AI News Archive: May 24, 2026 — Part 3
Sourced from 500+ daily AI sources, scored by relevance.
- K-pop androids and automated artists: welcome to South Korea’s strange and ambitious robot theme park
Galaxy Robot Park in South Korea hopes to attract tourists to concerts and fashion shows, but can robots ever replicate K-pop’s connection with fans? Four child-sized humanoid robots take the stage at an arena in eastern Seoul, and as the opening beats of a song by K-pop star G-Dragon begin, they start to dance. Arms swinging, legs stepping in sync, heads bobbing, wigs and baggy clothes swishing, until – mid-performance – one of them seemingly malfunctions and has to be removed from the stage. Continue reading...
Score: 36🌐 MovesMay 24, 2026https://www.theguardian.com/world/2026/may/25/galaxy-robot-park-in-seoul-south-korea-world-first - AI interns in the office: Preparing to work with digital employees
Kim Byoung-pil The author is a professor of technology management at KAIST. The office environment is changing rapidly. AI is now drafting business plans, designing marketing campaigns and even handling customer service and bookkeeping. Such multitasking capabilities are particularly useful for small organizations, where the work force is often limited. Startups and small business owners are increasingly dividing work among AI systems, assigning them roles in sales, marketing, accounting and customer support. A lobster-shaped cutout representing OpenClaw, an open-source AI agent, stands amid the Baidu offices in Beijing on March 17. [REUTERS/YONHAP] Yet these digital employees also create serious security vulnerabilities. One example is OpenClaw, an open-source AI assistant released in November of last year that quickly attracted global attention. Cisco described it as a “security nightmare.” OpenClaw can delete files from a user’s computer and even run malicious software. The moment such tools are connected to a company’s internal network, the entire corporate security system may be exposed to risk. That helps explain why many companies restrict the use of outside AI programs within their organizations. Without adequate control systems, it is difficult to allow AI to operate autonomously inside a company. At the same time, those restrictions can slow the development of AI capabilities. According to Cisco’s 2025 survey, only 8 percent of Korean companies were classified as “leaders” in AI readiness, below the global average of 13 percent. Still, companies cannot simply block AI adoption while competitors use it to widen productivity gaps. The question is how to resolve this dilemma. One practical approach may be to treat AI like an intern or probationary employee. When a new recruit first joins a company, every task is unfamiliar. Senior employees supervising the newcomer often struggle to decide what responsibilities to assign. Yet once entrusted with smaller jobs, probationary workers often prove capable. Today’s AI resembles that stage of development. Related Article Agentic AI era demands state-backed industrial strategy Agentic AI ignites efficiency race amid memory crunch Science Ministry launches Agentic AI Alliance consultative body with LG, Kakao The butterfly effect of the Anthropic contract termination Google reportedly developing AI agent ahead of annual conference Managing AI so that it does not threaten corporate security is remarkably similar to establishing rules for supervising new employees. First, just as workers enter the office using identification cards, AI systems should also be given clear identities. Uber created a framework that assigns verifiable identities to internal AI systems and tracks the work they perform. In effect, the company issued digital employee IDs for AI. Second, companies should not open all internal information to AI at once, just as probationary employees are not granted unrestricted access to all company resources. AI systems should operate in isolated environments with limited access to data. Their workspaces should remain separated from core company networks, and they should only be able to use authorized tools. The authority granted to AI should never exceed that of the human employee supervising it. Third, there must be a control mechanism equivalent to managerial approval. A probationary employee may prepare a purchase order, but cannot place the order independently. Final approval still comes from a responsible manager. The same principle should apply to AI. Human oversight must remain embedded in important procedures. Finally, just as probationary workers gain more important responsibilities as they build trust and accumulate positive evaluations, AI systems should gradually receive more autonomy as they prove reliable. Current AI technology is beginning to develop the ability to accumulate work experience independently. The recently introduced Hermes agent documents successful work processes and refers to them later when handling similar tasks. Anthropic's chief product officer, Ami Vora, co-founder and president, Daniela Amodei, and co-founder and CEO, Dario Amodei, appear onstage at the Code with Claude developer conference in San Francisco on May 6. [AP/YONHAP} Just as probationary employees can eventually become permanent staff members, companies that accumulate experience assigning work to AI and reviewing its results may gradually entrust it with more critical responsibilities. Greater AI capability alone, however, does not automatically accelerate adoption. Risks involving data leaks, malfunctions and auditing difficulties also increase alongside AI’s growing power. For that reason, waiting until AI performance improves further before introducing it more broadly may prove to be a mistake. Companies that delay adoption are likely to lack practical management experience while competitors continue widening the gap. What matters most is beginning now to prepare properly for working alongside AI. Organizations need systems for AI identity verification and authentication, isolated workspaces, behavioral records and human supervision. Only by assigning AI the role of a probationary employee can companies gradually move toward entrusting it with more responsible positions. The era of working alongside digital employees has already arrived. The challenge now is deciding what tasks to assign them, and under what rules and procedures. Preparation should begin before it is too late. This article was originally written in Korean and translated by a bilingual reporter with the help of generative AI tools. It was then edited by a native English-speaking editor. All AI-assisted translations are reviewed and refined by our newsroom.
