标签：#AR

Every model launch comes with a chart. Bars, usually, or a spider diagram, showing the new model edging past its rivals on a row of benchmarks with acronyms most people cannot expand. The bar is taller. The press writes it up as a leap. And within a week, users report that the new state-of-the-art model is, for their actual work, about the same as the last one or occasionally worse. The benchmark said one thing. Reality said another. This happens so reliably that it is worth understanding the mechanics of the gap. The test is public, which ruins the test The most fundamental problem is contamination. Many popular benchmarks are published, discussed, and sitting on the open web — which is exactly where models get their training data. When the questions and answers to your exam are in the study material, a high score measures memorisation as much as ability. Nobody needs to cheat deliberately; the leak is structural. A model can score brilliantly on a benchmark it has effectively already seen and then flounder on a genuinely novel version of the same task. A benchmark stops measuring intelligence the moment it becomes famous enough to end up in the training data. Fame is the thing that breaks it. The number becomes the marketing, and the marketing corrupts the number There is a commercial feedback loop that makes benchmark figures even less trustworthy than their technical limitations alone would suggest. A high score is not just an engineering result; it is a marketing asset worth an enormous amount in attention, funding and credibility. That raises the stakes on every fractional improvement, and where the stakes are high, the temptation to select, frame and present the numbers favourably is irresistible. Vendors choose which benchmarks to headline, which comparisons to draw, and which unflattering results to leave in an appendix or omit entirely. The chart on the launch slide is not a neutral readout; it is a curated argument. This is not necessarily fraud — it rare

Watch enough AI launches and they begin to blur into a single, endlessly repeating event. There is the understated title slide. The claim that we are at an inflection point. The chart showing the new model clearing a row of benchmarks. The live demo that works flawlessly. The superlatives — most capable, most advanced, our best model yet. And the closing note that all of this will roll out “over the coming weeks,” which is to say, not today, and possibly not to you. It is a genre now, with conventions as fixed as a nature documentary, and once you see the template you cannot unsee it. The conventions of the genre Every mature format has its tropes. The AI launch has assembled a reliable set: The benchmark chart — which, as we argued in our piece on benchmarks , predicts your experience far less than its prominence implies. The cherry-picked demo — a single, gorgeous example that represents the top of the model's range, not its average day. The superlative — always “most capable,” because every model is the most capable at the instant it ships, until the next one three months later. The vague availability — “rolling out over the coming weeks,” a phrase that lets the announcement bank the excitement now and deliver the substance later, to some users, eventually. The safety paragraph — a brief, serious note about responsible deployment, positioned to reassure without committing to specifics. When every launch uses the same script, the script stops conveying information and starts conveying mood. The mood is always “inevitable progress.” The relentless cadence is part of the message The sheer frequency of these launches is itself a rhetorical device, whether or not anyone intends it that way. When a major model or feature is announced every few weeks, the cumulative effect is a drumbeat of perpetual acceleration — a sense that the field is moving so fast that to pause, to doubt, or to ask whether the last release actually delivered is to risk being left behind. The pace

There is a comedy sketch from Bob & Tom that starts with a hilariously impossible promise: overnight delivery by train, from New York to Los Angeles. At one point, someone asks if they can really get a 2,000-pound package across the country overnight by rail. The answer is delivered with absolute confidence: “Norfolk and Waypal, overnight. Absolutely. Positively.” The name is doing some careful work. It lets you hear the phrase that nobody has actually said out loud. No way, pal. When I end up leading a project with six weeks left and something that feels like four months of work to do, I start the internal kickoff by telling the team to go watch that sketch. No other explanation. Just go watch it, then come back. Then I tell them: “Absolutely, positively, we will get you through it. There's Norfolk and Waypal, we are gonna to do it.” That does not mean we are going to do the thing exactly as it was originally promised. It means we are going to get through it. Absolutely. Positively. There is a difference. Laugh at the impossible first I think newer developers especially need permission to laugh at impossible requirements. An 800-pound gorilla from New York to Los Angeles overnight by train is impossible in a way that is easy to laugh at. A project that needs a full cloud environment, API work, a mobile application in the app stores, production deployment, security approvals, and a dozen other things in six weeks? That can feel less funny when it is sitting in your sprint board. But it may be just as impossible if we take the requirements literally. The first danger on a crunch project is shame. A junior developer can look at an impossible deadline and wonder if they are missing something. Maybe everyone else understands how this gets done. Maybe it is a talent problem. Maybe if they just worked harder, they could turn six weeks into twelve. Nope. Sometimes the work is just Norfolk and Waypal . Humor does not solve the problem. It lowers the temperature enough that

