Lab students were supposed to ID a mild germ. They all identified a deadly pathogen.

Ariana Grande filed a lawsuit on Monday in the Los Angeles County Superior Court against the currently unidentified hackers who allegedly stole and leaked private content. It aims to "uncover the identities of these currently unknown and unscrupulous individuals," referred to as "John Doe 1 and John Does 2 through 100." The lawsuit also says […]

Run coding agents through the full development process Discussion | Link

10 days passed from OpenAI models exploiting JFrog Artifactory 0-day to release of a patch.

Spur Intelligence has raised a $200 million round from Insight Partners for its tech that can identify legit human traffic from bots.

Working with databases is a big part of backend development, and choosing the right ORM can save you hours of work. Here are five of the most popular Node.js ORMs, along with their strengths and weaknesses, to help you pick the right one for your next project. 1. Prisma A modern, type-safe ORM built for TypeScript with an excellent developer experience. Pros • Great TypeScript support • Easy migrations • Excellent DX • Large community Cons • Less flexible for advanced SQL • Requires client generation Drizzle ORM A lightweight, SQL-first ORM focused on performance and simplicity. Pros • Very fast • Full TypeScript support • SQL-first approach • Lightweight Cons • Smaller ecosystem • Better if you know SQL 3. TypeORM A mature ORM with broad database support, widely used in enterprise and legacy projects. Pros • Rich feature set • Supports many databases • Strong relationship support Cons • More complex API • Slower development than newer ORMs 4. MikroORM A powerful TypeScript ORM designed for large and complex applications. Pros • Excellent relationship handling • High flexibility • Strong TypeScript integration Cons • Steeper learning curve • Smaller community 5. Sequelize One of the oldest and most established ORMs in the Node.js ecosystem. Pros • Battle-tested • Supports many databases • Large legacy adoption Cons • TypeScript support is weaker • Feels outdated compared to modern ORMs Which ORM do you use the most? 👇

Every binomial confidence interval you have ever computed on an eval pass rate, Wald, Wilson, Clopper-Pearson, all of them, rests on one assumption: each example is an independent draw. Most eval sets violate it. You have 40 questions generated from the same 8 documents, or 200 turns from the same 30 conversations, or 150 examples that are really 50 cases with 3 paraphrases each. Those are not 200 independent observations. And when you feed a correlated set into a formula that assumes independence, the interval comes out too narrow, which means you declare differences significant that aren't. I want to walk through why, put a number on how much it matters, and show the fix, because this one is invisible: the code runs, the interval prints, and it is quietly wrong. Why clustering shrinks your real sample size Independent examples each carry their own information. Correlated examples carry overlapping information. If five questions come from the same document, and the model either understands that document or doesn't, those five outcomes move together. You did not learn five independent things about the model. You learned something closer to one and a half. The survey-statistics name for this is the design effect (Kish, "Survey Sampling," 1965). For clustered data it is approximately: Deff = 1 + (m̄ - 1) · ICC where m̄ is the average cluster size and ICC is the intra-cluster correlation, the fraction of total variance that lives between clusters rather than within them. Your effective sample size is: n_eff = n / Deff That is the number of independent examples your clustered set is actually worth. The number Take a realistic eval set: n = 200 examples, drawn from 40 source documents, so average cluster size m̄ = 5. Suppose the ICC is 0.3, which is unremarkable for "questions from the same document" (I have measured higher). Deff = 1 + (5 - 1) · 0.3 = 2.2 n_eff = 200 / 2.2 ≈ 91 Your 200-example eval is worth about 91 independent examples. The correct confidence interval

This is my first dev blog I'm making a terraria mod I'm not sure if i want to start right now but i am sure to start soon i already have some ideas so here are the ideas The Operator The operator is someone i have in idea for a while kind of the lore aspect is you work it, killing bosses and giving proof to the operator for certain rewards, at first it is an Npc but after moon lord you fight him. I might bring him back as the same dude but is occupied by the Fixer as a vessel which he is chained or has custom hand cuffs for the fixer to occupies the fixer without The Operator body, without the body dying. The Dulled One This is not my idea but a alternative version of it (Game: Craft-Wars Redux Roblox, Boss: Dulled Spectrum) for credits, so I really liked this boss idea but I'm really not sure if they did what I'm doing, but his power is to erase or turn into dust or "dull". To erase certain parts of the world either matter, or space i don't want to say time because i feel like that would be boring, it can either be like passive very weak erase which it can re-gain power. Second version is the Compacted version which does way more damage, maybe one shot if i do one shot then im adding middle attacks, but deplete the dust bar by a ton so if the boss is not careful or the player stops it then you can easily beat it. it might have a regen system where if the bar is full then it heals. Also hammer both the versions the game and mine has hammer. The lore is not very fleshed out right now but i will figure it out

A thousand workers from OpenAI, Anthropic, Google, Meta and more have signed the letter.