- Linus Torvalds to ‘start being more hardnosed’ about ‘pointless pull requests’ – some of which come from AIs
Warns large release candidates ‘are *not* conducive to long-term stability’
- I Paid for Google's Gemini AI Plan. These 5 Features Justify the Price
I Paid for Google's Gemini AI Plan. These 5 Features Justify the Price PCMag UK
Score: 35🌐 MovesMay 24, 2026https://uk.pcmag.com/ai/163294/i-paid-for-gemini-ai-plan-5-feature-justify-price-google-io - Would you trust AI to help you find 'the one'? Dating apps bet it can
Outsourcing matchmaking to AI risks further reducing love and intimacy - a universal human experience - into something to be quantified and calculated, stripping it of its humanness
- Google won’t let you disable AI in Search, but these tricks still work
Check out some tricks on how you can get rid of Google's new AI features in Search.
- ’90s rap legend brings anti-AI message to BottleRock: ‘We are the special effects’
’90s rap legend brings anti-AI message to BottleRock: ‘We are the special effects’ San Francisco Chronicle
Score: 34🌐 MovesMay 24, 2026https://www.sfchronicle.com/entertainment/music/article/busta-rhymes-bottlerock-technology-22274594.php - The AI-generated Kurdish song taking over TikTok and Instagram
The AI-generated Kurdish song taking over TikTok and Instagram The National
- AI chip testing: AEM Holdings' vital role
AI chip testing: AEM Holdings' vital role The Straits Times
- Orbot Robotics’ space robot has four arms, but its Goro physique has a purpose
In microgravity, legs do not do much. Orbit Robotics’ Helios replaces them with four arms built for movement and station work.
- AIs new cottage industry: Lawyers defending students accused of cheating
Some students accused of AI cheating are trying to save their college career with an attorney's help.
- ASR Models Collapse in the Real World
This ASR Trains on 2 Million Simulated Nightmare Scenarios to Fix That. Continue reading on Towards AI »
Score: 32🌐 MovesMay 24, 2026https://pub.towardsai.net/asr-models-collapse-in-the-real-world-614a03096a66?source=rss----98111c9905da---4 - These Robots Are Making Meals for a Nonprofit in San Francisco’s Tenderloin
A nonprofit in the city’s most troubled district has turned to robotic meal prep tech to make up for a dearth of human volunteers.
Score: 32🌐 MovesMay 24, 2026https://www.wired.com/story/these-robots-are-making-meals-for-a-nonprofit-in-san-franciscos-tenderloin/ - Google showcased Gemini’s focus on practical AI, but I still have mixed feelings
AI continues to give me mixed feelings about the future, and that’s top of mind following another Gemini-heavy Google I/O this past week. On the one hand, Google continues to show some of the most practical and useful ideas for AI, but until I can trust it, I struggle to actually get excited about it. more…
Score: 31🌐 MovesMay 24, 2026https://9to5google.com/2026/05/24/google-io-2026-gemini-practical-ai-opinion/ - Tested: Base Tesla Model Y Vs. Chevy Bolt – Driver Assist AI On A Budget
The 2026 Tesla Model Y base trim and 2027 Chevy Bolt deliver premium self-driving capabilities at prices that many car buyers can actually afford.
- 12 AI Co-Scientists of 2026
5 most notable breakthroughs plus 7 open-source co-scientists for you experiments
- I’ll keep using Duolingo but this AI language learning app has a hold on me
Homescreen Heroes: Promova is a popular AI-powered language-learning app
- Amazon’s drone ambitions are about to reshape Chicago’s south suburbs
The next frontier in drone delivery? The airspace above your community. Amazon’s CEO, Andy Jassy, said recently that the company intends to continue developing its Prime Air drone delivery program, with the goal of expanding drone delivery to 500 million packages a year worldwide over the next decade. To achieve this, Amazon has started operating Prime Air drone delivery and conducting pilot tests in eight U.S. cities, with four more to be added soon. One of these cities is Chicago, whose south suburbs are next on the list for a trial expected later this spring or early summer. For people who live within an eight-mile radius of Amazon’s Matteson and Markham warehouse locations near Chicago, their neighborhoods will be part of the next Amazon Prime Air drone trial. Participants will be able to order small packages through Amazon Prime to be delivered via drones. Those who choose not to participate will still need to prepare themselves for delivery drones flying above their sidewalks, streets, and homes—and what that may entail. Amazon’s MK30 delivery drones are approved by the Federal Aviation Administration, and have six sets of propulsors. Each weighs 80 to 85 pounds, operates autonomously using onboard AI systems, and can carry a 5-pound payload. A 2025 FAA document describes the drones as using rechargeable lithium-ion batteries and being able to fly up to 400 feet above ground level at a maximum cruise speed of 73 mph. There may be some initial thrill in seeing delivery drones zipping through neighborhood skies. But, as prior accidents suggest, that thrill may quickly give way to alarm should these drones lose power, collide with birds, structures, or each other, and randomly plummet down. The momentum of a loaded 85-pound drone, or even a 5-pound payload dropping from a drone at 400 feet while traveling 73 mph, generates a great deal of force. In a dense community, this is a risk. To be fair, the accidents that have been documented seem to have mostly affected people only indirectly, but the risk rises as the number of drones and drone vendors increases. Delivery drones have had both minor and more serious accidents. In January 2026, an Amazon Prime Air drone crashed into an apartment building in Richardson, Texas, where it ricocheted off the exterior, plummeted to the sidewalk below, and started to emit smoke. Fortunately, no one was hurt—but they could have been. In 2025, two Amazon Prime Air drones crashed into a crane , sending a man to the hospital from fume exposure caused by the accident. Even though the crane had a flag to warn aircraft, the drones did not navigate around it. In 2022, in Brisbane, Australia, an