AI News Archive: July 23, 2026 — Part 8
Sourced from 500+ daily AI sources, scored by relevance.
- Remember Jibo? Its Successor Is a Wearable That Turns Your Life Into AI Slop
With “blessings” from the original Jibo founders, iKairos is a wearable or desk-mounted “AI journal” that turns your family moments into AI images and video.
Score: 40🌐 MovesJul 23, 2026https://www.wired.com/story/the-beloved-jibo-robot-is-being-resurrected-as-an-ai-wearable/ - A side-by-side comparison shows stark differences between Meta and Anthropic's AI ad campaigns
A side-by-side comparison shows stark differences between Meta and Anthropic's AI ad campaigns Business Insider
Score: 40🌐 MovesJul 23, 2026https://www.businessinsider.com/meta-anthropic-ai-ad-campaign-compared-2026-7 - Top TrendAI Likes & Dislikes 2026
Top TrendAI Likes & Dislikes 2026 Gartner
Score: 40🌐 MovesJul 23, 2026https://www.gartner.com/reviews/market/email-security/vendor/trendai/likes-dislikes - How We Benchmark Deep Agents
An overview of the methodology and metrics used to evaluate Deep Agents performance.
- Shinsegae's AI paper accepted at ICML
Shinsegae Department Store said Thursday that its AI-powered hyperpersonalization technology, developed jointly with Seoul National University, has been accepted for presentation at the International Conference on Machine Learning, one of the world's most prestigious artificial intelligence conferences. The achievement stems from a yearlong industry-academia collaboration launched after Shinsegae and Seoul National University's Graduate School of Data Science signed a memorandum of understanding
- Clio’s AI legal workspace comes to Canada
Canadian product was built on case law dataset from Clio’s Jurisage acquisition. The post Clio’s AI legal workspace comes to Canada first appeared on BetaKit .
- Geekbench 7 introduces biggest overhaul yet — real-world CPU testing, new media workloads, AI benchmarks, and CUDA support
The latest update introduces more realistic CPU and GPU workloads, redesigned multi-core testing, AI-focused benchmarks, larger datasets, and CUDA support for Nvidia GPUs.
- AI-powered app aims to boost early childhood learning outcomes
The Unlimited Child has launched an AI-driven app to provide personalised coaching, classroom support and monitoring for ECD practitioners in low-resource and offline environments.
Score: 40🌐 MovesJul 23, 2026https://www.itweb.co.za/article/ai-powered-app-aims-to-boost-early-childhood-learning-outcomes/KBpdg7pmG5yMLEew - ChatGPT has no clear brand leader in most categories
Semrush found only 15% of ChatGPT categories have a consistent brand leader, underscoring how difficult AI visibility is to sustain. The post ChatGPT has no clear brand leader in most categories appeared first on MarTech .
Score: 40🌐 MovesJul 23, 2026https://martech.org/chatgpt-has-no-clear-brand-leader-in-most-categories/ - MoneySimpler Launches AI-Powered Quantitative Trading Platform to Simplify Automated Investing
MoneySimpler Launches AI-Powered Quantitative Trading Platform to Simplify Automated Investing USA Today
- Tilly Norwood's creator insists the AI actor is creating Hollywood jobs
The robot actor is generating work for Hollywood creatives, not stealing jobs from actors, her inventor tells CBS News.
- OpenAI won't let some customers export their chats, but this tool will
ChatGPT Business and Enterprise users lack the standard chat export option, so third-party utilities like scrapemychats fill the gap
- 400 Massachusetts high school students complete paid AI internships for real businesses
The four-week program paired students from 76 schools with businesses nationwide to develop deliverable AI projects. Each intern earned $750.
Score: 40🌐 MovesJul 23, 2026https://www.bizjournals.com/boston/news/2026/07/23/massachusetts-ai-internships-high-school.html?ana=brss_6150 - 20 NII papers accepted at ACL 2026; two win best theme and outstanding paper awards
20 NII papers accepted at ACL 2026; two win best theme and outstanding paper awards EurekAlert!
- DAC 2026: Users Are Not Waiting; DIY AI Is Now in Vogue
At DAC 2026, chip giants stop waiting for EDA vendors and build their own AI brains—see who’s seizing control. The post DAC 2026: Users Are Not Waiting; DIY AI Is Now in Vogue appeared first on EE Times .
