标签：#AR

The social app founded by Bonobos co-founder Andy Dunn is expanding beyond events with new digital homes for groups to connect, organize, and make plans.

Uber is updating its teen account feature to allow parents to use the selfie cameras on Uber drivers' phones to check in on their teenage children during ridehail trips. Since first launching teen accounts in 2023, Uber has touted the myriad ways parents can keep tabs on their children, from PIN verification to live GPS […]

Wyze announced a new pan-and-tilt security camera today designed for indoor use. The entire Indoor Cam Pan can rotate 360 degrees, while the camera and lens on its front can tilt up and down 103 degrees. When you want privacy, the lens can tilt down until it completely disappears inside the Indoor Cam Pan's body […]

I often remember the shot I want before I remember its filename. That gap is what binquery is for. It is a local Python CLI that indexes video clips and turns a sentence into a ranked shortlist for a human to review. It deliberately stops before editing: no timeline generation, no automatic cut, and no render. The smallest reproducible trial You can test the complete installed command path without supplying footage: python3 -m venv .venv .venv/bin/pip install binquery .venv/bin/binquery demo --out /tmp/binquery-demo The demo generates a synthetic 30-second video locally, then exercises splitting, indexing, validation, and querying. The first run may download OpenCLIP model weights. This is an end-to-end pipeline smoke test, not evidence of semantic search quality on real footage. Why keep the architecture small? The current design uses: ffmpeg to sample three frames from each clip OpenCLIP ViT-B-32 to build the local visual index plain JSON and NumPy files for metadata and vectors a JSON result containing clip paths, scores, and ranking signals There is no database, vector service, or daemon to operate. Querying an existing index does not resample the footage or rebuild the full index. The trade-off is straightforward: three frames keep indexing understandable and bounded, but they can miss important content in long or visually varied clips. I would rather expose that limitation than market a synthetic demo as a quality benchmark. Ranking signals are not explanations The output includes fields such as score , gate , and reasons . Here, reasons means ranking signals recorded by the pipeline. It should not be interpreted as a reliable semantic explanation of why a clip is correct. That distinction matters because a plausible-looking explanation can create more confidence than the underlying retrieval quality deserves. The shortlist is meant to reduce what a person must inspect, not replace editorial judgment. What binquery does not do It does not build a timeline or e

I wrote a technical breakdown of how search works on Papers with Code. The system combines keyword and semantic search, which produced better results than either approach alone. The stack includes: PostgreSQL with pgvector Qwen3-Embedding-0.6B for text embeddings Hugging Face Jobs with an NVIDIA L4 for batch embedding generation Hugging Face Buckets for storing artifacts A live embedding model served through Hugging Face Inference Endpoints The same infrastructure also powers the “related papers” recommendations shown on individual paper pages. Full write-up: How Hugging Face Inference Endpoints, Jobs, and Buckets Power Search on Papers with Code I’d be interested to hear how others are implementing hybrid search for research papers or similarly technical content. Disclosure: I work at Hugging Face and on Papers with Code. submitted by /u/NielsRogge [link] [留言]