Tools4AI is a 100% Java agentic AI framework that turns any annotated Java method into an AI-callable action. Ollama runs open models like Llama 3.1 and Phi-4 locally and exposes an OpenAI-compatible API. Point Tools4AI at http://localhost:11434/v1 and you get a fully offline, on-premise AI agent — no data ever leaves your network. In this tutorial we build an insurance claims triage agent that reads a claimant's free-text incident report, routes it to the right business action, extracts structured data, gates high-value payouts behind a human approval, and records a compliance audit trail. Who is this for? Java developers, solution architects, and engineering leaders in regulated industries (insurance, banking, healthcare) who want agentic AI without sending sensitive data to a third-party API . Table of Contents Why local AI agents matter for insurance Insurance runs on personally identifiable information (PII) : names, addresses, policy numbers, medical details, vehicle data, and loss descriptions. Sending that data to a hosted LLM API creates regulatory, contractual, and reputational risk. At the same time, claims teams are drowning in unstructured text — First Notice of Loss (FNOL) reports, adjuster notes, emails, and call transcripts. A local AI agent solves both problems at once: Data never leaves your premises. The model runs on your own hardware via Ollama. Deterministic business logic stays in Java. The LLM decides what to do; your audited, tested Java code decides how . Human-in-the-loop and audit trails are first-class, so you can satisfy compliance reviewers. That combination — private inference plus governed execution — is exactly what Tools4AI + Ollama gives you. What is Tools4AI? Tools4AI ( io.github.vishalmysore:tools4ai on Maven Central) is a lightweight, pure-Java agentic AI framework and ADK. Its core idea is simple and powerful: Annotate a Java class with @Agent and its methods with @Action . Tools4AI scans the classpath, and at runtime it maps

Process mismatch In tools built for Scrum, a task is an input: something you file, size, and work on. In Shape Up, a task is an output — something discovered while building work that was already shaped and bet on. That's the core mismatch, and it plays out differently depending on the tool. Jira Jira does exactly what it was built to do. Its shape is Scrum's shape: a backlog, estimates, sprints. Teams bring Shape Up in anyway and try to make it fit the tool's shape. A scope becomes an epic. A task becomes a ticket. The pitch — Shape Up's document for a problem, its appetite, and a proposed solution — has no equivalent object in Jira, so it ends up living in a Confluence doc, disconnected from the work it's supposed to govern. The substitutions are each small and reasonable on their own: An estimate field is there, so it gets filled in — and the velocity report looks broken without it. Losing bets need somewhere to go, so they land in the backlog. They aren't dead, they're waiting — and now someone has to groom them. Appetite ("how much is this worth") quietly reverts to estimate ("how long will this take"). Before long, the team is running Scrum, with a backlog-refinement meeting back on the calendar. The tool's requirements pull the ceremonies back in. Linear Linear is fast and well made. It even has cycles. The mismatch here isn't a quality problem — it's an inheritance problem. Linear carries the same assumptions as Scrum, just executed better. When a cycle ends with work unfinished, Linear rolls it forward automatically into the next one. It's meant as a convenience feature. It's also the inverse of Shape Up's circuit breaker. Shape Up's bet is that the deadline is real. The whole mechanism depends on a hard stop forcing a decision — cut the scope and ship what's done, while there's still time to make that call. A tool that quietly carries unfinished work forward removes the one moment the method needs. Every six weeks, it says: the deadline was just a suggestio

Every data team eventually asks the same question: what runs our pipelines, on what schedule, with what retry logic, and who gets paged when it fails. The answer used to default to Airflow because there wasn't a real alternative. Now there are three reasonable defaults, and they optimize for different things. Picking wrong doesn't break anything on day one — it shows up eighteen months later as either an operations team drowning in scheduler maintenance or an engineering team fighting a platform that won't do what they need it to. Here's the actual tradeoff, not the vendor pitch version. Databricks Workflows: the path of least resistance, if you're all-in on Databricks Databricks Workflows is the orchestrator built into the platform. Jobs, clusters, Unity Catalog permissions, and Workflows all share the same control plane, which means you're not maintaining a separate scheduler, not managing a second set of credentials, and not debugging why an external system can't see a table that Unity Catalog says it can. Task dependencies, retries, cluster reuse across tasks, and job-level alerting all come for free. The cost is exactly what you'd expect from a platform-native tool: it orchestrates Databricks well and everything else poorly. There's no first-class way to trigger a task in your orchestration DAG that waits on a Salesforce export, calls an internal API, or coordinates a dbt run against a warehouse that isn't Databricks SQL. You can bolt these in with webhooks and external scripts, but you're fighting the tool rather than using it. Workflows also doesn't give you the asset-lineage or testing story that Dagster does — it schedules tasks, not data assets. If your data platform genuinely is Databricks end to end — ingestion, transformation, ML, serving — Workflows removes an entire category of operational overhead you'd otherwise be paying for nothing. Teams in this position who reach for Airflow anyway usually do it out of habit, not need, and end up running two sch