标签：#AR

Electric-vehicle assistants rarely have just one job. A short Turkish question may ask for a nearby station, a charging-price comparison, help planning a route, or an explanation of battery health. Before an application can retrieve current data or generate an answer, it needs to identify that intent reliably. We created the Turkish EV Charging Intent Dataset as a small, transparent starting point for that routing problem. Version 1.0.0 contains 192 Turkish queries distributed evenly across eight intent classes. It is open under CC BY 4.0, includes fixed train, validation, and test splits, and is maintained by TekPedal , an EV charging map and vehicle decision platform for Türkiye. You can explore the dataset interactively , inspect the source and validation workflow on GitHub , or cite the permanent Zenodo release with DOI 10.5281/zenodo.22062688 . Why intent routing comes first An assistant should not answer every EV question in the same way. Different requests need different tools and freshness guarantees: a station request needs a map or location index; a price request needs current tariff data; route planning needs distance, range, and charging-stop logic; a battery question needs careful educational content; a vehicle comparison needs structured specifications. An intent router makes that separation explicit. It can send each query to the correct retrieval source, product page, or application workflow. This also makes evaluation easier: teams can test routing independently before measuring the quality of downstream answers. Dataset design The taxonomy contains eight balanced classes, with 24 records in each class: FIND_STATION COMPARE_PRICE ROUTE_PLANNING CHARGING_SPEED VEHICLE_COMPARISON HOME_CHARGING BATTERY_HEALTH OWNERSHIP_COST Every record includes a stable ID, the Turkish query, the intent identifier, a human-readable Turkish label, a suggested TekPedal content route, the assigned split, the language, and a provenance marker. Here is a simplified example

.NET 10 NU1015 turns a PackageReference without a version into a restore error. I like the stricter default because an unbounded direct dependency can quietly resolve the lowest package version. The catch is that versionless XML is also the correct shape for NuGet Central Package Management (CPM). A mechanical “add Version everywhere” repair can undo the policy your repository intended to enforce. I use a simple split: first decide who owns the version, then make restore prove the answer. Why .NET 10 NU1015 stops the build Before .NET 10, NuGet reported NU1604 when a direct reference had no inclusive lower bound. Restore could continue and select the lowest version available from the configured sources. Starting with .NET 10, the same mistake produces NU1015 and restore fails. Microsoft documents this as a stable behavioral change in the .NET 10 compatibility guidance . Here is the ambiguous project entry: <ItemGroup> <PackageReference Include= "Demo.Greeting" /> </ItemGroup> If this is a normal direct reference, the project is missing its version. If CPM is active, the project is correct and the version should live elsewhere. The NU1015 diagnostic reference calls out a common failure mode: a project that expected CPM was copied into a location where CPM is disabled or its props file is no longer discovered. That distinction matters more than silencing the error. It tells me whether the project file or the repository-level package policy is broken. The timing can be misleading. An SDK upgrade may expose an old direct reference that had always relied on lowest-version resolution, while a repository move may break a previously valid CPM import. I inspect the failing project's evaluated inputs, nearby props files, and recent path changes before editing package metadata. That keeps a restore migration from turning into an accidental package-management migration. Fix the owner, not only the XML For a direct reference, I add an explicit version: <PackageReference Include=

In June a reviewer on DEV who goes by @anp2network told me to stop storing a conclusion. I had a...

I had a paper accepted to a conference. This paper was originally published as a preprint. Subsequent works citing our preprint focused on the same topic and reused/extended our methodology. I am now preparing the camera-ready version of that preprint and I'm wondering how I should deal with this for the Related work section. It seems odd to me to cite my own preprint for the camera-ready version of the paper (and I am not even sure if this is allowed), but at the same time, I don't want to undermine the novelty of my original work (nor undermine the efforts of subsequent works). Has anyone dealt with such a situation before? What's the best way of solving this? submitted by /u/Vulcapulae [link] [留言]

Linkdaze's smart digital calendar stands out for not putting its features behind a paywall, including an AI meal planner tool.

