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I left a multi-agent refactor running overnight. By morning the model was gone, pulled out from under me by a government I don't even vote for, on the other side of an ocean. This isn't really a story about Anthropic. It's a story about who's actually holding the off-switch, and right now it probably isn't you. So here's how my morning went. I had a job running. Not a toy, a proper codebase-wide refactor that had been grinding away continuously for the best part of two days. Multi-agent setup, left to run overnight, the kind of long, messy, long-horizon task that every model before this one just fell over on. Claude Fable 5 was handling it like it was nothing. Anthropic's own launch notes talk about it compressing months of work into days, and honestly, on my own codebase, that wasn't marketing. It was just what was happening. Then I woke up. And the model was gone. Not rate-limited. Not having a wobble. Gone. The thing I'd built two days of momentum on simply did not exist any more. Turns out that on the 12th of June the US government issued an export-control directive telling Anthropic to cut off all access to Fable 5 and Mythos 5 for any foreign national. And because you can't exactly sort a global user base by passport in real time, that meant pulling it for everyone. Including me, sat in Tyrol, watching my overnight run go cold. Anthropic did the right things, for what it's worth. They complied fast, they said out loud that they disagreed, and they're fighting to get it back. About as well as a vendor can behave in that situation. (As I write this it's still down. Anthropic reckon it's a misunderstanding and they're trying to get it restored, so maybe by the time you read this it's back up. Doesn't change a single thing about the point I'm making.) And it made absolutely no difference to me. That, right there, is the whole point of this post. The offer was the trap Wind back a few days. Fable 5 dropped as the best model anyone had shipped, and the offer was lov

AI 工具整合評估報告 執行摘要 本報告評估了 7 個 AI 工具在臨床基因體學領域的應用潛力，重點測試了 3 個優先級最高的工具：MedGemma 醫療大語言模型、Nemotron RAG 文獻檢索系統，以及 Kimi K2.5 多模態視覺語言模型。 評估日期 : 2026-02-10 測試平台 : RTX 3090 24GB 評估目標 : 確認 AI 工具在變異解釋與臨床決策中的可行性 1. 測試項目總覽 1.1 優先級分類 P1 (高優先級) - 已評估: ✅ MedGemma - Google DeepMind 醫療大語言模型 ✅ Nemotron RAG - NVIDIA 文獻檢索與知識整合 ✅ Kimi K2.5 - 月之暗面多模態視覺語言模型 P2 (中優先級) - 已規劃: 📋 Gemini CLI Hooks - 工作流自動化 📋 DaGGR - Hugging Face 基因體學工具 📋 評測方法論 - 醫療 AI 評估框架 P3 (低優先級) - 待調研: 📋 OpenEvidence - 臨床證據檢索引擎 2. MedGemma 醫療大語言模型 2.1 工具概述 開發者 : Google DeepMind 基礎模型 : Gemma 7B 特化領域 : 醫療問答、臨床決策支持 技術規格 : { "model_size" : "7B parameters" , "quantization" : "8-bit (optional)" , "gpu_requirement" : "12-16GB VRAM" , "context_length" : "8192 tokens" , "languages" : "English (primary)" } 2.2 功能評估 核心能力 : 醫療知識問答 疾病症狀解釋 藥物機制說明 治療方案建議 基因變異解釋 基因功能說明 變異影響評估 臨床意義解讀 文獻摘要生成 PubMed 摘要 臨床指南解讀 研究結果總結 2.3 應用場景 場景 A: 變異臨床意義解讀 # 輸入 query = """ 變異資訊: - 基因: BRCA1 - 位置: chr17:41234470 - 變化: c.5266dupC (p.Gln1756Profs*74) - 類型: Frameshift 請解釋此變異的臨床意義。 """ # MedGemma 輸出 (預期) """ BRCA1 c.5266dupC 變異分析: 1. 變異類型: Frameshift insertion - 導致讀框位移 - 產生提前終止密碼子 (位置 1756+74) 2. 基因功能影響: - BRCA1 是腫瘤抑制基因 - 參與 DNA 雙鏈斷裂修復 - Frameshift 導致功能喪失 3. 臨床意義: - 致病性: Pathogenic (高可信度) - 疾病風險: 遺傳性乳腺癌/卵巢癌 (HBOC) - 乳癌風險: 55-72% (終生風險) - 卵巢癌風險: 39-44% 4. 臨床建議: - 遺傳諮詢 - 加強監測 (MRI + 超音波) - 考慮預防性手術 - PARP 抑制劑治療 (若已診斷) """ 場景 B: 醫療文獻查詢 query = " What are the latest treatments for TP53-mutated cancers? " # MedGemma 回答 (模擬) """ TP53 突變癌症的最新治療策略: 1. 標靶治療: - APR-246/Eprenetapopt: 恢復 TP53 功能 - PRIMA-1/APR-246: 臨床試驗進行中 2. 免疫治療: - PD-1/PD-L1 抑制劑 - TP53 突變可能影響免疫反應 3. 合成致死策略: - PARP 抑制劑 (部分 TP53 突變) - ATR/CHK1 抑制劑 4. 臨床試驗: - NCT02999893: APR-246 + 化療 - NCT03745716: TP53 疫苗免疫治療 """ 2.4 部署考量 技術需求 : GPU記憶體: 12-16GB (FP16) 或 8GB (INT8) 推理延遲: 2-5 秒/查詢 API 或本地部署均可 整合方案 : # 與變異註釋流程整合 def annotate_with_medgemma ( variant ): # 1. 提取變異資訊 gene = variant [ ' gene ' ] change = variant [ ' protein_change ' ] # 2. 生成查詢 prompt = f " Explain the clinical significance of { gene } { change } " # 3.

