AI News Archive: May 29, 2026 — Part 8
Sourced from 500+ daily AI sources, scored by relevance.
- ChatGPT for iOS and Android can now start Codex work on Windows
Earlier this month, OpenAI updated ChatGPT’s mobile app to include remote access to Codex for Mac. Starting today, ChatGPT for iPhone and Android can also start work on Codex for Windows as well. Plus Codex inside ChatGPT for iOS has some new features and improvements. more…
Score: 27🌐 MovesMay 29, 2026https://9to5mac.com/2026/05/29/chatgpt-for-ios-can-now-start-codex-work-on-windows/ - Gemini app users in India can now edit videos using Omni AI model
Google's Gemini Omni is now available in India, allowing users to upload and transform videos through conversational AI prompts without traditional editing tools
- Claude Code vs Codex vs Antigravity: Which AI Coding Agent Should You Use?
Every AI coding tool in 2026 claims to make developers faster. But which one actually performs best on a real engineering task? My engineering team uses AI coding tools daily. We got curious about whether the marketing matched reality, especially for a generation of tools that now claim to be “agents,” not just assistants. So we ran a hands-on experiment: install all three, give each the same real-world task, and document exactly what came out the other side. In this article, we compare the three agentic tools (Claude Code, Antigravity and Codex) and tell you which performed best. Which models did we use? Before comparing the platforms, it’s worth understanding the AI models that we used for our experiment. The diffenrences between these models is real, and it directly shapes the code you get out the other end. 1. GPT-5.5 (Codex) OpenAI’s GPT-5.5 launched on April 23, 2026 and is the model currently powering Codex. Designed as an efficiency-first flagship, it brings significant improvements in tool use and agentic task completion over its predecessors. The standout number is Terminal-Bench 2.0 at 82.7% , the highest of the three models here, and a meaningful signal for a tool like Codex that runs terminal commands as part of its core workflow. 2. Claude Sonnet 4.6 (Claude Code) Released February 17, 2026, Sonnet 4.6 is the model powering Claude Code’s default experience. Anthropic positioned it as near-Opus performance at mid-tier pricing, and the benchmarks back that up. SWE-Bench Verified at 79.6% is the headline. SWE-Bench tests models on real GitHub issues from production codebases. It’s the closest benchmark to “can this model ship production code?” and Sonnet 4.6 leads the three models here. However, this model also only scores 59.1% on the Terminal bench. In Claude Code usage data, developers preferred Sonnet 4.6 over the previous flagship Sonnet 4.5 70% of the time , citing better instruction following and less overengineering. 3. Gemini 3.5 Flash (Antigravity) Announced at Google I/O 2026 on May 19 : Gemini 3.5 Flash is the default model in Antigravity. A Flash-tier model (fast, cheap) that actually beats last year’s Pro model on several coding benchmarks . Gemini 3.5 Flash benchmark performance across Terminal-Bench 2.1, MCP Atlas (multi-tool), and SWE-Bench Pro. MCP Atlas at 83.6% is the number that matters for Antigravity (it measures multi-step tool orchestration across search, file operations, and data handling). For an IDE that dispatches multiple agents in parallel, that’s the relevant signal. Gemini 3.5 Flash also runs roughly 4x faster on output tokens per second than comparable frontier models, which is how Antigravity can manage parallel agent workloads without becoming unusably slow. Model benchmark summary No single model dominates across the board. Sonnet 4.6 leads on real-world repo-level coding. GPT-5.5 leads on terminal operations. Gemini 3.5 Flash leads on multi-tool orchestration and is by far the cheapest and fastest of the three. Now that we know about the AI models, let’s talk about the coding agents. Claude Code vs Antigravity 2.0 vs Codex The model is only part of the story. The platform is where the experience diverges most sharply. 1. Claude Code is Anthropic’s terminal-native coding agent. It lives in your command line, has direct access to your filesystem and git, and operates on an approval-first model: it reasons about your full codebase, asks clarifying questions before starting, and requires explicit confirmation before any destructive action. 2. OpenAI Codex started as a CLI in April 2025 and has expanded to a desktop app, IDE integrations, and cloud-based execution. It’s built around an async task-delegation model. It’s bundled into ChatGPT Plus ($20/month) rather than sold separately, making onboarding easy for anyone already in the OpenAI ecosystem. 3. Google Antigravity is the most architecturally ambitious of the three. An agent-first IDE built around a “Mission Control” interface, it dispatches up to five autonomous agents working in parallel across your editor, terminal, and a built-in Chromium browser. The philosophy is that you’re a manager of agents, not a developer writing every line. Currently free during public preview. Let’s compare these coding platforms on some metrics before we begin our experiments. Platform comparison Now, let’s start building with these platforms. Our experiment: Setup and methodology The Prompt To keep it neutral, we gave the same prompt to all of the coding platforms: Design a basic email routing architecture with Google OAuth. Include: OAuth 2.0 flow, email ingestion endpoint, routing logic by sender domain, and a simple queue. Implement in Node.js/TypeScript, wire it up, and push it to GitHub. This task is broad enough to test judgment: it spans auth, API integration, data flow, and git workflow, involves a real external service, and leaves enough ambiguity that how each tool interprets it is revealing. Evaluation criteria We assessed each output across five dimensions: The outputs All three outputs are public, committed exactly as each tool produced them without cleanup: Claude Code: email-router-claude-code Codex: email-routing-google-oauth-codex Antigravity: email-routing-antigravity Now, let’s see how each IDE performed in this task. Results: How did each tool perform? Install and workflow experience Before any code was written, the experience itself was already telling. Claude Code installed cleanly. It executed the full task without interruption. It wrote the code, configured the environment, wired up the git remote, and pushed the commit. No manual steps required. Codex installed cleanly but stalled at the finish line. The git configuration and