AI News Archive: July 22, 2026 — Part 8
Sourced from 500+ daily AI sources, scored by relevance.
- Redington to distribute UAE-built AIREV OnDemand agentic AI platform across MEA
Redington to distribute UAE-built AIREV OnDemand agentic AI platform across MEA Gulf News
- Athennian and PwC UK Collaborate to Bring AI-Powered Intelligence to Global Subsidiary Governance
Athennian and PwC UK Collaborate to Bring AI-Powered Intelligence to Global Subsidiary Governance Toronto Star
- Singapore’s data analysts trust AI to work, not to think
Singapore’s data professionals are proving to be among the most cautious in the world when it comes to letting artificial intelligence (AI) operate unsupervised. According to a new global study, 61 per cent of the city-state’s data analysts prefer a human-in-the-loop approach to AI, the highest share recorded across all regions surveyed. Also Read: AI […] The post Singapore’s data analysts trust AI to work, not to think appeared first on e27 .
Score: 50🌐 MovesJul 22, 2026https://e27.co/singapores-data-analysts-trust-ai-to-work-not-to-think-20260722/ - Why real-time is the future of speech-to-text
Explores the benefits and future of real‑time speech‑to‑text technology.
- Govern natively, federate outward, and what breaks across trust domains
Govern natively, federate outward, and what breaks across trust domains By now the agent has its own identity and you can carry that identity through a chain of calls. The next question is where the rules live. Who decides what an agent is allowed to do, and where does that decision get made? Two answers,... The post Govern natively, federate outward, and what breaks across trust domains appeared first on DataRobot .
Score: 50🌐 MovesJul 22, 2026https://www.datarobot.com/blog/govern-natively-federate-outward-and-what-breaks-across-trust-domains/ - Leadership bottlenecks slow AI adoption
At Cisco, VP of engineering Jason Andrews deals with all the same technical issues as every other company deploying AI, including ensuring it’s governed, secure, and integrating multiple data sources, legacy systems, and AI models. But these issues are relatively straightforward compared to the bigger challenges relating to the fast pace of change, specifically how AI can touch and transform nearly every aspect of business. “We’re thinking about it every day,” he says. “My belief is we’ll be seeing a massive acceleration of everything.” In coding, for example, he’s witnessing productivity increases up to 110% with AI assistants. “I can build apps or custom integrations a lot faster,” he adds. And the real benefit of AI isn’t just in speeding up individual steps in a process, but in making AI the core of a new business process. But building it from scratch puts even more pressure on organizations trying to get employees up to speed on new ways of doing things. “We want to move fast, train people, and get them onboarded,” he says. “But what I thought AI was going to do for my organization nine months ago is different from three months ago.” So by the time something is rolled out, it’s changed three times. “I struggle with the change management aspect,” he says. “The legacy model of change management isn’t fast enough. How do you create that constant learning?” One of the ways Cisco approaches it is to create communities where people can talk about these issues and share best practices and governance, and you have to keep people’s minds open that every day is going to be different than the last, Andrews adds. Testing the AI waters Cisco isn’t the only organization struggling with change management in the face of the AI tsunami. In a survey of 2,000 global CEOs IBM released in May , 83% of them said AI success depends more on adoption than on the technology itself, and 77% said talent and technology roles are converging. “Thanks to Claude Code, our entire development cadence is exponentially greater than a year ago,” says Andrew Johnson, CIO at Brownstein Hyatt Farber Schreck, a Denver-based law firm with about 700 employees and clients around the US. But, as with Cisco, the biggest challenge isn’t technical. “In our industry, with our circumstances, we’re probably less constrained by technical capability than organizational constraints, culture, aptitude, the need to bind people to technology, and what helps me and the client,” he says. “There’s a tremendous amount of cultural shift that has to happen in our organization, which is far more demanding of my attention and complexity of thought than the technical stuff.” Companies that bill by the hour, such as law firms, may face additional challenges as attorney productivity increases because billable hours might go down. Alternatively, the total number of cases could go up as litigation becomes less expensive. Either way, firms that adapt will see competitive advantage, and the rest will fall behind, putting more pressure on the need for change management. “If people can’t embrace technology, we won’t be able to get a lot of value out of it,” says Johnson. “I’m talking to people about adapting their way of work. There are certainly a lot of people intrigued and anxious to dive in. They recognize the connection between the potential of the technology and what we do.” But helping everyone see that connection and then working with them to change their habits is difficult, and requires solid relationships and good communications. “That’s been far more of a bottleneck for us,” he says. To address the issue, the firm has developed a network of technology champions who also understand the legal side of the business. “Now we need lawyers who know how to use the technology and can articulate these things to the people we’re trying to reach,” Johnson says. But change management is only one leadership bottleneck slowing AI adoption. Companies also struggle with figuring out their vision for AI, with slow decision-making, and a tendency to focus on the past instead of the future. Vision and strategy In another survey, this time of 950 business leaders released by Grant Thornton in April, 51% said