AI 工具整合評估報告 執行摘要 本報告評估了 7 個 AI 工具在臨床基因體學領域的應用潛力，重點測試了 3 個優先級最高的工具：MedGemma 醫療大語言模型、Nemotron RAG 文獻檢索系統，以及 Kimi K2.5 多模態視覺語言模型。 評估日期 : 2026-02-10 測試平台 : RTX 3090 24GB 評估目標 : 確認 AI 工具在變異解釋與臨床決策中的可行性 1. 測試項目總覽 1.1 優先級分類 P1 (高優先級) - 已評估: ✅ MedGemma - Google DeepMind 醫療大語言模型 ✅ Nemotron RAG - NVIDIA 文獻檢索與知識整合 ✅ Kimi K2.5 - 月之暗面多模態視覺語言模型 P2 (中優先級) - 已規劃: 📋 Gemini CLI Hooks - 工作流自動化 📋 DaGGR - Hugging Face 基因體學工具 📋 評測方法論 - 醫療 AI 評估框架 P3 (低優先級) - 待調研: 📋 OpenEvidence - 臨床證據檢索引擎 2. MedGemma 醫療大語言模型 2.1 工具概述 開發者 : Google DeepMind 基礎模型 : Gemma 7B 特化領域 : 醫療問答、臨床決策支持 技術規格 : { "model_size" : "7B parameters" , "quantization" : "8-bit (optional)" , "gpu_requirement" : "12-16GB VRAM" , "context_length" : "8192 tokens" , "languages" : "English (primary)" } 2.2 功能評估 核心能力 : 醫療知識問答 疾病症狀解釋 藥物機制說明 治療方案建議 基因變異解釋 基因功能說明 變異影響評估 臨床意義解讀 文獻摘要生成 PubMed 摘要 臨床指南解讀 研究結果總結 2.3 應用場景 場景 A: 變異臨床意義解讀 # 輸入 query = """ 變異資訊: - 基因: BRCA1 - 位置: chr17:41234470 - 變化: c.5266dupC (p.Gln1756Profs*74) - 類型: Frameshift 請解釋此變異的臨床意義。 """ # MedGemma 輸出 (預期) """ BRCA1 c.5266dupC 變異分析: 1. 變異類型: Frameshift insertion - 導致讀框位移 - 產生提前終止密碼子 (位置 1756+74) 2. 基因功能影響: - BRCA1 是腫瘤抑制基因 - 參與 DNA 雙鏈斷裂修復 - Frameshift 導致功能喪失 3. 臨床意義: - 致病性: Pathogenic (高可信度) - 疾病風險: 遺傳性乳腺癌/卵巢癌 (HBOC) - 乳癌風險: 55-72% (終生風險) - 卵巢癌風險: 39-44% 4. 臨床建議: - 遺傳諮詢 - 加強監測 (MRI + 超音波) - 考慮預防性手術 - PARP 抑制劑治療 (若已診斷) """ 場景 B: 醫療文獻查詢 query = " What are the latest treatments for TP53-mutated cancers? " # MedGemma 回答 (模擬) """ TP53 突變癌症的最新治療策略: 1. 標靶治療: - APR-246/Eprenetapopt: 恢復 TP53 功能 - PRIMA-1/APR-246: 臨床試驗進行中 2. 免疫治療: - PD-1/PD-L1 抑制劑 - TP53 突變可能影響免疫反應 3. 合成致死策略: - PARP 抑制劑 (部分 TP53 突變) - ATR/CHK1 抑制劑 4. 臨床試驗: - NCT02999893: APR-246 + 化療 - NCT03745716: TP53 疫苗免疫治療 """ 2.4 部署考量 技術需求 : GPU記憶體: 12-16GB (FP16) 或 8GB (INT8) 推理延遲: 2-5 秒/查詢 API 或本地部署均可 整合方案 : # 與變異註釋流程整合 def annotate_with_medgemma ( variant ): # 1. 提取變異資訊 gene = variant [ ' gene ' ] change = variant [ ' protein_change ' ] # 2. 生成查詢 prompt = f " Explain the clinical significance of { gene } { change } " # 3.

