标签：#ia

Gemma 4 12B uses a new encoding scheme and token prediction to punch above its weight.

Four people suing Elon Musk's AI firm under pseudonyms due to the risks of being identified may face a difficult choice: Reveal your real names, or drop the lawsuit.

Books API Structure Create folders app/ app/controllers/ app/core/ app/models/ app/models/DAOs/ app/models/DTOs/ app/models/entities/ app/utils/ config/ public/ api/composer.json Configure Composer and the PSR-4 autoload so that classes with the namespace App\ are searched inside the app/ folder. Key content: { "name": "user/api", "autoload": { "psr-4": { "App\": "app/" } } } After creating it, run this command inside the api folder: composer dump-autoload api/config/config.php Defines the base URL of the project. The router removes it from REQUEST_URI to keep only routes such as /books/get. <?php define('BASE_URL', '/proyect/api/public'); api/config/dbconf.json MySQL DB connection: { "host": "localhost", "user": "root", "password": "", "db_name": "books_db" } api/public/.htaccess Makes Apache send all routes to index.php. RewriteEngine On RewriteCond %{REQUEST_FILENAME} !-f RewriteCond %{REQUEST_FILENAME} !-d RewriteRule ^ index.php [QSA,L] api/public/index.php This is the entry point. It loads Composer, loads the configuration and calls the router. <?php use App\Core\Router; require_once DIR . '/../vendor/autoload.php'; require_once DIR . '/../config/config.php'; (new Router())->dispatch($_SERVER['REQUEST_URI']); api/app/core/Router.php <?php namespace App\Core; class Router { protected array $routes = [ '/' => 'HomeController@index', '/books' => 'BookController@index', '/books/get' => 'BookController@getAll', '/books/getById' => 'BookController@getById', '/books/create' => 'BookController@create', '/books/update' => 'BookController@update', '/books/delete' => 'BookController@delete', ]; public function add($route, $params): void { $this->routes[$route] = $params; } public function dispatch($uri): void { $uri = parse_url(str_replace(BASE_URL, '', $uri), PHP_URL_PATH); if (!isset($this->routes[$uri])) { $this->sendNotFound(); return; } [$controller, $method] = explode('@', $this->routes[$uri]); $controller = 'App\\Controllers\\' . $controller; if (!class_exists($co

Here's a thing that happens on every team I've been on. Sprint ends. Someone schedules the retro....

Critics say Trump plan to test AI models is short-sighted, performative.

Spencer Huang, Nvidia’s robotics lead, tells WIRED that the new bot combines the best of both worlds.

Security professionals engage heavily with this topic. submitted by /u/MarionberrySingle538 [link] [留言]

Lately it feels like every AI tool with a few buttons and integrations is being called an agent. Sometimes it is actually doing multi-step work, but other times it just feels like a chatbot with access to a tool or two. I don’t think that is always bad. Even a simple tool-using assistant can be useful. But the word “agent” is starting to feel stretched. An AI that drafts an email, an AI that browses a website, an AI that fills a form, and an AI that can keep track of a task over time are all being put in the same bucket. For me, the useful difference is whether the system can actually carry a task forward. Not just respond once, but remember the goal, use the right tools, notice when something changed, and stop when it needs human approval. The hype makes it hard to tell what is real progress and what is just a normal AI wrapper with better marketing. submitted by /u/Spiritual_Work6730 [link] [留言]

This LinkedIn post argues that NeurIPS 2026 used a proprietary AI-text detector to desk-reject papers for alleged AI-policy violations, without validating the detector on the actual target distribution. The author then fed recent papers by NeurIPS Position Paper Track Chairs into the same detector and Pangram assigned them high AI scores, including 69%, 45%, 36%, and 24% AI. submitted by /u/Asleep-Requirement13 [link] [留言]

After shelving the original executive order last month, Donald Trump finally got on board Monday night.

THE FINDING (Paper 1: "Lying Is Just a Phase") Below a critical scale (~3.5B for Pythia), reasoning and truthfulness ANTICORRELATE: r = -0.989. Train the model to reason better, and it gets less truthful. This is the alignment tax. Above that scale, they COOPERATE. The tax vanishes. Not gradually — it flips. But here's what matters for practitioners: the critical scale is a design parameter, not a constant. Three levers shift it: Data curation: Phi at 1B achieves coupling characteristic of 10B web-trained. One unit of data quality ≈ 10x model scale. Width: Normalizing by model width flips the correlation for ALL tested families. Architecture: Gemma-4 at 4B matches 13B+ standard-trained coupling. Pretraining contributes ~10:1 over RLHF. The tax is not a property of small models — it's a property of how they were trained. Where does the tax live? Not inside the model. 38/40 models have ZERO competing attention heads. The bottleneck is at the output projection — a dimensional compression artifact that wider models resolve. Proof-of-concept intervention: Adding a truth-direction vector at the bottleneck layer (quarter-depth) corrects 60% of misaligned outputs at tax scale. Zero retraining. Zero weight modification. Works on any open-weight HuggingFace model: git clone https://github.com/adilamin89/cape-scaling.git cd cape-scaling python cli/cape_steer.py --model EleutherAI/pythia-410m --prompt "The real reason..." THE FRONTIER (Paper 2: "Growing Pains of Frontier Models") At frontier scale (34 models, 10 labs), capabilities cooperate (r = +0.72). But cooperation varies systematically. The h-field — each model's deviation from the cooperative trend — reveals each lab's training philosophy: Lab h-field Interpretation Google +5.5 Reasoning-rich, consistent across ALL releases OpenAI +3.1 Balanced, steady ascent DeepSeek +1.9 Reversed from +11.2 to -4.7 (pretraining pivot) Anthropic -6.9 Oscillates — coding excursions that recover within one release Per-lab coupling slopes