strategy is the biggest driver of ROI when it comes to AI adoption, but 79% of operations leaders said they don’t have a fully developed and implemented AI strategy. “Having leadership understanding why AI is needed and what objective they’re trying to achieve is very important,” says Shivi Verma, senior manager of engineering at Docusign. “Sometimes leadership doesn’t have a strategy for their organization on how AI should be adopted. Many times it’s bottom-up, which creates a chaotic experience.” When Docusign started adopting gen AI, different teams and organizational units wanted to go in different directions. “All were coming up with their own strategy and tooling,” he says. So Docusign brought business leaders together to understand the pain points, and decide on the technology. “Getting requirements and placing a bet on a specific technology was important,” he says, “as well as pivoting to a different technology if needed.” In order to adapt to changes, the company wanted to have a nimble approach, starting with smaller use cases, with power users, and problem areas. “We try to plan for four to six months,” he adds. “We set expectations for our leadership that we place a bet with a specific technology, but want to be able to pivot.” Today, the leadership challenge front lines have moved yet again, to agentic AI. “Folks are creating their own agents and deciding their own permissions,” Verma adds. “We’re still coming up with a governance strategy.” Slow decision-making When it comes to AI deployments, Dan Diasio, global AI consulting leader at EY and CTO for its US consulting business, admits he’s a bottleneck. There’s a great deal of interest in what AI can do, and using a variety of new AI tools. But since the firm deals with sensitive client data, safety is paramount. It’s a slow process, but important to build secure infrastructure, and to have trust in the technology. “That’s a reasonable bottleneck that makes sense,” he says. Trust in the tools they work with is essential because clients expect it. “Every tool we use has to go through a detailed security and information privacy impact assessment, as well as a whole other set of controls so they can be used appropriately and safely,” he says. These reviews can take a lot of time, though, and in the age of AI, speed is a highly valued currency. So how do you balance the two, when safety reviews can require input from a lot of different stakeholders and be extremely time intensive? “We’ve stood up a team to be able to quickly certify and address a variety of platforms,” Diasio says. “Instead of working with different departments in the way we used to, we’ve started identifying representatives from different departments into a cohort. Decisions we used to make in months now take weeks.” According to a West Monroe survey of more than 1,200 leaders released earlier this year, slow decision-making is already showing up on the bottom line. Nearly three out of four leaders said their organizations lose up to 5% of annual revenue to slow decision-making and delayed execution. And the top reasons for the delays? According to 40% of the managers surveyed, the problem was the skills gaps of overwhelmed teams, and 35% pointed to layers of management or approvals. Nearly half said they’re spending 10 to 25% of their time on rework, excessive approvals, and unnecessary meetings, and more than half say up to 50% of their projects fail or lose momentum to delays. Focus on the future, not the past When it comes to the decision about where to apply AI in an organization, the tendency, Diasio says, is to turn to the experts with the most expertise in the business. But these are the same people most likely to focus on improving on what they’re already doing. “And that often blinds people to what’s possible in the future,” he says. “That becomes a significant bottleneck.” So the solution is to revamp the decision-making process around the new reality. “What we see some advanced companies do is give people who don’t understand the process but understand the technology equal footing with people who don’t understand the technology but understand the process,” he says. “A lot of companies are disproportionately focused on just addressing their operating model right now.” Instead of focusing on what they’re currently doing, AI-native companies will start with a focus on the customer, he says. This shift in focus isn’t likely to show up immediately on the bottom line, or result in the highest possible number of pilots going into production. “If leaders are in a position where they’re justifying the use of a technology to the board or their CFO, they become a bottleneck when they start demonstrating their value in terms of the number of things they’re doing,” Diasio says. But 150 or 200 use cases deployed into production may feel like progress, like things are happening in the organization. But all these use cases are a waste of time and money if they’re applied to existing processes that don’t move the needle. “We see that happen in organizations today,” he says. “Maybe we need to reinvent the processes.” It’s no secret that companies will need to change in order to adapt to AI. Deloitte recently surveyed 660 global technology leaders and 81% said their current operating model can deploy and govern AI enterprise-wide, but 75% also said their organization must change its operating model within the next 12 to 18 months to drive greater value. AI ROI is real, says China Widener, Deloitte vice chair and US tech, media, and telecom industry leader. But it’s currently weighted toward efficiency gains, with broader business transformation and revenue upside still developing. Another Deloitte survey showed that the clearest results from AI were in productivity, with 66% of organizations reporting gains, and cost efficiency, with 40% saying AI reduces costs. “However, revenue impact is still emerging,” says Widener. “Only one in five companies says AI is driving top-line growth today.” But optimism prevails, with 74% expecting it to do so in the future.
