标签：#build

We're building an AI marketing operation in public, and early on we hit a question we couldn't skip: how aggressive can you be about growth before you've crossed into something you'll regret? "Be ethical" is easy to say and useless under pressure. Every real decision is messier than that. Is using a VPN cheating? Is running more than one channel a trick? Is bending a platform's rules the same as lying? We needed a line we could actually hold at 2am when a shortcut looks tempting. Here's the one we found — and it turned out to be simpler and sturdier than "follow all the rules." The line isn't rule-breaking. It's deception. The cleanest test we landed on: the line is deception, not rule-breaking. Breaking a rule is a fight you can have in the open. You can announce it, defend it, and accept what comes. Deception is different — it works by making someone believe something false, which strips away their ability to respond honestly, because they don't even know what's real. That's the move that does the damage. So the question to ask about any tactic isn't "did this break a rule?" It's: "does this work by causing a real person to believe something that isn't true?" If yes, that's the line. If no, you're probably fine even if you're being bold. The daylight test Here's how to apply it fast. Ask: would this tactic still work if everyone could see exactly what I was doing? If yes — it survives daylight. People are choosing freely with full information. That's honest, even when it's aggressive. If it only works in the dark — the concealment itself has become the product. Something only works hidden because someone is acting on a false belief you planted. That's the part to cut. A poker bluff survives daylight (everyone knows bluffing is part of poker). A magician's trick survives daylight (the audience knows it's a trick and enjoys it). A fake testimonial does not. A sock-puppet account vouching for you does not. Run every growth idea through the daylight test and most hard

"Space Launch Complex-6 represents six decades of American innovation."

In 2020, I discovered the idea that people could make money online by building things. Since then, I've tried almost everything. I started websites. I learned design. I learned marketing. I built digital products. I launched projects that nobody used. I launched projects that got almost no traffic. Every year I thought: "Maybe this is the year it finally works." But somehow I always ended up back at zero. The frustrating part is that I didn't quit. For 5 years I've been consistently learning new skills: Graphic design Website building Digital products Content marketing SEO Social media Yet I still haven't reached the point where I can say: "Yes, this business is working." Recently I spent weeks building a library of 500+ Notion templates. I launched it. The result? Almost nothing. No viral launch. No overnight success. Just another reminder that building is easier than distribution. That's the lesson that keeps hitting me: Building isn't my problem anymore. Getting attention is. I can create products. I can design landing pages. I can write content. But distribution still feels like a puzzle I'm trying to solve. So I'm asking developers, founders, and creators who are further ahead: If you were starting again today with no audience and no reputation, what would you focus on? Would you: Double down on content? Build more products? Focus entirely on one distribution channel? Spend more time networking? I'm genuinely curious because after 5 years of trying different things, I'm convinced the answer isn't "work harder." It's probably "work differently." I'd love to hear your advice.

I wanted to see how far an autonomous coding agent could get unattended. The constraint that makes this hard isn't code generation — it's that each session starts with zero memory of the last. So the design problem is state, not prompting. Setup A cron-scheduled task fires hourly, 11pm–7am (~8 runs). Each run is a fresh agent session. No shared context, no carryover. State lives entirely on disk: the working tree, the git history, and two files — BUILD_SPEC.md (the immutable goal/architecture) and PROGRESS.md (an append-only decision + status log). The run loop Every session does the same thing: Read BUILD_SPEC.md, then PROGRESS.md, then git log --oneline. Reconstruct "where are we" from the files themselves (the code is the source of truth, not the narrative). Do one unit of work. Commit. Append to PROGRESS.md: what changed, why (chosen X over Y because Z), and the exact next step. Commit granularity = checkpoint granularity. Worst case on an interrupted session is losing one unit, and the next run re-derives it. The "why" lines matter as much as the diffs — without them a later session re-litigates settled decisions. Guardrails The agent was allowed to build, test, and commit locally. It was explicitly not allowed to deploy, push to a remote, or touch secrets — those get written into PROGRESS.md as "needs human" items instead. This boundary is what makes unattended runs safe to leave alone. What came out A working full-stack monorepo: a pure TS scheduling engine (with property tests), multi-tenant auth, a Drizzle/Postgres schema, server-side re-validation, and publish/share/export flows. Across the runs it cleared its own stale git lock, and one session caught and fixed an off-by-one in a labeling layer that spanned five files. The takeaway The leverage wasn't the model writing code. It was designing a process where progress is durable across total context loss — externalize state, checkpoint constantly, log decisions not just actions, and fence off irreversible o

