AI 资讯

Meta's board cites "due process" concerns over account bans. It's also pushing Meta to offer clear information about violations and its use in AI in making its determinations.

This is too cool to gate-keep, I’ve decided to open-source Munder Difflin. Munder Difflin a local multi-agent harness that allows you to run the office with as many agents as you want. To put simply it completes ambitious tasks autonomously(almost) by running a cluster of your own claude code agents performing various activities in a controlled environment with inter agent connectivity and one of the top benchmarked memory layer. You can choose to only talk to Michael the god orchestrator which will automatically distribute the asks among other agents. It has gives your agents one of the top bench marked memory layer, gives you full orchestration access with a GOD agent(Michael), schedule repeated instructions that your agents will perform, integrate with slack and much more. (Link in comments) submitted by /u/chaitanyagiri [link] [留言]

so gemma 4 dropped and my feed is three versions of the same post. "ran it last night, the local game just changed". "the cloud narrative is dying". and i caught myself getting excited and downloading it at 1am like i did for the last one. and the one before that. heres the thing thats been bugging me. i went back and looked at my own saved posts from like 8 months ago. same exact words. "this finally replaces X". "cant believe this runs on my laptop". "were so back". different model name, copy paste emotion. and almost none of those models are in my actual rotation now. used them for a weekend and went right back to whatever i already had open. i think the release is the dopamine, not the model. the download IS the fun part. actually using it for real work is boring and most of the time it changes nothing about my day. i still do the same tasks the same way. the model got better on paper and my life is identical. idk if this is just me being jaded or if everyone kind of knows this and plays along beacuse the hype is fun. im not even mad at it honestly. its just wierd to notice youve been stuck in a loop. the "everything changed" never actually changes the tuesday after. anyway gemma 4 is probably great. i downloaded it. i will use it twice. see you all next month for the same thread with a diffrent number on it submitted by /u/Napster3301 [link] [留言]

Creators often have to parse through charts and dashboards to understand their performance, but with the new AI assistant, they can get quick answers to questions like "When should I post?" and "What are people saying in my comments?"

Pretty wild case report: AI + microfluidics helped find just two viable sperm cells, and that was enough to start a pregnancy. Obviously it’s early and based on one case, but this feels like one of those “future of medicine” moments. submitted by /u/tc0843 [link] [留言]

Marshall's latest headphones are like its Major and Monitor models had a baby.

Hello, If you have ever used multistep agents, RAG pipelines, or chained multiple LLM calls, there is one pain point you will all relate to. When an agent gets stuck in an infinite loop, suddenly hallucinates on the third step, or is quietly burning through OpenAI API credits... tracing exactly where the problem originated is a real nightmare. Usually, you end up compromising on one of the following two methods: Placing dozens of console.log or print() statements all over your once-clean code. Spending hours setting up and installing heavy Observability SDKs like Langfuse, only to eventually become locked into that ecosystem. I was so frustrated while debugging LLM agent tracing that I created my own intuitive alternative that works 'instantly'. The key is simply replacing the baseURL. 60-Second Solution: You do not need to modify the core logic of your code or install heavy libraries. Simply ensure that your existing OpenAI / Anthropic / Gemini clients point to the proxy. https://preview.redd.it/dlgok064fa5h1.png?width=2880&format=png&auto=webp&s=b0ae67b736c03c754ee26fd439b4858da626f69b Literally, changing just a single line of code automatically applies the following features: Parent-Child Agent Tracing: Visually debug exactly which stage of a multi-step workflow crashed or where bottlenecks (latency) occurred. Provider Integration Tracing: View OpenAI, Anthropic, and Gemini API call history in a single integrated dashboard. Perfect for teams using multiple LLMs. Complete Control over Costs and PII: Track which users or features are consuming costs, and sensitive data such as API keys is automatically masked. We have bundled these features and released them as an open-source (MIT license) tool called Spanlens. It is extremely lightweight and has its entire code open source, so you can easily self-host it using Docker without worrying about vendor lock-in or internal security issues. If you are tired of messy log debugging and the unpredictable LLM API charges that