Score: 50🌐 MovesJul 22, 2026https://www.cio.com/article/4186225/leadership-bottlenecks-slow-ai-adoption.html - GitLab previews auto-remediation of vulnerable dependencies
GitLab previews auto-remediation of vulnerable dependencies InfoWorld
Score: 50🌐 MovesJul 22, 2026https://www.infoworld.com/article/4200083/gitlab-previews-auto-remediation-of-vulnerable-dependencies.html - Why the future of AI depends on SMB adoption
There’s growing appetite for practical, accessible tools designed around the realities of running a small business rather than enterprise-scale workflows.
Score: 50🌐 MovesJul 22, 2026https://www.techradar.com/pro/why-the-future-of-ai-depends-on-smb-adoption - House of Lords questions Andy Burnham’s AI plans
The decision by the new Andy Burnham government to disband the Department for Science, Innovation and Technology (DSIT) has led the House of Lords to raise an urgent question over the UK’s artificial intelligence (AI), technology and science strategy. Peers in the House of Lords asked what will happen to science and technology funding while the Department for Business, Innovation, Science and Trade (DBIST) gets up and running. Crossbench MP life peer Lionel Tarassenko said: “Any organisation like this will take 12 months. And 12 months is an eternity in AI. It’s equivalent to three generations of frontier AI models.” He asked for reassurances that both the AI Security Institute and the Sovereign AI Unit will be able to keep up momentum, which he described as “essential to their activities during this unnecessary transition period”. Parliamentary secretary Ruth Anderson responded by emphasising the role Kanishka Narayan now has as AI minister in the Burnham cabinet. “I think we’re seeing technology move faster than at any point, never mind in my lifetime, but I think over the last hundred years, and it’s incredibly important we keep up, which is why we will now have a dedicated minister in the attending Cabinet to make sure these issues are reflected,” said Anderson. She went on to state that the AI Security Institute will be part of the Office for the Prime Minister and Cabinet, while UK Research and Innovation (UKRI) will be moved to the newly formed Department for Business, Innovation, Science and Trade (DBIST) . She also confirmed that UKRI’s £9.22bn budget for research and development will continue. Anderson said the Government Digital Service (GDS) will move to the newly renamed Department for Digital, Culture, Media and Sport (DCMS) and the Sovereign AI Unit will also be moving to DBIST. Liberal Democrat peer Mike Dixon questioned the government’s spending on technology. “The leading global companies are spending about £500bn this year on AI. Our spending as a country will be roughly 0.3% of that,” he said. Dixon enquired about the focus the government plans to take. “Given the scale of our investment compared to others, does the machinery of government changes reflect the government choosing to focus on an area where we can have stronger comparative advantages as a country, or does it mean that we will continue to try and lead on all of those areas?” he asked. In response to Dixon’s question, Anderson said: “We’re talking about how technology and how the advancements of technology are going to fuel economic growth going forward. That is a priority for the government.” Anderson responded to other questions relating to AI, saying: “Everything we’re talking about is AI-enhanced re-industrialisation, and I appreciate that there are many times where we probably use the words AI as short speak for tech or science. That’s just because of how quickly the technology is moving on. But across the eight industrial priority sectors, we will continue to make sure that AI and science and technology are at their hearts.” The government confirmed in answers to the peers that the changes it is making are designed to embed science and AI at the heart of government policy. This policy is being elevated in prominence through direct involvement of the Prime Minister’s Office and Narayan’s role as cabinet-level AI minister . Read more about DSIT Burnham government to drop digital ID and tech department : Controversial ID Scheme and government technology department to be tossed on scrap heap as new prime minister enters 10 Downing Street. DSIT gets sums badly wrong on AI datacentre carbon footprint : Government revises July 2025 projections for AI-driven datacentre carbon footprint upwards by around 100 times, but Carbon Brief suggests the numbers could be much higher still. DSIT aims to bolster expertise with year-long secondments : To drive forward its Plan for Change, the Labour government is looking to hire 25 experts for the Department for Science, Innovation and Technology Fellowship programme.
