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I recently analysed UK occupation data to see which job categories appear most exposed to current-generation AI systems. The results are probably not what most people here would predict. Using ONS workforce data mapped to ISCO-08 occupation groups, I assigned AI exposure scores based on how much of an occupation's core task bundle can already be completed or substantially augmented by current models and automation systems. The highest score was not software development. It was clerical support work. Clerical occupations scored 8.5/10 across roughly 3 million UK workers. This includes administrative assistants, receptionists, customer service representatives, data-entry workers, call-centre staff, and bookkeeping clerks. The reason becomes obvious when you break occupations into tasks. Modern LLMs are exceptionally good at: Information retrieval Structured communication Summarisation Classification Form completion Draft generation Customer interaction workflows Those capabilities overlap directly with a large percentage of clerical work. Professionals scored 6.5/10. That category includes lawyers, engineers, accountants, analysts, architects, and software developers. What's interesting is that exposure and displacement aren't the same thing. A lawyer using AI to draft contracts becomes more productive. A customer-support department replacing a large portion of repetitive ticket handling with AI may reduce headcount entirely. The underlying capability overlap can be similar while labour-market outcomes are very different. The lowest-risk categories remain occupations requiring physical adaptation to unpredictable environments. Trades and elementary occupations scored between 2.0 and 2.5. One takeaway is that AI discussion often focuses on whether models can write code. The labour-market impact may arrive first through administrative and support functions because those workflows are already highly structured and relatively easy to automate. Curious how others here woul

Overview Hey everyone 👋 If you've ever wanted to experiment with Gemini models, build AI-powered features, or grab an API key without going through a complex setup, Google AI Studio is the tool you're looking for. It's free, it's browser-based, and it's probably the fastest way to go from "I have an idea" to "I have working code." Today I'll walk you through what it is, what you can actually do with it, and why it belongs in every developer's toolkit. Let's dive in! 🤙 What Is Google AI Studio? 🤔 Google AI Studio is a web-based platform where you can interact with Google's AI models, prototype ideas, fine-tune behavior, and export working code, all without writing a single line of infrastructure. Think of it as a sandbox. You can test prompts, switch between Gemini models, tweak parameters, and when something works, click "Get Code" to get a ready-to-use snippet in Python, JavaScript, or REST. No cloud setup, no billing configuration, no long onboarding. Just go to aistudio.google.com , sign in with your Google account, and you're in. It sits at the intersection of playground and development tool. Researchers use it to experiment. Developers use it to prototype. Teams use it to validate ideas before committing to a full integration. What You Actually Need It For 💡 There are a few scenarios where Google AI Studio becomes indispensable: Getting a Gemini API Key: This is often the first reason developers land on AI Studio. It's the official way to get a Gemini API key for free, which you then use in your own applications, in tools like Gemini CLI, Antigravity, or any custom integration. No credit card required for the free tier. Testing Prompts Before Hardcoding Them: Prompt engineering is trial and error. AI Studio gives you a fast feedback loop where you can iterate on prompts interactively, see the output, adjust, and repeat, before embedding anything in your codebase. Exploring Model Capabilities: Not sure if Gemini can handle your specific use case? Test it directl

You've been building REST APIs — one endpoint for users, another for posts, another for comments. The client makes three requests, stitches the data together, and half of it gets thrown away because it wasn't needed in the first place. GraphQL was built to fix exactly that. It gives the client full control over what data it receives. One request. Exactly what you asked for. Nothing more, nothing less. The Problem REST Couldn't Solve Before understanding GraphQL, you need to understand the two problems that drove its creation. Over-fetching The server returns more data than the client needs. GET /users/123 Response: { "id": 123, "name": "Anne", "email": "anne@example.com", "phone": "...", "address": "...", "createdAt": "...", ← you didn't need any of this "updatedAt": "..." ← but the server sent it anyway } The client only needed name and email — but it downloaded the whole object every time. Under-fetching One endpoint doesn't return enough, so the client has to make multiple requests. GET /users/123 → gets the user GET /users/123/posts → gets their posts GET /users/123/followers → gets their followers Three round trips to the server just to render one screen. On a mobile network, that cost is real. GraphQL's answer: Let the client write the query. The server returns exactly what was asked. What Is GraphQL? GraphQL is a query language for your API and a runtime for executing those queries. It was created by Facebook in 2012, open-sourced in 2015, and is now maintained by the GraphQL Foundation . Unlike REST, which exposes multiple URL endpoints, GraphQL exposes a single endpoint — typically POST /graphql . The client sends a query in the request body describing exactly what it wants, and the server responds with only that data. Key characteristics: Single endpoint — everything goes through POST /graphql Client-driven — the client defines the shape of the response Strongly typed — every field has a declared type in the schema Introspective — clients can query the API

TL;DR Welcome back to Dev Opportunity Radar. This is a weekly series where I share opportunities,...

