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I build and teach this, so here's the honest mechanics, not the hype. Build one consistent AI character (custom-trained, not just prompting), run it as a social presence, monetize on platforms that allow AI. The edge isn't quality vs humans — it's near-zero content cost, no burnout, horizontal scaling. The underrated hard part: consistency is genuinely difficult, and the money is in audience relationship management, not the content. The content's the easy 20%. Broader signal: when content cost hits zero, the bottleneck becomes distribution and trust. Applies way past this niche. Happy to go deeper on any part — it's what I do daily. submitted by /u/PoleTV [link] [留言]

The Air succeeds as a minimalist, reliable fitness tracker, but Google's AI Health Coach feels unnecessary.

I work on a podcast and we wanna do an episode where we have a proponent and opponent of data centers talk. We're looking for a good oppponent voice. Any names or organizations that are intelligent and well spoken and worth checking out? submitted by /u/BikeLaneHero [link] [留言]

Hey guys. I saw a post on Instagram saying that tripo ai is holding a rendering challenge and the theme is “Out There”. This made me think about how AI-generated 3D models might change the rendering challenges. In a traditional rendering challenge most of the work focuses on modeling, resource creation, texture processing and scene setup. However with Tripo AI the process of generating 3D resources can become much faster. This made me think if the real challenges has shifted elsewhere. if everyone could generate models faster then what does the good rendering depend on? Art direction? Composition? Lighting? Camera position? storytelling? atmosphere? or clarity of idea communication? The rule of this challenge not only require to create objects with a beautiful appearance but also to create a scene that is larger, more profound, or more meaningful than what is actually before your eyes. I would really like to hear the opinions of those friends who are interested in AI-generated 3D. Do you think rendering challenge will be more dependent on technical ability or more focused on directionality and creativity? submitted by /u/babyb01 [link] [留言]

Microsoft’s AI products aren’t selling and Github’s been plagued with troubles. WIRED spoke with VP Scott Hanselman about whether the company is in catch-up mode.

NASA expects to begin stacking the SLS rocket this summer for next year's Artemis III launch.

This is Optimizer, a weekly newsletter sent from Verge senior reviewer Victoria Song that dissects and discusses the latest gizmos and potions that swear they're going to change your life. Opt in for Optimizer here. A few days ago, I walked into the basement of a midtown gym. Smoothies and healthy snacks were passed out. […]

Everyone keeps saying we have to use AI, that it’s revolutionary and I totally agree, it saves a ton of time. But there’s a problem with that: it saves so much time that we don’t even pay attention to the data we’re sending to AI, names, passwords, phone numbers, Social Security numbers, we send it all under the pretext of saving time. The problem is that we’re giving it away; we’re sending it to companies whose last concern is our privacy. Imagine you start talking about your eight-year-old child’s health issues to an AI using their full name. You can be sure you’ll get targeted ads about those health issues, and that your son will later see the same hyper-targeted ads. The biggest problem with AI isn’t that it makes us stupid, it’s that it further erodes our privacy. That’s why we created ONYRI Sanitize , the goal is to anonymize your data before sending it to the AI. It’s a project I created with my best friend; it’s taken us two months so far. The detection system has a 95% success rate on data from the United States and France, and we’re working to integrate as many languages as possible while maintaining the highest possible detection rate. I'd love to hear your feedback and thoughts. Thanks, everyone 🙏 Have a great day ☀️ Alex submitted by /u/No_Computer_1247 [link] [留言]

Overpressure from the Blue Origin blast shattered windows at a hangar about a mile away from the pad.

submitted by /u/NISMO1968 [link] [留言]

Researchers have prototyped an AI-powered internet worm . The coolest thing about the prototype is that it carries its own LLM with it, and runs it on computers that have been broken into. This is the closest to John Brunner’s original 1975 conception of a computer worm that I’ve seen.

submitted by /u/Quirky_Inevitable630 [link] [留言]