Score: 50🌐 MovesJul 22, 2026https://www.computerweekly.com/news/366645889/House-of-Lords-questions-Andy-Burnhams-AI-plans - Google’s Gemini lineup has a Pro-sized hole
PLUS: Sell a high-value AI workflow audit as a consultant
- Build an LLM Agent That Can Write and Run Code
A hands-on walkthrough of code execution with the OpenAI Agents SDK and Docker The post Build an LLM Agent That Can Write and Run Code appeared first on Towards Data Science .
Score: 50🌐 MovesJul 22, 2026https://towardsdatascience.com/build-an-llm-agent-that-can-write-and-run-code/ - Research-Grade EdgeBench Analysis: AI Agent Benchmarking, Leaderboard Analytics, Scaling Laws, and Evaluation Metrics
Research-Grade EdgeBench Analysis: AI Agent Benchmarking, Leaderboard Analytics, Scaling Laws, and Evaluation Metrics MarkTechPost
- The rise of shadow AI is exposing the gaps in enterprise data governance
The rise of shadow AI is exposing the gaps in enterprise data governance YourStory.com
Score: 50🌐 MovesJul 22, 2026https://yourstory.com/2026/07/rise-shadow-ai-exposing-the-gaps-enterprise-data-governance - Tesla's push into AI and robotics is proving costly
Tesla's massive investments in humanoid robots , self-driving cars and AI chips are hurting profits, but the company says it'll all pay off down the line. Why it matters: CEO Elon Musk indicated he's "never been more optimistic about the future," but acknowledged the investments could lead to uneven results. Zoom in: Tesla revenue jumped on record vehicle deliveries in the second quarter, but the company saw a significant dip in operating profit because of its heavy spending on R&D. Tesla plans to spend more than $25 billion on capital investments this year, and that spending rate will grow over the the next two or three years, CFO Vaibhav Taneja told investors and analysts on a call late Wednesday. Additionally, Tesla plans to borrow as much as $30 billion to accelerate its investments in robotaxis, Optimus robots, semiconductors, solar manufacturing and AI compute infrastructure, he said. "We're investing a lot in growing the core business and really preparing for the future," Musk said on the call. "So this is a massive capex year, but I'm confident that all the things that we're investing in will yield incredible returns — really, maybe the best capex returns that we've ever seen." By the numbers: Tesla reported Q2 revenue of $28 billion, up 23% year over year. Net income was $1.1 billion, down 5% compared to a year ago, and essentially flat with the last three quarters. Operating margin fell to just 1.4%, compared to 4.1% a year ago. State of play: Tesla began production of its driverless Cybercab in Texas during the quarter, but Musk said its Robotaxi service is rolling out cautiously to ensure safety. Tesla Semi remains on track for production later this year at a new factory in Nevada, but Musk said autonomous trucking is not a priority until next year. The company said it's making progress to expand battery pack manufacturing capacity, which it called "the main limiting factor to near-term vehicle production volume increase." Tesla tore out assembly lines for its discontinued Model S and X cars at its Fremont, Calif., factory and expects Optimus humanoid robot production to begin there later this year. Musk acknowledged that scaling Optimus production will be difficult. "This is going to be the hardest product to scale manufacturing that we've ever made at Tesla, because everything on the robot is new." The bottom line: Tesla's focus is always over the horizon.