Never attended a conference before, so apologies if these are dumb questions. I’m an author of an accepted paper at COLM 2026. One of my coauthors registered during the author-only registration period, so I joined the waitlist on August 10. I later received an email saying: “Your access to reserve tickets remains active until Aug 24 7:06 p.m. EDT.” I thought I had until August 24 to register, so I didn’t register immediately. When I checked again today (8/23), registration was sold out. I also can’t seem to rejoin the waitlist. Unfortunately, I also missed the financial assistance deadline because at the time I wasn’t even sure whether I would be able to attend. I really really want to attend the conference. Does anyone know what I can do at this point? Is there a chance that more registration spots will be released later? And is there any possibility of getting financial assistance after the deadline? Thanks a lot for any advice. submitted by /u/mziycfh [link] [留言]

Originally published on michael-kaminski.io . The Genome of Games publishes the same 1,180 records four different ways, and one command writes all four: node build.js , 0.39 seconds, zero npm dependencies. Out come 1,245 static HTML pages for crawlers, an interactive canvas graph for humans, a 129,037-byte search index for the site's own search box, and a Model Context Protocol server exposing 8 tools to agents. The decision worth copying is the one that sounds like a downgrade. The MCP server does not query the site and does not read the source data. It statically imports a 1.9 MB index that the build wrote. There is exactly one place where slugs, lineage, and adoption edges get joined, so an agent and a crawler cannot come back with different answers. The dataset is an ontology of video game mechanics — 168 mechanics, 618 games, 394 companies, 4,366 recorded links, 1962 to 2025. What the records are about does not matter here. The shape of the problem shows up anywhere a structured knowledge base has to serve both a search engine and a model. Four surfaces, one build, a twelve-fold expansion Six hand-edited JSON files under data/ are the source of truth: the feature ontology, the graph, the prose, the company registry, the site copy, and the verified outbound links. Together they are 1,312,577 bytes. The build turns that into 16,644,215 bytes of generated read surface. A 12.7× expansion, and every byte of it is disposable. Surface Consumer Bytes Per entity 1,245 static HTML pages Crawlers, humans 14,613,203 11,728 / page mcp-index.json → MCP server Agents 1,901,975 1,612 search-index.json The site's own search box 129,037 109 /graph/ canvas Humans exploring lineage data injected at build — The build also emits sitemap.xml with 1,245 entries, llms.txt , robots.txt , and a 404 page. The same run reports 96,843 internal links across those pages. Nothing in that list is authored. Delete the whole output directory and the next build restores it in under half a second.

Envelope encryption con clave por usuario, qué se cifra y qué no, cómo lo puso a prueba una auditoría externa, y el incidente de rendimiento que provocó nuestro propio hardening de seguridad. Montaste tu app con IA rápido: le pides unos datos al usuario, llamas al modelo, guardas el resultado en la base de datos y a producción. Cómodo, sin complicaciones. Hasta que un día miras bien qué estás guardando. En Cuentopia generamos cuentos personalizados para niños. Para personalizar, un padre nos cuenta cómo es su peque: su carácter, qué le da miedo, qué está pasando en casa. El modelo no improvisa sobre la marcha: se apoya en un marco de criterios clínicos y pedagógicos para decidir cómo abordar cada situación, y luego lo reescribe todo en prosa. Visto de golpe, lo que teníamos en la base de datos era el diario emocional de un montón de menores. El RGPD lo trata como categoría especialmente protegida. El sentido común, también. ¿Y si se filtra la base de datos? ¿Y un backup mal guardado? ¿Y un acceso indebido con privilegios de admin? Relájate —bueno, primero asústate un poco; luego relájate—. Te voy a contar cómo pensamos el cifrado en reposo en serio: una arquitectura de tipo envelope encryption , con una clave maestra que no sale nunca de Cloud KMS (Google Cloud) y una clave por usuario que cifra los campos sensibles antes de que toquen la base de datos. Un aviso antes de seguir: te cuento el criterio y las decisiones, no el plano. No vas a encontrar aquí nombres de recursos, rutas de repositorio, ni el detalle exacto que le serviría de receta a alguien con ganas de probar suerte con nuestros datos. Y porque la seguridad honesta se cuenta entera, también te cuento dónde decidimos no llegar y por qué. ✨ Promesa: al terminar vas a entender, con criterio real de producto, cómo una familia sin ser expertos en cripto se planteó cifrar datos de menores — y por qué ciertas decisiones muy concretas no se hacen públicas nunca, ni en el artículo más honesto. El mapa Lo constru

Engineers who only understand the technology, and never the business it serves, hit a ceiling early. The best ones develop commercial awareness — a real sense of how value is created, funded, and sold. Two days at 21BY72 Season 4, one of Bharat's leading startup summits — eighty-five ventures on the floor and live pitches in front of six hundred investors — was a concentrated lesson in exactly that, and I wrote about it in this reflection . Technology is a means; the business is the point It's easy, as an engineer, to treat the product as the whole world and the commercial side as someone else's problem. Sitting in a room where eighty-five ventures pitched to investors makes the truth obvious: the technology is a means to a business end, and understanding that end makes you a better engineer, not a distracted one. Watching founders pitch — being judged not on how clever the build was but on whether it solved a real problem people would pay for — reframes how you think about your own work. It pushes you to ask "who is this for and why does it matter" before "how do I build it." What the summit floor teaches an engineer Investors buy problems solved, not features built. The pitches that landed were about a real need and a credible path to meeting it — a discipline that improves engineering priorities directly. Commercial context sharpens technical decisions. When you understand the business constraints — cost, speed to market, who the customer actually is — you make better trade-offs in the architecture, not worse ones. Exposure recalibrates ambition. Being around people building real ventures at scale resets your sense of what's possible and what "serious" looks like. The takeaway The most rounded engineers I've come to admire pair technical depth with genuine commercial awareness. Spending two days inside a major startup summit, watching how businesses are pitched, funded, and built, was a deliberate investment in the half of the picture that a pure engineering educ

