标签：#state

Eric Wu, who built and ran Opendoor before stepping away in 2022, has had his new company, NavigateAI, out of stealth since May — building AI copilots that give construction workers real-time, hands-free guidance through smartphones and Meta's AI glasses, backed by $25 million from Elad Gil, Khosla Ventures, and Lennar to tackle a labor shortage severe enough that data center projects alone now need 4,000 to 5,000 workers apiece.

The Ubiquitous Infinite Scroll Pagination Bug Almost every Flutter engineer has encountered the dreaded infinite scroll race condition in production. The user opens a list, flings their thumb down the screen on a spotty cellular connection, and triggers multiple scroll notifications past the bottom threshold within milliseconds. Before the first asynchronous HTTP network request finishes, the scroll listener fires again. Suddenly, your list duplicates items, page counters jump ahead, or the state machine locks up entirely. Recently, mobile developer Ali Wajdan published a widely discussed article titled 3 Lines of Dart async* Code That Fixed My Infinite Scroll Pagination . In his article, Ali accurately diagnoses the root cause of standard pagination headaches: "Most Flutter pagination code I have seen, including my own for years, wraps a mutable state object around a scroll listener. A page counter, a loading boolean, a hasMore flag, and a fetch method the UI calls when it hits the scroll threshold. It works until two scroll events fire close together, or a rebuild triggers a second load before the first future resolves... It is a classic race condition, and it gets worse once the state lives across a page counter, a hasMore flag, and a loading flag that all need to stay in sync." To escape this trap, Ali suggested encapsulating pagination logic inside a Dart async* generator and consuming it with a StreamIterator : // The pattern proposed in Ali Wajdan's article Stream < List < Post >> fetchPostsPaginated ( String query ) async * { var page = 0 ; var hasMore = true ; while ( hasMore ) { final batch = await api . fetchPosts ( query , page: page ); hasMore = batch . isNotEmpty ; page ++ ; yield batch ; } } final iterator = StreamIterator ( fetchPostsPaginated ( query )); Future < List < Post >> loadNextPage () async { if ( ! await iterator . moveNext ()) return const []; return iterator . current ; } On the surface, moving mutable state into local generator variable

Most WhatsApp AI projects start with a simple goal: Receive a message → send an AI-generated reply. For real estate, I think that's only the beginning. A useful real-estate AI system should do more than generate text. It should understand the buyer's intent, capture important information, qualify the lead, preserve conversation context, organize that information in a CRM, and know when a human salesperson should take over. That's the system I'm currently building with Vaxyro . The problem: a WhatsApp conversation is not a lead record A typical real-estate enquiry might look like this: "Hi, is the 3 BHK available?" Then: "What's the price?" Then: "Is there anything around 80L in Gurgaon?" Then: "I can visit this weekend." The messages themselves are simple. The difficult part is turning the conversation into structured information that a sales team can actually use. The system should be able to understand something like: Property type: 3 BHK Location: Gurgaon Budget: ₹80 lakh Timeline: This weekend Intent: High Next action: Site visit discussion Instead of leaving all of that information buried inside a WhatsApp conversation. What a WhatsApp AI lead qualification system should do I think the workflow can be broken into six stages: WhatsApp message ↓ Message understanding ↓ Intent detection ↓ Lead qualification ↓ Structured CRM data ↓ Follow-up ↓ Human handoff The important part is that the AI is not only generating a reply. It is also producing structured sales information. That distinction changes the architecture. 1. Message understanding The first step is understanding what the buyer is actually asking. For example: "Looking for a 3 BHK in Gurgaon under 80L" could produce structured information such as: { "property_type": "3 BHK", "location": "Gurgaon", "budget": "8000000", "intent": "property_search" } This gives the rest of the system something useful to work with. The goal is not to perfectly understand every sentence. The goal is to extract the information tha