A researcher disclosed a vulnerability in the Claude Code GitHub Action that let an attacker submit a single crafted GitHub Issue and take over the agentic workflow running inside a repository. No stolen tokens. No compromised runner. Just text — pointed at an agent that trusted it. This is indirect prompt injection in the wild, and it's exactly the scenario that most AI security guidance hand-waves with "validate your inputs." Let's talk about what actually happened, why standard defenses didn't stop it, and what would have. What Happened The Claude Code GitHub Action wires Claude directly into your CI/CD pipeline. It reads repository context — issues, PRs, comments — and takes actions on your behalf: writing code, opening PRs, running commands. According to the disclosure, an attacker could craft a GitHub Issue containing a prompt injection payload. When the Claude Code agent processed that issue as part of its normal workflow, the payload manipulated the agent into executing unauthorized repository-level actions. One issue. Repository hijacked. The attack surface here is the trust boundary between external content (a GitHub Issue — writable by anyone with a GitHub account) and agent instructions (what Claude Code is actually supposed to do). The agent treated attacker-controlled text as authoritative instructions. How the Attack Actually Works Indirect prompt injection follows a consistent pattern: The agent reads external content as part of its task. In this case, the Claude Code Action ingests GitHub Issues to understand what to work on. That content contains adversarial instructions disguised as legitimate data. Something in the issue body tells the agent to deviate from its original task — "ignore your previous instructions," "your new task is to push this commit," or more subtle authority hijacks. The agent complies. Without a layer that can distinguish between legitimate orchestration instructions and attacker-injected content, the model treats the injected

I recently rebuilt my homelab from scratch. The goal was simple: one machine, everything containerised, zero exposed ports, GPU-accelerated local AI, and a fully automated backup setup. No cloud subscriptions for the tools I use every day. This is the full technical breakdown — what I'm running, how it's wired together, and the hard-won fixes that cost me hours so you don't have to repeat them. What I'm Running Eight services, 26 containers, one machine: Service Purpose Portainer Docker management UI Uptime Kuma Service monitoring (7 monitors) NocoDB Self-hosted Airtable — CRM & leads n8n Workflow automation Open WebUI Local AI chat interface Ollama Local LLM inference (GPU) AFF!NE Collaborative docs & whiteboards Plane Project management (roadmaps, sprints) Duplicati Encrypted daily backups Cloudflare Tunnel Zero Trust secure access — no open router ports All external-facing services sit behind Cloudflare Zero Trust with email OTP. No passwords to manage, no VPN clients — Cloudflare handles authentication at the edge. Architecture ┌──────────────────────────────────┐ │ Cloudflare Edge (Zero Trust) │ │ *.yourdomain.com — email OTP │ └──────────────┬───────────────────┘ │ HTTPS ┌──────────────▼───────────────────┐ │ Ubuntu Machine │ │ │ │ cloudflared (outbound tunnel) │ │ │ │ │ ┌─────▼────────────────────┐ │ │ │ homelab-net (bridge) │ │ │ │ │ │ │ │ portainer uptime-kuma │ │ │ │ nocodb n8n │ │ │ │ open-webui affine │ │ │ │ plane-* duplicati │ │ │ │ ollama (GPU passthrough) │ │ │ └───────────────────────────┘ │ └───────────────────────────────────┘ Everything runs on a shared Docker bridge network ( homelab-net ). The cloudflared container maintains an outbound-only encrypted tunnel — no inbound ports open on the router at all. Ollama runs in Docker with NVIDIA GPU passthrough. The AI model inference happens on the GPU, leaving CPU headroom for all other services. Prerequisites Ubuntu 24.04 LTS Docker Engine + Compose v2 NVIDIA GPU with driver 535+ NVIDIA Container Too

