标签：#Go

AI has made it a lot harder for tech companies like Amazon and Google to deliver on their net-zero pledges.

The EU went after Google for the practice of bundling its search engine and browser with Android.

This tutorial was written by Néstor Daza . This is the second article in a series about building Claudius , my own Claude-based chatbot ( Github ). The prologue made the case for building it, and for choosing MongoDB as its foundation. Open the conversations collection in Claudius’ database and you find the usual fields of a thread header but nothing else: a userId , a title , some timestamps , and so on, but no array of messages, no messages collection sitting beside it either! The text of every conversation lives somewhere else entirely, in the LangGraph checkpointer, which I wire up later in this series. This absence is a modeling decision, and how I came up with the database schema for my chatbot is the theme of this article. If you come from a relational background, you're used to modeling the data first when designing a database. For a project like this, you would start by finding the entities and normalizing them, and the final schema would come out of the data's structure: a conversations table and a messages table with a foreign key between them, because that is what the data looks like. Document modeling runs the other way. You start from how the application reads and writes, and the shape of the document follows the access patterns. Claudius never reads conversation messages without the agent's full working state wrapped around them, and that state is persisted using the LangGraph checkpointer. A separate messages table would add nothing, since the app would always have to join it back to that state on every read. The access pattern says the messages belong with the agent state, so that is where they go, and conversations are left as the lightweight header the list view actually needs. That inversion, modeling around use rather than around the data, runs through everything below. Schema-flexible is not schemaless This is the misconception lots of people often carry, and it is worth killing on the way in. A document database does not mean no schema; it mea

OpenAI CEO Sam Altman has reportedly proposed giving 5% of the company’s equity to a U.S. sovereign wealth fund, reviving discussions about letting the public share in the financial gains from the AI boom.

A top Democrat on the Senate's Intelligence Committee warned that the information accessed on a Homeland Security intelligence-sharing network may risk national security.

Even the most advanced enterprise systems are tethered to a costly paradox: manual bottlenecks that introduce critical errors, security risks, and slow innovation. These hidden operational anchors are the friction preventing your organization from realizing its full potential. The Challenge: Manual Bottlenecks in Modern Enterprise Operations In an era defined by cloud-native architectures, microservices, and declarative infrastructure, a persistent and costly paradox remains at the heart of enterprise operations. We have built systems capable of immense scale and resilience, yet they are often tethered to manual, human-driven processes that act as operational anchors. These bottlenecks aren't just minor inefficiencies; they are critical points of failure, introducing latency, human error, and security vulnerabilities into our most important workflows. They represent the friction that slows down innovation, drains resources, and prevents organizations from realizing the full potential of their digital investments. Before we can orchestrate an autonomous workspace, we must first dissect the anatomy of these manual constraints. Identifying the High Cost of Manual Invoice Reconciliation To ground this challenge in reality, consider a ubiquitous and deceptively complex business process: accounts payable invoice reconciliation. On the surface, it seems simple. In practice, it's a classic example of a high-friction, manual workflow that silently bleeds enterprise resources. The typical process is a gauntlet of context-switching and swivel-chair integration: An invoice arrives, often as a PDF attached to an email, with no standardized format. A finance professional must manually open the document and visually identify key data points: invoice number, date, vendor, line items, and total amount. They then pivot to an ERP system like SAP or NetSuite to find the corresponding Purchase Order (PO). Next, they might need to access a separate logistics or warehouse management syste

Google tries balancing AI data center emissions with clean energy efforts.

Interesting paper: “ Cybersecurity Mission Creep .” Abstract: Cybersecurity is experiencing mission creep. Policymakers are casting more and more problems as issues of cybersecurity. So reframed, wildly different policy issues, from misinformation, to child social media safety laws, to antitrust regulations, to alleged journalist misconduct, to anti-sex trafficking statutes become what this Article calls “cybersecuritized.” Before this reframing, these issues present as important but not existential. But once cybersecuritization positions the issues as threats intensified by their technological nature, they gain access to the politics and law of urgency and exceptionalism and invite troubling governance responses...

