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Jotai v2.20.0 has been released, focusing on improved performance for high-throughput scenarios. Key changes involve adjustments to internal building blocks and addressing previous performance regressions. While the API for everyday use remains unchanged, library authors should note some deprecations as the groundwork for Jotai v3 begins. By Daniel Curtis

Live Project: tv.cadnative.com What Is FireViston TV? FireViston TV is a full-stack streaming solution designed for the modern living room. It consists of two parts that work in tight coordination: FireViston TV Android App — a native Android/Android TV application that delivers a polished, remote-friendly viewing experience. https://github.com/akshaynikhare/FireVisionIPTV/releases FireViston TV Server — the backend streaming infrastructure that powers content delivery, user management, and playback control hosted at tv.cadnative.com Together, they form a complete, self-contained streaming platform. The Android App Built for the 10-Foot Experience The FireViston Android app is designed for television screens — large fonts, d-pad navigation, and a layout that works from across the room. No pinching, no scrolling hunts, no mobile-style UI crammed onto a 55-inch display. Smooth Playback Video streaming requires more than just playing a file. FireViston handles adaptive bitrate streaming, buffer management, and codec compatibility to ensure smooth playback across the broadest range of Android TV devices — from budget sticks to high-end smart TVs. Content Discovery A clean, browsable content grid lets users find what they want quickly. Categories, search, and a "continue watching" row reduce friction from intent to playback. The Server Backend Reliable Infrastructure The FireViston server at tv.cadnative.com manages content ingestion, transcoding pipelines, and delivery. It's built to handle concurrent streams without degrading quality for any individual viewer. API-Driven Architecture The Android app communicates with the server through a REST API, making the backend flexible enough to support additional clients — web players, other mobile platforms — without rewriting core logic. User & Session Management Account creation, authentication, playback progress sync, and device management all happen server-side. Users can pick up on any device exactly where they left off. W

Six failed CI runs. Thirty-six hours of GitHub Actions time. Every run timing out at exactly the 6-hour limit. The culprit was one line in tests/setup.js . The Setup We were building a multi-tenant platform with a PostgreSQL backend — around 76 database models handling everything from user accounts and billing to visitor logs and real-time notifications. The test suite had grown to roughly 1,140 test cases across 36 files. Standard stuff. CI ran on every PR. Tests passed locally. And then one day, CI just... never finished. The Anti-Pattern Here's what the test setup looked like: // tests/setup.js beforeEach ( async () => { const tableNames = await getTableNames (); // 76 tables await sequelize . query ( `TRUNCATE TABLE ${ tableNames . join ( ' , ' )} CASCADE;` ); }); The intent was clean isolation — every test starts with a blank slate. Reasonable in theory. Catastrophic in practice. The Math Do the multiplication: 76 tables × 1,140 tests = 86,640 TRUNCATE operations Each TRUNCATE TABLE ... CASCADE is not a cheap operation. PostgreSQL has to: Acquire exclusive locks on all referenced tables Walk the foreign key graph to find dependent tables Truncate each in dependency order Release locks With a moderately complex schema where most tables reference others (users → societies → members → invoices → payments → ...), a single TRUNCATE ... CASCADE on a central table can fan out into dozens of implicit truncations. Multiply that by 86,640 and you have a test suite that will never complete within any reasonable timeout. Why It Wasn't Caught Sooner Two reasons: 1. It used to be fast. When the suite had 50 tests and 20 tables, this pattern worked fine. 50 × 20 = 1,000 truncations — uncomfortable but survivable. Nobody noticed when the suite crossed a tipping point. 2. Local runs used a different database state. Locally, developers often ran a subset of tests with --grep or file-specific runs. The full suite was only ever run on CI, and CI was slow enough that most assumed i