- SpaceXAI’s Explores Texas Data Center, Oracle’s Costly Surprises, AI-Generated Movies Incoming — TITV [Video]
SpaceXAI’s Explores Texas Data Center, Oracle’s Costly Surprises, AI-Generated Movies Incoming — TITV [Video] The Information
- 'Gemini who?': Rivals dunk on Google's delayed frontier AI
'Gemini who?': Rivals dunk on Google's delayed frontier AI Business Insider
Score: 50🌐 MovesJul 22, 2026https://www.businessinsider.com/rivals-mock-google-gemini-3-5-pro-delay-2026-7 - We must reject any notion of AI consciousness | Letters
Artificial intelligence systems won’t become conscious for the same reason they won’t become pregnant, says Dr John Pickering Anil Seth is right to point out that to overestimate artificial intelligence is to underestimate ourselves ( Once again we are told AI may be conscious – I study consciousness, and I have my doubts, 15 July ). But he is wrong only to have doubts about whether AI systems like Claude may become conscious. He should be certain, and should say so more forcefully. It’s not like anything to be Claude, just as it’s not like anything to be a washing machine. An academic education is not required to realise that, common sense will do. Artificial intelligence systems won’t become conscious for the same reason they won’t become pregnant; they’re not that sort of thing. Continue reading...
Score: 50🌐 MovesJul 22, 2026https://www.theguardian.com/technology/2026/jul/22/we-must-reject-any-notion-of-ai-consciousness - Datavault AI (NASDAQ: DVLT) to Tokenize $1B Project Qestrel Edge AI Infrastructure Program
Datavault AI (NASDAQ: DVLT) to Tokenize $1B Project Qestrel Edge AI Infrastructure Program USA Today
- S’pore proposes focus on AI, STEM and student development under new five-year ASEAN education plan
S’pore proposes focus on AI, STEM and student development under new five-year ASEAN education plan The Straits Times
- Tesla's Summer Update makes Grok the smart assistant it was always meant to be
Tesla's latest update makes Grok much more useful behind the wheel
- Robot AI company Noetra is 'last chance' for Japan, CEO says
Robot AI company Noetra is 'last chance' for Japan, CEO says Reuters
Score: 50🌐 MovesJul 22, 2026https://www.reuters.com/business/media-telecom/robot-ai-company-noetra-is-last-chance-japan-ceo-says-2026-07-22/ - iTmethods Appoints Przemek Tomczak as Chief AI Governance Officer
iTmethods Appoints Przemek Tomczak as Chief AI Governance Officer azcentral.com and The Arizona Republic
- Fusepay expands beyond payments with AI operating system for trade businesses
The launch comes eight months after Fusepay launched its digital payments platform in Seychelles, and raised $350,000 in pre-seed funding in August 2025.
Score: 49🌐 MovesJul 22, 2026https://techcabal.com/2026/07/22/fusepay-expands-beyond-payments-with-ai-operating-system/ - AI agents aren't confidently wrong because of bad context — they're wrong because of bad data engineering
You spend weeks tuning an AI chatbot. Answers are accurate. Stakeholders sign off, and you ship it. Three months later, the system is confidently wrong about a third of what users ask. Nobody changed the model, and nobody touched the prompts. The world moved, pricing changed, a policy updated, a product spec shipped a new version, and the underlying knowledge store didn't move with it. This is not a hypothetical. It's one of the most common production failure modes in enterprise AI right now, and most data engineering teams don't have the right tooling to catch it, regardless of how the AI system retrieves the data. The failure that doesn't look like a failure An AI application doesn't care whether it's retrieving from a vector store, a document index, or an API call. Whatever the mechanism, nothing in a standard retrieval pipeline checks whether what it's serving is still correct. A stale pricing document retrieves just as confidently as a current one, because the system is scoring relevance or availability, not correctness. A record with a silently missing field passes through just as cleanly as a complete one, for the same reason. So the failure is invisible by design. Outdated or incomplete data still scores high on relevance, or passes every check a data pipeline was built to run. The model answers with full confidence because the retrieved context looks authoritative. Every dashboard you're watching stays green. The system looks like it's working. It's just wrong. I’ve watched a similar version of this happen outside the AI context, in a fintech pipeline. An upstream system changed a field without notifying downstream users. The pipeline did not fail; it simply propagated bad values into dashboards because the system only checked whether the job completed, not whether the data was still correct. The issue surfaced only when a customer noticed something inconsistent. By then, the bad data had already moved downstream. Whether it's a document that's gone stale or a field that's gone silently missing, the failure shape is the same: the absence of an error is not the presence of correctness, and without building proper validation layers, nothing in the pipeline could identify the problem. Why this is a data engineering problem Teams that hit this failure tend to misdiagnose it, and they tend to do it twice. Blaming the model: The first instinct is to blame the model, try a different LLM, adjust the