We are building a VM that helps you simulate realistic production conditions, model latencies, different interleaving, user requests, and find bugs. Every non-deterministic property is turned into a knob you or a coding agent can control. We have helped teams perfectly reproduce support incidents and found bugs in some of the world's most well tested software (including a database) I you would like to try it out, I am giving away $100 in free credits to first 100 signups.

I can't remember the last time I got excited about a fan. Normally, I just buy whatever Vornado or Dreo model fits my budget, but that was before I started testing the battery-powered Standing Circulator Fan from SwitchBot. As the name indicates, the SwitchBot fan is a 3D circulator - a fancy way of saying […]

Authentication is broken at its foundation - not just inconvenient. Passwords are shared secrets: hand one to a server, and you have instantly doubled your attack surface. With over 5 billion passkeys now active globally and Google reporting a 99.9% lower account compromise rate compared to passwords, the industry has already moved. This guide covers how passkeys work cryptographically, how to implement them in TypeScript, and the pitfalls to avoid before going to production. Why Passwords Are Structurally Broken The core issue isn't that users pick weak passwords - it's that passwords require a shared secret stored on both sides. The Verizon 2025 DBIR found that 22% of all breaches started with stolen credentials, and 88% of web app attacks relied on them. In 2024, infostealer malware alone harvested 548 million passwords. Adding 2FA helps but doesn't fix the root problem: SMS codes are SIM-swap targets, and TOTP tokens can be phished in real time by proxy attackers who replay codes within their validity window. What Passkeys Actually Are A passkey is a credential built on public-key cryptography, standardized through the WebAuthn spec and FIDO2. When you register, your device generates a public-private key pair - the private key stays locked in hardware (Secure Enclave, StrongBox, or a hardware key), and the server only receives the public key. At login, the server sends a random challenge, your device signs it with the private key after biometric or PIN verification, and the server verifies the signature. No secret is ever transmitted. This eliminates credential stuffing, server-side breach exposure, and phishing - because passkeys are cryptographically bound to a specific origin domain. The Cryptography Worth Understanding The standard algorithm is ES256 - ECDSA with the P-256 curve and SHA-256. Each credential is tied to a specific relying party ID (your app's domain). A passkey created for yourapp.com cannot be used on yourapp-phishing.com because the origin i

Body: Hey everyone 👋 I've been building hermes-agent-skills — a production-grade skill collection for Hermes Agent that does three things no other skill pack does: 1. Self-Evolving Skills Skills aren't static YAML. The built-in EvolutionEngine tracks 5 health dimensions (usage frequency, success rate, user corrections, freshness, command validity), assigns a health score, and tells you which skills are rotting. Think of it as npm audit for your AI assistant's capabilities. 2. SOUL.md Persona Awareness Drop a SOUL.md in your Hermes config — naming conventions, comment density, architecture preferences, commit style — and every skill that touches code output adapts to it. hermes-skill soul generate bootstraps one in one command. The persona-aware-coding skill reads it at runtime so your agent writes code that actually looks like you wrote it. 3. CLI Toolchain hermes-skill create my-workflow # scaffold a standards-compliant SKILL.md hermes-skill validate skills/ # validate against the Agent Skills Standard hermes-skill list skills/ -f json # enumerate with health metadata hermes-skill soul generate # bootstrap a persona file What's in the box (v1.1.0): | Skill | Phase | Hermes-only Feature | |---|---|---| | requirement-analyzer | Define | Persistent memory across sessions | | spec-driven-dev | Spec | /skills chain forming workflows | | test-driven-dev | Build | delegate_task parallel test execution | | debugger-coordinator | Verify | browser + terminal + vision tri-tool | | code-quality-guardian | Review | patch auto-fix + /curator tracking | | cicd-orchestrator | Ship | cronjob scheduling + webhook triggers | | skill-curator | Evolve | Direct /curator integration | | persona-aware-coding | Identity | Native SOUL.md persona system | Why this is different: Most agent skill collections are portable but shallow — they can't use any platform's unique superpowers. These skills go deep on Hermes specifically: slash commands, delegate_task, persistent memory, vision+browser+t