Apple chose Google Cloud to run Private Cloud Compute outside its own data centers for the first time, using NVIDIA Blackwell GPUs, Intel TDX, and Google's Titan chip. Apple maintains an independent append-only hardware ledger and dual-vendor attestation roots. AWS and Azure are not part of the collaboration. By Steef-Jan Wiggers

When I checked Google Search Console after a month, only 2 of my 8 sites were indexed. The other 6 had zero pages in Google's eyes. No penalty, no error banner. Just silence. The bug My build script generated the sitemap by mapping over page objects. Somewhere a URL field was an object, not a string. So the sitemap shipped lines like: <url><loc> https://example.com/[object Object] </loc></url> Google fetched the sitemap, saw garbage URLs, and quietly skipped the whole file. No crawl, no index. How I caught it GSC > Sitemaps > it said "Success" but "Discovered pages: 0". That mismatch is the tell. I opened the raw sitemap.xml in the browser and searched for [object . There it was. Root cause: url: page.url where page.url was itself { path, params } , not a string. The fix // before loc : page . url // -> [object Object] // after loc : `https://livephotokit.com ${ page . path } ` Redeployed, resubmitted the sitemap, and requested indexing on the core pages. Pages started landing in the index within a couple of days. Takeaway A "Success" status on your sitemap does not mean Google read your URLs. Always open the raw XML and eyeball it. One bad [object Object] can silently sink an entire site. I'm building LivePhotoKit and a handful of other small tools solo with AI. Sharing the real bugs as I hit them.

📚 Series: Databricks on AWS (Part 0, prologue) The Target Architecture ← you are here Building a Databricks AI Platform on AWS RBAC with Function-Role Groups Compute Governance: Pools, Policies, Clusters The BOOTSTRAP_TIMEOUT Mystery Fixing It with AWS PrivateLink How We Structure the Terraform Before the build story, here's the destination. This is the target-state data architecture we designed the whole platform toward — the three principles that shaped every later decision, and the Unity Catalog governance model that keeps production data safe from human hands. The rest of this series is a build log: workspaces, RBAC, compute, the networking rabbit hole, the Terraform layout. But every one of those decisions was made in service of a target picture we drew first . This post is that picture — the "to-be" architecture, not the scaffolding we happened to have up on any given week. It's built on three things Databricks basically hands you if you lean into them: the Lakehouse (one store, ACID tables, no separate warehouse to sync), the Medallion architecture (raw → cleaned → integrated → business, each layer a promotion), and Unity Catalog as the single governance plane across all of it. The interesting part isn't reciting those three buzzwords — it's the specific way we wire them so that prod, dev, and analyst sandboxes never step on each other. Three principles, and everything follows Almost every concrete rule later in this series is a consequence of one of these three. 1. Nobody touches production by hand. Create, update, delete in prod data happens only through an automated, code-reviewed pipeline running as a service principal. Human accounts don't get write on prod — not analysts, not engineers, not admins. The blast radius of a bad afternoon is capped at whatever a person can do with read-only. This one principle is why the whole "promote" flow later exists. 2. Never copy production to look at it. If an analyst wants to explore the gold layer, they read it in p

Meta says usernames improve privacy, but critics question whether its safeguards can prevent impersonation.

Cloudflare is giving AI companies until September 15 to separate web crawlers used for search from those used for AI training and agents, or risk being blocked by default on many publisher sites.

The government has removed restrictions on Anthropic’s Fable 5 and Mythos 5 AI models—but there were strings attached.

Venice AI is already profitable, with annualized run-rate revenues of over $70 million, CEO Erik Voorhees said.

