标签：#ai

While LLMs are great, there are some limitations in using LLMs: LLMs can hallucinate, presenting factually incorrect information when they don't know the answers, and their knowledge gets frozen at the time of training. That's when Retrieval Augmented Generation (RAG) addresses both of these problems. It is the process of optimizing the output of the LLM. This article walks through what RAG is, why it matters, and how to build a working RAG pipeline using two of the most popular tools in the space: LangChain , a framework for building LLM-powered applications, and Pinecone , a managed vector database designed for fast similarity search at scale. A typical RAG pipeline has three core steps: Retrieve : When a query is entered, the system searches an external data source (like a vector database) for the most relevant documents. Augment : The system attaches those relevant retrieved documents to the original user prompt. Generate : The LLM reads the appended context and formulates a highly accurate, grounded answer. RAG is popular because it solves practical problems that pure fine-tuning or prompting can't easily solve: Freshness — You can update the knowledge base without retraining the model. Domain specificity — You can ground responses in your company's internal documents, product manuals, or proprietary data. Traceability — Because answers are based on retrieved documents, you can cite sources and reduce hallucination. Cost — Retrieval is far cheaper than fine-tuning a model every time your data changes. Why LangChain and Pinecone? LangChain drastically speeds up AI development. It is an open-source orchestration framework that provides pre-built components to connect Large Language Models (LLMs) to external data, manage memory, and create multi-step workflows. It abstracts away the complex boilerplate usually required to build production-ready AI applications. Pinecone is a purpose-built vector database. Once your documents are converted into embeddings (numerica

The previous code-graph series was about reshaping a static analysis graph so AI could query it. The same kind of reshaping is needed on the observability side. This post walks through four axes — application / infrastructure / CI / LLM — and the deliberately different shapes each one ends up in. The design judgments worth calling out: computing Gemini cost client-side instead of from billing API, sending Claude Code OTel straight to BigQuery instead of Loki, and shipping CI logs via post-hoc pull instead of webhook push.

Abstract Cada vez más equipos quieren consultar datos sin escribir SQL a mano. El problema es que un sistema Text-to-SQL no solo debe “traducir preguntas”, sino también entender el esquema, restringir permisos, validar consultas y explicar por qué una consulta es rápida o lenta. Ese enfoque coincide con la ruta propuesta por el tutorial oficial de LangChain para agentes SQL: listar tablas, inspeccionar esquemas, generar la consulta, revisarla, ejecutarla y corregir errores hasta obtener una respuesta; y el propio tutorial advierte que ejecutar SQL generado por modelos tiene riesgos y exige permisos mínimos. En paralelo, la documentación oficial de Python recomienda usar placeholders en sqlite3 para enlazar parámetros y evitar inyección SQL, mientras que la documentación de SQLite explica que EXPLAIN QUERY PLAN permite inspeccionar si una consulta hace SCAN, SEARCH y si usa índices. manueldongo23 / sql_ai_sales_assistant_demo SQL AI Sales Assistant A safe Text-to-SQL demo that converts natural language business questions into SQL queries, executes them on a local SQLite retail database, and shows the generated SQL plus EXPLAIN QUERY PLAN . This project was created as evidence for an article about SQL AI Database Solutions . Topic Talk to your database with AI: build a safe SQL query extractor with Streamlit and SQLite. Features Natural language prompts such as sales by month , top customers , sales in Lima . Rule-assisted NL→SQL generation, designed to be transparent and auditable. SQLite demo database with customers, products, orders and order items. Read-only SQL validator that blocks destructive commands. Parameterized queries for user-provided filters. Streamlit interface. CLI demo for quick testing. Query plan inspection with EXPLAIN QUERY PLAN . Architecture User question ↓ NL → SQL interpreter ↓ Read-only SQL validator ↓ SQLite execution ↓ Results + EXPLAIN QUERY … View on GitHub Cuerpo del artículo La promesa de SQL + IA suena sencilla: le haces una pregunta

