标签：#ia

Hi! Not sure if this community is suitable for this, if not, please let me know and I will take it down. My parents love sharing online content with me, we love animals so a lot of that is cute animal stuff, and lately I've been getting a lot of AI cats. I gave them some hints so they spot the obvious ones but not all the time. We haven't yet had an election cycle with AI content being this common, and it scares me a bit. I appreciate your support! submitted by /u/hakansan [link] [留言]

submitted by /u/esporx [link] [留言]

"Mummified remains sold online exhibit signs of biodeterioration, yet sellers provide no safety guidance."

AI agent observability means capturing your agent's reasoning cycles, tool calls, and token usage as...

Raspberry Pi's head of social, Ashley Whittaker, acknowledged the cyberdeck trend today, saying "we haven't been able to get away from cyberdecks this year." Tiny portable computers made out of things like purses, jewelry boxes, and other thrifted or recycled parts have gone viral on TikTok and other social media platforms over the past year, […]

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. Chatbots took many schools by surprise upon their release a few years ago. Suddenly, students carried an app in their phones that could magically answer almost any…

Microsoft has outlined an AI governance architecture spanning nine governance domains and four functions: policy, control, visibility, and proof. The approach connects policies with runtime enforcement, continuous evaluation, observability, identity, security, and audit evidence to help organizations verify governance requirements as AI applications and agents operate in production. By Leela Kumili

What is Git and Github This is a version control system or tool used to track changes by developers. When one installs git it comes with an inbuilt terminal called gitbash Github is a cloud based platform for storing git repositories online. Just sign up for free,verify via email and your account is created. git and github are connected using a SSH KEY. How Git works. We start by installing git on my Pc, after installation check if git is installed by opening a terminal eg powershell on windows and run git --version Stages Git/Github is broken into four simple stages: working directory is where we write code and amend and delete files. Here changes are made but cannot be tracked unless they are instructed to commit. staging phase is an area where files are modified. commit phase is where git takes everything from the staging area and sends it to our local repository. push phase is where the saved commits are sent to a remote repository like GitHub. Creating folders and files on git bash First identify where we want the folder to be located ls is used to list mkdir "name of the folder" (means make directory) cd " name of the folder" (change directory) Readme texts README.md end with .md since they are written using markdown language.Can use echo,touch or nano commands to write a readme file. If i want to know the contents of my readme file we use: cat README.md git config-this is basically telling it my identity git config --user.name"user" git config --user.email "useremail" git init-this command is used to create or initialize a repository in main/master. git init main git status-shows the repository status. This command shows changes and what is happening in git git status git add-stages changes made git add . this means stage all or one can specify what to be added e.g i want to add only a javascript folder git add script.js git commit-commits records that have been staged in the local git repository.Its like getting a snapshot or memory of the file. git commit -

You probably know how it goes: you give a complex prompt to a LLM, it spits out a highly confident answer, and you just sort of... hope it’s right. If you ask the same question in a different tab, Claude might give you a completely different answer. Gemini might say they are both wrong. I've done it this way for a long time, and many of my friends seem to do the same. I wanted to see what happens if you don't just compare answers, but actually bring AI models into a shared chat to discuss the question together. Here is how it went when they could discuss each other's replies in real-time: - ChatGPT went first. It wrote a beautiful, highly structured, and completely wrong answer. It hallucinated a tax rule that didn't apply to the prompt. - Claude stepped in next. It immediately flagged GPT’s tax hallucination, but overcorrected and messed up the final math equation. - Gemini acted as the final Judge. It took ChatGPT’s original structure, applied Claude’s logical correction, fixed the math, and spat out a flawless final output. The takeaway: Letting an AI model review itself is like a student grading their own work. It just repeats the same assumptions. When you force different models (OpenAI vs Anthropic vs Google) to fact-check each other, they actually expose each other's blind spots and hallucinations. I got so obsessed with this multi-AI workflow that I built a site to let these models debate in real-time without having to copy-paste between different tabs (I posted about it earlier here). If anyone wants to try it or testing their own complex questions, curious to hear what kind of workflows you guys would use it for. submitted by /u/capibara13 [link] [留言]

Asgaut Mjølne Söderbom and Ola Hast discuss the evolution of their software engineering practices past continuous deployment and pair engineering. The conversation continues where it left off in the previous episode and focuses on the experiments in adopting Claude Code and the reasons why they consider it good for everything else, but not coding. By Asgaut Mjølne Söderbom, Ola Hast