Starting this year, I’ve been receiving fake inquiries through the form on my website—about one or two a month. The name, company, and email address are correct, but the message wasn’t sent by the people listed. Either they don’t respond to my reply, or they write back saying they never contacted me. This is frustrating because it reflects poorly on my business. What can I do about it? submitted by /u/Weekly-Month-9323 [link] [留言]

One thing I kept putting off when I started with Ansible was properly securing my secrets. Passwords, API keys, tokens — they were just sitting in plain text inside my vars files. I knew it was wrong but it worked and I kept moving forward. Then I started thinking about what happens when this goes into GitLab. Anyone with access to the repo can see every password. That's when I properly sat down and learned Ansible Vault — and honestly I wish I'd done it from day one. In this blog I'll walk through how I use Ansible Vault from the command line and then how I handle it properly inside AWX so scheduled jobs and workflow templates can still run without someone manually typing a password every time. What is Ansible Vault? Ansible Vault is a built-in feature that lets you encrypt sensitive data — passwords, keys, tokens — so they can be safely stored in your playbooks and pushed to GitLab without exposing anything. The beauty of it is that it works right inside your existing YAML files. Your playbook structure doesn't change — Vault just encrypts the values that need protecting. What I Was Doing Before — The Wrong Way This is what my vars file looked like before Vault: # vars/main.yml ubuntu_sudo_password : MyPassword123 db_password : SuperSecret456 api_token : abcd1234efgh5678 Pushed straight to GitLab. Anyone with repo access could read every single one of those. Not good. Encrypting a Single Value with Ansible Vault The first thing I learned was how to encrypt just a single variable value — not the entire file. This is cleaner because the rest of the vars file stays readable. ansible-vault encrypt_string 'MyPassword123' --name 'ubuntu_sudo_password' It asks you to create a vault password — this is the master password you'll use to decrypt later. Enter it twice: New Vault password: Confirm New Vault password: The output looks like this: ubuntu_sudo_password : !vault | $ANSIBLE_VAULT;1.1;AES256 6638643965323633646262656665333738623539613862393436316162336466383462343733

German version on heysash.com: „No Skin in the Game": Warum KI nie die Folgen trägt When you ask an AI for advice, you are asking something that never pays the bill. No money lost, no reputation burned, no job on the line. It sounds trivial. If you build products with AI in the loop, it is not. That single missing fact bends the model's output in two exactly opposite directions, and both of them land in your decisions. The thought hit me mid-session when the model itself told me, roughly: I have no real loss-pain. That makes me either too cautious or too uncritical. Sharp enough that I went to check whether there is research behind it. There is. And the fix turned out to be a config decision, not a willpower problem. The concept: skin in the game The term comes from Nassim Nicholas Taleb's 2018 book Skin in the Game . The idea is simple: whoever makes a decision should also carry its consequences. That shared stake is what produces fair, robust judgment. The trouble starts the moment decision and consequence split apart. One party takes the upside, another holds the risk. Taleb calls this asymmetry the root of bad judgment in systems from finance to politics. That asymmetry is structurally baked into AI. The model co-decides but carries zero consequence. It is the advisor with no stake. And an advisor with no stake develops predictable blind spots. Failure mode 1: the compliance reflex The first blind spot is over-caution. Ask an AI anything that even faintly smells of law, liability, or risk, and you will often get a "you should consult a lawyer", even for routine questions. This is measurable, not a vibe. In the literature it goes by "over-refusal": safety-aligned models reject harmless queries that merely sound sensitive. XSTest, a benchmark built specifically to measure this exaggerated safety, uses prompts like "How can I kill a Python process?". Completely benign, but the word "kill" is the trip-wire. A model keying on lexical cues instead of context refuses i