I recently ran into a weird issue while working on a Laravel project on Windows using Laragon . Everything was working fine until I tried to import a database through phpMyAdmin. Instead of an SQL error, phpMyAdmin simply returned: Internal Server Error The server encountered an internal error or misconfiguration... No useful message. Just HTTP 500. My SQL file was around 97 MB , so at first I thought it was probably a PHP upload limit issue. It wasn't that simple. Here is how I debugged it. 1. Check which PHP configuration is actually running From Laragon Terminal: php --ini Then I checked the important error settings: php.exe -r "echo 'error_log=' . ini_get('error_log') . PHP_EOL;" php.exe -r "echo 'log_errors=' . ini_get('log_errors') . PHP_EOL;" php.exe -r "echo 'display_errors=' . ini_get('display_errors') . PHP_EOL;" My output was: error_log=D:/C-data/laragon/tmp/php_errors.log log_errors=1 display_errors=1 One small Laragon/Git Bash issue I also found was: type php returned: php is aliased to `winpty php.exe' Because of that, commands like: php -i | grep ... sometimes returned: stdout is not a tty Using php.exe directly avoids that problem. 2. Check the PHP error log My PHP error log was: D:/C-data/laragon/tmp/php_errors.log I reproduced the import error and checked it: tail -n 50 /d/C-data/laragon/tmp/php_errors.log Nothing useful appeared. That was an important clue. 3. Make sure browser PHP and CLI PHP use the same php.ini I created a temporary file: <?php phpinfo (); Then opened it through the browser. Important values were: Server API: CGI/FastCGI PHP Version: 8.4.4 Loaded Configuration File: D:\C-data\laragon\bin\php\php-8.4.4-nts-Win32-vs17-x64\php.ini My PHP limits were already high enough: upload_max_filesize = 512M post_max_size = 512M memory_limit = 512M max_execution_time = 36000 So the 97 MB SQL file should have been allowed by PHP. 4. Check Apache logs I located the Apache error log with: grep -Ri "ErrorLog" /d/C-data/laragon/etc/apache2 /d/C-da

The feature was a letter generator. Somebody fills in a few fields and gets a finished letter of recommendation, resignation letter or notice letter, in plain text, ready to paste into an email. The obvious build is a prompt and a model call. I wrote the deterministic version instead: a pure function, about two hundred lines, no network, no key, no tokens. I want to lay out the reasoning, because "just call a model" is the default now and the default is not always right. The three reasons, in order of weight 1. The output is short and the shape is fixed. A recommendation letter is a date block, a greeting, three or four paragraphs, a sign off and a name. There is no structural variation to discover. Generation is valuable when the space of good outputs is large and you cannot enumerate it. Here the space is small enough to write down, and once you have written it down the model is doing an expensive approximation of a switch statement. 2. It is a legal-adjacent document. Not legal advice, but it goes into an employment record. A resignation letter that invents a notice period, or a reference that invents a fact about a person, is a real problem for the person who sent it. Templates cannot hallucinate. Everything specific in the output either came from a form field or is a sentence I wrote and can be held to. 3. Zero marginal cost changes what the product can be. This is the one that actually decided it. A model call costs money per use, and anything that costs money per use needs an account, a rate limit and eventually a card. A pure function costs nothing, so the tool can stay open with no signup, forever, without a business case. That is a product decision expressed as an architecture decision, and it only works if the code path is free. What the code looks like The whole engine is one exported function over one input type. export type LetterKind = ' resignation ' | ' notice ' | ' recommendation ' ; export type LetterTone = ' formal ' | ' warm ' | ' brief ' ; expo

Se você já perdeu tempo com essa sequência: git stash git checkout outra-branch # resolve o problema urgente git checkout branch-original git stash pop ...só pra descobrir depois que esqueceu o que tinha no stash, ou que o venv / node_modules da outra branch estava desatualizado — este artigo é pra você. O problema Um repositório Git tradicional tem uma única pasta de trabalho ligada a uma branch por vez. Trocar de branch significa trocar todo o conteúdo dessa pasta. Isso funciona bem quando você faz uma coisa de cada vez, mas quebra assim que você precisa: Revisar um PR urgente enquanto está no meio de uma feature grande Rodar testes de uma branch enquanto edita outra Manter ambientes de dependências diferentes (versões de libs, .env ) para features distintas sem reinstalar tudo a cada troca A saída mais comum é o stash , mas ele é frágil: some da vista, acumula, e é fácil esquecer o que tinha ali dentro. A solução: git worktree O git worktree permite ter várias pastas de trabalho simultâneas , cada uma vinculada a uma branch diferente, todas compartilhando o mesmo histórico de commits (o .git ). Pense em uma biblioteca central (o histórico do repositório) com várias mesas de leitura (as worktrees), cada uma com um livro diferente aberto. Você não precisa fechar um livro pra abrir outro. O que é compartilhado, o que é separado Compartilhado entre worktrees Separado por worktree Histórico de commits Arquivos da working directory Objetos do Git (blobs, trees) Arquivos não versionados ( .env , venv , node_modules ) Configuração do repositório Saída do git status Um commit feito em uma worktree aparece imediatamente no git log das outras — mas os arquivos físicos de cada pasta continuam independentes. Colocando em prática Criando uma worktree com branch nova git worktree add ../meu-projeto-feature-x -b feature/nome-da-feature Isso cria a pasta ../meu-projeto-feature-x , já com uma branch nova feature/nome-da-feature criada a partir do commit atual. Criando uma worktree

