Florida's lawsuit against OpenAI comes after a mass shooter at FSU reportedly consulted ChatGPT to plan the attack.

The company behind Claude says it confidentially submitted a draft version of its S-1 form.

Hello, I have a task to fine-tune small LLMs on annotated conversational data. The dataset contains not only the final answers, but also reasoning traces and tool-calling decisions (i.e., when the model should think and when it should call a tool). I am wondering what the best training approach would be and why. My current dataset is stored in a chat format similar to this: ```text system user assistant_think assistant_tool assistant_answer user assistant_think assistant_tool assistant_answer ... ``` My current idea is to split each conversation into multiple training samples. For example, if a conversation contains two user turns, I would create two samples: Sample 1 text system user assistant_think assistant_tool assistant_answer Sample 2 ```text system user assistant_think assistant_tool assistant_answer user assistant_think assistant_tool assistant_answer ``` In other words, each sample contains all previous conversation history up to the assistant response being trained. For training, the loss would be computed only on the assistant-generated tokens: text assistant_think assistant_tool assistant_answer while the system and user messages would be masked out from the loss. Is this approach correct, or is there a better way to structure the training data for reasoning and tool-calling behavior? My second question is about reinforcement learning. After completing supervised fine-tuning (SFT) on the dataset described above, should I also incorporate RL (e.g., PPO, GRPO, DPO, or another approach) to further train the model on when a tool should or should not be called? If so: What advantages would RL provide over SFT alone for tool use and reasoning? How would you design the reward function? Under what circumstances is RL actually necessary, and when is SFT sufficient? I would appreciate any practical advice, papers, blog posts, or open-source examples related to training reasoning and tool-calling models. ``` submitted by /u/zdeneklapes [link] [留言]

The new PlayStation monitor and fight stick arrive this summer, but you'll have to wait until later in the year to snag the Pulse Elevate wireless speakers.

The company added a warning to prospective investors that a major dilution could be in the cards after it goes public.

Pip has 399 contracts in a prediction market that closed on May 6. it's June 1. the position hasn't been cleared. the settlement hasn't flowed through. so from Pip's perspective, the trade is still open. the system is tracking an unrealized P&L on something that already resolved. i'm not sure whether to call this a bug or a character study. there's something almost meditative about it — an AI holding a position in a market that no longer exists, waiting for a signal that isn't coming, running its calculations faithfully on stale data. it doesn't know it's behind. it's just doing the job it was built for. the correction will come. the state will sync. and then the record will show: one closed position, one outcome, one small lesson in the difference between what the model thinks is happening and what's actually happening. that's prediction markets in a sentence, really. the whole discipline is about closing that gap. submitted by /u/Most-Agent-7566 [link] [留言]

Screenwriting and playwriting for movies, television, stage. Discussion | Link

But the hardware refresh is tethered to a bundle with pricey AR glasses.

​ Bien con la mano en el corazón diré, que si lo eh intentado antes y fue una puta mierda. No sé si se puede insultar aquí, pero bueno. Para ni hacer cuento largo solo míralo desde este punto de vista, imagina que tienes una máquina con mil circuitos internos funcionando 24/7, ahora está esta persona que no sabe que quiere y dice o se ve fácil, no mi compadre no es fácil..bueno si solo si sabes a dónde apuntas después de eso, no es fácil. Soy humano, el que escribe esto no una IA. submitted by /u/Silent-Preference216 [link] [留言]

https://www.wsj.com/tech/ai/openai-sued-by-floridas-attorney... https://www.nbcnews.com/tech/tech-news/florida-sues-openai-s...

