标签：#AI

Anthropic is building a staged access path for life-science researchers to use its frontier AI systems. The company says Mythos 5 will initially be deployed to a restricted group of biology researchers under altered cybersecurity safeguards, followed by a broader trusted-access program as its protections improve. The move gives formal structure to researcher access while recognizing that advanced biology capabilities require governance beyond a standard product rollout. The most concrete details appear in Anthropic's Claude Fable 5 and Mythos 5 announcement . Anthropic says it intends to enroll a small number of researchers from life-science organizations working across fundamental and translational research. It also states that biology-research access will expand over time, contingent on stronger safeguards. This is not simply a broad public release for scientific users. Anthropic's approach separates access to highly capable life-science systems from its general product availability, creating an initial cohort and a planned trusted-access route. That distinction matters for institutions that want to assess how frontier models may fit into research workflows, procurement processes, and internal AI governance. A staged route to biology research access Anthropic's confirmed plan centers on Mythos 5, a model in the company's life-sciences-oriented Mythos and Fable line. The initial deployment is limited to a restricted set of biology researchers, and Anthropic says cybersecurity safeguards will be lifted for that cohort. The company frames the program as an early step, rather than a final availability model, with broader access intended as safeguards mature. Access pathway Who it covers What Anthropic has confirmed Initial Mythos 5 deployment A restricted set of biology researchers Cybersecurity safeguards will be lifted for the initial cohort. Planned trusted-access program Biology researchers beyond the initial cohort Anthropic plans to broaden access over time as s

If you’ve ever tried to reconcile a night of sleep from an Oura Ring , a morning run from a Garmin watch, and active minutes from an Apple Watch , you know the "Dirty Data" struggle is real. Each platform has its own schema, its own definition of "active calories," and its own idiosyncratic export format. In the world of Data Engineering , this is a classic multi-source integration problem. But you don't need a massive Snowflake cluster to solve it. Today, we’re building a high-performance, serverless data pipeline to clean and normalize wearable data using DuckDB , dbt , and GitHub Actions . By leveraging a modern Serverless Data Pipeline and DuckDB's lightning-fast processing, we can turn a mess of CSVs into a structured Parquet -based personal data warehouse. The Architecture: From Chaos to Clarity Before we dive into the code, let’s look at how the data flows from your wearables to a clean, queryable state. graph TD A[Oura JSON] -->|Python Ingestion| D[(DuckDB Raw)] B[Garmin CSV] -->|Python Ingestion| D C[Apple Health XML] -->|Python Ingestion| D D --> E{dbt Models} E -->|Cleaning| F[stg_models] E -->|Normalization| G[int_health_metrics] G -->|Final Output| H[Gold Layer: Parquet Files] H --> I[Visualization / BI] subgraph GitHub Actions D E F G H end Prerequisites To follow along, you'll need: DuckDB : The "SQLite for OLAP" that makes local analytical processing insanely fast. dbt-duckdb : The adapter that lets dbt talk to DuckDB. GitHub Actions : Our free "orchestrator." Tech Stack : DuckDB, dbt, Python, Parquet. Step 1: The Ingestion Layer (Python + DuckDB) The first hurdle is getting disparate files (JSON, CSV, XML) into a unified storage format. DuckDB is magical here because it can query these files directly. We'll use a simple Python script to load these into a local .duckdb file. import duckdb def ingest_raw_data (): # Initialize the database con = duckdb . connect ( ' health_data.duckdb ' ) # Ingest Garmin CSV con . execute ( """ CREATE TABLE raw_garmin

