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The flow will be: Authentication foundation Session vs JWT JWT deep dive JWT security Access/Refresh tokens OAuth2 relationship with JWT End-to-end production flow PKCE Storage strategies summary 1. Authentication Fundamentals Every secure application needs answers to two questions: Authentication "Who are you?" Example: User enters: username password MFA System verifies identity. Result: User is Bhargav Authorization "What are you allowed to do?" Example: User: Bhargav Permissions: READ_ORDERS CREATE_ORDER DELETE_ORDER Authentication happens first. Authorization happens after. Authentication | v Authorization 2. Traditional Session-Based Authentication (Stateful) Before JWT, applications commonly used sessions. Flow User logs in: Browser | | username/password | v Server Server creates: Session ID = abc123 Stores: Database / Memory abc123 | | User: Bhargav Role: ADMIN Browser receives: Cookie: SESSION_ID=abc123 Every Request Browser sends: GET /orders Cookie: SESSION_ID=abc123 Server: Receive Session ID | v Search session storage | v Find user | v Allow request Problems with Sessions 1. Server maintains state The server must remember: Session ID | v User Information 2. Scaling problem Imagine multiple servers: Load Balancer / \ Server A Server B User logs in: Server A Session stored here Next request: Server B No session found Solutions: Sticky sessions Shared session database 3. JWT Authentication (Stateless) JWT solves this by putting information inside the token. JWT: JSON Web Token It is a compact, signed representation of claims between two parties. Example: eyJhbGciOiJIUzI1Ni... JWT vs Session Session Server stores user state: Server Session ID | v User Data JWT Token contains information: JWT Header + Payload + Signature Server does not need to store session information. 4. JWT Structure A JWT has three parts: HEADER.PAYLOAD.SIGNATURE Example: xxxxx.yyyyy.zzzzz Part 1: Header Contains metadata. Example: { "alg" : "RS256" , "typ" : "JWT" } Meaning: JWT uses RS

After a domain migration or HTTPS switch, "all plugin settings are gone" or "Elementor layouts are broken" is a common outcome. The cause, in most cases, is running a string replacement against the WordPress database without accounting for PHP serialized data. WordPress stores plugin configurations, custom field values, and widget settings in PHP’s serialized format. Standard SQL replacements — phpMyAdmin’s find-and-replace, raw UPDATE statements, sed on a .sql dump — rewrite the string value without updating the length metadata that serialization embeds alongside it. The result is a database that appears intact but returns false on every read of the affected values. wp search-replace handles this correctly. Understanding why makes the pre- and post-execution steps more deliberate. What PHP serialization stores alongside the value A serialized entry in WordPress looks like this: a : 2 : { s : 4 : "home" ; s : 22 : "http://example.com/top" ; s : 5 : "title" ; s : 8 : "My Site" ;} The segment s:22:"http://example.com/top" means "a string of 22 bytes." The s:N: prefix records the byte length. When a simple string replacement changes http://example.com to https://example.com : Before: s:22:"http://example.com/top" (22 bytes) After: s:22:"https://example.com/top" (23 bytes) The s:22 stays unchanged even though the actual string is now 23 bytes. PHP’s unserialize() detects this mismatch and returns false . The plugin reads false instead of its configuration array and behaves as though the settings were never saved. phpMyAdmin’s find-and-replace executes a SQL UPDATE at the storage layer. No PHP context exists there — it can’t know the column contains serialized data, and it doesn’t adjust the length prefix. How wp search-replace handles it wp search-replace operates at the PHP layer, not the SQL layer: Reads each column value Checks whether it’s serialized using is_serialized() If serialized: calls unserialize() to expand it into a PHP array or object Applies the string r

X Money, a core part of Elon Musk's mission to turn X into an "everything app," is rolling out starting today, 9to5Mac reports. The payment platform offers a digital wallet and peer-to-peer payments similar to Venmo, along with a metal Visa card users can emblazon with their X username. Other perks include Apple Wallet support […]