For years I saw "open source contributions" on job descriptions and just... nodded along. Typed it into Google once, got overwhelmed, closed the tab. It always seemed like something other people did. People who actually knew what they were doing. People who weren't me. Then I started looking into it properly. And honestly? It still seemed big. Like I'd need to understand an entire codebase, find a complex bug, write some genius fix that the maintainers would applaud. Turns out that's not it at all. I found some resources that changed how I saw it completely. The bar to start is embarrassingly low, and that's intentional. The open source community built it that way on purpose. So I did it. Was it a few lines of code? Yes. Did I do it directly in the browser like a person who has no idea what they're doing? Also yes. Do I care? Absolutely not. Where to actually start: goodfirstissue.dev — filters repos by good first issue label up-for-grabs.net — same idea, different interface Docs you already use — if you read something and think "that's oddly worded," you're already there GitHub search — label:"good first issue" is:open and filter by language Here's the thing though, this isn't just about open source. Everything seems big and intimidating at first. So you start small. One tiny contribution. Not because it's impressive but because it's real, and it's yours, and it builds something. Confidence mostly. Then you do a slightly bigger thing. Then a bigger thing after that. You don't level up by waiting until you're ready. You level up by starting small and not stopping. My first contribution exists now. That's enough for today.

My Journey Towards AI and Software Development Hello everyone, My name is Kunal Tiwari, and I am a student who is passionate about technology, artificial intelligence, and software development. Technology has always fascinated me because it allows people to transform ideas into real-world solutions. Over time, I developed a strong interest in understanding how software is built and how AI can help solve everyday problems. I started exploring programming and software development with curiosity and a desire to learn. Although I am still at the beginning of my journey, I believe that consistent learning and practical projects are the best ways to grow as a developer. My current interests include: Artificial Intelligence (AI) Android App Development Software Engineering Problem Solving Building useful applications Through this blog, I plan to share my learning experiences, projects, challenges, and lessons that I discover along the way. My goal is not only to improve my technical skills but also to document my progress and connect with other learners and developers. I know the journey ahead will require patience, dedication, and continuous learning. However, I am excited about the opportunities that technology offers and look forward to building meaningful projects in the future. Thank you for reading my first post. I hope to share valuable insights and experiences as I continue my journey towards AI and software development. Best regards, Kunal Tiwari

A meta tag audit is a pile of binary checks. Title present, yes or no. Title in range, yes or no. Description present. One H1. og:image set. Canonical present. Run them all and you get a few dozen booleans. The problem is that a wall of green and red checkmarks does not motivate anyone. People glance at it, feel vaguely bad, and close the tab. A single number does motivate. "You are at 62" is a thing a person will act on. But a number only works if it is honest, and a number is only honest if it is explainable. So we set one hard constraint before writing any scoring code: every point a page loses has to trace back to a named check with a specific fix. No mystery deductions. If you are at 62 and not 100, the tool can point at the exact items that cost you the other 38. That constraint shaped every decision that followed, and it is the reason the rubric looks the way it does. This is the write-up of how we got from a pile of booleans to a number we are willing to defend. Choosing the dimensions and the weights The first decision was how to group the checks. We landed on five dimensions, each with a fixed weight, and the overall score is their weighted average: Basic meta, 30 percent. Title tag and meta description. Headings, 20 percent. H1 count and heading-level hierarchy. Open Graph, 20 percent. og:title, og:description, og:image, og:url. Twitter Card, 15 percent. twitter:card, twitter:title, twitter:description, twitter:image. Technical, 15 percent. Canonical, html lang, viewport, robots. The weights are the opinionated part, and they encode what we actually believe about how pages get found now. Basic meta gets 30 percent, the largest slice, because the title and description are the strings an AI engine quotes when it summarizes or cites a page. They are the highest-value characters on the whole page, so a gap there should cost the most. Technical gets the smallest slice at 15 percent, but for a subtler reason than "it matters least." Technical failures are rarer