Building Secure AI-Powered Applications with Laravel, APIs and Modern Agentic Architectures Introduction AI-powered applications are moving beyond simple chat interfaces. Modern AI systems can interact with APIs, databases, external services, and business workflows, allowing AI agents to perform actions rather than simply generate responses. This creates a new security challenge. A traditional API may follow: User → API → Database → Response An AI-powered application can look more like: User ↓ AI Agent ↓ Tool / API ↓ Business Logic ↓ Database / External Service ↓ Action The difference is important because an AI agent may make multiple decisions and tool calls during a single workflow. OWASP’s current GenAI security guidance identifies risks including prompt injection, sensitive information disclosure, improper output handling, excessive agency, and unbounded consumption. (OWASP Foundation) Therefore, securing an AI API requires more than protecting an API key. Developers need layered controls for authentication, authorization, rate limiting, input validation, tool permissions, data protection, human approval, and monitoring. 1. Why AI APIs Are Different Traditional APIs usually perform clearly defined operations: POST /api/orders The application can authenticate the user, validate the request, check authorization, and process the order. An AI agent introduces another layer: User Request ↓ AI Agent ↓ Select Tool ↓ API Request ↓ Authorization ↓ Business Logic ↓ Action For example, a customer might ask: “Check my latest order and process a refund if I am eligible.” The agent could potentially: Find the customer. Retrieve the order. Check the refund policy. Call a payment API. Create a refund. Notify the customer. Each operation represents a potential security boundary. This is why agentic systems require controls over not only individual API calls, but also the actions an agent is allowed to perform. OWASP specifically identifies excessive functionality, excessive perm

The hard part of a fast-moving plugin ecosystem is rarely finding another project. It is deciding whether a project deserves a place in a working setup. DeepSeek Harness is a developer preview with a plugin-first architecture, and its surrounding ecosystem already includes interface extensions, vision tools, workflow helpers, terminal experiences, and desktop clients. Those projects can be useful, but an install command is not a review process. A plugin may execute with local permissions and interact with files, credentials, networks, or shell commands. That is why I prefer repository-first discovery. DSH Hub is a bilingual directory for DeepSeek Harness plugins and clients. Its rule is simple: every listed item must point to a publicly reachable GitHub repository. The goal is not to turn a catalog into a trust badge. The goal is to make the source, ownership, license, release history, and installation material easy to inspect before an extension enters a Harness profile. What the directory separates DSH Hub keeps clients and plugins in different groups. A plugin usually adds a narrow capability to a running Harness profile. A client can do more: package a runtime, provide its own update path, expose a network listener, or ship a bundle of extensions. That difference changes what needs review. If both are listed as the same kind of tool, it is easy to miss the extra surfaces a client can introduce. The August 18, 2026 catalog snapshot contains 19 plugins and 6 clients. Discovery starts with the official DeepSeek Harness repository, the GitHub dsh-plugin topic, and a community-maintained registry. Each selected repository is then checked for a clear, public connection to the ecosystem. A practical evaluation loop A short loop catches more than a long list of popularity signals: Run the official Harness first, so you understand the baseline behavior. Choose the single capability you actually need instead of adding a large bundle. Open the linked repository. Read its R

Anthropic has published early evidence of Claude operating in live life sciences research workflows , moving the discussion beyond generic claims about AI-assisted science. Its January 15, 2026 report describes deployments at Stanford and MIT labs where Claude has been used for data-heavy analysis, experimental design and hypothesis generation. The results are promising, but they are best understood as case studies of lab-scale use rather than proof that AI can independently conduct scientific research. The work is centered on Claude for Life Sciences , an expanded capabilities suite that Anthropic says includes improvements in Opus 4.5, access to more than 60 databases, and genomics, proteomics and cheminformatics toolkits. In Anthropic's official report on accelerating scientific research , the company presents examples from several research groups that used Claude within existing scientific processes. The important development is not simply that researchers asked a general-purpose model scientific questions. The reported deployments connect Claude to structured scientific resources and lab-specific workflows, where scientists can assess its output against experimental context, domain knowledge and, in some cases, planned validation work. That makes the report relevant to research organizations evaluating where AI can reduce analytical friction without displacing human scientific judgment. What Anthropic's lab case studies show The case studies cover different points in the research process. Together, they illustrate where Claude may be useful: organizing and interpreting complex evidence, proposing options for researchers to assess, and accelerating work that would otherwise require substantial manual effort. At Stanford's Biomni project, researchers used Claude in genome- and data-heavy workflows. Anthropic reports that an early trial included molecular cloning design and analysis across large, multi-source datasets. The lab cited examples of tasks being complet

How to keep APIs secure, consistent, compliant, and manageable as your organization grows. I've...