prompt. The real problem lies further upstream, at the data engineering layer, the same instinct behind the fintech failure above: monitoring built for the pipeline, not the data. Blaming the retrieval layer: Once the model's ruled out, the next instinct is to blame the retrieval or context layer instead and buy a better one. The timing isn't a coincidence: as enterprises push these systems into the real production world, this gap is exactly what's starting to surface, and the vendor response has been everywhere. AWS just entered the "context layer" race with a knowledge graph that learns from agent usage. Snowflake's new Horizon Context and Cortex Sense target the exact symptom this piece opened with : agents giving confident wrong answers because nothing governs the business logic underneath them. Both are real responses to a real problem, but they sit one layer above it; a knowledge graph still depends on whatever feeds it. The real problem lies further upstream, at the data engineering layer. Teams check whether a job ran, not whether the data it moved is still true, an instinct that predates AI by years. Monitoring is built for the pipeline, not for the data. What's actually missing: Data observability Data observability is a well-known concept that doesn't get enough attention in how it's actually implemented. The relevant metric isn't a percentage — it's coverage: what fraction of critical datasets have lineage that's actually queryable, versus only living in someone's head. Uber built a dedicated data quality and observability platform long before retrieval-augmented generation existed. Their Unified Data Quality platform supports more than 2,000 critical datasets and detects around 90% of data quality incidents before they reach downstream consumers. Netflix solved a different piece of the same problem, building a company-wide data lineage system so anyone could answer where a dataset came from and what touched it along the way. It maps dependencies across Kafka topics, ML models, and experimentation, not just warehouse tables. Similar to Uber, the platform was built for humans and now it has become more important with the rise in AI/LLM applications. Between them, Uber and Netflix cover two of the four things worth building for. In practice, I think about it as four dimensions, each measurable on its own terms. Correctness: Does each record conform to the shape and rules it's supposed to, right field types, no unexpected nulls, values in range. Tools like Great Expectations and Soda handle this well: automated row and column-level validation instead of manual checks after something breaks. Track percentage of records passing validation per run. Freshness: Is the data still current relative to its source, not just current as of its last check. Track time since last successful update per source, with an SLA per dataset rather than one blanket threshold, since some sources need hourly refresh and others don't. Consistency: Does the same fact read the same way everywhere it's stored or indexed. This fails silently, it only shows up when two systems fed by the same source start disagreeing. A periodic cross-check between downstream destinations, flagging mismatch rate above a threshold, is enough to catch it early. Lineage: Can you trace any output back to its source and every transform it passed through, the same question Netflix built its system to answer. None of this requires infrastructure most data teams don't already have. I know because I've built it, not just argued for it. At Socure , client data arrived in whatever shape the client felt like sending it, and occasionally, quietly wrong. The challenge was building a system where incorrect data could be identified before it propagated downstream. The same principles applied: Validate what arrived, understand where it came from, and prevent bad data from becoming someone else's problem. Great Expectations became part of that foundation: schema and range validation at ingestion, per-source SLAs for freshness, cross-system checks for consistency, and file-level lineage. All of it sat behind a write-audit-publish pattern, where data landed in staging, was validated, and only moved downstream if it passed the required checks. The result showed up downstream: better accuracy across the board, in reporting, in the ML models, and in AI retrieval built on top of that same data. What to do Monday morning If you're running retrieval-based AI systems in production, the diagnostic question isn't which model to try next or which retrieval architecture to migrate to. It's four narrower questions: Is the underlying data validated against the standards required by its consumers? What's the oldest piece of content currently being served with high confidence? Would two chunks of the same source ever disagree with each other in the same retrieval result? Could you trace where it came from if it turned out to be wrong? If you can't answer those questions, then the gap lies in the pipeline between your source systems and whatever your agent reads from. That’s a data engineering fix, not a model swap or a vendor migration. Whether you're building reporting pipelines, ML systems, or AI agents, correctness, freshness, consistency, and lineage are what make data trustworthy. AI simply exposes weaknesses that have existed in data engineering all along.
