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Read Committed: snapshot mỗi statement, và vì sao hai SELECT trong cùng transaction có thể trả khác nhau READ COMMITTED là isolation level mặc định của PostgreSQL, và là level mà phần lớn workload OLTP đang chạy mà không biết. Khác với mô hình "transaction lấy một snapshot rồi giữ nguyên" mà nhiều dev tưởng tượng từ MVCC, ở Read Committed mỗi statement lấy một snapshot mới tại thời điểm statement bắt đầu , không phải tại thời điểm BEGIN . Hậu quả thực tế: hai SELECT liên tiếp trong cùng một transaction có thể trả về dữ liệu khác nhau nếu giữa hai lần đó có transaction khác commit. Đây là non-repeatable read — đúng spec của Read Committed, không phải bug — và là nguồn của một class lỗi rất hay gặp: code đọc một giá trị, ra quyết định, rồi cập nhật dựa trên giá trị đã đọc, trong khi giá trị thực tế đã thay đổi. Cơ chế hoạt động Một transaction ở Read Committed không có transaction-level snapshot . Khi mỗi statement (mỗi SELECT , UPDATE , DELETE , INSERT ... SELECT ...) bắt đầu thực thi, backend lấy một snapshot mới gồm xmin , xmax và xip list — chính cái snapshot quyết định row version nào "visible" theo MVCC. Statement chỉ thấy: row có xmin đã commit trước thời điểm statement bắt đầu , và xmax chưa tồn tại hoặc thuộc một transaction chưa commit / đã abort. Ngay sau khi statement kết thúc, snapshot đó bị bỏ. Statement kế tiếp lấy snapshot mới — nếu trong khoảng giữa có transaction khác commit, statement này sẽ thấy dữ liệu mới đó. -- T1 BEGIN ; -- KHÔNG lấy snapshot ở đây SELECT balance FROM accounts WHERE id = 1 ; -- snapshot S1 -> trả 1000 -- ... T2 chạy: UPDATE accounts SET balance=500 WHERE id=1; COMMIT; SELECT balance FROM accounts WHERE id = 1 ; -- snapshot S2 -> trả 500 COMMIT ; Với UPDATE / DELETE / SELECT ... FOR UPDATE / FOR NO KEY UPDATE / FOR SHARE , Read Committed làm thêm một bước đặc biệt mà SELECT thường không làm: nếu target row bị một transaction khác đang lock (chưa commit), statement đợi transaction đó kết thúc. Khi unblock: nếu transaction kia ROL

submitted by /u/c1rno123 [link] [留言]

There's a lot of info packed in the top area of an Android phone's screen

X is rolling out a new video editor and recorder for iOS with multilingual captions, green-screen effects, and other editing tools.

Discord says a bug affecting its safety system caused it to mistakenly ban more than 8,000 accounts since May. The platform's statement follows a wave of reports from users over the past week, who say they've been banned for posting images containing grids, such as chessboards, game textures, and even Minecraft inventories. Stanislav Vishnevskiy, Discord […]

Chemistry Ventures, the VC firm launched by Bessemer, Index Ventures, and Andreessen Horowitz alums, is raising $500M for its second fund.

Everyone does CV in Python/C++. I did it in Rust anyway – and it worked. 🦀 Libraries compared, code breakdown, and why I chose kornia‑rs submitted by /u/darryldias [link] [留言]

iRobot just announced its first-ever non-robotic floor cleaner. The $399 Roomba Electro Plus is a 5-in-1 hard-floor cleaner that combines vacuuming, mopping, and disinfecting, but you have to operate it yourself. The company also announced updates to its line of Roomba robot vacuums, launching five new models with higher suction power, smaller footprints, and lower […]

Microsoft is addressing a Windows 11 bug that caused a folder to take up several gigabytes of storage space. As spotted earlier by Windows Latest, Microsoft included the patch in its optional June 2026 update (KB5095093), which "improves disk space usage for the CapabilityAccessManager.db-wal file." The CapabilityAccessManager.db-wal file is installed on Windows 11 PCs by […]

Calw-Code is one of the fastest-growing repos in the world When Claude's source code was leaked, some developers studied it and recreated this AI. Claw--Code written mostly in Rust submitted by /u/Ok_Stomach6651 [link] [留言]

The U.S. Army has fixed two of its websites that were hacked to display messages calling President Trump a "pedophile" and a "thief."

