AI 资讯

Runs on a daily GitHub Action so it stays current, thought it was neat and wanted to share in case anyone else wanted to fork it or use it https://github.com/colincode0/github-readme submitted by /u/anotherinternetlad [link] [留言]

I’ve been working on TrailVerse for a while now, and it’s slowly becoming the kind of national parks planning tool I always wished existed. The idea is simple: find parks, compare options, check useful details, and turn a trip idea into a day-by-day plan with Trailie. Still improving things, still adding more, but I’m happy with where it’s heading. If you like national parks, road trips, or just exploring new places, check it out: https://www.nationalparksexplorerusa.com/explore submitted by /u/peakpirate007 [link] [留言]

Built bag-radar.com after getting tired of wondering whether my cabin bag would actually get checked. It lets travellers view real experiences of how strict airlines and airports are with baggage size and weight checks. Still early, but I'd love to hear what people think. submitted by /u/mub2010 [link] [留言]

Tired of Buffering and Scam IPTV Providers? Here’s What I Found After Testing 7 Services If you’ve spent any time looking for a reliable IPTV service, you already know how frustrating it can be. Most providers overpromise and underdeliver. Fake channel counts, endless buffering, poor support, and in some cases, services that disappear right after payment. After testing seven different streaming services over the last three months, one stood out as genuinely reliable: 𝐗𝐭𝐫𝐞𝐚𝐦𝐨.𝐜𝐨𝐦 ⸻ ⚠️ The IPTV Scam Problem in 2026 The streaming market is full of questionable providers. Common red flags include: Services disappearing after payment No working customer support Channels that never load Fake “4K” labels on low-quality streams No free trial offered Zero transparency about who runs the service 𝐗𝐭𝐫𝐞𝐚𝐦𝐨.𝐜𝐨𝐦 has been the opposite of that in my experience. It offers a free trial, transparent pricing, responsive support, and has been consistently stable. ⸻ ✅ Why 𝐗𝐭𝐫𝐞𝐚𝐦𝐨.𝐜𝐨𝐦 Stands Out 🔴 Live TV & Sports Coverage NFL, NBA, MLB, UFC, WWE Premier League, Champions League, FA Cup, La Liga, Serie A Sky Sports, TNT Sports, ESPN, FOX Sports and more PPV events included 📺 Entertainment Channels BBC, ITV, Channel 4, Channel 5 (UK) NBC, ABC, CBS, FOX (USA) Large VOD library with movies and TV series International channels including French, German, Arabic, Spanish, and Italian ⚡ Stream Quality HD and 4K streams Fast channel switching Anti-buffering infrastructure Stable performance during peak hours and major sporting events ⸻ 📱 App Compatibility One thing I liked was how easy it was to use with different IPTV apps. Supported Apps ✅ TiviMate ✅ Chillio ✅ IBO Player ✅ BOB Player ✅ IPTV Smarters Pro ✅ GSE Smart IPTV ✅ Lazy IPTV ✅ Perfect Player ✅ OTT Navigator ✅ Sparkle TV ✅ VLC Media Player ✅ Kodi (PVR IPTV) ✅ XCIPTV Player ✅ Net IPTV ⸻ 🖥️ Supported Devices ✅ Amazon Firestick & Fire TV ✅ Android TV & Android Phones ✅ Apple TV & iPhone/iPad ✅ Samsung Smart TVs ✅ LG Smart TVs ✅ MAG Boxes ✅ Win

I keep seeing "AI took these jobs" every time a company does layoffs, and I'm not convinced it's the main driver. A few things I keep coming back to. The industry cut around 122,500 jobs in 2025, down from about 153,000 in 2024. AI was named as a direct reason in fewer than 8% of those announcements. So for the other 90 percent plus, something else was going on. Actual AI adoption inside companies is also lower than the marketing suggests. Full org-wide rollout is still in the single digits in the surveys I've seen. Plenty of teams have a ChatGPT subscription and call themselves "AI-driven", but that is not the same as AI doing real work in the pipeline. My read: AI usually isn't replacing people directly. Managers see devs shipping more code and assume they can cut headcount, and companies are moving tight budgets toward expensive AI infra and tooling. But coding is a small part of the job, so "more code per dev = fewer devs" rarely holds up. I don't think AI is taking most jobs. I think it's adding pressure to a market that was already rough for other reasons (economy, over-hiring in 2021-2022, investor expectations). For people who work in eng or hiring: when you've seen layoffs up close, how often was AI genuinely the reason versus the convenient public explanation? submitted by /u/Empiree361 [link] [留言]

It'll be available for PC and mobile, and maybe Nintendo Switch down the line.

