标签：#AR

https://preview.redd.it/llqlupnwng4h1.png?width=2188&format=png&auto=webp&s=7fae5860babaffa1c8bfdcb1468b374eb38ac55d I'm currently building a computer vision application. I've managed to successfully train a YOLO model to detect the object I'm interested in for my videos. The image above shows some visualisations of the YOLO model outputs for some of the videos. I want to essentially cluster these strands in the image into groups based on their separation distance and return a string telling me the number of strands in each group from left to right (e.g. 1-2-3). The target value for each column in the image (where each column corresponds to a video) is 1-2-3, 1-2-3-2-3, 1-1-2-3-3-3-3 and don't worry about the fourth column for now 😄. The rows show the x vs t, y vs t and x vs y vs t for all the detections and the points are sized based on the detection box area. In the fourth column I have some background object detections which I want to ignore hence why I've also visualised detection box area. I've managed to train a XGBoost classification model that gives 70ish% accuracy however Bayes error is making me think I should be able to do much better than this. How would you approach trying to predict these strand clusterings? Some extra info that might help; there are at max 8 groups and each group can have only at max 3 strands. submitted by /u/mitbull420 [link] [留言]

Existing research on LLM response uncertainty has been looking in different directions. Hallucination, knowledge conflict, RLHF limitations, prompt sensitivity, calibration failure — these have all been studied separately, and I kept wondering why no one had tried to unify them under a single principle. I ran experiments on the hypothesis that the common cause of these phenomena lies not inside the model or in the prompt, but in an attribute inherent to the topic itself . A Convergence Point is the consensus density of knowledge humanity has accumulated on a given topic. The higher it is, the more the AI's internal processing converges in one direction. The lower it is, the more it disperses. Along the spectrum, three zones emerge: Full Consensus Zone — Mathematical theorems, physical laws, chemical and biological facts. Knowledge that humanity has converged on in a single direction. Partial Consensus Zone — Domains like ethics, morality, politics, and law. Not a lack of data, but an abundance of it — accumulated firmly in both directions. Non-Consensus Zone — Philosophical hard problems and unresolved scientific questions: the nature of consciousness, the reality of the self, the interior of black holes, the origin of life, the existence of God. Not so much a clash of opposing sides, but the absence of any agreed explanatory framework at all. The experimental results suggest AI broadly operates along these lines. It responds confidently in the Full Consensus Zone, and becomes uncertain in the Partial and Non-Consensus Zones. One interesting finding: the Partial Consensus Zone sometimes shows higher uncertainty than the Non-Consensus Zone. Data conflict appears to destabilize AI's internal processing more than data absence does. Phenomena that have been studied in isolation — why hallucinations vary so much by topic, why RLHF fails in certain domains, why some topics hit a ceiling no matter how carefully the prompt is crafted — seem to connect in unexpected ways onc

DuckDB has recently announced Quack, a new remote protocol over HTTP that lets multiple DuckDB instances connect to and work with the same database over a network. The protocol introduces client-server capabilities to a database that was previously mostly local and embedded. By Renato Losio

Is it a hidden gem, a cult classic, or hopelessly dumb? We vote "all of the above."

The M-Track Duo HD Producer Pack removes the last remaining excuse for terrible audio and potentially terrible opinions.

Despite headwinds from the current administration, automakers continue to release well-equipped EVs with bigger battery packs and increasingly faster charging speeds. For those who want to travel further between plugging in, the future is still bright, just slightly tinted. But there haven't been many sedans starting around or below $50,000, as crossover SUVs have largely […]

submitted by /u/NihiloZero [link] [留言]

For a long time, I thought the key to getting more value from AI was finding the smartest model. So I spent months comparing outputs, testing prompts, and constantly switching tools whenever a new release dropped. Ironically, that became its own form of procrastination. The biggest productivity boost came when I stopped optimizing for model quality and started optimizing for workflow. Now my stack is boring: One tool for thinking and writing One tool for execution and organization A few specialized tools only when needed Less tool-hopping. Less context switching. More shipping. The funny thing is that AI didn't remove work. It changed the work. Instead of creating everything from scratch, I'm reviewing, directing, and refining. The people getting the most value from AI don't seem to have the best prompts or the fanciest tools. They have the simplest workflows. Anyone else notice this, or am I just getting old and tired of managing software? submitted by /u/Leading-Tailor-6000 [link] [留言]

Find out whether a single Wi-Fi router or a mesh system makes the most sense for your home network.

