标签：#m

It’s the first estimate of how many Americans are sneaking onto Polymarket’s banned crypto-based platform.

If you're someone who needs to (or likes to) take their work on the go, a portable monitor will make a huge difference. These are my favorite that I tested.

After getting its hands on a Trump phone and tearing it apart, iFixit has confirmed what I first reported back in February: the T1 Phone is an almost exact duplicate of the HTC U24 Pro. iFixit partnered with NBC to get hold of the network's media sample of the Trump phone, along with a U24 […]

The musician created his own line of loopers that record and layer riffs in a loop. The pricey Looper X does what it claims, but it isn’t without quirks.

It might not feel all that different from older World Cups—for better or worse.

Deezer will now help you find AI slop in your music playlists even if you're on another platform.

YouTube is reintroducing private messaging after testing new ways for users to share videos and "have conversations about them" last year. In an announcement on its official blog, YouTube says it's now starting to expand the in-app video sharing and messaging feature to users in the US and "other global regions" who are 18 or […]

as in the title, my goal is to predicting failure and RUL of machine, dataset is timestamp and when machine is failure it will labeled with 1 that only have 56 https://preview.redd.it/plbydmenmm6h1.png?width=1205&format=png&auto=webp&s=2fefe3cc2e3fe554b81c9e0b4012c5345e73ec3f From this data im ditching operating hours and humidity because it didnt show correlation for machine failure, what algorithm or deeplearning suit for it? submitted by /u/False-Seesaw-1899 [link] [留言]

Travel bans and other visa issues are creating problems for World Cup participants even before the whistle blows.

It’s a tale of two nuclear industries. In China, large reactors are coming together at a stunning pace. The country has nearly doubled its nuclear fleet since 2016, reaching nearly 60 gigawatts of total power capacity. The new facilities are nearly all gigawatt-scale pressurized-water reactors. Meanwhile, the US has built just two reactors in that…

In 2018, after nearly two decades working in Big Pharma, chemist Tim Cernak was ready to put his skills to a new use. For Merck, he’d developed precision therapies for cancer, HIV, and diabetes that could target disease while minimizing harm to healthy cells. But as a lifelong nature lover, he was increasingly concerned about…

Venues hosting the 2026 World Cup must meet high standards to obtain environmental certifications, but FIFA also requires that they use natural grass, which is water-intensive to maintain.

WWDC26 brought a substantial round of updates to SwiftUI — not a ground-up redesign, but a lot of small limitations removed, new APIs that were clearly driven by real-world pain points, and meaningful performance improvements. This post walks through every major announcement so you know exactly what's available and when to reach for it. Look and Feel: Liquid Glass and the 2027 Releases The most immediately visible change costs you zero code. Apps built with SwiftUI automatically pick up the updated Liquid Glass appearance on the 2027 OS releases. The glass tint responds to the new system-level Liquid Glass slider without any changes on your part. On iPad, windows now dim when inactive, reinforcing which window has focus — again, automatic. On Mac, custom interactive Liquid Glass elements respond more fluidly to the mouse pointer. There are a few opt-in refinements available when you want tighter control: Responding to active state — use the appearsActive environment value to reduce opacity on custom elements when the window is inactive: struct SidebarFooterView : View { @Environment (\ . appearsActive ) private var appearsActive var body : some View { MyAccountView () . opacity ( appearsActive ? 1 : 0.5 ) } } Menu bar icons — the menu bar now shows a minimal set of icons by default. Add .labelStyle(.titleAndIcon) to a specific menu item to make its icon visible: CommandMenu ( "Stickers" ) { Button { openStore () } label : { Label ( "Store" , systemImage : "bag.fill" ) . labelStyle ( . titleAndIcon ) } } Resizability on iPhone iPhone apps become resizable on iOS 27, which matters for iPhone Mirroring and running iPhone apps on iPad. Xcode 27's Live Previews now include resize handles so you can test this interactively without running on a device. If your app mixes UIKit and SwiftUI, check the session "Modernize your UIKit app" for specifics around screen geometry, size classes, and orientation handling. Toolbar APIs The toolbar has been a source of friction on smalle