The company behind Claude has two openings on its creative team. The enterprise copy lead pays up to $320,000. The head of copy and content goes up to $400,000. Both roles come down to the same task: take dense, technical product features and write about them so people actually want to read. So the company building a tool that writes is paying engineer money for humans who write. Andrej Karpathy joined Anthropic this month and recently rated copywriting an 8 or 9 out of 10 for AI exposure, a job the machines are coming for fast. Anthropic posted the roles anyway. Their president, Daniela Amodei, studied literature in college and keeps arguing that the humanities get more valuable as the models get smarter, not less. I think she is right, and these salary numbers back her up. Generating text was never the bottleneck. The hard part is taste. Knowing your audience. Cutting the line that does not earn its place. Deciding what to leave out, which almost nobody gets credit for and everybody notices when it is missing. Writing more is easy. Writing the right thing, for the right people, at the right moment is what companies are paying for. submitted by /u/evankirstel [link] [留言]

If you are a Software Developer of some form or another, chances are that you follow what are considered best practices for "Clean Code"or "Clean Architecture". It's considered generally best practice according to these books to keep functions down to a few lines, ensure classes have exactly one reason to change, and wrap implementation details behind abstract interfaces. It’s an approach designed to isolate responsibilities and keep the long-term cost of software modifications flat. Yet, as codebases grow under this paradigm, engineers frequently encounter a subtle friction. In the drive to decouple every moving part, applications often accumulate a massive web of boilerplate and multi-layered abstractions. This raises a fundamental question: does hyper-decomposing code actually reduce complexity, or does it simply scatter it across dozens of shallow files, making a single linear operation difficult to follow? This article revisits the baseline assumptions of Clean Architecture by examining a growing yet subtly different software design philosophy championed by systems engineers and computer science pragmatists. We will explore how different software environments define code quality, look at actual case studies of algorithmic decomposition, and map out alternative patterns like John Ousterhout's "Deep Modules." Along the way, we will examine how our design choices interact with mathematical correctness proofs, functional programming paradigms, and a modern toolchain increasingly driven by automated AI agents. The bubbles that shape your opinions The frameworks championed by the "Clean" movement were largely forged in the world of large-scale corporate IT consulting. They were explicitly designed to manage risk in massive organizations where hundreds of engineers with varying levels of experience write code against a single, shared repository. In a setting like a sprawling insurance platform or a legacy banking app with shifting corporate rules, Clean Architecture s

Every AI coding tool can write Python — Cursor, Claude Code, Windsurf. None of them can run it safely in production. That gap between "AI wrote the code" and "the code ran safely" is exactly what I'm building jhansi.io to close. This series documents the journey. One layer of the problem at a time. The execution gap When AI generates code, four things still stand between you and prod: Dependencies — Install the right packages, with versions and licenses you trust Isolation — Run it hard-sandboxed. No host access, no outbound network, no surprises Secrets — Let AI use your API keys without ever letting it see or leak them Audit — Log every execution. Prompt, code, result, timestamp. Compliance-grade. Most teams stop at step 1. Banks and fintechs can't. FCA, SOC2, and the EU AI Act require audit trails for AI actions. You can't eval() your way through an audit. jhansi.io is the missing run() for AI-generated code. Open core, cloud sandbox, built to close each part of the gap — layer by layer. The series Part 1 — Persistent sandboxes Why "ephemeral" breaks debugging, state, and compliance. The case for giving every AI a home directory. → Read Part 1 Part 2 — Dependency management (coming soon) Detecting, installing, and locking deps across Python, Node, Go, and Java. With SBOMs and policy built in. Part 3 — Isolation (coming soon) What "hard isolation" actually means. Containers, Firecracker, zero trust networking, and the metadata service attacks you haven't thought of yet. Part 4 — Secrets (coming soon) Kernel-level proxies. AI can call Stripe without the key ever entering the sandbox. Part 5 — Audit (coming soon) Who ran what, when, with which prompt. Hash-chained logs that satisfy auditors, not just engineers. Building this in public. Follow the series on Dev.to , Linkedin , and X . Code is Apache 2.0 at github.com/jhansi-io .

