submitted by /u/fagnerbrack [link] [留言]

Fable is delayed again, putting it outside the GTA blast radius.

Im a beginner trying to create a website for my luxery home staging business. I tried using templates in canva but hate how generic it looks and find it very limiting Is there a better way ro use canva? What combination of tools would help me create an editorial style website. Would be nice if there were some AI tools i could use in conjunction to make some of the work easier . submitted by /u/Grand_Respect_9176 [link] [留言]

submitted by /u/esporx [link] [留言]

CNN is the latest media company hauling Perplexity into court.

Google’s new AI agent combed through my emails, documents, and calendar to plan a birthday party and still didn’t clock the person most important to me.

submitted by /u/Altruistic-Dirt-2791 [link] [留言]

A practical 5-10 minute checklist for vetting npm dependencies before adding them to production. It focuses on provenance attestations, install scripts, CI quality signals, maintainer responsiveness, and security handling. submitted by /u/OtherwisePush6424 [link] [留言]

The Acer Nitro Blaze Link might run on Linux, but it's no Steam Deck. Acer says it's a "streaming-first handheld and companion device," like a PlayStation Portal for your PC. Announced ahead of Computex on Friday, it's launching in Q4 2026 with a 7-inch (1920 x 1200) display, Wi-Fi 6, just 1GB of LPDDR4 RAM, […]

Checklist for evaluating third-party npm packages before install submitted by /u/OtherwisePush6424 [link] [留言]

At what point does AI-powered monitoring become incompatible with a free society? At what point does this Wild West of tech advances lead to dystopia? We know we can’t stop AI, it’s already here and growing fast. But we can expect better protections and limits of government and corporate use of these tools for surveillance. The big question on this topic - what rules would we put in place if we could even get Congress to ever take action? We will be sharing some thoughts on that in subsequent posts and would love to see what people think. As a political strategist, I think we may need to work at the state levels first to create an intolerable patchwork of regulations to then force Congress to act. If this is done correctly, big AI companies may well beg DC to create something that is nationally standardized. submitted by /u/amfreedomfoundation [link] [留言]

The rise of AI has brought an avalanche of new terms and slang. Here is a glossary with definitions of some of the most important words and phrases you might encounter.

Bun is fast. The README will tell you 4x on bun install , 3-5x on Bun.serve() , 2x on bun:sqlite . Some of this matters for AI agents. Some of it doesn't. We've been running production agents on Bun for about 3 months — a mix of Hono-on-Bun HTTP agents and standalone Bun scripts called from Claude Code and OpenClaw. This post is what we'd tell ourselves 3 months ago about where Bun actually helps and where it bites. Where Bun's speed actually matters for agents Cold starts on agent scripts Agents are spawned. A lot. Every Claude Code hook, every npx invocation, every cron-fired worker. Node's startup is ~80-120ms cold; Bun's is ~15-25ms. For interactive agent loops where the user is waiting on a hook to populate context, that's a noticeable UX difference. The pre-task hook that takes 250ms to do its retrieval feels totally different when the runtime ate 100ms vs 20ms of that budget. This is the strongest case for Bun in agent workflows. Concrete win. bun install for ephemeral agent containers If you spin up containerized agents (Daytona, E2B, Modal, your own ECS task), each cold container does a package install. npm install on a fresh container is 30-90s; bun install is 5-15s. Over thousands of agent runs per day, that's real money. For Workers / serverless / persistent processes, this doesn't matter — you only install once. bun:sqlite for local agent memory If you're building a per-agent local cache (recent tool calls, recently-seen embeddings, scratchpad state), bun:sqlite is genuinely 2x faster than better-sqlite3 on simple selects. It's also zero-install — no native bindings to compile, no Python build chain, just import { Database } from 'bun:sqlite' . If your agent runs on a Bun runtime AND uses SQLite for state, the math works. If you're on Node, just use better-sqlite3 . Where Bun's "speed" doesn't matter LLM inference latency The agent is going to wait 800-4000ms for the LLM to respond. The 50ms of runtime overhead you saved is round-off. Your bottleneck is

Hello Dev Community 👋 I recently built my first web project called Namastra — a simple tool to explore the meanings, origins, and insights behind names. 👉 Live Demo: 💡 Why I built this I noticed that many people are curious about: What their name means Where their name comes from What personality or cultural meaning it carries But most websites are: Too slow Full of ads Hard to navigate So I decided to build something simple, fast, and clean. ⚙️ What Namastra does With Namastra, users can: 🔍 Search any name instantly 📖 Get meaning and origin 🌍 Learn cultural background ⚡ Use a clean and fast interface 🛠️ Tech Stack I built this project using: HTML CSS JavaScript GitHub (version control) Netlify (deployment) Hosted here: [Netlify] Code managed via: [GitHub] 🚧 Challenges I faced As a beginner developer, I faced challenges like: Designing a clean UI Making search functionality smooth Deploying with GitHub + Netlify Structuring data properly But I learned a lot through building it step by step. 🎯 What I learned How to build and deploy a full project How important UI simplicity is How real users think differently than developers How deployment pipelines work (GitHub → Netlify) 🚀 Future improvements I plan to add: More name data Better UI design Categories (religion, origin, country) Possibly AI-based name insights 🙌 Feedback welcome This is my first real web project, so I’d really appreciate your feedback and suggestions. Try it here: 👉 Thanks for reading ❤️ Happy coding!

fd vs find vs ripgrep: I Created 10,000 Files to Settle This Debate TL;DR: fd is ~2.5x faster than find for filename searches, rg demolishes grep by ~3x for content searches, and find + grep combined lose on every single benchmark I ran. But there's a catch: both fd and rg skip hidden files by default, which can bite you if you're not paying attention. Here are the receipts. Why I Did This Every time someone posts a shell one-liner using find on Reddit, there's always that guy in the comments: "jUsT uSe fD, iT's fAsTeR." Then someone else chimes in with "actually ripgrep can do that too." I got tired of the anecdotes. I wanted numbers. Real ones. On real files. So I fired up WSL, generated 10,900 files across 1,506 directories (~143 MB of mixed content), and ran actual benchmarks with hyperfine . No synthetic microbenchmarks, no "I feel like X is faster" — just cold, hard terminal output. Methodology The Test Bed I created a directory at /tmp/fd-benchmark containing: Category Count Details Plain text files 2,000 file_*.txt — 20 bytes each, contains "test content line N" Binary files 2,000 data_*.bin — 15 bytes each Log files 1,500 match_*.log — contains unique "match_this_test_N" strings Config files 1,000 nested_file_*.cfg Nested dir files 1,000 level1_*/level2/level3/deep_*.txt + level1_*/shallow_*.txt Hidden root files 1,500 .hidden_* + .config_*.yml Hidden dir files 500 .hidden_dir/subdir/deep_hidden_*.txt Git objects 500 .git/objects/obj_* Multi-ext source files 800 src_*.{py,js,ts,rs,go,java,rb,php,cpp,h,css,html,json,xml,yaml,md} (50 each) Large binary files 100 large_*.dat — 1 MB each (random data) Total 10,900 $ du -sh . 143M . $ find . -type f | wc -l 10900 $ find . -type d | wc -l 1506 Tools Tested Tool Version What It Does find (GNU) 4.9.0 The OG. Ships with every Linux distro. fd 10.2.0 Rust-based find alternative. Smarter defaults, colored output. grep (GNU) 3.11 Content search. Also the OG. rg (ripgrep) 15.1.0 Rust-based grep alternative. Respects .gi

The botnet was reportedly tied to a Russia-based residential proxy network.