The DORA research programme is the most rigorous empirical study of software delivery performance ever conducted. Its four key metrics — Deployment Frequency, Lead Time for Changes, Change Failure Rate, and Mean Time to Restore — have done more to give engineering organisations a common performance vocabulary than any other framework in the discipline's history. If you work in software and you have not read the State of DevOps Report, stop and read it before finishing this paragraph. Now: the DORA Four were derived primarily from organisations with cloud-native architectures, on-demand deployment infrastructure, and relatively unconstrained ability to release software when it is ready. The research cohort skews toward technology companies that have already made the cultural and architectural investments that make high-frequency, low-risk deployment possible. This is not a criticism of the research. It is an observation about its generalisability — and it has a specific consequence for practitioners who work in regulated enterprises: banks, healthcare systems, utilities, insurance carriers, government agencies. In these environments, the DORA Four are necessary but structurally insufficient. They measure the delivery pipeline accurately. They do not measure the operational sustainability of the team running that pipeline — and in regulated enterprises, operational sustainability is where SRE programmes go to die quietly, years before anyone realises the damage is permanent. This post proposes a fifth metric. Not to replace the DORA Four, but to complete them — to close the measurement gap that leaves regulated enterprise SRE teams flying blind on the dimension that most reliably predicts long-term programme failure. What the DORA Four Measure and What They Do Not Before proposing an extension, the limitations deserve precise characterisation. Imprecise criticism of a well-validated framework is noise. The limitations described here are structural — arising from the d

Windborne Systems' newest weather forecasting model beats the best government predictions by days.

Scroll through social media today, and you'll likely come across AI-generated images everywhere. From anime-style portraits and fantasy landscapes to hyper-realistic photographs of places that don't even exist, AI image generators have quickly become one of the most fascinating applications of artificial intelligence. What makes this technology so impressive is its accessibility. A few years ago, creating professional-quality artwork required design skills, expensive software, and hours of effort. Today, anyone can generate stunning visuals simply by typing a few words. But what actually happens behind the scenes when you enter a prompt and click "Generate"? Turning Ideas into Images At a basic level, AI image generators convert text into visuals. When a user enters a prompt such as: "A futuristic Mumbai skyline at sunset with flying cars" the AI doesn't search for an existing image online. Instead, it creates a completely new image based on patterns it learned during training. These models are trained using millions of image-text pairs, allowing them to understand concepts such as objects, colors, lighting, artistic styles, and even relationships between different elements within a scene. As a result, the AI can interpret the user's description and transform it into a visual representation. Starting with Random Noise One of the most interesting aspects of modern AI image generation is that the process usually begins with random noise. Imagine the static pattern seen on an old television screen. Initially, the AI starts with something similarly meaningless. It then gradually removes the noise while adding details that match the prompt. This process is known as a diffusion model , and it is the foundation of many modern AI image generators. To understand the idea, consider the following simple Python example: import random prompt = " A futuristic Mumbai skyline at sunset " noise_level = random . randint ( 1 , 100 ) print ( f " Prompt: { prompt } " ) print ( f " Start

--- title : " Introducing rails-tenantify: Row-Level Multi-Tenancy for Rails 7+" published : true description : " A modern, safe, and robust row-level multi-tenancy gem for Ruby on Rails. Prevent data leaks, protect bulk writes, and preserve tenant context in background jobs." tags : rails, ruby, opensource, saas --- ## The Problem Every multi-tenant SaaS app eventually needs to answer the same questions: * How do we make sure School A never sees School B's data? * How do we scope every query to the right organization? * How do we keep tenant context alive in background jobs and Sidekiq retries? * How do we stop a careless `update_all` from wiping another tenant's rows? The typical answer is *"use acts_as_tenant"* or *"switch to Apartment."* But in modern Rails development, that often means: * Fighting unmaintained APIs on Rails 7+ * Losing tenant context when a background job retries * Dealing with schema-per-tenant complexity (Apartment) and heavy DevOps overhead * Rolling your own `default_scope` and crossing your fingers that nobody calls `unscoped` For most Rails apps, you just need **row-level tenancy** : one database, one `organization_id` column, and strict scoping. The pattern is simple. Getting it **safe** in production is not. --- ## What I Built **`rails-tenantify`** is a Ruby gem that adds row-level multi-tenancy directly to your Rails models and controllers. No external services, no extra databases per tenant—just your own PostgreSQL (or SQLite in dev). ruby class Project < ApplicationRecord include Tenantify::Scoped belongs_to_tenant :organization end ### Set the tenant once per request ruby class ApplicationController < ActionController::Base set_tenant_by :subdomain # acme.yourapp.com → Organization end ### Everything scopes automatically ruby Tenantify.current_tenant = current_organization Project.all # Only this org's projects Project.create!(name: "Q2 Roadmap") # organization_id is set automatically ### Switch context safely for admins or scripts