- AI stocks lead Wall Street higher, even as Brent oil's price tops $91
AI stocks lead Wall Street higher, even as Brent oil's price tops $91 Austin American-Statesman
Score: 48🌐 MovesJul 22, 2026https://www.statesman.com/news/world/article/asian-shares-mostly-gain-and-south-korea-and-22353399.php - These Clothes Were Designed to Trick AI Surveillance Cameras
The AI backlash has led brands to sell so-called adversarial fashion at wildly different price points.
- China’s Newborn Town targets global viewers with AI-made short dramas
China’s Newborn Town targets global viewers with AI-made short dramas The Straits Times
Score: 48🌐 MovesJul 22, 2026https://www.straitstimes.com/asia/east-asia/newborn-town-targets-global-viewers-with-ai-made-short-dramas?ref - Bluehost promises its new AI agents will deliver "a site or store that runs itself"
“AI shouldn’t stop at building a website,” says Bluehost CEO Sachin Puri
Score: 48🌐 MovesJul 22, 2026https://www.techradar.com/pro/bluehost-promises-its-new-ai-agents-will-deliver-a-site-or-store-that-runs-itself - Simplify AI agent orchestration with Lakebase Postgres
IntroductionTraditionally, auditing is a tedious process that often requires detailed...
Score: 48🌐 MovesJul 22, 2026https://www.databricks.com/blog/simplify-ai-agent-orchestration-lakebase-postgres - How To Build Your Own LLM Runtime From Scratch
If you have ever wanted to actually build an LLM inference runtime yourself — pack your own weights, own every barrier, capture your own CUDA graphs — this is what that journey looks like on an H100. A step-by-step tour of a small runtime called annotated-llm-runtime, and the three bugs that produced most of the annotations. The post How To Build Your Own LLM Runtime From Scratch appeared first on Towards Data Science .
Score: 48🌐 MovesJul 22, 2026https://towardsdatascience.com/how-to-build-your-own-llm-runtime-from-scratch/ - Suck at bedtime stories? Meta has an AI to tuck kids in
Meta’s experimental StoryKit app generates personalized stories, illustrations, narration, and music using a child, toy, setting, or lesson selected by a parent
Score: 48🌐 MovesJul 22, 2026https://www.digitaltrends.com/phones/suck-at-bedtime-stories-meta-has-an-ai-to-tuck-kids-in/ - Africa’s AI-driven lender to enter Ethiopia, Egypt
Opstasia is betting its algorithm stack can score 200 million unbanked consumers in frontier markets.
Score: 48🌐 MovesJul 22, 2026https://www.semafor.com/article/07/22/2026/africas-ai-driven-lender-to-enter-ethiopia-egypt - Earnings at Musk's car company fall as research spending cuts into profit from selling cars
Earnings at Musk's car company fall as research spending cuts into profit from selling cars Houston Chronicle
Score: 47🌐 MovesJul 22, 2026https://www.houstonchronicle.com/business/article/earnings-at-musk-s-car-company-fall-as-research-22356195.php - Uncanny AI: Why AI bots remember random, sometimes useless information
Uncanny AI: Why AI bots remember random, sometimes useless information marketplace.org
- Elon Musk says Grok Imagine will make ‘historically accurate’ AI adaptation of Homer’s Odyssey
The billionaire says the AI-generated film will stay true to Homer’s original, after repeatedly criticising Christopher Nolan’s blockbuster over its casting choices Elon Musk has said his AI platform Grok Imagine will make a “historically accurate” adaptation of Homer’s Odyssey, after the success of Christopher Nolan’s blockbusting treatment which the SpaceX founder has regularly criticised over its casting. In a post on X , Musk said: “Before this year ends, Grok Imagine will make a full-length movie of The Odyssey that is historically accurate and true to the art of Homer.” Musk also linked to a post containing a three-minute clip of footage that the user said had been generated from Grok Imagine, showing a scene between Odysseus and the nymph Calypso. Continue reading...
