标签：#Product

Privacy-First Personal Finance for iOS Your finances. On your phone. Nowhere else. A privacy-first personal finance app that connects your banks, brokerage, and investment accounts into one unified dashboard — entirely on-device, no backend, no account required, no subscription. View on GitHub At a Glance 🏦 Multi-source 🔒 100% On-device 📊 Full picture Banks, Revolut, Trading 212 and more No server. No account. Your data stays in your Keychain. Net worth, spending, investments — all in one place Screenshots Log Insights Budget Transaction Entry Settings About Escudo Built out of two frustrations: every decent finance app costs a monthly subscription, and none of them support Trading 212. Escudo connects your banks, brokerage, and investment accounts and gives you a single view of your net worth, spending, and investments — without your data ever leaving your phone. Key Features Net worth dashboard — aggregated balance across all accounts and investment portfolio P&L Unified transaction log — every account in one feed, auto-categorised Spending insights — breakdown by category and trends over time Budget tracking — per-category budgets with visual progress dials Multi-currency — EUR, GBP, USD with stored exchange rates Recurring transactions — template-based recurring transaction engine Shortcuts support — deep linking via escudo:// URL scheme Integrations Source Method Trading 212 REST API — portfolio, orders, dividends Revolut Enable Banking OAuth 2.0 Bankinter PT Enable Banking OAuth 2.0 SIBS SIBS Open Banking (PT market) CSV import Revolut & Bankinter statements All credentials live in the iOS Keychain — never in UserDefaults, never in iCloud, never on a server. Known Limitations Enable Banking does not expose credit card accounts — only bank accounts and transactions are available through the PSD2 API; credit card balances and transactions are not accessible No token auto-refresh for Enable Banking — manual re-auth when tokens expire Categorisation rules are hard

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Hi everybody. We have created this app around two months and this is third version with fixed bugs. Now it is amazing app that synchronization your tasks throughs iPhone iPad Mac via iCloud with no Sign In! And in pocket you will already have a useful notes. It was a huge code work. Hours and hours. Such a pleasure. What I want to note here for you fellas, we were going from these scheme: What's really happening The self-overwrite loop User edits subtask title ↓ onChange fires → scheduleSave() → debounce 1s ↓ ...debounce fires → DataManager.save() writes todos.json to iCloud ↓ NSMetadataQuery detects file change on disk ↓ ↑ └── todosChanged() ───────┘ ↓ taskManager.loadTodos() ↓ self.todos = loadedTodos ← 💥 replaces entire array mid-edit The core problem is that NSMetadataQuery watches the file at the OS level. It has no concept of who made the change — your own app writing the file looks identical to another device syncing a change over iCloud. So every save you make triggers a reload that cancels whatever the user is currently doing. Why subtask titles are worst affected A TextField bound to $subtask.title is live — it reflects the array value character by character. The moment self.todos = loadedTodos runs, SwiftUI throws away the in-memory array and rebuilds from the freshly decoded JSON. If the save hasn't happened yet (debounce still counting down), the loaded file has the old title, and the field visually snaps back. Progress sliders have the same issue but it's less noticeable because a slider value is a Double — the snap-back is a jump rather than disappearing characters. Why it only shows up on real devices The simulator runs everything on the same Mac so iCloud writes are near-instant and the race window is tiny. On a real device the file system is slower and iCloud sync adds latency, making the timing gap between "user is editing" and "reload fires" much more visible. The three fixes needed Fix 1 — Ignore self-triggered reloads in DataManager Track a isS

LinkedIn inbox and feeds without the chaos. Discussion | Link

Microsoft’s OpenClaw-style agent appears in Teams, just like a human colleague, and automates your dull office tasks.

At Microsoft Build 2026, GitHub introduced new tools, updates, and surfaces so agents can work the way you already work. The post GitHub Copilot app: The agent-native desktop experience appeared first on The GitHub Blog .

Native Android AI chat with chats stored locally Discussion | Link

In the social event planner’s first major move toward monetization, Partiful is getting ticketing directly in the app.