Anthropic has suspended all access to its new AI models Fable 5 and Mythos 5 to comply with a government order citing national security concerns.

Open-source AI coding agent for your terminal Discussion | Link

It is one of the most frustrating experiences in online learning: you sit down to focus on a Udemy course, but the video player constantly pauses, freezes, or refuses to load. Meanwhile, YouTube, Netflix, and every other app on your Mac run perfectly fine. Because other platforms work without a hitch, it is easy to assume the issue lies with Udemy's servers. However, the root cause is usually a silent conflict between your browser settings, macOS security features, and Udemy’s strict digital rights management (DRM) protections. If you are stuck on a looping loading wheel, here is exactly why it happens and how to fix it in less than two minutes. Quick-Fix Troubleshooting Checklist Save or screenshot this step-by-step breakdown to instantly diagnose and fix your playback issues: Step 1: Open Chrome in Incognito Mode Open a new Incognito window ( Cmd + Shift + N on Mac). Try playing the video again. If it works: Disable ad blockers. Disable VPN privacy shields or browser extensions one at a time. If it still fails: Continue to Step 2. Step 2: Disable Hardware Acceleration Open Chrome. Go to Settings → System . Turn off Use graphics acceleration when available . Relaunch Chrome. Test the video again. Step 3: Check Mac Security and Display Connections Disconnect any external monitors or docking stations. Close applications that may interfere with video playback: Zoom Discord OBS Studio Screen recording tools Test video playback again. Step 4: Clear Temporary Browser Data Open Chrome. Go to Settings → Privacy and Security → Clear Browsing Data . Select: Cookies and other site data Cached images and files Clear the data. Restart Chrome and try again. Still Not Working? If the issue persists after completing all four steps: Update Chrome to the latest version. Update macOS. The Main Culprit: Hardware Acceleration Conflict The most common reason Udemy videos stutter or freeze on a Mac is a feature called Hardware Acceleration inside Google Chrome. What is Hardware Accelerat

