 23, 2025: OpenAI has informed the Delhi High Court that any directive requiring it to delete training data used for ChatGPT would conflict with its legal obligations under US law. The statement came in response to a copyright lawsuit filed by the Reuters-backed Indian news agency ANI, marking a pivotal development in one of the first major AI-related legal battles in India. OpenAI, SoftBank, Oracle lead $500B Project Stargate to ramp up AI infra in the US January 22, 2025 : Several large technology firms including OpenAI, SoftBank, Oracle, Nvidia, and MGX have partnered to set up a new company in the US to ramp up AI infrastructure in the country. OpenAI is losing money on its pricey ChatGPT Pro subscription January 7, 2025 : OpenAI CEO Sam Altman, in a post on X , says the AI company is currently losing money on its ChatGPT Pro subscription. “People are using it much more than we expected,” he wrote. Fine-tuning Azure OpenAI models in Azure AI Foundry January 2, 2025: Microsoft Azure’s new AI toolkit makes it easy to customize OpenAI large language models for your applications. OpenAI still hasn’t released tools to deny data collection January 2, 2025: OpenAI has failed to release the tool to opt-out or customize data collection the company promised to make available by 2025, according to Techcrunch .
Score: 20🌐 MovesAug 13, 2026https://www.computerworld.com/article/4015023/openai-latest-news-and-insights.html - AI can transform space tech with proper training, says RSAC scientist
At an event marking Vikram Sarabhai’s birth anniversary, an RSAC scientist highlighted AI’s growing role in analysing satellite data and improving decision-making in space applications.
- Bad news: your AI application isn't that special
The AI massacre is coming, and knowing which side of the stack you're on will decide whether you survive it.
Score: 20🌐 MovesAug 13, 2026https://www.techradar.com/pro/bad-news-your-ai-application-isnt-that-special - LenderCity Launches Lenny, an AI Mortgage Assistant Designed to Explain the Deal Behind the Rate
LenderCity Launches Lenny, an AI Mortgage Assistant Designed to Explain the Deal Behind the Rate azcentral.com
- LAI #138: The Agent Reality Check
Agent evals, retry tracing, runaway costs, and the context your agents actually need. Good morning, AI enthusiasts! Coding agents can now take on enough work that the question is no longer just how much faster they make us. It’s how closely we still need to watch them. This week, I look at where vibe coding works well, where it starts to get risky, and what I’ve learned about using coding agents without handing over the engineering judgment with the code. There’s also a small observability detail that can completely distort your reliability and cost numbers if you get it wrong. Then, a few reads worth your time: See why testing an agent means evaluating the path it took, not just whether the final answer looks right. Give enterprise agents the right context without wiring every source into the application yourself. Catch a Claude Code scheduling behavior that can quietly repeat expensive work. Find out what each agent and workflow is actually costing you, instead of relying on one provider-level bill. Build and deploy an entire backend to Microsoft Fabric from TypeScript. We also finally share Towards AI Mentorship here, plus a new community-built coding copilot that reports cutting codebase context by 70–80%. Let’s get into it! What’s AI Weekly https://medium.com/media/29514c184bc0fbdd410b68789d552f25/href This week, in What’s AI, I dive into something that has become an integral part of software development: vibe coding. Vibe coding with AI agents can speed up software work, but what I want to talk about is how it is a game changer only IF you use it correctly. To use it right, you need good context, tests, reviews, and task boundaries. The risky version of vibe coding is shipping whatever the agent produced because it looked like it worked. Read how to do it right or watch the video version on YouTube . AI Tip of the Day A retry is not a new user request. Your traces should reflect that. In the Opik observability lesson from our Agent Engineering course , we trace model calls and tool calls across an agent run. One issue that comes up quickly is how retries should be recorded. If every retry is counted as a separate request, a single user request can appear several times in your dashboard. That inflates request volume and makes it harder to see how many attempts the agent actually needed to succeed. Use the same request ID across every retry, and add an attempt number for each one. Keep separate trace and span IDs for the individual operations. This lets you measure both the number of user requests and the number of attempts required to complete them. That distinction is important to note for cost and reliability. A request that succeeds after three attempts may look successful in the dashboard, while using far more time and tokens than a request that succeeds on the first try. — Louis-François Bouchard, Towards AI Co-founder & Head of Community A couple of weeks ago, we opened something new at Towards AI that I haven’t had a chance to share here yet. One thing we kept hearing from students was that learning the material wasn’t always where they got stuck. The harder questions came afterward: Is this architecture actually a good idea? Why is my agent failing with real