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New satellite imagery reveals how much terrain has shifted in the wake of the twin quakes.

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。

Every open-source CVE backlog has that one line item you keep sliding into next quarter. The library is a couple of majors behind, the upgrade breaks four services, and the fix upstream ships against a version you cannot ride to. So you file the ticket again. (Everyone's doing great, thanks for asking.) On June 30, Aikido Security said it had acquired Root, whose whole pitch is to make that ticket go away by another route: patch the vulnerability directly into the version already resolved by your build, and skip the upgrade entirely. Per The New Stack, the deal is worth $70 million, and Root's patching technology gets folded into a new Aikido product. Let me phrase what has just moved as plainly as I can. A vendor now edits open-source packages on your behalf and hands you back a version string upstream never shipped. If that sentence made you flinch, hold the flinch. It is doing useful work. The problem this is actually solving The dirty secret of dependency remediation is that a lot of "known" CVEs sit unfixed because remediating them means a version bump that carries breaking changes. You do not get a security patch for the 2.x line, you get a "fixed in 4.0" release note and a laugh track. Backporting the fix is the right operational move: keep the API surface, change only the vulnerable bytes. Linux distributions have done exactly this for decades. The reason your app team is not doing it too is that nobody has the muscle to maintain a patched fork of every transitive dependency in a lockfile. If Aikido now makes that muscle available to the average CI/CD owner, teams get a lever they simply did not have. That is the honest upside. Own it. Who is signing what, exactly Here is the part I care about, which is trust. When your build resolves a package by name and version, you rely on a chain: the registry answers, the digest matches what upstream published, the SBOM you generate downstream still refers back to that same identity. A backported build breaks that chai

The White House is easing restrictions on Anthropic’s most advanced AI models weeks after ordering the company to suspend access for foreign nationals.

Also, the science of poop's distinctive shape, boron buckyballs, and the secret to a soccer feint.

At an event for pharmaceutical executives, biotech founders, and researchers on Tuesday, Anthropic announced Claude Science, a major new product intended to support scientific research in the same way that Claude Code supports software engineering. Like Claude Code, Claude Science can autonomously carry out meaningful work when given concise, high-level instructions, and it has access…

They may not look as good, but Nano Banana 2 Lite images only take a few seconds to create.

The Quest Begins (The "Why") Honestly, I was tired of playing “guess the state” every time I spun up a new environment. One day I clicked “Apply” in the AWS console, watched a handful of EC2 instances, S3 buckets, and IAM roles appear, and then realized I had no idea how to recreate that exact setup six months later when the team needed a staging copy. It felt like trying to rebuild the Death Star from memory after a single glance at the blueprints—frustrating, error‑prone, and definitely not the heroic saga I signed up for. That moment was my “aha!”: I needed a repeatable, version‑controlled way to describe infrastructure. Enter Infrastructure as Code (IaC). I’d heard the buzz, but the real question was which tool to wield—Terraform or CloudFormation? Both promised declarative provisioning, but they spoke different dialects. I decided to embark on a quest to learn both, slay the configuration drift dragon, and come out with a reusable spellbook I could share with anyone on the team. The Revelation (The Insight) The breakthrough came when I stopped thinking of IaC as “just another config file” and started seeing it as a storytelling language . Every resource block is a character, every variable a plot twist, and the state file the ever‑growing script that remembers what happened in previous chapters. When I wrote my first Terraform module, it felt like Neo realizing he could bend the spoon—suddenly the impossible became trivial. I could define a VPC, subnets, security groups, and an RDS instance in a few dozen lines, run terraform init , terraform plan , and watch the plan show exactly what would change before any resources touched the cloud. No more surprise “you created a public‑facing DB!” moments. CloudFormation, on the other hand, felt like the loyal sidekick that already lives in the AWS universe. Its JSON/YAML templates are native to AWS, so there’s no extra provider to install, and drift detection is built‑in. The trade‑off? A bit more verbosity and a steepe

Anthropic's Claude Science is a workbench that gives scientists one environment to do computational research, saving them from the need to bounce between databases, pipelines, and tools.

The updates include translations, new tools for hosts, and more.

A year in, National Design Studio delays plan to update government web standards.