Score: 40🌐 MovesJul 23, 2026https://www.eetimes.com/dac-2026-users-are-not-waiting-diy-ai-is-now-in-vogue/ - Larry Magid: TrustCon Explores Trust and Safety in the Age of AI
Larry Magid: TrustCon Explores Trust and Safety in the Age of AI The Mercury News
Score: 38🌐 MovesJul 23, 2026https://www.mercurynews.com/2026/07/23/larry-magid-trust-and-safety-professionals-in-age-of-ai/ - How some companies are using AI to clear technical debt
Can LLMs help deal with decades of legacy code and the cruft that builds up in massive codebases?
- Can an LLM make a feature-length movie on its own?
How it did Betteridge's Law of Headlines says the answer is no [1] , but I was able to make a feature-length adaptation of William Hope Hodgson's The House on the Borderland. I have a YouTube channel where I mostly get LLMs to write albums and make them into music videos. This naturally made me curious whether an LLM could make a feature-length movie on its own. It doesn't seem like it would be harder to do than to disprove the Jacobian conjecture [2] and if the challenge is to just work for a long time, it should be noted that METR's time-horizon benchmark is saturated. [3] Even with my assistance, I would still say the result is a failure. It's good enough that I'm willing to put my name to it as the first feature-length movie I've made, but that is judging by the standards of small YouTube channels, not movies as an overall category. Why I think it failed I would not say agency is an obstacle for this work. Claude Fable 5 knew what to do and when I gave it permission it was perfectly capable of babysitting hours of video-generation runs, debugging API issues and pipeline bugs, coming up with and implementing edits to the storyboard, and many other tasks without oversight. The main obstacles were LLMs' poor sense of timing, struggles with audio and vision, poor integration of multimodality, and losing details after lengthy sessions. Timing issues The initial storyboard done by Claude Fable 5 had a runtime of 49 minutes, despite being told it was to make a feature-length movie of a book that has 50,945 words and takes 5 hours as an audiobook. GPT 5.6 Sol reviewing the storyboards didn't think the runtime was problematic either. I eventually got it to rewrite the storyboard for an hour and forty-five minutes, which seems both appropriate for a movie and adapts the book without being consistently too fast or too slow. These timing issues recur at the smaller level just as much. I had to repeatedly correct the model when it tried to fit multiple beats of action as well as dialog within eight-second shots and scripted thirty seconds of narration over forty seconds of footage. Video is expensive Throughout this project, I was using Fable and Sol with very little restraint and under the (very affordable to me) $200/month subscription plans. [4] Video is much more expensive: at Gemini Omni Flash's current price of $0.10/second, it would cost $645.40 to generate this movie, but that would balloon massively to $5,146.10 when you add in the 45,007s of footage I generated which didn't make the cut [5] . Compared to an actual Hollywood movie, that's a bargain, but again this was done on the budget of a hobbyist YouTube channel. Video generators are still pretty bad Omni Flash may be the best video generator [6] , but there are still hard limits on its quality. For one, the inability to extend its clips means that it can only be used for short videos of between 4 and 12 seconds. I used Veo 3.1 for the rest of the film and that had recurring issues: the book's swine-things routinely came out looking like literal pigs, left/right were routinely confused, telling the model not to include something rarely worked, faces were a constant struggle, action clips routinely had beats dropped, dialog was routinely given to the wrong character, and there were frequent unnecessary cuts and dissolves. This interacts with the timing issues mentioned earlier very poorly. Fable struggles to write prompts for Veo that match the length of the video generated and Veo punishes prompts that give it too much and too little to do on-screen very heavily. We could reroll shots with all those issues, but shots that failed the first time were much more likely to fail the second time. If you see issues in the YouTube video, rest assured that those issues survived even more than two generation attempts. This all means that we hit diminishing returns hard after massive effort, but before we actually do a good job. Coordination difficulties I wasn't using one large model to do all this work; it was a combination of Claude Fable 5 (coding, coordinating the others, reviewing their work), GPT 5.6 Sol (for grunt work that needed to be done at high quality), Claude Sonnet 5 (for independent perspectives on reviews), Gemini 3.5 Flash (for video review), Whisper (for audio review), Nano Banana 2 (for creating Veo's keyframes and reference images), gpt-image-2 (for editing reference images), edge-tts (for narration voice-overs), Suno v5.5 (for soundtrack), Gemini Omni Flash (for high-quality standalone clips), Veo 3.1 - Fast (for mid-quality chains of clips) , and Veo 3.1 - Lite (for cheap video