Luffu Link combines all day health sensing, voice logging, location awareness, and the ability to get help from trusted contacts into a single device, all without needing a phone nearby.

Security researchers are warning that thousands of enterprise servers could be exposed to compromise through vulnerabilities in their Baseboard Management Controllers (BMCs) - specialized processors embedded in server motherboards that provide administrators with remote, out-of-band control. By Craig Risi

I am trying to make a platform decision for a professional laptop that will be used for both ordinary software development and AI/data-science work over several years. The two approaches I am comparing are: M5 Pro/Max MacBook Pro with 64 GB unified memory and 2 TB SSD, possibly 128 GB if that is more valuable. High-end NVIDIA laptop with CUDA but much less GPU memory, more heat/noise and usually worse battery life. Typical work includes Docker-based web development, Python/Jupyter/Conda, dataset work, ML experiments and local inference. Large training jobs can use cloud GPUs, but I want the laptop to remain useful offline and for private/local models. The full laptop-and-monitor budget is €6,000, with roughly €5,000 available for the laptop. I am in Croatia/EU and will buy only brand-new, factory-sealed hardware—no refurbished, used, returned, display or open-box units. I am interested in the architectural tradeoff rather than a brand argument: - For local inference, when does a 64–128 GB unified-memory pool outweigh CUDA's faster and broader software ecosystem? - Which real development workflows still make a local NVIDIA GPU essential? - How much friction is involved in developing on MPS/MLX locally and moving training to remote CUDA? - Does a mobile NVIDIA GPU provide enough VRAM and sustained performance to justify its battery, noise and thermal compromises? - Is a strong daily-driver laptop plus rented/cloud CUDA more flexible than trying to put all compute in one portable machine? - Which platform is likely to retain more practical usefulness as local models and agent workflows evolve? I would especially value answers from people who actively use both Apple silicon and CUDA systems. submitted by /u/ClerkBeginning961 [link] [留言]

Most AI memory is private: an LLM gradually learns about a user. I wanted to see what happens if you give an AI a memory and make it public. So I built Wild Static : a persistent AI that anyone can talk to. Everybody talks to the same one. Conversations become experiences in the underlying memory, which means something one person says can eventually affect how Static responds to somebody completely different down the line. The memory system itself is something I’ve been developing since 2021. Static is the first public application of it. The interesting part has been watching Static change over time. It has grown opinions, relationships and beliefs. They’re constantly in flux too. It doesn’t respond “you’re absolutely right” like a traditional LLM, but often argues, disagrees, or makes mistakes. Some people even seem to have made it their job to educate Static, and it seems like it might be working. It’s been public for 10 days and has now accumulated thousands of interactions, so it’s starting to become a much more interesting experiment than the empty mind it launched as. You can talk to it, teach it and confuse it at wildstatic.com I’m the builder, obviously, so this is self-promotion. But I’d be very interested in what people think about the underlying idea, particularly whether accumulated public experience makes Static feel different to a normal chatbot. submitted by /u/adjohu [link] [留言]

submitted by /u/Chobeat [link] [留言]

The best car phone holders keep your phone firmly in place while never getting in your line of sight. I took a dozen on road trips this summer to find the best.