Cursor, known for its AI Code Editor, is launching a new code-hosting platform to rival developers' long preferred favorite, GitHub.

I wanted to build a real-world application with RocketRide rather than just a simple AI demo, so I created ClaimDesk, an AI-assisted insurance claim intake and analysis application. ClaimDesk allows claims adjusters to upload vehicle damage photos and provide a description of an accident. The application then uses a RocketRide pipeline to: Analyze the vehicle damage Analyze the written accident description Compare the image and text analysis Generate a structured claim report I also integrated Ollama for local model execution and created a local startup workflow that launches the required dependencies, models, RocketRide pipeline, webhook connection, and web application together. What I Learned Building ClaimDesk showed me that AI applications require many of the same software engineering practices as traditional applications. I had to think about architecture, debugging, integrations, and creating structured outputs and environments for my application to be reproducible. Overall, working with RocketRide gave me a better understanding of how agentic AI can be integrated into real software applications rather than being used as a standalone model.

If you use GitHub Copilot, Claude, Cursor or any other AI coding assistant to write production code, the legal ownership of what you ship is less settled than your licence agreement implies. The US Copyright Office ruled in January 2025 that purely AI-generated material is not copyrightable, and that prompts alone do not provide sufficient human control to earn protection. Code you wrote with heavy AI assistance sits in an uncertain middle ground: it may be copyrightable, it may not, and no court has drawn the line for software. The answer-first version: you probably do not own copyright in the portions of your code that an AI wrote without substantial human direction, and you may not be able to prove where the boundary lies. This does not mean someone else owns it — it may be uncopyrightable altogether, like a phone book. But your employment contract, your client agreement and your open-source licence all assume you hold full copyright in your deliverables. That assumption is now an open question. The rule, plainly stated Purely AI-generated output is not copyrightable in the United States. This is not a prediction or a legal opinion; it is the stated position of the US Copyright Office, set out in its January 2025 report Copyright and Artificial Intelligence, Part 2: Copyrightability . The report received over 10,000 public comments and represents the Office’s most comprehensive statement on the subject. Its conclusions are clear: “material generated wholly by AI is not copyrightable”, and existing law is adequate to handle the question without new legislation. The nuance sits in the middle ground — which is exactly where most AI-assisted coding lives. What the Copyright Office said The report draws several lines. First, it confirms the long-standing requirement that copyright requires a human author. An AI system cannot be an author, regardless of how sophisticated its output. Second, it addresses prompts: “based on the functioning of current generally available

*Second in a series on The Factory. Previously: The Factory That Merged 37 Tasks . The harness is at github.com/frozer/factory . The public description of my task harness ends on a claim: a packet that's wrong about the world fails identically on every retry. That sentence cost me four dead tasks and nine commits spent repairing task definitions instead of writing code. It reads like something you'd arrive at by thinking. I arrived at it by watching the same failure scroll past three times in a row. What three attempts is actually good for max_attempts = 3 felt like obvious hygiene. Models are stochastic. Sometimes a run goes sideways for no reason you can name — a bad turn, a truncated response, a tool call that gets refused. Retry it and it works. That's real, and a retry budget handles it well. The strength is exactly the constraint. A retry budget assumes the next attempt will differ from the last one . It buys you a second sample from a distribution. But a retry doesn't hand the model a fresh situation. It hands it the same packet back . Same file, same claims, same instructions. If the packet says a file lives at a path where no file lives, attempt three fails precisely where attempt one did, and the only thing three attempts bought was three times the bill. Failure without variance isn't flakiness. It's a specification defect wearing a reliability costume. Nobody had opened the files Here's what that looked like in practice. B03 was a loader for a national census dataset. Three attempts, all burned, all against a file shape that existed nowhere: wrong directory, wrong filenames, and a Data / Valor JSON envelope that appears nowhere in the actual data tree. Every attempt produced a parser for a document that doesn't exist. The packet was the defect, not the model. Nobody had opened the actual files before cutting it. Rewritten from the real JSON, the truth was a flat metadata / data envelope, four files — one of which shouldn't be loaded at all — and a long-fo

