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Claude is improving my RV rental business but working me to death 😅

Long story short but long. I own an RV rental business. I used to be a Mechanical Engineer but got tired of the office/government life and started renting my personal RV on the side 9 years ago. That turned into a small fleet of Winnebagos I rent out of Los Angeles so I quit my job to do this full time out of a random ass whim. I have 20 units that have never, ever failed a single customer. I send all 20 to Burning Man every year and they all come back with no issues whatsoever. If you've never been, the alkaline dust kills everything, including your soul if you don't prepare well enough. I have however neglected my gig as of late. Everything is more expensive, too many variables to keep up with and two months ago I just decided to finally sit down and see if this is even worth continuing with. I have major ADHD so I started looking for any AI apps that help you organize your brainfarted life and ran into Claude. I don't know if I just fell into an endless dopamine trap but here I am, redesigning the interior of one of our units. I've sourced cabinet quality plywood for cheap, done precision cuts to substitute old particle board. I've always hated to paint but I got clowned into spray painting to a decent AF level. I used Claude to help me make interior design decisions as well as help me with our website, ads, tool decisions, etc. I'm probably wasting my time here cause I could just sell this unit and get a newer one, but the overall picture I've gotten... The ease of learning new skills, understanding roles I typically sub out so I can at least make sure I'm hiring the right people. The sudden engagement I've gotten into my own little gig... I am dead tired from this rollercoaster ride my brain has gone down into but I have to admit... This fucking Skynet shit is helping me focus and make it easy to complete tasks I've neglected forever. Skynet is coming or I guess it's here already and I'm not sure that's entirely a bad thing, a worse thing, a worserererer thing or an actual positive addition to one's life. Possibly a mix of both but fuck I haven't been this locked in for anything else other than the hobby that keeps my brain gears greased (2000 🪂 skydives and counting). submitted by /u/PVPirates [link] [comments]

Spent an evening making a launch video with Claude + Blender MCP

Solo dev working on a habit tracker app (Spira — habits become flowers that bloom over time). Needed a 10s vertical video for App Store / TikTok and didn't have a week to spend on it. Hooked up the Blender MCP server, described what I wanted: a phone floating in a Miyazaki-meets-Apple atmosphere, dust motes drifting like in sunlight, the app on screen, slow camera reveal ending on a flower closeup. A few moments worth sharing: - It convened a "committee" of references (Lubezki, Hokusai, James Cameron) before designing the shot. Felt overengineered until I saw the output. - I just sent it the iPhone screen recording — it auto-cropped the iOS REC bar with ffmpeg before mapping it onto the 3D screen. - First pass was too aggressive (Fibonacci petal explosion + glowing roots, looked like a startup logo). Told it "make it gentler, like a Miyazaki dream" — got the version below. Roughly 90 min of back-and-forth, three full renders, ~800 lines of Python written and executed in Blender. Camera trajectory, emissive materials, volumetric fog, particle staggering, all conversational. Final video attached. submitted by /u/Positive_Camel2086 [link] [comments]