When you're building a multi-tenant SaaS, the first architectural question is usually: how do you keep tenant data isolated? The options range from separate databases per tenant (maximum isolation, maximum cost) to a shared database with row-level filtering (minimum cost, more careful coding required). But there's an equally important question that gets less attention: how does your API know which tenant context a request belongs to? This post covers a pattern we've used in production: a custom request header for tenant scoping, combined with JWT authentication. Simple to implement, easy to audit, and flexible enough to support multi-tenant access from a single user account. The Three Common Approaches 1. Subdomain-based ( tenant.yourdomain.com ) The tenant is encoded in the hostname. Each subdomain routes to the same backend, which extracts the tenant from the Host header. Good: Intuitive, visible in the URL. Bad: Requires wildcard TLS certs, more complex DNS setup, awkward in development, doesn't work for mobile API clients the same way. 2. URL path-based ( /api/tenants/{tenantId}/... ) The tenant identifier is part of every route path. Good: RESTful, self-documenting. Bad: Bloats all route definitions, requires every endpoint to include the tenant segment, makes API versioning messier. 3. Header-based ( x-tenant-id: <id> ) A custom header carries the tenant context. Routes stay clean. The tenant scope is resolved in middleware before the handler runs. Good: Routes stay simple, middleware handles scoping uniformly, works well with JWT auth, easy to test. Bad: Less visible (the tenant isn't in the URL), requires clients to always include the header. We use the header approach. The Implementation The API accepts two forms of auth: A JWT token in the Authorization header — identifies who is making the request A tenant ID in the x-tenant-id header — identifies on behalf of which tenant POST /api/v1/members Authorization: Bearer eyJhbGciOiJIUzI1NiIs... x-tenant-id: ten

The bug report was simple: long-pressing the d-pad center button on a channel card should toggle the favorite — it wasn't working reliably. On some devices it fired once and stopped. On others it didn't fire at all on long-press. The fix was changing onKeyEvent to onPreviewKeyEvent in one Composable. The reason why is worth understanding. Background: Two Event Handlers in Compose Jetpack Compose exposes two modifier-level hooks for key input: Modifier . onKeyEvent { keyEvent -> .. . } Modifier . onPreviewKeyEvent { keyEvent -> .. . } They sound equivalent. They're not. The difference is where they sit in the event propagation chain . How Android TV Routes D-Pad Events When a user presses a key on a TV remote, Android routes the event through a dispatch tree: Activity └─ ViewGroup (root) └─ FocusedComposable └─ Child Composables The event travels down first (capture phase), then up (bubble phase): Capture (top → focused node): onPreviewKeyEvent handlers fire here, outermost first. Bubble (focused node → top): onKeyEvent handlers fire here, innermost first. onPreviewKeyEvent is the capture phase. onKeyEvent is the bubble phase. Why This Matters for Long-Press Android TV handles long-press recognition at the framework level. When you hold the d-pad center button: A KeyEvent.ACTION_DOWN fires immediately. If the key is held, the framework generates repeated ACTION_DOWN events at the key repeat rate. ACTION_UP fires when the button is released. The long-press callback that Compose's focus system uses for "confirm" actions (select, activate) consumes ACTION_DOWN during the bubble phase — specifically to prevent the holding action from also triggering the tap action. When the ChannelCard had a click handler wired for the primary action and onKeyEvent for the long-press toggle, the click handler's bubble-phase consumption of ACTION_DOWN was racing with the long-press handler. On some devices the click handler won, swallowing the event before the long-press code ran. The Fix

Most teams treat "monorepo vs multi-repo" as a binary choice. Pick one, commit, move on. We ended up with a hybrid, and it turned out to be the right call — not out of indecision, but because our apps have genuinely different deployment and ownership characteristics. Here's what we built, why, and what it costs. The System The platform consists of six applications: App Type Primary Users REST API backend Node.js + TypeScript — (consumed by all apps) Society dashboard React web app Society managers, admins, accountants Company admin panel React web app Internal operations Marketing website Next.js Public Resident mobile app React Native (Expo) Residents Guard mobile app React Native (Expo) Security personnel All six apps talk to the same API. But they have very different deployment cycles, team ownership, and testing requirements. The Structure: 4 Repositories repo: main-platform (monorepo) ├── api/ — Express + Prisma backend ├── web-society/ — Society dashboard ├── web-admin/ — Company admin panel └── web-marketing/ — Marketing site repo: mobile-resident — Resident app (React Native) repo: mobile-guard — Guard app (React Native) repo: mobile-staff — Society staff mobile app (React Native) The web apps and the API live together in one monorepo. The three mobile apps each have their own repository. Why Split Mobile From Web? The driving factor was deployment cadence and review process . Web apps deploy on push — merge to main, CI builds, CDN updated within minutes. The feedback loop is fast, rollbacks are instant, and there's no approval gate between code and production. Mobile apps go through app store review. A release cycle includes building a release APK, submitting to Google Play (and Apple App Store), waiting for review, and then a staged rollout. The cadence is measured in days, not minutes. Mistakes are expensive to reverse — a bad release means submitting a patch, waiting again, and potentially having a broken version live for days. Given that difference, mob