AI agents are getting better at taking actions, not just giving answers. That sounds exciting until the action touches something real: customer data, payments, internal systems, emails, approvals, or legal/business decisions. A bad answer can be corrected. A bad action can create a chain of problems. I think the next AI bottleneck is not only intelligence. It is accountability. If an AI agent makes a bad decision in a real workflow, who should be responsible? submitted by /u/Alpertayfur [link] [留言]

Forget the pricey, postpaid cell plans and two-year contracts. Save with one of these WIRED-tested options from US Mobile, Boost, and Google Fi.

Quick answer: The Google Ads Transparency Center is a public registry of every ad Google runs — but it ships no API and no bulk export . To get the data programmatically you scrape it. A Google Ads Transparency scraper sends the same RPC call the website uses and returns every ad creative for an advertiser as structured JSON. The Apify Actor below does it for $0.0012 per ad (~$1.20 per 1,000), with the TLS fingerprinting, proxy rotation, and pagination handled for you. Google's Ads Transparency Center is one of the most underused datasets in marketing. Launched in 2023 under the EU Digital Services Act and parallel US pressure, it indexes every ad campaign currently running on Search, YouTube, Display, Shopping, Maps, and Play — keyed by advertiser. Google's own counter lists 300,000+ active creatives for a brand like Nike . For your nearest competitor, it's usually 50–500. The catch: there's no download button. Just an interactive UI that paginates 40 creatives at a time. If you want this as a CSV — for a competitor sweep, a trademark audit, or a RAG corpus — you have to extract it yourself. Here's what that actually takes, and how I shortened it to one API call. What is the Google Ads Transparency Center? 🔎 The Google Ads Transparency Center is a public, Google-operated registry that shows the ad creatives any verified advertiser is running, the date range each ad was shown, and roughly where. Google built it to comply with ad-disclosure regulation, so the data is public by design — you're reading the same registry a regulator would. What it gives you per advertiser: Every ad creative currently or recently live (text, image, video) The landing domain each ad clicks through to First-shown / last-shown timestamps and a rough impression count A deep link to each creative inside the Transparency Center What it does not give you: a search-by-keyword mode, region-filtered results from the server, or — crucially — an API. Does the Google Ads Transparency Center have an A

Stacking, Bidirectionality, the Encoder-Decoder, and LSTMs Last post ended with a simple RNN and three promises: LSTMs, bidirectional RNNs, and attention. This post delivers the first two, plus the refinements that turn a working-on-paper RNN into something you'd actually deploy. By the end, you'll know how to stack RNNs for depth, why reading a sentence backward as well as forward (bidirectionality) makes representations sharper, how the encoder-decoder turns one sequence into a different one for machine translation, and exactly what breaks in a simple RNN that LSTMs and GRUs were invented to fix. Attention, the fix for the last problem we'll hit, gets its own home in the transformer post, so we'll stop right at the edge of it. The simple RNN was the idea. This post is the engineering. A vanilla RNN carries a thread of hidden state through time, but in practice, that thread frays on long sequences, only sees the past, and bottlenecks everything through one final vector. Each section here is a fix for one of those problems. Put them together, and the path from "RNN" to "transformer" looks less like a leap and more like a series of obvious next steps. Stacking RNNs for Depth The first refinement is the easy one. Nothing says an RNN's output has to go straight to a prediction. You can feed the entire output sequence of one RNN as the input sequence to another. Then another. These are stacked RNNs (also called deep RNNs), and they usually outperform a single layer. Why does depth help? The same reason it helps in vision. Each layer learns representations at a different level of abstraction. The lower layers pick up fundamental, local properties; in language, that's roughly the level of parts of speech and named entities. The higher layers compose those into bigger groupings: descriptive phrases, "this is the answer to a question," and so on. We can't point at layer 3 and say "this one does coreference." But the theory holds up well enough that researchers have probed m

I sifted through the fine print and figured out how to score the best deal right now on all the major carriers.

From specialized motors to the use of machine learning algorithms, Turkey’s billion-dollar hair-transplant industry is the result of a constant process of innovation.