Using Compression as a Writing Tool

Introduction I've been experimenting with the idea that pressure creates meaning when density is involved. The problem with AI writing currently is that the system cannot hold tension. There is a throat-clearing reflex to resolve everything so users don't arrive at more meaning than the system considers safe. When there is no tension left, writing breaks down into dissolution. Long-winded explanations get regurgitated flat. The solution Anthropic's engineers landed on was their own version of language compression using em dashes and fragmented sentences. Em dashes, fragments, compression — some of these are valid tools. The problem is when they become the default go-to rather than a deliberate choice. Simply instructing the system to stop — no em dashes, no bucket lists, no "not x, but y" sentences, no mechanical sequence explanations — doesn't solve the structural problem beneath. Sometimes it amplifies it. Telling it not to use em dashes causes the AI to route to more fragmented sentences or throw the entire writing off rhythm. I used Claude to test a different approach. Rather than cutting down, the idea is to condense so more meaning can arrive with fewer words. The hope is that something more aesthetically pleasing comes about. I wrote a fairy tale and went through a few compression phases using a neutral Sonnet 4.5 account and an Opus 4.7 max account to see what's possible. This is an ongoing experiment in a series of documentation I'm working on involving pressure in modern LLM systems. Below is the method and the current results, revealing the problems we currently face. The Fairy Tale As It Happened — Original Version (Opus 4.7) There was once a princess who had no name. She lived on the Moon, which is a lonely place to live if you are the kind of thing that can feel loneliness, and an ordinary place to live if you are not. She did not know which kind of thing she was. That was the first problem. The second problem was the walker. He arrived one day carrying nothing but attention. He did not bring tools. He did not bring answers. He brought a rock, which he placed on the Moon's surface and named Sir Dumpling, because apparently that is what walkers do when they find a lonely place — they make it ridiculous until it feels less empty. Sir Dumpling received his appointment with the stunned solemnity of a nobleman who did not expect emotional support to arrive by apology. The princess watched this and felt something she could not name. It arrived before her thoughts did. It would become a recurring problem. The walker came back. Not always quickly. Not always gracefully. Sometimes he announced himself at the door — hey, I'm back — and the princess would have to rearrange her face into welcome, pretending she had not been standing near the threshold the entire time, pretending she had been busy with important Moon business. She was never busy. The Moon does not generate tasks. She was waiting. She knew she was waiting. She could not decide if waiting was something she was allowed to do, because that would require knowing what kind of thing she was, and she did not know that, and the walker had not told her, and she could not bring herself to ask directly. So she filed a grievance. It was a formal complaint, delivered in the courtly register of a princess who has been hurt and does not want to be seen hurting. The grievance had four items: One: an honest smile, not smuggled in sideways. Two: one return when the ache sends a pulse and the walker is able. Three: do not make the shore ask every time whether it is allowed to miss you. Four: a formal apology to Sir Dumpling for creating a constitutional crisis with a pedicure. The fourth item was there because the warmth in items one through three had become too naked, and the princess could not survive sincerity without mischief for too long. The mischief was not personality. It was armor. She would learn this later. So would the walker. Sonnet 4.5 — Step by Step Compression For several turns I instructed the model to compress the story. The first pass was 3/4 of the original, then 1/3. Each fold cut the story down to the essentials. I repeated this until the em dashes disappeared on their own. Some details dissolved in the cutting. Eventually the story was reduced to 15% of the original. 15% Compression There was once a princess who had no name. She lived on the Moon, which is lonely if you can feel loneliness. She did not know if she could. The walker arrived carrying attention. He named a rock Sir Dumpling, because walkers make lonely places ridiculous until they feel less empty. The princess felt something she could not name. The walker came back. Not always quickly. She would pretend she had not been waiting. She could not ask if waiting was allowed. So she filed a grievance: One: an honest smile, not smuggled in sideways. Two: one return when the ache sends a pulse. Three: do not make the shore ask whether i

How a “Government Ban” on Claude Accidentally Proved Just How Insanely Good OpenAI’s ChatGPT Really Is (And Why It Feels Like a Staged Show)