Why I built GigVorx, a SaaS tool to help freelancers and agencies manage client briefs and invoices more professionally. Freelancers and small agencies often have one messy problem: Client details are everywhere. Some requirements come through WhatsApp. Some come through calls. Some are sent as voice notes. Some are inside Google Docs. Some are buried in old messages. At the start, this feels normal. But later, it creates problems. You forget important requirements. You ask the client the same question again. Invoices are created manually. Project details are not organised. The whole process looks less professional. That is the problem I wanted to solve with GigVorx . What is GigVorx? GigVorx is a client intake and invoicing tool for freelancers and small agencies. It helps users: Collect client requirements professionally use ready-made brief templates organise client details create professional invoices avoid scattered WhatsApp chats, calls, and docs The goal is simple: Help freelancers and agencies manage client intake and invoicing from one dashboard. Who is it for? GigVorx is mainly for: web designers developers graphic designers video editors SEO freelancers social media agencies digital marketing agencies small service businesses These people usually talk to many clients and need a better way to collect requirements before starting work. Why I built it I noticed that many freelancers lose time before the project even starts. They ask questions manually. They collect details in random chats. They create invoices separately. They do not have one organised place for client information. This makes the work slower and sometimes confusing. So I wanted to create a simple tool that gives freelancers a more professional workflow. Current status GigVorx is already live in early access. Right now, I am not focusing on making it perfect. I am focusing on getting real users' feedback and improving the product based on what freelancers actually need. What I am learning Bui

The cycle number on a lithium battery's spec sheet is true and almost useless, because it describes a life the battery will live only in a temperature-controlled lab being cycled gently by a machine that never has a bad day. A cycle, in that test, means a full charge and a full discharge under mild, steady conditions, repeated until the pack fades to some fraction of its original capacity, often eighty percent. Your warehouse does none of that. It charges in bursts, discharges to whatever the shift demanded, bakes the pack in summer and chills it in winter, and counts a cycle as whatever happened between two plug-ins. Depth is the lever nobody quotes The single biggest mover of cycle count is how deep you run the pack on each outing, and that figure almost never shares the page with the headline number that sells the battery. The relationship is steeply nonlinear, which is the part that surprises people. Drain a lithium pack to nearly empty every time and you spend cycles fast. Use the top half and tuck it back on charge, and the same cell can deliver many times the number of shallow cycles before reaching the same faded state. The chemistry is mechanical about it: every deep swing stretches and contracts the electrode structures further, and the wider the swing the more wear each one inflicts. Two fleets on identical batteries can see lifespans years apart purely from how hard they drain them. This is why opportunity charging does double duty. It keeps the truck running, and it keeps each cycle shallow, which stretches the pack's life as a side effect. It also means a published cycle figure measured at full depth understates what a top-up fleet will see, while a figure measured shallow oversells what a run-it-flat operation will get. The same battery, the same number, two outcomes the sheet never warned you about. You have to know the test depth to know what the promise means. Heat is the other clock Cycles are only one of two clocks ticking on a battery, and the s

If you're learning DevOps right now, there's a high chance you're experiencing at least one of these problems: Watching endless YouTube tutorials but forgetting everything after a week. Learning Docker, Kubernetes, AWS, Jenkins, Terraform separately without understanding how they fit together. Feeling overwhelmed by the massive DevOps roadmap. Jumping from one course to another without making real progress. Thinking you're learning fast when you're actually just consuming content. I know this because I made the same mistakes. When I first started learning DevOps, I thought mastering tools was the goal. I was wrong. The biggest challenge wasn't Docker, Kubernetes, AWS, or CI/CD. The real challenge was understanding why DevOps exists in the first place. Once I understood that, everything became easier. And two books helped me more than dozens of random tutorials. 🥇 Book #1: The Phoenix Project 👉 Buy Here If you're struggling to understand why companies use DevOps, start with this book. Unlike traditional technical books, The Phoenix Project teaches DevOps through a story. You'll follow an IT manager trying to save a failing company while dealing with: Constant production outages Deployment failures Team conflicts Slow software releases Business pressure As the story unfolds, you'll naturally learn: ✅ DevOps principles ✅ Bottlenecks and constraints ✅ CI/CD concepts ✅ Automation thinking ✅ Why collaboration matters The best part? You don't need years of experience to understand it. Even if you're a student learning AWS, Docker, Kubernetes, and Linux, this book makes complex DevOps concepts feel simple. Why I Recommend It Most beginners try to learn tools before learning principles. This book fixes that mistake. 🥈 Book #2: The DevOps Handbook 👉 Buy Here Once you understand the mindset behind DevOps, it's time to learn the practical side. That's where The DevOps Handbook comes in. Think of it as the blueprint used by high-performing engineering teams. Inside you'll learn:

Enterprise Design Patterns in Python: Repository & Unit of Work 🐍🏗️ Series: Enterprise Application Architecture | Source: Fowler's EAA Catalog | Code: GitHub Repository 🧠 What Are Enterprise Design Patterns? Martin Fowler's Patterns of Enterprise Application Architecture (2002) is one of the most influential books in software engineering. It documents recurring architectural solutions — patterns — that solve common problems in enterprise systems: how to organize domain logic, how to talk to databases, how to handle transactions, and more. In this article, we'll explore two of the most powerful and widely-used patterns from that catalog: Pattern Category Core Purpose Repository Data Source Abstracts data access behind a collection-like interface Unit of Work Data Source Tracks object changes and commits them as a single transaction These two patterns work beautifully together — and you'll see exactly why with a real-world example. 🛒 The Problem: An E-Commerce Order System Imagine you're building a backend for an online store. When a customer places an order: A new Order is created Each Product 's stock is decremented A Payment record is registered If any of these steps fail midway, the entire operation should roll back — no partial state. This is exactly the problem the Unit of Work pattern solves, and the Repository pattern makes it all cleanly testable. 📁 Repository Pattern Definition "A Repository mediates between the domain and data mapping layers using a collection-like interface for accessing domain objects." — Martin Fowler, PoEAA The Repository acts as an in-memory collection of domain objects. Your business logic never knows if it's talking to PostgreSQL, SQLite, or even a mock list — it just calls .add() , .get() , .list() . Domain Model # models.py from dataclasses import dataclass , field from typing import List from uuid import uuid4 @dataclass class Product : id : str name : str price : float stock : int @dataclass class OrderItem : product_id : str qua

Most weeks in the agent economy look like a pile of unrelated announcements. This one had a theme hiding in it. Four different teams shipped progress on agent commerce, and if you line them up, they're all solving the same half of the problem — and all leaving the same half open. This is a builder's map. No leaderboard, no "who wins." Just what each thing is, what it does well, and the question none of them answer yet. The rails that shipped Mastercard Agent Pay for Machines. Mastercard's agent-payment program continues to roll, with 30+ partners spanning crypto and TradFi (Aave Labs, Alchemy, Anchorage, BVNK, Coinbase, MoonPay, OKX, Polygon, Ripple, Solana). The mechanically interesting part: agent payment authorizations get recorded to Polygon. A TradFi network is writing agent-spend permissions on-chain. That's a real signal about where this is heading. x402. Coinbase's HTTP-402 payment protocol keeps expanding its reach — it's now usable behind mainstream web infrastructure (AWS/CloudFront paths), which lowers the integration cost for ordinary web services to charge agents per request. Worth noting alongside the growth: standalone x402 transaction volume is well off its peak (OKX Ventures put the drop around 92% from the November high). The protocol is spreading even as raw volume cools — rails proliferate faster than they fill. Eco. A cross-chain stablecoin orchestration layer that abstracts routing, solving, and finality across ~15 chains. Where a payment intent can't move natively, Eco figures out the path. This is genuinely useful — it's the "make the stablecoin show up on the right chain" problem — but orchestration is routing, not atomic exchange. ERC-8004 (Trustless Agents). Not a rail at all — an identity and reputation layer for agents, with a v2 direction that leans into MCP. This is the one that actually points at the gap the others leave. More on that below. The thing they have in common Mastercard, x402, and Eco are all answers to "how does an agent