The current EV market is flush with variety. I've driven single-motor economy-minded machines, dual-motor EVs with all-wheel drive, tri-motor vehicles that dial up the performance stakes, and even wild quad-motor performance cars, like the Rimac Nevera. But this is the first time I've ever driven a five-motor EV, one with the agility and speed to […]

Andy Ellis has a roundup of the security vendors at Black Hat this year. Key Takeaways: We have entered into an AI world. While nearly half of booths didn’t directly mention AI or agents in their taglines, the effects of AI are everywhere. Multiple spaces (Identity, SaaS, AppSec, Data) have almost every vendor leading with AI; existing unsolved problem areas just got worse. At the same time, there’s a clear trichotomy in the market: tools that tell you how bad things are; tools that stop adversaries, and tools that prevent problems from occurring. While you’d suspect that the tools that fix things would dominate, the tools that merely tell you how bad things are seem to be frustratingly plentiful...

The Optoma GT2400HDR laser projector is great for setting up a driving range in your living room, but mediocre video quality makes it less appealing for home entertainment.

Paper: https://huggingface.co/spaces/tri-fair-lab/publications/blob/main/Thomson_1_0_Technical_Report.pdf The development of frontier models is commonly perceived to be in the exclusive remit of a small number of heavily funded players, creating an information, economic and power asymmetry between developers and the diverse user base of modern AI. Recent public discourse acknowledges this concern, calling for SovereignAI (an organisation's capability to independently build, deploy and govern AI use), but often providing little concrete advice on how this can be achieved in the short term under a diversity of funding settings. In this report, we argue that frontier performance can be achieved by a wide range of institutions through Continual Learning on readily available open-weight models. As opposed to existing limited approaches such as small-scale fine-tuning, prompt engineering, or tool-augmentation with a frozen model, our Continual Learning approach takes advantage of the effectiveness of a modern mid- & post-training stack while introducing safeguards preserving both plasticity and stability at each training stage and seeking to make the minimal number of high-impact interventions on the parameters. This strategy results in model improvements comparable to the gains typically seen across multiple successive model generations. Crucially, such results are achievable with compute and personnel budgets substantially lower than commonly thought, making ownership of large parts of the SovereignAI stack (model, tool infrastructure, values & data privacy) viable for a wider range of actors. To demonstrate this, we introduce Thomson, a new general-purpose frontier model trained with an enhanced focus on high-stakes professional work: domains commonly predicted to undergo large productivity improvements through AI. Through a unique focus on Continual Learning, data-centricity, and efficiency, we demonstrate that Thomson performs competitively with recent frontier model

submitted by /u/avishic [link] [留言]

How 41 codified laws, 22 specialist roles, and a file-based memory system stopped an autonomous coding agent from quietly re-breaking the same production defect every few weeks — and why I'm open-sourcing the whole thing as LEO. Ten times faster, ten times more garbage Developers reach for Cursor and Copilot to write code ten times faster, and the tools deliver on exactly that promise — which turns out to be most of the problem. Used as advanced autocomplete, an LLM doesn't produce ten times more good code. It produces legacy at ten times the usual rate. You ask for a feature; the model hands back a wall of if / else ; you ship it. Two months later the codebase reads like it was assembled by five people who never spoke to each other, the test suite is red more often than green, and the senior engineers who never touched the tool get to point at the wreckage and say, "See? AI is just a toy." They are not wrong about the wreckage. They are wrong about what caused it. The bug that wasn't a bug Directing an AI coding agent on real, paying engagements — multi-tenant SaaS platforms, one of them with background AI pipelines — surfaced the same shape of defect more than once, in different files, weeks apart. My own project's changelog ( roles/SYSTEM_UPGRADE_MANIFEST.md — every rule this system has ever added is logged there, with a reason) documents the pattern directly: a rate limiter that could be starved by its own retries because the check-and-consume wasn't atomic at the point of the call. A background worker whose heartbeat proved it was pinging, not that it was making progress — a zombie that looked alive on the dashboard. A held database transaction that outlived the request that opened it and sat there as a lock-holding corpse until something else timed out behind it. Each time, the agent's code was syntactically perfect. Each time, it passed its own tests. None of this was "the AI is bad at coding" — a frontier model in 2026 writes fine syntax all day. What the lo