How to get a photo below a strict KB limit Many government portals, job forms, school applications, and support websites reject an otherwise valid photo because it is larger than a fixed limit such as 100 KB or 200 KB. Standard gallery apps usually offer cropping or a quality percentage, but they do not tell you whether the final file will meet a specific upload limit. That is the problem I built FormFit to solve on Android. Why exact-KB compression is tricky File size depends on more than width and height. Image detail, color variation, output format, and compression quality all affect the result. A quality setting that works for one photo may leave another photo far above the required size. FormFit works toward a maximum KB target and adjusts the generated copy for you. The practical goal is to create a file at or below the limit while keeping it as clear as possible. Compress a photo on Android Install FormFit from Google Play . Open the photo-compression tool and select the image you need to upload. Enter the maximum file size required by the website or form. Optionally resize the image dimensions or choose JPG, PNG, or WebP for the generated copy. Run the compression, review the result, and save or share the new file. The original photo is not replaced. FormFit creates a separate output copy, so you can compare the result before uploading it. Remove metadata from generated copies Photos can contain metadata such as device or capture information. When you only need to submit the visible image, FormFit can remove metadata from the generated copy. This does not change the original file. Turn several photos into one PDF Some forms ask for a single PDF instead of multiple image files. FormFit can combine up to 20 selected photos into one PDF directly on the phone. This is useful for receipts, scanned notes, application documents, and other small document sets. On-device processing The selected photos and PDFs are processed on the Android device. FormFit does not req

The deepfake epidemic in schools is affecting more than students. Four teachers tell WIRED about becoming targets of sexualized, AI-generated content—and how difficult it was to find accountability.

People have been talking to each other for at least 100,000 years, as best we can tell. And in all that time, there has been only one thing in the world that could learn a human language to perfect fluency: a human child. Now there are two. Four short years after the release of ChatGPT,…

Ask a team how their RAG pipeline works and they will tell you about the embedding model, the vector database, and maybe the reranker. Ask them how they chunk their documents and you will usually get "uh, 500 tokens with some overlap? Whatever the default was." That default is quietly deciding the quality of every answer the system gives. Chunking is the highest-leverage, least-discussed decision in a RAG pipeline , and I want to convince you of that with concrete examples rather than hand-waving. The refund policy that got sliced mid-sentence Say your docs contain this refund policy: ## Refund policy Customers may return items within 30 days of delivery for a full refund. Items must be unopened and in original packaging. Opened electronics are subject to a 15% restocking fee. Sale items are final and cannot be returned unless defective. Defective items can be returned within 90 days regardless of sale status. Now run it through a fixed-size chunker, the kind that cuts every N characters. Depending on where the boundary lands, you can get a chunk like this: original packaging. Opened electronics are subject to a 15% restocking fee. Sale items are final and cannot be returned unless A user asks "can I return a sale item?" The retriever finds this chunk (it literally contains "Sale items are final and cannot be returned unless") and hands it to the model. The model reads it and answers "sale items are final and cannot be returned." The critical exception, "unless defective," was decapitated by a character boundary. The 90-day defective window lives in a different chunk that scored lower and never made it into the prompt. Nothing in your stack is broken. The embedding model is fine, the vector database is fine, the LLM did exactly what the context told it to. The answer is still wrong, and it is wrong because of an off-by-one in a splitting function nobody has looked at since the prototype. A heading-aware chunker would have kept the whole "Refund policy" section toget

The Evolution of Web Forms Part-1 — From Plain HTML to AJAX Modern React forms can feel unnecessarily complicated when you first encounter tools such as React Hook Form, Zod, resolvers, controlled inputs, refs, formState , and server-error handling. Why do we need all of that? Why not simply read the value from an input and send it to the server? To understand why modern form libraries exist, we need to understand the problems developers faced before those libraries were created. In this series, we will evolve the same idea step by step: Plain HTML ↓ Native HTML validation ↓ JavaScript validation ↓ AJAX submission ↓ React controlled forms ↓ Form libraries ↓ React Hook Form ↓ React Hook Form + Zod ↓ Production form architecture This first part covers the first four stages: Plain HTML forms Native HTML validation Vanilla JavaScript validation AJAX form submission By the end, you will understand how forms worked before React and why each new approach became necessary. Stage 1: Plain HTML Forms Before React, AJAX, or even large amounts of client-side JavaScript, browsers already knew how to submit forms. HTML forms are not just visual containers. They are a built-in browser mechanism for collecting data and sending an HTTP request. A basic registration form <!DOCTYPE html> <html lang= "en" > <head> <meta charset= "UTF-8" /> <meta name= "viewport" content= "width=device-width, initial-scale=1.0" /> <title> Registration Form </title> </head> <body> <h1> Create an account </h1> <form action= "/register" method= "POST" > <div> <label for= "username" > Username </label> <input id= "username" name= "username" type= "text" /> </div> <div> <label for= "email" > Email </label> <input id= "email" name= "email" type= "email" /> </div> <div> <label for= "password" > Password </label> <input id= "password" name= "password" type= "password" /> </div> <button type= "submit" > Register </button> </form> </body> </html> There is no JavaScript in this example. The browser handles the ent