generation). That is literally twelve models from five companies. Even if you assume that each model could do its work without errors [7] , there were still large time sinks where Fable would describe what it wants to Suno or Veo , get back mediocre work, struggle to verify its quality, and have trouble deciding whether the issue was with the instructions it gave or the other model struggling. Forgetfulness and context rot The final issue I encountered was LLMs losing details of the work I requested them to do while working on it. After watching a cut of the movie, I would typically have around 400 lines commenting what I thought was wrong and ask them to fix it. None of the work should have been too difficult for them, but they would usually focus on the big classes of problems first and after those were dealt with, they would assume the work was done instead of checking the full list for remaining work. To me, this seemed more like them losing things in the long-context , not a knowing attempt to reward-hack or pass incomplete work off as complete, but the end result was the same, in that my third or fifth watch saw errors that I already reported and expected to be fixed in earlier watches. Where we go from here I expect these issues to all be fixed by the end of 2027. It's a running gag that Claude gives wildly wrong estimates for how long software projects take and we can see similar issues elsewhere; an AI deficiency with such a wide impact is certainly one that will see major effort to resolve once it becomes the obstacle to anything important. I have full confidence that it will be solved, both from looking at lines on the graph and from comparing it to prior issues that were also solved. I'm open to work if you know a company hiring to fix these issues, but I have some ideas of my own. As for multimodality, I'm not sure whether the solution is just one god-model that can storyboard movies, write code, generate video, and refine all these outputs all on its own or for models to get better at coordinating with other specialized models, but I don't see a wall. Not until we hit the singularity. My current goal with the video pipeline is to create movie adaptations of every classic sci-fi novel (i.e. the ones that are public domain). I think that's doable by end-of-year; whether those movies get immediately upstaged by the next generation of models is an open question. ^ And it holds here. ^ https://x.com/__alpoge__/status/2079028340955197566 ^ https://metr.org/time-horizons/ ^ If you check out my Straude , I probably used $5,000 of tokens, but the actual subscriptions are $200/month. ^ The full details are that the final cut is 6,454s of runtime, made from 849 clips and the rejects are 5,509 clips with 45,007s of runtime, which means there are 6,358 clips in total with 51,461s of runtime. Thus only about 13% of the clips I made were included in the final cut. ^ As of 2026/07/22, it's listed #1 on https://artificialanalysis.ai/video/leaderboard/text-to-video ^ Which you can't. Discuss
Score: 38🌐 MovesJul 23, 2026https://www.lesswrong.com/posts/24RKHEkwgZ6Hm6ygY/can-an-llm-make-a-feature-length-movie-on-its-own - The robot byline is quietly disappearing
The most obvious use of generative AI is writing. It’s right there in the name—large language models (LLMs) are all about reading, organizing, analyzing, and conjuring words—which is exactly why many in the journalism profession have been going through a kind of existential crisis these past few years. And the crisis isn’t just theoretical. As artificial intelligence systems get better at writing, a growing number of newsrooms are using AI to help not just with analysis, process, and ideas, but the actual words, too. That’s leading to growing pushback from editorial teams, like when reporters at The Sacramento Bee recently objected to having their bylines put on content written primarily by AI. As I teach in my AI trainings , using AI to write public-facing content is an inherently dicey business. I should know, since AI-generated articles are a component of The Media Copilot ‘s editorial strategy. At minimum, you need to ask yourself several ethical questions: What is the medium? What exactly was the AI’s role? What’s the worst that could happen? And several more. From the answers, you need to develop a clear rulebook. One of the most important parts of such a rulebook is its policy around disclosure—the ways you signal to the reader that a piece of content is AI-generated or AI-assisted. The most direct way to do so is with an AI byline. Robot bylines typically have clinical names, such as the AI News Desk or Generative AI Services , along with their own author pages. That unambiguously lets the reader know that AI didn’t help just with research or ideas, but also with the words on the page. To what extent—which is often crucial—is usually revealed via an accompanying disclaimer, often at the bottom of the page. Transparency comes with a cost The AI byline feels intuitively correct: It checks the transparency box, it’s an easy-to-read label, and it slots neatly into an existing system. Moreover, audiences say they want them, with a Trusting News study showing that 94% of readers want