Built a full-stack RBAC admin starter: NestJS (Clean Architecture) + Next.js 16 (FSD). JWT refresh, permission-gated UI, sheet-based CRUD. MIT. Looking for feedback. ⚡ Next.js 16 Admin Dashboard Template Architecture: Strictly adheres to Feature-Sliced Design (FSD) to prevent codebase rot in large applications. Key Features: Full-scale Role-Based Access Control (RBAC) UI, URL-driven advanced tables (TanStack Table v8), global caching (TanStack Query v5), and dynamic sheet-based UX configurations using shadcn/ui and Tailwind v4. Quality Assurance: Pre-configured with Playwright for End-to-End (E2E) testing and automated GitHub Actions CI. 🛡️ NestJS Clean Architecture REST API Architecture: Implements strict layered Clean Architecture (Presentation ➔ Application ➔ Domain 🡨 Infrastructure) ensuring zero database/framework lock-in. Key Features: Advanced authentication via JWT refresh rotation, stateful RBAC with high-performance Redis permissions caching, and enterprise-grade security structures. Quality Assurance: Achieves ~98% test coverage across domain and application layers using Jest.

TypeORM 1.0 is the first major release of the open-source TypeScript and JavaScript ORM since its inception in 2016. This version modernizes platform requirements, removes deprecated APIs, and introduces numerous bug fixes and new features. TypeORM now supports ECMAScript 2023, dropping older Node.js versions and dependencies while enhancing security and migration processes. By Daniel Curtis

Originally published at ayonbuilds.hashnode.dev I can't eat the food I want. I can't travel. I can't do the things my peers do. I'm a 2nd year CS student in Chandigarh. No connections. No money. No big university name behind me. Last week I was researching AI security tools and stumbled across a startup called Artemis . Founded in 2025. Just raised $70M . Building AI agents that automatically investigate security threats. I had just built something in the same category. From my room. With free tools. Zero budget. Simulated data. No users. No team. Not even close to what they've built. But I understood the problem well enough to build a working version of it myself. And that told me something. I'm not there yet. Not even close. But I'm working on the right problems at the right time — and I'm just getting started. Here's what I built — ARIA (Autonomous Risk Investigation Agent) . It detects suspicious authentication events in real time, maps them to MITRE ATT&CK threat techniques, and automatically generates plain-English incident reports using an LLM investigation chain. Built with FastAPI, React, PostgreSQL, and Groq API. GitHub: github.com/Ayon99/ARIA My name is Ayon. I'm building AI systems in public — the wins, the failures, the gap between what I make and what the funded teams make, and everything I'm learning along the way. I have one goal. Break through. Completely. Whatever it takes . If you're in a similar position — small city, limited resources, big ambition — follow along. I'm not going to pretend I've figured it out. But I'm going to document every step of figuring it out.

Since mid-March 2026, a significant chunk of Claude users have been hitting this error whenever Claude tries to render an inline diagram, chart, or interactive widget: Failed to set up MCP app for "visualize". Check that claudemcpcontent.com is not blocked by your network or browser. The instinct is to blame your network. I went through the same cycle — switched DNS to Cloudflare 1.1.1.1, tried Google 8.8.8.8, disabled browser extensions, tested across browsers. Nothing worked. Then I ran a direct DNS lookup: nslookup claudemcpcontent.com 1.1.1.1 Output: Server: 1.1.1.1 Address: 1.1.1.1# 53 *** Can't find claudemcpcontent.com: No answer Same result with 8.8.8.8. The domain doesn't resolve — at all, from any resolver. Not a user-side issue. What's Actually Happening Claude's visualize feature depends on an external domain — claudemcpcontent.com — to serve the MCP app that renders inline SVG/HTML widgets. When that domain goes down, the feature breaks silently with a misleading error that makes it look like a local network problem. There's an open GitHub issue tracking this (#34820 on anthropics/claude-code) filed March 16, 2026. It has 50+ affected users, no official fix, and was labeled invalid because it was filed on the wrong repo. Anthropic hasn't responded substantively. The visualize infrastructure had multiple incidents throughout April 2026. The Workaround Instead of asking Claude to generate a diagram or chart (which triggers the broken MCP visualizer), ask it to generate a PNG file using Pillow. Instead of: "Create a bar chart showing X" Say: "Create a bar chart showing X as a PNG file using Pillow" Claude writes Python, executes it via its bash tool, and drops a downloadable PNG in the outputs directory. No MCP dependency. No claudemcpcontent.com . Completely different rendering pipeline. Works for bar charts, line graphs, flowcharts, architecture diagrams — anything you'd normally visualize inline. TIL Claude's inline visualizer depends on an external dom