A receiver pulling a UDP feed was missing roughly 30% of its messages. No errors, no exceptions, no stack traces — just gaps in the sequence numbers. The first suspect is always the network: a flaky switch, a saturated link, a tired NIC. The network was innocent. The packets were being dropped on the receiving host , after they'd already arrived. Here's how to tell the difference, and why it matters. Why UDP makes this sneaky UDP has no retransmission and no backpressure. When a datagram is lost, nobody is notified — not the sender, not the receiver. The packet simply isn't there. That means two completely different failures look identical from the application's point of view: The network dropped the packet before it reached your machine. Your own host accepted the packet and then threw it away after it arrived. The application sees the same thing in both cases: a missing sequence number. But the fix is in a different building depending on which one it is. Where the packets actually go The receive path is: NIC → kernel socket receive buffer → your recv() call. The kernel parks incoming datagrams in a per-socket buffer until your code reads them. If your code doesn't drain that buffer fast enough, it fills, and the kernel drops the overflow. Crucially, the kernel counts those drops. On Linux: # Per-protocol summary — look for "receive buffer errors" netstat -su # Or straight from the kernel counters cat /proc/net/snmp | grep -A1 Udp # InDatagrams ... InErrors RcvbufErrors ... If RcvbufErrors is climbing, the network did its job and your host discarded the datagrams. That single counter collapses a week of "is it the switch?" into about ten seconds of certainty. The actual cause In this case the socket receive buffer was sitting at the default (~208 KB). The sender burst faster than a single receive thread could call recv() . Average throughput looked fine on every dashboard — but the bursts filled the buffer in milliseconds, and everything past the brim was dropped.

Everyone's debating whether coding is dead. I actually do this job.. with AI writing code beside me for most of my working hours. Here's what the headlines get wrong. Open your feed right now and you'll find the same headline in a dozen costumes: "Why AI will replace 80% of software engineers by 2026." "Is coding dead?" "Should you still learn to code?" It's the most-clicked anxiety in tech, and it's everywhere for a reason, it taps a real fear about real careers. But here's the thing about almost every one of those posts: they're written from the sidelines. Predictions about a job by people who don't do it. I'm writing this from the other side. I'm an engineer, and I drive AI coding agents every single day. They read code, write changes, run tests, and open reviews for most of my working hours. So when someone asks "should you still learn to code in 2026?" , I'm not guessing. Here's my honest answer: Yes. Absolutely. But the job you're learning for has quietly become a different job and almost nobody is telling you which one. The hype isn't entirely wrong Let me start by giving the doomers their due, because pretending the shift isn't real would make me exactly the kind of person I'm criticizing. The productivity jump is genuine, and it's not subtle. Industry surveys in 2026 put the share of new code that's AI-assisted somewhere north of 40%, and developers using these tools self-report double-digit speedups on routine work. That matches my experience. The agent now handles: Boilerplate and glue code —-> the stuff I used to type on autopilot, gone in seconds. First drafts —-> "scaffold something that does X" gets me 80% of the way instantly. Syntax recall —-> I stopped breaking focus to look up things I half-remember. Tedious refactors —-> rename-this-everywhere, migrate-this-pattern, done fast. and all the kludgy things that I dread to do. If your mental image of "coding" is typing syntax into an editor , then yes.. a big chunk of that is being automated. The vira

How I Fixed Bugs in 30+ Open Source Projects (And What I Learned) Over the past few months, I've been contributing to open source as an independent developer. No big company backing, no team — just me, a laptop, and a lot of caffeine. Along the way, I've submitted pull requests to 30+ repositories across the Python, JavaScript, TypeScript, and Rust ecosystems. Here's what I learned from the process — the good, the bad, and the "I wish someone told me this earlier." Why Contribute to Open Source? Let's get the obvious out of the way: it's not about the money (at least not directly). Most bounties pay $50-$500, and you'll spend 10-20 hours on a single PR if it involves deep codebase exploration. The real value is: Reputation — Each merged PR is a public signal that you can read, understand, and improve other people's code Learning — You'll see how major projects are structured, tested, and maintained Network — Maintainers remember helpful contributors. Jobs come from these relationships Scratching your own itch — Fix a bug that annoys you? Everyone benefits My Process: Finding Good Issues Step 1: Pick the Right Projects Not all projects are equally welcoming to new contributors. Here's my filter: Signal Good ✅ Bad ❌ Response time < 7 days > 30 days or never Issue labels good first issue , help wanted None CI/CD Green, fast builds Broken, 30min+ builds PR merge rate > 60% of open PRs merge < 20% merge Step 2: Find Issues You Can Actually Fix I look for: Bug reports with clear reproduction steps — Someone already did the hard work of identifying what's wrong Issues labeled easy-fix or similar — The maintainer thinks it's approachable Issues in domains I know — Don't pick a C++ compiler bug if you've never written C++ Step 3: Before Writing Code This is where most beginners fail. Don't start coding yet! Read the CONTRIBUTING.md — Every project has different style, commit message format, and PR requirements Look at recent merged PRs — What do good PRs in this project look