disclosures on content that’s AI-written. As use of AI rises in newsrooms and editorial operations, it seems logical that AI bylines will become more common. Except that’s not what’s happening. A 2025 audit of 186,000 articles in 1,500 newspapers in the U.S. estimated that about 9% of the content was partly or fully AI-generated, yet only about 5% of those articles included a disclosure. Prominent AI-byline experiments at Fortune and Business Insider were discontinued, though that’s not an indicator of a broad policy shift against AI writing; indeed, Nick Lichtenberg, business editor at Fortune , famously used AI to produce more than 600 stories in six months. The Cleveland Plain Dealer —which uses writing tools to turn raw reporting into stories with final sign-off from the reporter—generally doesn’t use an AI byline unless the human contribution is extremely minimal. Anecdotally, as someone who covers AI use in media closely, I see fewer AI bylines than I used to. Which isn’t to say they’re gone: CoinDesk marks AI assistance with the byline “AI Boost” and ESPN’s writing bots still get top billing, but generally there’s been a retreat from the AI byline as a best practice. What’s going on? A few things: 1. Visibility in search and AI answers. Google says it does not downgrade content simply because AI was used to produce it. But its guidance consistently stresses clear authorship, first-hand expertise, and accountability, and it has said that giving AI an author byline is probably not the best way to disclose its use. A robot byline may not be a direct negative ranking signal, but it also provides none of the human authority signals that search and discovery systems are designed to reward. Data supports this: In Graphite’s 2025 analysis , human-written articles made up 86% of the pages ranking in Google Search and tended to rank higher than AI-generated material. That doesn’t prove that AI authorship or attribution caused the difference, but it shows the broader disadvantage AI-heavy content faces. That pattern also matches our limited experience at The Media Copilot . Our human-bylined articles show up in Google Discover and rank higher in Google Search than those published under our AI byline, The Copilot . 2. The trust paradox. Trusting News found that, although the vast majority of audiences want AI disclosures, their presence made 42% of respondents less likely to trust an article. Separate research from Reuters Institute found an even wider comfort gap: 12% of readers are comfortable with fully AI news, versus 62% comfortable with fully human-written news. 3. Institutional memory. When generative AI was new, there were several high-profile failures of AI content. By putting forward an AI byline, an outlet can’t avoid association with those mistakes, and it paints a target on every article under it that the screenshot industrial complex will fire at all day the moment there’s an error. In short, AI bylines have acquired a lot of baggage in the short time they’ve been around, and many publications seem to be taking the stance that they’re just not worth it. However, that doesn’t translate into a pullback on AI generally, or even a pullback on AI-assisted content. The most direct way to square this circle is to reserve bylines for people, and disclose the AI’s contribution in some other way, usually via one of those notes at the end of the article. A byline is a promise At the heart of this issue is what the byline actually means. The idea that the person named at the top of the article wrote each and every word has always been a fallacy. Editors, wire copy, fact checkers, headline writers, and spellcheckers all contribute actual words and sometimes whole passages to articles. Automated editing software takes this even further—anything substantially edited through Grammarly or a similar tool already contains wording shaped by AI. The byline is not “I wrote all these words”; it’s “I stand by all these words.” And this is the core problem with AI bylines—they obfuscate responsibility. Without ownership, without someone prominently standing by what’s actually written, there’s little incentive to make sure it’s great writing. It inherently turns the writing into something second class, regardless of how the AI was used and how many words it actually contributed. At the same time, what happened at The Sacramento Bee shows just how much writers will defend the use of their names. And rightly so. The path forward isn’t to force writers’ names onto AI content without their consent, but to give them the freedom and the accountability on the use of AI. The disclosure should scale with the machine’s contribution: Routine editing may require nothing, substantial drafting should require a specific note, and largely automated reporting should explain both the system and the human review. Ultimately, though, the only label that really matters is whether a named human stands behind the result. With the right policies and training, publications can give writers broad freedom to use AI while keeping responsibility attached to a human name. If the content is worthwhile, then over time those who use AI to amplify and accelerate their judgment will be successful. Those who use it as a substitute for thinking will inevitably fail. The human accountability signal The AI byline isn’t dead, but it is being repriced. As machine text gets cheap, a human name that carries real accountability becomes the scarce and valuable signal. As the tools get better, they’ll continue to blur who wrote what. The constant, however, is simple: A human has to stand behind it. No one gets to outsource accountability to a machine.