When an application starts using more than one language model provider, the hard part is rarely the first API call. The hard part is everything that follows: separate credentials, different request shapes, provider-specific errors, billing dashboards, and model migrations scattered across the codebase. A useful way to reduce that surface area is to keep one OpenAI-compatible client contract and move provider choice into configuration. This guide shows the smallest working setup with Routara , plus the production checks I recommend before sending real traffic. 1. Keep the SDK, change the endpoint If your project already uses the OpenAI Python SDK, the client initialization is the only part that needs to change: import os from openai import OpenAI client = OpenAI ( api_key = os . environ [ " ROUTARA_API_KEY " ], base_url = " https://api.routara.ai/v1 " , ) response = client . chat . completions . create ( model = " deepseek-chat " , messages = [ { " role " : " user " , " content " : " Explain idempotency in two sentences. " } ], ) print ( response . choices [ 0 ]. message . content ) Store the key in an environment variable. Do not put it in browser code, a public repository, screenshots, or support messages. The same pattern works in Node.js: import OpenAI from " openai " ; const client = new OpenAI ({ apiKey : process . env . ROUTARA_API_KEY , baseURL : " https://api.routara.ai/v1 " , }); const result = await client . chat . completions . create ({ model : " deepseek-chat " , messages : [{ role : " user " , content : " Return one short test sentence. " }], }); console . log ( result . choices [ 0 ]. message . content ); 2. Treat model IDs as configuration Do not spread model names throughout the application. Put them in environment variables or a typed configuration object: model_id = os . environ . get ( " ROUTARA_MODEL " , " deepseek-chat " ) That makes model evaluation and rollback much safer. Routara's live model catalog is the source of truth for current availa

Kennedy has focused on nutrition but has neglected food safety, critics say.

The deal means content will be integrated into the YouTube experience, allowing viewers to discover and watch Peacock content without leaving the platform.

The screwup shows how tricky it can be to stop web crawlers from making ostensibly private conversations with AI chatbots entirely too public.

INTODUCTION Creating Greenwood academy database is essential for managing the students, subject and exam results efficiently. PostgreSQL, a powerful open-source relational database system, offers the perfect foundation for such a project. The main areas areas in SQL covered in this projects are : 1. DDL (Data Definition Language) DDL commands define, modify, and change the physical structure of database objects like tables and schemas. The first step is to create a greenwood academy schema using the create command. create schema greenwood_academy ; set search_path to greenwood_academy ; Next is to crete tables in the schema; The schema has 3 tables students,subject and exam results. create table greenwood_academy . students ( student_id INT PRIMARY key , first_name VARCHAR ( 50 ) NOT null , last_name VARCHAR ( 50 ) NOT null , gender VARCHAR ( 1 ), date_of_birth DATE , class VARCHAR ( 10 ), city VARCHAR ( 50 ) ); create table greenwood_academy . subject ( subject_id INT PRIMARY key , subject_name VARCHAR ( 100 ) NOT null unique , department VARCHAR ( 50 ), teacher_name VARCHAR ( 100 ), credits INT ); create table greenwood_academy . exam_results ( result_id INT PRIMARY key , student_id INT NOT null , subject_id INT NOT null , marks INT NOT null , exam_date DATE , grade VARCHAR ( 2 ) ); ALTER - This command changes the structure of tables in a database. Core Actions You Can Perform Add columns : Insert a new column and its data type into a table. The school realised that the nthey forgot to add phone numbers in the students table. The following command is used to add the data alter table greenwood_academy . students add column phone_number VARCHAR ( 20 ); Rename colums : Change the name of a table or a column. The column credit has to be changed to credit hours alter table greenwood_academy . subject rename column credits to credit_hours ; Drop columns : Delete an unwanted column from a table. Later the school relised that the phone number column is nolonger needed. a

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Reading OpenAI’s account last week of how some of its models broke their containment and hacked into the computer systems of Hugging Face, another AI company, was the first time I got…

