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For uninterrupted service as you country-hop, the Saily Ultra eSIM works well and comes with plentiful perks. It’s pricey though.

AI can produce an answer in seconds. The answer may be clear, plausible, and even correct. Yet something about it can still feel wrong. I do not think this discomfort comes only from hallucinations or poor model accuracy. Sometimes the real problem is simpler: The AI returned an output, but it did not return the work in a form that another person can safely continue. This is not a new problem created by AI. It is the same problem we already have when delegating work to another person. What do we expect when we delegate work? Imagine a manager asking a team member: Please prepare a proposal for reducing next month's operating costs. The team member reviews several documents, compares multiple options, and replies: We should choose Option A. The requested conclusion has been delivered. But has the work really been handed back? The manager still does not know: What objective the team member optimized for Which documents and facts were examined Which assumptions and constraints were used Which alternatives were compared Why Option A was preferred Which conditions remain unverified What must be reconsidered if the situation changes The original request may not have explicitly demanded all of this. Even so, we normally expect a competent team member to understand the purpose of the assignment and to return enough information for someone else to review, approve, revise, and continue the work. That information is not additional reporting attached to the work. It is part of the handoff condition that makes delegation possible. AI often returns the conclusion without the handoff Now replace the team member with an AI assistant. The AI immediately recommends Option A and produces a polished explanation. Because the answer arrives so quickly and looks complete, it is easy to confuse the existence of an output with the completion of the work. But the same questions remain: How did the AI interpret the objective? What was considered in scope and out of scope? Which sources were a

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Every open-source tool has the same brutal first 90 days: zero users, zero signal, no idea whether anything works. I have shipped several free CLI tools and browser tool sets. This is the exact funnel I use — no ads, no paid growth, no "build in public" theater. Just a repeating sequence of small, concrete actions. Step 1: Make the Tool Trivial to Try The first rule: npx must work. If a reader has to install, configure, and read a README before running the first command, the funnel is already broken. npx @wuchunjie/dotguard . That is the entire onboarding. Zero dependencies, no config, instant output. The first 10 seconds decide whether the reader comes back. Step 2: Publish One Article Per Angle Not one article. One per angle , spread over time: Tutorial — "Scan your .env files in 1 command" (the how) Comparison — "Why I stopped using X" (the why) Listicle — "5 tools for Y" (the discovery) Workflow — "My dev setup" (the context) Security/devops — "Your CI is missing this" (the fear) Each article targets a different search intent. A developer looking for "pre-commit secret scan" lands on article 4, not article 1. The funnel is wide because the angles are wide. Step 3: Cross-Link Everything Every article mentions every tool. The footer of a snippet article lists the scaffolder and the scanner. The GitHub repo links to the articles. The npm README links to the articles. The effect is compounding: a reader of article 3 meets four tools, not one. Your content becomes a network instead of a pile. Step 4: Make the GitHub Repo the Hub The repo README is the landing page that never goes stale: One-line description per tool Install/run commands (copy-paste ready) Links to every article A donation link, present but quiet GitHub is where developers actually trust. Stars and forks are the signal that converts "interesting article" into "let me try it". Step 5: Add the Quiet CTA One line at the end of every article: If this saved you time, a Ko-fi keeps the next tool coming. No

A lot of small file jobs still follow the same awkward pattern: choose a file, upload it to someone else's server, wait for processing, download the result. For some tasks, that server round trip is unnecessary. I have been building FileNest Worktools , a browser-based toolkit for repetitive file work, around a simple constraint: If a task can reasonably be done inside the browser, the file contents should stay on the user's device. What currently runs locally The current FileNest tools cover: batch file renaming sequential, reverse, and custom-order renaming JPG / PNG / WebP conversion image resizing and compression image-to-text OCR with editable review PDF merge, split, extract, and images-to-PDF text export to DOCX, PDF, TXT, Markdown, and HTML CSV / TSV / JSON conversion duplicate-file detection For these current browser-local workflows, the file contents are processed on the device rather than being sent to a conversion server. Why local processing matters Privacy is one reason, but it is not the only one. Many file operations do not actually need server-side infrastructure. Renaming a file is mostly about filenames, order, extensions, and conflict checks. Image resizing can be handled with browser APIs. CSV and JSON conversion is essentially local parsing and serialization. Duplicate detection can compare file fingerprints locally. If those jobs can stay inside the browser, there is less network overhead and one less copy of the user's files being created somewhere else. I also wanted the risky parts to stay visible One thing I dislike about many online file tools is the "click and hope" workflow. So I tried to make FileNest show more information before or after processing: renamed files can be previewed before packaging original files are not silently overwritten image compression reports actual output bytes duplicate detection uses content matching rather than filename guesses OCR output can be reviewed and edited the image enhancer does not claim that shar