- Zaptiva Introduces Automation Readiness Framework to Help Organizations Prepare for AI-Driven Business Operations
Zaptiva Introduces Automation Readiness Framework to Help Organizations Prepare for AI-Driven Business Operations USA Today
- What it's like inside the data centers powering the AI boom
ASHBURN, Va. – A test server hall tucked inside America's "data center alley" offers a rare firsthand look inside the AI boom. Why it matters: Seeing how AI is physically made helps sharpen our understanding of the unprecedented data center buildout that is becoming increasingly unpopular around the country. Behind the scenes: I recently toured an innovation lab by Digital Realty in Northern Virginia, home to the world's largest concentration of data centers. Digital Realty is one of the world's oldest and most established data center operators. The lab, which opened in September, allows the company to test new technologies and show visitors — like customers, lawmakers and journalists — what it's like to be inside. A similar one opened in London in June. Inside the room: I was immediately hit with what I can only describe as "demented white noise." The sound comes from fans cooling racks of AI chips that generate enormous amounts of heat. It felt like standing inside one giant computer. Even beneath my feet, a glass floor revealed pipes and other infrastructure helping keep the servers cool. Zoom in: At the bottom of one of the closet-sized racks sat a gold-colored Nvidia AI system packed with multiple AI chips. The gold is intentional — but not real. "Gold is associated with high value and performance," Nvidia spokesperson Kira Sarkisian said by email. "That's about $1 million right there," said my tour guide, Digital Realty engineer Tor Nyström, referring to the entire rack. From L to R: Inside the lab with a glass floor exposing the closed-loop cooling system of pipes and the server racks on either side; A close-up of the golden Nvidia AI system; A full rack of AI infrastructure one engineer estimated costs about $1 million. Photos: Amy Harder/Axios Reality check: Getting inside a data center is not easy. Some companies flatly declined my request for a tour, while others require signing a non-disclosure agreement to get inside the walls of a campus (server halls are typically off-limits altogether). Friction point: This type of secrecy is part of what's fueling the backlash against data centers, alongside strains on electricity and local water supplies. My thought bubble: Digital Realty let me photograph its innovation lab and tour a nearby campus under construction. I came away convinced companies can show the public how these facilities work without revealing proprietary information. The big picture: AI feels invisible. It isn't. It's racks of servers, cables, cooling systems and enormous amounts of electricity — and sometimes water. Compared with an offshore wind farm or an underground coal mine, it's remarkably uneventful. That's by design. If excitement is happening inside a data center, something has probably gone wrong. Zoom out: After the tour of the test server hall, we drove a few miles to what Digital Realty calls its Digital Dulles campus, which could eventually include 13 data centers with an investment north of $10 billion over the next decade. State of play: The most notable parts of a data center are also the most controversial: the infrastructure needed to power and cool AI. Otherwise, from the outside, data centers look like massive, windowless warehouses. Multiple electricity substations are visible, along with whatever cooling technology is used, which includes either huge water-filled cooling tours or rows of dozens of industrial air conditioners that require large amounts of power. Although plans are still being finalized, Digital Realty anticipates these data centers will use a type of cooling that relies more on energy than water. This means no huge cooling towers were on site, which would be easily visible at those that do use water-based cooling. Stunning stat: I counted almost 40 diesel generators hooked up to one data center building. Digital Realty spokesman Kevin Gundersen said those are only used for backup power purposes and occasional testing, so they aren't on most of the time. Normally, the campus draws power from the broader electricity grid. In Virginia, that's largely natural gas and nuclear power. A row of nearly 40 diesel generators are attached to this Digital Realty data center near Dulles Airport in Virginia. Photo: Amy Harder/Axios What's next: In a sign of the AI's global footprint, Digital Realty is eyeing at least three more locations for new innovation labs: Singapore, São Paulo, Brazil and Johannesburg, South Africa.
