AI 资讯

Hello Reddit, I'm 33, currently in school for my Bachelor's in IT (~2 years from graduation). I am taking Calculus (Math 110 at Penn State) for the 3rd time. I failed it twice before. I am currently a nurse of 10 years going back to school. I am taking a summer class version of it, which I did not realize is accelerated / asynchronous, and am slowly starting to do poorly in it. My end goal after school is to work remotely in a programming-related job. But math has always been hard for me. So I am asking: -Any tips / resources / insights for passing Calculus -Any way I can pursue a degree in programming / computers without having to take Calculus -what other degree options that aren't math heavy will allow me to pursue tech-related careers? Open to suggestions, but my necessity list is being able to work remotely and earn a comfortable living abroad. Thanks! submitted by /u/Born_Cod_8931 [link] [留言]

While building with OpenAI, Anthropic, and other AI providers, I realized something surprising. I monitored my servers, databases, and application performance—but I had almost no visibility into my AI API spending until I checked the provider dashboard or received the monthly invoice. That led me to build AICostPass . It helps developers, indie hackers, startups, and agencies: ⚡ Track AI API costs in near real time 📊 Monitor spending by project or client 🚨 Get budget threshold email alerts 📧 Receive weekly spending summaries 💰 Export billable CSVs for client invoicing The goal is simple: help developers understand and control AI costs before the invoice arrives. 👉 https://aicostpass.com I'd love to hear how you're currently tracking AI API costs. Are you using provider dashboards, spreadsheets, or another tool?

OpenAI's first device is set to be a smart speaker that lets you talk with ChatGPT, according to a report from Bloomberg. The device apparently won't have a screen, but will use a camera and additional sensors to "understand" your environment. The report comes just days after Apple filed a lawsuit against OpenAI that accused […]

Meta cut its workforce by 10 percent in May.

If Claude can already search the web, read a 10-K, and explain what a rate cut does to long-duration equities, the fair question is why you would connect anything to it at all. It is the right question, and the honest answer is that for a large class of questions you should not. Raw Claude is enough. The gap is narrower and sharper than "Claude does not know finance." Claude knows finance. What it does not know is you. What raw Claude already does well Be clear about this before the sales pitch, because pretending otherwise would insult anyone who has actually used it. Claude with web search will look up a current quote, summarize an earnings call, explain a valuation multiple, walk you through how a Monte Carlo simulation works, and reason about a macro scenario better than most of the commentary you would read instead. If your question is about the world, and not about your own balance sheet, a connector adds nothing. Ask Claude directly. The trouble starts the moment the answer depends on what you actually own. Four things that break when the question is about your money 1. It starts from zero every time A chat has no memory of your holdings. You can paste them in, and many people do, and it works for exactly one conversation. There is no cost basis, no purchase date, no daily snapshot series behind it. So "how concentrated am I really", "what is my realized gain this year", and "how correlated are my top five positions over the last 90 days" are not questions it can answer. It can only answer them about the numbers you re-typed, this once, from memory. 2. The same question gives a different answer twice LLM inference is not deterministic, and it is not deterministic even at temperature zero. Thinking Machines Lab traced the cause to batch-invariance in inference kernels: the batch your request lands in varies with server load, so the arithmetic varies with it. They fixed it in a research setting and got 1,000 bitwise-identical runs, which tells you how much engi