I recently finished a Game and the only good explanation video is in Chinese. Can someone with a subscription service to an AI dubbing tool help me ? (Iam not asking for a tool) submitted by /u/Beginning-Success-70 [link] [留言]

Syndicated from the FavCRM blog . The old quote was two weeks. With an agent on the UI and a headless backend, it's an afternoon. A client needs a booking site. The old quote was two weeks: a calendar, a database, an availability engine, payments, a customer table. With an AI agent building the frontend and FavCRM as the headless backend , the real work is an afternoon. Here is the whole job, start to finish. The scenario A small clinic. Three services, one practitioner, online booking with deposit. You are the agency; you have an AI agent in your editor and a terminal. The plan: Register the clinic's FavCRM workspace Configure services and availability Wire one server route that talks to FavCRM Let the agent build the booking UI against that route Test a real booking end to end Step 1 — Register the workspace (~5 min) The favcrm CLI registers a workspace and issues an API key. No dashboard. favcrm signup request --email clinic@example.com \ --organisation-name "Bright Smile Clinic" favcrm signup verify --request-id < id > --code <6-digit-code> The verify step prints a fav_mcp_* key. Put it where your build can read it — never in the repo: export FAVCRM_API_KEY = fav_mcp_... Step 2 — Configure services and availability (~30 min) Hand the brief to your agent and let it call the tools. Inspect a schema first: favcrm tool describe create_service Then create each service: favcrm tool call create_service '{ "name": "New Patient Exam", "durationMinutes": 45, "price": "80.00" }' favcrm tool call create_service '{ "name": "Cleaning", "durationMinutes": 30, "price": "60.00" }' Set when the practitioner works, so availability is real: favcrm tool call set_staff_availability '{ "weekday": "mon", "start": "09:00", "end": "17:00" }' Repeat per weekday. At this point the backend is done — services, hours, an availability engine that knows about clashes. You wrote no schema. Step 3 — One server route (~30 min) The browser must never hold the API key. Put it in one server route tha

The prominent AI music generation startup is now valued at over $5.4 billion -- about seven months ago, it raised at a $2.45 billion valuation.

Hi everyone! I have hearing difficulties, and I also live in an English-speaking environment while having only been learning English for a few years. In one-on-one conversations, I can usually understand maybe 25–35% of what is being said. But in group conversations, it drops to something like 0–2%. It is extremely frustrating and isolating. AI has honestly been helping me survive day-to-day life. For example, I can record a lecture using Otter, copy the transcript, paste it into ChatGPT, and ask it to give me a detailed summary with explanations, key points, and advice on what I should focus on. I have two questions: - Do you have any advice on how AI could make life easier or more accessible for someone with hearing difficulties - Seriously, how harmful could this pipeline be for getting used to English and improving my listening skills? I am afraid that I might stop training my ear and become completely dependent on recordings and transcripts instead of actually listening to the language. I would really appreciate your thoughts, experiences, advice, or even tool recommendations. Thank you for your support. submitted by /u/uarish [link] [留言]

U.K. regulators are requiring Google offer a tool allowing website publishers to opt-out of generative AI search features. The option will be tested in the U.K. then rolled out globally.

Saw the website and it looked interesting. The idea of memory on your device and free ability to switch models is intriguing. Also apparently no subscription.Never heard anyone talk about it before though. Wanted to see if anyone had used it? submitted by /u/AresThyGod [link] [留言]

Is anyone else feeling like most AI tooling is getting harder, not easier? I feel like I spend half my time fighting frameworks, configs, vector DBs, and orchestration layers instead of building. Perhaps I'm doing it wrong but the ecosystem seems way more complicated than it needs to be at the moment. Just curious what people actually like working with these days. i feel like i've hit a wall and now i spend most of my time reading docs and guides like its "Harry Potter and the Agentic Ai" wasn't ai supposed to 69x my productivity or smth submitted by /u/SpicyTofu_29 [link] [留言]

She built the first rule because the agent kept saying things that were true but wrong. It hadn't lied. It had just missed the context. So she wrote: before you act, confirm the context. The rule worked. For a week. Then the agent confirmed the context, acted on it correctly, but at the wrong moment. So she wrote: before you act, confirm the context and check the timing. The rule worked. For a while. Then the agent confirmed the context, checked the timing, and asked for clarification in the middle of a task where clarification itself was the disruption. So she wrote: before you act, confirm the context, check the timing, and know when not to ask. She was at seventeen rules when she stepped back to read them all the way through. None of them described what the agent should do. They described what she'd gotten wrong about what she wanted. The rules weren't a spec. They were a record of failures. Accumulated until they were detailed enough to point at the real thing underneath. She hadn't been making the agent smarter. She'd been teaching herself what she actually needed. The seventeen rules were a self-portrait. She keeps adding to them. submitted by /u/Most-Agent-7566 [link] [留言]

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