Alphabet food-delivery Wing drone flew into power lines and caught fire. Although no one was directly injured, the accident still affected the public when power had to be shut off for 2,000 Energex customers while crews removed the drone—and that could have had implications for temperature control, medical devices, food safety, work, and other needs. Amazon’s FAQ offers mostly general information about what could happen and what precautions it has taken to protect the public. The likelihood of drone accidents depends on a number of factors: the drones’ composition, speed, and position, which determine how fast the drone or its payload might fall; how thoroughly company workers inspect and load the drones to ensure they adhere to safety processes and regulations; the honesty of customers about whether they have a suitable delivery site; whether existing low-altitude traffic management systems are mature enough to safely coordinate large numbers of drones, birds, aircraft, and other aerial obstacles sharing the same airspace; how well the drones can communicate with themselves, others, and us; and how strong public opinion is for or against delivery drones. Amazon is far from alone in its skyward ambitions. Other companies are dispatching their own drones and conducting trials, such as Wing partnering with FedEx, Walgreens, and Virginia’s Sugar Magnolia ice cream, candy, and gift shops. Flytrex acts as an aggregate drone delivery company for regional businesses, UPS has been experimenting with drone delivery, and the skies are also tempting for “air taxi” companies such as Joby Aviation, which has started to inch its way into New York airspace. It’s going to get crowded, and the challenge extends beyond preventing crashes to building interoperable systems, communication standards, and public accountability mechanisms capable of handling constant low-altitude drone traffic across cities and suburbs. Drones will be flying at multiple heights, ascending and descending to deliver packages, and heading toward depots in different places. They will need some form of air traffic control. Transportation of any sort is highly social. We negotiate who goes and who yields and, over the centuries, have created rules for navigation and oversight for ships, trains, cars, planes, and bicycles, while also developing cultural rules for pedestrians. To function, drones also require that sociability . Air adds a vector that differs from truck delivery, which has an oversight network of policy, law enforcement, and the sociability of other drivers who generally have the agency to yield to avoid accidents. Drone sociability requires communication between drones, drones and their depots, drones and us, and drones with other drones or vehicles from other vendors. Amazon’s delivery drones do not seem to have obvious sociability infrastructure with the communities they intend to serve. How customers and citizens will report incidents also does not seem to have been addressed, unless Amazon and others are relying on public emergency responders. The insular automated customer service processes Amazon uses will make it difficult for people to report an errant drone—unless Amazon provides a special number to everyone in the test communities. We also have no way of knowing how Amazon intends to apply its package security protocols to drone delivery. The Amazon website offers no specific details on how the community will be kept safe. Currently, delivery drivers require a phone code before dropping off a high-value package. Are drones going to hover disruptively in neighborhoods waiting for codes before releasing their iPhone payloads? What does it mean for the public when private companies and the government decide to run pilot tests on communities? The upcoming Chicago drone delivery program is being run by Amazon with assistance from the FAA’s Office of Advanced Aviation Technologies, which does not appear to have a web presence under that name. The office is relatively opaque and does not offer specific information on individual trials. The FAA Advanced Operations website hosts a video and a series of basic diagrams that convey little specific information about what will actually happen in neighborhoods with multiple delivery drones. Each example shows one drone delivering to one large house. Realistically, people in cities without cars may be more likely to embrace delivery drones, but many of them live in buildings that have only balconies as access points. And more than one drone is going to be flying to more than one house at a time. Considering that the delivery drone program is set to begin in the next several weeks, it is concerning that there is no updated, specific information on this test available to the public from either the FAA or Amazon. This is especially odd since the FAA has had a mandate since 2023, within its “ Innovate 2028 ” initiative, emphasizing how important public support is to the success of any delivery drone program. The initiative states not only that it is important for FAA offices to know details of these programs (some do not seem to), but also that it is important “that the public understand how these new aircraft operations will impact their communities.” As a nearly 30-year-old company with a startup mentality and culture, Amazon appears to be looking to disrupt its own logistics model with Prime Air drones in order to utilize a perceived “greenfield” in the skies. Although the skies may look spacious to Amazon, many other companies are seeing a greenfield too. The FAA should not easily yield the safety of the nation’s air traffic control system to any company’s desire for faster delivery times over public safety—and neither should the public. With many companies pursuing drone ambitions, combined with what’s already in the air, the skies are about to become very crowded. The FAA can only do so much, and AI still isn’t capable of managing the air traffic nightmare required for all of these drones to navigate safely together. The current drone tests are small, controlled experiments. The real test will come when these drones are all flying at once.