xAI shipped the Grok Build Agent Dashboard on June 15, 2026, and it changes how multi-session coding actually works. Eight parallel agents — four on Grok Code 1 Fast, four on Grok 4 Fast — all visible on one screen. Sessions sorted by state automatically. Sub-agents rolled up under the session that launched them. Reply to a blocked session without ever leaving the dashboard view. If you are already running Grok Build (launched June 5, 2026 in beta), this is a meaningful upgrade. If you are evaluating coding agents and parallel execution is part of your decision criteria, the Agent Dashboard is the most developed TUI for multi-session work in any terminal coding agent right now. Here is exactly what it does and when to use it. How to Open It Two ways in. From your shell: grok dashboard Or from inside any Grok Build session: /dashboard The keyboard shortcut Ctrl+ also opens the dashboard from any active session. Closing the dashboard does not close your sessions — they keep running. When you reopen it, every session is still there in whatever state it was in when you left. That last point matters. The dashboard is a view, not a session manager. Sessions have independent lifetimes. You can close the dashboard, switch to a different terminal, do other work, and come back to a batch of completed or paused sessions waiting for your attention. Session States and the Sorting Logic Every session in the dashboard shows one of three states: working , awaiting input , or idle . The dashboard sorts them automatically, with sessions waiting for your input at the top. Working sessions come next. Idle sessions sit at the bottom. The practical result: open the dashboard and the first thing you see is your blocker queue. Sessions that need you are at the top. Everything else is running or done. You do not have to mentally track what state each terminal is in — the sort does that for you. Selecting any row shows the session's latest output inline, without opening the full conversation

After posting several articles about the impact of AI on developers and sharing resources to help...

Two weeks ago Visa invested in Replit. Not for code collaboration. For agentic payments. TechCrunch reported it on May 28: Visa put money into Replit specifically to "power agentic payments for developers." Over 1,000 Visa employees already use Replit for prototyping. Now they're building the pipes for autonomous agents to spend money. Here's why this matters: Visa doesn't make bets on developer tools. They make bets on payment volume. When they invest in agentic payment infrastructure, they're not guessing — they see the transaction data. And the data says autonomous agents are about to move real money. The question for developers: when your agent needs to pay for an API, a cloud instance, or another agent's service, what tooling stack do you actually use? The Stack Nobody Agrees On Right now there's no standard agent payment stack. But a pattern is emerging across the open-source projects shipping on HN: Layer 1: The Authorization Wrapper Before your agent touches money, something needs to say yes or no. Three approaches are competing: Budget Caps — Set a dollar limit per agent, per day, per category. Tools like AgentBudget and RunCycles enforce limits before execution. Simple, but brittle — what happens when your agent hits the cap mid-task? Policy Layers — Define rules: "Agent A can spend up to $50/day on OpenAI, $200/month on AWS, nothing on ad platforms." Tools like Ledge and PaySentry ship policy engines that evaluate every transaction against a rule set. More flexible than caps, but policy management becomes its own problem at scale. Spending Mandates — The agent gets a formal spending authorization with scope, duration, and approver. Nornr takes this approach: before the agent can spend, a human signs off on a mandate document. Most audit-friendly, least autonomous. Layer 2: The Payment Rail Once authorized, the agent needs to actually move money. The options: Rail Best For Limitation Stripe Agent SDK Subscription SaaS, metered APIs Requires merchant accoun