Every Flutter developer has run into the Sticky State Dilemma . You build a login screen. When authentication fails, your state container emits an error. You catch it in your UI and show a SnackBar . Everything works—until the user rotates their phone, pulls down the notification shade, or types on the virtual keyboard. Suddenly, the widget tree rebuilds. The state container is still holding AuthErrorState("Invalid password") . The UI listener fires again. And a duplicate snackbar appears out of nowhere. In this article, we’ll explore why domain state machines struggle with transient UI events, how the classic BLoC community worked around this with package:bloc_presentation , and how BlocSignal lets you handle one-shot side effects cleanly with zero additional package dependencies . 1. The Root Problem: Persistent State vs. Ephemeral Actions State management in Flutter is designed to model persistent truth over time: Is the user logged in? AuthState.authenticated(user) Is data loading? TodoState.loading What is the cart total? $49.99 Persistent state answers: "What is the system's current condition?" In contrast, UI presentation actions are ephemeral pulses : Show a brief SnackBar toast. Pop up an alert confirmation dialog. Push a new route on the Navigator stack. Vibrate the haptic motor. These actions answer: "What just happened that requires a one-time reaction?" ┌────────────────────────────────────────────────────────┐ │ State vs. Effects │ ├────────────────────────────┬───────────────────────────┤ │ Persistent State │ Ephemeral Side-Effect │ ├────────────────────────────┼───────────────────────────┤ │ • Survived by UI rebuilds │ • Consumed once & gone │ │ • Represented in signals │ • Triggered by an event │ │ • Backed by equality diffs │ • Zero domain state footprint │ └────────────────────────────┴───────────────────────────┘ 2. The Legacy Workarounds (And Their Hidden Costs) Historically in package:bloc and package:flutter_bloc , developers used one of three

Interpretability lenses get fitted to one exact checkpoint, and as far as I can tell nobody had tested what a version update does to one. So this was my question: when a model line updates, does the fitted instrument survive, or do you refit every release? I tested the published Jacobian lens for Qwen3.6-27B (Neuronpedia, from Anthropic’s July workspace paper) applied unchanged to Qwen3.8-27B. Setup: 3.8-27B shipped 113 days after 3.6-27B. Same 64 layers, same hidden dim, same tokenizer, training relationship undocumented. One protocol, both models, two readouts each: the transported Jacobian readout and the raw logit lens as baseline. bf16, greedy, single seed. Reading result: the main task is 40 two-hop prompts where the middle entity is never stated. Example: “Fact: The currency used in the country shaped like a boot is”, where the target is Italy and Italy appears nowhere in the prompt. The transferred lens keeps the latent entity near the top of the 248,320-token vocab. Median rank at layer 48 is 4 on the home model vs 17 transferred. At layer 24 it’s 121 vs 38, so the successor is actually better at mid-depth (paired sign tests, p < 1e-3). The raw logit lens sits at rank 1e3 to 1e4 through the same band on both models. On WikiText teacher-forced next-token (700 positions), transfer costs 1.2 to 1.3x mid-network and about 2x by layer 48. Latent-content readout transfers nearly clean; surface next-token readout pays more, and pays late. Steering result: I took pullback directions for “ paradox” / “ paradoxical” / 悖论 / 矛盾 from the 3.6 lens, orthogonalized within layer, and projected them out of 3.8’s residual stream at layers 18 to 47 during generation. Prompt: “Describe Escher’s impossible staircase”. The word paradox disappears from the output in all cells, on both models, while the description stays coherent (lithograph, closed loop, illusion all intact). Directions derived entirely from the old checkpoint still find the concept in the new one. Scope: one lens

Repo with dataset links: https://github.com/tesselwait/Starfield_Fauna Image classification dataset: 20,000 images from 50 fauna species in the video game Starfield. Images were extracted from video capture. About 2 minutes of footage was shot in all or most of the species biomes. One minute of daytime and nighttime footage respectively, usually in two 30-second takes to vary the background. A PowerShell script is used to establish a frame extract rate and extract the 400 frames plus some extra to replace images that were obstructed/blurry or contained other fauna species ignoring birds/critters. The shots are for the most part close-up and centered to keep the task focused on discerning between 50 species rather than finding the creature in the image. The images are initially randomized however some normalization was done if the ratio of images from some biomes was heavily skewed between the training, validation, and test sets. submitted by /u/eccLykta [link] [留言]

AI coding startup Cursor is now officially a part of SpaceX.