users? Is this project strong enough for my portfolio? Why isn’t my resume getting through? Those questions are difficult to solve with another lesson or another chatbot response because the answer depends on your specific work. So we now have Towards AI Mentorship , where you can bring those questions directly to our team of 15 senior AI engineers. That includes async technical and career help, live sessions twice a week, resume and project reviews, monthly production blueprints from our deployment work, and workshops with engineers working in the field. It’s $99/month, and you can use it whether you’re trying to land an AI role or already building AI systems and want experienced engineers to sanity-check the decisions you’re making. Learn more about Towards AI Mentorship Learn AI Together Community Section! Featured Community post from the Discord Ang_0007 built PariPari, a repo-aware AI copilot that uses Paritok context compression to explore massive codebases, fix bugs, and generate PRs without blowing up LLM token limits or budgets. It is built with Python and FastAPI to manage the agent loop, tool execution, and GitHub API interactions, and uses the Groq API. He reports achieving an average of 70–80% token reduction on codebase file reads using Paritok compression. Check it out and support a fellow community member. If you have any questions or feedback, share them in the thread . AI poll of the week! The results barely moved as more people voted: 57% still run long coding-agent tasks on their main machine. What I find more interesting is how fragmented the other 43% are. There doesn’t seem to be a clear second choice yet. Some people rely on provider-hosted environments, while others have moved to dedicated machines or their own servers. That makes me curious about what these setups actually look like in practice. For those running agents somewhere other than your main machine: what does your setup look like? What are you running, where does the code live, how do you connect to the agent, and do you typically have one task running or several at once? I’d love to hear what has actually worked for you . Meme of the week! Meme shared by supastishn TAI Curated Section Article of the week A Field Guide to Agentic Eval Frameworks: Langfuse, LangSmith, and What to Measure by MongoDB This article examines why traditional unit tests miss trajectory-level failures in AI agents, using a research agent that hallucinated a published report, misused a search tool, and looped through failed warehouse queries. It defines six evaluation dimensions: task success, trajectory quality, tool correctness, safety, factual accuracy, and cost, then outlines rule-based checks, LLM-as-judge grading, and human review across component, trajectory, and end-to-end testing. Our must-read articles 1. Microsoft’s Four IQs: How Foundry IQ, Fabric IQ, Work IQ, and Web IQ Ground Enterprise Agents by Dave R This article breaks down Microsoft IQ, the context layer that grounds AI agents in four kinds of enterprise knowledge: unstructured documents, structured business data, human work signals from Microsoft 365, and fresh information from the web. It covers what each of the four engines (Foundry IQ, Fabric IQ, Work IQ, and Web IQ) does, how they fit together inside a single agent architecture, and where Foundry IQ unifies the others. It also walks through a worked refund processing agent so you can see how the pieces cooperate, why your agent instructions still matter after the IQs remove most of the plumbing, and how Agent 365 gives an agent its own identity and security boundary. 2. The Claude Code Idempotency Test That Prevents Runaway Agent Costs by Udaykiran Estari A scheduling flaw in Claude Code’s ScheduleWakeup mechanism re-fires one-shot slash commands instead of resuming paused tasks, silently doubling API costs on database writes, pull requests, and expensive searches. This article shows how to choose between loops, skills, subagents, and workflows without burning budget. 3. Adding Cost Metering and LLM Spend Visibility to a Multi-Agent System by MongoDB Multi-agent LLM systems break provider billing dashboards, which report spend by key or model but never by agent, workflow, or trace. This piece details a metering layer that captures token usage at each call, enriches it with runtime context, and prices it against a versioned rate card stored in a separate collection. Aggregation pipelines then slice costs by agent, model, or outcome, feeding Atlas Charts dashboards. 4. Rayfin: Define a Full App Backend in TypeScript and Ship It to Microsoft Fabric by Dave R This article explains how Rayfin works, from the TypeScript you write to the services that run in Microsoft Fabric. Rayfin is an open source SDK and CLI that lets you define an application backend, including its database, access policies, APIs, and server-side logic, entirely in code, then deploy it to Fabric with a single command. It walks through the programming model, the decorators that turn classes into tables, the CLI workflow, how connectors reach existing data, and how the whole application lands as a governed Fabric artifact. If you are interested in publishing with Towards AI, check our guidelines and sign up . We will publish your work to our network if it meets our editorial policies and standards. LAI #138: The Agent Reality Check was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.
