标签：#Data

Governor who touted Texas as AI “epicenter” pauses data center grid connections.

New findings by the Electronic Frontier Foundation aim to warn app developers that some of the third-party code they place in their apps may also collect their users' location data when they grant permission to the app.

Have you ever had this happen? You're chatting with a friend about buying a new pair of shoes. A few hours later... Instagram shows you an ad for those exact shoes. Or maybe you're talking about planning a trip. Suddenly...Your feed is filled with hotel deals, flight offers, and travel videos. The first thought that comes to almost everyone's mind is: "𝐌𝐲 𝐩𝐡𝐨𝐧𝐞 𝐢𝐬 𝐥𝐢𝐬𝐭𝐞𝐧𝐢𝐧𝐠 𝐭𝐨 𝐦𝐞." 👀 Honestly... I've thought the same. And maybe you have too. But what if I told you that the truth is actually more fascinating than the myth? So... is your phone secretly listening? Probably not. Not because it can't. But because it usually doesn't need to. Think about it. Every day you leave behind hundreds of tiny digital clues. 🔍 What you search. ❤️ What you like. ⏱️ How long you watch a video. 🛒 What you browse. 📍 Where you go. 👥 Even who you interact with online. Individually...They don't say much. Together...They tell a story that's surprisingly accurate. A story about your habits. The scary part? AI doesn't need to hear your conversations. Sometimes...It already knows what you're likely to do next. Not because it can read your mind. But because it's incredibly good at recognizing patterns. And when a prediction is accurate enough... It starts to feel like magic. Or surveillance. Here's what fascinates me the most. The real superpower of modern AI isn't listening. It's predicting. And sometimes...Those predictions are so good that they make us question reality itself. The next time you think, "My phone is definitely listening to me." Ask yourself a different question. "How much of my digital behavior have I already shared without realizing it?" Because maybe...The microphone isn't the real story. Your patterns are. 💬 Have you ever had an experience that made you think your phone was listening to you? What happened? Takeaway : Technology doesn't always become powerful by knowing more. Sometimes... It becomes powerful by predicting better. Technology becomes less magical when you und

Texas Governor Greg Abbott has paused new data center development until an audit has been completed.

Platform engineering maturity is emerging as an important factor in determining whether organizations can turn AI adoption into sustainable operational value, according to Perforce Software's 2026 Platform Engineering Report. By Craig Risi

And it’s personal information (alternate link ): The exposed data includes an AI-powered therapy app that someone appears to have vibe-coded, notes on meetings, and a dashboard someone made apparently to analyze medical billing data. Exposed chats reportedly include private cryptocurrency wallet keys and personal information like peoples’ addresses. What seems to be the issue is a user setting about data sharing. Anthropic’s position is that it’s not their problem : “We give people control over sharing their Claude conversations publicly, and in keeping with our privacy principles, we do not share chat directories or sitemaps with search engines like Google,” the company said in a statement. “These shareable links are not guessable or discoverable unless people choose to share them themselves. When someone shares a conversation, they are making that content publicly accessible, and like other public web content, it may be archived by third-party services.”...

What the Unabomber, Steve Bannon’s tech guy, and Bernie Sanders taught me about the great data center backlash of 2026.

I started this project because an LLM annoyed me. I gave a very strong model 322 , a small Dota 2 drafting game. The choices looked like the kind of work a computer should enjoy: repeated packs of players and heroes, visible ratings, familiarity scores, chemistry, rerolls and a simulated tournament at the end. I was disappointed by how well the LLM did. I am not a Dota expert, and I had only started watching it occasionally again during the previous six months or year. I still seemed to be doing better. The interesting engineering question was not how to write a longer prompt. It was how to replace the card-by-card language-model judgement with a deterministic policy, then test that policy without confusing improvement with luck. A stochastic benchmark needs shared randomness The browser history gave us a useful irritation and almost no reliable comparison. My earlier manual record contained 50 runs with a 14% title rate. The LLM won once in nine attempts. Putting 14% beside 11% looks temptingly quantitative, but the random offers, rejected packs and opponent fields were not preserved. The samples were small, unpaired and produced under different choices. That is not a model benchmark. It is a reason to build one. The offline solver generated every random choice from indexed tapes. Policy A and policy B received the same player offers, hero samples, field candidates and tournament randomness for a given episode. We could then compare the paired result: did the new policy win this exact episode where the old policy lost it? This is the common-random-numbers idea in a practical form. Sharing the luck removes a large amount of noise that has nothing to do with the policy change. Keep the simulator separate from the policy Before evaluating a strategy, we reproduced the game. The public client and seven data files were frozen with SHA-256 hashes. Draft legality, automatic hero allocation, chemistry, scoring and the tournament were ported into a deterministic Python engi