You already run Claude Code by hand: copy issues into a prompt, watch it work, check the diff, and if something breaks halfway through, you restart it. This works fine for one task at a time, but it falls apart when you have 10 tasks simultaneously. Claude Code is good at handling routine engineering tasks: bug fixes, dependency bumps, and small features, when the prompt is clear and the task is scoped. But when it comes to scaling, you need an infrastructure with isolated workspaces, retry logic, state that survives a restart, and tracker integration, not to waste time on babysitting. Sortie removes the manual work. You label an issue, Sortie picks it up, creates an isolated workspace, runs the agent, retries it if it stalls, and opens a pull request when it's done. This article describes how to set the entire process from an empty directory to a GitHub issue turning into a PR without you touching the keyboard in between. What you need A GitHub repository you control Export two environment variables: ANTHROPIC_API_KEY - authenticates Claude Code GITHUB_TOKEN - it's read by tracker.api_key: $GITHUB_TOKEN for polling/updating issues, and it's the same token gh pr create inside the after_run hook uses to open the PR, so it needs Issues: read/write, Contents: read, and Pull requests: read/write scopes on that repository, all on one fine-grained PAT. Push access to the repository over SSH. The after_create hook below clones with git@github.com:... , so git authenticates with your SSH key, not with GITHUB_TOKEN . Verify with ssh -T git@github.com . If you'd rather stay on one credential, swap the clone URL for https://${GITHUB_TOKEN}@github.com/yourorg/yourrepo.git and give the token Contents: read/write. In your repository, create the agent-ready label — you need it to exist before you can put it on an issue, and query_filter finds nothing without it. Creating in-progress , review , and done up front is also worth doing: GitHub does create a missing label when Sortie ap

After two years in stealth, Safe Superintelligence has announced a long-term partnership with Nvidia as it prepares to scale to its next phase.

Starting with Spotify, Snapchat users will be able to link their accounts, choose who can see their listening activity, and see what their friends are listening to in real time.

Overview mise is one of the cleanest ways to make a repository's tool environment explicit. It can tell contributors, CI, and agents which runtime versions and command environment a project expects. That matters. But a resolved environment is not the same thing as accepted repository execution. mise helps answer: Which tools and versions should this repo use? Ota answers a different question: Is the selected repo path ready, safe, canonical, and verified? Those two layers work well together because they solve different parts of the same operational problem. For modern repositories, especially ones touched by AI agents, that distinction matters. The failure is not always "wrong Node version" or "missing Python". Many repo failures happen after the right tools are already present. The repo still needs to declare setup, task truth, readiness, verification, agent safety, runtime mode, and proof boundaries. That is Ota's layer. What mise Is Good At mise is good at environment and tool resolution. It gives a repo a compact way to declare things like: which Node version should be used which Python, Go, Ruby, or other tools matter which tool versions should be installed or activated which command environment should be entered which repo-owned task aliases should exist That is valuable for contributors and agents because it reduces host guesswork. Without a tool manager, a new contributor may have the wrong runtime, a stale global binary, or an old package manager sitting on PATH . With mise , the repo can make more of that explicit. That is a strong foundation. Where Environment Resolution Stops The problem starts when teams treat environment resolution as repository acceptance. They are not the same thing. The right tools can be installed and the repo can still be ambiguous about: which setup path is canonical whether dependencies have been hydrated whether services are required whether test , check , ci , or verify is the accepted lane whether local and CI execution agree

Nvidia on Monday said it is joining forces with Microsoft, SpaceX, IBM, and other tech companies to build and share open-source AI security tools. The new Open Secure AI Alliance said open tools are required to effectively defend against attacks from frontier models. The initiative is a direct response to mounting concerns over the safety […]

Imagine a healthcare system made up of multiple AI agents: one that manages symptom assessment, another scheduling, a third insurance, and a fourth pharmacy. Each is an expert in its domain. But they all have their own distinct knowledge and objectives. Today they can exchange data, but they are not yet able to actually coordinate…

Drug discovery is a high-cost, high-risk endeavor that is under growing pressure from a market increasingly defined by first-mover advantage. Since the 1950s, the cost of developing new pharmaceuticals has roughly doubled every nine years—a phenomenon known as Eroom’s Law. Today, bringing a new drug to market takes an average of 10-15 years and costs…

For the enterprise, the promise of agentic AI is much more than just a better chatbot. It is software agents that execute business tasks end-to-end across people, business workflows, data, and systems. The platform best-suited to run agents is built with proper CPU capacity, resilient data access, policy-aware tool use, observability, memory management, and the…

On Monday, the network announced a $1.6 million seed round from top players in the media ecosystem, including Powerhouse Capital, Axel Springer SE (which owns Business Insider and Politico), the popular media publication LadBible, and angel investors from OpenAI and DeepMind. With this fresh capital, the network is announcing its largest expansion yet.