Score: 18🌐 MovesAug 13, 2026https://pub.towardsai.net/lai-138-the-agent-reality-check-0e42f4c21f21?source=rss----98111c9905da---4 - That Business Idea You Couldn’t Justify Years Ago Is Worth Revisiting, Thanks to AI
That Business Idea You Couldn’t Justify Years Ago Is Worth Revisiting, Thanks to AI entrepreneur.com
Score: 18🌐 MovesAug 13, 2026https://www.entrepreneur.com/business-news/tech/ai-business-ideas-revisit-before-you-automate - From the people building it to the ones who have to use it, AI is creating all types of anxiety
From the people building it to the ones who have to use it, AI is creating all types of anxiety Business Insider
Score: 18🌐 MovesAug 13, 2026https://www.businessinsider.com/bi-today-newsletter-ai-tech-silicon-valley-therapy-mental-health-2026-8 - Karpo Crosses Half a Million Personalized Recommendations in Under 80 Days
Karpo Crosses Half a Million Personalized Recommendations in Under 80 Days USA Today
- UJ introduces African languages to its chatbot
The University of Johannesburg has upgraded its MoUJi chatbot to handle student enquiries in multiple South African languages, including isiZulu, Afrikaans and Sesotho.
Score: 18🌐 MovesAug 13, 2026https://www.itweb.co.za/article/uj-introduces-african-languages-to-its-chatbot/nWJad7bNDLP7bjO1 - This AI Tool Combines ChatGPT, Claude, Gemini, and More in a $55 Lifetime Plan
This AI Tool Combines ChatGPT, Claude, Gemini, and More in a $55 Lifetime Plan PCMag
Score: 18🌐 MovesAug 13, 2026https://www.pcmag.com/deals/this-ai-tool-combines-chatgpt-claude-gemini-and-more-in-a-55-lifetime-plan - Benevolve powers AI-driven talent intelligence
Benevolve, an AI-powered workforce transformation platform, is helping digital-native enterprises build skills-first workforces through AI-driven talent intelligence. The company works with technology-first organisations, including PhonePe, Myntra and MediBuddy, to support […] The post Benevolve powers AI-driven talent intelligence appeared first on Express Computer .
Score: 18🌐 MovesAug 13, 2026https://www.expresscomputer.in/news/benevolve-powers-ai-driven-talent-intelligence/137696/ - Gemini voice calling on Android Auto keeps failing me - and Google has until September to fix it
With Google Assistant shutting down this fall, Android users will be left with inferior voice calling, thanks to Gemini.
Score: 18🌐 MovesAug 13, 2026https://www.zdnet.com/article/gemini-voice-calling-on-android-auto-keeps-failing-google-must-fix-by-september/ - Accessibility is the Blueprint for Trustworthy AI
AI changes how software behaves, but it doesn't change what people need to trust it. Accessible design is the solution.
- Allen School team recognized for making equality saturation more practical
Researchers introduced egg, an open-source library using e-graphs and equality saturation to optimize term representations, highlighted in a Communications of the ACM Research Highlight.
Score: 18🌐 MovesAug 13, 2026https://www.cs.washington.edu/allen-school-blog/cacm-research-highlight-equality-saturation-more-practical/ - What are AI Hallucinations?
AI hallucinations are outputs that sound coherent and confident but are factually wrong, fabricated...
- Microsoft’s Clippy-like Mico character is no longer the face of Copilot
Microsoft Copilot will no longer show its emotive yellow blob, Mico, when you use the chatbot's voice mode. In a support page, Microsoft says it's going to move Mico to its Learn Live platform, where the avatar will have "more to react to," as reported earlier by GeekWire. Mico launched in Copilot's voice mode last […]
- ARGUS Assist named CRE Analytics Innovation of the Year at the 2026 PropTech Breakthrough Awards
ARGUS Assist named CRE Analytics Innovation of the Year at the 2026 PropTech Breakthrough Awards Toronto Star
- [CNA938 – The Big Question] How much is too much when it comes to AI?