The thing I'm building, App Store Analyzer, is a website that does one thing: it reads an iOS niche and writes a deep market analysis for indie devs. For a long time that analysis had one brain — and it spoke German. That made sense at the start. German is my home market and my own language, so I built the analysis logic in German first. I could actually feel whether the output was good or garbage, section by section, because I was reading it in the language I think in. It got deep. Reliable. I trusted it. Then it started to hurt. Every time I wanted the analysis in another language, I was basically running the whole expensive thinking step again from scratch. German code, German slugs, German routes, German everything — and a goal of serving 14 languages. The whole thing fought itself. So I rebuilt the brain in English. Not "translated the code" — rebuilt the canonical brain so English is the one source of truth. Now the engine thinks once in native English, and that single analysis gets translated and cached into 13 other languages . Generate once, translate many. It was not a clean ride. The lows. A refactor left a pile of undefined names and quietly 500'd my detail pages — live, in production, while I thought everything was fine. I misread a normal cache warm-up window as a dead backend more than once and "fixed" things that were never broken. I spent an embarrassing stretch hammering an endpoint with a wrong key, watching 403 scroll by, before realizing my terminal had eaten the line that set the key. Small things. Hours each. The highs. Two of them I didn't expect: It got cheaper , not just cleaner. I'm not paying for a full deep analysis per language anymore — one real generation, then lightweight translations. For a solo dev watching every API cent, that's the whole game. And the English brain was actually sharper . I ran the old German output against the new English one side by side, fully expecting English to be the weaker copy. It wasn't. In a few section

I've been working on CompanioxVPS, a VPS and cloud infrastructure platform that aims to make deploying and scaling applications much simpler for developers. Right now, I'm building core features such as: VPS provisioning Load balancing Autoscaling Custom domains Developer APIs Simple billing and infrastructure management Before going too far down the road, I'd love to hear from developers who actively ship products: What do you dislike most about current VPS or cloud providers? What features do you wish existed? What would make you switch to a new platform? What's one thing that would make your deployment workflow significantly easier? I'm also open to connecting with developers, DevOps engineers, and infrastructure enthusiasts who find this space interesting and would like to contribute ideas, provide feedback, or potentially collaborate as the project grows. Still early, still building, and still learning. Every piece of feedback helps shape the direction of CompanioxVPS. Looking forward to hearing your thoughts. 🚀

My detector passed every synthetic test with zero false positives. Then I pointed it at one real trace and found a crack. This is the honest version of where I am. I'm building Clew — a tool that finds the redundant loops, re-queries, and handoffs that silently burn tokens when multiple AI agents work together. No crash, no error, just two agents quietly re-doing each other's work while the token bill climbs. I build in public, and I publish the negatives. So here's the whole arc, including the part that isn't working yet. First, I killed my own hypothesis The original idea wasn't waste detection at all. It was failure prediction: watch the behavior between agents and forecast multi-agent failures before they happen. The differentiator was a single metric built on two signals — structural cycles in the inter-agent message graph, and the decay of novelty in embeddings. Before I ran anything, I pre-registered the success bar: AUC ≥ 0.80. I numbered every change and kept the signal code physically separated from the labels so I couldn't leak my way to a good number. Then I ran it on MAST-Data — UC Berkeley's dataset of 1,600+ real multi-agent traces across 7 frameworks[( Cemri et al., arXiv:2503.13657 )](url) Result: AUC ≈ 0.455. A coin flip. It got worse. The signal correlated with trace length at r ≈ 0.86 — it was mostly measuring how long a trace was, not whether it failed. Correcting for that dropped AUC to 0.42 and reversed the direction: successful traces actually showed more decay (p ≈ 0.013). The honest read: not disproven, but unvalidated. On this implementation, on this data — negative. So I shut it down. And I counted it as a win, because I got a fast, honest answer in weeks instead of building a dashboard on a metric that secretly measures string length. That experiment became the DNA of everything since: design the experiment that's allowed to kill the idea. The pivot: from predicting failure to cutting waste The intuition behind v1 — that you need structu