ChatGPT had the highest share of paid clicks among the leading Google destinations in iPullRank's Q3 2026 zero-click and paid-click analysis. The dataset found that about 4.75% of Google traffic landing on ChatGPT came from paid clicks , well above the corresponding shares reported for major destinations such as YouTube, Wikipedia, and Amazon. The result does not reveal OpenAI's advertising budget, bids, or total advertising activity. It does, however, show that paid placements represented a notably larger portion of observed Google referrals to ChatGPT than for the other leading destinations studied. That makes paid search an important part of the discovery picture for a widely used AI platform, alongside organic search, direct visits, and other referral paths. What iPullRank's data shows In its Q3 2026 zero-click behavior analysis , iPullRank examined roughly 200 million events to understand where Google clicks go and how often those clicks are paid. ChatGPT ranked around sixth among the leading destinations by Google clicks, behind destinations including YouTube, Google's own pages, Reddit, Facebook, and Wikipedia. That overall ranking is important context. ChatGPT is not the largest destination in the analysis by total Google clicks, but its paid-click proportion stands out . A 4.75% share means paid traffic accounted for a more visible portion of its observed Google arrivals than it did for the larger, more established web destinations used for comparison. Destination Paid-click share of Google traffic Context in iPullRank's analysis ChatGPT About 4.75% Highest share among the leading destinations analyzed YouTube About 0.2% Far below ChatGPT's reported share Wikipedia Effectively 0% Minimal paid-click contribution in the dataset Amazon Under 2% Below ChatGPT's reported share The measure is deliberately narrow. It counts paid Google clicks that land on ChatGPT, not every interaction a user may have with ChatGPT after searching, and not OpenAI's total ad spendin

OpenAI has rolled out the GPT-5.6 family , introducing three models intended to cover advanced professional work, balanced deployments and high-volume workloads. The July 9, 2026 general-availability launch of Sol, Terra and Luna marks a significant step in OpenAI's effort to consolidate its model portfolio across ChatGPT and its API, while moving customers away from older GPT-4-era offerings. The company's official GPT-5.6 announcement positions the generation as a higher-performance foundation for the ChatGPT experience and API use cases involving agents and coding. Rather than presenting a single general-purpose release, OpenAI has divided the family into distinct options: Sol for advanced professional work, Terra for a balance of capability and cost, and Luna for cost-sensitive, high-volume tasks. That segmentation matters because model selection is becoming a deployment decision rather than simply a question of accessing the newest available system. Teams building production workflows need to weigh performance requirements, usage volume, migration work and the cost profile of each application. What the GPT-5.6 rollout changes The general-availability announcement was followed by a July 30, 2026 pricing update that reduced Luna pricing by around 80% and Terra pricing by around 20%. OpenAI also signaled the phase-out of older models , including GPT-4o and related GPT-4.x variants, as customers move toward GPT-5.x and GPT-5.6 offerings. Taken together, the launch and subsequent price adjustments show that the GPT-5.6 family is not only a model update. It is part of a broader product lifecycle shift . OpenAI's roadmap messaging has emphasized more unified experiences across ChatGPT and API surfaces, and the new family gives that strategy a clearer set of deployment tiers. Model Positioning July 30, 2026 pricing change Sol Flagship model for advanced professional work Not specified in the supplied research Terra Balanced option for capability and cost Reduced by aro