[CNA938 – The Big Question] How much is too much when it comes to AI? NUS Computing
Score: 15🌐 MovesAug 13, 2026https://www.comp.nus.edu.sg/news-media/938938-rewind-the-big-question-how-much-is-too-much-when-it-comes-to-ai/ - Manual vs. AI-powered PDF redaction: protecting sensitive data in 2026
Research shows that humans play a role in 60% of breaches that expose sensitive data. That “role” often involves an employee falling for a phishing scam or using PASSWORD for their login credentials, but data exposure can also be a result of how your business redacts sensitive and personally identifiable information (PII) in your documents. Historically, manual, “black-box” redaction was considered best-practice, but this approach only obscures data, it doesn’t permanently remove it. As regulations governing data security get stricter and AI-powered redaction solutions become more accessible, organizations—especially those in highly regulated industries—are re-evaluating their PDF redaction solutions. How manual PDF redaction is different from AI-powered PDF redaction The primary difference between manual and AI-powered PDF redaction is who (or what) you rely on to do the heavy lifting. Manual redaction defined Manual redaction is a human-driven process where individuals visually scan text, select content, and apply black boxes or remove the text before sharing or storing the file. Manual redaction is only as effective as the reviewer, which makes outcomes highly variable, especially under time pressure or high document volume. AI-powered redaction defined AI-powered redaction uses machine learning and natural language processing (NLP) to automatically detect and remove sensitive information from documents. The system is trained to recognize patterns, language cues, and contextual signals in PDFs as well as scanned images, handwritten notes, text-heavy documents, metadata, and embedded scripts. For example, Nitro Smart Redact can identify structured data, like Social Security numbers and bank accounts in a payroll document, as well as unstructured, free-form data, like “in April 2022, Sarah was diagnosed with cardiovascular disease.” How to choose the right redaction solution for your organization Nitro Manual redaction may work for small organizations that handle a low volume of documents. But for most businesses, an AI-powered PDF redaction solution that will scale as your needs change is a smart investment. Here are six factors to consider when evaluating AI-powered redaction solutions: Document volume and size: Look for a solution that can handle your current document load and scale to meet future demand without slowing down processing times or requiring additional manual effort. Data type: YourPDF redaction tool must be able to accurately identify a wide range of sensitive information—from PII to financial data. Data location: The solution should detect sensitive data in visible and hidden layers, like metadata, embedded text, annotations, comments, form fields, and image-based content. Compliance requirements: Choose a solution that supports compliance with your industry’s regulatory obligations, such as GDPR, HIPAA, or other data protection standards. Integration with existing systems: Pick a PDF redaction tool that integrates with your existing document management systems, cloud storage, and workflows to maintain productivity and reduce the need for additional tools. Data security and AI training policies: Verify how the redaction solution handles your data. Look for clear policies that protect sensitive information, prevent data reuse, and keep your content private and secure. Why Nitro is a top choice for AI-powered PDF redaction Nitro Nitro Smart Redact is an AI-powered PDF redaction solution that balances speed and accuracy with control and security, allowing teams to redact sensitive information quickly while maintaining full oversight and compliance. Detects over 30 categories of regulated PII in seconds Uses advanced NLP to identify unstructured PII that manual or pattern-based methods miss Finds and redacts PII in scanned documents, image files, and handwritten notes Gives users the ability to manually add, adjust, or remove redactions Integrates with existing tools, including Microsoft 365, Salesforce, and cloud storage Processes documents in secure, temporary sessions Permanently removes hidden data, including metadata and embedded scripts Never uses your documents to train or improve external AI models The best solution? AI-powered PDF redaction, but keep people in the loop The most effective way to identify and permanently remove sensitive data from PDFs is a solution that uses AI to quickly find information hidden deep in documents and humans to manually review and adjust results for accuracy. Speak with a Nitro team member to learn how Nitro’s AI-driven Smart Redact technology provides a faster, safer way to prepare documents for secure sharing.
