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Reshape any website with plain words via Sider extension Discussion | Link

I switched my testing order so the phone goes first and the desktop goes second, on every RAXXO tool without exception A desktop-first habit hid layout and tap-target problems for months because the biggest screen forgives the most mistakes Testing on a phone first forces the same discipline as writing a short sentence instead of a long one, cut what does not fit The rule survives even for tools built for a keyboard and a terminal, because the landing page and the first impression are still mobile The Habit I Had Backwards For a long time I built and tested everything in the same order: open the code editor on a wide monitor, ship the feature, check it on desktop, call it done. If I had time left over, I would open it on my phone to confirm nothing was broken. That last step felt like a formality, a quick glance rather than a real check, because the tool had already passed on the screen I spent most of my day looking at. The problem with that order is that the desktop is the most forgiving screen there is. Extra padding does not matter when there is space to spare. A button that is slightly smaller than it should be is still easy to click with a precise mouse pointer. Text that wraps awkwardly at narrow widths never shows up because the window is never narrow. Every mistake that a small screen would expose gets absorbed by the size of a big one, which means desktop-first testing is really desktop-only testing wearing a disguise. I noticed this the hard way, not through a single dramatic failure but through a slow accumulation of small ones. A support message here about a button that was hard to hit. A review there that mentioned the site felt cramped on a phone. None of them were urgent enough on their own to stop what I was doing, so I patched each one individually and moved on, the same reactive pattern I try to avoid everywhere else in the studio, including the check I run on every tool before I call it shipped . It took stepping back and counting the pattern to

Your Free AI Server Will Fail Quietly. Five Gates to Make It Loud. The model can be innocent. The server cannot. Earlier this week I wrote a fail-closed checklist for AI-generated code. That list guards against the model writing something dangerous. This list guards against something duller: the server around it dying at 2 a.m. while the model stays online the whole time. Nobody sees that failure until a user does. The setup I am testing MonkeyCode for a small side build: a log-summarizing API. The project gives you free model access and a free server option, which is exactly the toy setup I like. Ten lines of app logic. Zero dollars. One honest problem: free infrastructure is someone else's best effort. Disclosure: This article was prepared as part of MonkeyCode's product outreach. Before you judge, my plan was simple. I deliberately killed my own server to see where the stack would fail. Then I wrote gates that make each failure loud. The kill test Here is the failure sequence, reproduced on purpose. The server process died. No restart policy. Connections hit a dead socket. Nothing answered. The client had no timeout and waited forever. No health probe. No alert. No log line. Four hours later, the model was still happy. The server was still dead. The tool was still broken. The model was innocent the whole time. The harness was the guilty one. The problem was never intelligence. It was silence. So here are five gates, ordered from cheapest to most annoying. Gate 1: A kill switch that outlives the process A crash bug can take down your app. It can also take down your ability to disable the app. So the switch lives outside the app. KILL_FILE = " /tmp/disable-monkeycode " @app.post ( " /summarize " ) def summarize ( logs : str ): if os . path . exists ( KILL_FILE ): raise HTTPException ( 503 , " disabled by operator " ) ... Why a file and not a database row? Because the DB may be down when you need the switch most. A file survives restarts. You can touch it from cron.

In my first post, I wrote about why I spent my first week writing zero business logic and instead built rig - our lightweight, POSIX-compliant local provisioning tool. It was my way of rejecting "wiki-ops" and applying Infrastructure-as-Code (IaC) discipline to our local environments so that a hardware failure means minutes of downtime, not a week. But as I transitioned into Week Two, I was hit by a different kind of operational reality check. For years, I had been building a comprehensive repository of system architecture, design decisions, and guidelines on Confluence. It was my digital home. So, knowing I would be creating a startup, I set to work writing my documentation in my spare time in preparation. But during a brief hiatus of inactivity, the space was silently, unceremoniously deleted. It was gone. Late nights of ideas, patterns, templates, and reference materials vanished into the cloud ether. That loss was a violent reminder of a lesson I thought I'd fully mastered: if your documentation doesn't live alongside your code, you don't truly own it. Relying on third-party SaaS wikis to store the soul of your system architecture is just another form of "click-ops". It creates an artificial separation between the craftsmen writing the logic and the documentation that defines it. But rather than mourning my lost Confluence space, I treated it as a catalyst. I decided that our young startup would not have a bloated, detached corporate wiki. Instead, we would treat Documentation as a Contract - a unified, git-backed human-and-machine contract that serves as the precise, zero-maintenance boundary for our AI systems. Here is how losing my documentation led to a new architectural philosophy, and how we built a zero-overhead, "Anti-AI AI Strategy" that uses GitLab CI/CD and Google Workspace to run a secure, managed RAG pipeline. The Anti-AI Strategy: Why We Refuse to Let AI Write Our Code Walk into almost any tech startup today, and you’ll find developers blindly feed