local run required manual intervention. The code it produced was good; the end-to-end workflow automation was not. Antigravity crashed twice before the application opened. The tool failed on launch on a supported OS configuration twice. When it eventually loaded, there were bugs in the UI. It produced output, but the install experience is a data point in itself. The new Antigravity 2.0 just launched and has documented bugs. Gemini 3.5 Flash is also not the most competent model. Both deficiencies have been documented by Theo from t3.gg https://medium.com/media/654446fd9e7157e8f7793fb0221eac7c/href Code Audit 1. Claude Code: a real Gmail ingestion pipeline TypeScript: 100% | Commits: 2 | Module system: CommonJS via ts-node | Port: 3000 Architecture Claude Code is the only tool that actually reads Gmail. It uses the googleapis package directly: authenticating, querying the Gmail client with is: unread, downloading message metadata, and extracting From, Subject, and Date headers sequentially. It also supports Pub/Sub push via /webhook/gmail, which is the production-appropriate ingestion pattern rather than polling. Routing: Simple linear array scan matching r.domain === job.senderDomain, with an empty string “” catch-all at the end. It routes to real email destinations (engineering@company.com, billing@company.com). The table lives in code, and to change a rule, you edit router.ts. Queue: A single global in-memory FIFO queue, drained by a continuous async worker loop. If a job fails, the error is caught and logged to stderr before moving to the next item — a dead-letter pattern. There is no retry counter. OAuth: Basic state-free redirect flow. It generates a login URL, accepts the callback code, and stores tokens in a Map under a default user key. Token refresh is handled automatically by google-auth-library events. Critically, there is no state parameter validation on the callback, and the endpoint is technically vulnerable to CSRF. Codex fixed this; Claude Code and Antigravity did not. Production guidance: The README includes an explicit swap-out table: The verdict: The only output of the three you could point at a Gmail inbox and have it actually work. The routing and queue are deliberately simple, and the production notes are honest. The OAuth gap is real and worth one targeted fix, but the structural foundation is sound. 2. Codex: An enterprise-grade security skeleton TypeScript: 100% | Commits: 6 | Module system: ESM (“type”: “module”) via tsx watch | Port: 3000 Architecture Codex built a validated API scaffold, not a Gmail integration. It accepts email payloads via POST /email/ingest; there is no googleapis dependency, no Gmail polling, no Pub/Sub. The ingestion model is passive: it expects upstream systems to forward normalized email payloads to the endpoint. This is a deliberate architectural choice, not an oversight, and it’s the right pattern for a microservice that shouldn’t own the mail transport layer. Input validation: Codex is the only tool to use zod for schema validation, enforcing typed runtime contracts on incoming API requests. Claude Code and Antigravity both use manual parsing: custom string methods, regex, and basic JavaScript presence checks (!sender). Routing: Constant-time O(1) dictionary lookup (domainRoutes[senderDomain(email)]) falling back to a defaultRoute object when unmatched. Faster and more predictable than Claude Code’s linear scan, though at the scale of email routing, the difference is academic. Queue: In-memory structures segmented by target queue name (Map ), covering customer-success, vendor-ops, partnerships, and general-triage. There is no background worker in the committed code — jobs are enqueued but not actively drained. OAuth: The strongest implementation of the three. Codex signs the OAuth state parameter using crypto.createHmac(“sha256”) with a user nonce and timestamp, validates the signature on callback return, checks expiry, and uses timing-safe comparison to prevent timing attacks. This is the correct CSRF mitigation for an OAuth 2.0 callback, and neither Claude Code nor Antigravity implemented it. The README also explicitly flags “encrypt refresh tokens at rest” as a production requirement. Six commits versus Claude Code’s two reflect Codex’s iterative style. The ESM module system, dedicated types.ts, and separated tokenStore.ts all signal a codebase designed to be extended without stepping on itself. The verdict: The cleanest, most security-conscious skeleton of the three. Not a working Gmail integration, but if you’re building something you’ll take through a security review, this is the foundation with the fewest “why didn’t you handle this?” questions in code review. 3. Antigravity: A full-stack demo that exceeds the brief TypeScript: 36.9% | CSS: 27.6% | JavaScript: 22.4% | HTML: 13.1% | Commits: 3 | Module system: CommonJS via ts-node | Port: 3050 Architecture The language breakdown says everything before you open a file. Antigravity built a product. The public/ folder contains a complete monitoring dashboard with real-time queue visualization, a route management UI, and a webhook sandbox. No one asked for this. But examining the backend closely, the picture becomes more nuanced. Antigravity’s implementations of routing and queuing are technically the most sophisticated of the three. Routing: Antigravity translates wildcard patterns (*@google.com, sales@*, *) into operational regex blocks (.*), sorts rules by numeric priority fields, and tests each email against the sorted rule set sequentially. This supports complex, prioritized matching that neither Claude Code’s linear scan nor Codex’s dictionary lookup can express. Rules can also be added and deleted at runtime via API — no code edit required. Queue: A stateful job tracker with explicit state boundaries (pending → processing → completed / failed), attempt tracking, and a setInterval worker polling every 500ms. If a job fails and the attempt count is under the maximum, the status resets to pending, and the job is retried automatically (up to 3 times). Neither of the other two tools implements retry logic. OAuth: A toggleable service class. If Google credentials are present, it uses the standard OAuth flow. If not, it falls back to a mock mode that renders a visual HTML consent screen and generates dummy tokens locally, useful for testing without cloud credentials. No HMAC-signed state validation. The cost of all this: TypeScript covers only 37% of the codebase because of the unrequested frontend. Three large-batch commits suggest generation in bulk rather than