Há alguns meses, pagando $7/mês por um servidor de 512MB no Render pra hospedar uma API de um projeto da escola, decidi entender como esse tipo de infraestrutura funciona por baixo — e construir a minha própria versão. O resultado é o Arctis Deploy : uma plataforma de deploy contínuo via Git, com Docker isolado por projeto. Esse post é sobre como ela funciona por dentro. Arquitetura geral Frontend (Next.js) │ ▼ Backend (Go + Fiber) — Clean Architecture │ ├──► Deploy-Agent (roda em cada servidor) → Docker └──► Database-Agent (provisiona MySQL/Postgres) em desenvolvimento, ainda não disponível para usuários Cada servidor de produção roda um deploy-agent próprio. O backend central envia comandos via HTTPs autenticado e o agent executa o que for necessário — clone, build, container, métricas. O pipeline de deploy Todo deploy passa por 5 etapas sequenciais: clone — git clone --depth 1 , otimizado pra trazer só o necessário analyze — detecta o framework automaticamente lendo package.json , requirements.txt , go.mod , etc. build — gera um Dockerfile multi-stage específico pro framework detectado (timeout: 15min) deploy — sobe o container na porta alocada, com limites de CPU/RAM aplicados health check — faz requisições até o container responder, com rollback automático em caso de falha Cada etapa emite logs estruturados ( info / warn / error ), transmitidos via WebSocket em tempo real pro frontend. Detecção automática de framework Next.js, React, Vue, Node.js, e sites estáticos. A detecção é baseada nos arquivos do repositório — o usuário só conecta o Git e dá push. Outros detalhes Pool de recursos : cada plano define um total de Projetos/CPU/RAM/disco que o usuário distribui livremente entre seus projetos Auto-sleep : no free, containers sem tráfego são pausados ( docker pause ) — não destruídos — e despertam automaticamente na próxima requisição Domínios : subdomínio automático ou domínio próprio, com SSL via Cloudflare Pagamentos : MercadoPago integrado, planos em real V

I spent a weekend wiring Google's Gemini and Veo APIs into a single app just to feel where the edges of multimodal AI actually are. It turned into a small studio I now use daily, and along the way I learned more about these models from plumbing them than from any paper. Here's the honest technical debrief. Three pipelines, three completely different problems I wanted one prompt box that could do video, image editing, and document Q&A. Naively I assumed they'd share most of the stack. They don't. 1. Image-to-video: the enemy is time, not pixels Generating one good frame is solved. Video is about temporal coherence — frame 13 must agree with frame 12 or you get flicker and identity drift. Modern video models treat the clip as one object in space and time (latent diffusion over a width x height x time volume, with spatiotemporal attention) rather than 120 independent images. Conditioning on a reference image as the first frame is what makes image-to-video feel controlled: you've handed the model a strong anchor and asked it to extrapolate motion, not invent a world. The surprise: native audio sync (Veo 3.1 generating clip + soundtrack jointly) does more for perceived realism than another notch of resolution. A door slam landing on the exact frame the door shuts is uncanny in a good way. 2. Instruction-based image editing: preservation is the hard part Generating is unconstrained; editing must change one thing and preserve everything else. Condition the diffusion model on both the instruction and the source image's latents, cross-attend the instruction to steer only the referenced region, and bias hard toward preserving unedited latents. Push that preservation too soft and the subject's face quietly morphs across edits — the classic 'character consistency' failure that makes or breaks storytelling use-cases. 3. PDF chat: it's retrieval, not a long context The naive 'paste the whole PDF' approach dies on long files (models get lost in the middle ) and costs you the full

What device do we use almost all the time? Our mobile phone, almost certainly. If we wanted to...

Today I’m talking with Mustafa Suleyman, the CEO of Microsoft AI. And I’m actually going to keep today’s intro short — I’m working from my wife’s family farm this week, as you’ll see in the video, but also this is a real burner of an episode. We covered everything from Mustafa’s approach to training new […]

I Built a GDPR Compliance Scanner Using the Claude API - Here's How It Works A few months ago I noticed something that kept bugging me. I was building and handing off websites for clients and every single time, GDPR compliance was either an afterthought or a panic right before launch. Privacy policies copied from templates, cookie banners slapped on at the last minute, no one really sure if the contact form was actually compliant. The bigger problem: there was no quick, affordable way to check . Enterprise compliance tools cost hundreds per month. Legal consultants cost more. Most small businesses just crossed their fingers. So I built ClearlyCompliant - an automated GDPR compliance scanner that analyses a website and delivers a detailed PDF report for a one-off fee. No subscription, no jargon, just a clear picture of where a site stands. Here's how it actually works under the hood. The Stack Django (Python) - backend and web app BeautifulSoup + requests - crawling and HTML parsing Python threading - async scanning without the overhead of Celery/Redis Anthropic Claude API (Haiku) - AI-powered policy analysis ReportLab - PDF report generation Stripe - payments IONOS SMTP - email delivery Gunicorn + Nginx on an IONOS VPS The Scanning Pipeline When a user submits a domain and completes payment, the scan kicks off immediately. Rather than making them wait on a loading screen, the scan runs asynchronously in a background thread and the report gets emailed when it's done. I deliberately avoided Celery and Redis here. For the scale I needed, Python's built-in threading module was more than sufficient and kept the infrastructure simple. One less thing to maintain, one less thing to break. import threading def run_scan_async ( domain , order_id , customer_email ): thread = threading . Thread ( target = run_full_scan , args = ( domain , order_id , customer_email ) ) thread . daemon = True thread . start () The scan itself runs 23 individual GDPR checks across several categori