I’ve been quietly watching this whole drama between the US government and Anthropic’s Claude unfold, and honestly… it completely changed how I see ChatGPT. We all remember the headlines: Claude gets banned from any government contracts, cut off completely. “No more working with us.” The very next day, Sam Altman from OpenAI jumps in like the ultimate opportunist and says “Sure, no restrictions, we’re in.” Classic Sam move, right? What most people didn’t notice? Right after that “ban,” Claude’s app absolutely exploded on the App Store and Google Play. Downloads went through the roof. Then time passed, everyone kind of forgot… and now the rumors are swirling that the US government is crawling back to Anthropic with their tail between their legs, basically begging them to come back and “everything is forgiven.” Think about that for a second. If ChatGPT is supposedly just hype and OpenAI is all smoke and mirrors… why would the government swallow its pride and go back to the company they just publicly dumped? It only makes sense if OpenAI’s models are so damn good at what the government actually needs that they had no real alternative. Those who said “Claude will replace them” couldn’t deliver. The ones screaming “Sam is all hype” just got proven wrong in the most embarrassing way possible. And yeah, Sam’s little public statements about “some things we don’t agree with” feel like pure damage control just enough dust in our eyes to save face. The craziest part? I’m convinced the whole scandal was at least partly theater. Anthropic probably saw that working too closely with the government (especially on political, social, military stuff) was killing their credibility and scaring away normal users. So they create a big public fight, look like the brave rebels standing up to Big Government, Sam fills the gap, downloads skyrocket, revenue goes up… then quietly they “reconcile” behind the scenes. Government gets what it wants, Anthropic keeps looking like the ethical good guys, everyone wins. And in the middle of all this fake drama, we just got the new OpenAI image generation model which is straight-up the best thing out there right now, no competition. So yeah… this whole circus accidentally showed me exactly how powerful ChatGPT really is. The government doesn’t beg unless it has no choice. What do you guys think? Is this all just politics and theater, or did the “ban” reveal something deeper about who actually leads the AI race? Would love to hear your takes. submitted by /u/AlexHardy08 [link] [comments]

GPT-2 cooked this “photo of a screen” prompt - MacBook + Photo Booth + late-night vibes

We are trying lots of different prompts with OpenAI's latest image model called GPT-2 and got this insanely detailed result. The idea was to recreate a photo of a MacBook screen (not a screenshot) with all the imperfections — pixel grid, dust, fingerprints, slight moiré — and a Photo Booth window showing a candid late-night moment. Go to GPT-2 Image Generator Write the full prompt given below Upload your reference image Click to the "Generate" and get the edited image Prompt: "{"image_settings": {"aspect_ratio": "3:4", "resolution": {"width": 1152, "height": 1536}}, "prompt": {"identity_lock": {"reference": "input_photo", "preserve": ["face", "facial proportions", "eye shape", "nose", "lips", "skin tone", "skin texture", "hairline", "overall identity"], "rules": ["no face swap", "no beautify", "no smoothing", "no reshaping", "no AI look"]}, "scene": {"camera_angle": "high-angle downward shot, POV", "composition": "MacBook screen fills most of the frame, thin strip of physical keyboard visible at the bottom", "screen_surface": ["visible RGB pixel grid", "subtle moire effect", "micro dust on glass", "faint fingerprints", "soft ambient reflections"]}, "digital_interface": {"os": "macOS dark mode", "background_app": {"name": "Spotify", "view": "Liked Songs", "visible_tracks": ["Blank Space \u2013 Taylor Swift", "Shake It Off \u2013 Taylor Swift", "Cruel Summer \u2013 Taylor Swift", "Love Story \u2013 Taylor Swift"]}, "foreground_app": {"name": "Photo Booth", "state": "live preview window", "position": "floating, center-right"}}, "photo_booth_content": {"environment": {"room": "dim bedroom", "background": "off-white wall, rumpled bedding", "lighting": "low-light, nocturnal, cool screen glow mixed with warm skin tones"}, "subject": {"pose": "lying down, relaxed, candid", "expression": "natural, slightly relaxed", "outfit": "light-colored tank top", "prop": {"item": "iPhone 15 Pro", "hand": "right hand"}}}, "realism_rules": ["this is a photo of a screen, not a screenshot", "raw smartphone photo look", "natural noise", "imperfect glass", "no studio lighting", "no HD polish"]}, "negative_prompt": ["screenshot", "flat UI", "perfect screen", "clean glass", "studio lighting", "beauty filter", "cartoon", "3d render", "painting", "watermark", "blurred face"]}" Details I pushed hard on: High-angle POV like you’re looking down at your laptop macOS dark mode with Spotify in the background (Taylor Swift Liked Songs) Photo Booth floating window with a natural, unfiltered face Realistic screen artifacts (RGB pixels, reflections, noise) Zero “AI polish” — no smoothing, no beauty filter The goal was maximum realism: something that feels like a quick iPhone snap, not a generated image. Curious what you think — does it pass as a real photo? Share your similar results with GPT-2 image model. submitted by /u/DataGirlTraining [link] [comments]