- United Telecoms launches enterprise-ready AI voice agent platform for SA corporates, SMEs
The AI voice agent platform is available through the United Voice Cloud ecosystem.
- macOS 27 Beta Hides Trippy Siri 'Voice Pad' Interface With 13 Voices
For macOS 27 Golden Gate, Apple appears to be working on an immersive new interface for choosing Siri AI's voice, based on findings in the latest beta. MacRumors forum member " mactracker " has shared what they call a hidden "voice pad" in the macOS 27 beta 4, which was released on Monday. When enabled, the interface replaces the System Settings menu for Siri's voice options with a nearly black canvas, with the currently selected voice marked by a colored dot. Pressing, holding, and moving around the pad with your pointer brings the interface to life, causing various colors to transition across the view as more dots appear and the voice modulates through different characteristics. Each dot emerges from the ether like a revealed star in a hidden constellation. The visuals are even responsive to cursor movement via spatial audio, so that when the pointer approaches a new voice on the pad, it sounds clearer and more present, while voices further away become distant and ambient. Meanwhile, a spatial mixer tracks the pointer's distance, angle, and velocity, which adjusts volume, reverb, occlusion, and filtering across the voice and background audio layers as you move. The pad lays out 13 voice slots in a loose grid, suggesting Apple may be preparing to support up to 13 custom voices for Siri AI. You can see the UI in action in the video embedded below. Currently, the latest macOS 27 beta doesn't include the required voice models, so it falls back to the two custom voices available. However, a related file found in the beta describes the 13 expected American English voices by gender, age, and character, spanning everything from a soft-spoken young female voice to a deep adult male voice and two older-sounding voices. If the interface makes the final macOS 27 cut, it would be a big jump from where things stand today. As of the current betas, American is the only voice option for Siri AI, whose expressiveness and pace can only be adjusted via sliders. In this context, the interface likely serves as an onboarding experience for picking Siri's underlying voice, with the existing picker and its Pace and Expressivity controls used to further adjust the voice after selection. While the interface remains unfinished, it is already said to be localized for 44 languages. It also shares the same Siri setup framework with iOS 27, so we could see it on iPhone and iPad, too. Your browser does not support the video tag. Unlike the hidden Siri popover we covered earlier this week , there's no easy way for beta testers to switch on this feature flag, as it requires disabling System Integrity Protection (SIP) and overriding internal code with a debugger. The new, more advanced Siri AI, which is backed by large language models, will arrive in supported regions with the release of iOS 27, macOS 27, and Apple's other software updates this September. Tag: Siri This article, " macOS 27 Beta Hides Trippy Siri 'Voice Pad' Interface With 13 Voices " first appeared on MacRumors.com Discuss this article in our forums
Score: 38🌐 MovesJul 23, 2026https://www.macrumors.com/2026/07/23/macos-27-beta-hidden-siri-voice-ui/ - Google Just Handed Marketers a 4-Step AI Playbook. Most Will Ignore It
Behind the blowout numbers, Alphabet described an AI-first marketing machine. Here’s what any business can steal from it.