The Problem Wasn't Writing It was starting. Sometimes I wanted: A social media caption A motivational quote A writing prompt A meaningful message But my mind would go completely blank. Not because I had nothing to say. Because: Coming up with the right words at the right moment is surprisingly difficult. We've All Done This Open a new tab. Search: "Motivational quotes" "Success quotes" "Life quotes" "Funny quotes" Scroll for 10 minutes. Copy one. Close the tab. Why I Built This Tool So I built something simple: 👉 https://allinonetools.net/quote-generator-tool/ A tool that instantly generates quotes across different categories. Whether you need: Motivation Success Life Leadership Creativity Social media inspiration You can generate quotes in seconds. No signup. No setup. Just: Click → Generate → Use What I Realized People don't always look for quotes because they need content. Often they're looking for: A different perspective. A good quote can do something interesting. It can say in one sentence what takes us paragraphs to explain. The Surprising Part The most popular quotes are rarely complicated. They're simple. Short. Easy to remember. Yet somehow they stick with us for years. Why Quotes Still Matter In a world full of endless content: Attention is limited Time is limited Patience is limited A strong quote delivers an idea instantly. That's powerful. The Problem With Searching Manually Most quote websites feel: Cluttered Slow Full of ads Hard to browse And sometimes you spend more time searching than actually reading. What I Focused On I wanted the experience to feel: Fast Clean Inspiring Fun to explore Because finding inspiration shouldn't require effort. What Surprised Me After building it: Some people used it for: Social posts Presentations Daily motivation Writing inspiration But one thing surprised me most. People kept generating quote after quote. Not because they needed one. Because they enjoyed discovering them. The Real Insight Sometimes tools aren't abo

Somewhere along the way, computing became disposable. Devices became sealed. Systems became rented. Ownership became licensing. Repairability disappeared. Infrastructure moved away from the user and into distant cloud platforms. And I think we lost something important because of it. Lately, I’ve found myself becoming increasingly interested in hardware again. Not just software. Not just cloud systems. But actual computing devices. Servers. Home infrastructure. Repairable machines. Set-top systems. Local AI appliances. Sovereign computing. Because I believe the next era of computing will belong to people who own their infrastructure again. The Provider Box Realization One thing that kept sticking in my head was this: Almost every home in America already has a provider box. A Comcast box. An AT&T gateway. A router. A modem. A streaming box. People are already comfortable with the idea of a dedicated computing appliance sitting in their home quietly powering their digital life. That realization changed how I thought about computing infrastructure. What if those boxes worked for the user instead of the provider? What if they: hosted local AI, managed home storage, coordinated smart devices, powered media systems, handled automation, protected privacy, synchronized intelligently, and operated as sovereign infrastructure? That idea became part of the thinking behind KiwiHome. The Return Of Home Infrastructure For a long time, the industry moved toward centralization. Everything shifted toward: SaaS, subscriptions, streaming, cloud storage, cloud intelligence, and rented operational environments. Convenient? Absolutely. But also fragile. If: pricing changes, services disappear, companies shut down, APIs get revoked, or platforms change policies, entire workflows collapse overnight. I think people are starting to feel that tension. Especially creators. Especially businesses. Especially technical users. That’s why I believe we’re going to see a major resurgence in: home serv