I Built a Manga Reader That Works on Every Platform — Here's How Nyora is a free, open-source manga/manhwa/manhua reader for Android, iOS, macOS, Windows, Linux, Web, and even Docker — with AI-powered on-device translation and cross-platform sync. The Problem Every manga reader makes you choose: Free but ad-riddled (most Android readers) Polished but paywalled (commercial apps) Powerful but single-platform (Tachiyomi, Aidoku) I wanted one library — same titles, same progress, same bookmarks — on my phone, laptop, and browser. No ads. No account required. So I built it. What Nyora Does Every Platform, One App Platform Distribution Android APK (sideload) iOS/iPadOS IPA via AltStore/SideStore macOS .dmg or brew install --cask nyora Windows .exe (x64 + ARM64) Linux .deb , .rpm , or curl installer Web web.nyora.xyz — zero install Docker Single container, self-hosted No account needed to read. Cloud sync is opt-in. AI Translation That Understands Manga This is the flagship feature. Instead of dumping translated text over the artwork: Detects text baked into speech bubbles and captions Translates using on-device ML Typesets the result back over the original artwork Each platform uses the best local engine: Android : Google ML Kit + ONNX Runtime iOS : Apple Intelligence + Google Translate macOS : Apple Vision + MangaOCR CoreML Windows : Windows OCR Linux : Tesseract There's also an Ensemble AI Narrative Engine that tracks character names and speaking styles across chapters so translations stay consistent. 1,100+ Sources The Android app pulls from 1,100+ manga sources via 35 generic engine templates (Madara, FoolSlide, MMRCMS, etc.). Web has ~390 live, health-checked sources. Desktop ports are growing toward parity. Free Cloud Sync Sync library, categories, reading history, bookmarks, and exact page progress across all six platforms. Two sign-in methods: Google OAuth Nyora Cloud (email + password, free) Self-hostable — the backend is just Supabase/PostgreSQL with row-level s

A validation failure is not an exception to hide. It is a record your system does not yet know how to trust. That distinction matters in LLM extraction pipelines. A malformed invoice, an unexpected OCR layout, a model response that violates the schema, and a semantically impossible value may all reach the same line of validation code. If the only outcomes are “retry” or “drop,” the pipeline will either waste money repeating the same failure or silently lose work. The production answer is a dead-letter path: a durable place for failed records to wait with enough evidence to explain, triage, and safely replay them. The queue itself is the easy part. The hard part is designing the failure contract around it. Validation is where routing begins Constrained decoding and post-hoc validation solve different problems . Even with both, some records should fail. Real documents are messy, schemas change, OCR corrupts values, and models sometimes return plausible nonsense. A robust validation boundary should produce more than true or false . It should emit a reason the rest of the pipeline can act on: which schema and model versions were used which fields failed and why whether the payload was malformed, incomplete, or semantically invalid the confidence signal attached to the extraction whether a retry is likely to change the result That result becomes a routing decision. High-confidence, valid records can flow forward. Recoverable transport failures can use a bounded retry. Ambiguous or invalid records belong in review or a dead-letter queue. Confidence-based routing is useful precisely because “trust everything” and “review everything” are both bad operating models. A dead-letter record needs evidence, not just payload Putting the original input on another queue is not enough. Without context, the team investigating the failure has to reconstruct the run from scattered logs—if those logs still exist. I would store a dead-letter envelope containing: a stable record ID and idem

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"When you multiply 0588235294117647 by 2, 3, 4, 5, …, 16, the same sequence of digits appears in a cyclic order. That is, the digits remain the same but start from a different position, and when they reach the end, they continue again from the beginning." Would be useful for hash tables? or something else that needs rotations/permutations? looks like a reversible data transformation what if instead of 16 values (4 bits), we get a bigger number for 65536 values (16 bits) ChatGPT says it's related to a particular property of 17 (a prime number), but I want to know the opinion of actual programmers, mathematicians and engineers. Thanks submitted by /u/digital_n01se_ [link] [留言]

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Local-first desktop agent for everyday work Discussion | Link