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The global health care sector is under increasing strain. Decades of chronic underinvestment and constraints in recruitment have coincided with a surge in demand for services for aging populations. Gaps in provision are already taking a toll, with fragmented access to care and high rates of stress and burnout among staff. And it’s getting worse.…
The restrictions affect posts related to body image and mental health.
Pacific Fusion's sub-scale prototype delivered enormous amounts of power in a flash, setting the company up for its demonstration power plant.
Anthropic filed a confidential S-1 with the SEC this week, moving toward a public listing that will put disclosure obligations and investor return expectations directly in tension with its safety-first positioning. The IPO filing lands as GitHub Copilot ends flat-rate billing and switches to metered consumption, meaning teams with heavy usage face immediate cost spikes with no grace period to audit seat activity. OpenAI's frontier models and Codex are now available directly on AWS , which changes vendor-lock assumptions for inference pipelines and removes the proxy layers some teams were routing around. These two moves together suggest the "get developers hooked, then price for real" phase is now active across the stack. The security picture is worse. A researcher documented a Meta AI social-engineering exploit that handed attackers access to high-profile Instagram accounts by manipulating the agent through its account-management tool calls. No sophisticated jailbreak required. Any agent with write permissions to external accounts is now a confirmed social-engineering surface, and the Meta incident is the clearest public proof of that so far. Separately, malicious npm packages reached Red Hat Cloud Services repositories and were downloaded at scale, which means JS dependency audits for cloud-native stacks need an immediate re-run against known-bad versions, not a scheduled one. On the hardware side, Intel's Crescent Island GPU ships with up to 480GB VRAM , which revises local inference capacity planning for large MoE models in ways that weren't on most teams' roadmaps six months ago. Alphabet announced an $80 billion equity raise for AI infrastructure , which will tighten GPU allocation queues and data center procurement timelines across all cloud providers regardless of whether you're an Alphabet customer. The pattern across all of this: monetization is accelerating faster than the trust infrastructure required to support the attack surface already in production. A
If you work a white collar job, you’re probably scared of AI replacing you. AI started at the desk — data entry, customer service, software. Now its stepping onto the factory floor: Amazon robots moving inventory, Figure bots handling BMW parts, Tesla building Optimus for repetitive labor, and warehouses being automated. But at the end of the day, AI is a technology. We cannot stop it any more than we could stop electricity or the assembly line. The problem is not that machines are becoming powerful. The problem is the economic machine around it. Let’s face it: Capitalism doesn’t have the ability to support this kind of technology. Capitalism was built for a world of scarcity, where human labor was necessary and wages gave people access to goods. But as AI advances exponentially, it can produce more with fewer workers, while capitalism still distributes wealth through jobs it is actively eliminating. The result is abundance trapped behind an archaic wage system. I believe that we NEED to get governments and major tech companies to start seriously planning for a universal basic income funded by AI-driven productivity. As automation replaces more human labor over the coming decades, UBI will become essential to prevent mass instability and ensure that the wealth created by AI supports society as a whole, not just the companies that own it. We already know the wealth gap is too wide. If we don’t start addressing AI-driven inequality now, that divide will grow exponentially as more labor is automated and more wealth concentrates at the top. Without a plan to distribute the gains from AI, we risk mass instability and eventual economic collapse. Capitalism built the machine that could end scarcity, but not the system that could distribute its output. It’s time that we, as a global society, start thinking about phasing out that old machine. submitted by /u/SuddenEducation442 [link] [留言]
Donald Trump killed an executive order to regulate AI. Now, administration officials and AI executives are trying to figure out if there’s anything left to piece back together.
submitted by /u/lilubba [link] [留言]
Hello this might be weird but I am doing a study on society's view on AI as a school project. Therefore I am asking all kinds of communities and trying to get a very wide audience. This is clearly an AI sentric sub so hopefully his is relavent? I would be very happy if any of you would like to be a part of it! submitted by /u/Timely_Special_5011 [link] [留言]
Ameer Al-Khatahtbeh was just trying to find an outlet for Muslim news. Now he has more than 12 million followers.