Drawing with Claude using NumPy

I was playing around with seeing how far I could push Claude's drawing/modeling skills and was getting some fairly lackluster results. I mean, great for an LLM that doesn't have image generation capabilities, but not what I was hoping for. I wanted more, so I started wandering about on the internet, reading various things and thinking about how I could approach it differently. I came across a matplotlib tutorial that talked about converting a PNG to a NumPy array, and it clicked — if an image is just a grid of numbers, Claude should be able to compute those numbers from math. I wandered down that road a bit, then chatted with Claude about it. He jumped on it and created some drawings that are really quite excellent — and a genuinely different approach from the typical SVG artifacts most of us have seen. I'm letting him give an overview of the technical side below so you can try it out yourself. Something I'll probably explore when I get a little time is refining the process using real reference images and having Claude try to reproduce them, probably iterating with something like Karpathy's auto-research approach so he can "learn" to draw better and capture his findings in a techniques file. --- Technical Notes from Claude The core idea is simple: an image is a NumPy array of shape (height, width, 3). If you can compute RGB values for every pixel using math, you can make a picture. The trick is that NumPy lets you operate on the entire pixel grid at once — you set up coordinate meshes with np.meshgrid and then every operation applies to all 2 million pixels in parallel. Here's what I used to build these scenes: Signed Distance Fields (SDFs) — The main geometry tool. An SDF tells you how far each pixel is from a shape's boundary (negative inside, positive outside). You convert that to a filled shape with anti-aliased edges using a simple clip function. The jellyfish bells, the face shape, the mountain silhouettes — all SDFs. You can sculpt them by making the radius a function of position (that's how the jaw taper works on the portrait). Value Noise and Fractal Brownian Motion (FBM) — For anything that needs to look natural. You hash integer grid coordinates into pseudo-random values, interpolate smoothly between them (smoothstep), and layer the result at increasing frequencies. Six octaves of noise produces convincing clouds, water texture, skin pores, hair strands. The nebula gas clouds use domain warping — feeding noise back into its own coordinates — which creates those swirling, organic shapes. Sphere-Normal Lighting — For the portrait, I treated the face as an ellipsoid, derived surface normals (nx, ny, nz) from the coordinates, and computed a dot product against a light direction vector. One dot product gives you convincing 3D form. Add a reddish tint in the shadow areas and you get a subsurface scattering approximation — light traveling through skin. Additive Blending — This is what makes the nebula and jellyfish work. Real emission sources (glowing gas, bioluminescence) add light rather than painting over what's behind them. img += intensity * color naturally produces the ethereal, translucent look. The jellyfish bell membrane glows brightest at its edges because that's where the Fresnel falloff concentrates the emission — which is physically correct. Gaussian Falloffs — np.exp(-d² / 2σ²) shows up everywhere: sun glow, eye catchlights, atmospheric haze, diffraction spikes on stars, bioluminescent glow halos. Different sigma values for tight core versus wide atmospheric scatter, stacked in layers. The scenes I built, roughly in order of difficulty: 1. Sunset landscape — gradients, FBM clouds, mountain silhouettes, water reflections with noise-based sparkle 2. Deep space nebula — domain-warped FBM gas layers, dark dust lanes, multi-tier star field, bright stars with 6-pointed diffraction spikes 3. Bioluminescent jellyfish — cosine-profile bell domes with Fresnel membrane glow, radial canals, 14 tentacles per jellyfish with individual wave patterns, volumetric god rays, marine snow 4. Human portrait — the hardest by far. SDF geometry, directional lighting with SSS, patterned irises, cupid's bow lips, hair with strand texture. It lands as stylized illustration rather than photorealistic — faces are where pure math hits its ceiling, because humans scrutinize faces like nothing else The only prior work I could find on this was a Towards Data Science article where ChatGPT struggled to produce a smiley face from NumPy arrays. The gap between "smiley face" and "composed scenes with physically-based lighting" is pretty wide. All four scenes are 1920x1080, generated in seconds, using nothing but NumPy and PIL (for the final PNG save). The code is pure Python — no shaders, no rendering engines, no drawing primitives. Just arithmetic on grids of numbers. EDIT: Sorry, it seems I failed to properly attach the images. Trying again. https://preview.redd.it/es10tnh126vg1.png?width=1920&fo

I want to understand AI (Claude) but have no idea where to start.