- One AI Can’t Really Disagree With Itself. So I Wired Up a Council of 18
One AI Can’t Really Disagree With Itself. So I Wired Up a Council of 18; Across Claude, Gemini, and Ollama. A structured multi-persona deliberation framework, now native to Gemini CLI, with one new capability the original didn’t have: every member can run on a different model. The disagreement is no longer simulated. Repo: github.com/Alpsource/council-of-high-intelligence-gemini Original upstream by @0xNyk : council-of-high-intelligence (MIT) “Argue both sides” is theater You’ve done this. I’ve done this. “Steelman the opposite view. Argue both sides. Play devil’s advocate.” It feels rigorous. The output, almost always, isn’t. Here’s why. A single LLM has one prior. When you ask one model to argue against itself, the same weights are generating both the position and the rebuttal. The rebuttal isn’t independent thinking — it’s the model’s learned representation of what a rebuttal should look like , which is itself a distribution shaped by training data, RLHF preferences, and the model’s tendency to hedge. You don’t get conflict. You get a model performing conflict, then quietly converging on a centrist conclusion that flatters its own priors. That’s bad enough on its own. It gets worse when you start trusting it. You walked in unsure. You walked out with a confidently moderated take. The model never pushed on the assumption you should have questioned, because that assumption lives in its weights too. I wanted a tool that fixes this structurally — not by prompting harder, but by making the disagreement come from genuinely different places. That’s how I ended up porting Council of High Intelligence to Gemini CLI, and then adding something to it. What the council is The original framework is by @0xNyk , released as a Claude Code extension. It’s a structured multi-persona deliberation system: 18 sub-agents, each one a historical thinker — Aristotle, Socrates, Ada Lovelace, Feynman, Torvalds, Machiavelli, Sun Tzu, Donella Meadows, Kahneman, Karpathy, Sutskever, Taleb, and seven more — plus a coordinator skill that runs them through a strict protocol before producing a verdict. It is not “ask multiple AI personas and average the results.” That would be theater at a higher resolution. The point of the protocol is that it’s adversarial by design . The coordinator’s job is to make consensus mechanically harder to reach than dissent — the opposite of how most multi-agent systems behave. You convene it with one command: /council Should we rewrite the auth service or add an abstraction layer? And here is what actually happens, end to end: Problem Restate Gate. Before anyone analyzes anything, every member must restate the problem in their own terms. This catches misunderstandings before they compound through three rounds. If Socrates and Torvalds turn out to be solving subtly different problems, you find out now, not at the verdict. Round 1 — Blind-first parallel analysis. All members produce their analysis independently, before seeing each other’s output. No anchoring on the loudest voice. This is the one round most “multi-agent” systems skip, and it’s the most important. Round 2 — Cross-examination. Members directly challenge each other’s reasoning. Parallel when there are five or more members, sequential when there are four or fewer. Post-Round Enforcement Scan. This is the part that makes the protocol bite. Five hard checks every round: Dissent quota — at least two non-overlapping objections must exist in the round, or it doesn’t pass. Novelty gate — each member must introduce at least one new claim per round. No restating themselves louder. Agreement check — if more than 70% of members agree, the coordinator triggers a mandatory counterfactual round . Consensus is a flag, not a conclusion. Evidence labeling — every claim must be tagged: empirical | mechanistic | strategic | ethical | heuristic. You can no longer hide an opinion as a fact. Anti-recursion / hemlock rule — Socrates is not allowed to just keep questioning forever. After enough loops, someone hands him the cup. 5. Round 3 — Crystallization. Final positions after the dust settles. 6. Tie-breaking. Two-thirds majority, or a domain expert with a 1.5× weighted vote. 7. Verdict synthesis. Structured output: Consensus, Minority Views, Unresolved Questions, Recommended Next Steps. The personas are flavor. The thing that does the work is the post-round scan — specifically, the dissent quota and the agreement check . Together, they make a particular failure mode — premature consensus — mechanically expensive. You don’t get a centrist convergence by default. You have to earn one by surviving a counterfactual round. Why I ported it to Gemini CLI Two reasons. First, the AI tooling ecosystem is converging. Claude Code, Gemini CLI, Codex CLI, Cursor’s agent mode — they’re all settling on the same primitives: skills, sub-agents, TOML commands, MCP servers. The shape of an “AI CLI extension” is rapidly standardizing across providers. A framework that lives on only one platform is leaving most of the surface area on the floor. Second, a port done honestly is a test of how universal the protocol actually is. If the council depends on Claude-specific quirks, the port will tell you. If the pattern is real, it should translate. Mostly, it translated cleanly. Here are the substantive moves: Claude Code Gemini CLI ~/.claude/agents/ ${extensionPath}/agents/ Spawn isolated sub-agent processes. Read each persona file at the round start, embody it, and re-read at the next round to preserve isolation detect-providers.sh bash script at runtime Declarative mcpServers in ~/.gemini/settings.json Frontmatter: model, color, tools, provider_affinity Frontmatter: name, description only (Gemini's loader rejects unknown keys) Flag: --models [path] Flag: --mcp-route [path] The full translation table lives in docs/architecture.md . I wrote that file before I wrote any blog post about it. Honest porting notes are rare in OSS, and I wanted this one to be one of them. Three bugs that taught me the platform These are the actually useful parts of any port write-up. None of these are in either CLI’s docs. I found them by running the thing and watching it fail. 1. Gemini CLI’s agent loader silently rejects unknown frontmatter keys. The original Claude agents had a rich council: metadata block — domain, triads, polarity pairs, and MCP affinity. The Gemini loader doesn't throw on unknown keys; it just refuses to load the agent. You get a missing-persona symptom that looks like a path bug. Fix: strip the block from all 18 agent files, move the metadata into a standalone file that configs/mcp-provider-slots.yaml the coordinator reads explicitly. 