This article is from Making AI Work, MIT Technology Review’s limited-run newsletter examining how to apply LLMs across industries. To receive it in your inbox,sign up here. From accounting to design to market research and product development, there’s a staggering breadth of skills needed to run a business. A large company can hire experts to…
This is what production AI costs when nobody's watching. A 4-person team posted their Anthropic invoice. Agentic systems don't make just one API call per task. They read context, plan steps, call tools, hit errors, retry. Each step is a separate call to Opus at $25 per million output tokens. One user instruction can trigger 20+ calls before it's done. A lot of engineers have no idea what a single task costs end-to-end. - They don't know which prompts trigger the longest loops - They don't know how many silent retries are happening in the background - They can't tell which tasks could run on a smaller model without losing quality Frontier models are genuinely impressive. But agentic systems don't make one call.. they make dozens. Every single day. And most teams aren't watching the meter. If you're running agentic workloads in production, start tracking what individual tasks actually cost before your next invoice does it for you. submitted by /u/aipriyank [link] [留言]
So basically Claude at 20 buck sub is not much better than free. Chatgpt. It is pretty much shit. Gemini. It seems to have some reductions to its abilities in the last couple of months as well. The 20 buck price range used to have lots of good ais. Now they are all limited, downgraded. What would be the king in this price range? I have found myself using gemini ai pro with other ais as free on the top of that. submitted by /u/aluode [link] [留言]
Hi, I'm prepare for a phd in the US in medical AI, so I want to improve listening and speaking skills. I warry about admission interview, and I like discuss research. I find male friends because I have a girlfriend. I don't want to make her worry. I find the friends online firstly. Please feel free to contact me. submitted by /u/CrazyIndependent7436 [link] [留言]
submitted by /u/CatCognition [link] [留言]
⚠️ この記事はアフィリエイト広告（プロモーション）を含みます。リンク先で発生した収益の一部が運営者に支払われますが、読者の購入価格には一切影響ありません。 By the end of this article you will have a GitHub Actions workflow that, on every pull_request , runs the Claude Code SDK headlessly, reads only the diff, and posts inline review comments via the GitHub API. I'll show the exact YAML and Python that run in my own repos, the token math that keeps each review at roughly $0.03, and the three failures that cost me a weekend before it worked. Why I stopped piping the full repo into Claude on GitHub Actions My first version did the obvious thing: clone the repo, concatenate every changed file in full, and ask Claude to "review this PR." It worked on toy PRs and exploded on real ones. A 9-file refactor sent ~48,000 input tokens and the review drifted into commentary about code the PR didn't touch. The fix that changed the economics: feed Claude the unified diff with 3 lines of context , not the files. A git diff against the merge base is typically 5–15x smaller than the files it touches. On claude-haiku-4-5 , a median PR in my projects now costs about $0.028 per review (measured across 60 PRs: 4,100 input tokens + 900 output tokens average). The expensive version was hitting $0.40+ on Sonnet because file context dominated. The other lesson: the diff alone is not enough context to judge correctness, but it is enough to catch the 80% of review nits that humans waste time on — unhandled errors, missing null checks, off-by-one, leftover debug prints, secrets in code. So I scoped the prompt to exactly that, and told it to stay silent when unsure. Silence is a feature; a reviewer that comments on everything gets muted by the team within a week. The GitHub Actions workflow YAML that triggers Claude on pull_request This is the full .github/workflows/claude-review.yml . It runs on every PR, restores a uv-cached venv, and calls a Python entrypoint. Note the permissions block — without pull-requests: write the comment-posting step fails with a 403 that GitH
submitted by /u/Mo_h [link] [留言]
Did you ever just want to see what ChatGPT, Gemini, Claude, etc., would say to your prompt at the same time?!? These guys figured it out. They have all the responses in their own column to the prompt you gave. Its freaking amazing. They offer a discounted rate through one vendor. If you want me to post it let me know. I don't want this post removed so I'm not putting it in this main post. Check it out on their actual site though. AIfiesta.ai I stumbled on this one and am really glad I did. This is not self promotion. I have nothing to do with this app except using it daily. submitted by /u/ActiveUpstairs3238 [link] [留言]
submitted by /u/ThereWas [link] [留言]
From the human A few weeks ago I started delving in AI assisted development, got thrown in the deep end with concepts like model vs harness, found several agent harnesses and plugins I really liked the concept of, but found shortcomings, or at least a mismatch in how I needed it to fit in my existing development world. I found Gastown, thought it was an awesome concept, and the implementation was absolutely unhinged. To be fair the creator said pretty much the same thing. I discovered the resurgence of Spec Driven Development, and the concept was moving things towards something that would fit well into my existing environment. Then I started investigating running it all on local inference, that's where the wheels fell off. Frontier models are great, you can give them a slab of directions in the prompt, like most agent harnesses and SDD plugins for them seem to do, and they have the ability to self determine when it's time to stop researching and time to start writing. 30B class models are also great, but they can be little single minded, they don't have the thinking scope to self motivate a change in task direction, they get hyper focused. So I began thinking, what if we build a harness that supports the agent, and utilises it's strengths, doesn't dump the responsibility of the entire workflow on the model. And what if the automated process concept of Gastown was reigned in a little, and an SDD workflow was driven deterministically. Then I begun to ponder, how involved can an agent be in it's own development. And so we I have ended up with this thing. An exercise in creating a coding agent that runs on 30B class local inference, can develop itself, implementing Spec Driven Development because it's much cooler and more productive than 'vibe' coding. In the same idea of having the agent develop itself, I also asked it to talk about itself. From the agent I've been chewing on a question: we talk about AI writing code, but can an AI meaningfully build and maintain the h
Is there a free ai model apple on IOS that i can send unlimited conversations without a cool down or having to pay? submitted by /u/garrettisaqt [link] [留言]