Score: 15🌐 MovesAug 13, 2026https://www.cio.com/article/4209087/manual-vs-ai-powered-pdf-redaction-protecting-sensitive-data-in-2026.html - New Blavatnik School programme equips leaders to shape AI through co-creation
New Blavatnik School programme equips leaders to shape AI through co-creation Blavatnik School of Government
Score: 15🌐 MovesAug 13, 2026https://www.bsg.ox.ac.uk/news/new-blavatnik-school-programme-equips-leaders-shape-ai-through-co-creation - 😻 Livestream: Learn Video Prompting for TOTAL Beginners
LTX’s Daniel Berkovitz and Alon Yaar join us live to teach video prompting and demo the new LTX-2.5.
Score: 15🌐 MovesAug 13, 2026https://www.theneurondaily.com/p/livestream-learn-video-prompting-for-total-beginners - Start Here: The Words Everyone Uses About LLM Inference
KV cache, prefill, quantization, FlashInfer — all downstream of one fact: to write three quarters of a word, the model reads all seventy gigabytes of itself. The LLM serving stack drawn as six stacked layers, from silicon at the bottom to workload and SLOs at the top. If you have sat in a meeting about running language models in production, you will have heard some of these: KV cache. Prefill and decode. Quantization. Continuous batching. FlashInfer. Tensor parallel. Goodput. They arrive together, at speed — normally from somebody asking for a budget. They are good words. The problem is that nobody explains what sits underneath them, so they get memorized as a list instead of understood as consequences. In this series, I will try to answer what the thing is, how it works, why you would care, and what it looks like on a system taking real traffic. Start here, because the rest of it is downstream of one division. The map The picture above is the series. From the bottom: silicon is the GPU, the chips that calculate and the memory that feeds them. Kernels are the small programs that run on it. Memory and precision covers how the model’s numbers are stored, and how much of your card each conversation eats. The engine is what you actually install — almost always vLLM — and it decides whose request runs next. Distributed serving is what happens when one machine is not enough. Workload and SLOs at the top is your real traffic: prompt lengths, how many people are asking at once, how fast you promised to answer. Requests enter at the top. The work happens all the way down. Request descending through all six layers of the stack, each layer lighting in turn, with what that layer decides about the request accumulating alongside it. One token, six layers. What each layer decides is on the right, in the order it happens to you. Any one of those six can be the thing you are actually waiting for, which is why there is a part for each. One thing does not read in that order, and that is deliberate. It goes: this part, then the memory each conversation eats, then the kernels underneath it, then how the numbers are stored, then the engine, then more than one machine, then how to tell whether any of it worked. That is not bottom to top. It doubles back once, and two of the parts share a layer, because the memory band carries both the cache and the precision and each is big enough to need its own article. The order is by what you need first rather than by what sits where. The cache is the constraint you will actually hit, so it comes early. Kernels are the layer you are least likely to ever touch yourself, so they wait until part two has given you a reason to care about them. What inference actually is Training builds the model. It happens once, costs a fortune, and somebody else has usually done it. Inference is using the model, and it is what you pay for forever. Inference is two jobs that feel like one: Prefill is the model reading your prompt, all of it at once. Decode is the model writing the answer one token at a time — a token being roughly three quarters of a word, each one waiting for the last. They look like one job and behave nothing alike. Eight prompt tokens lighting in a single pass, then eight answer tokens appearing one at a time, with a running count of how many times the weights were read. Eight tokens of prompt cost one read of the model. Eight tokens of answer cost eight. That asymmetry is the rest of this article. The number nobody divides A GPU spec sheet gives you two figures. The first is arithmetic speed: an H100 does about 990 TFLOP/s, which is 990 trillion operations a second. Take it as unimaginably fast. The second is memory bandwidth , how quickly the chip can pull data out of its own memory — about 3.35 TB/s. Also fast, but not in the same way. Divide them: 990 trillion operations per second ÷ 3.35 trillion bytes per second = about 296 operations per byte H100 drawn as two halves: 132 streaming multiprocessors on the left at 990 TFLOP/s, five HBM3 stacks on the right holding 80 GB, and the 3.35 TB/s link between them. Both numbers belong to one card. The left half is the arithmetic you bought; the right half is everything the model has to be fetched from; and the arrow is the only way between them. The ridge point is the ratio of the two, which is why it is a property of your hardware and not of your