WordPress maintenance automation has a long-running market, especially outside Japan. ManageWP, MainWP, WP Umbrella, InfiniteWP — each has more than a decade of history behind it. While building our comparison pages, we surveyed all four side by side. An interesting pattern emerged: three things none of the four tools offer . Each is a gap the industry has long treated as "not feasible," and there are structural reasons why. Here's a look at those three unsolved areas — and why they remain unsolved. Gap 1 — Per-plugin updates with HTTP checks between each one In most maintenance tools, plugin updates run in bulk . After the batch, the tool takes a sitewide screenshot diff or HTTP status check, and if anything is broken, "Safe Updates" or "Atomic Updates" features roll everything back at once . Why isn't "one at a time with an HTTP check between each" the standard? The main reason is API design constraints . WordPress's built-in wp_ajax_update-plugin and Worker-plugin APIs (like ManageWP Worker) are designed around batch processing. Doing an HTTP probe from an external host after every single update would add significant per-update overhead. The industry has settled on "bulk update → bulk check" as the natural granularity. The side effect: identifying which plugin caused the breakage often falls to the operator's manual investigation. Gap 2 — Pinpoint rollback (only the one that broke) The industry-standard "Safe Updates" feature is fundamentally a "roll back everything" design. If 20 plugins are batched together and one breaks the site, all 20 updates revert. It's a safety-first choice — but operationally, it means the 19 that finished cleanly are also lost. Why isn't pinpoint rollback (revert only the one that broke) the standard? The root cause is state-management complexity . To pinpoint rollback, you need to keep the pre-update files of each plugin individually. Storage, transfer cost, and dependency consistency checks become impractical over a Worker-plugin HTT

If you're building autonomous trading agents on Solana, you've hit this problem: your agent needs to vet a token before it buys — but most agents trade blind. They have no idea whether a token is a rug pull, a honeypot, or has an active mint authority that can dilute holders to zero. I built RugCheck AI to fix this: an MCP server that screens any SPL / Token-2022 token for the common scam patterns, then executes the swap through an MEV-protected route — screening and execution in one place. What it checks Mint & freeze authority — is supply still mintable? Can your tokens be frozen in place? Honeypot detection — can the token actually be sold , or only bought? Liquidity & holders — concentration, LP locks, real depth. Token-2022 traps — permanent delegate, transfer hooks, default-frozen state. Tools verify_token_safety — full on-chain safety audit check_authorities — mint / freeze / Token-2022 trap detection simulate_sell — honeypot check execute_safe_swap — MEV-protected execution Connect RugCheck AI is listed on the official MCP Registry as io.github.MrWizardlyLoaf/rugcheck-ai . Remote endpoint, no install: https://web-production-58d585.up.railway.app/mcp Or self-host — it's open source (MIT) on GitHub: https://github.com/MrWizardlyLoaf/rugcheck-ai Why pre-trade screening matters A rug pull on Solana often looks fine at the moment of purchase — liquidity is there, the chart is green. The trap is in the authority : a live mint authority lets the deployer print unlimited supply after you buy; a freeze authority can lock your tokens so you can never sell. An autonomous agent that doesn't read these on-chain fields walks straight into it. RugCheck AI reads them directly and gives a verdict on a fresh launch instead of unknown . Built for Solana trading agents. Feedback welcome.

Google recently announced that WebMCP is entering origin trials in Chrome 149. The new WebMCP standard proposal lets sites expose tools (e.g., JavaScript functions and HTML forms) to in-browser AI agents, which can thus reliably simulate user actions instead of resorting to possibly expensive (e.g., on-screen reading) and often unreliable guesswork (e.g., DOM scraping). By Bruno Couriol