Tenho aproveitado meu tempo sem trabalhar pra estudar, enfim a vida de quem trabalha com tecnologia né? E um dos meus maiores focos tem sido IA, seus usos, como ela entra e pode ser aplicada em áreas diferentes, e todas as novidades que saem todos os dias. Hoje vim compartilhar uma coisa bem legal que aprendi no curso AI-Native Engineering Foundations do Addy Osmani , o problema dos 70%. Existe um padrão claro que tenho observado na prática ao acompanhar dezenas de equipes de engenharia: a Inteligência Artificial resolve com impressionante eficiência 70% de quase qualquer tarefa técnica. Falo daquela camada previsível, repetitiva e baseada em padrões exaustivamente documentados na internet. Coisas como código boilerplate, arquivos de configuração, implementações de CRUDs simples, conversão de sintaxe entre linguagens e a escrita de testes unitários básicos. A IA já "viu" milhões de exemplos disso em repositórios públicos e consegue reproduzir o padrão em segundos. Para essa fatia do trabalho, ela é uma aceleradora fantástica. O grande problema, e o motivo pelo qual muitos projetos com IA começam bem mas falham no meio, é que os outros 30% são justamente os que sustentam o software. É nesses 30% que entram as decisões que inteligência nenhuma consegue tomar sozinha: Contexto de Negócio: A IA não sabe por que aquela feature está sendo construída ou como ela impacta o usuário final. Arquitetura e Manutenibilidade: Escrever código que funciona hoje é fácil; escrever código que outra pessoa consegue alterar daqui a seis meses sem quebrar o sistema é outra história. Casos de Borda e Segurança: A IA tende a gerar o "caminho feliz". Tratar falhas de concorrência, vazamento de memória e vulnerabilidades específicas do seu ecossistema exige malícia técnica. Essas questões não se resolvem apenas digitando linhas de código, elas exigem contexto, experiência, histórico de dores passadas e, acima de tudo, julgamento humano. E é exatamente aqui que a IA ainda não entrega. O Parado

Using threat modelling to make system dependability observable, testable, and actionable Executive summary Security and Reliability address system degradation. Security addresses degradation from intentional actions, such as denial-of-service attacks, while reliability addresses degradation from failure, load, dependency behaviour, operational change, or complexity. The underlying engineering question is the same: which critical system property can degrade, how would users experience that degradation, how would we detect it, and what controls would prevent, contain, or recover from it? This document proposes a practical way to bridge the two disciplines: anchor analysis on Critical User Journeys, express expected behaviour through SLOs and SLIs, use RAMSS to ensure coverage across dependability dimensions, and adapt PASTA-style threat modelling to reliability scenarios. The goal is not to merge Security and Reliability into one generic practice. The goal is to reuse the strongest habits of each discipline: security's adversarial modelling and reliability's production-oriented measurement, validation, and recovery loops. The most useful outcome is a shared model of degradation scenarios. A degradation scenario links a critical user journey to a concrete reliability or security threat, the system weakness that makes it possible, the signal that would detect it, the objective it would violate, and the mitigation or experiment that would validate the control. This makes risk easier to discuss with engineering teams because it connects abstract concerns to user impact, SLO burn, business loss, and testable remediation. 1. The problem: two disciplines, one degradation model After working in both Reliability and Security, I found that the two domains share much in common: both focus on objectives, weaknesses, control effectiveness, incident response, prioritisation, and residual risk, but often use different rituals, terminology, metrics, and boundaries. This separation ca

Voice agent for MacOS Discussion | Link

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Review AI edits like stacked PRs at generation time. Discussion | Link

Every inventory tool says real-time. Every single one. Open the settings. Find the sync frequency configuration. It says 15 minutes. Or 10. Or 30 on the cheaper plan. That's not real-time. That's a cron job. There's a meaningful architectural difference and the industry has collectively decided to pretend there isn't. I want to make the technical case for why this matters — and ask why so few tools have actually fixed it. What "real-time" actually means technically Real-time in distributed systems has a specific meaning. It means the system responds to events within a bounded, predictable latency — not on a schedule. javascript// This is NOT real-time — this is scheduled // Latency: up to 15 minutes (the full interval) setInterval(async () => { const stock = await getSourceOfTruth(); await syncToAllChannels(stock); }, 15 * 60 * 1000); // This IS real-time — event-driven // Latency: network round-trip (~milliseconds) orderEventBus.on('order.confirmed', async (event) => { const updated = await decrementStock(event.sku, event.qty); await propagateToAllChannels(updated); }); The first example responds to state changes on a schedule. The second responds to events as they happen. These are fundamentally different architectures with fundamentally different latency guarantees. Calling the first one "real-time" is technically incorrect. It's scheduled sync. The schedule is just short enough that most users don't notice — until they do. When users notice The failure mode is predictable and well documented: javascript// Flash sale scenario — 10x normal velocity const normalOrdersPerWindow = 500 / ((24 * 60) / 15); // ~5.2 const flashSaleOrdersPerWindow = normalOrdersPerWindow * 10; // ~52 // 52 orders processed against potentially stale stock // per 15-minute window // across multiple channels simultaneously // none of which know what the others have sold 52 orders per window. At 2% oversell rate — just over 1 oversell per window. Across 96 windows per day — nearly 100 oversel

I’ve rounded up the best smart speakers that let you talk to Alexa, from the popular Echo Dot to the newest Echo Studio.

Session reports for Claude Code & Codex to improve your code Discussion | Link

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