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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.

Andrew Yang made a list of everything Americans overpay for — housing, food, wireless — and thinks the next startup gold rush is giving that money back.

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