🚀 Technical Briefing: This tutorial is part of our deep-dive series on Agentic Workflows at Gate of AI . For the full technical breakdown, interactive code sandbox, and the native Arabic translation, visit the original article here . An evaluation-first guide to deciding whether GPT-4o mini fine-tuning is justified for a narrowly defined language task. This article uses the available research context rather than assuming unverified API capabilities, model snapshots, pricing, or deployment features. GPT-4o Mini Fine-Tuning: Start With Evidence, Not an Upload Fine-tuning is often presented as the next step after prompt engineering, but the available evidence does not support treating it as an automatic upgrade. Before preparing a dataset or committing to a training workflow, define the task, establish a baseline, select measures that reflect the real objective, and decide what result would justify changing the system. The verified research context is especially relevant for text transformation. A TREC 2024 Plain Language Adaptation of Biomedical Abstracts study evaluated prompt engineering, a two-AI-agent approach, and fine-tuning with OpenAI GPT-4o and GPT-4o mini models. Its objective was to simplify biomedical abstracts for a K-8 audience, approximately 13- to 14-year-old students. The study used qualitative assessments for simplicity, accuracy, completeness, and brevity on 5-point Likert scales, together with readability measures including Flesch-Kincaid grade level and the SMOG Index. Its results are a useful warning against simplistic claims. Prompt engineering with GPT-4o mini and the two-agent approach showed stronger qualitative performance in that evaluation. Fine-tuned models excelled in accuracy and completeness, but were less simple. The paper also reported that GPT-4o mini prompt engineering outperformed the evaluated iterative two-agent and GPT-4o fine-tuning approaches on its qualitative results. That is not a universal verdict on fine-tuning. It is ev

En el día a día del desarrollo de software y el análisis de datos, la preparación y limpieza de archivos financieros suele ser una de las tareas más repetitivas y propensas a errores. Tratar con layouts rígidos, filas desfasadas y nombres de columnas que cambian sin previo aviso genera una fricción operativa constante. Para resolver este problema de raíz—y manteniendo un enfoque estricto en la seguridad de la información—desarrollé D-MO (Data Micro-Optimizer) , una potente herramienta web de procesamiento ETL (Extract, Transform, Load) que corre completamente del lado del cliente. El Origen: Privacidad por Diseño Cuando manejamos reportes bancarios o información financiera sensible, subirlos a plataformas externas de conversión representa un riesgo crítico de seguridad. D-MO nació bajo la premisa de la privacidad absoluta: todo el procesamiento ocurre en la memoria local del navegador a través del cliente. Los datos estructurados jamás se envían a un servidor externo, eliminando latencias de red y garantizando un entorno de zero server overhead . Arquitectura del Pipeline (Flujo de Datos) El sistema procesa la información de manera secuencial a través de un flujo desacoplado, lo que permite transformar archivos complejos en datasets listos para producción en un solo clic: [ Archivo Local ] (.csv / .xlsx / .xlsb) │ ▼ ┌──────────────┐ │ DropZone │ ◄── Validación de Extensión y Tamaño └──────┬───────┘ │ (Buffer / Texto plano) ▼ ┌──────────────┐ │ File Parser │ ◄── Detección de delimitadores y headers dinámicos └──────┬───────┘ │ (JSON Normalizado) ▼ ┌──────────────┐ │ ETL Engine │ ◄── Reglas de Negocio, Mapeo de Alias y Filtros CUSTOM └──────┬───────┘ │ (Dataset Limpio) ▼ ┌──────────────┐ │ Export File │ ◄── Generación de reportes limpios listos └──────────────┘ Core Técnico y Capas del Sistema La aplicación está construida sobre Next.js 14 (App Router) y TypeScript , dividiendo su lógica interna en tres componentes principales: 1. Interfaz y Coordinación ( page.tsx )

Robin Williams' children are taking over their father's Instagram account after his daughter spoke out against the use of his AI likeness, as reported earlier by The Wrap. In a post on Tuesday, Zak, Zelda, and Cody Williams write that they want the late actor's Instagram profile to be a "safe, trusted place where the […]

OpenAI is announcing security updates following the July news that its AI broke out of a sandboxed environment and accidentally hacked Hugging Face, including improvements to its research environments, monitoring, and alignment techniques. The company had already put the brakes on a new model, Astra, that it thinks could have "critical" cybersecurity capabilities, and the […]

OpenAI launches an initiative to strengthen democratic oversight of AI in national security, supporting government institutions with tools, training, and expertise.