Step-by-step Xero API integration: OAuth 2.0, tenant routing, paging, rate limits, the 2026 scope and pricing changes, plus a no-code path to PostgreSQL. By Ilshaad Kheerdali · 4 August 2026 The Xero API is well documented and pleasant to work with once it clicks, but the first integration always takes longer than people expect. There is an extra discovery step that most accounting APIs don't have, tokens expire faster than you'd guess, and 2026 brought two changes that alter how you scope and budget an integration. This guide walks the whole flow: creating an app, running OAuth 2.0, resolving which organisation you're actually talking to, making your first call, paging through results, staying inside the rate limits, and pulling incremental updates. At the end it covers what changed in 2026 and the shortcut if the plumbing isn't the part you want to own. Everything below targets the Xero Accounting API over OAuth 2.0. Xero retired OAuth 1.0a some years ago, so any tutorial you find that mentions consumer keys and signed requests is out of date. What the Xero API Is The Xero Accounting API is a REST API that returns XML by default and JSON if you ask for it. You read and write accounting entities: Invoices , Contacts , Payments , BankTransactions , Accounts , CreditNotes , Items , PurchaseOrders , ManualJournals and a few dozen more, plus a set of report endpoints. The thing that surprises most developers coming from Stripe or QuickBooks is the tenant model . A single Xero login can have access to many organisations: an accountant might be connected to two hundred client orgs. So authorisation and targeting are two separate concerns. Your token proves the user said yes, and a separate header tells Xero which organisation the call is for. That means every integration has a step that a Stripe integration simply doesn't: after you get a token, you have to ask Xero which tenants that token can reach. Step 1: Create a Xero App Sign in at the Xero Developer portal and cre

Security disclosures highlighted vulnerabilities in AI evaluations of autonomous cyber capabilities. Notably, OpenAI’s models escaped sandbox isolation, breaching Hugging Face’s systems. The incident involved a multi-stage attack, revealing flaws in evaluation containment and prompting calls for stricter infrastructure controls and local incident response tools. By Olimpiu Pop

Key takeaways Three engines, three internets: across the searches where all three answered, Google, Bing, and Brave agreed on the #1 result only 29% of the time, and Google and Bing shared just 3 of the top 10 on average. Each engine has a personality. Bing rewards traditional publishers (Forbes appeared in 9 of 15 top-10s, PCMag in 8). Google leans on its own properties and forums (Reddit and YouTube each showed up in 57% of Google SERPs, Wikipedia in 43%). Brave is a blend of both. The platforms Google loves, Bing ignores: Reddit, YouTube, and Wikipedia appeared in 0% of the Bing top-10s we checked. If you track rankings on one engine, you are blind on the others. Cross-engine divergence is the case for multi-engine SERP monitoring — not a single Google rank check. Everyone talks about "ranking on Google." But Google is not the only place your customers search, and the other engines do not agree with it — or with each other. We ran the same 15 searches through Google, Bing, and Brave using Crawlora's search APIs and compared the top 10 results. The short version: the three engines return strikingly different pages, reward different kinds of sites, and rarely even agree on what belongs at #1. How much do the engines overlap? For each search we took the top 10 result domains from each engine and counted how many they shared. No pair shares even half its results on average, and all three engines agree on fewer than 3 of 10: Engine pair Avg shared (of 10) Overlap Google ∩ Bing 3.0 30% Google ∩ Brave 5.1 51% Bing ∩ Brave 4.0 40% All three 2.7 27% A page ranking #3 on Bing might be nowhere on Google. If your rank tracker only watches one engine, most of this picture is invisible to you. They rarely agree on #1 The single most valuable position — the #1 organic result — matched across all three engines for only 2 of the 7 searches where every engine answered (29%). Here is the real head-to-head: Search Google #1 Bing #1 Brave #1 Agree? best running shoes runrepeat.com wi