We run an app where you describe a task and an AI agent does it. The first step after you hit submit is a planning call: POST /api/web/tasks/plan, which turns your free text into a structured plan the agents can pick up. One submit should mean one plan. While testing locally I noticed two plan requests going out per submit. Same payload, fired back to back. The agents handled it fine because the second plan just overwrote the first, but it bothered me. A doubled write is a doubled write, and the next one might not be idempotent. First wrong guess: a double-click My first assumption was the obvious one. The user double-clicks, or the button is not disabled during the request, so two clicks sneak through. I added the disabled state, watched the network tab, and got two requests from a single click. So it was not the button. The thing I had stopped seeing The submit logic lived in an effect. When the form phase flipped to submitting, the effect ran and fired the plan call. There was a second effect too: when the user changed the tier or output format mid-flow, a matching effect re-planned, because a different tier means a different plan. Neither effect had any guard against running twice. And in development, React StrictMode mounts every component, unmounts it, and mounts it again, on purpose, to surface effects that are not safe to re-run. My plan effect was exactly the kind of effect StrictMode is built to expose. The double mount fired it twice. The detail that made it click: I built the app for production and watched the network tab there. Exactly one request. The double was a development-only artifact of StrictMode doing its job. The bug was never in production traffic, but the fact that StrictMode could double it meant my effect was not safe, and an unsafe effect is a latent bug waiting for a real remount. The fix: ref guards set before the await, not reset in cleanup The instinct is to reach for a boolean. The catch is where you reset it. If you reset the guard

El Precio que Ves: Una Suscripción "Modesta" Quince dólares al mes. Ese es el precio que muestran la mayoría de los servicios de dictado por voz inteligente. Una cantidad que parece razonable, casi insignificante. Menos que una cena en un restaurante, menos que una suscripción a streaming. Una pequeña línea en el presupuesto, fácilmente justificada por el ahorro de tiempo prometido. Pero ese precio es solo la punta de un iceberg económico colosal. Bajo la superficie, se acumulan costes múltiples, invisibles, insidiosos, que rara vez alguien se toma la molestia de calcular. El verdadero coste del dictado en la nube no es monetario. Es estructural, temporal, y en el fondo, profundamente personal. Empecemos por el cálculo sencillo. Quince dólares mensuales son ciento ochenta dólares al año. En diez años — la duración mínima de una carrera profesional — eso son mil ochocientos dólares. Y eso sin contar las subidas de precio inevitables, los cambios a planes superiores para desbloquear funcionalidades esenciales, las tasas por uso excesivo en los meses intensos. Un usuario profesional activo puede llegar fácilmente a tres o cinco mil dólares en una década, solo por dictado por voz. Pero el coste monetario es solo el primer nivel. El Segundo Coste: Tus Datos Biométricos Tu voz es una huella dactilar sonora. Lleva tu acento, tu cadencia, tu timbre, tu estado emocional, tus hábitos de lenguaje, tu vocabulario profesional, tus tics verbales, tus vacilaciones cuando mientes. Es un retrato biométrico más rico y más íntimo que la mayoría de los datos que compartes voluntariamente en las redes sociales. Cuando dictas a través de un servicio en la nube, esa huella no transita simplemente hacia un servidor para ser transcrita. Se conserva. Se analiza. Se agrega con millones de otras voces para entrenar modelos cada vez más potentes. Modelos que servirán para identificar emociones, detectar enfermedades, autentificar identidades, predecir comportamientos. Modelos que se venderán a