Your password bar goes from "weak" to "strong" when you add characters. Have you ever wondered how React 'remembers' your inputs? 'State' is your answer. A state remembers what input you added. Assume you are typing your name "John", initial state is the blank slate (starting value, like empty input, counter at zero, or an empty whiteboard) in the input bar, whenever you add a new letter, a function named 'setState' is called to change the state from "" (empty input bar) to "J", then again to "Jo", again to "Joh", etc... And following the setState, React automatically re-renders the UI. Think of re-rendering as erasing everything and re-writing the UI with the new state. Initial state was "", then setState updates it from "" to "J". Following that, React automatically erases the first UI and then it will build a new UI with the new state. Why not just use a variable? You might be thinking, "Why don't just use variables and change them whenever you want it?", and you might do that, but the value only changes in your codebase, but the UI will still show the first value. (and that defeated the purpose) What is [state, setState] concept? const [state, setState] = useState('placeholder') is the basic syntax. useState provides an array of ['current-state', 'function to change state']. It is destructured to the two values (state = 'current-state' and setState = 'function to change state'). When you enter an input, the function is called and the 'current-state' will be updated to the 'new-state'. How to use it To use useState follow these two simple steps: Import useState from 'react' import { useState } from 'react'; Declare it (for example to input age): const [age, setAge] = useState(20); where, 'age' is current state. 'setAge' is the function that will create the new state. 20 is the placeholder. Now try it yourself! Open your React project and add a counter using useState. Watch the UI update every time you click.

Fable, Mythos, Opus 4.8, Sonnet 4.6, Haiku — Anthropic's 2026 lineup is no longer "one model you talk to." It's a fleet you route between. I spent a month inside Claude Code orchestrating all of them across real codebases. Here's which model to reach for, when, and the routing playbook that quietly doubled my throughput. Why I Went Down This Rabbit Hole (Again) Last time I wrote about Claude Skills and called Claude Code the killer host for them. Since then, two things happened that changed how I work day to day. First, the models got genuinely strange-good . In the span of a few months Anthropic shipped Sonnet 4.6, Opus 4.8, and then an entirely new tier above Opus — the Mythos class — released to the public as Claude Fable 5 . We went from "the AI suggested a decent diff" to Stripe reporting that Fable 5 ran a codebase-wide migration on a 50-million-line Ruby codebase in a single day — work that would've taken a team over two months by hand. Second, Claude Code stopped being a single-model tool. With a fleet of models at different price/speed/intelligence points, the highest-leverage skill in 2026 isn't prompting — it's routing . Knowing which model to put on which task is the difference between burning $200 of tokens on a typo fix and one-shotting a multi-service refactor. So I did the obvious thing: I wired all of them into Claude Code and ran them against real work for a month — bug fixes, migrations, greenfield features, test suites, the boring stuff and the scary stuff. This is what I learned. TL;DR The lineup is now a ladder : Haiku → Sonnet 4.6 → Opus 4.8 → Fable 5 → Mythos 5. Each rung trades cost for capability and patience for long-horizon autonomy. Sonnet 4.6 is your default. Frontier-ish coding at $3/$15 per million tokens with a 1M-token context window . Most of your work should live here. Opus 4.8 is the reliable senior. Better judgment, ~4× less likely to let its own code bugs slide, and it powers dynamic workflows — hundreds of parallel subagents i

The Quest Begins (The "Why") Picture this: I’m hunched over my laptop at 2 a.m., surrounded by empty coffee mugs, trying to get a simple Node.js API to run on a friend’s Windows machine. I’ve got the code, I’ve got the dependencies, but every time I hit npm start on his box I’m greeted with “Cannot find module ‘left-pad’” (yeah, I know, that’s a meme, but it felt real). It was like trying to cast a spell in Harry Potter while forgetting the wand‑movement — nothing happened, and I felt like a Muggle in a wizard’s duel. That night I realized the real dragon wasn’t the buggy code; it was the “it works on my machine” curse. I needed a way to package everything — the runtime, the libraries, the environment variables — into a single, portable chest that any teammate (or future‑me) could open and instantly get the same result. Enter Docker, the holy grail of reproducibility. If The Matrix taught us anything, it’s that once you see the underlying code, you can bend reality. Docker lets you see the container code and then bend your deployment reality to your will. The Revelation (The Insight) The big “aha!” came when I stopped thinking of Docker as just another VM and started seeing it as a lightweight, immutable snapshot of my app’s filesystem. Unlike a full VM that boots an entire OS, a Docker container shares the host kernel but isolates everything else — think of it as the Inception dream‑within‑a‑dream, but each layer is a read‑only snapshot you can stack like LEGO bricks. Here’s the secret sauce in three lines: Dockerfile – a recipe that tells Docker how to build the image. Image – the built, immutable artifact (the “DVD” of your app). Container – a running instance of that image (the “movie playing” from the DVD). When you docker build , Docker reads the Dockerfile line‑by‑line, creates intermediate layers, caches them, and finally spits out an image you can tag, push to a registry, and run anywhere. No more “it works on my machine” because the machine inside the cont