- More Texas Home Sellers Are Turning to AI Before Contacting Real Estate Agents
More Texas Home Sellers Are Turning to AI Before Contacting Real Estate Agents azcentral.com and The Arizona Republic
- Elon Musk to make ‘historically accurate’ AI version of ‘The Odyssey’
Elon Musk to make ‘historically accurate’ AI version of ‘The Odyssey’ The Mercury News
Score: 36🌐 MovesJul 23, 2026https://www.mercurynews.com/2026/07/23/elon-musk-the-odyssey-ai-movie-remake/ - I use Anthropic's Claude AI tools for very different jobs: How to pick between models, Code, and Cowork
Here's how Anthropic's AI tools work, what they can accomplish, and why security, cost, and your oversight still matter.
- Inside AI Builders Week: How teams work in the AI era
Inside AI Builders Week: How teams work in the AI era Atlassian
Score: 35🌐 MovesJul 23, 2026https://www.atlassian.com/blog/how-we-build/ai-builders-week-how-teams-work - Introducing the official Trello MCP server! Connect Trello to your favorite AI tools
Introducing the official Trello MCP server! Connect Trello to your favorite AI tools Atlassian Community
- How to optimize token efficiency in agentic systems
Learn how to reduce token usage in agentic systems with better retrieval, structured memory, routing, and loop control without hurting answer quality.
Score: 35🌐 MovesJul 23, 2026https://www.glean.com/blog/how-to-optimize-token-efficiency-in-agentic-systems - 5th Weixin Mini Program Global Innovation Challenge Launches Southeast Asia Regional Competition, Inviting Youths to Build Mini Programs with AI Tools
5th Weixin Mini Program Global Innovation Challenge Launches Southeast Asia Regional Competition, Inviting Youths to Build Mini Programs with AI Tools The Straits Times
- Darrow Now Available in the Microsoft Marketplace
Darrow Now Available in the Microsoft Marketplace Toronto Star
- Michaels launches AI shopping assistant
The retailer is positioning the Ask Mike tool as a way shoppers can move beyond keyword filtering and receive personalized recommendations.
Score: 35🌐 MovesJul 23, 2026https://www.retaildive.com/news/michaels-ai-shopping-assistant-ask-mike/826016/ - Singapore's AI-generated National Day banner erases flag's crescent and stars, invents fake stadium
An AI-generated National Day banner in Singapore drew public criticism after it rendered the national flag without its crescent moon and five stars, leaving a plain red-and-white design.
- follow Inc announces followOS, a fully autonomous AI advertising Platform Built for Small and Mid-Sized Businesses
follow Inc announces followOS, a fully autonomous AI advertising Platform Built for Small and Mid-Sized Businesses azcentral.com and The Arizona Republic
- Why Founders Need a New Operating System to Lead Through AI Disruption
Why Founders Need a New Operating System to Lead Through AI Disruption entrepreneur.com
Score: 35🌐 MovesJul 23, 2026https://www.entrepreneur.com/leadership/why-founders-need-a-new-operating-system-to-lead-through-ai/503716 - The Robots Cometh
The Robots Cometh Time Magazine
- AI notetakers promise easy meeting recaps, but some professionals question their use
AI notetakers promise easy meeting recaps, but some professionals question their use Boston Herald
- Stop asking AI nicely: Here’s how to get work-ready results every time
Over the past few years, I have learned that basic prompts produce inconsistent, hallucination-prone results that no executive would trust in production. What turned the tide was my move to advanced prompting techniques. These weren’t theoretical experiments; they became a practical foundation for reliable, measurable outcomes. I want to share the techniques that consistently delivered the biggest gains in my projects, complete with real before-and-after examples, copy-paste templates, lessons from failures and guidance on when to evolve beyond prompting to agentic systems. Why advanced prompting still matters in enterprise settings Sophisticated prompting remains essential for control, reliability and compliance. If you “ask nicely” and hope for the best, you need deterministic behavior, auditable reasoning and minimal risk of hallucination. Here’s what worked for me. 1. Chain-of-Thought (CoT) and its variants: Unlocking step-by-step reasoning The problem: Models would jump to conclusions on complex analysis tasks, especially involving data interpretation or multi-step logic. What I did: I started explicitly instructing the model to “think step by step” and show its reasoning. Before (basic