deliberate iteration. The tool crashed twice on launch before producing any of it. The verdict: The most feature-complete output of the three, and the least suitable for a production codebase. The routing and queue engines are better-designed than the alternatives. The monitoring dashboard is scope creep. The type safety is the weakest. For a prototype or stakeholder demo, it delivers more than either competitor. For a codebase you’ll maintain, the 63% non-TypeScript ratio is a liability. Full technical comparison https://medium.com/media/1020d7a4f430f3780e80259a18c3f540/href https://medium.com/media/25bc8b32f488481c46e0c92f0a391deb/href Our Verdict Claude Code delivered the most complete working Gmail ingestion pipeline. It built the core workflow end to end, but it missed OAuth state validation and needs targeted security hardening before production. Codex produced the strongest security skeleton, including signed OAuth state protection and schema validation, but it did not implement a full Gmail ingestion pipeline. Antigravity created the richest prototype, with dynamic routing, retry logic, and a dashboard, but it expanded the scope significantly and had the weakest TypeScript coverage. Conclusion Running all three tools on the same task produced a cleaner answer than we expected, but because each tool’s output maps precisely to a specific type of work. Claude Code won the workflow test. It handled the full task without hand-holding, and it’s the only tool that built something you could actually point at a Gmail inbox. The approval-first model maps well to how professional teams want to work with an AI agent: it asks before acting, stays in scope, and leaves the codebase in a state another developer can pick up immediately. The OAuth state gap is real and worth one targeted fix. Everything else is a solid foundation. For teams building anything that processes real email, the googleapis integration alone makes it the practical starting point. Choose Claude Code if you need real Gmail integration, you want an agent that executes the full workflow without you finishing the job, and you’re working on a codebase where scope discipline and maintainability matter. Codex: for security-conscious teams building to last. Codex produced the most secure and structurally sound codebase of the three. HMAC-signed OAuth state, zod schema validation, ESM module system, isolated type definitions — these aren’t style choices, they’re the decisions that make a codebase easier to audit, extend, and hand off. A developer picking up Codex’s output would ask the fewest questions in code review. The trade-offs are real: no Gmail integration, no background worker, manual git steps. It’s better understood as a disciplined pair programmer than a fully autonomous agent. Choose Codex if you’re already in the ChatGPT ecosystem, your team does thorough code review, and you’d rather have a clean, security-hardened skeleton you extend yourself than an end-to-end output you need to audit. Antigravity: for prototyping and demos Antigravity produced the most ambitious output of the three. Its wildcard routing engine, stateful retry queue, and runtime route management are technically better-designed than the equivalent components in either competitor. If you’re building a demo, shipping a prototype to stakeholders, or stress-testing what agent-first development looks like, it gets you further faster. But it crashed twice on launch. TypeScript covers only 37% of the output. It rewrote the scope of the task without asking. And the broader reliability story suggests a tool still finding its production footing. Choose Antigravity if you’re prototyping, running a hackathon, or need to show stakeholders something that looks and feels like a complete product. Don’t put it at the center of a codebase you need to maintain. The honest summary No tool won across the board. Each tool is suited to distinct development philosophies, and that philosophy shows up directly in the code it writes. Claude Code is the engineer: scoped, end-to-end, ships what was asked. Codex is the security architect: disciplined, extensible, conservative by design. Antigravity is the ambitious prototype: it builds more than you asked for, with less type coverage, and crashes on the way in. The right answer depends less on benchmark scores and more on where you sit in the development lifecycle. All three tools are moving fast. The gap between them is closing. Check back in six months. Claude Code vs Codex vs Antigravity: Which AI Coding Agent Should You Use? was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.
- Nordic AI in Media Summit 2026: A deep look into how AI is about to revolutionise the news ecosystem
Nordic AI in Media Summit 2026: A deep look into how AI is about to revolutionise the news ecosystem reutersinstitute.politics.ox.ac.uk
- Think Beyond Data Classification: Unlock Contextual Data + AI Intelligence
Think Beyond Data Classification: Unlock Contextual Data + AI Intelligence IT Pro
- Happiest Minds Sees AI-led Momentum in FY26, But Can it Escape the Mid-Tier Trap?
Happiest Minds’ AI business is growing rapidly, but the real challenge is whether the mid-tier IT firm can scale fast enough to stand out in an increasingly crowded AI services market.
- You can now choose how hard Claude thinks before answering your queries
Anthropic's Claude Opus 4.8 update lets you control how hard Claude thinks before it answers.
- How to Automate AI Model Documentation with the NVIDIA MCG Toolkit
As AI models grow in complexity and regulatory scrutiny intensifies under frameworks including California’s AB-2013 and the EU AI Act, software teams...
Score: 26🌐 MovesMay 29, 2026https://developer.nvidia.com/blog/how-to-automate-ai-model-documentation-with-the-nvidia-mcg-toolkit/ - AI has fundamentally changed enterprise support
Technology enables a transition from reactive troubleshooting to predictive, preventive and insight-led operations, say enterprise software support and AI specialists.
Score: 26🌐 MovesMay 29, 2026https://www.itweb.co.za/article/ai-has-fundamentally-changed-enterprise-support/DZQ587V8RyjqzXy2 - People prefer to talk to chatbots that share similar personality traits to their own, research shows
It's well understood that people tend to be naturally drawn to those with bubbly and extroverted personalities. And those outgoing and gregarious types may naturally consider themselves people-persons and gravitate toward others. But the feeling may not be mutual when it comes to the people with whom these extroverts are interacting.