Aerospace completely revolutionized my workflow after 15 years of using macOS the way Apple intended. I no longer hunt for apps and windows in Mission Control or drag them around spaces to organize. I can open as many windows as I need and have them all under my fingertips. And instead of swiping around to find one, I instantly teleport to where they are. This incredible software is technically aimed at advanced users. It’s installed from the command line and offers extensive configuration options. For basic use though, you don’t need to configure it at all, and if you have opened the Terminal application before and know what running a command means, you should be good to go. Rest assured, I will not show you how to configure Aerospace with Vim, or show you how to create an elaborate but useless dashboard! Just the essentials to get you started. How to set up Aerospace Aerospace is a menu bar application, but you can’t download it from an App Store or get it as a DMG file. You need a package manager. Go to the Homebrew website and follow the installation guide. Make sure to accurately follow the on-screen instructions. This may include any of the following: A prompt to enter your password. When you type passwords in Terminal, you will not see stars or anything. Just make sure you’re typing the correct one and hit Enter. A prompt to install XCode Command Line Tools . Somewhere around the end of the installation process, you may get a prompt to run some extra commands, which depend on your system. Make sure you run them as instructed. To test if you have correctly installed Homebrew, run which brew in Terminal. If you see a path printed out, like /opt/homebrew/bin/brew , you’re good to go. If not, something has gone wrong. Try searching for other, more focused guides on installing Homebrew. With Homebrew, you can install applications from the Terminal app using the brew command. For Aerospace, you would run the following command: brew install --cask nikitabobko/tap/ae

You locked down your human logins years ago: SSO, MFA, a joiner-mover-leaver process, access reviews every quarter. The machine identities never got that treatment, and they bred. Service accounts, API keys, OAuth tokens, SSH keys, CI jobs, RPA bots, and now AI agents. In cloud-native shops these non-human identities (NHIs) outnumber people 144:1 (Entro Labs, H1 2025); even cautious enterprise-wide counts sit at 45:1. They rarely expire, nobody owns them, and SOC 2, ISO 27001, PCI DSS, and NIST 800-53 mostly leave them in a grey zone. OWASP cared enough to publish a Non-Human Identities Top 10 for 2025, and the headline risks are boring on purpose: improper offboarding, leaked secrets, over-privilege, and long-lived credentials. If someone just handed you "go govern the machine identities," here is what actually moves the needle, in roughly the order I'd do it. The tips Build one correlated inventory before you touch a single permission. The thing that kills most NHI programs on day one is partial visibility: secrets in a vault, service accounts in IAM, tokens scattered across SaaS apps, certs in a fourth place. Stop inventorying by storage location and key it by identity instead, joining each credential to an owner, a last-used timestamp, and its permissions. Start with what the cloud APIs hand you for free. # AWS: IAM users acting as service accounts + when their keys last worked aws iam list-users --query 'Users[].UserName' --output text \ | xargs -n1 -I {} aws iam list-access-keys --user-name {} \ --query 'AccessKeyMetadata[].[UserName,AccessKeyId,CreateDate]' --output text Replace static cloud keys in CI with OIDC workload identity federation. A long-lived AWS_SECRET_ACCESS_KEY or a GCP JSON key file sitting in CI secrets is the classic NHI breach path, and rotating it is a chore nobody does on schedule. GitHub Actions can trade a short-lived OIDC token for cloud access that expires in about an hour and is scoped to one job, so there's no stored secret to leak

Prompt length doesn't equal quality. Most people write paragraphs. Short, visual, specific prompts almost always win. Consistency is the real challenge. Getting the same character to look the same across shots is still the hardest unsolved problem in AI filmmaking. Audio kills or saves the whole thing. Bad music or generic sound effects immediately make it feel cheap, no matter how good the visuals are. People overthink the tools and underthink the story. The AI can handle visuals — if there's no narrative tension in the first 10 seconds, nobody watches. Iteration speed is the actual superpower. Treat it like editing — make 20 versions, pick the one that works. What tools are you all using for AI video right now? submitted by /u/AcanthisittaTall127 [link] [留言]