Look inside almost any modern connected device -- a smartphone, a smartwatch, a Wi-Fi thermostat, a battery-powered sensor node -- and you will find a processor core designed by ARM. It is one of the most successful engineering ideas in computing history. And here is the strange part: ARM has never manufactured a single one of those chips. It does not own a factory. It sells blueprints. A three-person project in Cambridge The story starts at Acorn Computers in Cambridge, England, in the early 1980s. Acorn had built the BBC Micro, a hugely popular educational computer in the UK, and it needed a faster processor for its next machine. The commercial chips available at the time were disappointing, so a tiny team decided to design their own. The acronym everyone knows today originally stood for Acorn RISC Machine . Sophie Wilson designed the instruction set and wrote the simulator; Steve Furber led the physical chip design. RISC -- Reduced Instruction Set Computing -- was the key bet. Instead of piling on complex instructions, they kept the instruction set small and simple, which made the chip easier to build, cheaper, and remarkably power-efficient. The first silicon, the ARM1, was fabricated by VLSI Technology and delivered to Acorn on 26 April 1985. When the team powered it on, it simply worked -- first try. For anyone who has designed hardware, that is almost unheard of; new processors normally need several rounds of revisions to shake out design errors. A famous piece of Acorn lore is that the early ARM chips drew so little current they could keep running on leakage alone after the power was disconnected. From a British computer to the whole world Acorn the computer company faded, but the processor design did not. In 1990 the ARM team was spun out into a separate joint venture, and the acronym was quietly re-expanded to Advanced RISC Machines . The new company made a decision that defined its future: it would not build chips. It would license the designs and let oth

Scope creep usually does not start with a dramatic disagreement. It starts with a reasonable-sounding request: "Can we also add this small thing?" For freelancers, consultants and small agencies, the hard part is often not the work itself. The hard part is that nobody has a clean shared record of what changed, what was included originally, what is now extra, and whether the timeline or price should move. The workflow I find most useful is deliberately simple. 1. Write the baseline before work starts Before the project begins, write down: the goal of the work what is included what is excluded assumptions dependencies what the client needs to provide This does not need to be legal language. Plain language is often better because both sides can understand it quickly. 2. Separate included and excluded work Many scope problems happen because "not included" was never written down. For example: Included: one landing page with copy and layout Excluded: email automation, paid ad setup, analytics dashboard, extra page variants That makes later conversations less emotional. You are not saying "no" from nowhere. You are comparing the new request to the original scope. 3. Pause before doing the extra work When a new request appears, write it down before starting. A useful change note can be short: requested change why it is needed impact on price impact on timeline what will be delivered approval status The point is not bureaucracy. The point is to stop invisible work from becoming normal. 4. Make timeline impact explicit Freelancers often talk about price but forget schedule. Even if the client accepts the extra cost, the original delivery date may no longer be realistic. A small change can still interrupt review cycles, dependencies or other client work. 5. Get written approval before starting This can be as simple as: "Confirmed. Please go ahead with this change at the updated price and timeline." The approval does not need to be fancy. It just needs to exist before the addit

Agentic AI for Healthcare Triage and Care Automation Discussion | Link

It's the latest in a barrage of efforts to win compensation from AI companies over training materials.

Meta denies using AI to terminate workers with disabilities and medical problems.

Patreon sends billions of notifications each year. As our largest creator audiences grew, a legacy task responsible for generating millions of recipient-specific notifications began consistently timing out. This post explains how our team introduced a two-stage fanout architecture, isolated email, push and in-app processing, improved observability, and migrated more than 200 notification types across a 13-year-old codebase. submitted by /u/patreon-eng [link] [留言]

If you’ve been waiting to try Apple’s revamped Siri without installing a developer beta, you now can. The company on Tuesday released the iOS 27 public beta, giving iPhone owners early access to its AI-powered assistant and other new features before the software’s official launch this fall.

Anthropic has consistently attempted to depict itself as the ethical foil to other AI companies. This latest marketing stunt — which leans into criticism of AI as a way to make Anthropic seem aware of the responsibility it carries — would appear to be more of the same.

Overtone describes itself as "a voice- and audio-forward service, enabled by AI, that provides highly curated introductions."

SpaceXAI's Grok Build AI coding tool was spotted uploading users' entire codebases to Google Cloud before it was reported, and the company turned it off. The Register reports that Cereblab published findings on Monday showing how the Grok Build CLI was packaging and uploading entire code repositories, "including files it was told not to open […]

Microsoft Corp. today released software updates to plug at least 570 security holes in its Windows operating systems and other software, almost triple the number of vulnerabilities the software giant fixed in its record-smashing Patch Tuesday release last month. Microsoft attributed the burgeoning patch counts to vulnerability discoveries aided by artificial intelligence.