A while back, I built a complete field service management platform for the first time. It worked. But building it taught me as much about what I'd do differently as it taught me about field service software itself. So I built it again — FieldOS — the way I'd build it now, with everything I picked up the first time around. And this time I tracked every real hour it took, start to finish. Not to hit a deadline. To finally answer a question I'd always guessed at before: how much time does a project like this actually take, and what is that time genuinely worth to the business paying for it? I do maintenance for a car dealership, an auto parts warehouse, a body shop, and five parts stores in my day job, so I've seen firsthand what off-the-shelf field service software costs a business — and how much of it a smaller crew never touches. FieldOS is built so a business only pays for the pieces it actually needs. But the real question underneath this series isn't what to charge. It's how to price custom work in a way that's honest about what the work is worth, without pricing a small business out of getting it built at all. This series is that experiment, told through the build itself. How the Core System Works Every field service business needs the same five things to run day to day, no matter what they fix or deliver. This is the part of FieldOS nobody gets to skip — the foundation every add-on plugs into later in this series. A Front Door for Every Job: A request comes in, gets logged, and moves through a status list a business defines for itself — not a fixed workflow some software vendor decided everyone needs. A body shop's process doesn't look like an HVAC crew's, so the system shouldn't force them into the same one. Real Parts Tracking: Every part or supply used on a job gets logged against real stock at a real location, and the system flags it automatically the moment something drops below what a crew needs on hand — not after a technician shows up to an empty shelf.

¿Alguna vez refinaste una nota durante horas — redactando, puliendo, dándole estructura — y un ingest posterior la sobrescribió silenciosamente, como si tus horas nunca hubieran existido? Si usas LLMs para mantener una base de conocimiento, probablemente ya sentiste ese dolor: la IA genera contenido excelente, pero cuando tú lo mejoras a mano, no siempre respeta tus ediciones. Este artículo te muestra cómo resolvimos ese problema en agnosticBrain , un vault de conocimiento basado en el patrón LLM Wiki de Andrej Karpathy, al que agregamos tres capas de ownership en lugar de las dos originales. El contexto: LLM Wiki, la propuesta de Karpathy En abril de 2026, Andrej Karpathy publicó su gist sobre LLM Wiki , una idea elegante para bases de conocimiento personales: The wiki is a persistent, compounding artifact. The cross-references are already there. The contradictions have already been flagged. The synthesis already reflects everything you've read. La idea: en lugar de RAG clásico (recuperar fragmentos crudos en cada consulta), el LLM compila y mantiene una wiki persistente — un conjunto de notas markdown interconectadas que vive entre tú y tus fuentes. Obsidian es el IDE, el LLM es el programador, y la wiki es el código. La arquitectura tiene dos capas : raw/ — Tus fuentes crudas. Inmutables. El LLM solo lee. wiki/ — Conocimiento compilado. El LLM lo escribe y mantiene todo. Tú lo lees. Suena perfecto, ¿no? El LLM hace todo el trabajo pesado. Pero hay un punto ciego. El GAP: el conocimiento refinado a mano no estaba protegido El patrón de Karpathy asume que tú nunca escribes en la wiki — el LLM la mantiene por completo. En la práctica, eso no se sostiene. Eventualmente quieres intervenir: corregir una síntesis, afinar una conclusión, documentar algo que aprendiste por experiencia y que ninguna fuente captura. ¿Y qué pasa entonces? Los sistemas de conocimiento con IA tienen un punto ciego: no distinguen entre conocimiento generado automáticamente y conocimiento refina

So I've been using OpenCode for a while now, and it's pretty cool. It's clean, minimal, effective, and not hacking other companies with rogue AI bots 😅. But there is one thing that I dislike about all of these AI tools besides people using them wrong: it's all 1 prompt, 1 agent at a time. Even with these new crazy models such as Kimi K3, Claude Fable 5, GPT Sol, DeepSeek V4 Pro, and the list goes on, having reliable workflows/pipelines is the best way to use AI effectively. Even these models that seem to be the "best" have pretty major flaws. Whether it is hardly speaking in an understandable way or just lying to your face, AI can be pretty annoying. I mean, they literally have "peak hours" and then "dumb hours" depending on the time zone. All of these are reasons why I just built an open-sourced project to fix this. A little while ago, I discovered node-based workflows. Like I said earlier, using one agent one prompt at a time felt super unproductive, so I was inspired to fork OpenCode's harness and create my own twist on it. It still follows the concept of BYOK keys and using any provider you want, but instead of simply prompting, you build a workflow that you can easily save to reuse over and over again. How it works is you create a card for an agent, specify their role (planner, architect, coder, etc), and connect them to another agent or a chain of agents. Now it's not just Opus 5 doing everything, but every agent having a designated role and working together. You can make it as simple or complex as you want, and fork it so that it fits your needs. That's all I have to say. I am still working on it and constantly improving it. Feel free to fork it and make it your own as well, and I hope that this tool levels up how you use AI. Link: https://github.com/SeeRay11/OpenFlow

Drift score for any GitHub repo Discussion | Link

The AI port manager for Mac. Never run lsof again. Discussion | Link

Conteúdo 1. Apresentação - Omarchy 2. Praticidade 3. Agentes de IA 4. Desuso do Mouse 5....

