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Upgrading CI Workflows: From Node 20 to Node 22 and Actions v5/v6 TL;DR: I upgraded the CI workflows for the content-automation repository from Node 20 to Node 22 and Actions v5/v6, addressing compatibility issues and improving performance. Key changes included updating upload-artifact from v5 to v7 and implementing retry with backoff. The Problem The CI workflows for the content-automation repository were using Node 20 internally, despite the configuration specifying Node 20. This discrepancy caused compatibility issues with newer versions of the GitHub Actions. Specifically, the upload-artifact action was still on version 5, which was internally targeting Node 20. What I Tried First Initially, I attempted to update the upload-artifact action to version 7, which supports Node 22. However, this change alone did not resolve the issue, as other actions like checkout and setup-python were still on older versions. The Implementation To address the compatibility issues, I updated the following actions: upload-artifact from v5 to v7 checkout to v5 setup-python to v6 Here are the specific code changes: // .github/workflows/main.yml steps: - name: Checkout code uses: actions/checkout@v5 - name: Setup Python uses: actions/setup-python@v6 - name: Upload artifact uses: actions/upload-artifact@v7 Additionally, I implemented a retry mechanism with backoff for the CI workflows: // .github/workflows/main.yml steps : - name : Retry with backoff run : | for i in {1..3}; do if ./script.sh; then break else echo "Retry $i failed, backing off..." sleep $((i * 2)) fi done Key Takeaway The key takeaway from this experience is the importance of keeping CI workflows up-to-date with the latest versions of GitHub Actions. This not only ensures compatibility but also improves performance and reliability. What's Next Next, I plan to monitor the CI workflows for any issues and continue to optimize the retry mechanism for better performance. I will also explore other ways to improve the reliabili

Disallow: GPTBot is a wall. Walls don't pay rent, and the crawlers that matter most either ignore them or route around them. If your content is worth training on, the interesting question isn't "how do I keep the bots out" — it's "what do they owe me, and how do I say so in a way a machine can read." That's what RSL (Really Simple Licensing) is for. It shipped 1.0 in December 2025 with around 1,500 publishers behind it — Reddit, Yahoo, Quora, O'Reilly, Medium, Vox. This post is a from-scratch walkthrough of what the format actually is, the six places you can put it, the one mistake that makes crawlers silently ignore your terms, and where the declaration stops and enforcement begins. No tooling required to follow along — it's all plain XML and HTTP. The format is an XML vocabulary, not a config file An RSL document says: for this content, here's what's permitted, what's prohibited, and what it costs. Minimal example: <?xml version="1.0" encoding="UTF-8"?> <rsl xmlns= "https://rslstandard.org/rsl" max-age= "7" > <content url= "/" > <license> <permits type= "usage" > search </permits> <prohibits type= "usage" > ai-train </prohibits> <payment type= "crawl" > <amount currency= "USD" > 0.015 </amount> </payment> </license> </content> </rsl> Read it out loud: search engines may index this; training on it is prohibited; if you want to crawl it anyway, the rate is $0.015. usage tokens include search , ai-train , ai-use (inference/grounding), and a few more. You can scope rules by user and geo too. One rule that trips people up: prohibition wins . If the same token shows up under both permits and prohibits , the content is prohibited. Don't try to express "allowed except for X" by listing X in both — just prohibit X. The namespace is the thing crawlers actually key on The single most common way to publish RSL that quietly does nothing: getting the namespace wrong. It must be exactly: xmlns="https://rslstandard.org/rsl" http instead of https , a trailing slash, or a plausible

SteelSeries has the Arctis Nova Pro Wireless gaming headset on sale for $239.99 (currently between $300 and $350 at other retailers). The Xbox version that supports a host of other platforms including PlayStation, PC, and Switch is available in both black and white. The PlayStation version, which supports the same platforms except for Xbox, is […]

A few years ago I made a pen plotter attachment for Prusa MK4 ( https://www.printables.com/model/827264-pen-plotter-attachme... ) and at the time I didn't have a good way to turn artwork into G-code for it, and I put the project on ice for a while. I recently wanted to dabble in line art again and made a small browser app to make it easier. As agentic AI tools of 2026 are quite addictive, it rather quickly grew into something quite a bit more - an integrated browser CAD/CAM for pen plotters that

If you're reading an Oura Ring 5 review at The Verge, you likely fall into one of two camps: newcomers looking for a smartwatch alternative, or Oura users pondering an upgrade. In the case of the former, this is a great casual health tracker and the best smart ring on the market - but not […]