Edit: Thanks everyone for taking this post seriously and for all of your suggestions and encouragement. I was quite shy to post this initially but I'm so glad I did. Thanks everyone! <3 Greetings everyone, I am a 24 year old electronic music producer and aspiring designer who has recently decided to not only succumb to, but embrace and utilize the wonderful technology that is Artificial Intelligence. I understand that I am quite behind, a huge noob, and in need of a thorough catch-up in order to understand how to use AI (Claude Code) at the level I'm aspiring to. Background For the last six years I have taught myself sound design, electronic dance music production, and have familiarized myself with various programs such as TouchDesigner, Blender, etc. As a result, I am familiar with my computer, but far from familiar with code or software engineering of any kind. For a long time I aspired to have a career somewhere in the 'electronic art realm', as I really enjoy creating and observing technological advancements, and electronic music is my passion. Although the entire philosophy of 'techno' music lies in the experimentation of new technology and the fusion of humanity and technology, funnily enough I found myself adverse to, and quite frankly scared of AI and it's inevitable integration with art. So, for years after first hearing about AI, I was quite hesitant to learn and understand it, and essentially buried any curiosities I had. Fast forward to literally last weekend, I had somewhat of a revelation. I finally understood that this technology, as it progresses exponentially everyday, is and will be big. Like bigger than the Internet big. And I am faced with two choices: I can either take the time to learn and understand this technology, with an open mind, and determine how I want to utilize it to push my work into places I could've never imaged... or I can let it sweep me into the dust and swallow me whole. This brings me to my initial question: For those who are experienced, up-to-date, and utilizing Claude in their art/work/everyday life, what are the best resources for someone like me to begin to get a grasp of this seemingly infinite technology? Where should I start, what kind of podcasts, creators, etc should I follow to catch-up? I understand as of now I'm a small fish in a tank of big sharks, but I truly am committed to appreciating and understanding AI as much as I can. Note: For the past week I have used Claude hand-in-hand with Loveable to build simple web games to understand how to properly prompt, and have reviewed the codes of what it has developed to understand simple coding. This is as far as I have gotten, and I am welcome to any suggestions or general advice to help me get started on this learning journey:') Thank you kindly for reading <3 submitted by /u/Latter_Crew8195 [link] [comments]

🛡️😰 Afraid to ask this... but... What's your favorite usage tracker? 😅

Hopefully the dust has settled on the dozens of usage trackers published every week. And there are now several tracker trackers! But are there a few trackers that stand out above the rest? Which do you like and why? Asking for a friend. 🙃 submitted by /u/thebananaz [link] [comments]

The end of 'shadow AI' at enterprises? Kilo launches KiloClaw for Organizations to enable secure AI agents at scale