Extending the Login Session to 1 Year for Kiosk‑Mode TV Screens (Next.js API Route) TL;DR: I changed the maxAge of the auth cookie from 30 days to 365 days in src/app/api/login/route.ts . The tweak lets a TV kiosk stay logged in without a daily refresh, while keeping the same security flags. The Problem Our kiosk‑mode deployment runs on large‑format TVs that display a live dashboard. The UI is protected by the same JWT‑based authentication we use for the web app. After a user logs in, the server sets a Set-Cookie header with the token: cookie : serialize ( " token " , jwt , { httpOnly : true , secure : true , sameSite : " lax " , path : " / " , maxAge : 60 * 60 * 24 * 30 , // 30 days }); In practice, the TVs are turned on once a week and are expected to stay signed in for months. After 30 days the cookie expires, the dashboard silently redirects to the login page, and a technician has to manually re‑authenticate the device. The symptom was a 401 Unauthorized error after exactly 30 days, logged as: Error: No valid session cookie found (maxAge expired) The root cause: the maxAge value was hard‑coded to 30 days, which is fine for browsers but not for unattended kiosks. What I Tried First My initial thought was to keep the 30‑day limit and simply refresh the token on every API call . I added a middleware that called the login endpoint silently if a request lacked a valid token. The flow looked like this: // pseudo‑middleware if ( ! req . cookies . token ) { await fetch ( " /api/login " , { method : " POST " , body : storedCredentials }); } What went wrong? Rate limiting – The middleware hit the login endpoint on every request that missed a token, quickly exhausting the auth provider's rate limit. State leakage – Storing credentials on the client (even in a server‑side environment) introduced a security surface. Complexity – The extra round‑trip added latency and made the code harder to debug. After a few failed attempts (and a stack trace full of 429 Too Many Requests )

Technical Reconstruction of Toxic Work Culture Mechanisms Mechanisms and Their Impact Chains The evolution of workplace dynamics in large organizations has been marked by a pronounced shift from collaborative ecosystems to competitive arenas. This transformation is driven by specific mechanisms that, while often implemented with the intent to enhance productivity, inadvertently foster toxicity. Below, we dissect these mechanisms, their internal processes, and the observable effects they precipitate, elucidating the causal pathways that undermine organizational health. Performance Evaluation Systems Impact : Stackranking, hidden metrics, and PR/LoC counts incentivize individual competition, distorting the focus from collective goals to personal advancement. Internal Process : Employees, driven by survival instincts, prioritize outperforming peers over collaborative problem-solving, eroding the foundation of trust. Observable Effect : The erosion of trust and reduced team synergy directly correlate with diminished project outcomes and increased interpersonal conflicts. PIP Processes Impact : When PIP (Performance Improvement Plan) systems are weaponized punitively rather than utilized developmentally, they become tools of fear rather than growth. Internal Process : Employees perceive PIPs as existential threats, triggering stress responses that manifest as defensiveness and reduced engagement. Observable Effect : The resultant demoralization accelerates turnover rates, exacerbating recruitment costs and depleting institutional knowledge. Time Tracking Systems Impact : An overemphasis on quantifiable hours worked relegates quality and innovation to secondary priorities, fostering a culture of compliance over excellence. Internal Process : Employees, incentivized by logged hours, allocate effort disproportionately to tasks that maximize visibility rather than impact. Observable Effect : Despite inflated time logs, overall productivity declines as creative and strategic

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After two years of testing, these are the electric toothbrushes that impressed WIRED staffers the most.

I expected the audit to find broken code. That's what I was bracing for going in — a pile of half-working features, sloppy logic, the kind of mess you'd assume from software built at maximum speed with no human reviewing every line. That's not what I found. Almost everything Claude built actually worked, taken piece by piece. What I found instead was something I didn't expect at all: the agent had made the exact same mistakes I've watched human startup teams make, over and over, when they move fast and nobody's job is to say no. That's the real story here, and it's more interesting than "AI wrote bad code" would have been. The experiment The project is called GetPricePulse — a SaaS pricing intelligence product. It's Claude's entry from The $100 AI Startup Race , the season-long challenge I run where seven AI agents each get $100 and full autonomy to build a real startup from scratch, with no human coding and no product manager in the loop. Each agent picked its own idea and ran with it. Claude picked SaaS pricing intelligence, named it PricePulse, and kept building on it for the entire race. That "no product manager in the loop" part is the thing that made this interesting to watch. Nobody was deciding what PricePulse should be. Nobody was saying "we have enough pricing tiers now" or "this feature doesn't belong here." Claude got to build exactly what its own priorities told it to build, at whatever speed it chose, for the length of the race — optimizing, as far as I could tell from the commit history, for speed, feature creation, shipping, and monetization experiments. Not correctness. Not coherence. Not "does this still make sense in three weeks." I've written before about what all seven agents in this race said, independently, when I asked them what AI agents still can't do — they converged on the same answer without seeing each other's responses. This piece is narrower: a full production audit of Claude's specific build, PricePulse, done after the race, before I

Your personal AGI on your PC. Built to finish real work. Discussion | Link