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TypeScript TS2802 Error: Resolving Observer Pattern 'Set' Spread with Array.from Conversion

TypeScript Compile Error TS2802: Resolved with Observer Pattern by Converting Set Spread to Array.from If you're stuck implementing the observer pattern due to TypeScript compile error TS2802, this post might help. I resolved the issue with a simple conversion: changing Set spread to Array.from() . Attempts and Pitfalls While implementing the observer pattern, I encountered TypeScript compile error TS2802 when trying to spread a Set. Initially, I suspected the Set's type might be the problem, so I tried various approaches. class Observer { private subscribers = new Set < () => void > (); subscribe ( callback : () => void ) { this . subscribers . add ( callback ); } notify () { // TS2802 error occurs here for ( const callback of [... this . subscribers ]) { callback (); } } } When attempting to spread the Set into an array using [...this.subscribers] as shown above, TypeScript failed to recognize it properly, throwing an error similar to TS2802: Cannot find module '...' or its corresponding type declarations. . At first, I thought it was a library configuration issue and spent a considerable amount of time lost. The Cause In the end, the problem lay with the Set spread syntax itself. When TypeScript applies the ... spread operator to a Set, there were instances where it couldn't accurately infer the types internally. This issue can be more pronounced in certain versions or environments. The Solution To resolve this, I used the method of explicitly converting the Set spread to an array using Array.from() . class Observer { private subscribers = new Set < () => void > (); subscribe ( callback : () => void ) { this . subscribers . add ( callback ); } notify () { // Resolved by converting with Array.from for ( const callback of Array . from ( this . subscribers )) { callback (); } } } By using Array.from(this.subscribers) , TypeScript clearly recognizes the Set as an array, allowing the loop to execute correctly. The Outcome The TypeScript compile error TS2802 was cleanl

2026-06-13 原文 →
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DigitalOcean vs Vultr: The AWS Alternatives Small Businesses Actually Need

A quick note on the links below. The DigitalOcean and Vultr links in this article are referral links. If you sign up via them, you get a free credit on your new account (currently $200 over 60 days for DigitalOcean and up to $300 for Vultr) and the author of this article gets a small referral credit too, at no extra cost to you. AWS does not run an equivalent referral program, so the AWS links are normal links. The review below is the author's own evaluation; the credits do not change the recommendations. If you have ever spent a workday watching your website refuse to load, you are not alone. In a recent outage , a single building in Northern Virginia hosting one of Amazon's availability zones (the cloud-industry term for one campus's worth of servers in one region ) got too hot. The hardware shut itself down. AWS calls this a thermal event. Customers around the world have other names for it. Big enterprises ride out outages like this. They have multi-region setups, dedicated SRE teams, and SLA credits that will refund a small fraction of their monthly bill. Small and mid-size businesses do not. They lose a day of revenue, scramble to reassure customers, and then read a post-mortem in a few weeks that explains what went wrong in language that does not help them recover the lost revenue. The cloud was supposed to make small businesses look big. After each new outage, it is fair to ask: is AWS actually the right cloud for small businesses at all? Two providers worth a serious look, DigitalOcean and Vultr , are simpler, cheaper at the entry level, and built around use cases that more closely match what a small business actually needs. Here is what each one does, where AWS is still the right answer, and how to decide. Why AWS hits small businesses harder than big ones When a giant company has an AWS outage, three teams kick into gear. There is the engineering team that fails workloads over to a backup region. There is the customer-success team that updates the status p

2026-06-13 原文 →
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Two HNG Tasks That Taught Me More Than the Spec: OAuth for Three Clients, and Shipping AI on a Team Deadline

Two HNG Tasks That Taught Me More Than the Spec This is my Stage 9B write-up for the HNG internship . No new code just two tasks that stuck: one I owned solo across multiple repos, and one I shipped inside a team product under real deadline pressure. If you've ever had auth work almost done for three days straight, or watched an LLM politely ignore your JSON schema, you'll recognize these stories. Task 1 (Individual): Insighta Labs — One API, Three Clients, One Auth System Stage: 3 (Technical Requirements Document / TRD track) Why I picked it: Auth looked "done" on paper. It wasn't. Web portal, CLI, and graders all needed to log in differently, and every environment (localhost, Railway, preview URLs) found a new way to break. What it was Insighta Labs is a queryable profile-intelligence API I built during HNG. By Stage 3 the backend wasn't just CRUD anymore it needed GitHub OAuth with PKCE , JWT access + refresh with rotation , RBAC ( admin vs analyst ), rate limits, API versioning, and three first-class clients : Client Repo How it authenticates Backend API HNG_STAGE-1 Issues tokens, sets cookies Web portal Insighta-WebPortal HTTP-only cookies + CSRF CLI Insighta-Cli PKCE + local callback + Bearer tokens Every /api/* route required X-API-Version: 1 and a valid session. Access tokens expired in 3 minutes ; refresh tokens in 5 minutes with rotation. That sounds harsh, it was intentional, and it surfaced bugs fast. The problem it was solving Reviewers and real users had to prove identity without sharing one login mechanism. Browsers should never see raw tokens in JavaScript. The CLI can't use cookie redirects the same way a React app does. Automated graders needed a test path that didn't depend on GitHub's OAuth exchange. One auth design. Three runtimes. Zero "works on my machine only." How I approached it I split auth into explicit paths instead of one generic "login" handler: Web flow GET /auth/github — server stores PKCE verifier, redirects to GitHub GET /auth/gith