Score: 26🌐 MovesMay 29, 2026https://techxplore.com/news/2026-05-people-chatbots-similar-personality-traits.html - US stock markets today (May 30, 2026): S&P 500, Nasdaq extend record run as Dell jumps 33% on AI demand
US stocks continued their record-setting rally, driven by strong earnings and AI optimism, pushing major indices higher. Despite inflation and geopolitical concerns, investor sentiment was buoyed by hopes of a US-Iran ceasefire extension, easing energy market pressures. Technology stocks, particularly Dell, led the gains as markets closed the week on a positive note.
- It’s Summertime and AI Means Internships Aren’t Easy
With chatbots like Claude around, more companies are cutting back on internships.
- Sargasso Capital Management Launches With Research Paper on the AI Implementation Gap in Public Equities
Sargasso Capital Management Launches With Research Paper on the AI Implementation Gap in Public Equities azcentral.com and The Arizona Republic
- Why AI can’t match human creative work
It’s hard for people to tell the difference between AI-generated advertising and writing. So why do they respond better to the human-made stuff? AI vs. Mad Men Ipsos, along with faculty members from Syracuse University’s S.I. Newhouse School of Public Communications, just published a unique advertising study . They took 20 real ads from major brands, including Cheerios, Chewy, Febreze, Fiat, H&M, Old Navy, Herbal Essences, Ray-Ban Meta, TurboTax and Visa. They fed the same creative briefs used by the human ad creatives into Google Gemini, then used OpenAI’s Sora to generate fully AI-produced counterparts with no human intervention. They showed the ads to 3,000 consumers. Only 25% of AI ad viewers were at least somewhat confident the spot was AI-made, and 40% of all viewers were uncertain either way — suggesting the public isn’t great at spotting ads that are AI generated. But here’s the interesting part: While most people didn’t register that ads were AI-generated, they also didn’t respond to them like they did with human-generated ads. They consistently rated human-made work as more eye-catching and more imaginative. In other words, people assumed AI ads were made by people, but didn’t particularly like them compared to human-generated ads. And that means human-generated ads performed much better. Ads made by people without AI were 14% stronger on short-term sales impact and 17% stronger on long-term brand health. To me, the data here suggests that while people can’t easily discern the difference between AI- and human-generated content, the AI stuff hits wrong on a subconscious level. And I think that’s happening with AI social posts, AI blog posts and AI slop in general. In fact, I’ve noticed it strongly in my own response to AI-generated content. It often looks perfect but bothers me for reasons that aren’t immediately obvious. The researchers explained AI’s inability to match human ad creativity by pointing out that AI draws from what already exists, while great advertising breaks new ground. AI can replicate the conventions of advertising, but it can’t transcend them, make a creative leap or engender emotion like people can. A broad range of research beyond the Ipsos study suggests that skillful people working with AI tools will always outperform AI alone, and often outperform people not using AI tools. Ipsos’ advice? Ad agencies should keep people at the center of brand storytelling and emotive assets. Can AI write right? Another recent study looked at written web content and compared how human-written articles “performed” on search engines compared to AI-generated content. Semrush analyzed 42,000 blog pages across 20,000 keywords , ran every single one through GPTZero’s AI detector, and cross-referenced the results with actual Google Search results. It also surveyed 224 search-engine optimization (SEO) professionals about their AI habits and beliefs. They found a disconcerting disconnect between what SEO people believe and what is actually true. Some 72% of SEO professionals who use AI content say it performs just as well or better than human-written content in search rankings. But it turns out that human-generated posts strongly outperform AI-generated. Content classified as purely AI-generated appeared in the top spot in search result just 9% of the time. Content classified as human-written was there 80% of the time.” That’s a roughly 8-to-1 advantage. (Note that the coveted top link in search results typically gets around one-third of the clicks.) For lower page-one positions — from the fifth position down (which get relatively few clicks) — AI- and human-generated posts perform more similarly. (The researchers also found that when people write posts with a little help from AI, their posts rank better much than AI-only content.) Those Semrush results are consistent with previous research. NP Digital conducted an oft-cited study two years ago that found that human-written content ranked higher 94.12% of the time on Google than AI content. A Graphite/Common Crawl analysis found that 86% of articles ranking high on Google Search are human-written (only 14% AI-generated), and ChatGPT and Perplexity cite human-written articles 82% of the time (only 18% AI). On LinkedIn, more than half of site’s long-form content in 2025 was classified as “Likely AI” by Originality.ai . Engagement on verified human content was 61% higher than the AI-marked posts. Note that engagement performance varied by industry; that 61% result is an aggregate average across all industries. Ironically, in the category of “Leadership & Inspiration,” AI posts outperformed human posts by 75% . The absurd lesson here: If you want to be a thought leader on LinkedIn, don’t lead with your own thoughts. Quantity vs. quality What all this research boils down to is that human-generated content (with or without help from AI) attracts far more traffic and higher engagement than AI-generated content. AI content is essentially invisible in high-value channels and while it might be high in quantity, it’s low in quality where it really matters — with reach and influence. As with the ad creative study by Ipsos, the conclusion of all this research is the same: People (and search engines) respond much better to creative content produced by people compared with AI-generated content. In short, AI is great at “flooding the zone” at high speed and low cost — and there’s a ton of AI-generated content out there. A quick check reveals that: More than half of all written content on websites is now AI-written. Almost half of all music uploaded is now AI-generated. Nearly one-quarter of all videos uploaded are AI-generated or manipulated. Around 40% of all podcast episode uploads are AI-generated. More than 70% of all images uploaded to social media may be AI-generated or manipulated. And wll over half of all social posts are AI-generated The specific numbers are my best estimates, and they’re changing fast each month. The takeaway is that AI-generated content is exploding in volume. But it isn’t reaching people the way human-generated content does. Take podcasts, for example. While roughly 40% of new podcast episode uploads are AI-generated, that 40% captures less than 1% of the listening hours. Of the top 100 podcasts, zero are AI-generated. A clear picture is emerging about the use of AI for content generation. AI is great for churning out a lot of content at low cost. It can be good for some kinds of content — if a skillful person directs it. And AI can be a helpful tool for content creators. But when it comes to direct comparisons between people and AI, it’s clear that the winning content — the stuff with the best “performance” on search, best reception by people and the most engaging — is always human-generated.