In remote software demos, the biggest problem is not always the product itself. Sometimes the audience simply doesn’t know where to look. A button may be visible. A setting may already be on screen. The presenter may be explaining everything correctly. But if attention isn’t directed clearly, people can still get lost. After doing a lot of screen sharing and software demos, I kept noticing the same small UX problems. 1. The cursor is visible, but not necessarily noticeable When you're presenting your own screen, you always know where your pointer is. The audience doesn’t. On a large monitor, a compressed video call, or a busy application UI, the pointer can easily disappear visually even though it is technically visible. This becomes especially obvious when you say something like: “If you look over here…” You know exactly what “here” means. The audience may need another second or two to find it. That delay sounds minor, but during a demo it can happen again and again. A presenter moves on to the next step while part of the audience is still trying to locate the previous one. 2. Moving the pointer is not the same as directing attention A common workaround is to move the mouse around whatever you want people to notice. I’ve done this many times myself. Circle the button with the cursor. Move back and forth over a chart. Quickly point between two settings. It works, but it also adds visual noise. Eventually I realized there are really two different actions happening: Navigation — using the mouse to operate the software. Attention — telling the audience where to look. During a demo, those aren’t always the same thing. Sometimes I don’t want to click anything or change the interface. I just want to say: Look here. 3. Highlighting something can interrupt the demo There are plenty of powerful screen annotation tools available. They make sense when you want to draw arrows, write notes, add shapes, or explain something in detail. But during a live product demo, switching int

Last month I shared how I built 143 free online tools that run 100% in your browser — no signup, no uploads, no watermarks. That post got a great response (and a lot of "how is this free?" comments — answer: it stays free because files never touch a server, so there are no processing costs). Today's update: every single tool now has a full guide series. What's new 144 how-to articles — one per tool — live at toolfyra.vercel.app/blog : Step-by-step guides — every input explained, common pitfalls, pro tips Real competitor comparison tables (we scraped and analyzed who ranks for what, and where their tools annoy users with account walls) FAQ sections with schema markup so answers surface directly in search and AI assistants Unique generated illustrations per article Smart related-tools clusters — finish one task, the next tool is one click away Why guides for calculator tools? Because "how to use a calculator" is what people actually search for. Tools win clicks; guides win trust and rankings . Each article is built from real search-engine data: live SERP results, keyword expansions, and competitor FAQ analysis — zero guesswork. The engineering side (for the dev readers) Every tool is a single HTML page with vanilla JS — calculators run client-side, file tools use Canvas/FileReader APIs The blog is generated (Python build script): schema.org BlogPosting + FAQPage + BreadcrumbList, per-post OG images as optimized SVGs, canonical URLs, sitemap + IndexNow pings on every deploy New site-wide: instant search (type "pdf" → live results dropdown, keyboard-first: / to focus, ↑↓ to navigate), a Tools dropdown with 11 categories, and a mobile hamburger panel — all vanilla JS, no dependencies Privacy by architecture: there is literally no upload endpoint to breach What's next More waves of content (FAQ, mistakes-to-avoid, and comparison articles for every tool) A batch of new tools from our demand-research pipeline (we score thousands of real search phrases before writing a line

Your filesystem is already a database. Most tools just don't treat it that way. That's the core idea behind the Markdown Database Pattern — written up properly on The Way of Markdown , a site we've been contributing to that makes the case for building things on plain markdown instead of locked-in platforms. We think it's a pattern worth more attention, so here's the short version. Treat a folder of markdown files as a database. Each file is a record. Frontmatter fields are columns. Directories are tables. Tags, wikilinks, and tasks in the body become queryable relations. Filesystem Database ────────────────────────────────── markdown file → record frontmatter field → column directory → table #tag → tag relation [[wikilink]] → link relation - [ ] task → task relation You get portability, version control (git works perfectly on plain text), no framework lock-in, and full queryability. You give up scale and real relational joins — this isn't for millions of records. It's a lightweight database, honest about its limits. Once you name it, you start seeing it everywhere. Obsidian Bases and Dataview already do versions of this, half-consciously. A team wiki where every page has a status and owner field is one. A blog with date and tags in frontmatter is one — it just doesn't know it yet. Sweet spot: up to roughly 10k files. Past that, reach for a real database. Below it, this gets you almost everything a database gives you, at a fraction of the complexity, with none of the lock-in. The full writeup — the complete tradeoff analysis, a worked example with actual queries, how to implement it in a weekend, and the tool ( MarkdownDB ) that does it for you — is here: wayofmarkdown.com/markdown-database