- Unsloth vs Axolotl vs TRL vs LLaMA-Factory: A Fine-Tuning Framework Comparison on Speed, VRAM, and Multi-GPU
Unsloth vs Axolotl vs TRL vs LLaMA-Factory: A Fine-Tuning Framework Comparison on Speed, VRAM, and Multi-GPU MarkTechPost
- What AI Trading Agents Are Up to on Robinhood
What AI Trading Agents Are Up to on Robinhood The Information
Score: 45🌐 MovesJul 22, 2026https://www.theinformation.com/newsletters/the-information-finance/ai-trading-agents-robinhood - Nigeria’s AI-skilled workforce is moving faster than its businesses, creating the widest adoption gap among major outsourcing markets
Nigeria’s AI-skilled workforce is moving faster than its businesses, creating the widest adoption gap among major outsourcing markets Business Insider Africa
- Intel results to test if AI-fueled rally has room to run
Intel results to test if AI-fueled rally has room to run Reuters
Score: 45🌐 MovesJul 22, 2026https://www.reuters.com/business/intel-results-test-if-ai-fueled-rally-has-room-run-2026-07-22/ - Tesla spending skyrockets as Cybercab, Semi, Megapack production timeline slips
Tesla's 26% boost in revenue wasn't enough to offset rising operating expenses and capital expenditures as it pushes to launch a new generation of products.
Score: 45🌐 MovesJul 22, 2026https://techcrunch.com/2026/07/22/tesla-spending-skyrockets-as-cybercab-semi-megapack-production-timeline-slips/ - DataMEDS AI Officially Begins Trading Under Stock Symbol 'MEDS' on the NASDAQ Capital Markets
DataMEDS AI Officially Begins Trading Under Stock Symbol 'MEDS' on the NASDAQ Capital Markets USA Today
- Best voice agent API for startups building their first voice product
Review of top voice agent APIs suitable for startups launching voice products.
- AI server maker Supermicro’s stock gains on $60B order backlog and stronger margins
Artificial intelligence server maker Super Micro Computer Inc. told investors in a preliminary earnings forecast today that it has secured more than $60 billion worth of new orders for its products in its fiscal fourth quarter. As a result, it now expects its gross margin to significantly exceed its prior forecast. The news sent shares of […] The post AI server maker Supermicro’s stock gains on $60B order backlog and stronger margins appeared first on SiliconANGLE .
Score: 45🌐 MovesJul 22, 2026https://siliconangle.com/2026/07/21/ai-server-maker-supermicros-stock-gains-60b-order-backlog-stronger-margins/ - Suspicious content? NordVPN's new AI bot will check it for you on WhatsApp, X, and Instagram
NordVPN's experimental hub, NordLabs, has launched NordBot, a free-to-use AI agent that acts as your personal scam checker for suspicious links, messages, and images on platforms like WhatsApp and Instagram.
- Exploring a smarter way to build climate-resilient roads
Every year from June to September, India experiences the monsoon season. While the visible heavy rainfall often takes the blame for many roads requiring repairs much sooner than expected, a far less visible yet critical force is at play long before the first raindrop falls on the road.
Score: 45🌐 MovesJul 22, 2026https://techxplore.com/news/2026-07-exploring-smarter-climate-resilient-roads.html - NTT DATA Group cuts incident analysis to 30 minutes with Codex
NTT DATA Group uses ChatGPT Enterprise and Codex to help 9,000 employees automate work, cut incident analysis to 30 minutes, and scale secure AI adoption.
- For AI Value, Focus on Your Use Cases
For AI Value, Focus on Your Use Cases Gartner
- Towards Automating Eval Engineering
Explores automating evaluation processes for AI agents, improving efficiency and reliability in agent development.
- AI startup Cascade raises $3.5M to predict construction projects before bidding begins
The company's platform uses AI to analyze bond filings, permits and capital plans to help firms decide which projects they're most likely to win. Customers include firms working on major airports and data centers.
Score: 45💰 MoneyJul 22, 2026https://www.bizjournals.com/newyork/news/2026/07/22/startup-cascade-raises-35m-construction.html?ana=brss_6150 - Phoenix Motor Participates in AMD Advancing AI 2026 as EdisonFuture Accelerates Expansion into AI Infrastructure
Phoenix Motor Participates in AMD Advancing AI 2026 as EdisonFuture Accelerates Expansion into AI Infrastructure azcentral.com and The Arizona Republic