Hi HN — I'm Scott. Skillscript is a small language I built to write what I want my local agent to actually do, in a form I can read and version, instead of hoping the model gets it right each time. The itch started with something small. I wanted my NanoClaw agent to run my morning brief the same way every day. Check overnight tickets, summarize the deploy pipeline, flag anything urgent. Every session, it would re-figure out how to do this from scratch, drift a little, and cost tokens for what's

I've always been the gamer in the family. When my son was born nearly 11 years ago, gaming was one of the things I looked forward to sharing with him. Pulling up a chair next to me, he would watch as I played Sea of Thieves with friends, often wearing the too-big-for-his-head headset to talk […]

I built a voice assistant that controls 45 AI tools. I say "Hey Jarvis" and it executes. What It Does Command Action "generate content" Creates YouTube scripts for 9 channels "research quantum computing" Deep research via Tavily + AI "write email about meeting" Drafts email, copies to clipboard "start focus" Starts Pomodoro + blocks apps "code review" Reviews git diff with AI "summarize" Summarizes clipboard content "find file tax PDF" Natural language file search Architecture Mic → Whisper (offline) → Intent Classify → Router → Ollama → say (TTS) Key Features Offline speech (Whisper local) Wake word: "Hey Jarvis" Global hotkey: Ctrl+Space Command chaining: "research AI then write blog" Memory across conversations Hindi + English Setup brew install portaudio pip install SpeechRecognition pyaudio openai-whisper python voice_commander_pro.py 🔗 github.com/amrendramishra/ai-tools 🌐 amrendranmishra.dev

hi, i am Aniruddha Adak, a full-stack developer from kolkata who spends way too much time building things with ai tools, shipping apps, and reading way too many github readmes at 2 am. i built 27 apps in 45 days using no-code and ai tools last year. that experience taught me one thing very clearly: the landscape of ai tooling for developers is moving insanely fast, and it is genuinely hard to keep up. so i sat down and did something about it. this is my deep research post on every ai tool, agent, builder, reviewer, and framework that developers, software engineers, and ai engineers should actually know about right now. i have organized it into categories so you can find what you need quickly. no fluff. just the tools, their sites, and what they do. why i wrote this i keep seeing developers waste time because they do not know the right tool exists. someone is manually reviewing pull requests for a week straight, not knowing coderabbit exists. someone else is hand-writing supabase schemas when emergent can do it in seconds. another person is spending days on a landing page when v0 can scaffold it in one prompt. this post is my attempt to fix that. i went through github repositories, dev communities, product hunt launches, and research aggregators to compile this. it is long. that is intentional. bookmark it. section 1: ai-native ides these are not just editors with a chatbot plugged in. these are environments built from the ground up around how language models think and work. tool site what it does cursor https://www.cursor.com forked vscode, codebase-aware context windows, multi-file edits with copilot-style background indexing windsurf https://windsurf.com cascade ai agent that writes files, runs terminal checks, and fixes things in real-time zed https://zed.dev built in rust with gpui, super low latency, native multiplayer coding support replit https://replit.com cloud ide with a full autonomous agent that runs inside serverless virtual workspaces google antigravit

Hey everyone, I change servers more often than I probably should. A discounted VPS or a good coupon is usually enough to convince me, but manually recreating the same web stack every time stopped being fun a long time ago. That is why I built HostShift , an Apache-2.0 licensed Go CLI for discovering, planning, migrating, and verifying Ubuntu and Debian servers. The rule I would not compromise on The source server must remain read-only. HostShift does not install packages, stop services, enable maintenance mode, create temporary archives, or change configuration on the source. It reads approved facts and streams data directly to the target. Any target mutation requires an explicit CLI apply command. What it currently covers Docker Compose projects and standalone containers MySQL/MariaDB, PostgreSQL, and Redis Nginx, Apache, Caddy, and systemd services SSH and firewall configuration PHP-FPM, Supervisor, Fail2ban, Certbot, and Logrotate Migration planning, audit journals, status, resume, rollback metadata, and verification checks The migration engine is deterministic Go code and does not need AI. I also added an optional Codex plugin and a deliberately non-apply MCP interface for discovery, planning, review, and dry runs. Actual changes stay in the human-operated CLI. Testing real migrations I did not want to call it tested just because a few unit tests passed. The repository includes Docker migration matrices and real Lima VM matrices covering Ubuntu 22.04, Ubuntu 24.04, Ubuntu 25.10, Debian 12, and Debian 13, including cross-distribution moves. The VM tests also reboot the target and verify persistence while comparing source snapshots before and after the migration. The project is still new, so I expect real-world edge cases. I am sharing it now because feedback from people who actually move and maintain servers will be more useful than polishing it alone forever. GitHub: https://github.com/oguzhankrcb/HostShift Documentation: https://hostshift.karacabay.com