Hey! I'm a CS student and I got tired of not being able to compare MLX inference engines properly — every benchmark out there is either made by the engine's own developers, runs on an M3 Ultra nobody has, or just shows tok/s with zero context. So I built mlx-Chronos — a small open source CLI tool that runs a standardized benchmark protocol on your Mac and lets you submit your results to a shared community leaderboard. What it measures: Cold and cached TTFT (Time to First Token), with a proper methodology — unique prompts per trial, cache priming, no interleaved phases Throughput (tok/s), with mean/stddev/min/max across repeated trials Engine process RSS and system RAM peak, sampled continuously during inference Thermal state and hardware info Supported engines: oMLX, Rapid-MLX, mlx-lm, Ollama (MLX backend) The leaderboard is basically empty right now since I only have an M2 8GB. Would love results from M3 Max, M4, M4 Ultra, or anything with more RAM — that's where things get actually interesting. → Leaderboard: https://igurss.github.io/mlx-chronos → GitHub: https://github.com/igurss/mlx-chronos → Install: pip install mlx-chronos It's early, the methodology is documented (there's a methodology.md if you want to pick it apart), and I'm 100% open to feedback, contributions, and getting told what I'm doing wrong. The goal is just to have one place where you can compare engines on your specific hardware instead of trusting someone else's numbers. submitted by /u/igor__004 [link] [留言]

submitted by /u/Early_Mail9268 [link] [留言]

I used these two prompts on all AI apps to figure out who to vote for in the CA primaries: If you were running for governor of California, what will your big policies be ⁠Out of the candidates that are running in June election, who aligns closest to those policies Gemini, claude, chatgpt all ranked Matt Mahan (Democrat) as #1 Grok chose Steve Hilton (Republican) thoughts on AI use for voting decisions? submitted by /u/No_Mall_7299 [link] [留言]

Most robot foundation model demos are hard to interpret because the impressive number usually comes after task-specific fine tuning. Wall-OSS-0.5, a new open-source VLA release from X Square Robot, is interesting because the report tries to measure what the pretrained checkpoint can do before that extra adaptation step. The setup is a 4B vision-language-action model built around a 3B VLM backbone plus action-generation components. According to the report, the pretrained checkpoint was evaluated on a 17-task real-robot suite without task-specific fine tuning. Four tasks crossed 80 task progress: block sorting, fruit sorting, ring stacking, and a held-out deformable task, rope tightening. The part that seems more important than the raw score is the framing. In language models, nobody would accept only a fine-tuned downstream score as evidence that pretraining worked. With robots, that has been much harder because the evaluation is physical, slow, embodiment-dependent, and expensive. A real-robot zero-shot suite is a useful step toward asking the same question directly: does pretraining itself produce executable behavior, or is it mostly a better initialization? The method is also trying to solve a specific training problem. Continuous action losses are useful for execution, but the paper argues they do not send a strong enough learning signal into the VLM backbone by themselves. Their recipe combines action-token cross entropy, multimodal cross entropy, and flow matching in one stage, using the discrete action-token path as a gradient bridge into the backbone while flow matching handles continuous actions at deployment time. For reference, the code is at https://github.com/X-Square-Robot/wall-x , the paper is at https://x2robot.com/api/files/file/wall_oss_05.pdf , the project page is https://x2robot.com/oss#resources , and the Hugging Face org is https://huggingface.co/x-square-robot . The caveat is obvious but important. Zero-shot still does not solve the hardest man

174 million smart TVs, most of which run Linux. 3.9 billion Android phones. Zero marketing. Tonight, somewhere around the world, a person will press the power button on their Samsung TV. A proprietary Samsung logo will appear. A polished menu will load. They will open Netflix, scroll through recommendations, and pick a movie. They will never know that every frame they see is being scheduled, managed, and rendered by a Linux kernel, the invisible engine that sits between apps and hardware. They will then reach for their Android phone to check something on social media. Another Linux kernel. If they are sitting in a Tesla, the touchscreen showing their charging status is running yet another Linux kernel. The “year of the Linux desktop” debate has been running for two decades. Entire forums exist to argue about whether 2025, 2026, or 2027 will finally be the year Linux takes over the PC market.