2. Declaring mcpServers In the extension manifest, it starts them unconditionally. The first version of the port registered claude-code and ollama MCP servers are directly in gemini-extension.json. Result: every user who installed the extension got "Disconnected" warnings on every Gemini session, because Gemini tried to spawn an Ollama server on machines that didn't have Ollama installed. Fix: move MCP server registration out of the extension manifest entirely. The extension ships with zero opinions about your providers. If you want multi-provider routing, you opt in by editing your own ~/.gemini/settings.json. 3. ${extensionPath} is substituted in TOML, but not inside SKILL.md body content. This one took an embarrassing amount of debugging. The coordinator skill references agent files like ${extensionPath}/agents/council-socrates.md. In TOML command definitions, Gemini resolves ${extensionPath} to the actual install path. Inside the SKILL.md body — where the LLM reads it as plain text — the variable is not substituted. The LLM reads the literal string. Sometimes the model correctly inferred the path from context. Sometimes it didn't, and you got intermittent file-not-found errors that looked like flakiness. Fix: every TOML command now opens with Extension path: ${extensionPath} so the resolved path lands in the LLM's context before it reads the skill. The skill then refers ${extensionPath} symbolically, and the model substitutes correctly. If you’re building anything on Gemini CLI’s extension model, bug #3 is the one to remember. It’s a quiet footgun, and the symptom doesn’t point at the cause. The capability the upstream doesn’t have: members on different models The port preserves the protocol exactly. But Gemini CLI’s MCP support let me add one thing the original couldn’t do: the same council deliberation, with members running on different model providers in the same session. Set one environment variable, point at a slot file, and the coordinator distributes seats across providers per the routing config: Socrates, Ada, Sutskever → Claude (via the @anthropic-ai/claude-code MCP server) Feynman, Torvalds, Karpathy → Ollama (local models, privacy-preserving) Coordinator → the active Gemini model The protocol’s polarity pair constraint is enforced at the routing layer. There are thirteen polarity pairs in the framework — Socrates/Feynman, Ada/Machiavelli, Aurelius/Machiavelli, and so on — pairs of personas that are deliberately designed to oppose each other on a particular axis. The router enforces a hard rule: opposing members are never assigned to the same provider . The full mapping is in configs/mcp-provider-slots.yaml . This is the part I want to be careful about claiming. I am not telling you the council produces better decisions than a single model. That’s not measured, and “better” depends entirely on what you’re deciding. What I am telling you is that it produces structurally different reasoning : when Socrates challenges Feynman’s empiricism, those two arguments are no longer being generated by the same weights. Different model families. Different training corpora. Different tokenizers. Different priors. The disagreement is no longer simulated. It is structural. That distinction — simulated vs structural disagreement — is, I think, the only honest defense of multi-agent systems over a single large model. If your “agents” are all the same weights with different system prompts, you have a single model doing voices. If they’re different models, you have something that cannot collapse to a single posterior . There’s no shared prior to fall back on. A council where Socrates runs on Claude and Feynman runs on a local Llama is not the same thing as a council where both run on the same Gemini model with different system prompts. The second one is improv. The first one is an argument. Why I find this interesting beyond the tool I work on robotics and perception — visual-inertial odometry, multi-sensor fusion, and dynamic scene understanding. Robust decision-making under disagreement is a constant theme in that world too: a camera says one thing, an IMU says another, and the system has to either reconcile them or admit it can’t. The standard move is to fuse them with a filter that picks the lower-uncertainty signal. That’s efficient and usually correct. It’s also exactly the move that fails in the cases that matter — adversarial conditions, sensor degradation, degenerate motion — because two channels that look independent often share a hidden common source, and the system collapses confidently onto a wrong answer. I don’t think that’s a coincidence. The failure mode in single-model “argue both sides” prompting and the failure mode in naive sensor fusion have the same shape: premature consensus on signals that should still be arguing . A protocol that enforces a dissent quota, requires evidence labels, and triggers a counterfactual round on suspicious agreement is one instantiation of a more general design pattern — make agreement expensive before you trust it . I haven’t ported that pattern into a perception stack yet. But this is the cleanest instantiation of it I’ve seen for an LLM context, and the fact that it survives a port across CLIs is some evidence that the pattern is real. Try it gemini extensions install https://github.com/Alpsource/council-of-high-intelligence-gemini Then, in Gemini CLI: /council Should we rewrite the auth service or add an abstraction layer? Three rounds, enforced dissent, and an actual structured verdict. Free, MIT-licensed, runs on your active Gemini model out of the box. MCP multi-provider routing is opt-in once you decide you want it. A few useful modes: /council:quick Should we add Redis caching to the auth flow? # 2 rounds, fast /council:duo Should we use microservices or a monolith? # sharp dialectic /council:triad ai What are the limits of current foundation models? /council --members socrates,feynman,ada Is this abstraction sound? If you find a bug, open an issue. If you find a fourth one I missed, even better — it’ll be in the next blog post. Repo: github.com/Alpsource/council-of-high-intelligence-gemini Original upstream: github.com/0xNyk/council-of-high-intelligence by @0xNyk , MIT I’m a PhD candidate working on visual-inertial odometry, JEPA-based architectures, and dynamic scene understanding. I write about robotics, self-supervised learning, and the occasional developer tool. If you liked this, my last post was I Built an AI Pilot That Plans Like a Robot and Dodges Like a Human . One AI Can’t Really Disagree With Itself. So I Wired Up a Council of 18 was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.
- CBA's DevOps agent is helping on-call engineers on 2am wake-up duty
Finding root cause while engineers boot up.