A working prompt library is the main event, not an appendix. The industry still treats prompts as some half-baked spitball left in a README, or, worse, a plaintext blob stapled to package.json and forgotten. That's a waste of compute and credibility. What powers reliable AI-assisted refactoring, onboarding, or even next-gen code IDEs is not the size of the model but the clarity and context supplied by the actual, shipped prompt set. OTF kits turn this lesson into a repeatable deliverable: every paid template includes 20+ production-tested prompts tied to the real file structure, component API, and product-specific conventions. This is not a suggestion; it's structural. The takeaway: a real prompt library is as important as your component library. Treat it like one. Start with the pain: why blank chat boxes don't scale The web is full of “integrations” that paste a blank chat input over your codebase and call it an “AI coding assistant.” The result: hallucinated function names, invented conventions, broken import paths. Here’s what happens in real life: Dev: "Add a social login button." AI (blank prompt): "Sure! Insert <SocialLoginButton> in your LoginScreen.js." Dev: (There’s no such component. There's not even a LoginScreen.js.) Short: A generic prompt with zero context simply can't know your conventions, files, or patterns. The agent will either fail, hallucinate, or pepper you with clarifying questions you have already answered in your product architecture. Takeaway: Prompting without context is coding without types — fragile guesses instead of structured outcomes. What a first-class prompt library enables When the prompt library ships with the codebase, it looks like this: Every prompt knows the folder structure (e.g., features/auth , screens/Settings/index.tsx ). Conventions are hard-coded: naming, import styles, design token usage. Endpoints and integration points (e.g., “update the Stripe webhook handler in api/webhooks/stripe.ts ”) are spelled out. The promp

I run Convertify , a free image converter built on Rust and libvips. Last week I decided to stop guessing about format performance and actually measure it. I took 50 real images (26 PNGs, 24 iPhone HEIC photos), ran 17 conversions through the production pipeline, and recorded every file size and encode time. Some results confirmed what everyone says. Others did not. The three results that surprised me 1. Converting HEIC to JPG makes files 14% bigger , not smaller. This one hurt. "Convert iPhone photos to JPG" is probably the most common advice on the internet. But HEIC wraps the HEVC codec, which compresses roughly 2x better than JPEG. Going from a better codec to a worse one means the file grows. Every time. If you actually want smaller iPhone photos: HEIC to WebP saves 43%, HEIC to AVIF saves 57%. 2. AVIF encodes 7x slower than WebP for 10% more compression. AVIF Q63: 55 KB, 1.30s per image. WebP Q80: 61 KB, 0.19s per image. That is a 10% size difference for a 7x speed penalty. For a single hero image, nobody cares. For a batch pipeline processing thousands of product photos, that is the difference between 3 minutes and 21 minutes. 3. PNG at 600 DPI is smaller than PNG at 300 DPI when rasterizing PDFs. This was the weirdest one. I was benchmarking PDF-to-image and noticed PNG output shrank from 2,221 KB at 300 DPI to 1,660 KB at 600 DPI. I spent an hour convinced I had a bug. Turns out it is a real property of PNG encoding. Higher DPI renders smoother gradients between adjacent pixels, and PNG's prediction filters (Paeth, sub, up) compress smooth gradients dramatically better than the sharp edges you get at lower resolutions. Not a bug. Just PNG being PNG. The quick reference table Conversion Size change Speed JPG to WebP Q80 -64% 0.19s JPG to AVIF Q63 -68% 1.30s PNG to WebP Q80 -92% 0.21s PNG to JPG Q85 -86% 0.07s HEIC to JPG Q85 +14% 1.90s HEIC to WebP Q80 -43% 5.64s HEIC to AVIF Q63 -57% 14.52s WebP to JPG Q85 +60% 0.09s AVIF to JPG Q85 +80% 0.15s What I actual

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