Score: 25🌐 MovesMay 29, 2026https://www.computerworld.com/article/4178383/why-ai-cant-match-human-creative-work.html - Gap Lowers Sales Outlook; Dell Soars on AI Demand | Stock Movers
On this episode of Stock Movers with Alexis Christoforous: - Dell (DELL) shares are soaring after the company gave an outlook for annual sales that far surpassed analysts’ estimates, fueled by demand for servers that power artificial intelligence work. - Gap (GAP) shares plunged after the company lowered its sales outlook due to struggles with its product mix. - NetApp (NTAP) shares jumped after the data storage provider reported its latest earnings. Analyst say the report is a strong print from the company, showing strong growth, with upgrades to consensus expected. (Source: Bloomberg)
Score: 25🌐 MovesMay 29, 2026https://www.bloomberg.com/news/videos/2026-05-29/gap-lowers-sales-outlook-dell-soars-on-ai-demand-video - Amazon kills internal AI leaderboard after employees gamed it with pointless tasks
Amazon is pulling an internal AI ranking system, the Financial Times reports, after employees inflated their scores through meaningless AI usage and driving up the company's cloud costs in the process. The article Amazon kills internal AI leaderboard after employees gamed it with pointless tasks appeared first on The Decoder .
Score: 25🌐 MovesMay 29, 2026https://the-decoder.com/amazon-kills-internal-ai-leaderboard-after-employees-gamed-it-with-pointless-tasks/ - Check out real-life AI prototypes from the Futures Lab.
University of Waterloo students develop AI prototypes like sign language tutors to reshape the future of education and work.
Score: 25🌐 MovesMay 29, 2026https://blog.google/innovation-and-ai/technology/ai/university-waterloo-labs/ - Leaning into disruption: How Perficient delivers real outcomes in an AI-first world
Five years ago, enterprise technology roadmaps stretched across half a decade. Then they shifted to three years. Today, many organizations are planning just 18 months ahead, and even that horizon is getting shorter. As AI accelerates innovation, the challenge is no longer access to technology. The challenge is applying it effectively in a world that […] The post Leaning into disruption: How Perficient delivers real outcomes in an AI-first world appeared first on IDC .
- Trees are mostly made of air and a generalizable lesson for AI safety
At the risk of embarrassing myself, I’ll share a confession. For context, I took five years of Latin: four in high school and one in college. In addition to learning the language, all my Latin classes taught a lot about Roman history. Emperors, internal politics, Caesar, etc. I was always learning some random bag of facts about Roman history. In high school, I won the award for top Latin student in my graduating class. So I wasn’t a bad Latin student. Here’s the confession: I somehow don’t even vaguely remember the rough timespan the Roman Empire existed. Maybe Jesus time? I know he was killed by the Romans (is that right?). Were they around for a long time after? A long time before that? When was Romulus and Remus allegedly fighting? Virgil wrote the Aeneid when? I don’t have a clue. Despite being a kind of “Latin expert” I am missing a much more important foundational fact: when all of this was happening. When I say trees are made out of air I’m not talking about the fact that there is a lot of empty space inside a tree (or actually anything made out of atoms). I mean something more mind-blowing. Imagine you are holding a piece of dry wood. Where did that wood come from? A tree. Okay sure, but what are you actually holding? Where did the tree get that stuff? It turns out that almost all of the mass in dry wood comes from the in the air. This follows from a simple fact about how photosynthesis works: The carbon and the oxygen in the glucose ( ) come from the and the hydrogen comes from the water. Since hydrogen is the lightest atom in the universe, it adds almost no mass, so nearly all the mass of glucose traces back to the . Wood is mostly built from glucose, so by mass, a dead tree is mostly the carbon and oxygen that came out of the air. It’s kind of unintuitive. Wood is hard and stiff and air is not even close to either of those things. But if you are a biologist or know some basic chemistry you would think that this is the kind of obvious thing you would want to know. At the very least you would expect the best math and science students in the world to be familiar with such basic biology. But take a look at this video of a documentary film crew asking MIT graduates where they think wood comes from: One student, upon being told that most of the mass in the wood comes from and not the dirt in the ground, said “that's very disturbing and I wonder how that could happen.” And that’s an MIT graduate. “Trees come from mostly air” is pretty fundamental for biology because it follows from photosynthesis which is in many ways the basis for life on earth. In a sane world, every 8th grader would know that dry wood is mostly , not like minerals found in the ground or something. MIT grads probably have vast amounts of detailed knowledge of math, physics, biology, chemistry or all of the above. But they don’t know some basic facts about biology. There is this assumption that you should first learn the foundational facts of an area of study and then move to more and more specific questions and ideas. As you move up levels of classes, the foundational stuff seems more and more basic and less and less relevant. Some stuff is continually hammered in because it’s useful background knowledge. In a perfect world, this helps you internalize the basics and learn how to reason about them to solve harder and harder problems. There are some areas where, without much effort, this may work out. For example, you learn fractions in first or second grade but will probably understand them more deeply by the time you get to calculus because you need to know fractions to do calculus and all the classes that come before calculus. But not all foundational knowledge is like this! Knowing where trees come from doesn’t help you answer organic chemistry or evolutionary biology questions. Being 1. foundational and being 2. useful for answering increasingly specific questions are different things. They are certainly not orthogonal but they are also not perfectly correlated. When 1 & 2 diverge, you get MIT grads who are confused about what wood is. In AI safety, this can be a serious problem. I have had one-on-ones or interviewed dozens of students who want a career in AI safety. There are many examples of students who are something like this: they know what alignment faking is, read LessWrong, know who Neel Nanda is, know what METR is, have done an interp project, etc. But when you ask them why they care about AI safety they don’t provide a particularly coherent answer. So I get more specific: “Why should we think AI is an existential