Smart speakers have spent the past few years searching for a compelling second act. Beyond music, timers, and controlling your lights, they've struggled to justify taking up space on the kitchen counter. AI promised to change that. Amazon debuted its new hardware powered by a revamped Alexa last fall, and now it's finally Google's turn. […]

Papa Johns is spying on people’s buying activities to predict when they are low on food: The pizza chain recently tapped NBCUniversal, Instacart and the dentsu-owned media agency Carat for help reaching consumers when they’re low on groceries—and thus more likely to be swayed by a mouth-watering ad. The idea is to reach hungry consumers by “knowing what is in their fridge without being too creepy,” said Carrie Drinkwater, chief investment officer at Carat. To achieve that goal, NBCU and Instacart created a custom audience of shoppers who regularly purchase grocery staples on Instacart, such as eggs, milk, meat and produce. Based on that data, Papa Johns can determine which days of the week certain consumers are likely to run out of groceries and serve them an ad on NBCU streaming content accordingly. The brand served custom creatives to consumers based on their food preferences—such as whether they buy meat regularly—with QR codes and calls to action such as, “Light on groceries?” or “Empty fridge?”...

Adjust your options for things like account recovery, ad personalization, and which parts of your Google profile are shared publicly.

客戶開價太低嗎？Freelancer 接案前的 3 問決策樹 客戶說：「就改幾行代碼，收這麼多？」 你是不是也曾這樣懷疑過自己？ 每個 freelancer 都遇過這種時刻——客戶開了一個數字，你直覺「好像太低了」，但又說不出具體原因。以下是三個問題，幫你在 30 秒內判斷一個報價是否值得接。 3 問決策樹 Q1：這個價格是否覆蓋你的實際時間成本？ 別只算「改了幾行代碼」。真實成本包括： 讀懂陌生的 codebase（新手可能 3 小時起跳） 本地環境折騰（特別是別人維護的老項目） 測試和部署風險（部署壞了誰負責？） 客戶來回溝通的成本（「再大一點」「這個藍再淺一點」） 未知因素：如果代碼原作者已經不在，你是在維修「別人的技術債」 快速算法 ：把報價 ÷ 你估計的總小時數 = 每小時實際時薪。拿這個數字和你的底線比（建議：不是你「想要」的時薪，而是你「能接受吃飯」的時薪）。 如果低於底線 30%，進 Q2。 Q2：需求是否清楚到可以控制風險？ 報價低且需求模糊 = 高危信號。 以下任一癥狀存在，提高風險溢價或拒絕： 「就簡單改一下」——沒有定義邊界 沒有明確定義「完成」的標準——上線了算完成？客戶滿意了算完成？ 對方說「你先做再說」——這句話幾乎等於「我打算白嫖你」 沒有提供任何文件或代碼庫 access——等於讓你盲開 決策樹 ： 需求不清楚 + 報價低 → 報價必須上浮 50%，否則不接 需求不清楚 + 報價合理 → 可以談，先付定金再動工 需求清楚 + 報價低 → 進 Q3 Q3：這個案子是否帶來明確後續價值？ 有兩種情況可以在低報價下仍然接： 確定的後續項目 ：客戶明確說「這個做好了，下個月還有 X 個功能要做」 戰略性客戶 ：這個客戶有公開作品價值（大厂案例、知名公司、能寫進 portfolio 的上線項目） 如果兩者都沒有，低報價等於純粹的自我低估。 真實案例：隱藏成本解析 案例 1：$200 改 3 行 CSS 客戶說：「就改導航列的顏色，$200 應該夠了吧？」 表面看：3 行 × $66/行 = 天價。 現實： 理解整個樣式系統、找到正確的 CSS 檔案：2 小時 本地環境折騰（別人的專案，Node 版本衝突）：1 小時 反覆修改確認視覺效果：3 小時（客戶說「那個藍再淺一點、再加個 hover 效果」） 部署時發現壞了其他頁面：2 小時 客戶最後說「還是原來的好」：情緒成本 實際時薪 ：$200 ÷ 8 小時 = $25/小時，低於 freelancer 最低生存線。 案例 2：$2,000 報價改 2 天的「簡單項目」 客戶說：「做一個登入系統，就基本功能，2 個禮拜夠了吧？」 報價 $2,000，看起來還不錯。 現實： 需求訪談：4 小時（客戶一開始說「就登入」，後來才說「還要有第三方登入、密碼重置、邀請機制」） 設計資料庫結構：3 小時 實現 Registration + Login + OAuth：6 小時 測試覆蓋：4 小時 文件撰寫和交接：2 小時 實際 ：19 小時 × $105/小時 = $1,995 ——這個案子壓根不賺錢 常見陷阱：為什麼低報價 freelancer 總是吃虧 1. 「就幾行代碼」陷阱 代碼行數 ≠ 工作量。真正的成本在「理解上下文」——你得讀懂別人的代碼邏輯，這可能比你自己寫慢三倍。 2. 「簡單的 SQL」陷阱 每一條看似簡單的 UPDATE 語句，背後可能是： 凌晨 3 點資料庫突然鎖死 備份失敗、沒有測試環境 正式資料一個失误就沒了 3. 「長期合作」陷阱 客戶說「我們長期合作」通常是好事，但前提是—— 報價不能因為「長期」而打折 長期合作應該帶來穩定收入，不是穩定低價 你現在有一個具體報價嗎？ 如果客戶給了你一個數字，你不確定是否該接—— For $10, I'll review one client offer and tell you whether it looks underpriced, risky, or worth taking. 直接發報價截圖或文字到 paypal.me/cheapuno ，標註「報價審查」，24 小時內回覆具體分析。 快速決策檢查表（列印出來放桌邊） □ 報價 ÷ 預估時數 > 我的底線時薪？ □ 需求有明確定義邊界嗎？ □ 有隱藏的技術債或未知因素嗎？ □ 客戶有明確的後續項目或品牌價值？ □ 我有權利說「不」嗎？ 如果以上有任何一個「否」，這個報價需要重新談。 如果你想系統性学会如何報價、報價低了怎麼談、客戶不接受怎麼辦——歡迎從 Freelance Pricing Master Index 開始，這裡有 14 篇文章覆蓋 freelancer 定價的各種場景。