Ask a large language model for a specific statistic, then ask where it found that number. More often than not, the citation it gives you doesn't exist. The model will hallucinate a plausible-looking reference, confidently present outdated conclusions, or simply make things up without any internal signal that something is wrong. This failure mode has a well-known name — hallucination — and the most widely adopted engineering solution for it is RAG. RAG in One Sentence RAG stands for Retrieval-Augmented Generation. The idea is straightforward: before the LLM generates an answer, retrieve relevant document chunks from an external knowledge base, then feed those chunks to the model as context so it can compose its response based on real source material rather than parametric memory alone. Think of it like writing a research paper. You don't cite statistics from memory; you look them up first, then write your argument around verified data. RAG gives language models the same "look it up, then write" workflow. Three Structural Limitations of LLMs To understand why RAG is necessary, we need to identify the specific gaps it fills. Knowledge cutoff. Every model has a training data deadline. GPT-4's cutoff is late 2023; Claude's is early 2025. Anything that happened after that deadline simply doesn't exist in the model's world. It will either admit ignorance or, more dangerously, fabricate an answer that sounds current. Bounded parametric capacity. Even a 100-billion-parameter model can only "memorize" so much. Long-tail facts, niche domain knowledge, your company's internal documentation, yesterday's meeting notes — none of these are in the weights. No built-in fact-checking. Token generation is probabilistic sampling. The model has no mechanism to distinguish whether it's recalling a training fact or pattern-matching its way into a plausible-sounding fiction. RAG addresses all three: it supplies up-to-date, verifiable, externally sourced evidence at inference time. How RAG W

Every tool sprawl thread I read starts with license math, and license math is a decoy. Last quarter I added up what our seven-tool delivery stack actually cost us, and the subscriptions came to about 15% of the total. The other 85% never appears on an invoice, which is exactly why nobody budgets for it and nobody fixes it. Some background so you can judge whether my numbers transfer to your team. I spent years building automation in banking before running my own product team, so I am professionally allergic to process waste. Despite that, our stack had drifted into the usual shape: Jira for tickets, Confluence for docs, Lucidchart for architecture, TestRail for test cases, two spreadsheets doing unpaid overtime in the gaps, and an AI chatbot bolted on the side that had never seen any of it. The licenses for all of that, for six people, ran about $700 a month. Annoying. Not a crisis. And that is precisely why the "consolidate your tools" pitch dies in so many budget conversations. Saving a few hundred dollars a month does not justify a migration, and everyone in the room knows it. If licenses were the real cost, I would side with the skeptics. The audit: two weeks of logging every re-key So we measured the part nobody measures. For two weeks, everyone on the team logged every re-key: any moment a human moved or restated information that already existed in another tool. Copying acceptance criteria from Confluence into a Jira ticket. Updating TestRail because a story changed shape. Redrawing a Lucidchart flow that had drifted from the code. Reassembling a status update by hand from three tabs. Pasting project context into the chatbot, again, because it forgot everything since yesterday. The rules were strict so the number would survive scrutiny. Log transfer time only, not thinking time. Round down when unsure. If the same fact got re-keyed twice, log it twice, because it cost twice. Each entry went into a shared CSV with four columns, and this script turned it into th

PostgreSQL Partitioning for Multi-Tenant Audit Logs: Querying 100M Events Without Table Scans I'll be direct: if you're running a SaaS with compliance requirements and your audit_logs table is approaching 50M rows, you're three months away from pain. I've watched audit queries go from 200ms to 8 seconds in production at 2am because someone ran a "give me all logs for tenant X" report. Partitioning isn't optimization theater—it's table-stakes infrastructure. At CitizenApp, we store 9 months of audit logs across 50+ tenants. Without partitioning, a single compliance query would full-table scan 100M+ rows. With it, we hit the same data in <100ms. This post is exactly how we do it. Why Partitioning Matters (The Reality Check) Most developers treat audit_logs like any other table. You add an index on tenant_id and created_at , call it done, and move on. Then your compliance officer runs a query like: SELECT * FROM audit_logs WHERE tenant_id = 'acme-corp' AND created_at >= '2024-01-01' ORDER BY created_at DESC ; At 50M rows, even with a composite index, PostgreSQL has to: Index scan → finds millions of matching rows Random I/O all over the table Spill to disk if sorting is large Hope the OS cache is warm Partitioning solves this by eliminating the data you don't need from day one . Instead of scanning a 100GB table and filtering it down, PostgreSQL can skip entire partitions. A query against January 2024 data simply ignores partitions for February–December. I prefer partitioning because it's native PostgreSQL—no external caching layer, no read replicas, no Redis gymnastics. It's boring infrastructure that works. The Partitioning Strategy: Composite Partitioning (Range + List) I use a two-level partitioning scheme: Range partition by month ( created_at ) — keeps each partition to ~5–10GB List subpartition by tenant — ensures compliance queries are single-partition scans This is deliberately opinionated. You could do range-only, but then a multi-tenant query still scans the