When a free endpoint returns 429, most agents do the most expensive thing possible: retry. Retrying looks harmless. A 200-millisecond request becomes a 2-second wait, then another attempt. But under peak load, that loop becomes a 30-second stall while your agent clicks refresh on an empty response. If the quota window resets during the stall, every retry burns tokens you could have spent on actual work. The retry loop assumes the failure is temporary. For rate limits, that assumption is usually wrong. Quota counters reset on a fixed schedule, not on your convenience. You are not just waiting; you are burning wall-clock time that could have gone elsewhere. The Cascade Pattern A cascade router is the alternative. It sends requests to the free endpoint, backs off on rate-limit signals, then degrades gracefully to a backup endpoint. The free tier carries the load; the backup exists only when needed. You get the cost advantage of the free tier and the reliability of the paid tier. The design has three parts: an endpoint abstraction layer, a rate-limit detector, and a circuit breaker that trips when the free endpoint fails repeatedly. Here is the core code: # cascade_router.py — free tier first, paid/self-hosted as fallback. import json import os import time import urllib.error import urllib.request from dataclasses import dataclass @dataclass class Endpoint : name : str url : str api_key : str model : str cooldown_until : float = 0.0 consecutive_failures : int = 0 def available ( self ) -> bool : return time . time () >= self . cooldown_until class CascadeRouter : def __init__ ( self , endpoints : list [ Endpoint ]): self . endpoints = endpoints def _call_one ( self , ep : Endpoint , messages : list [ dict ]) -> tuple [ int , dict ]: body = json . dumps ({ " model " : ep . model , " messages " : messages , " max_tokens " : 256 }). encode () req = urllib . request . Request ( ep . url , data = body , headers = { " Content-Type " : " application/json " , " Authorization "

Google is introducing HEIR (Homomorphic Encryption Intermediate Representation), an open-source compiler and development toolchain designed to make encrypted computation easier to deploy. In particular, HEIR can compile pre-trained AI models built for conventional, unencrypted inputs so they can instead operate on encrypted data. By Sergio De Simone

If you've ever wondered whether you can just charge your phone with the cable for your laptop, read on and find out how to tell.

My dumbass just realized all NeurIPS workshops are non-archival. In terms of grad school applications, would there be a difference in how much they value ur paper if u get it in a proceeding submitted by /u/Wonderful_Entry9371 [link] [留言]

A whole bunch of smart watches are getting phased out this year. Is your watch on the list?

Flock Safety faces a growing public outcry over concerns that its surveillance technology could be misused.

Voice and chat assistants for the home share a deceptively hard job: turning messy natural language into precise, structured commands. “Make it cozy in here” has to become a concrete intent plus the right slots — which device, which room, which value. Domux is an open model from iFlytek that focuses on exactly this problem: command understanding for smart-home assistants, framed as intent parsing and slot filling. What it is Task: smart-home command understanding — intent parsing + slot filling Base model: fine-tuned on google/gemma-4-E2B-it Modality: multimodal (image + text input) Target: edge / on-device deployment rather than large cloud models License: Gemma Why the compact base matters Building on the small Gemma-4-E2B base keeps Domux in a size class meant to run close to the device. For home assistants, that direction is attractive: keeping command understanding on-device can reduce round-trips and keep more interaction local, instead of routing every utterance to a large hosted model. Try it The model card is on Hugging Face (access is gated — you may need to log in and request access): 👉 https://huggingface.co/iFlytekOpenSource/Domux We're sharing open work like this because on-device, task-focused models are a practical piece of the foundation-model and serving story — not everything needs to be a giant cloud model.