A frozen desktop window is a state, not a diagnosis. Windows adds Not Responding when the UI thread stops processing messages for long enough. That can happen because the application is doing legitimate work, waiting for disk or network I/O, blocked by another process, stuck behind a modal dialog, or caught in a real deadlock. Reinstalling may replace files, but it does not tell you what the process was waiting for. Preserve a few minutes of evidence first. Define the symptom precisely Keep these cases separate: Slow: the window still repaints and eventually accepts input. Not responding: the frame is visible, but Windows reports that the app is not processing messages. Blank: the frame appears while the content surface fails to render. Invisible: the process runs without a visible main window. Crash: the process exits and may create an application error event. This distinction matters. A blank WebView surface and a blocked UI thread can look similar to a user, but they leave different evidence. Use one repeatable action Restart the application once and perform the smallest action that reproduces the freeze. Record: the exact click or file that triggers it; the time the action starts; how long the window remains responsive; whether CPU, disk, or network activity changes; whether the process recovers without being terminated. Avoid opening several test files or clicking repeatedly. Extra input can queue more work and hide the original transition. Watch the process before ending it Open Task Manager and identify the correct process ID. Expand child processes if the application uses helpers or a web-rendering runtime. Useful observations include: High sustained CPU: a loop, intensive parsing, OCR, compression, or rendering work is plausible. Near-zero CPU with disk activity: the process may be waiting for storage. Near-zero CPU with network activity: an online request, proxy, DNS, or TLS operation may be blocking progress. Near-zero activity everywhere: look for a hidd

Building a Scalable, HIPAA‑Compliant Healthcare Document Processing Pipeline in .NET & Azure Quick Answer A deep dive into architecting a production‑grade Healthcare Document Processing Pipeline—covering AI extraction, FHIR integration, vector search, and compliance at scale. In my experience, the biggest cost is not the AI model, but the orchestration that turns raw scans into audit‑ready FHIR resources. The right mix of services can reduce latency by 30‑50% while keeping the bill below 10% of the raw compute budget. Choose services that expose a BAA and native hybrid search (Azure Cognitive Search) to avoid a second compliance layer. Prioritize deterministic scaling (Container Apps + Aspire) over elastic serverless when real‑time SLAs are tight. Version your embeddings; treat the vector index as a first‑class contract. HIPAA‑Ready High‑Volume Document Ingestion When a health system starts ingesting thousands of paper‑to‑digital documents per day, the naïve “scan‑and‑store” approach quickly becomes a compliance and performance nightmare. The real challenge is to produce HIPAA‑ready, FHIR‑compliant, low‑latency data that can be consumed by downstream clinical decision support or billing systems. Compliance is not a checkbox; it’s a series of audit trails that must survive a 30‑day retention policy and survive a forensic review. In production, the cost of a single PHI exposure can exceed the annual budget of the entire platform. Real‑World Example Consider a mid‑size hospital that receives 25,000 inpatient discharge summaries, 8,000 lab reports, and 12,000 imaging PDFs every month. Each document is a mixture of scanned images, PDFs, and legacy forms. The billing team needs structured diagnoses and procedure codes within 30 seconds to avoid claim denials, while the analytics team wants similarity search for rare disease cases in the last 12 months. The pipeline must: Extract structured entities with ≥95% accuracy. Redact PHI in transit and at rest. Provide audit logs

How to Extract Colors From an Image Using JavaScript and Canvas Have you ever looked at an image and wanted to know the exact HEX color of a particular pixel? Designers often need to extract colors from photographs, screenshots, logos, UI designs, and illustrations. You can do this directly in the browser without uploading the image to a server. The browser Canvas API gives us everything we need. Reading pixels with Canvas The basic process is: Load an image. Draw it onto a canvas. Read the pixel data. Convert the RGBA values into a color format such as HEX or RGB. The important API is getImageData() . javascript const imageData = ctx.getImageData(x, y, 1, 1); const pixel = imageData.data; const r = pixel[0]; const g = pixel[1]; const b = pixel[2]; const a = pixel[3];