I’ll say it plainly: the first health-adjacent agent workflow I’d trust is not an AI doctor. It’s a narrow pipeline that takes 6 months of Apple Watch sleep data, cleans timestamps, maps records into a fixed sleep-diary schema, flags broken rows, and stops for human review before anything reaches a clinician. That sounds unsexy. Good. That’s exactly why it’s the first version I’d trust. I landed on this after reading a post on r/openclaw where someone said they had their AI assistant turn months of Apple Watch sleep data into the diary their sleep clinic requested, and the data gotchas were brutal. That sentence contains the whole product. Not “AI healthcare.” Not “autonomous wellness.” Not a GPT-5 wrapper with a soothing UI pretending it understands sleep medicine. Just a very practical engineering problem: parse ugly export data normalize time boundaries fit it into a clinician-friendly format fail loudly on bad rows require a human to approve it That is a real use case. And if you build automations in n8n, Make, Zapier, OpenClaw, or Python, it should feel familiar: the hard part is not the final prompt. The hard part is the ugly middle. The hard part is ETL, not reasoning Most health-agent demos skip the only part that matters. They show the polished summary. They show Claude or GPT-5 saying something calm and articulate. They show a dashboard. I don’t think that’s the hard part. The hard part is ETL: extraction transformation loading For sleep data, that means dealing with stuff like: timestamps crossing midnight timezone normalization naps vs overnight sleep missing start or end times overlapping intervals gaps from the device not recording clinic-specific diary formats If you get any of that wrong, the model summary at the end is not helpful. It is actively misleading. That’s why I think the boring pipeline is the real product. The workflow I’d actually ship If I had to build this today, I would keep the architecture aggressively narrow. Apple Health export ->

link - https://arxiv.org/abs/2606.06158 Abstract : Adaptive video tokenisation seeks to dynamically allocate token budgets based on the underlying visual complexity of a sequence. Current continuous-regime approaches achieve this via iterative binarised searches or trained neural regressors, while discrete methods often require a full-rate decoder pass to estimate information content. We demonstrate that such computational overheads are not strictly necessary. We show that the latent space of a frozen continuous video tokeniser inherently encodes temporal redundancy that can be exploited directly: spatial positions whose latent representations change minimally between consecutive frames carry near-zero additional information. We introduce a parameter-free adaptive token allocation mechanism that applies a fixed threshold to per-position temporal-L1 differences, identifying and dropping redundant latent positions. Consequently, the compression rate emerges naturally from the input content rather than being enforced top-down: static scenes get compressed aggressively, while highly dynamic sequences retain more tokens. To reconstruct the dropped positions, we propose the Latent Inpainting Transformer (LIT), a lightweight factorised spatial-temporal attention architecture. The resulting inference pipeline is highly efficient, requiring only a single encoder pass and one LIT forward pass, eliminating the need for auxiliary routing networks. Evaluations across TokenBench and DAVIS, which are the standard benchmarks used by recent tokenisers, indicate that our framework yields meaningful, content-driven token allocation while maintaining competitive reconstruction fidelity, and delivers a 31x inference-time speedup over the continuous adaptive baseline (ElasticTok-CV) and an 2x speedup over the discrete information-theoretic baseline (InfoTok) submitted by /u/chhaya_35 [link] [留言]

Elon Musk is set to make hundreds of billions even as communities in Mississippi and Tennessee are fighting to stop the gas turbines powering xAI's supercomputers.

OpenAI's GPT-5.5, GPT-5.4, and Codex are now generally available on Amazon Bedrock, one month after OpenAI revised its exclusive Azure arrangement. Pricing matches OpenAI's direct rates with usage counting toward AWS commitments. Codex shifts to pay-per-token billing with no seat fees. GPT-5.4 is the first OpenAI model available in AWS GovCloud. By Steef-Jan Wiggers