risk?” Again, incoherent answer. This may be because they don’t really care much about AI safety and they just like to hang out with EA/rationalist types. An alternative reason (which I think is more likely) is because AI safety basics are something you learn and don’t exercise. When you do a BlueDot reading group you hopefully learn AI safety basics. But when doing interp experiments or your first SPAR research project, you think about specific empirical questions, not the orthogonality thesis. You don’t think about the basics and you don’t internalize them and certainly cannot reason about them. Instrumental convergence, inner alignment, reward misspecification, etc. are our “trees are made out of air” or “the Roman Empire was 27 BC to 476 AD”. But lots of people in AI safety programs or those applying for them don’t know the basic facts. They know a lot of specific things but somehow not the foundational things. This strikes me as a genuine failure mode. A lot of focus goes into having great fellowship programs and university groups but some conceptual knowledge seems to be slipping through the cracks. I hope the next generation of AI safety researchers have all of the conceptual knowledge of earlier researchers and more. I’ll leave you with an additional confession. 6ish years ago, before I started college, this was probably me, at least partly. I understood the basic alignment problem but understood it mostly through outer-alignment issues and didn’t fully internalize the difficulty of the problem until I started college. That was roughly 4 years ago when I moved into the UChicago dorms. From the very beginning, I was a CS major because I wanted to be an AI safety researcher. A few hours ago I turned in my last paper, and now I’m done. My primary reflection is this: I would not have become a CS major, would not have worked so hard, would not have been so laser-focused on AI safety if I didn’t actually understand it. I would have got distracted and ended up probably on Wall Street or worse. This is because knowing a problem tells you why you should care. So if there is one reason to embrace the basics, it is that. There is so much fucking power in actually understanding something. Discuss
Score: 25🌐 MovesMay 29, 2026https://www.lesswrong.com/posts/xiTBpBDwubnr4MLRe/trees-are-mostly-made-of-air-and-a-generalizable-lesson-for - I made an AI clone of myself based on my Google and Reddit history — and it understood me better than I expected
I used years of Reddit comments and Google history to create an AI version of myself in ChatGPT
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You start with one AI agent to save time. A month later, you've got prompts in a doc, outputs in Slack, half-finished automations in three places, and the same request getting handled a dozen different ways depending on who saw it first. That's what happens when businesses try to "do AI" by building roughly 43 agents with no plan in place to coordinate them. AI agent orchestration solves this problem. Instead of relying on a single, general-purpose AI agent to do everything (which rarely works),
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Score: 25🌐 MovesMay 29, 2026https://uk.pcmag.com/ai/165232/are-googles-gemini-pro-or-plus-worth-it-5-features-that-justify-the-price - BulkQuant Highlights Expansion of AI-Powered Market Analytics Technology
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Score: 24🌐 MovesMay 29, 2026https://www.itpro.com/security/6-ways-to-design-a-resilient-strategy-for-ai-workloads-with-dell-powerprotect - Public lecture by Prof. Kathleen Thelen: "Cloud capitalism and the AI transition"
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Score: 24🌐 MovesMay 29, 2026https://www.landecon.cam.ac.uk/event/public-lecture-prof-kathleen-thelen-cloud-capitalism-and-ai-transition - Adobe’s conversational AI agent is a mediocre design intern
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Score: 24🌐 MovesMay 29, 2026https://www.theverge.com/tech/939686/adobes-conversational-ai-agent-is-a-mediocre-design-intern - AI is only as good as the data you can reach, while integration is the real foundation
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Score: 22🌐 MovesMay 29, 2026https://techxplore.com/news/2026-05-tiny-underwater-antenna-robots-dark.html - AI and Content Reshape Attractions & Tours at Trip.com Group's Global Partner Forum
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Score: 22🌐 MovesMay 29, 2026https://www.lesswrong.com/posts/T5aWBLDdkqPEzDhjZ/claude-doesn-t-know-who-you-are - Software Engineering Institute Webcast: Rethinking and Maturing AI Adoption
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Score: 20🌐 MovesMay 29, 2026https://www.businessinsider.com/wayve-launches-ai-lab-to-look-beyond-self-driving-cars-2026-5 - AI chatbot for customer service: a 2026 guide for CX leaders
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- 20 incredibly useful things you didn’t know Google’s Gemini AI could do
When we hear about Google’s Gemini AI engine these days, it’s almost always the result of some wildly ambitious and futuristic-sounding advancement. You don’t have to look far to find examples. Gemini, like other generative AI systems, is increasingly being positioned as an agent that can handle complex tasks for you , as we heard about throughout Google’s I/O conference keynote last week. They span everything from shopping and purchasing tickets to planning travel and even meandering around the web on your behalf. And, of course, there’s vibe-coding your own custom apps without needing to know a lick of code. That’s all well and good, but for most of us, it isn’t exactly the sort of stuff we’re relying on in day-to-day life. In reality, it’s Gemini’s more mundane and less marketing -worthy wizardry that’s likely to be most useful in an ordinary moment. And those are exactly the types of tricks that are underemphasized and go unnoticed—often because they’re off the beaten path and buried. So today, we’re going to skip over the standard superlatives and focus instead on the wow-worthy little gems lurking within Gemini that you don’t usually hear about and might otherwise never encounter. Check out the 20 truly useful Gemini abilities below and see how Google’s AI can actually help you. (Note that, Gemini, like all generative AI systems, can at times be inconsistent and may relay inaccurate info. The use cases I’m highlighting here generally minimize that risk and focus on more confined data sets and task-oriented missions that play to the technology’s strengths—but, as always, proceed with caution and approach all results with a critical eye. AI may be powerful, but the human touch around it very much still matters. And that part’s on you to provide.) 