The Networking Architecture Dilemma in Production Flutter If you survey ten seasoned Flutter developers about how they structure networking in production, you will almost certainly see the same multi-tiered pipeline: ┌────────────────────────────────────────────────────────────────────────┐ │ Traditional Flutter Networking Pipeline │ ├────────────────────────────────────────────────────────────────────────┤ │ [Dio / HTTP Client] ─▶ [API Service] ─▶ [Repository Layer] ─▶ │ │ [Cubit / BLoC] ─▶ [UI Builders & Banners] │ └────────────────────────────────────────────────────────────────────────┘ The underlying architectural principles are sound: separation of concerns, testability, and isolating network transport details from UI widgets. However, in practice, this classical layered stack demands an enormous amount of repetitive boilerplate: Async State Union Ceremony : Defining four separate state classes ( Initial , Loading , Success(data) , Failure(error) ) or union types for every single API endpoint. Race Conditions & In-Flight Cancellation : When users type queries or switch tabs rapidly, requests finish out of order. Preventing stale responses requires complex Dio CancelToken plumbing or heavy rxdart switchMap streams. Offline Caching & "Stale-While-Revalidate" : Showing cached data on Frame 1 while fetching fresh updates in the background usually requires database synchronization and stream merging logic. The Repository vs. Controller Divide : Repositories hold data and caching logic, while BLoCs or Cubits hold reactive state. Because Dart only allows single inheritance, developers end up maintaining two separate class hierarchies connected by verbose dependency injection glue. With bloc_signals , we can preserve complete separation of concerns while eliminating 70% of the friction. Let us examine how to architect a modern, clean, production-ready networking layer using CubitSignalMixin , HydratedMixin , and .toAsyncBlocSignal() . ⚡ 1. Symmetrical Async Projection

Making the Migration from In-View Asynchrony to Synchronous State Management Truly Mechanical After our recent discussions on why FutureBuilder and StreamBuilder are architectural anti-patterns when placed inside Flutter widget trees, I started thinking: how can we make it even easier—even completely mechanical—to convert from a FutureBuilder or StreamBuilder to a BlocSignalBuilder ? Every Flutter developer knows the history. Years ago, I recorded a video breaking down the hidden traps of placing asynchronous builders in UI views: Why you shouldn't put FutureBuilder in your build method . Even the original official Flutter video on FutureBuilder initially instantiated the network future directly inside the build() method, until I filed an issue to get it corrected (which is why the official Flutter YouTube video still proudly bears "Take 2" on its clapperboard!). The fundamental issue has never been that developers want bad architecture. The issue was friction . FutureBuilder was simply the path of least resistance. To do it "properly" in traditional state management, developers had to create an entire BLoC or Cubit, declare separate Event and State classes (or union types), write boilerplate event handlers, wire asynchronous repository methods, manage subscription lifecycles, and inject everything into the widget tree. With bloc_signals 1.1.0 , that friction disappears completely. We have introduced universal, symmetrical adapter extensions that allow any Dart Future , Stream , ReadonlySignal , or lifted primitive ( value.$ ) to adapt into a synchronous BlocSignalBase container with a single method call. 🧭 The Universal Dual-Track Mental Model When bridging asynchronous sources into synchronous state management, developers typically have one of two distinct intents: Raw Domain Values ( T ): You want raw domain objects (for example int , UserProfile , ThemeMode ) with zero wrapper ceremony, and you have an immediate default or fallback value for frame 0. Rich Asynch

Have you ever tried to use the classic Gang of Four (GoF) State Pattern in real code? You might have hit a wall. You might have thought, "Wait, this feels way too connected." You are not wrong about that. In school and many engineering interviews, the GoF State Pattern looks great. It promises to fix big, ugly switch statements. But real business rules are hard. When you use this pattern in real life, it can become a huge mess. Every state knows too much about the other states. Let us look at why this happens. We will learn the difference between the GoF pattern and a Finite State Machine (FSM). We will also learn when to use each one. The False Promise of the GoF State Pattern The main idea of the GoF State Pattern is to spread out the work. The main object gives its work to state objects. But there is a catch. The state classes themselves must trigger the change to the next state. Example: The Traffic Light Think about a simple traffic light. It goes Red to Green to Yellow to Red. It does this forever. class RedState implements TrafficLightState { change ( context : TrafficLight ): void { console . log ( " RED light, Stop " ); context . setState ( new GreenState ()); // Very connected! } } The Problem: RedState is forced to know about GreenState . This is fine for a simple traffic light. It is a closed loop. The rules will never change. But what happens when business rules change? Imagine the city council makes a new rule. From midnight to 5:00 AM, the light must flash yellow. Now, you must open your RedState and YellowState classes. You have to add new time checks. You have to add the new flashing state. The more states you add, the messier your code gets. The Better Choice: The Central FSM In the real world, things do not always happen in a straight line. An online order does not just go from Pending to Shipped to Delivered. It can jump from Pending to Cancelled. It can go from Shipped to Returned. If you use GoF here, your PendingState needs to know about many

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Mark Zuckerberg just bought a cozy abode somewhat close to Meta’s international headquarters in Ireland.

Sonic Fire Protection raised its new funding to help get its sound-powered fire protection system into everything from commercial kitchens to apartment buildings.