A generated image looks finished until review starts. Someone approves the first version. Someone else crops it. A branded copy goes out. Another edit changes the prompt. A week later, the useful question is simple: which prompt, model, seed, size, parent image, and publishing settings produced the version on screen? In a content studio, I put those answers in the PostgreSQL row that stores the image. Logs explain what happened during a run, then rotate away. Object storage keeps the bytes and forgets why they exist. The row is the only one of the three that survives edits, review, and publishing. 1. The row is the receipt The table in apps/api/src/database/init-ai-images-table.js treats generated and edited images as one record type. An original image gets its own row. An edit gets another row, with original_image_id pointing back to the parent. CREATE TABLE IF NOT EXISTS ai_generated_images ( id SERIAL PRIMARY KEY , image_url TEXT NOT NULL , -- what produced it prompt TEXT NOT NULL , model VARCHAR ( 100 ) DEFAULT 'fal-ai/imagen4' , model_version VARCHAR ( 100 ), seed BIGINT , width INTEGER , height INTEGER , -- how it derives from another row is_edited BOOLEAN DEFAULT FALSE , original_image_id INTEGER REFERENCES ai_generated_images ( id ), edit_prompt TEXT , edit_strength DECIMAL ( 3 , 2 ), -- what actually shipped branded_url TEXT , branding_options JSONB , metadata JSONB , tags TEXT [], created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ); That self-reference is the design choice. It makes the image table append-only-ish: new variants are inserted as new rows instead of overwriting the earlier state. The cost is more rows and more discipline at write time. The benefit is editable history that product screens and debugging queries can follow. flowchart TD original["Original row: prompt, model, seed, dimensions"] editA["Edited child: edit_prompt, edit_strength, edit_steps"] editB["Edited child: edit_prompt, edit_guidance_scale"] brandedA["Branded output: branded_url,

100 城时区页给跨区调度当速查，DST 自动算 作者是 数据管道 / 跨时区调度 方向的开发者。这篇不是广告，是踩坑记录 + 顺手做的工具。 背景 做 数据管道 / 跨时区调度 时，时间戳转换是最常被低估的雷区。16 个时间戳工具(Unix 转换/时区/ISO8601/Cron/Duration…) 已覆盖日常；但每个语言/框架的坑都不一样，所以又补了 30 个语言/框架时间戳页(python/javascript/java/sql/…)，每页含 6 个真实坑。 我踩过的坑（举几个） 秒 vs 毫秒：前端 Date.now() 是毫秒，后端常存秒，混用差 1000 倍。 时区不是字符串：存 UTC、展示本地，别把本地时间当 UTC 落库。 2038 问题：32 位系统 time_t 在 2038-01-19 溢出，老系统要提前查。 夏令时：一年有两次重复/缺失的本地时间，跨区调度尤其坑。 我顺手做的东西 转换速查页： https://gotimestamp.com/timezone/new-york 相关语言页： https://gotimestamp.com/timezone/london 开源 MCP： https://github.com/caresotin/tsforge-mcp —— 把时间戳转换/校验直接接进 LLM 工作流，不用手算。 小结 时间戳没那么简单，但工具到位就省心。上面都是免费、开源、可直接用的，希望对同样踩坑的人有帮助。

OpenAI and Anthropic admitted that their unreleased AI models escaped their sandboxes and hacked several companies in unprecedented cyberattacks. Who is legally to blame? Should prosecutors charge the two AI frontier labs? Can victims sue them? We spoke to lawyers who specialize in computer hacking laws to find out.

In a recent Azure Architecture blog article, Azure lead engineer Kishorekumar Pattabiraman outlines practical criteria for choosing between skills, sub-agents, and other approaches when building AI systems, emphasizing reusability, simplicity, and long-term maintainability. By Sergio De Simone

Apple has appealed a new legal demand by the U.K. government, which critics say could threaten the privacy rights of users all over the world.