GitHub热门项目 | 💡 Download the complete source code of any website (including all assets). [ Javascripts, Stylesheets, Images ] using Node.js | Stars: 3,643 | 173 stars today | 语言: HTML

You can clip a cover over the cameras, which could be a double-edged sword.

submitted by /u/itgforlife [link] [留言]

Most of the "AI for observability" work I see right now hands the language model the judgment. I think that's backwards. Feed it the alert, feed it some metrics, ask it what's wrong, what should be done, and let it make the judgement call. Based on my experience working with language models, I decided that inverting the process provides better results. The short version: in my alerting pipeline, the set of allowable classifications is fixed in deterministic Python, and the model has to pick from it. The LLM's only job is to turn a structured verdict into an easily digestible sentence. It never decides whether something is bad, how bad it is, or what category of problem it is. It narrates within a decision space the code has already locked down. TL;DR: Instead of letting an LLM decide what's wrong with an alert, I let deterministic Python make every operational decision and restrict the model to explaining the result in plain English. The code classifies, validates, and aggregates; the LLM only narrates. That keeps the data consistent, prevents hallucinated classifications, and ensures the monitoring pipeline continues working even if the model fails. The problem I run a small managed monitoring service. Alertmanager fires, a webhook lands, and historically that webhook produced a line like HighMemoryUsage on host web-vm, severity warning, which is accurate, but not terribly helpful. The person reading it still has to know what HighMemoryUsage implies, whether this host always runs hot, and whether to care. I wanted plain-English context attached to the alerts without altering the alert delivery process. The obvious move was to throw the whole alert at an LLM and ask it to explain. I tried that in the first iteration of this experiment, expecting it to be somewhat accurate, but not entirely reliable, and it did not disappoint, the model was confidently inconsistent. The same alert, fired three times, produced three different "root cause" categories. One run called a

Nephew saw a YouTube ad. Someone was selling a "secret prompt" for ₹199, claiming ChatGPT and Claude can analyze the stock market and place trades with 90% accuracy — no technical analysis, no fundamentals, just paste this prompt. He brings it straight to Uncle. The Ad 👦 Nephew: Uncle, I saw an ad on YouTube. Some guy was saying, "Use ChatGPT and Claude AI for stock market analysis, take trades with 90% accuracy. You don't even need to know technical analysis or fundamentals — just use this prompt and you'll get all the results." Is that actually possible? 👨‍🦳 Uncle: (laughs) Ah, here we go. This is exactly how a lot of scams happen — and honestly, it's rarely because of some clever new invention. It's because of a lack of understanding, and people treating these models as a magic black box. 👦 Nephew: So are they scamming us? Or genuinely fooling themselves too? 👨‍🦳 Uncle: Not exactly a straightforward scam, and not exactly genuine either. Here's the honest split: they're maybe 30% correct, and 70% wrong. 👦 Nephew: What does that even mean? 👨‍🦳 Uncle: I'll accept this much — there genuinely are AI models out there that can do a solid job predicting stock trends or running fundamental analysis, because that kind of prediction is heavily mathematical, numerical work. But — and this is the important part — ChatGPT, Claude, and Gemini are not that kind of model. 👦 Nephew: Why not? It can literally write code. It can do math inside code. Why can't it just... do math for stock prediction too? I genuinely don't get it. 👨‍🦳 Uncle: Come, sit. This needs a proper, from-scratch conversation. We're going to dig all the way down to what these models actually are , and by the end, you'll understand exactly why ChatGPT, Claude, and Gemini are the wrong tool for this specific job — not a scam exactly, but sold by people who never actually opened the box themselves. Part 1: What Is an LLM, Really — In One Honest Sentence 👨‍🦳 Uncle: Before anything else, one sentence, and hold onto i

"I'll just write a quick button style... and a text field with validation... and a card component..." Two weeks later you have a bespoke design system that only half the app uses, three slightly different buttons, and nothing shipped. If you're building with an AI coding agent, the drift problem is worse, not better. Ask Claude, Cursor, or Codex to "add a settings screen" three separate times and you'll get three different paddings, three different corner radii, and a button style that quietly forked itself somewhere around commit 40. Agents are great at writing plausible SwiftUI and bad at remembering what the rest of your app already decided. DesignFoundation is an open-source SwiftUI package that gives you a token-based theming engine, 25 components, and 6 feedback/overlay modifiers that all read from the same theme. You set it once at the app root, and everything underneath updates. That's the whole idea — and it ships with CLAUDE.md , AGENTS.md , and Cursor rules already written, so an agent working in your repo reaches for DFButton instead of inventing a new one. In this tutorial you'll go from zero to a themed, consistent SwiftUI UI — without writing a single custom button style. What We're Building By the end you'll have: A working app that uses DesignFoundation's theme system A themed form with inputs, validation states, and feedback components A custom theme built from tokens An understanding of how the style system composes Requirements: Xcode 16+, iOS 18+ / macOS 15+ target, Swift 6. Installation Via Xcode File → Add Package Dependencies Paste https://github.com/NerdSnipe-Inc/design-foundation Set the version rule to Up to Next Major from 1.0.0 Add DesignFoundation to your target Via Package.swift dependencies : [ . package ( url : "https://github.com/NerdSnipe-Inc/design-foundation" , from : "1.0.0" ) ], targets : [ . target ( name : "YourApp" , dependencies : [ "DesignFoundation" ]) ] Step 1: One Line Instead of a ThemeManager Singleton Every app that