How to design, ship, and operate an AI agent that is reliable, efficient, performant, scalable, and secure enough to serve real companies — from a 5-person startup to a 50,000-person enterprise. This guide distills hard-won lessons from production agents (Claude Code, OpenHands, SWE-agent, GoClaw, Hermes, nanobot, PicoClaw, ZeroClaw, Multica, Paperclip) and grounds them in current engineering guidance from Anthropic and OpenAI plus the security and compliance standards you'll actually be audited against (OWASP Top 10 for Agentic Applications, NIST AI RMF, the EU AI Act, and 2025–2026 prompt-injection research). It focuses on the parts most articles skip: the enterprise tax — governance, security, compliance, integration, cost control, and the operating model — that separates a demo from a system a CISO will sign off on. 📖 How to use this guide Read Parts 0–2 to decide whether and what to build. Most failed agent projects die here. Read Parts 3–7 for the architecture and reliability engineering. Read Parts 8–10 for the enterprise gates: security, compliance, multi-tenancy, observability, cost. Read Parts 11–15 for delivery, scale & rollout: deployment topologies (SaaS/self-hosted/hybrid), how to adopt from pilot to org-wide, how to handle thousands of concurrent requests, the operating model, and a 30/60/90 plan. Every part ends with an ✅ Actionable checklist . Skim those for a design review. 📋 Table of Contents 🧮 Part 0 — The Core Equation 🧭 Part 1 — Decide Before You Build: Workflow vs Agent, Build vs Buy 🏛️ Part 2 — The Enterprise Tax: What Actually Changes 🏗️ Part 3 — Reference Architecture: The Layered Stack 🔄 Part 4 — The Reliable Kernel: The Agent Loop 🛠️ Part 5 — Tools & Enterprise Integration 🧠 Part 6 — Context & Memory: The Cost Center 🛟 Part 7 — Reliability Engineering 🔐 Part 8 — Security, Compliance & Governance 🧱 Part 9 — Multi-Tenancy & Isolation 📊 Part 10 — Observability, Evals & Cost Governance 🚀 Part 11 — Deployment & Delivery Models 📈 Part 12 — The

Most teams building AI agents treat manual override queues as a last resort, a safety net for when the agent goes off the rails. That view keeps agents locked in read-only mode on anything risky. The real insight is the opposite: a well-designed AI agent manual override queue enables you to trust your agent with high-value actions you would otherwise never automate. An override queue is not a punishment for poorly trained models. It is a design tool that widens the feasible autonomy boundary by giving you a structured escape hatch for the 5% of cases the agent cannot confidently handle. Table of Contents What Is an AI Agent Manual Override Queue? What Makes a Manual Override Queue Different from Other Queue Types The Lifecycle of an Action in a Manual Override Queue Four Steps to Implementing a Manual Override Queue in Your Agent Workflow How to Evaluate a Manual Override Queue Solution: Key Dimensions Three Pitfalls That Sabotage Manual Override Queues When to Use a Manual Override Queue, and When to Skip It Why We Built AwaitHuman: Escalation-as-a-Service for Agentic Workflows Frequently Asked Questions What Is an AI Agent Manual Override Queue? An AI agent manual override queue is a structured holding area where an autonomous agent's action is paused and routed to a human operator for review, approval, or rejection before execution. This is the formal definition from our guide to safe autonomous workflows (internal page, but we'll keep the reference general). The queue sits between the agent's decision and its execution, intercepting only the actions that cross a configurable risk threshold. For example, an agent managing cloud deployments might trigger a terraform apply command. Without a queue, that action happens immediately. With one, the agent serialises the full reasoning trace, the proposed diff, and the affected resources into a queue item. A human operator receives a notification, reviews the packet, and approves or rejects. The agent then proceeds or ba