Every Flutter developer has run into the Sticky State Dilemma . You build a login screen. When authentication fails, your state container emits an error. You catch it in your UI and show a SnackBar . Everything works—until the user rotates their phone, pulls down the notification shade, or types on the virtual keyboard. Suddenly, the widget tree rebuilds. The state container is still holding AuthErrorState("Invalid password") . The UI listener fires again. And a duplicate snackbar appears out of nowhere. In this article, we’ll explore why domain state machines struggle with transient UI events, how the classic BLoC community worked around this with package:bloc_presentation , and how BlocSignal lets you handle one-shot side effects cleanly with zero additional package dependencies . 1. The Root Problem: Persistent State vs. Ephemeral Actions State management in Flutter is designed to model persistent truth over time: Is the user logged in? AuthState.authenticated(user) Is data loading? TodoState.loading What is the cart total? $49.99 Persistent state answers: "What is the system's current condition?" In contrast, UI presentation actions are ephemeral pulses : Show a brief SnackBar toast. Pop up an alert confirmation dialog. Push a new route on the Navigator stack. Vibrate the haptic motor. These actions answer: "What just happened that requires a one-time reaction?" ┌────────────────────────────────────────────────────────┐ │ State vs. Effects │ ├────────────────────────────┬───────────────────────────┤ │ Persistent State │ Ephemeral Side-Effect │ ├────────────────────────────┼───────────────────────────┤ │ • Survived by UI rebuilds │ • Consumed once & gone │ │ • Represented in signals │ • Triggered by an event │ │ • Backed by equality diffs │ • Zero domain state footprint │ └────────────────────────────┴───────────────────────────┘ 2. The Legacy Workarounds (And Their Hidden Costs) Historically in package:bloc and package:flutter_bloc , developers used one of three

Building Pure Dart Web Apps Without Compromise When developers evaluate Dart for the web, they typically face a stark tradeoff: Flutter Web : Exceptional for canvas-driven applications, design systems, and cross-platform desktop/mobile parity—but heavy for content-first landing pages, docs, and fast-loading SEO sites. Jaspr Web : A lightweight, component-driven framework that compiles pure Dart to HTML and CSS with instant first paint and full search engine indexing. When we built the official documentation and showcase site for BlocSignal , we knew Jaspr was the perfect foundation. But like many engineers diving into a new UI paradigm, our initial implementation took a shortcut: we used raw StatefulComponent lifecycles and manual .subscribe() callbacks to wire up our state machines. It worked—but it wasn't idiomatic. In this behind-the-scenes case study, we walk through the process of dogfooding bloc_signals_jaspr across blocsignal.dev , replacing manual subscription glue with declarative consumer components, achieving 100,000 operations/sec in compiled JavaScript , and exploring the sheer developer ergonomics of Dart 3.13 primary constructors . The "Manual Subscription Trap": Why Raw .subscribe() Fails at Scale In classic Flutter or Jaspr development, when you create a state machine without framework-level consumer widgets, you might be tempted to subscribe inside initState() : // ❌ THE ANTI-PATTERN: Manual subscription glue in StatefulComponent class LiveVisualizerState extends State < LiveVisualizer > { late final LiveCounterBloc _bloc ; @override void initState () { super . initState (); _bloc = LiveCounterBloc (); // ⚠️ Flaw 1: Every state change triggers a full component setState _bloc . state . subscribe (( _ ) { if ( mounted ) setState (() {}); }); } @override void dispose () { // ⚠️ Flaw 2: Manual dispose tracking _bloc . close (); super . dispose (); } } While this appears harmless in a simple counter demo, it introduces three severe architectural flaws:

A Practical Guide to Faster, Deterministic Flutter & Dart Unit Testing If you’ve ever written unit tests for classic package:bloc applications using bloc_test , you know the drill: build your BLoC, dispatch an event in act , and assert state emissions in expect . Under the hood, classic BLoC processes state updates asynchronously via Dart microtask-queue Streams . While robust, testing asynchronous streams can introduce microtask timing headaches, race conditions, or the need to drain queues or use fakeAsync when testing complex side-effects. In BlocSignal , state updates propagate synchronously . Calling emit(newState) updates the underlying signal graph in the exact same call stack frame. This handbook is a practical, recipe-based guide to testing BlocSignal and CubitSignal applications using package:bloc_signals_test . Whether you’re coming from classic BLoC or brand new to Signals, this guide shows you how to test every scenario cleanly—and why it’s significantly easier than classic stream-based testing. 🤖 AI Assistant Tip : Working with an AI coding assistant (like Antigravity, Gemini CLI, or Cursor)? The official bloc-signals plugin includes a pre-built testing skill ( plugins/bloc-signals/skills/bloc-signals/ ) that automatically teaches your AI assistant these exact testing conventions, observer scoping rules, and declarative blocSignalTest patterns! 🛠️ Quick Reference: BLoC Streams vs. BlocSignal Testing Testing Task Classic BLoC ( package:bloc_test ) BlocSignal ( package:bloc_signals_test ) Why it’s easier in BlocSignal Execution Environment Often requires flutter test engine Pure dart test execution Blazing Speed : Business logic tests run in pure Dart CLI without booting Flutter UI engine. Simple State Assertions Requires async stream listener or blocTest Direct expect(cubit.state, 1) or blocSignalTest Synchronous : State updates on the next line of code without microtask delay. Failure Diagnostics Legacy Instance of 'CounterCubit' Built-in toString() :