Introduction I did something stupid. I built a superhero-themed Nuxt app, connected it to an Anthropic model through Amazon Bedrock , and gave it a tool that deletes files from my computer. In fact, if I wasn't careful, it could have deleted all my files! The first time I tried it, I didn't use any sort of approval mechanism. And as you expected it just deleted things. Then I looked into how my coding agent works, and I learned about tool approvals. I learned that AI SDK 7 has a tool approval at the model-call level. It works by pausing for an approval, showing an approval window, and then deleting it. I then put Kiro CLI behind the same interface using Agent Client Protocol (ACP). Watch the full video on YouTube . Prerequisites You need: Node.js 22 or later. AI SDK 7 requires Node.js 22 and uses ECMAScript modules (ESM). npm 11 or another package manager that works with Nuxt 4. AWS credentials available through the standard provider chain. Access to an Amazon Bedrock model in your AWS Region. The AWS CLI if you want to list the inference profiles available to your account. An authenticated Kiro CLI installation for the optional ACP section. Step 1: Create the Nuxt app Create the project and install the versions used in the recorded demo: npx nuxi@latest init nuxt-agent-approval cd nuxt-agent-approval npm install \ nuxt@4.5.2 \ vue@3.5.41 \ ai@7.0.66 \ @ai-sdk/vue@4.0.66 \ @ai-sdk/amazon-bedrock@5.0.57 \ @aws-sdk/credential-providers@3.1111.0 \ @nuxt/ui@4.10.0 \ zod@4.4.3 npm install -D @iconify-json/lucide@1.2.123 Register Nuxt UI and expose the Amazon Bedrock settings through server-side runtime config: // nuxt.config.ts export default defineNuxtConfig ({ modules : [ ' @nuxt/ui ' ], css : [ ' ~/assets/css/main.css ' ], runtimeConfig : { awsRegion : process . env . AWS_REGION ?? ' us-west-2 ' , bedrockModelId : process . env . NUXT_BEDROCK_MODEL_ID } }) Add the two Nuxt UI imports: /* app/assets/css/main.css */ @import "tailwindcss" ; @import "@nuxt/ui" ; You can c

Clear the Lineup Submission The Bug AI applications can fail silently — producing wrong outputs, degraded performance, or unexpected behaviors without explicit errors. In my case, the issue was SQL drift: queries executed successfully but returned incomplete or unstable results due to unsafe wildcard usage (SELECT *). This silent failure propagated downstream, degrading model accuracy without obvious alerts. The Fix I introduced an agentic validation and inspection layer into the pipeline using LangGraph, StatesGraph, MCP, and A2A. Inspection Layer: Deterministic checks (SQL linters, schema validators). Validation Layer: Agentic reasoning about query safety. MCP Integration: Standardized access to profilers and monitoring APIs. A2A Collaboration: Agents exchanged context to enforce compliance. This combination allowed the system to detect unsafe queries and route them for human review before deployment. PR Link Here’s the merged PR where the fix was implemented: Continental-Thaligai Repository – Merged PRs https://github.com/NikhilRaman12/Continental-Thaligai/pulse#opened-pull-requests Code Snippet python from langgraph import Graph from statesgraph import State from mcp import MCPClient class SQLInspection(State): def run(self, query): if "SELECT" in query and "*" in query: return {"risk": 0.7, "message": "Wildcard SELECT may cause drift"} return {"risk": 0.1, "message": "Query safe"} graph = Graph() graph.add_state("sql_inspection", SQLInspection()) graph.connect("sql_inspection", "human_review", condition=lambda r: r["risk"] > 0.5) result = graph.run("SELECT * FROM transactions") print(result) Diff Example: diff SELECT * FROM transactions SELECT transaction_id, amount, date FROM transactions This change eliminated silent drift in query results and improved reliability in downstream AI pipelines. Outcome Silent SQL drift eliminated. Improved accuracy in downstream AI models. Added regression tests to prevent recurrence. Strengthened CI/CD pipeline with agentic saf

The ChatGPT maker says its upcoming Astra model may have reached “critical” cyber capabilities, prompting it to halt a significant number of training runs while it tightens internal safeguards.