If you run a team on Jira or Confluence, the deal changed on 17 August and the change was opt-out. From that date, by Atlassian’s own account, the content your team writes into its Cloud products — Confluence pages, Jira tickets, the descriptions and comments where the actual work lives — is used by default to train Rovo, Atlassian’s AI assistant. You were not asked to opt in. You were, at best, given a switch and left to find it. Answer first, because the detail matters more than the outrage: there are two settings, and they are not equal. One governs your in-app data — the text itself. The other governs metadata — the derived signals about that text. On the Free, Standard and Premium plans you can turn off the content, but the metadata switch is greyed out; Atlassian’s support page reads, flatly, “You can’t change this setting.” The full off switch, the one that also stops metadata contribution, is available only on Enterprise. Privacy, in other words, is now a plan tier. What actually changed, with the switches named Atlassian’s data-contribution documentation lays out a matrix that is worth reading slowly, because the defaults are doing the heavy lifting. In-app data contribution defaults to on for Free and Standard customers and off for Premium and Enterprise. Every tier can toggle that one. Metadata contribution is a different story: it is on across the board and can only be switched off by Enterprise. So the customer contributing the most by default — content and metadata, both on, no ability to fully stop it — is the one on the cheapest plan who never opened the settings page. The categories are broad. In-app data, per Atlassian’s materials, covers Confluence page titles and body text, Jira work-item titles, descriptions and comments, and custom status and workflow names. Metadata covers the derived layer: readability scores, task classifications (that a ticket is “sales work,” say), story points, sprint end dates, SLA values, and semantic-similarity measure

从 Demo 到生产：那些真正让 AI Agent 敢上线的护栏 开场钩子： 你在网上看到的多数「AI Agent」都是 demo。它们之所以上不了生产，原因往往 只有一个 —— 而下面这个开源的小脚手架，专门解决它。 我们已经过了「能调通大模型」就算赢的阶段。现在真正难的是那没人讲的 10%： 是什么阻止 Agent 做出伤害性的事？ 我在微软跑过一套约 25 个 Agent 的生产平台，现在也帮团队把 Agent 从笔记本推进到真实用户面前。两边的体会是一致的。 一个不太舒服的真相：能调 5 个工具的聊天机器人， 不是产品 。周末项目和你敢放到客户面前的 系统之间，差的只有三件事 —— 而且全都是不酷、不性感的工程： 你怎么给输出质量打分 （质量门）。 你怎么决定什么时候必须人签字 （审批门）。 你如何让整套东西模型无关 ，不被某个厂商锁死。 所以我写了一个很小的 harness，把这三件事摆在最显眼的位置。它故意做得很小 —— 一小时能 读完 —— 因为价值不在「框架」，在 模式 本身。 仓库： github.com/zhasun0818/ai-agent-scaffold 1. 质量门：别发布你无法打分的东西 Agent 的输出是「预测」不是「承诺」。上线前它必须过一道 检查 ：是否达到你的标准。脚手架里 这是一个可插拔的 QualityGate ，你可以换成 LLM 裁判或测试套件： # agent_harness/eval.py @dataclass class EvalReport : passed : bool score : float checks : List [ str ] class QualityGate : def grade ( self , proposal : str , context : str = "" ) -> EvalReport : return self . grader ( proposal , context ) 循环在门没过之前拒绝执行： result . report = self . quality . grade ( proposal , f " state= { state } " ) if not result . report . passed : self . approval . log ( " quality-gate " , " blocked " , result . report . __str__ ()) return result 注意它 把拦截记录下来了 。生产里你会想把这些被拦的尝试都进可观测性系统。「这周我们拦下 了 12% 的 Agent 提议」是个真实 KPI —— 它说明门在工作。 2. 审批门：所有人都忘掉的那一步 这才是让企业真正点头说「可以」的东西。当 Agent 想加急订单、取消订阅、或动钱的时候，它应该 停下来问人 。沉默不等于同意。 # agent_harness/approval.py class ApprovalGate : def request ( self , action : str , detail : str ) -> bool : # 生产里：推一条通知到 Teams / Slack / 邮件，然后等待。 decision = input ( f " Approve { action } ? [y/N] " ). strip (). lower () self . audit . append ( AuditEntry ( time . time (), action , " human-reviewer " , decision , detail )) return decision . startswith ( " y " ) 在脚手架里，标记 needs_approval=True 就够了： @tool ( " expedite_order " , " Mark an order as expedited. " , needs_approval = True ) def expedite_order ( order_id : str ) -> str : return f " PO { order_id } : marked expedited " 而且因为有 审计链 ，你永远能回答「谁改的、为什么」—— 这通常是合规团队问的第一个问题。 3. 模型无关的 provider：别跟一个厂商结婚 模型每几周就变，价格也是。你的 Agent 循环不该知道自己在对谁说话： # agent_harness/providers.py class ModelProvider ( Protocol ): def