To this day, we have yet to see a quantum computer conclusively perform a single useful task. Existing machines are simply too small and error-ridden to solve commercially relevant problems. That hasn't stopped Donald Trump's science adviser from promising a "quantum computer powerful enough for scientific discovery by 2028" and Trump from issuing a new […]

If your Terraform plans are slow, your blast radius is too wide, or multiple teams are stepping on each other's changes, it's time to split your monolith. See The Problem with Large Terraform States for how to diagnose whether you've reached that point. This guide walks through the process of breaking a monolithic Terraform state into smaller, focused states — and how Snap CD can manage the dependencies between them so you don't have to. The approach 1. Identify natural boundaries Look at your resources and group them by lifecycle and ownership. Common boundaries: Networking — VPCs, subnets, route tables, NAT gateways. Changes rarely, underpins everything. DNS — Zones, records. Usually owned by a platform team. Compute — Kubernetes clusters, VM scale sets, container services. Changes more often, depends on networking. Application infrastructure — Databases, caches, queues, storage accounts. Owned by application teams. Monitoring — Dashboards, alerts, log sinks. Changes frequently, depends on everything but nothing depends on it. A useful test: if two resources would never be changed in the same PR by the same person, they probably belong in different states. 2. Map the dependencies Before you move anything, draw the dependency graph. Which groups produce values that other groups consume? networking dns │ ▲ ▼ │ compute ──────────►─┘ │ ▼ application │ ▼ monitoring The outputs that cross these boundaries are what you'll need to wire up after the split. Typical examples: Networking → Compute: vpc_id , private_subnet_ids Compute → DNS: load_balancer_ip Compute → Application: cluster_endpoint , cluster_ca_certificate Application → Monitoring: database_id , cache_name 3. Use terraform state mv to migrate resources Terraform's state mv command lets you move resources from one state to another without destroying and recreating them. # Initialize the destination state cd modules/networking terraform init # Move resources from the monolith to the new state terraform state mv \

Sources suggest Musk may be mulling big donation to Trump Accounts.

At some point every growing Terraform project hits a wall. Plans that used to finish in seconds now take minutes. Applies feel risky because hundreds of resources share a single blast radius. Colleagues avoid running terraform plan because it hammers cloud APIs hard enough to trigger throttling. The state file itself becomes a liability — large, slow to lock, and one bad write away from corruption. This guide covers the symptoms of an oversized state, the band-aids teams reach for, and the structural fix that actually works. How Terraform state works under the hood Every terraform plan does two things: Refresh — for every resource in state, Terraform calls the provider's API to read the current real-world status. A state with 500 resources means 500+ API calls, often more when resources have nested data sources. Diff — compare the refreshed state against the desired configuration and produce a change set. The refresh phase is the bottleneck. It's sequential per provider (parallelism helps across providers, not within one), and every resource pays the cost whether you changed it or not. Adding ten resources to a 500-resource state doesn't make plans 2% slower — it makes the refresh 2% slower on every single plan, for every engineer, forever. Symptoms of a state that's too large Slow plans The most visible symptom. Plan time scales with resource count because every resource is refreshed on every plan, regardless of whether its configuration changed. The exact speed depends on provider — AWS resources with complex nested structures (IAM policies, security group rules) are slower to refresh than simple ones, and Azure resources that require multiple API calls per refresh are worse still. These aren't edge cases — users regularly report 2,900-resource states taking 20–25 minutes to plan and 1,600-resource states taking 8+ minutes . Even starting Terraform with a large state can take minutes before a single API call is made . There's a long-standing proposal for terraform

Extreme heat coupled with humidity will make it feel like 109 degrees Fahrenheit as the holiday weekend approaches.

X has launched a hosted MCP server, making it easier for developers to connect AI applications with the company’s API.

Sriram Madapusi Vasudevan discusses industry-converging patterns for securing autonomous AI agents in production. He explains the critical vulnerabilities hidden inside the ReAct loop across context, reasoning, and tool execution. He shares how to mitigate risks like memory poisoning and rogue tool execution using defense-in-depth strategies, LLM-as-a-judge critics, and MAESTRO threat modeling. By Sriram Madapusi Vasudevan

From October 10-16, host a Side Event and command the room during the week of TechCrunch Disrupt 2026.

Gov. Ron DeSantis calls it a crackdown on "radical climate policies."