1. Act as your on-demand memory expansion Sometimes, the simplest feats really are the most valuable of all. The next time you find yourself facing some manner of random fact you need to remember—the name of someone’s partner or kids, the gate or door code at a particular place, the license plate on your vehicle or rental vehicle, or anything else imaginable— just tell Gemini : “Remember that Susan’s husband is named Carl.” “Remember that the gate code at Josh’s apartment is 8934.” And so on. Then, whenever you next need that nugget of info, all you’ve got to do is ask. Gemini—your no-cost memory expander. 2. Set a timely reminder in no time Speaking of remembering, don’t forget that Gemini can also perform the simple but supremely useful task of helping you recall specific things at specific times—thanks to its native integration with the oft-forgotten Google Tasks service . No matter what device or interface you’re using, ask Gemini to remind you about anything at any date and time you want. It’ll set the reminder in Tasks and then pop up an alert when the right moment arrives. Just make sure you’ve got the Google Tasks app installed and set up on your phone—be it Android or iPhone —so you see the notification. 3. Help you find your way back anywhere One final reminder-related resource that’s worth tucking away in your memory bank—a two-parter: First, if you’re using the Gemini mobile app on a phone, make yourself a mental note that you can always ask Gemini the only slightly embarrassing question of “Where am I?” So long as you’ve allowed the app the proper location-sensing permissions, it should then tell you roughly where you are—with a city name and, depending on your whereabouts, also potentially the name of a specific business or address. Then, if it’s a place you want to remember for the future, ask Gemini to “remember that location as”—followed by whatever description you want (e.g., “remember that location as the best place to park in Westwood”). You can then ask Gemini for that info anytime down the road, and it’ll zap you right back to the spot you need. 4. Dig up details from a video You probably know that Gemini can summarize most any text you show it. One of its even more mind-blowing powers is its ability to summarize and analyze any video you feed into its metaphorical maw. Now, when you’re watching something for pleasure, this probably isn’t a power you’ll need. But when you encounter a video that you need to parse for purely informational purposes and you don’t feel like sitting through 22 minutes to get a shred of knowledge that’d take you 10 seconds to read, you can upload the video file or simply copy and paste its URL directly into Gemini—then tell Gemini to “summarize this video” or “give me a short bulleted summary of the high points.” Want important info without watching a lengthy video? All you’ve gotta do is ask. If you’ve got something super-specific you’re seeking, you can also just ask Gemini about it: “What does this person say about battery life?” “Does the interview reveal anything about when the product will be released?” “What sort of screwdriver does this say to use for installation?” You get the idea. 5. Create your own personal podcast On the flip side of that last item, if you’ve got a dense document that you need to digest and you think you’d do better hearing it as a conversation, try uploading the doc into Gemini and asking it to “Generate a 10-minute conversational podcast between two experts discussing the findings.” You can get as nuanced as you want with your request, and Gemini should spit back out a personalized play-ready creation that’s ready for your aural consumption. 6. Skim over your emails Provided you’ve got Google’s Personal Intelligence option available and active, you can always ask Gemini to summarize your most recent incoming emails—or even get more specific. For example, you can ask it what the last email from your lawyer said, what your roofer quoted as the estimate for repairs, or anything else that might make sense for your inbox. 7. Find you a killer deal If you aren’t in a rush to make a purchase, try telling Gemini to monitor the price of a specific item and alert you if a certain kind of sale ever comes along. You can get as broad or as specific as you want with it: “Monitor the price of the Pixel 10 Pro and notify me if it goes on sale.” “Monitor the price of the Pixel 10 Pro on Amazon and notify me if it goes on sale.” “Monitor the price of the Pixel 10 Pro on Amazon and notify me if it drops below $900.” Your future self will thank you. 8. Create custom product comparisons All deal-seeking aside, Gemini can work wonders when it comes to comparing products and serving up exactly the info you need. Ask it to compare the battery life on two phone models you’re considering or to compare a series of specific refrigerators you see in a store and then tell you how they’re actually different—or even just to give you a table-style comparison of the most important differences across certain products from a purely practical perspective. 9. Decipher doctor-style handwriting Got a note that you can’t for the life of you read? Snap a pic of it and ask Gemini to decipher the writing. Say what? Gemini can interpret any handwriting for you—no matter how illegible it may be. You’d be surprised how often it manages to interpret even the messiest script. 10. Act as your error-interpreting technician No matter the device or appliance, whenever you next encounter an error code that looks like gibberish, ask Gemini to help figure out what it means and how you can fix it. The more specific you can get, the better—telling it the manufacturer and model name of whatever’s giving you the error, for instance, or just showing it a picture—but even if you don’t know all the details, there’s a decent chance it’ll be able to point you in the right direction. 11. Serve as your handyman helper While we’re on the subject of repairs, you can show Gemini a photo of a random screw, connector, or component of any sort and ask what it’s called and where you can find a replacement—or anything else you might need to know. Whether you’re a seasoned repair pro or a befuddled homeowner with next to no handy knowledge, the answer it coughs back may be invaluable. 12. Parse an impossible document With the hopefully obvious caveat that you should absolutely consult with a lawyer for anything truly important and before making any consequential decisions, Gemini can be surprisingly helpful when it comes to going through dreadful-seeming documents filled with endless clauses and clusters of legalese. If nothing else, it can help you wrap your head around the info within and any sticking points you might want to mull over. For instance, I fed in an agreement for an upcoming bouncy-house rental for a kiddie (and, if I’m being fully honest, also adult) party we’re having in our backyard. Gemini identified a couple of potentially problematic and not-at-all-necessary sections that were easy enough to ask the vendor to remove. Similarly, I used it to compare a few vexingly similar insurance policies and translate the differences into real-world terms. For those sorts of scenarios or even as a pre-lawyer-meeting preparation, Gemini’s ability to ingest mountains of complex material and then identify and explain important points can be indispensable. 