I build a web service that preserves evidence of harassment on social platforms. The core feature is a single thing: automatically capture a real screenshot of the offending post. There was no substitute for it. I built an alternative that pulled the text through an API and rendered a tidy "evidence card" image, and threw it away. An image you can author freely afterwards proves nothing. Here's the conclusion first. Third-party wrappers eventually die, and when they do, the failure comes back as a plausible-looking image rather than an error. The first approach was refused by the other side I started with Cloudflare Browser Rendering. The wiring worked. The capture didn't. X blocks headless browsers. The request times out YouTube refuses script injection under a Trusted Types CSP. There's no way to make it render the comment Neither is a bug in my implementation — that is how they are built. So I declared Cloudflare alone impossible for this and moved to a service with a real browser and bot avoidance behind it. Both captures started working. For X, open the post page and clip the tweet element. For YouTube, open the URL with &lc= and screenshot just that comment element. Element screenshots have one trap worth knowing: selector_algorithm=clip returns a blank image when the element sits below the fold. The selector matches, the capture "succeeds," and the file is empty. That took a while to see. ytd-comment-thread-renderer :has ( a [ href *= "lc=ID" ]) A parameter that had worked started returning 400 I wanted timestamps rendered in Japan time, so I passed time_zone: Asia/Tokyo . One day every request started coming back 400. Every capture failed. The provider had narrowed which timezones they accept. Nothing changed on my side. I could diagnose it immediately only because I was storing the raw error body in the database. The response went into rawPayload.screenshotError , so opening one row told me why. Without that, this starts as "captures stopped working, no ide

Somewhere in your codebase there is a line that looks like this: if ( user . plan === ' enterprise ' || user . tenantId === ' acme-corp ' ) { // ... } Nobody remembers who wrote the second half of that condition. It has been there for two years. It is almost certainly still load-bearing. Here is the thing I want to convince you of: that line isn't code. It's data, and it's stored in the worst possible place. Every conditional that encodes a business decision is really a row. It has a condition, an outcome, and a bunch of implicit context about when it applies. You have hundreds of these rows. They're spread across a dozen services, written in four different styles, and there is no way to list them. You have a database. You just can't query it. Five things a database gives you that your code doesn't Once you look at it this way, the problems stop feeling like sloppiness and start feeling structural. There's no schema. One service decides a customer is premium by checking plan === 'premium' . Another checks subscription.tier > 2 . A third checks a flag that was set during a migration in 2023. All three are "the same rule" until the day they aren't, and there's nothing in the system that would notice the drift. There's no way to query it. Try to answer a simple question: what rules are live in production right now? You can't. Someone has to read the source. And grep won't save you, because the interesting conditions are compound, spread across guard clauses, and half of them are expressed as an early return rather than an if . There are no migrations. Changing a rate limit from 100 to 200 requires a pull request, a review, a CI run, and a deploy window. You're pushing a code change through the full pipeline to change a number. It's a schema migration with none of the tooling that makes schema migrations tolerable. There's no audit log. Git tells you who edited the line. It doesn't tell you who decided the rule, when it was supposed to expire, or whether the customer it

"Flamingo missiles were used. A good achievement."

Season two of Star Wars: Ahsoka is still months away, but Lucasfilm still took the opportunity to tease it a bit at D23. The company dropped the first trailer for the new season ahead of its January 20th, 2027 debut. The clip shows a darker, witchcraft-filled take on the Star Wars universe, with Grand Admiral […]

The date for NeurIPS 2026 author notifications is September 24th. First of all, is it normal for AC and reviewer discussion phases to be this long? This is particularly frustrating given that 5 out of the 6 reviewers in my two papers did not address the rebuttals. In any case, I was also wondering, given that ICLR's paper deadline is literally the day after (September 25th) whether you guys are preparing ICLR submissions for your papers in case of rejection. Cheers and good luck! submitted by /u/_Sarcastrophe_ [link] [留言]

The annual Disney fan event showed off the cast of Marvel’s X-Men movie, plus a new trailer for Avengers: Doomsday, and our first look at the VisionQuest TV show for Disney Plus. For Star Wars fans, there was a teaser trailer for season two of Ahsoka, plus a special look at Star Wars: Starfighter with […]

With Spider-Man: Brand New Day behind us, and its reveal of Sadie Sink as Jean Grey, Marvel is finally ready to officially welcome the X-Men into the MCU. At the D23 event in Anaheim yesterday, the company revealed the cast for its upcoming, untitled X-Men film, which is scheduled to hit theaters on May 5th […]

Fusion startups have raised $7.1 billion to date, with the majority of it going to a handful of companies.

Utility-scale solar leads by a mile, followed by batteries. Fossil fuels, not so much.

This affordable electric BMX delighted my entire family, even if its range, ride comfort, and actual BMX abilities are limited.

Noware’s Puff necklace whimsically reimagines flash drives, turning something functional into personalized jewelry.

Sudeep Das shares how DoorDash shifts from legacy one-shot predictions to an agentic recommendation platform. He discusses leveraging language-native consumer memory, RQ-VAE semantic IDs for catalog representation, and grounded search to dramatically boost relevance and conversion metrics. By Sudeep Das

Cloudflare launched agent tracing, adding spans for agent invocations, model calls, tool runs, and approvals to existing Workers traces. Sessions replay turn by turn, though the docs warn traces are not lossless and payloads may be truncated. Payload recording defaults differ by framework, and from October 1, 2026 every span counts as a billable event. By Steef-Jan Wiggers