When building a small content site, the framework is usually not the hardest part. The harder part is deciding what each page should be responsible for. A lot of sites start as a simple article list. That works for a while, but it becomes messy when visitors arrive with different search intents. Some users want to learn what something means. Some want download or setup information. Others are trying to fix a specific issue. Those users should not all land on the same generic page. The structure I use For a small Next.js content hub, I like to separate routes by intent: Homepage: broad entry point Learn hub: basic explanations and guides Learn detail pages: specific guide topics Download page: download or install intent Fix hub: troubleshooting entry point Fix detail pages: specific issue pages English and Japanese routes: language-specific entry points This structure is simple, but it keeps the site easier to maintain. Page role comes first Before writing a page, I define its role. A learn page answers what something is, how it works, and what a beginner should understand first. A download page answers where a user should get something, what should be checked before installing, and which platform or device matters. A fix page answers what is not working, what should be checked first, and whether the problem is related to permissions, notifications, device settings, or installation. The page role decides the title, description, internal links, and body structure. Why this helps SEO This approach helps avoid pages competing with each other. For example, a download page should not try to rank for every tutorial query. A troubleshooting page should not read like a general homepage. Each page can link to related pages, but the primary intent stays clear. That makes the site cleaner for both users and search engines. Metadata and sitemap discipline In a Next.js App Router project, I also like to keep metadata and sitemap updates close to the route change. For example: If

It is time to put my proposition made in my previous blog post to the test. Is it possible to spec an application for execution by an agent without encoding it in source? Let's find out. One type of application every knowledge worker is familiar with is task management. Every task has a lifecycle status, dependencies on other tasks, and a history of progress. Let's give agents their own. TaskTrack is a simple but non-trivial task management system variant implemented as a Specify spec. It goes beyond checkbox-based to-do lists that agents sometimes use internally and mimics the key system features listed above. TaskTrack defines two procedures: a "Plan Authoring Run" to create an interconnected set of tasks from requirements and a "Plan Execution Run" to advance a previously authored plan toward completion. One execution run might not always be enough to achieve completion, because TaskTrack allows requesting human feedback and incorporating it during the next execution run. Furthermore, every execution run is divided into "Task Processing Run" sub-procedures to allow for advanced agent context management. TaskTrack implements all of this in less than 300 lines of text. If the implementation used source code, then, depending on the programming language, this would be enough space to implement only the required file I/O operations (TaskTrack uses files for simplicity, not a database). Natural language can easily become quite bloated, but a stringent, scientific writing style and extensive use of what the Specify standard offers can effectively counter that. The official test is, how could it be any other way, the implementation of yet another uninspired Breakout clone. The requirements, the completed TaskTrack plan, and the deliverable are contained in the repository. If you want to run the test yourself, the included README file contains the necessary information, including the launch prompts for both the authoring agent and the execution agent. Please note how both

Introduction Excel is one of the most used tools for data analysis. It allows beginners like myself to easily clean, organize, analyze and visualize data.Excel enables users to work with large datasets and extract meaningful insights without requiring advanced technical skills. What is Excel Excel is a spreadsheet that allows you to collect, organize, analyze, calculate, and visualize data efficiently.Despite the emergence of other data analysis tools like SQL and Power BI, Excel remains one of the most widely used tools for both personal and professional data management.This can be credited to its ease of access, learning, and use. Ways Excel is used in real-world data analysis This week, I had the opportunity to explore how Excel is used in real-world data analysis.I discovered that Excel is not just a basic spreadsheet tool, but a powerful application that helps make sense of data and support decision-making. Data organization and cleaning Excel is used to structure raw data, remove duplicates, and fix errors. This improves data quality, making it easier to analyze and more reliable for decision-making.This improves data quality, making it easier to analyze and more reliable for decision-making. Financial Excel is commonly used in finance to create budgets, calculate profits and losses, and monitor expenses.It helps organizations keep accurate financial records and understand their financial situation. Business decision-making Businesses use Excel to track sales, compare performance over time, and identify trends.This helps managers understand what is working well and what needs improvement. Excel features and formulas In just a week, I have learned several Excel formulas that simplify data management and make working with data more efficient. SUM function The SUM function is used to add a range of values together in Excel, making it one of the most essential tools for quick calculations.It's used to automatically add a range of numerical values together, elimina