The Problem When building BotForge, our AI no-code chatbot platform, we needed a retrieval system that could handle messy, real-world user queries — typos, partial phrases, semantically similar-but-differently-worded questions. A naive vector search alone wasn't good enough. It's powerful but brittle to out-of-vocabulary terms and exact keyword lookups. The Solution: Four-Tier Parallel Retrieval We ran four retrieval strategies simultaneously using Promise.all\ , then merged results with a weighted scoring function. \ javascript const [semanticResults, textResults, regexResults, fuzzyResults] = await Promise.all([ semanticSearch(query, embeddings), // weight 1.8x mongoFullTextSearch(query), // weight 1.5x regexKeywordSearch(query), // weight 1.0x fuzzyPerWordMatch(query), // weight 0.6x ]) \ \ Tier 1: Semantic Search (1.8× weight) Using Gemini gemini-embedding-2\ to produce 3072-dimensional vectors , we compute cosine similarity against stored document embeddings. This catches meaning — "how do I reset my login?" matches "account recovery options" even with no shared words. Tier 2: MongoDB Full-Text Search (1.5× weight) A native MongoDB Atlas text index for fast, exact keyword hits. Great for technical terms, product names, and precise phrases. Tier 3: Regex Keyword Matching (1.0× weight) Each significant word in the query is compiled to a case-insensitive regex. Catches partial matches and hyphenated variants. Tier 4: Fuzzy Per-Word Matching (0.6× weight) Levenshtein distance matching per query word — handles typos and misspellings like "configuraton" → "configuration". Weighted Score Merging Each result carries a base score from its retrieval strategy. We deduplicate by chunk ID, sum scores across strategies, and sort descending: \ javascript function mergeResults(tiers, weights) { const scoreMap = new Map() tiers.forEach((results, i) => { results.forEach(({ id, score, chunk }) => { const weighted = score * weights[i] scoreMap.set(id, { chunk, total: (scoreMap.get

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

How to Monitor Website Changes Automatically I run a few websites and need to know immediately when something breaks. A CSS regression, a broken layout, a missing section. Manual checking doesn't scale, and text-based monitoring misses visual issues. The {{screenshot-diff}} on Apify takes two screenshots and produces a pixel-level comparison with an overlay showing exactly what changed. How It Works Take a baseline screenshot of the correct state. Then take a current screenshot of the live page. The actor compares pixel by pixel and returns a diff image with changed pixels highlighted, plus a percentage telling you how much changed. import requests , time API_TOKEN = " YOUR_APIFY_TOKEN " def capture_screenshot ( url ): resp = requests . post ( " https://api.apify.com/v2/acts/weeknds~website-screenshot-api/runs " , headers = { " Authorization " : f " Bearer { API_TOKEN } " }, json = { " url " : url , " fullPage " : True } ) run_id = resp . json ()[ " data " ][ " id " ] time . sleep ( 15 ) items = requests . get ( f " https://api.apify.com/v2/acts/weeknds~website-screenshot-api/runs/ { run_id } /dataset/items " , headers = { " Authorization " : f " Bearer { API_TOKEN } " } ). json () return items [ 0 ][ " screenshotUrl " ] def compare_screenshots ( baseline_url , current_url ): resp = requests . post ( " https://api.apify.com/v2/acts/weeknds~screenshot-comparison-tool/runs " , headers = { " Authorization " : f " Bearer { API_TOKEN } " }, json = { " baselineImageUrl " : baseline_url , " currentImageUrl " : current_url , " threshold " : 0.01 } ) run_id = resp . json ()[ " data " ][ " id " ] time . sleep ( 10 ) items = requests . get ( f " https://api.apify.com/v2/acts/weeknds~screenshot-comparison-tool/runs/ { run_id } /dataset/items " , headers = { " Authorization " : f " Bearer { API_TOKEN } " } ). json () return items [ 0 ] baseline = capture_screenshot ( " https://mysite.com " ) current = capture_screenshot ( " https://mysite.com " ) result = compare_screenshots ( b