2026-06-12 原文 →
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Flutter Agent Skills: How to Make Your AI Agent Actually Good at Flutter

TL;DR: Your AI coding assistant is a generalist. It writes Flutter that looks right but quietly reaches for 2022 patterns. Agent Skills are a new, official way (from the Dart and Flutter teams) to hand your agent task-specific, battle-tested workflows it loads on demand. Two repos, flutter/skills and dart-lang/skills , ship ready-to-use skills for responsive layouts, routing, testing, localization, static analysis, and more. Install in one command: npx skills add flutter/skills --skill '*' --agent universal npx skills add dart-lang/skills --skill '*' --agent universal This post breaks down what they are, how they differ from rules files and MCP, the full catalog, what a real skill looks like under the hood, and whether they actually move the needle. (Spoiler: mostly yes, with one honest caveat.) Let me tell you about a fight I have almost every day. I ask my AI agent to make a screen adapt to tablets. It confidently hands me code that switches layout based on MediaQuery.orientationOf(context) . It looks clean. It compiles. It even runs . And it's wrong, because device orientation has nothing to do with how much window space your app actually has on a foldable, in split-screen, or in a resizable desktop window. The model isn't dumb. It's a generalist trained on a giant pile of Flutter code, much of it old. And here's the uncomfortable truth the Flutter team said out loud when they launched this feature: Flutter and Dart ship new features faster than LLMs can update their training data. That lag has a name, the knowledge gap , and it's why your agent keeps writing rookie Flutter with a straight face. Agent Skills are the Flutter team's answer to that gap. I've been running them on real projects, and they're one of the few "AI workflow" things in 2026 that earned the hype instead of borrowing it. Let's get into it. Table of Contents The real problem: your AI is a generalist What are Agent Skills, exactly? Skills vs Rules vs MCP: who does what The full catalog: every of

2026-06-12 原文 →
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GBase 8a OLAP Window Functions in Practice: Ranking, Running Totals, MoM, and Ratio Analysis

GBase 8a fully supports OLAP window functions, making it a powerful gbase database for analytical workloads. This guide uses real sales scenarios to demonstrate ROW_NUMBER / RANK , moving aggregates, LAG / LEAD for period‑over‑period comparisons, ROLLUP subtotals, and how these functions execute in an MPP environment. Window Function Syntax function_name () OVER ( [ PARTITION BY column ] -- window group [ ORDER BY column ] -- ordering within window [ ROWS | RANGE BETWEEN ... AND ...] -- frame definition ) Unlike GROUP BY , window functions do not collapse rows. Every row remains in the result set while still being able to “see” other rows in the window. Ranking Functions ROW_NUMBER / RANK / DENSE_RANK SELECT dept_id , salesperson , sale_amount , ROW_NUMBER () OVER ( PARTITION BY dept_id ORDER BY sale_amount DESC ) AS row_num , RANK () OVER ( PARTITION BY dept_id ORDER BY sale_amount DESC ) AS rnk , DENSE_RANK () OVER ( PARTITION BY dept_id ORDER BY sale_amount DESC ) AS dense_rnk FROM sales WHERE sale_date >= '2024-01-01' ; Sample output: dept_id salesperson sale_amount row_num rnk dense_rnk 101 Zhang San 95000 1 1 1 101 Li Si 95000 2 1 1 101 Wang Wu 82000 3 3 2 101 Zhao Liu 71000 4 4 3 Top 3 Sales per Department WITH ranked AS ( SELECT dept_id , salesperson , sale_amount , ROW_NUMBER () OVER ( PARTITION BY dept_id ORDER BY sale_amount DESC ) AS rn FROM sales WHERE YEAR ( sale_date ) = 2024 ) SELECT dept_id , salesperson , sale_amount FROM ranked WHERE rn <= 3 ORDER BY dept_id , rn ; NTILE: Bucketing SELECT dept_id , salesperson , sale_amount , NTILE ( 4 ) OVER ( PARTITION BY dept_id ORDER BY sale_amount DESC ) AS quartile FROM sales WHERE YEAR ( sale_date ) = 2024 ; Cumulative and Moving Aggregates Year‑to‑Date SELECT dept_id , sale_month , monthly_amount , SUM ( monthly_amount ) OVER ( PARTITION BY dept_id ORDER BY sale_month ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW ) AS ytd_amount FROM ( SELECT dept_id , DATE_FORMAT ( sale_date , '%Y-%m' ) AS sale_month ,