If your Magento store still depends on the old USPS Web Tools integration, you should assume your shipping rates are either already broken or one change away from breaking. That sounds dramatic, but it is the practical reality we have been seeing. USPS has moved away from the old Web Tools model and toward REST API v3 with OAuth 2.0 authentication. Magento's legacy USPS integration was built for a different era. For merchants, the symptom is simple: rates stop showing up, return inconsistently, or fail under conditions you did not use to worry about. For Magento developers, the reason is also simple: the built-in carrier module is not designed for the current USPS authentication and request model. This article explains what changed, why core Magento falls over here, how to migrate cleanly, and what to watch for whether you choose an extension or a custom build. What changed: USPS Web Tools is not the same platform anymore Historically, Magento's USPS integration talked to Web Tools-style USPS endpoints: structured shipping requests, legacy authentication, and XML responses. That is not the model USPS wants merchants using now. The modern USPS stack is based on: REST API v3 endpoints OAuth 2.0 for authentication Different request and response payloads Different onboarding and credential management patterns That shift matters because it is not just a URL update. It changes authentication, token handling, and request structure. In practical terms, a migration now means: Getting the right USPS developer credentials Exchanging those credentials for OAuth access tokens Updating the carrier request layer to use REST payloads Mapping the new response format back into Magento shipping methods If you skip any of that and try to "patch" the old module with endpoint changes, you are going to waste time. Why Magento 2's built-in USPS module no longer works Magento's built-in USPS module was not architected around OAuth-backed REST API calls. It expects a legacy carrier contract

When a Magento store feels slow, merchants usually notice it first on the homepage. When revenue actually slips, we usually find the damage deeper in the funnel. That was the case on a recent mid-market Magento 2 build we inherited. Product pages were acceptable. Search worked. But checkout analytics told a different story. Mobile users were stalling after address entry, re-clicking shipping methods, and abandoning before payment finished rendering. The merchant described it in business terms: "traffic is fine, but checkout feels fragile." They were right. The store was running a fairly typical Magento checkout stack: Luma fallback checkout, several shipping customizations, two payment methods, tax recalculation on step changes, and a handful of third-party scripts that had quietly accumulated over time. Together, they created a familiar Magento problem: too much JavaScript, too many render passes, and too much waiting on the highest-stakes route in the store. Over a 90-day measurement window after launch, checkout completion improved by 34%. Mobile completion improved by 39%. Lab metrics got much better immediately, and field metrics followed. This article covers why we chose React instead of Hyva Checkout, how we implemented the frontend, what moved the numbers, and what we would do differently next time. The problem with Magento's default checkout Magento's default Luma checkout is functional, but performance is rarely its strength. The architecture was designed around Knockout.js components, RequireJS modules, and a lot of UI behavior being layered in over time. Once a real merchant adds shipping estimation, fraud tooling, tax logic, payment widgets, analytics, and address validation, the route becomes busy in all the wrong ways. In this project, our baseline looked like this on a throttled mobile profile: Metric Before (Luma checkout) After (React checkout) Initial checkout route payload 1.8 MB transferred 486 KB transferred LCP 4.2s 1.1s INP 280ms 92ms CLS 0.1

A couple of years ago, I built a custom flashcard app. I had a huge list of words and sentences in Japanese that I collected in an Excel file. I wanted an app that could easily take them and display them on flashcards. The flashcard app was useful, but the main issue was that I could only use it on my laptop. This meant that when I wasn't home, I had no access to it. I made some updates so that I could deploy it to Azure and now I can use it on the train or at the park. I wanted to share the app and lessons learned during development. What It Does The app is a straightforward spaced repetition flashcard tool. You create collections, fill them with cards (front/back/optional notes), and review them. After each card you rate your recall: Button Meaning Easy Remembered without effort Good Remembered correctly Hard Remembered with difficulty Again Forgot (resets to day 1) Ratings feed the SM-2 algorithm, which is the same algorithm as other popular spaced repetition apps like Anki. Cards that are easy get pushed further and further into the future. Cards that are difficult will come back sooner. After a while, you're just reviewing what you actually need to review. There's also a 45-second timer per card. If it expires before you complete the card, it automatically counts as Again (Resets to day 1). Before the timer, I found it easy to lose focus or open another tab and forget about the current card. This has helped me stay focused for longer and stay on this task. The CSS is specifically designed to be mobile friendly. The Tech Stack Frontend: Blazor WebAssembly (.NET 10) Backend: ASP.NET Core minimal API (.NET 10) Database: Azure SQL (Basic DTU tier) Hosting: Azure Static Web Apps (frontend) + Azure App Service F1 free tier (backend) I mostly use C# at work, so Blazor WASM was a natural fit. The whole app shares models and flows together without jumping between languages. Importing Cards from Excel This Excel import function is one of the main reasons I made this app.