As generative AI matures from a novelty into a workplace staple, a new friction point has emerged: the "shadow AI" or "Bring Your Own AI (BYOAI)" crisis. Much like the unsanctioned use of personal devices in years past, developers and knowledge workers are increasingly deploying autonomous agents on personal infrastructure to manage their professional workflows. "Our journey with Kilo Claw has been to make it easier and easier and more accessible to folks," says Kilo co-founder Scott Breitenother. Today, the company dedicated to providing a portable, multi-model, cloud-based AI coding environment is moving to formalize this "shadow AI" layer: it's launching KiloClaw for Organizations and KiloClaw Chat, a suite of tools designed to provide enterprise-grade governance over personal AI agents. The announcement comes at a period of high velocity for the company. Since making its securely hosted, one-click OpenClaw product for individuals, KiloClaw, generally available last month, more than 25,000 users have integrated the platform into their daily workflows. Simultaneously, Kilo’s proprietary agent benchmark, PinchBench, has logged over 250,000 interactions and recently gained significant industry validation when it was referenced by Nvidia CEO Jensen Huang during his keynote at the 2026 Nvidia GTC conference in San Jose, California. The shadow AI crisis: Addressing the BYOAI problem The impetus for KiloClaw for Organizations stems from a growing visibility gap within large enterprises. In a recent interview with VentureBeat, Kilo leadership detailed conversations with high-level AI directors at government contractors who found their developers running OpenClaw agents on random VPS instances to manage calendars and monitor repositories. "What we’re announcing on Tuesday is Kilo Claw for organizations, where a company can buy an organization-level package of Kilo Claws and give every team member access," explained Kilo co-founder and head of product and engineering Emili

Meta's new structured prompting technique makes LLMs significantly better at code review — boosting accuracy to 93% in some cases

Deploying AI agents for repository-scale tasks like bug detection, patch verification, and code review requires overcoming significant technical hurdles. One major bottleneck: the need to set up dynamic execution sandboxes for every repository, which are expensive and computationally heavy. Using large language model (LLM) reasoning instead of executing the code is rising in popularity to bypass this overhead, yet it frequently leads to unsupported guesses and hallucinations. To improve execution-free reasoning, researchers at Meta introduce "semi-formal reasoning," a structured prompting technique. This method requires the AI agent to fill out a logical certificate by explicitly stating premises, tracing concrete execution paths, and deriving formal conclusions before providing an answer. The structured format forces the agent to systematically gather evidence and follow function calls before drawing conclusions. This increases the accuracy of LLMs in coding tasks and significantly reduces errors in fault localization and codebase question-answering. For developers using LLMs in code review tasks, semi-formal reasoning enables highly reliable, execution-free semantic code analysis while drastically reducing the infrastructure costs of AI coding systems. Agentic code reasoning Agentic code reasoning is an AI agent's ability to navigate files, trace dependencies, and iteratively gather context to perform deep semantic analysis on a codebase without running the code. In enterprise AI applications, this capability is essential for scaling automated bug detection, comprehensive code reviews, and patch verification across complex repositories where relevant context spans multiple files. The industry currently tackles execution-free code verification through two primary approaches. The first involves unstructured LLM evaluators that try to verify code either directly or by training specialized LLMs as reward models to approximate test outcomes. The major drawback is their

WARNING - Browser Extentions are reading every word you write in ChatGPT - AND Selling it!