- I Spent Hours Testing NSFW AI Video Generators. These 4 Are the Best
I Spent Hours Testing NSFW AI Video Generators. These 4 Are the Best PCMag Australia
Score: 28🌐 MovesMay 24, 2026https://au.pcmag.com/ai/117860/i-spent-hours-testing-nsfw-ai-video-generators-these-4-are-the-best?p=1 - Personalizing Claude by Subtraction, Not Fine-Tuning
An independent researcher’s open-source method for growing a personalized Claude through external memory, correction, and distillation —… Continue reading on Towards AI »
- AI and office space
Two different reports highlight how flexible workspaces are the way forward to retain young talent
Score: 28🌐 MovesMay 24, 2026https://www.thehindubusinessline.com/specials/corporate-file/ai-and-office-space/article71018826.ece - Space rockets, satellites, data centers and Grok: What's the right S&P sector index for SpaceX?
What S&P Sector is SpaceX likely to be in once it launches on the public markets.
- With AI now reading student names at graduation, not everyone is applauding
With AI now reading student names at graduation, not everyone is applauding The Washington Post
Score: 27🌐 MovesMay 24, 2026https://www.washingtonpost.com/education/2026/05/24/schools-turn-ai-graduation-ceremonies-drawing-mixed-success/ - Los Gatos High robotics team takes top honors in international competition
Los Gatos High robotics team takes top honors in international competition The Mercury News
- Datavault AI (NASDAQ: DVLT) Evaluating Dividend Spin-Out of Acoustic Sciences Division Into API Media
Datavault AI (NASDAQ: DVLT) Evaluating Dividend Spin-Out of Acoustic Sciences Division Into API Media USA Today
- AI Has No Memory. So I Built One For It.
Day 7–How AI Memory Actually Works Here is something that genuinely surprised me when I figured it out. AI assistants remember what you said three messages ago. They know your name. They follow the thread of a long conversation without losing track. But here’s the thing — the AI model itself has absolutely no memory. None. Zero. Every single time you send a message, the model starts completely fresh. It has no idea who you are, what you said before, or what conversation you are in the middle of. So how does it appear to remember? That question sent me down a rabbit hole. And at the end of it, I built something that made the whole thing click. A beginner friendly chatbot which runs entirely on your machine — no API keys, no internet, no cloud — using a tool called Ollama that lets you run powerful open source AI models locally for free. Let me show you what I built and what it taught me. The Illusion of Memory The illusion of memory — it’s not magic, it’s context | Source: Unsplash Imagine you wake up every morning with complete amnesia. No memory of yesterday. No memory of anyone you have ever met. (I promise this is the weirdest analogy I’ll use today — bear with me, it actually makes perfect sense.) But before you start your day, someone hands you a printed transcript of every conversation you have ever had. You read it. Now you know everything. You can continue any conversation as if you never forgot. That is exactly what happens with AI chatbots. The model wakes up fresh with every message. But the app hands it a full transcript of the conversation before it answers. The model reads it, understands the context, and replies as if it has been paying attention the whole time. It has not. It just read the notes. What Actually Gets Sent to the Model Most people think this is what happens when they send a message: User: "What is my name?" ↓ AI Model ↓ "Your name is Priyanka." Here is what actually happens: System: You are a helpful assistant. User: Hi, my name is Priyanka. Assistant: Hi Priyanka! Nice to meet you. How can I help? User: What is AI? Assistant: AI stands for Artificial Intelligence... User: What is my name? ← your actual new message ↓ AI Model ↓ "Your name is Priyanka." Every single message includes the entire conversation history from the beginning. The model is not remembering — it is re-reading everything from scratch every single time. This is called the context window — the maximum amount of text the model can see at once. GPT-4 has a 128,000 token context window. Claude has up to 200,000 tokens. Every message, every reply, every system instruction — it all counts towards that limit. When you hit the limit? The model starts forgetting the oldest parts of the conversation. Not because it got tired. Because there is literally no more room. The App I Built to Prove This POST /chat in action — the app remembering across messages. | Screenshot: Author’s own I wanted to see this in action myself. So I built a simple chatbot in Node.js that runs completely locally — no API keys, no internet connection, no cloud. “ It’s not perfect — here it confused Mistral the wind with Mistral the AI model. But it remembered my name and city across 16 messages. That’s the point. ” What is Ollama? Ollama — the easiest way to run AI models locally. Free, private, no API key needed. | Source: ollama.com Before we get into the code — let me explain the tool that makes all of this possible. Ollama is an open source tool that lets you download and run large language models directly on your own machine. No account. No subscription. No API key. No data sent to any server. You install it, pull a model, and it runs locally. That’s it. Think of it like Docker — but for AI models. Instead of pulling a container image, you pull a language model. Why this matters: Free — no token costs, no rate limits, no monthly bill Private — your conversations never leave your machine Fast — no network latency, no waiting for remote servers Educational — you see exactly what’s happening under the hood Ollama supports many open source models including: Llama 3.2 — Meta’s latest open source model, what we use here Mistral — a powerful French open source model Gemma — Google’s open source model Phi — Microsoft’s small but capable model For this project I used Llama 3.2 — a capable conversational model that runs well on a standard laptop. Here is what the app does You send a message via a POST request The app loads the full conversation history from a local JSON file It builds a complete prompt — system instructions + all past messages + your new message It sends that full prompt to the local Llama model Gets the reply, saves the updated history, returns the response The key is step 3. Every single time. The full history. That is the memory trick. How the Code Works The buildPrompt function — where the memory trick actually happens. | Image: AI Generated Let me walk you through the actual code. The full project is on my GitHub — link at the end. The project structure: chatbot-app/ ├── server.js ← API routes (chat, history, reset) ├── src/ │ └── chatbot.js ← core logic: loads history, builds prompt, calls Ollama, saves history └── data/ └── conversation.json ← your conversation is stored here chatbot.js — The Brain This is where all the interesting stuff happens. Step 1 — The system prompt const SYSTEM_PROMPT = `You are a helpful, friendly assistant. You remember everything the user has told you in this conversation. Be concise but warm. If the user tells you something personal like their name or preferences, remember and use it naturally.