How do you usually build a login page in an application? The first project Imagine you are working on a project that needs a login page. Let's call it Aurora (Project A). The login page is the entry point to the application. Users who have access can log in to the application with the permissions they have. We are not going to talk about the details of the login method yet, such as email + password, username + password, phone + password, social login, magic link, or others. Let's say we use email + password for this example. For this, we usually need user data for the application, for example a users table in the database. If we use email and password as the login method, the users table would at least need email and password columns. Of course, the password should be hashed. After the application is developed, users can log in using the email and password registered in the database. During development, we can simply inject user data directly into the database. Adding one or two users manually is still fine. If we need more users, we can create a database script to insert them. Then another requirement appears. We need to manage users directly from the application. Previously, user data could only be accessed directly from the database. Now the application needs to show a list of users, user details, and provide features to create, update, and delete users. We need to build several new pages for this user management feature. Eventually, the feature is completed. Now you can add users whenever you want, and they can immediately use their account to log in to Aurora. At this point, the user requirements for Aurora might be enough. The second project Then you have another project that also needs a login page. Let's call it Borealis (Project B). This is a different project from Aurora, but the login works in a similar way. Since you already built the login feature in the previous project, you can duplicate the existing code into Project B, including the user management

The single biggest operational risk for early-stage founders remains hiring traditional hourly dev shops. Before partnering with any external dev team, I've learned to run them through this 5-point evaluation framework: The 5-Point Evaluation (co-founder approved) 1. Quality of Questions If a team asks zero questions, it’s an immediate red flag. It's impossible to deeply understand a project without asking anything. But quality matters. Weak devs ask easily googled questions about basic blockchain mechanics. Strong engineers ask highly specific questions focused entirely on your business logic, edge cases, and tokenomics. 2. Proposing Solutions, Not Problems (obvious one) A weak team will message you saying, "We have a problem, how should we fix it?" A mature team says, "We hit a blocker. Here are three architectural workarounds, the trade-offs for each, and our recommendation." 3. Deep Ecosystem Knowledge Coding isn't enough. If an agency claims they can build a top-tier lending protocol but doesn't understand the role of risk engines and oracles, they are tourists. Your developers need to know top-tier market leaders like Gauntlet, Steakhouse, Chaos Labs, and RedStone, and understand how their risk modeling and data feeds directly dictate market parameters. If they lack this context, their expertise is strictly surface-level. 4. Full Product Lifecycle Understanding Writing code and calling it a day is a massive mistake. A real partner understands what happens outside the IDE. They account for security audit buffers, integration with risk providers and oracles before mainnet, and the proper setup of on-chain governance and admin functions. 5. High Agency & Proactivity Elite teams care about your overall success, not just their Jira tickets. To quote a BD i work closely with: “When a client is about to make a massive mistake, you have two choices: stay silent and watch them fail, or step in with your expertise, even uninvited, and say: 'We hear what you want to do,

If/else statements - We all know and love them. While they are incredibly powerful, there comes a point where a long chain of conditions only makes your code look messy. Choosing between if/else and switch depends on readability, but there's a hidden pro tip that makes switch much more powerful than many people think at first. Traditional Approach: If/Else Normally, we use if/else when our logic depends on complex ranges and multiple variables: // Hard to scan, bulky, and prone to typos let weatherAdvice = "" ; if ( temperature < 15 && isRaining ) { weatherAdvice = " Grab a heavy coat and an umbrella! 🌧️🧥 " ; } else if ( temperature < 15 && ! isRaining ) { weatherAdvice = " It's cold but dry. Just a jacket is fine! 🧥 " ; } else if ( temperature >= 15 && isRaining && isNightTime ) { weatherAdvice = " Warm, rainy night. Stay indoors if you can! 🌧️🌃 " ; } else if ( temperature >= 15 && isRaining && ! isNightTime ) { weatherAdvice = " Warm rain during the day. Don't forget your umbrella! 🌧️🌦️ " ; } else if ( temperature >= 30 && ! isRaining ) { weatherAdvice = " It's scorching hot! Stay hydrated! ☀️🥤 " ; } else { weatherAdvice = " Weather seems pleasant today! 😎 " ; } Pro Tip: Using switch(true) Many developers think you can only use switch when you're checking a single variable against fixed values. However, you can use a switch statement for complex ranges by passing the boolean value true into the switch condition. Here is a cleaner switch statement version of the above code block: // Much easier on the eyes let weatherAdvice = "" ; switch ( true ) { case ( temperature < 15 && isRaining ): weatherAdvice = " Grab a heavy coat and an umbrella! 🌧️🧥 " ; break ; case ( temperature < 15 && ! isRaining ): weatherAdvice = " It's cold but dry. Just a jacket is fine! 🧥 " ; break ; case ( temperature >= 15 && isRaining && isNightTime ): weatherAdvice = " Warm, rainy night. Stay indoors if you can! 🌧️🌃 " ; break ; case ( temperature >= 15 && isRaining && ! isNightTime ): weather

The Asus PG27UCWM brings a new sub-pixel layout to the world of OLED gaming monitors, and in my testing, I appreciated the improvements it brings to the table.