prompt): “Analyze last quarter’s sales data and recommend three actions.” After (CoT prompt): “You’re a senior business analyst. Analyze the following sales data step by step: [data]. First, identify the key trends. Second, calculate the rates and anomalies. Third, link findings to business context. Finally, recommend the three prioritized actions with expected impact. Explain your reasoning at each step.” Results: Accuracy and depth improved dramatically. Variants that worked well: Self-consistency. I ran the same CoT prompt multiple times and took the majority consensus. This reduced variability significantly. Template you can use: You are [expert role]. Solve this problem by thinking step by step. [Task or question] For each step: 1. State your observation or calculation. 2. Explain the implication. 3. Proceed only when confident. Final answer in this format: [structured output] 2. Tree-of-Thoughts (ToT): Exploring multiple reasoning paths For truly complex decisions such as resource allocation or risk assessment, linear CoT isn’t enough. Tree-of-Thoughts lets the model generate and evaluate multiple branches. Example: I was helping a client evaluate three potential vendor platforms for an AI deployment. A standard prompt gave a superficial comparison. With ToT Prompt Snippet: Explore three different reasoning paths for selecting the best vendor platform: Path 1: Focus on cost and scalability. Path 2: Focus on security, compliance and integration. Path 3: Focus on innovation and long-term roadmap. For each path, evaluate pros/cons against our requirements [list]. Then, compare the paths and recommend the strongest overall option with justification. Outcome: The model surfaced nuanced trade-offs (e.g., one vendor had superior security, but higher integration cost). When to use: Strategic planning, troubleshooting or scenarios with high uncertainty and multiple viable approaches. 3. ReAct (Reason+ Act) and prompt chaining: Moving toward agentic behavior One of the biggest leaps I have noticed comes from combining reasoning with tool use and chaining prompts. ReAct example : (used in data analytics workflow) You are an AI analyst with access to tools. For the query below: 1. Reason about what information you need. 2. Choose the appropriate tool or action. 3. Observe the result. 4. Repeat until you can answer confidently. Query: [user request] In practice, I chained this with retrieval tools. One automated quarterly compliance reporting; the system reasoned about required data, pulled relevant records, validated them, and generated the reports. 4. Meta-prompting and self-reflection: Letting the model improve itself Use the model to refine its own prompt. This is a huge time-saver. You are an expert prompt engineer. Improve the following prompt for clarity, structure and effectiveness with [target model]. Make it more precise while preserving intent. Original prompt: [paste] Provide the improved version and explain your changes. Self-reflection loops (asking the model to critique its own output and revise) are a game-changer for content generation and code-review tasks. 5. Multimodal and structured output techniques With vision-enabled models, I started combining text with images (e.g., uploading architecture diagrams or dashboards). Tip from experience: Be extremely specific in describing what the models should focus on. Best practices I learned the hard way Start simple, then layer complexity : Over-engineered prompts from Day One usually backfire. Model specific tuning: Some models respond better to XML delimiters; others to explicit reasoning. Evaluation and versioning: Treat prompts like code if you track versions and run automated evals. Security guardrails: Always include instructions against prompt injections and respect data boundaries. When to stop prompting : For repetitive, high-stakes workflows, move to full agents or an orchestration framework. Final takeaways for technical leaders Advanced prompt engineering has now become a core competency for anyone responsible for enterprise AI outcomes. Start by picking one technique and apply it rigorously to a real business problem. Document before/ after and you will notice why it’s worth mastering. The field continues evolving towards more automated and agentic systems, but the ability to precisely direct AI reasoning remains foundational. This article is published as part of the Foundry Expert Contributor Network. Want to join?
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