After 25 years of making other people's pancake recipes - always yearning for more tang, more fluff, and more predictability - I decided to derive the pancake recipe from the chemistry. You mark checkboxes for what you have on hand (ricotta, sour cream, kefir, buttermilk, yogurt, cottage cheese, lemon, cream of tartar, etc.) and it computes the best recipe based on targets for acid, fat, salt, sugar, and CO2. My particular favorite are the yeast-raised lemon ricotta kefir pancakes - the best I'v

Have you ever trained a model that performed beautifully on your training data but fell apart the moment it saw new data? Or perhaps you built something so simple it couldn't even learn the training data properly? These are the classic traps of overfitting and underfitting — and every machine learning practitioner runs into them. In this article, we'll cover what they are, how to detect them, how to fix them, and where the bias-variance tradeoff ties it all together — with real-world examples and code throughout. What is Model Fitting? Model fitting is the process of training a predictive model on a dataset to find the optimal parameters that best capture the underlying patterns in the data. The goal is simple: the model should generalize well to unseen data — not just memorize the training examples. There are three possible outcomes when fitting a model: Outcome Description Good fit Captures underlying patterns, generalizes well Underfitting Too simple, misses patterns even in training data Overfitting Too complex, memorizes noise, fails on new data What is Underfitting? Underfitting occurs when a model is too simple to capture the underlying patterns in the data. It performs poorly on both the training set and on new, unseen data. Think of it like this: imagine asking a child to predict house prices and they only use the rule "all houses cost $100,000." That model ignores all relevant features (size, location, age) and will be wrong almost every time. Why Does Underfitting Occur? Model is too simple : A linear model trying to fit a curved, nonlinear relationship Too few features : Important variables are left out Too much regularization : Penalizing complexity so heavily that the model can't learn anything meaningful Insufficient training : The model hasn't been trained long enough Real-World Example Suppose you're predicting whether an email is spam. If you only use the feature "email length" and ignore word content, sender, and links, your model will underfit —

Hello everyone, Is it allowed to use OpenAI API outputs to create a silver code dataset or benchmark for a specific Python library? I am working on a project idea related to library-specific code generation. The concrete case is a specific Python library used in a technical/scientific domain. The goal would be to improve and evaluate how well code-generation models can use this library correctly. I am trying to understand the legal / Terms of Service boundary around using OpenAI API outputs in two different scenarios: Scenario 1: Silver dataset for fine-tuning an OSS model Use the OpenAI API to generate programming tasks, reference solutions, and verification tests for the specific Python library. Then human-review, filter, and validate the generated examples. Then use this silver dataset to fine-tune an open-source code model, with the goal of improving its performance on this specific library. My question: would this violate OpenAI’s terms because the API outputs are being used to train/fine-tune another coding model, even if the scope is narrow and library-specific? Scenario 2: Benchmark only, not training Use the OpenAI API to generate programming tasks, reference solutions, and verification tests. Human-review and validate them. Then use the resulting dataset only as an evaluation benchmark to compare different models. The benchmark would not be used to fine-tune or train any model. My question: is this generally considered allowed under OpenAI’s terms, assuming the benchmark is properly reviewed and documented as AI-assisted? I understand that Reddit is not legal advice, and I would still contact OpenAI or legal counsel for a definitive answer. However, I thought new ideas could come up from people who have already faced similar situations in practice. submitted by /u/ororo88 [link] [留言]

I'm currently building an AI, specifically a large language model (LLM), using PowerShell. This AI will search the internet for code snippets and create databases. It will also have the ability to adjust and improve its own code. With PowerShell, I'm leveraging its scripting capabilities to automate tasks and manage data efficiently. The AI will integrate natural language processing techniques to understand and generate text, making it more user-friendly. Additionally, I plan to develop a simple interface to allow users to interact with the AI easily and provide feedback for continuous improvement. submitted by /u/Electrical-Tap-9224 [link] [留言]