AWS released an official Agent Toolkit that plugs Claude Code, Codex, and Cursor directly into your AWS account through a single MCP server. Instead of wiring up IAM roles and endpoints by hand, you install one plugin and the agent can search AWS docs, run sandboxed Python, and follow curated cloud skills with full CloudTrail audit logging. What the AWS Agent Toolkit Actually Is The Agent Toolkit for AWS is an open-source project (published on GitHub at aws/agent-toolkit-for-aws ) that bundles two things: The AWS MCP Server — a managed server that gives agents access to AWS through the Model Context Protocol. Agents can search AWS documentation and pull service information without authentication. To actually execute AWS API calls, run Python in a sandboxed environment, or follow curated skills, the agent authenticates through your existing IAM credentials. Agent plugins — single-install packages that bundle the MCP server configuration and a curated set of agent skills, so you don't configure endpoints and install skills one by one. The point is consolidation. One endpoint, IAM-based access controls, CloudWatch metrics, and CloudTrail logging of every API call for audit visibility. Which Agents It Supports Per AWS's own documentation, plugins ship for Claude Code, Codex, and Cursor . Kiro connects to the AWS MCP Server directly without needing a plugin, and any MCP-capable agent can point at the server manually. The install flow is refreshingly short for Claude Code: /plugin install aws-core@claude-plugins-official /reload-plugins The aws-core plugin is the recommended default — it bundles the MCP server config and skills covering service selection, infrastructure as code (CDK and CloudFormation), serverless, containers, storage, observability, billing, SDK usage, and deployment. A second plugin, aws-agents , provides additional agent-oriented capabilities. Why This Matters for Coding-Agent Users Until now, getting an agent to safely touch your cloud account meant h

This is a submission for Weekend Challenge: Passion Edition What I Built Every developer has a graveyard of side projects — started with fire, abandoned quietly on a Tuesday. They deserved better than an empty GitHub repo gathering digital dust. DevGraveyard is a gothic memorial platform where developers give their abandoned passion projects a proper burial. Connect your GitHub, pick a dead repo, carve its epitaph — and watch Gemini AI write a dramatic breakup letter from you to the project. Here's what it does: ⚰️ Bury a project — 3-step burial wizard: pick a repo → choose cause of death ( "Never Made it Past Localhost" , "Ran Out of Weekend" , "It Was Complicated" ...) → write an epitaph 🪦 Real tombstone data — pulls your actual commit history: peak obsession streak, most commits in a single day, last commit message ( your final words ) 🤖 AI Eulogy — Google Gemini writes a dramatic breakup letter from you to the project, referencing your real commit data 🕯️ Community mourning — light candles, leave RIP messages, vote to resurrect projects 🌐 3D Graveyard — a full Three.js interactive cemetery: bare trees, fireflies, flickering candles, soul wisps, resurrection pulse rings. Click any tombstone to interact My own ARweave repo had 56 commits, a 2-day peak streak, 30 commits on its best day. Cause of death: "Never Made it Past Localhost." Last words: "feat: overlay plane in 3D builder — drag/scale image on marker, position saved to DB and restored in AR viewer." It worked until it worked. Demo 🔗 Live → devgraveyard.varshithvhegde.in Code Varshithvhegde / devgraveyard Give your abandoned passion projects a proper burial. A gothic graveyard for dead side projects. ⚰️ DevGraveyard A memorial for your abandoned side projects. They deserved better than an empty GitHub repo gathering digital dust. Live → devgraveyard.varshithvhegde.in What is this? Every developer has a graveyard of passion projects — started with fire, abandoned quietly on a Tuesday. DevGraveyard gives them