Menubar file delivery with your brand and tracking Discussion | Link

A self-serve platform for building AI phone agents Discussion | Link

Indexar o código fora do repo: como economizar tokens sem jogar o projeto no contexto Pessoal, o agent precisava achar um símbolo. Trabalho de um minuto. Na prática, ele abria arquivo atrás de arquivo, colava dump de teste no papo e a janela sumia. Às vezes a fatura também. Não era o modelo burro. Era eu pagando o monorepo inteiro pra responder a pergunta errada. A pergunta mudou. Deixei de ser “qual tool faz o agent entender o repo?” e virei: o que é memória de domínio, e o que é só custo de ler código nesta sessão? Tem um segundo motivo, e ele não é economia. Um índice de símbolos é um mapa do seu sistema : quem chama o quê, onde está o fluxo crítico. Se esse mapa mora no git, no cache de CI ou num serviço que o agent também escreve, o blast radius não é só token. É superfície. Duas contas, um prompt Memória de domínio é política. O que pode ser lembrado, por qual porta se entra, o que é canônico. Notas, contratos, “onde a gente decide X”. Indexer de código não resolve isso. Code-read barato é custo de sessão. Achar caller e símbolo sem despejar o working tree no prompt. Isso não deveria virar a sua base de conhecimento. Eu misturava. O indexer virava KB. O vault virava grep sem porta. Os dois falhavam, e a sessão inchava igual. Economizar token aqui não é trocar de modelo da semana. É separar camada. E decidir onde o mapa vive . O que eu mudei na mesa O mapa de símbolos saiu do working tree. Cache local, fora do repo , fora do git. Reindex é operação de máquina, não de PR. O agent consulta o índice; não precisa reler o monorepo pra “quem chama essa função?”. Quatro perguntas que eu faço antes de indexar um repo (vale colar no README do setup): O índice vive na minha máquina ou sai dela (cloud, CI, cache compartilhado)? Entra em contexto de agent que também tem tool de escrita ? Como eu apago e revogo? Quem mais lê isso? Índice ≠ fonte de verdade versionada. Least privilege no que entra no contexto continua valendo. Depois, parei de mandar firehose de CLI cru. tes

I combed through the Labor Day sales to find the power tools and other essentials that cover nearly every household repair.

Netflix has walked back plans to put AAA games on its streaming platform. Instead, it's pivoting to marketing highly anticipated titles like Grand Theft Auto VI.

I'm part of an awesome community where senior devs, product managers, founders, and experienced folks from different domains discuss how they use AI and automation tools to boost productivity — without sacrificing real learning. The group is a goldmine . Tool recommendations. Automation workflows. Latest trends. Migration war stories. I've learned more from this group than from most tutorials. Last week, I needed to find something specific about running local LLMs. I searched with keywords. I scrolled. I found a whole lot of messages — but none of them answered my question directly. I still had to manually read through dozens of messages , open the links they shared, and try to piece together the context myself. It took me over an hour, and I wasn't even sure I'd found everything. Someone might say: "Just Google it." Or "Ask an LLM." But that defeats the purpose. The value here isn't just information — it's the context . The "this library is a game changer" comment only makes sense if you know what the person was working on before, and who else agreed or disagreed. That context is lost in scrollback. And I'm tired of trying to keep it all in my head . I've seen people send important links to themselves on WhatsApp. But that becomes a messy pile with no structure. No connections. No way to see how one message relates to another. So I'm curious: How do you deal with this? Have you lost important knowledge in group chats? Do you have a system for recovering it? Or are you also just scrolling endlessly? I have an idea I'm working on. But I'd love to hear your approaches first. Drop your thoughts in the comments — I'll document what I learn .

Build one customer-facing AI agent and launch it everywhere Discussion | Link

Alienware’s latest gaming monitor explores a new size and resolution for ultrawide monitors, and I have a feeling PC gamers are going to love it.

You’re hunched over your desk and phone for hours. I rounded up gadgets, a DIY trick, and even some yoga advice to help you straighten up.

The first open-source price index for GPU compute Discussion | Link

A read-later app sending a typeset digest to your e-reader Discussion | Link

Let us help you choose the right Pixel phone. Plus, check out our Pixel accessory recommendations and smart software tricks to try.

Trajectory-aware LLM routing that cuts agent cost Discussion | Link