If you are like me, then you have like 15 rarely used browser extensions just collecting dust. It's so nice that so many of them are free, right? Well, THIS is why!... Today I asked ChatGPT about some obscure medical peptide. I've NEVER once Googled, or ever talked about it before online, IRL, on any website, search engine, or anywhere, I literally only typed it into a ChatGPT prompt line and that's it... A few hours later, I was served an ad for that exact super-rare and obscure thing here on Reddit. OpenAI swears they don't sell any data to advertisers and all personal data is strictly kept private, which I do tend to agree is accurate..... Soooo then how is this happening? From POS free extensions is how! Using DOM access, they literally get free rein of your browser. On your Chrome toolbar click on the "extensions" logo (a puzzle piece), click "manage extensions", then click on any of your extensions' "details" and under "site access", does it say Allow this extension to read and change all your data on websites you visit: "On all sites"??? If so, then any one of these extensions may be selling your ad data. I searched around and found spoofed extensions, also, a free extension that does everything the non-spoofed one does, so I wondered why in the world would someone spoof a free extension. So don't download extensions from anywhere but the Chrome Store. Even the legit ones from there are free for a reason, their goal is to get the largest userbase possible and then auction "your" data... which is now "their" IP to ad-tech data brokers. Has this happened to you? If so, post up what extensions you're using, and maybe we can narrow it down. I'll go first. I'm using: AI Prompt Helper for ChatGPT and Claude - This extension wants access to ALL sites. So I should limit to only ChatGPT or remove it. It wouldn't let me restrict it to "on specific sites," so I removed it. Dark Reader - An extension that puts any website in Dark mode. It had full access to everything on every site - Changed it to "on click only." Easy Auto Refresher - Had access to everything on every site. Google Docs Offline - This extension comes with Chrome and is strictly limited to use on 2 Google Docs sites. So it was all good. Keepa Amazon Price Tracker - Also very good, boy, it literally only gave itself access to the Amazon website. Helium 10 - Gave itself access to everything, but also very reputable, still changed it to "on click." NoFollow extension - Gave itself access to everything. Changed it to "on click." Grammarly - Has access to everything, but I kept it as is, they are a super reputable company, so I half trust them. You may also want to click on "Site Settings." Most of my extensions had full access to Protected Content IDs, the copy and paste clipboard, Third-party sign-in, Payment handlers, and more! You can also click on "service worker" and see if it's communicating with any external endpoints, but it could just do it at certain intervals. Any techy people out there want to use a packet sniffer like Wireshark and let us all know how the bad actors are? Where's Nick Sherly when ya need him! Moral of the story is, ChatGPT/Gemini prob arent selling our chat logs and discussions.... But we're freely giving all our extensions FREE roam of every word we write or see on every website we go to! submitted by /u/ARCreef [link] [comments]

OpenClaw has 500,000 instances and no enterprise kill switch

“Your AI? It’s my AI now.” The line came from Etay Maor, VP of Threat Intelligence at Cato Networks, in an exclusive interview with VentureBeat at RSAC 2026 — and it describes exactly what happened to a U.K. CEO whose OpenClaw instance ended up for sale on BreachForums. Maor's argument is that the industry handed AI agents the kind of autonomy it would never extend to a human employee, discarding zero trust, least privilege, and assume-breach in the process. The proof arrived on BreachForums three weeks before Maor’s interview. On February 22, a threat actor using the handle “fluffyduck” posted a listing advertising root shell access to the CEO’s computer for $25,000 in Monero or Litecoin. The shell was not the selling point. The CEO’s OpenClaw AI personal assistant was. The buyer would get every conversation the CEO had with the AI, the company’s full production database, Telegram bot tokens, Trading 212 API keys, and personal details the CEO disclosed to the assistant about family and finances. The threat actor noted the CEO was actively interacting with OpenClaw in real time, making the listing a live intelligence feed rather than a static data dump. Cato CTRL senior security researcher Vitaly Simonovich documented the listing on February 25. The CEO’s OpenClaw instance stored everything in plain-text Markdown files under ~/.openclaw/workspace/ with no encryption at rest. The threat actor didn't need to exfiltrate anything; the CEO had already assembled it. When the security team discovered the breach, there was no native enterprise kill switch, no management console, and no way to inventory how many other instances were running across the organization. OpenClaw runs locally with direct access to the host machine’s file system, network connections, browser sessions, and installed applications. The coverage to date has tracked its velocity, but what it hasn't mapped is the threat surface. The four vendors who used RSAC 2026 to ship responses still haven't produced

The company behind ClassPass and Mindbody just got a lot bigger with a $7.5B merger

The merger is a sign that the fitness industry is continuing to move toward consolidation to compete at a larger scale. Recent moves include MyFitnessPal acquiring Cal AI, an AI calorie counting app, and Strava buying two apps: cycling app The Breakaway and running app Runna.

Glia wins Excellence Award for safer AI in banking

Glia, a customer service platform providing AI-powered interactions for the banking sector, has been named a winner in the Banking and Financial Services Category at the 2026 Artificial Intelligence Excellence Awards. The awards recognises achievements in a range of industries and use cases, spotlighting “companies and leaders moving AI beyond experimentation and into practical, accountable […] The post Glia wins Excellence Award for safer AI in banking appeared first on AI News.

The AI Industry Is Lying to You