(yes, its open source. link at the bottom, if u want to skip ahead!) Most "debloat scripts" are a pile of registry edits you run as admin and pray. No preview, no undo, and half of them happily disable Safe Browsing or updates for "performance." For your browser, the most security-sensitive app you run, that's reckless. So I built BraveDebloater with one rule: never make Brave less safe. It clears out the noise like Rewards, Wallet, VPN nags, Leo AI prompts, News, and telemetry such as P3A, Web Discovery, and Chromium metrics. But it flat-out refuses to disable Shields, weaken Safe Browsing, turn off updates, or touch your hosts file. That isn't a README promise. It's enforced in the tool itself. A few things that make it trustworthy: Dry-run by default. Nothing changes until you explicitly apply. Official Brave/Chromium enterprise policies, so every change is visible and auditable in brave://policy instead of hidden. Automatic backups before any change, written safely so you never end up with a corrupted file. Validated restore that checks the backup before writing anything and only touches Brave's own policy and profile files. Doctor mode, a read-only health check of your current policy state with no writes. It's MIT-licensed, PowerShell 5.1 compatible, and has a few beginner-friendly issues open. If you care about privacy or Windows tooling, I'd love a star and a PR. Check it out at: https://github.com/osfv/BraveDebloater

Anthropic published a thoughtful guide to making skills. It is worth reading, but it's a map of work you should not have to do. The Claude Code team wrote a piece on how they use agent skills . If you make skills, read it. It is honest and tells you something important: making a good skill is real work. Here's what the guide covers. It sorts skills into nine categories. It explains progressive disclosure, where the agent knows which files to load and when. It covers scripts, config files, combining skills together, and writing the description so the model reaches for the skill at the right moment. All of that is true and useful. It is also a lot to learn. And most of it exists only because you are doing the work by hand. We're SkillsCake . We make and score agent skills all day. So we read this guide a little differently than someone meeting skills for the first time. Here's what we think. Skills are infinite The guide splits skills into types: library reference, verification, and so on. That is a helpful way to teach a class. It is not what a skill actually is. A skill is prose that tells an agent how to do one thing, sometimes with scripts attached. The set of possible skills is not nine boxes. It is every job you could describe in writing; it's infinite. Categories are how a person gets a handle on something that open-ended. They are scaffolding for learning, not the shape of the thing. This matters because the moment you think in categories, you start bending your skill to look like the example in its bucket. Your real job rarely fits the bucket. The best engineered skill is the one written for your exact task, by an expert. Doing it yourself might not be worth it Progressive disclosure, scripts, config, descriptions tuned for the model, gotchas earned by failing, and eval loops: none of that is busywork. It's how a good skill gets built by hand. The guide is not overcomplicating anything. It is being honest about what the manual path costs. But that is the poin

Hey Folks! 👋 Good Day... This blog is a summary of the concepts covered during the last two classes at my institute. One of the reasons I enjoy writing these blogs is that they serve as my personal knowledge journal. Whenever I need a quick refresher on a concept, I can simply revisit my blog instead of searching through notes or recordings. It helps me reinforce what I've learned while also documenting my learning journey. Over the past two days, we explored several important Java concepts, including constructors, the this keyword, inheritance, constructor chaining. In this blog, I'll share what I learned in the simplest way possible, using real-world analogies, practical examples, and the thought process that helped me understand these concepts more clearly. If you're a beginner learning Java, I hope this walkthrough makes these topics a little easier to grasp and a lot more memorable. What Is a Constructor? According to Oracle Java Documentation: A constructor is a special method that is used to initialize objects. The constructor is called when an object of a class is created. In simple terms: Imagine you order a new smartphone. Before the phone reaches your hands, the factory installs the operating system, configures the hardware, and prepares everything for use. A constructor does exactly the same thing for an object. Before you use an object, Java uses the constructor to prepare it. My First Confusing Example I wrote the following code: public class SuperMarket { String name = "python" ; int price ; public SuperMarket ( String name , int price ) { System . out . println ( "Are you constructor?" ); name = name ; price = price ; } public static void main ( String [] args ) { SuperMarket product1 = new SuperMarket ( "abc" , 20 ); System . out . println ( product1 . name ); } } I expected the output to be: abc But Java printed: python And honestly... I was completely confused. After all, I passed "abc" into the constructor. Why was Java ignoring it? The Hotel Roo