Your AI doesn't know which button you mean Discussion | Link

Meta has been ordered by the European Commission to restore free WhatsApp access for chatbots made by rival AI providers while the regulator finishes its antitrust investigation. The rare interim measure announced on Tuesday was deemed necessary "to prevent serious and irreparable damage to competition" in the general-purpose AI assistant market. This is only the […]

> For Fable 5, Mythos 5, and future models on Bedrock with similar or higher capability levels, Anthropic will require 30-day retention for all traffic on Mythos-class models. Retaining data for a limited period allows Anthropic to detect patterns of misuse that are not visible from a single exchange. Once you opt into data retention, your data will leave AWS’s data and security boundary. From the announcement here: https://aws.amazon.com/blogs/aws/anthropic-claude-fable-5-on-aws-mythos-class-ca

Every QA team knows the feeling. The home screen works. Browse works. Search works. Cart works. And...

submitted by /u/feross [link] [留言]

AI-native billing & payments for usage-based AI products Discussion | Link

Google pushed a 4GB local AI model to Chrome through silent updates and did not provide a disable switch until version 149. Users had to delete the file manually and it would be re-downloaded on restart. The reason this matters is not the storage. It is the consent. An AI model running in my browser is a category different from a calculator widget. It sends data to an inference engine, consumes power, generates heat, and runs code. Not having a clear off switch is not an oversight. It is a product philosophy about whether the user is in control. I do not think local AI is inherently bad. For real-time search suggestions or on-device content filtering it is useful. But the deployment model matters. If I install something, I should know what it does and how to turn it off. The update that installed the model was silent and the documentation was buried. The switch to disable it only appeared after sustained user complaints. The lesson is that capability is not what builds trust. The ability to turn it off is. submitted by /u/Fantastic-Place5501 [link] [留言]

submitted by /u/feross [link] [留言]

The 168-megawatt facility will support Meta's global AI computing needs and can be expanded over time.

Logitech finally announced its new ultraportable travel mouse following leaked marketing images that spoiled the surprise last month. As the name implies, the Mobi Fold is a compact mouse that can fold in half using a hinge that can pivot about 130 degrees. At $79.99 in graphite, off-white, lilac, and sand color options, the Mobi […]

The Mobi Fold is an excellent travel accessory for anyone who wants something that's better than the built-in touchpad on their laptop.

Earlier parts examined the performance characteristics of sequential and random access under single-threaded execution, and noted in passing the destructive effect of random access on the TLB. This part devotes full attention to the TLB: what it is, why a TLB miss is more severe than a cache miss, why a page table walk constitutes one of the longest dependency chains a CPU can encounter, how huge pages fundamentally alter TLB reach, and where memory-level parallelism falters in the face of TLB misses. Page Boundaries: Where the Prefetcher Halts Part III, in its discussion of prefetchers, noted a hard constraint: a prefetcher must not cross page boundaries on its own authority. The operating system manages virtual memory in units of pages (typically 4 KB, i.e., 64 cache lines). When a program reaches the end of one page and is about to step into the next, the prefetcher cannot proceed. The reason is that the next page may not reside in physical memory (it may have been swapped out to disk), or it may be an entirely invalid virtual address — if the prefetcher were to speculatively initiate an access to the next page, it would trigger a page fault: the OS would have to suspend the process and swap the page in from disk; in the case of an invalid address, the OS would terminate the process outright. From a security standpoint, the prefetcher neither can nor is permitted to autonomously cross page boundaries without TLB approval. Hence a performance brake appears every 4 KB — even when traversing an array sequentially, after every 64 cache line accesses the prefetch pipeline must pause and await confirmation of an address translation. This is not to say that modern CPU prefetchers are completely unable to cross pages. Intel's Next Page Prefetcher and AMD's equivalent mechanism can consult the TLB when approaching a page boundary — if the address mapping for the next page is already registered in the TLB, the prefetcher receives clearance to continue prefetching across th

Building a "paste a YouTube link, get a transcript" feature sounds trivial until you deploy it to a server. The moment your request comes from a datacenter IP instead of a residential one, YouTube responds with LOGIN_REQUIRED or quietly serves nothing. Here's how VidTranscriber handles it. The problem There are two ways to get text from a YouTube video: Existing captions — if the uploader (or YouTube's auto-caption) provides them, you can fetch the caption track directly. Fast, free, no transcription needed. Transcribe the audio — pull the audio stream and run it through a speech-to-text model (Whisper-family). Works for any video, but costs compute. Both start with talking to YouTube from your server — and that's where it breaks. YouTube aggressively gates datacenter traffic: the watch page and InnerTube API return LOGIN_REQUIRED , and naive audio fetching gets reCAPTCHA'd. The approach The fix is to separate where the request originates from where the work happens : A Cloudflare Worker handles the user request and orchestration. Caption/audio fetching is routed through a path whose egress isn't treated as a bot — so the LOGIN_REQUIRED wall doesn't trigger. Captions, when available, become the primary path (no transcription cost). Only when there are no usable captions do we fall back to downloading audio and running Whisper. Long jobs go onto a queue (Cloudflare Queues) so the request returns immediately and the transcript streams in as it completes. Why captions-first matters Most "transcript generator" traffic is for videos that already have captions — talks, tutorials, news. Serving those from the caption track is instant and free, which means the expensive Whisper path is reserved for the minority of videos that actually need it. That's the difference between a tool that's cheap to run and one that isn't. What's still hard IP reputation drifts — what works today can get throttled tomorrow, so the extraction path needs monitoring and fallbacks. Caption quality