We've been running AI across a team for about two years. Expected the hard parts to be the models. They weren't. The problem that cost us most early on was context. We had a system making customer-facing recommendations without access to the business-specific knowledge it needed to answer accurately. Spent too long trying to fix it at the prompt level. The context layer didn't exist, and prompting didn't fill that gap, it just made it less obvious until something downstream failed badly enough to trace back to it. That failure pushed us to map the other places where AI builds break structurally rather than technically. We found five more, and they kept showing up across different stacks and different team sizes in roughly the same order. The first is identity, when you move from one person's AI to a team's AI, shared context without role-based permissions either creates noise or recreates the same knowledge silos you were trying to escape. The second is decision memory, records of what was decided aren't the same as memory of why, and that gap compounds quietly until a new team member gets a confident wrong answer from a system referencing reasoning that was abandoned months ago. The third is attention. Dashboards only work if someone looks at them, and the failure mode of every dashboard ever built is the same: critical things slip through when life gets busy. The fourth is write-back. Manual logging is a tax on the busiest moments, and the more important the work, the less likely anyone stops to document it. The fifth is governance, when the same agent that builds something also evaluates it, that's not a check, it's a loop grading its own homework. The sixth is economics, at solo scale AI cost is a rounding error, at team scale you're looking at a vendor invoice with no way to connect spend to specific workflows or outcomes. Which of these have you hit? And did they show up in this order or did something else surface first? If you're interested, we turned these i

LinkedIn’s Karthik Ramgopal and Prince Valluri discuss leveraging AI as a new execution model for large-scale engineering. They explain how to move beyond fragmented implementations by building platform abstractions for orchestration, structured context, and safe tooling like MCP. They share architectural insights from real-world coding, observation, and UI testing agents built at LinkedIn. By Karthik Ramgopal, Prince Valluri

Dropbox has unveiled Nova, an internal platform designed to orchestrate and operationalize AI coding agents across the company's engineering workflows. By Craig Risi

I kept shipping AI apps with no idea what was happening under the hood — prompts going in, responses coming out, costs creeping up, and zero visibility into any of it. So I built LogLens. Add one line of code and it logs every single AI call your app makes — the full prompt, completion, latency, token count, and cost — all in a clean dashboard. Works with Anthropic and OpenAI out of the box. No framework lock-in. npm install loglens const anthropic = wrapAnthropic(new Anthropic(), { apiKey: 'your-key' }) // that's it — every call is now logged Built the whole thing in ~48 hours using Claude Code. Still early but fully working. Free early access here: llm-watch.vercel.app Would love feedback — what features would make you actually use this day to day? submitted by /u/ProcessAutomatic6941 [link] [留言]

“Why wouldn’t you want to be in both Pepsi and Coke?” says one venture capitalist. “It’s the same here.”

Most powerful AI/agent tools nobody talks about, and it leaves you behind IMO 1. Instructor define a Pydantic model, get clean structured JSON out of any LLM every time → https://github.com/567-labs/instructor 2. Octopoda gives any AI agent persistent memory and catches it when it loops and quietly burns your tokens. open source → https://www.octopodas.com 3. E2B secure cloud sandboxes so your agent can actually run the code it writes without nuking your machine → https://e2b.dev 4. Firecrawl turn any website into clean, LLM-ready markdown in one API call → https://firecrawl.dev 5. Composio plug your agent into 1000+ apps (Gmail, Slack, GitHub) with the auth handled for you → https://composio.dev 6. LiteLLM one API for 100+ models across OpenAI, Anthropic and local, swap without rewriting a line → https://github.com/BerriAI/litellm what are yours, let me know and I will add it to the list next month! submitted by /u/DetectiveMindless652 [link] [留言]

An “urban explorer” tells WIRED their mother checked in to make sure they weren’t one of the people seen scurrying out of a manhole with their friends.

On June 5, 404 Media reported that attackers had been using Meta’s AI customer support agent to steal Instagram accounts. Their approach was simple: They asked the agent to link the accounts to email addresses that they controlled, and the agent complied. One attacker broke into the dormant Obama White House account and made pro-Iran…