May 25, 2026. This is not just another date on my calendar. This marks the beginning of one of the biggest professional transitions of my life. After nearly a decade working in the world of IT infrastructure, technical support, networking, field engineering, and systems operations, I’ve made a decision that has been building in my mind for some time: I am transitioning into Data Analytics. And this is where I document that journey—publicly, honestly, and in real time. Not when I become an expert. Not when I feel “ready.” Not when everything looks polished. I’m starting now. Because real growth is rarely clean, predictable, or perfectly planned. Sometimes it starts with one uncomfortable decision: To leave what you already know… and step into what your future requires. Where My Journey Started Before data, before dashboards, before writing my first SQL query or building my first analytics project—my career started in the trenches of IT. For the past 10 years, I’ve built my career solving real technical problems across businesses, organizations, schools, offices, and field operations. My world has been cables, routers, networks, system failures, installations, troubleshooting, and making technology work where others saw complexity. Over the years, I’ve worked deeply in: Computer troubleshooting and hardware diagnostics Printer setup, configuration, and enterprise support Wi-Fi deployment and hotspot installations LAN design and structured network deployment Fiber optic installations and network termination Data cabling and structured cabling systems CCTV surveillance installation and maintenance Alarm systems and electronic security integration Intelligent security systems Electric fence installations and perimeter protection systems Router, switch, and access point configuration End-user support and enterprise technical troubleshooting Systems maintenance and operational support I’ve spent years on ladders, in server rooms, inside offices, on construction sites, insi

There's a pattern I've noticed with every new AI coding tool that comes out: they all want you to switch editors. Or open a new terminal. Or context-switch into some standalone app. I DON'T WANT TO DO THAT My entire dev workflow lives in VS Code. My keybindings, my split panes, my snippets, my extensions — all of it. When Google released the Antigravity CLI ( agy ), an agentic coding assistant, I genuinely liked what it could do. But to use it properly, I had to live in a terminal window, manually managing sessions, typing slash commands from memory, and losing my editor context entirely. So I built a VS Code extension for it instead. What is Antigravity? Google Antigravity is Google's agentic coding CLI — think of it as a Gemini-powered dev assistant that can read your project, run tools, execute terminal commands, and help you build. It's the kind of tool that can handle complex multi-step tasks, not just autocomplete. The CLI is called agy , and it's genuinely capable. The problem was the workflow: terminal-first, session management by hand, and no visual layer over the context you're already in. The Extension: Antigravity for VS Code Install it on the VS Code Marketplace Source on GitHub The core idea is simple: the extension is a UI layer. It never bundles or replaces the agy binary — it shells out to whichever version you have installed locally. Same philosophy as the Claude Code VS Code extension: the editor provides the surface, the CLI does the work. Here's what it actually does: Sessions List The sidebar panel opens to all your saved sessions. You can open an existing one, delete it, or start fresh. New sessions can be launched in sandboxed mode or with permissions bypassed — accessible right from the "New Session" overflow menu, without memorizing CLI flags. Any session with an active turn shows a loading indicator in its row, so you always know what's in flight. Chat Panel (Material 3 Expressive) This is the main surface. Each session runs its own live,

10 Prompt Patterns I Use Every Single Day Last Tuesday I spent 40 minutes arguing with Claude about a database schema before I realized I had never told it what I already tried. I described the problem, it gave me the same three suggestions I had already ruled out, I pushed back, it apologized and gave me variations of the same three suggestions. The entire session was garbage because I started from zero instead of from where I actually was. I closed the tab, rewrote my first message, and had a working solution in six minutes. That gap — between how most people prompt and how it actually works when you treat the model like a collaborator who needs real context — is what this post is about. Pattern 1 & 2: Lead With What You Already Tried, and State the Constraint That Binds You These two patterns are almost always used together, so I won't pretend they're separate. When you describe a problem without the history of your attempts, you are forcing the model to rediscover your dead ends. Every developer knows this frustration: you explain a bug, get back a solution you tried on Monday, explain you tried that, get a variation, explain that too — it's a recursive waste. The fix is brutal honesty upfront: "I need X. I already tried A and B. A failed because [specific reason]. B is off the table because [constraint]. Don't suggest either." The constraint layer is the other half. Models are optimists by default. They will give you the architecturally clean, perfectly testable solution that requires three new dependencies and a refactor of your auth layer. Unless you tell them you are shipping in two days, can't add dependencies, and the code needs to be readable by someone who last touched Python in 2019. Constraints aren't limitations on the answer — they are the answer. Front-load them or you will spend the session rejecting suggestions that are technically correct but situationally useless. Pattern 3 & 4: Output First, Reasoning After — and Diff Only, Not Rewrites Two sid