📝 Originally published on unfoldcms.com — reposted here for the DEV community. (I work on UnfoldCMS.) A coupled CMS puts the admin login on the same hostname visitors reach. A headless CMS puts it on a different hostname behind auth. That single architectural difference is why headless CMS security is meaningfully better than traditional coupled-CMS security on most real-world dimensions — and it's also why the comparison gets oversimplified into "headless is more secure" when the truth is more interesting. This post is the architectural take on headless CMS security : why decoupled is safer on most dimensions, where it can be less safe if you don't handle API hygiene properly, and what the honest comparison looks like in 2026. TL;DR : headless wins on attack-surface reduction (admin off the public hostname, smaller plugin attack surface, API-first auth model) but loses on dimensions teams typically don't think about (exposed APIs without rate limits, JWT misuse, secrets in frontend code, draft preview tokens leaking). A well-built headless CMS is meaningfully more secure than a typical WordPress site; a poorly-configured headless CMS can be worse than a maintained WordPress site. The architecture biases toward safer; the implementation determines actual outcomes. The audience: technical decision-makers and security-conscious teams comparing CMS architectures with security as a deciding factor. If you're earlier in the architectural decision, see headless CMS vs traditional CMS: key differences . For the WordPress-specific security picture this post compares against, WordPress security problems in 2026 . The Attack Surface Difference The single biggest architectural difference between coupled and headless CMS security is where the admin lives . A traditional WordPress site puts the admin login at yourdomain.com/wp-admin . The same hostname your visitors reach. The same SSL cert. The same Cloudflare config. Every brute-force attempt, every credential-stuffing bot, ev

📝 Originally published on unfoldcms.com — reposted here for the DEV community. (I work on UnfoldCMS.) WordPress's market share among CMS-using websites dropped from 65.2% in 2023 to 60.2% by Q1 2026, contracting -2.9% year-over-year for the first time in over a decade. The platform that built the modern web is losing share to Wix, Squarespace, Shopify, and an emerging headless category — and the trend is steeper among newly-built sites than the headline number suggests. This post is the data-driven take on WordPress market share declining in 2026 — where the numbers actually come from, where the lost share is going, what age-cohort analysis reveals about the trajectory, and what the developer-signal data (Stack Overflow, GitHub) shows about who's still building on WordPress versus moving on. TL;DR : WordPress isn't collapsing — 60% market share is still dominant — but the lead has shrunk for the first time in 10 years, the cohort of new sites is breaking away faster than the overall number suggests, and the developer mindshare is leaving even faster than market share. The structural pressures (security, performance, plugin tax, modern stack expectations) all point the same direction. The audience: developers, agencies, and CTOs trying to read the WordPress market trend before committing to a multi-year platform decision. If you've been told "WordPress is sinking" or "WordPress is fine, market share gossip is overstated," this post puts numbers behind the actual movement. For the broader context on why WordPress is losing share, see why developers are leaving WordPress: 7 pain points and WordPress vs modern CMS: honest feature comparison . The Headline Numbers Three primary sources track CMS market share. They don't agree exactly, but they all show the same direction: W3Techs (the most-cited dataset, scans the top 10M websites): Metric 2023 Q1 2024 Q1 2025 Q1 2026 Change WordPress share among CMS-using sites 65.2% 64.1% 62.4% 60.2% -5 points in 3 years WordPress shar

The demo is impressive. ✅ The demo works in your environment, with your data, with you watching. ✅ Production? Silent failures. Cost overruns. Wrong tool calls. Stuck loops. No fallback. ❌ Agents in 2026: The Real Problem Here is the thing most people are not talking about when they ship AI agents: A demo agent and a production agent are completely different things. A demo is: "watch this work once." A production agent is: "what happens when it is wrong, stuck, expensive, over-permissioned, or called 10,000 times by real users?" That second question is what separates a cool technical proof-of-concept from something a business can actually rely on. Demos are not systems. 1️⃣ The 7 Things That Break in Prod In every agent hardening sprint I run, the same failures show up: Failure Mode What It Costs No logging You have no idea what the agent did or why No eval set You cannot measure quality or catch regressions Unlimited tool access Agent calls tools it should never touch No retry logic Transient failures become permanent failures No memory rules Context leaks between sessions or inflates cost No fallback path Agent loops or crashes instead of escalating No cost checks 1 misconfigured prompt → $400 API bill overnight If your agent is in production with 3 or more of those missing — you are one bad prompt away from a very expensive incident. 2️⃣ The Production Hardening Checklist Before you call an agent production-ready, run through this: Eval set exists — at least 20 test cases covering happy path + edge cases Structured logging — every tool call, every input, every output, every error — logged and searchable Retry logic — transient API failures handled gracefully, not crashed Tool limits — agent cannot call tools outside its defined scope Memory rules — what carries over between sessions, what gets cleared, how context is compressed Fallback paths — when the agent gets stuck or uncertain, it has an exit: escalate to human, return partial result, surface an error Cost