13. Become your manual magician Now that Google’s NotebookLM system is essentially integrated into Gemini , the feat I suggested in my recent collection of practical NotebookLM revelations can also apply to Gemini itself. That involves creating confined notebooks to hold specific manuals and then asking natural-language questions anytime there’s knowledge you need. I did this with the digital version of a manual for a recently acquired vehicle and was blown away by how much easier it became to find info simply by asking what a particular button does. On-demand answers are never more than a question away when you keep your manuals in Gemini for reference. The same strategy can work equally well with manuals for appliances, electronics, you name it. 14. Perform fast web fixes for you Human designers and developers are undeniably important when it comes to creating high-quality web work, but for those teensy little tweaks and frustrating fixes where you used to have to pester a professional, Gemini can now step in to help you come up with a correction—and help your coding-minded colleagues focus their time on higher-level concerns. Try showing Gemini a screenshot of a website you’re responsible for and then explaining what’s wrong or what you want to have changed, while providing any pertinent details about your setup—that you’re using WordPress with a particular theme, for instance—and see what it suggests. It can sometimes take several rounds of back-and-forth iteration, but if you’ve got the patience (and a solid staging site for low-risk experimentation), it can get you to the finish line much more easily than you’d expect. 15. Cook up some spreadsheet sorcery Speaking of coding chops, one area where specialized knowledge has traditionally been required is in the ever-confounding arena of spreadsheets. And while there’s certainly still a place for spreadsheet expertise , you can make your life a heck of a lot easier by letting Gemini guide you toward crafting complicated formulas. Just fire it up, explain what you want to have happen, and ask it to give you the formula you need—for Google Sheets, Microsoft Excel, or whatever specific program you’re using. Or, on the flip side, paste a formula you’ve found somewhere into Gemini and ask it to tell you what, exactly, it accomplishes. 16. Create custom Chrome extensions While full-fledged vibe coding can help you dream up all sorts of insanely customized complete programs , you might not be ready to take the plunge just yet. But you can dabble with the same sort of superpower and feel a sense of its addictive effects by asking Gemini to create a custom browser extension on your behalf. Unlike native applications, web-centric extensions require no compiling or separate steps beyond just taking a series of plain text files Gemini gives you and then plopping them into your browser. (It’ll even walk you through the exact process of doing that.) And with all the time you probably spend working on the web, you can accomplish some pretty spectacular stuff by doing that—like changing the appearance of web apps to better suit your preferences or giving yourself pop-up panels with simple tools you’ve never quite been able to find. A custom Chrome extension I created for storing and easily accessing commonly used color codes. It’s also incredibly fun and empowering to play around with—without requiring you to venture into exceptionally geeky waters. 17. Become your prompt-mastering guide Oftentimes, the biggest challenge with Gemini—or any AI chatbot—is figuring out the right approach for wording a request and getting the bot to do what you want. In an amusingly meta-twist, Gemini is actually quite good at advising you on the best way to phrase prompts for itself. The next time you’re struggling to get the service to do your bidding properly, consider asking it what the best prompt would be for the purpose you have in mind. It seems silly, but it often works astonishingly well. 18. Wear the hat of an AI detection agent In a similarly entertaining sense, Gemini is impressively effective at detecting images that were generated by Gemini—or another similar AI tool. It’s not foolproof, but it’s right up there with the best options we’ve got at the moment. If you’re ever trying to decide if an image is genuine or AI-generated, ask Gemini and see what it says. 19. Answer in whatever way you like Maybe you’re someone who prefers reading things in conversational paragraphs—or in short, succinct lists. Or maybe you like having a detailed, in-depth answer with a “TL;DR”-style summary at the start. Whatever the case may be, if you ask, Gemini shall oblige. Tell the service exactly how you prefer to have your info provided as part of your next prompt—or, if you want it to always follow a specific formula, tell it to always answer in that way, and it’ll adjust your account-wide preferences. You can also check any saved settings along those lines and modify them directly on the Gemini instructions page . 20. Transform into an entirely new personality Why stop at formatting? Gemini has the ability to completely adjust its personality and act in any way you want—again, either for a specific prompt or in a more generalized and ongoing sense. You could ask it to become a tough but supportive coach , for instance, or a lifelong friend who’s always brutally honest and direct with you. Or you could request it to take on the role of specific jobs, like a veteran software engineer, a travel agent, or a legal adviser—or even some combination of different identities. Welcome to generative AI—the one place where having a split personality is actually an asset. The possibilities are practically endless, and you never know what might resonate and prove to be useful until you try. For even more next-level Google knowledge, check out my free Android Intelligence newsletter — three new things to try in your inbox every Friday.
- MyCommunityToday Launches Emotion-Aware Deal Chief AI for Meetings, Video Calls, Webinars & Interactive Social Shopping
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- Interesting Times: Why Are We Still Driving?
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Modern LLM serving is hard to tune because each deployment is a stack of interacting choices: model backend, tensor-parallel shape, prefill/decode split, worker...
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Score: 20🌐 MovesMay 29, 2026https://www.c2d3.cam.ac.uk/index.php/events/archive-algorithm-how-ai-transfo - Google fixes Gemini Omni bug, stops charging users for failed requests
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