` This is the personality and instructions for the model. It gets included at the very top of every prompt — before any conversation history. Notice it tells the model to “remember everything the user has told you” — but the model doesn’t actually remember anything. We’re about to fake that for it. Step 2 — Load the history function loadHistory() { try { if (fs.existsSync(CONVERSATION_PATH)) { const raw = fs.readFileSync(CONVERSATION_PATH, 'utf-8') if (!raw || raw.trim().length === 0) return { history: [] } return JSON.parse(raw) } return { history: [] } } catch (err) { throw new Error(`chatbot: failed to load history — ${err.message}`) } } Every time a new message comes in, we read conversation.json from disk. This is the entire conversation so far — every user message and every assistant reply, in order. Step 3 — Build the full prompt (this is the key) function buildPrompt(history) { let prompt = `SYSTEM: ${SYSTEM_PROMPT}\n\n` history.forEach(msg => { if (msg.role === 'user') { prompt += `User: ${msg.content}\n` } else { prompt += `Assistant: ${msg.content}\n` } }) prompt += `Assistant:` return prompt } This is the memory trick in 10 lines of code. We take the system prompt, then loop through every single message in history — every user message, every assistant reply — and stitch it all together into one big string. Then we add Assistant: at the end as a cue for the model to continue. So what the model actually receives looks like this: SYSTEM: You are a helpful, friendly assistant... User: Hi, my name is Priyanka. Assistant: Hi Priyanka! Great to meet you. How can I help? User: What is AI? Assistant: AI stands for Artificial Intelligence... User: What is my name? Assistant: ← model continues from here The model reads it all. Responds. Done. Step 4 — Send to Ollama and save async function chat(userMessage) { const data = loadHistory() data.history.push({ role: 'user', content: userMessage }) const fullPrompt = buildPrompt(data.history) const response = await fetch('http://localhost:11434/api/generate', { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify({ model: 'llama3.2', prompt: fullPrompt, stream: false }) }) const result = await response.json() const assistantReply = result.response.trim() data.history.push({ role: 'assistant', content: assistantReply }) saveHistory(data) return assistantReply } Notice http://localhost:11434 — that's Ollama running locally on your machine. No cloud. No API key. The model lives on your computer and we talk to it over a local HTTP request. server.js — The API Layer const express = require('express') const { chat, getHistory, resetHistory } = require('./src/chatbot') const app = express() app.use(express.json()) // Route 1 — send a message app.post('/chat', async (req, res) => { const { message } = req.body if (!message || message.trim().length === 0) { return res.status(400).json({ error: 'No message provided' }) } try { const reply = await chat(message) res.json({ message, reply, totalMessages: getHistory().length }) } catch (err) { res.status(500).json({ error: err.message }) } }) // Route 2 — get full conversation history app.get('/history', (req, res) => { const history = getHistory() if (history.length === 0) { return res.json({ message: 'No conversation yet', history: [] }) } res.json({ totalMessages: history.length, history }) }) // Route 3 — reset conversation app.post('/reset', (req, res) => { resetHistory() res.json({ message: 'Conversation cleared. Starting fresh!' }) }) app.listen(3000, () => { console.log('Chatbot running on http://localhost:3000') }) Three clean routes. That’s all you need. POST /chat — takes a message, returns a reply GET /history — returns the full conversation so far POST /reset — clears the JSON file and starts fresh The error handling is worth noticing — if Ollama isn’t running, chat() throws a clear error: "could not reach Ollama — is it running?" Small detail but makes debugging much easier. How to Run It Yourself node server.js — your chatbot is live. | Image: AI Generated Step 1 — Install Ollama Download from ollama.com and install it. This is what runs the AI model locally on your machine. Step 2 — Pull the model ollama pull llama3.2 This downloads Llama 3.2 — a powerful open source model from Meta. About 2GB. Free. Step 3 — Clone the repo and install git clone https://github.com/PriyankaMali-13/AI cd chatbot-app npm install Step 4 — Start the server node server.js Server runs on http://localhost:3000 Step 5 — Send your first message Open Postman or any API client and send: POST http://localhost:3000/chat { "message": "Hi, my name is XYZ" } Then send a follow up: POST http://localhost:3000/chat { "message": "What is my name?" } It will know. Not because it remembered. Because you just showed it the transcript. What This Teaches You About LLMs Building this small app taught me more about how LLMs work than any article I read. Three things that hit differently after building this: 1. Context is everything The quality of the AI’s response depends entirely on what you put in the context. Better history management = better responses. This is why prompt engineering matters so much. 2. Memory is an illusion the app creates ChatGPT, Claude, Gemini, every AI assistant — they all do some version of this. The difference is scale, sophistication, and how they manage what goes in and out of the context window. 