The engineering team at Stripe recently described how they automated database incident recovery by modeling their global infrastructure as a graph. Using graph search algorithms together with state machines, the team computes and executes remediation plans automatically. By Renato Losio

Hi, my name is Dan, I'm a frontend engineer and open-source maintainer. I've spent the last couple of years building Nano Kit — a lightweight, modular state management ecosystem for modern web apps: signals-based stores , a router , data fetching , i18n , and SSR support , all built on the same tiny reactive core. It recently hit 1.0 , and in this post I want to give you four honest reasons to try it. 1. It's fast At the heart of Nano Kit is a push-pull reactivity system based on the algorithm from alien-signals — one of the fastest signal implementations in the JavaScript ecosystem. I didn't use alien-signals directly, though. Nano Kit needed things it doesn't provide, so I built a dedicated fork called Agera : Signal lifecycles — you can listen to signal activation and deactivation, which powers Nano Kit's mountable stores (run setup logic on first listener, clean up on last). Real tree-shaking — Agera is designed so that only the code you use ends up in your bundle; alien-signals is not well tree-shakable. The result keeps almost all of alien-signals' raw speed. Here is how @nano_kit/store compares to other popular state management libraries in a reactivity benchmark : Library Latency avg (ns) Throughput avg (ops/s) alien-signals 294.00 ± 2.24% 3,559,763 @nano_kit/store 303.55 ± 0.75% 3,365,816 svelte/store 428.58 ± 0.61% 2,479,118 rxjs 454.74 ± 0.07% 2,250,397 nanostores 1,373.2 ± 5.96% 952,399 mobx 3,474.7 ± 1.86% 306,094 valtio 5,041.3 ± 11.46% 254,109 jotai 9,454.6 ± 16.45% 157,853 effector 24,885 ± 11.78% 62,744 @reatom/core 59,430 ± 15.61% 22,741 Benchmark was run on AMD Ryzen 5 PRO 3400G with Node.js v24.14.1 That's ~3.5× faster than nanostores and an order of magnitude faster than most atomic state managers — while shipping lifecycles and mountable stores on top. 2. It's small Nano Kit exists largely because of Nano Stores . I love its philosophy: atomic stores, mountable resources, logic moved out of components, and an obsessive focus on bundle size. Nan

If you've ever tried converting a bank statement PDF to CSV and ended up with a jumbled mess of merged cells, missing rows, or split transaction descriptions — you're not alone. This is one of the most common data pain points for accountants, bookkeepers, and anyone who does their own finances. In this post, I'll walk through why this happens, what the right approach looks like, and how to get clean, analysis-ready CSV output from any bank statement. Why Bank Statement PDFs Are So Hard to Parse Bank statements aren't structured documents — they're designed for printing, not data extraction. Here's what generic converters run into: Merged cells : PDF renderers often group date + description + amount into a single visual block. Naive converters pick one cell boundary and split it wrong. Multi-line transactions : A single transaction entry (especially with memos) can span 2–3 lines in the PDF, but gets split into separate rows in the spreadsheet. Negative vs. positive amounts : Debit/credit columns vary by bank.Chase uses a single "Amount" column with negatives for debits. BoA uses two separate columns. A generic converter treats them identically and produces wrong signs. Running balance drift : If even one row is misaligned, every balance figure below it is off. Method 1: Manual Copy-Paste (What You're Probably Doing Now) The baseline. Open the PDF, select all, paste into Excel, then spend 30 minutes fixing column alignment. Pros: Free, no tools required. Cons: Slow (20–40 minutes per statement), error-prone, completely unscalable if you have multiple accounts or months to process. Method 2: Python + pdfplumber For developers who want a scriptable solution: import pdfplumber import csv with pdfplumber . open ( " statement.pdf " ) as pdf : rows = [] for page in pdf . pages : table = page . extract_table () if table : rows . extend ( table ) with open ( " output.csv " , " w " , newline = "" ) as f : writer = csv . writer ( f ) writer . writerows ( rows ) This works reas

New York has become the first state to temporarily halt approval of large data centers, as Gov. Kathy Hochul argues the AI-driven building boom shouldn’t come at the expense of higher electricity costs, water supplies, or local control.