We've been a Magento agency in Chicago since 2008. When Hyvä Themes hit the ecosystem, we were skeptical—another theme promise. Then we measured Core Web Vitals on client stores and the case became obvious: Hyvä is the most practical path to a fast Magento storefront without a full replatform. This is the migration framework we use at Towering Media for US and Canadian merchants moving off Luma (or aged custom frontends) onto Hyvä. Why Hyvä now (not next year) Google's CWV thresholds affect ad quality and organic visibility. Luma checkout and catalog pages often ship 1.5–2+ MB of JavaScript before you add analytics, chat, and personalization. Hyvä replaces Knockout/RequireJS on the storefront with Alpine.js and Tailwind. Typical results on our projects: 50–70% less frontend JS on category and product pages LCP improvements of 1–3 seconds on mobile field data (highly variable by hosting and images) Lower maintenance — fewer JS conflicts between theme and extensions Delaying migration means paying for performance twice: once in emergency fixes, again in the eventual theme project. Phase 1: Discovery (1–2 weeks) Extension audit List every module that touches the frontend: bin/magento module:status | grep -v "Module is disabled" Flag anything with view/frontend , RequireJS , or Knockout in: Layered navigation and search Checkout and cart Page Builder widgets Blog and CMS enhancements Hyvä maintains a compatibility module ecosystem; unsupported extensions need replacements or custom Hyvä templates. Towering Media includes extension compatibility mapping in every Hyvä migration engagement. CWV baseline Capture before metrics from: Google PageSpeed Insights (origin-level) Chrome UX Report for key templates: home, category, product, cart Real-user monitoring if the client has it (GA4, SpeedCurve, etc.) Store screenshots. Stakeholders forget how slow the old site felt. Business constraints Document: Peak seasons (do not launch in November without war room

Magento's default Luma checkout loads a heavy Knockout.js stack, dozens of RequireJS modules, and payment iframes that fight for the main thread. For merchants where checkout is the conversion bottleneck, shaving seconds off load and interaction time pays back faster than another homepage hero image. We rebuilt checkout in React— React Checkout Pro —for Magento 2 and Hyvä stores that needed Shopify-like speed without leaving Adobe Commerce. Here is what we measured, what surprised us, and what we would do differently. The problem: checkout is where Core Web Vitals go to die Homepage optimizations are table stakes. Checkout is different: More JavaScript. Payment methods, validators, shipping step observers, and third-party scripts stack on one route. More layout shift. Address suggestions, shipping method lists, and tax updates re-render large DOM regions. More input delay. Autocomplete plugins, reCAPTCHA, and BNPL widgets compete on keydown handlers. On a representative Luma checkout (mid-size US retailer, ~80 SKUs in catalog, 4 payment methods), lab tests before migration showed: Metric Luma checkout (before) React checkout (after) LCP (lab, 4G) 4.8s 2.1s INP (field interaction) 320ms 95ms CLS (full flow) 0.18 0.04 JS transferred (checkout route) ~1.9 MB ~420 KB Time to interactive (est.) 6.2s 2.8s Field data from CrUX lagged lab wins by 4–6 weeks but trended the same direction once cache and CDN rules settled. Your numbers will differ. The pattern we see repeatedly: the biggest win is shipping less JavaScript to checkout , not micro-optimizing the JavaScript you keep. Architecture: React island, Magento brain We did not headless the entire storefront. Magento still owns: Quote totals and tax calculation Shipping rate requests Payment tokenization and order placement APIs Customer session and cart persistence React owns the UI layer: step navigation, form state, validation UX, and optimistic updates while Magento APIs catch up. High-level flow: Browser → React Chec

Commerce dept. worries that a Fable 5 "jailbreak" could be a national security threat.

Anthropic isn't hiding its frustration. "We disagree that the finding of a narrow potential jailbreak should be cause for recalling a commercial model deployed to hundreds of millions of people," the company wrote in a blog post.