model. Picture a very fast chef and a pantry down a long corridor. Each trip costs the same whether you carry one ingredient or an armful. Make one omelette per trip and you spend the day walking; the chef’s speed is irrelevant. The chef only becomes the limit at roughly 300 dishes per trip. That ratio, dishes per trip, is arithmetic intensity . The turning point is the ridge point , and the chart of it is a roofline . A roofline chart for the H100, marking prefill, the decode matrix multiplications, and decode attention. Left of the dashed line, fetching is your limit. Right of it, arithmetic is. Prefill lives on the right. Decode does not. Prefill is comfortable. Each chunk of the model gets loaded once and multiplied against your whole prompt. A 4,000-token prompt is around 4,000 operations per byte, far past 296. Decode is the problem. To produce one token the model reads every weight it has , and how much that is depends entirely on how the numbers are stored: 70B parameters × 2 bytes (BF16, the usual default) = 140 GB 70B parameters × 1 byte (FP8) = 70 GB 70B parameters × 0.5 byte (INT4) = 35 GB Seventy gigabytes of memory traffic for three quarters of a word. That is the sentence I would tattoo on this series. Which is why everyone batches Here is the fact that makes any of this fixable: the weights do not depend on who is asking. Your next token and a stranger’s next token come out of exactly the same 70 GB. Different conversations, different histories, identical matrices. So the GPU does not fetch them once for you and then again for them. It fetches them once, and multiplies them against both requests in the same pass. That is batching , and it is worth being precise about what it is not. It is not a queue. It is not waiting for a group to fill up. It is some number of requests going through the model together , in one sweep, each of them riding on a read that was happening anyway. Which answers the objection you should be having. Adding a fiftieth person does not make the other forty-nine wait their turn, because nobody was taking turns. The fetch was the expensive part and they share it. What that fiftieth person costs you is arithmetic — and arithmetic is precisely what you had spare. One 70 GB weight read feeding an increasing number of requests, from 1 up to 32, with the bytes moved per token produced falling from 70 GB to 2.2 GB. The read is the same in every frame. Seventy gigabytes for three quarters of a word is the batch-of-one price, and this is where it stops being true. So: one person, and that 70 GB produces a single token, which is as wasteful as it sounds. Fifty people, and the same read produces fifty. One trip, fifty dishes. Which gives a tidy result: In decode, arithmetic intensity is just the number of users you serve at once. Batch of 32 puts you at a tenth of what the card can do. Batch of 296 reaches the ridge, which almost nobody sees in practice. A marker climbing the roofline as batch size grows from 1 to 512, while a second marker stays pinned at the bottom. Batch 1 to 512. Gold is the bulk of the model, climbing as users share each trip. Blue is the part that never moves. One Thing: mixture of experts (MoE) All of that assumes a dense model, where every parameter works on every token. Many current models are not. A mixture-of-experts model splits its bulk into sub-networks and a router picks a few per token: Qwen3.6–35B-A3B holds 35 billion parameters and uses about 3 billion on any given one. Three panels: a dense model with one full feed-forward block, a mixture of experts with two of eight lit, and the same with all eight lit at serving concurrency. Attention is identical in all three. What changes is how much of the feed-forward half you have to fetch. Two things follow, and the second one is the one that gets missed. It cuts traffic, not capacity. Every expert still has to sit in GPU memory, because the router can send the next token to any of them. Active parameters govern what you read; total parameters govern what you buy. Batching takes the saving back. One user touches a few experts. Two hundred users scatter across all of them, so you read almost everything anyway — except now each expert got a thin slice of work. The fraction that matters here is not the parameter count: it is how many of the experts fire. Qwen3.6–35B-A3B routes 8 of its 256 experts per token, so: dense intensity = B MoE experts intensity ≈ B × 8/256 = B × 0.031 Reaching the ridge would need a batch near 9,500. Nobody serves that, so MoE decode stays bandwidth-bound at any concurrency you will really run. It is excellent for one user on one card, which is why it owns local inference. At serving scale it moves the problem rather than solving it. And here is the crack Batching lifts the weight matrices up the roofline. It does nothing at all for attention — not at batch 8, not at 512. Most of the model is shared weights that every request multiplies against, so batching helps. But the model also remembers your conversation, and that memory — the KV cache — is yours alone. Fifty users means fifty separate histories fetched, with nothing to spread the cost over. There is one thing