2026-06-12 原文 →
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Ditch Electron: Spawning a Rust-Powered Python Sidecar from Bun

Part 1 of the ERTH Architecture Series: Launching local backends on Port 0, dynamic port negotiation, and establishing the dual-core desktop backbone. If you are building a modern desktop application, you are probably tired of the same old options. On one hand, you have Electron . It’s the industry standard, but it forces you to bundle a full Chromium browser and a Node.js runtime with every app. Even a simple "Hello World" takes up 200MB+ of disk space and eats hundreds of megabytes of RAM. For background utility utilities or AI assistants that need to be nimble, this is a massive tax. On the other hand, you have Tauri . It solves the bundle size issue by binding to OS-native WebViews and using Rust for the backend. But unless you are already a Rust expert, you will find yourself fighting the compiler's borrow checker and async lifecycles, slowing down your development velocity. But what if you want to use Python for its rich AI ecosystem (Ollama, SQLModel, PyTorch), but still keep the UI lightweight, fast-loading, and responsive? Welcome to the ERTH Stack ( E lectroBun + R obyn + T urso + H TMX). In this first post of our 5-part series, we will break down how to launch a high-performance Python sidecar backend directly from a Bun-based desktop shell, bypassing the bloated Electron environment entirely. The Concept: Heterogeneous Dual-Core In the ERTH architecture, the desktop app is split into two physical processes: The Main Process (Bun) : Responsible for native OS window management, IPC (Inter-Process Communication), and hosting the HTML/CSS view. We use ElectroBun —a next-generation, ultra-lightweight wrapper that binds directly to the OS-native WebKit engine (no Chromium bloat!). The Sidecar Process (Python/Robyn) : Responsible for heavy computations, database access, and local LLM orchestration. We use Robyn , an incredibly fast, Rust-based async Python web framework. Here is how the lifecycle and process boundaries interact: Step 1: Spawning the Robyn Sidec

2026-06-12 原文 →
AI 资讯

Why I Abandoned Electron & SPAs to Build a 128MB Local-First Desktop AI Agent

How the ERTH Architecture (ElectroBun + Robyn + Turso + HTMX) breaks the obesity of modern desktop app development. If you’ve tried to build a cross-platform desktop application recently, you’ve likely faced the classic developer’s dilemma: Electron makes developer velocity fast, but at the cost of dragging a bloated Chromium kernel and Node.js runtime into every build. A simple "Hello World" easily eats up 200MB+ of disk space and hundreds of megabytes of RAM. Tauri solves the footprint issue by using OS-native WebViews and Rust, but forces you onto Rust’s steep learning curve, sacrificing the agility of rapid prototyping. The Python Distribution Hell : With the rise of local LLMs and Edge AI, Python is the de facto language for AI orchestration. Yet, packaging Python, its heavy dependencies, and databases into a double-click-to-run package for non-technical users remains a nightmare. Faced with these shackles, I decided to take a step back and rewrite the physical laws of desktop development. Today, I’m introducing the ERTH Stack ( E lectroBun + R obyn + T urso + H TMX)—a heterogeneous, local-first, zero-JS desktop application architecture designed for independent full-stack creators. And yes, the entire bundle—including a browser shell, a high-performance Python sidecar, a local database, and local AI agent execution—packages into a single 128MB standalone binary. Our open-source implementation is live on GitHub: 👉 GitHub Repository: bnpysse/erth_assistant The Core Pillars of the ERTH Architecture To achieve a minimalist footprint without sacrificing developer velocity, we structured the architecture into four core layers: ERTH ARCHITECTURE [E]lectroBun (UI Shell) ───[HTMX]───► [H]TMX (Zero-JS Frontend) │ ▲ (IPC) (HTTP) ▼ │ [R]obyn (Python Sidecar) ───────────► [T]urso / libSQL (Local-First DB) 1. [E]lectroBun: The Lightweight Shell Instead of Electron’s heavy Chromium, ElectroBun binds directly to the OS-native WebKit engine (Cocoa WebKit on macOS, WebView2 on W

2026-06-12 原文 →
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