Alzheimer's disease affects over 55 million people worldwide, yet the precise molecular changes happening inside individual brain cells remain poorly understood. I wanted to dig into that question - not at the tissue level, but at single-cell resolution. So I built a full scRNA-seq analysis pipeline in Python using Scanpy, working with a publicly available dataset of 63,608 nuclei from human prefrontal cortex tissue (sourced from CZ CELLxGENE). The donors spanned three Braak stages: 0 (cognitively normal), 2 (early Alzheimer's), and 6 (severe Alzheimer's). Here's what I found and how I found it. The Dataset The data came from a study on the molecular characterisation of selectively vulnerable neurons in AD. It covers the superior frontal gyrus, a prefrontal region known to be hit hard by neurodegeneration - and includes seven major brain cell types: Glutamatergic neurons GABAergic neurons Oligodendrocytes OPCs (oligodendrocyte precursor cells) Astrocytes Microglia Endothelial cells 31,997 genes. 63,608 cells. Three disease stages. A lot to work with. The Pipeline 1. Quality Control No dataset is clean out of the box. I filtered cells to keep only those with between 200 and 6,000 detected genes, and excluded anything with more than 20% mitochondrial gene content (high mitochondrial reads usually signal a dying or damaged cell). This removed around 2,809 low-quality cells. 2. Normalisation Library sizes were normalised to 10,000 counts per cell, followed by log1p transformation, standard practice that makes cells comparable regardless of how deeply they were sequenced. I then identified 5,607 highly variable genes to focus the downstream analysis. 3. Dimensionality Reduction PCA (50 components) → neighbourhood graph (10 neighbours, 20 PCs) → UMAP embedding. The UMAP is where the biology starts to become visible. All seven cell types separated into distinct clusters, with clear separation between neuronal subtypes and glial populations. 4. Differential Expression For t

Hi all, I have been working on this method based on a hunch along with many llm for quite some time. Though first it was being engineered by me but I was learning in supervised ml area but this hunch took to semi-supervised ml and that to too deep. I then became llm orchestrator of sort while 4 llm's tried to figure it out. I put up a live demo on Hugging Face Spaces where you can try it yourself — set the number of labels, click run, see the accuracy. No installation, no code required. Brief about method Optimus — Graph SSL under Extreme Label Scarcity Key Results (PathMNIST, N=2000, 9 classes) Labels Total Optimus GCN 9(1 per class) 73.9 60.6 27(3 per class) 77.3 68.5 45(5 per class) 79.8 77.1 https://huggingface.co/spaces/Keshu007/optimus-graph-ssl Edit : You can can even run the code on your own dataset submitted by /u/Loner_Indian [link] [留言]

Hi everyone, I’m building Penform, a browser-based tool that turns handwriting into a real installable OTF font. The idea came from seeing people use AI tools to recreate handwriting for personal cards and notes. The results can be touching, but the workflow felt backwards to me. Personal handwriting should not require a black-box model, a server upload, a GPU, or a hidden training pipeline. Penform takes a more deterministic approach: Print an A4 Template or use a tablet Write characters into predefined Glyph Slots Upload a JPEG or PNG scan/photo Align four printed Alignment Markers Optionally add more filled templates for contextual alternates Review and optionally refine the extracted glyphs Preview the generated font in the browser Download an installable .otf Everything runs locally in the browser. There is no account, no upload, no OCR, and no AI. A TemplateManifest defines the page geometry, so the app knows where every Writing Box, Glyph Slot, Alignment Marker, and font metric reference is. The manifest is the source of truth instead of OCR or server-side inference. The part I’m considering open-sourcing is the browser engine behind it. It currently handles: image decoding and EXIF-normalized capture manual marker alignment homography-based perspective correction A4 warping at 150/300 DPI writing-box cropping from a Template Manifest thresholding and empty glyph detection glyph vectorization contour winding correction pixel-to-font-unit mapping OpenType font generation OTF validation before export per-glyph threshold, scale, offset, and rotation overrides I’m trying to figure out two things: Whether this engine is useful enough to open-source as a standalone package Whether the product itself is useful beyond my own use case It is not meant to replace professional font design software. The goal is narrower: preserve someone’s actual handwriting well enough that it becomes usable as editable text for cards, notes, labels, classroom materials, personal project