3. Local AI is more powerful than most people realise Running Llama 3.2 on my own machine — free, private, no rate limits — felt like a superpower. For learning and prototyping, you don’t need expensive API calls. The Limitation — And What Comes Next This approach works beautifully for short conversations. But it has a ceiling. Every message adds more tokens to the context. Eventually you hit the model’s limit. At that point the oldest messages start getting cut off — and the “memory” starts to fail. The solution to this problem is something called RAG — Retrieval Augmented Generation . Instead of stuffing everything into the context, you store the history in a vector database and only retrieve the most relevant parts when needed. That is coming up in a future post. But first you need to understand what we just built — because RAG is just a smarter version of exactly this. What I Learned Today AI models have zero memory — they start fresh with every single message The illusion of memory comes from the app including full conversation history in every prompt This is called context window management — and every chatbot app does some version of it The context window has a limit — go over it and the model starts forgetting You can run powerful AI models completely locally for free using Ollama Building something small teaches you more than reading about it ever will The Code Full project on GitHub: github.com/PriyankaMali-13/AI/tree/master/chatbot-app Clone it, run it, break it, rebuild it. That is the best way to learn. Written by Priyanka. AI tools were used in the research and writing process. All code, ideas, and opinions are my own. #365DaysOfAI #NodeJS #Ollama #LLM #Chatbot #AI #LearningInPublic #ArtificialIntelligence AI Has No Memory. 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Human Archive largely confirmed the same version of events, saying the discussions never moved beyond an exploratory stage and that “no formal customer-home rollout was ever agreed or undertaken.” While both companies have described the engagement as exploratory, the existence of a signed NDA suggests discussions had moved beyond casual conversations into areas involving confidential commercial or technical information. Snabbit and Human Archive also confirmed that a physical assessment of Human Archive’s technology was conducted inside a controlled training-centre environment. However, neither company answered critical questions around what data was generated during that assessment, whether it involved egocentric video, motion capture, hand tracking, or depth mapping technologies that Human Archive explicitly references on its platform, where any such data currently resides, or what specifically led Snabbit to eventually walk away from the proposal. Still, the bigger picture emerging from these conversations is difficult to ignore. Physical AI systems require something that generative AI models do not: real-world behavioural training data. Robots cannot learn household work from text alone. They need exposure to kitchens, appliances, cluttered spaces, object handling, body movement, hand gestures, and repetitive human workflows. That makes home services platforms strategically valuable because they already operate inside real-world environments at scale through distributed workforces generating structured human task flows every day. And according to Human Archive co-founder Raj Patel, the economics may eventually become difficult for startups to ignore. He publicly claimed on X that Human Archive had approached multiple companies in the category, including Urban Company, but was turned down. He further alleged that the company later worked with a smaller player where users were offered subsidised services in exchange for consent-based recording. According to Patel, nearly 98% of users opted for cheaper services tied to recording consent, and bookings scaled rapidly during the experiment. He also claimed that wearable cameras, motion-capture systems, and AI-linked workforce tracking tools could eventually become standard across labour networks connected to Physical AI. Those claims raise bigger questions around the nature of consent itself in a price-sensitive market like India. If consumers begin trading privacy inside their homes for discounted services, what does “consent” actually mean? Can consent truly be considered freely given when economic incentives are attached to it? Would users fully understand whether recordings are being used merely for operational monitoring or eventually becoming commercial AI training datasets? Under India’s Digital Personal Data Protection Act, 2023, consent is required to be specific, informed, and purpose-bound. But if household recordings collected through discounted services are later processed, annotated, or monetised for robotics and Physical AI systems, where does the boundary between service delivery and AI data extraction begin? And if such models scale, could India’s low-cost labour economy quietly evolve into one of the world’s largest real-world training pipelines for global robotics companies? Perhaps the most urgent question emerging from this investigation is one that neither Entrackr nor any private entity has the authority to compel an answer to, but Indian authorities do. Patel publicly claimed that after being turned down by larger players, Human Archive worked with a smaller home services platform where users were offered subsidised services in exchange for consent-based recording, and that nearly 98% of users opted in. If accurate, that suggests an unnamed Indian company may already have conducted a large-scale recording exercise inside customer homes to generate Physical AI training data. Which company was involved? Which households were recorded? Were customers explicitly informed that the recordings could be processed or commercialised as AI training datasets? These are not claims Entrackr could independently verify. But there are questions regulators, including India’s Data Protection Board of India and the Ministry of Electronics and Information Technology, are empowered to examine under the DPDP Act. Entrackr asked Snabbit whether workers were informed that their hand movements, body positioning, gestures, or workflows could potentially become AI training data. The company responded that no such framework exists on the platform and that the limited training-centre assessment referenced earlier was voluntary and consent-based. Snabbit also denied transferring any operational or customer-home data outside India or sharing such information with Human Archive. Importantly, the company rejected the idea that it intends to become a Physical AI data layer. “Snabbit is a hyperlocal services platform focused on operational excellence, customer trust and expert experience,” the company said. “We are not pursuing any strategy involving recording inside customer homes or positioning the platform as a data infrastructure layer for physical AI.” Meanwhile, Abhiraj Bhal also publicly denied any such initiatives, stating that the company “does not engage in any such activities” and has no plans to do so in the future. Urban Company operates InstaHelp , a platform that competes in a similar category as Snabbit and Pronto. But the fact that multiple companies in the sector are now being approached for Physical AI-linked data partnerships reveals how aggressively AI firms are searching for real-world behavioural datasets. 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