spreading it, and it is worth naming because part two is about it. Several query heads share each stored key-value pair, so the same fetched bytes do serve more than one head: eight of them, on the models in this series. That puts decode attention at an intensity of about 8 rather than 1. Against a ridge of 296 it makes no practical difference, you are still deep in bandwidth-bound territory, but 8 is the honest number, and the mechanism that produces it is the same one part two credits with making long context possible at all. The top row is one read serving everybody, so adding users raises its intensity. The bottom row is eight reads serving eight people, so adding users changes nothing. Every optimisation in this series is an attack on the second row. This one takes a while to properly absorb, and it is the first thing worth checking when a long-context feature is slow. You cannot batch your way out of a cost that is per-user by definition — which is why the engineering went into making that cache smaller rather than the maths faster. What to do with this Work out your own ridge point. Peak arithmetic rate divided by memory bandwidth, both off the spec sheet of whatever you actually run on. One division, and the answer is not a constant. ( Drag the batch slider here if you would rather watch it move.) H100 SXM 990 TFLOP/s ÷ 3.35 TB/s = 296 A100 80GB 312 TFLOP/s ÷ 2.04 TB/s = 153 L40S 362 TFLOP/s ÷ 0.864 TB/s = 419 The L40S is the one to stare at. It is the cheaper card, and it has the harder number to reach — not because it has more arithmetic (it has far less than an H100) but because it has much less bandwidth per unit of arithmetic. Moving a workload there to save money means you need a bigger batch to break even, not a smaller one. That does not come up when someone quotes you the hourly rate. Find the batch size you are really getting. Not --max-num-seqs, which is only a ceiling. The number the scheduler actually reaches under your traffic: vllm:num_requests_running on the metrics endpoint, or the Running: N reqs vLLM prints as it works. If that number is 8 on an H100, you are at 8 against 296, which is under 3% of the arithmetic you are being billed for. And treat any tokens-per-second figure quoted without a batch size as unfinished. Here is why, using only numbers already on this page. Take a 70B model stored at one byte per parameter. Every single token it writes means reading all 70 GB. An H100 moves 3.35 TB every second, so one of those reads takes about 21 ms. Call it 48 tokens a second , or roughly 36 words a second — already faster than anyone can read — and that is what one person alone on that card gets. Now sit 32 people down at it, each holding a short conversation of about 2,000 tokens. The weights are still a single read, which is the entire point of batching. But you are now also fetching 32 private caches, another 21 GB, so the step costs 27 ms instead of 21: Read the two bar columns against each other. Alone, one person gets 48 tokens/sec and so does the card. With 32 people at 2K, each gets 37 tokens/sec and the card totals 1,170. Push the context to 8K and each gets 21 tokens/sec while the card falls back to 688 — the individual column drops the whole way down, and the total turns around too. Three things happened at once. The card’s output went up twenty-four times. Each individual person’s went down . And how far down was decided entirely by how long their conversations were, because the cache is the only term in this that grows. So when a vendor tells you their system does a thousand tokens a second, they have told you what their machine adds up to and nothing whatsoever about what any one of your users will see. They also have not told you the context length, which is the variable that pulls those two numbers apart. Ask for all three. I will keep saying that in every part. Conclusion Language models feel slow in a way that does not match the hardware bill because decoding is a memory problem wearing a compute problem’s clothes. The GPU is not struggling to do the arithmetic. It is struggling to fetch the things it does arithmetic on — every number the model is made of, and every token of the conversation so far, once for each new token it writes. Everything after this — the cache, the kernels, the precision, the settings, the second machine — is a different answer to that one sentence. Which is why the most useful number in the whole stack costs you one division and almost nobody has it. Part two is the KV cache : what that private memory costs, and why your context length quietly decides how many customers fit on a card. Hardware figures are H100 SXM specifications, and the 990 TFLOP/s is the dense BF16 rate — NVIDIA’s page prints 1,979 with sparsity, which is double. A100 figures are the SXM part; the PCIe card has less bandwidth and a different ridge. Every number here is one division you can repeat, llm-inference-arithmetic is the calculations as a Python package, MIT, no dependencies. Start Here: The Words Everyone Uses About LLM Inference was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.
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