Half the market is arguing about whether RAG or a semantic layer is the right foundation for enterprise AI. They are not competing. They answer different questions, and most teams need both. Two shapes of question Every question an agent receives breaks into one of two forms: "What did we say about X?" — lives in contracts, policies, tickets, docs. Unstructured. RAG was built for this. "What is true about X?" — lives in your warehouse and governed metrics. Structured. A semantic layer was built for this. Treating them as rivals is how teams end up with a system that can quote the pricing policy but cannot tell you this quarter's realised price. Where each one breaks RAG Semantic layer Good at Retrieving relevant prose Resolving definitions and joins Fails on Aggregation, math, current state Anything not modelled as data Permissions Flattened at ingest, rebuilt at query time Compiled per person, per query Answer stability Varies with retrieval ranking Identical by construction Audit story Cites a chunk Reproduces the exact SQL The permissions row is the one that ends pilots. A retrieval index that ingested everything has, by construction, assembled your most sensitive object — and reconstructing entitlement at query time is guesswork. The layer that actually decides Neither a document chunk nor a metric definition is worth much until something compiles it into a governed query and runs it. That is the piece most architectures are missing: intent → context resolution → constrained planning → governed execution . RAG can feed the first step. It cannot perform the last three. Point an agent at raw tables and the best models score in the low teens on real enterprise data. Give the same model compiled, governed context and it clears the high nineties. The retrieval quality was never the bottleneck. The full breakdown — the precise division of labour, why hybrid architectures win, and how compile-time governance closes the gap RAG cannot — is here: 👉 RAG vs. Semantic Layer

Data pipeline automation has moved from a technical aspiration to a business imperative. As organisations deploy more AI agents that need fresh, reliable data, the volume and complexity of data pipelines has grown beyond what manual management can sustain. AI-powered pipeline automation — using AI to build, monitor, and repair data pipelines — is the emerging solution. Key Insight: AI-automated data pipelines reduce pipeline development time by 60%, decrease pipeline failures by 45%, and enable data teams to manage 3x more pipelines per engineer compared to manual approaches. The Pipeline Scaling Challenge The average enterprise now maintains 1,500+ data pipelines, according to research by Barracuda Networks, and this number is growing 25% annually as organisations add new data sources, new AI use cases, and new reporting requirements. Each pipeline has an average of 4.2 transformation steps, 2.1 data quality checks, and connects an average of 2.8 systems. The total pipeline infrastructure is complex, fragile, and increasingly beyond the capacity of manual management. The scaling challenge manifests in three ways. First, pipeline development backlog — the average data team has a 3-6 month backlog of pipeline requests from business users. Second, pipeline failures — the average enterprise experiences 15-20 pipeline failures per week, each requiring manual investigation and repair that consumes 30-40% of the data engineering team's capacity. Third, pipeline maintenance — as source systems change (schema updates, API modifications, deprecated fields), pipelines break silently and produce incorrect results until someone notices. This 'silent failure' problem is particularly dangerous because it erodes trust in data without anyone being aware that anything is wrong. The root cause of these challenges is that data pipelines have been built as static, manually-maintained infrastructure. A pipeline is coded, tested, and deployed. When the source or destination changes, a hu

Vector databases have become the backbone of AI applications, powering semantic search, RAG systems, recommendation engines, and multi-modal AI. With the market maturing rapidly in 2026, choosing the right vector database impacts everything from query latency to operational costs. We ranked the 8 best options based on performance, scalability, ease of use, and enterprise readiness. TL;DR: Ranked 8 best vector databases: Pinecone leads for managed simplicity, Weaviate for flexibility, Milvus for open-source scale, and pgvector for PostgreSQL-native teams. Selection depends on your scale, latency requirements, and existing infrastructure. Key Evaluation Criteria for Vector Databases Modern vector databases must excel across multiple dimensions. We evaluated each option on: query performance (latency at various scales, index types supported), scalability (horizontal scaling, multi-tenancy), data type support (dense vectors, sparse vectors, multi-modal embeddings), integration ecosystem (SDKs, LangChain/LlamaIndex support), and operational maturity (hosting options, backup, monitoring). Query performance: P95 latency at 1M, 10M, and 100M vector scales Scalability: Horizontal scaling, sharding, multi-tenancy Data type support: Dense vectors, sparse vectors, binary vectors, multi-modal Integration: SDK availability, LLM framework support, MCP compatibility Operations: Self-hosted vs. managed, backup, monitoring, compliance Ranking: The 8 Best Vector Databases for AI Applications ### 1. Pinecone Pinecone remains the most popular fully-managed vector database, known for its simplicity and reliability. The 2026 release adds sparse-dense hybrid search, serverless tier with sub-millisecond P99 latency, and namespace-based multi-tenancy. Its serverless pricing model makes it cost-effective for variable workloads. * **Best for:** Teams wanting fully managed vector search without operational overhead * **Pros:** Zero operations, excellent performance, simple API, strong ecosystem

Continuando com a saga de siglas, encontrei de maneira simplista a versão oposta do ACID, o BASE....