Broadcom accuses Allstate of dodging VMware audits.

Idle in transaction: connection ngồi không nhưng vẫn giữ snapshot, VACUUM đứng hình và bảng phình idle in transaction là trạng thái mà pg_stat_activity.state đặt cho một connection đã BEGIN , đã chạy xong statement gần nhất, và đang ngồi đợi statement kế tiếp hoặc COMMIT / ROLLBACK . Trên giấy nó "rảnh"; trong thực tế nó vẫn cầm một snapshot, vẫn giữ backend_xmin , vẫn ôm mọi lock đã claim từ đầu transaction. Một connection trong state này chỉ vài chục giây không nguy hiểm; cùng connection đó ngồi vài giờ trên cluster có write traffic là kịch bản kinh điển khiến bảng update-nóng bloat, autovacuum đứng yên, replica conflict, và DBA on-call bị page lúc 3 giờ sáng. Đây không phải bug Postgres mà là application code path đã lỡ rời transaction mở rồi đi làm việc khác (đợi user input, gọi HTTP ra ngoài, ngủ trong queue worker) — Postgres chỉ trung thực phản chiếu lại. Cơ chế hoạt động Khi một connection gửi BEGIN , Postgres ghi nhận transaction nhưng chưa cấp xid (chỉ cấp khi câu lệnh ghi đầu tiên xuất hiện) và lập tức gán cho backend này một snapshot : tập hợp các xid mà transaction sẽ coi là visible. Snapshot này được phản chiếu ra cột backend_xmin trong pg_stat_activity . Ngay sau BEGIN , state của connection là idle in transaction . Mỗi câu lệnh sau đó đẩy connection sang active trong lúc chạy, rồi trả về idle in transaction ngay khi statement hoàn tất — và nó ở đó vô thời hạn , cho tới khi client gửi câu lệnh kế tiếp, gửi COMMIT / ROLLBACK , hoặc connection bị terminate. -- Session A BEGIN ; -- pg_stat_activity.state = 'idle in transaction' -- pg_stat_activity.backend_xmin = <xid horizon ngay tại thời điểm BEGIN> SELECT count ( * ) FROM orders ; -- trong lúc chạy: state = 'active' -- xong: state = 'idle in transaction', snapshot KHÔNG bị nhả -- (client đi gọi HTTP ngoài, hoặc đơn giản là quên gửi COMMIT) -- nhiều giờ trôi qua. state vẫn 'idle in transaction'. -- backend_xmin vẫn pin xmin horizon của cluster. Điểm cốt tử là backend_xmin : VACUUM (và autovacuum ) chỉ đ

Redux setup is a ceremony. You create a store, compose your reducers into a root tree, wrap your app in a Provider, register middleware, and configure enhancers — all before you write a single line of feature logic. SDuX Vault™ replaces that entire ceremony with two function calls and zero root configuration. Redux Store Ceremony A typical Redux application requires several files and configuration steps before state management is operational. Here is what a minimal Redux setup looks like for a single feature: // store.ts import { createStore , combineReducers , applyMiddleware } from ' redux ' ; import thunk from ' redux-thunk ' ; import { userReducer } from ' ./reducers/userReducer ' ; const rootReducer = combineReducers ({ users : userReducer , }); export const store = createStore ( rootReducer , applyMiddleware ( thunk ) ); // App.tsx — Provider wrapper required import { Provider } from ' react-redux ' ; import { store } from ' ./store ' ; function App () { return ( < Provider store = { store } > < UserList /> < /Provider > ); } That is 20+ lines of configuration across multiple files — and it only covers one feature. Add a second feature and you are back in the combineReducers file, composing another slice into the tree. Add middleware and you are threading enhancers through applyMiddleware . Add DevTools and you are composing composeWithDevTools on top. Every new feature touches the root configuration. Redux Requirement What It Does createStore() Creates the single global store instance combineReducers() Composes feature reducers into a root tree applyMiddleware() Registers middleware (thunk, saga, etc.) Provider Makes the store available to all components via context composeWithDevTools() Enables Redux DevTools integration ⚠️ Warning: Every entry in that table is root-level configuration. Adding a new feature means editing the root reducer composition, possibly the middleware stack, and potentially the Provider hierarchy. Root configuration is a shared depende