Most things that ship under "browser MCP" are the same thing wearing different names: an autonomous agent with a do-anything tool, pointed at your browser, told to figure it out. The pitch is capability. The unspoken cost is that a runtime which can do anything can be steered into doing anything. We just published the opposite, and we published it in the open. github.com/e2llm/e2llm-sifr is now the canonical home for SiFR - the format spec, the taxonomy, the MCP server manifest, real page captures, per-client configs, and the model skill. MIT-licensed. The capture engine and the server stay a hosted product; the format and the interface are open. This post is about why that split is the whole point. E2LLM is not an agent This comes first because everything else follows from it. An agent decides and acts on its own. It plans, it loops, it takes steps toward a goal with you out of the path. That autonomy is the feature - and it is also the attack surface. A runtime that can do anything is a runtime that can be talked into anything. E2LLM is a perception layer, not an agent. It gives whatever model you already use senses for the browser: structured sight, and a small set of narrow, individually-gated actuators. It does not plan, does not loop, does not decide. Your model does the reasoning. E2LLM reports what a page is and carries out one explicit instruction at a time. Nothing runs while you look away. Perception substrate versus autonomous runtime. That line is the design, not a disclaimer on top of it. What SiFR is - and the three things it isn't SiFR (Salience-Indexed Flat Relations) is the capture format at the center of E2LLM. From a distance it can look like a tidy DOM dump or an accessibility tree. Mechanically it is neither, and the difference is the entire value. Not a DOM dump. A dump serializes the tree as-is: everything, in document order, noise included. SiFR selects and ranks. It scores every node by salience, drops scaffolding, and flattens the survivor

The first time a client said yes to a quote without hesitating, I felt sick. This was early on. I'd sent over a rate for a batch of articles, my palms were actually sweaty over the email, and the reply came back in under an hour. "Sounds great, when can you start?" No pushback, no negotiation, nothing. I should have been thrilled. Instead, I sat there doing the math on how much more I could have charged, and I knew, the way you just know sometimes, that I'd priced it too low. His enthusiasm was the tell. That queasy feeling taught me more than any pricing guide ever did. If a client says yes instantly and happily, you were cheap. I've been freelancing for about eight years now, all of it writing and content work, most of it solo from a spare room in my house. I've priced my work a dozen different ways over that stretch, and I've gotten most of them wrong at some point. So here's what I actually believe about pricing, after enough scars to have earned an opinion. Per-word pricing quietly punishes you for getting better I started out charging by the word, like a lot of writers do. Five cents a word, sometimes six if I was feeling brave. It felt safe because it was easy to explain and easy for a client to say yes to. A 1,500-word article costs this much. Clean. Predictable. The problem showed up slowly. The better I got, the worse that model treated me. Early on I'd pad a piece to hit a word count because more words meant more money, which is a genuinely insane thing to be incentivized toward as a writer. Then I spent years learning to cut. Learning that the sharpest version of an article is usually the shortest one that still does the job. And every ounce of that hard-won skill made me poorer, because a tight 900-word piece that took real judgment to shape paid less than a bloated 1,400-word one I could have written half-asleep. Think about how backwards that is. I was being paid the least for the writing I was proudest of. The stuff that took a decade to be able to d

Engineering prompts inside your source code is a recipe for deployment fatigue. If you've spent any time moving an AI feature from a prototype to production, you know the specific frustration of 'prompt drift.' You make a subtle tweak to a system instruction—perhaps changing how the model handles edge cases in JSON formatting—and suddenly you're forced into a full CI/CD cycle. A PR, a review, a build, and a deployment, all because of three words changed in a long string constant. In a mature engineering organization, your application logic should be decoupled from your prompt instructions. The code handles the orchestration, the plumbing, and the security; the prompts represent the dynamic configuration. This is what LLMOps aims to achieve, but until recently, there was a massive friction gap between managing these prompts in a dashboard and actually using them inside an agentic workflow. This is where the Humanloop MCP server changes the interaction model entirely. It's not just about having a central repository for strings; it's about bringing those strings into your execution context—your IDE, your Claude instance, or your Cursor agent—as actionable tools. The Architecture of Prompt-as-a-Service The core idea here is treating prompts as versioned assets rather than hardcoded constants. By using the Humanloop API via MCP, you're essentially turning prompt management into a service call. When I look at the toolset available in this server, the first thing that stands out isn't just the ability to read data—it's the ability to manipulate state. Take upsert_prompt for instance. You aren't just fetching text; you can create or update configurations directly from your agent. This transforms your development loop. Instead of context-switching between a browser tab with Humanloop and a terminal, you can instruct an agent to 'Refine the customer-support-reply prompt to be more concise and save it.' The agent performs the engineering work and updates the source of truth in