A reviewer that flags the same known issue on every run trains you to ignore it. The fix can't be "hide findings," because a tool that silently drops things is worse than one that nags. CommitBrief has two ways to accept a finding and move on — a per-developer baseline and an in-source suppression marker — and both are built so that what they remove is always counted, never quietly swallowed. The interesting part is how a finding keeps its identity when the code around it moves. TL;DR Baseline ( .commitbrief/baseline.json , gitignored): accept the current findings once; later runs drop anything whose fingerprint is already in the file. Inline suppression : a commitbrief-ignore: <reason> comment on or above a line removes that finding — and lives in committed source, so a reviewer sees it. A finding's fingerprint deliberately excludes its line number , so accepting it survives the code drifting up and down the file. Both are TRUE removals — they affect --fail-on and the JSON findings[] , not just the display — and both print what they removed. The limit. The baseline is per-developer, not a shared team policy; it quiets your runs, not CI's. The fingerprint that survives code drift The whole design rests on one question: when is a finding "the same finding" you already accepted? If the answer included the line number, a baseline would evaporate the moment you added an import above the issue. So it doesn't. A finding's identity is three fields, hashed: func normalizeTitle ( title string ) string { return strings . ToLower ( strings . Join ( strings . Fields ( title ), " " )) } func Fingerprint ( f render . Finding ) string { h := sha256 . New () h . Write ([] byte ( f . File )) h . Write ([] byte { 0 }) h . Write ([] byte ( f . Severity )) h . Write ([] byte { 0 }) h . Write ([] byte ( normalizeTitle ( f . Title ))) return hex . EncodeToString ( h . Sum ( nil )) } File, severity, and a normalized title — and nothing else. Line is out, so the same issue keeps its finger