Building an AI agent sounds complicated. It's not. By the end of this guide, you'll have a working agent that can search the web, remember conversations, and handle multi-step tasks. No frameworks, just TypeScript and an LLM API. What We're Building A research assistant agent that: Takes questions from users Uses tools (web search) when needed Remembers conversation history Handles errors without crashing Runs in about 150 lines of TypeScript This won't be production-ready, but it'll work and you'll understand every line. Prerequisites You need: Node.js 18 or higher Basic TypeScript knowledge An Anthropic API key ( get one free ) That's it. No prior AI experience needed. Setup (5 minutes) # Create project mkdir research-agent cd research-agent npm init -y # Install dependencies npm install @anthropic-ai/sdk dotenv # Install dev dependencies npm install -D typescript @types/node tsx # Initialize TypeScript npx tsc --init Create .env : ANTHROPIC_API_KEY = your-key-here Step 1: Define Your Types Create src/types.ts : export interface Message { role : ' user ' | ' assistant ' ; content : string ; } export interface Tool { name : string ; description : string ; input_schema : { type : ' object ' ; properties : Record < string , any > ; required ?: string []; }; execute : ( input : any ) => Promise < string > ; } Why these types matter: Strong typing prevents bugs. If you change how a tool works, TypeScript tells you everywhere that breaks. Step 2: Create a Simple Tool Create src/tools/search.ts : import { Tool } from ' ../types ' ; export const searchTool : Tool = { name : ' search_web ' , description : ' Search the internet for current information. Use this when you need facts, recent events, or data you do not know. ' , input_schema : { type : ' object ' , properties : { query : { type : ' string ' , description : ' The search query ' , }, }, required : [ ' query ' ], }, execute : async ( input : { query : string }) => { console . log ( `[Tool] Searching for: ${ input

Microsoft cut around 4,800 roles, or 2.1% of its global workforce, on Monday — the latest in a series of layoffs that’s stoking fears of AI replacing jobs. The layoffs will hit Xbox and commercial sales the hardest.

Reddit to combat AI with more AI

In the AI era, platforms have no choice but to fight fire with fire to cull spam.

Holes in socks have become a curious sight at this year’s World Cup. The reasons why are a weird mix of biomechanics, perception, and player habits.

FCA official makes case for greater powers for watchdog as millions use technology for personal finance decisions.

Nintendo is making a new version of the Switch 2 with a replaceable battery in Europe - but its predecessor has a very different future. As part of an updated FAQ about revisions to Nintendo hardware in Europe, the company confirmed that it will stop selling all iterations of the original Switch on the continent […]

One hundred dollars will buy you 8 pounds of glitter; 10 Domino's pizzas; 406 miniature disco balls from Temu; or 100 cans of Coors Light. For a friend's birthday party one year, Ayla D'Silva spent $100 on sour candy and made a "sour candy salad." Even sweeter was that she didn't have to foot the […]

A year after cutting around 9,100 employees, Microsoft is making further layoffs today as it begins its new financial year. The software maker is laying off around 4,800 employees today, approximately 2.1 percent of its workforce. Most of the employees affected by today's cuts are in Microsoft's commercial sales business or the company's Xbox division. […]

Station F, the Paris-based startup hub founded by French billionaire Xavier Niel in 2017, is gearing up for a new edition of its selective acceleration F/ai program that will cement its role as a stepping stone for AI startups.

I kept hearing about Kalshi. The commercials, the mentions, and then one morning CNN was talking about Kalshi prediction odds like they were a weather report. So I went and looked. And I had no idea what I was seeing. Kalshi is really hard to understand when you're new to it. You get markets, contracts, prices that are also probabilities, volume, movement, and none of it tells you what's actually worth paying attention to. I wanted something that would translate what I was looking at into something I could understand and, ideally, act on. That was the whole original goal: make the firehose legible. Then, of course, I kept adding to it, because once you can read the flow you start wanting an edge in it. Who doesn't. So Trade Hunter grew from a translation layer into a translation layer with detection on top. This is a writeup of what it became, why I made the design choices I made, and the parts I'm still not sure about. I'd rather you poke holes in it now than find the breakpoints the hard way. If you think a decision here is wrong, please let me know. The comments are the point of this post, not an afterthought. Where this came from Basically, I couldn't read Kalshi, and I wanted to. Trade Hunter is the original tool I built to fix that for myself, and it's the one I still run when I want a live view. So this isn't a polished sequel to anything. It's the thing I built because I wanted it to exist, flaws and design bets included, and I'd rather show you those directly. The core idea never changed even as I piled features on: watch live Kalshi WebSocket feeds and surface an unusual move while it's still moving, rather than reading about it after the fact, or on CNN the next morning. What it actually does Trade Hunter subscribes to live Kalshi feeds across every market you track. Multi-contract series like the Fed rate decisions or who will win Top Chef fan out automatically to all open contracts, so you point it at one thing and it watches the whole family. When some

The story behind why we built a WhatsApp CRM that actually understands how WhatsApp works. There's one scene I can't get out of my head. A friend's desk. She runs an online store. On it sat three phones. Not for show. One for customer service, one for the admin, one for the number that was "just for resellers." All three buzzing, nonstop. And there she was, eleven at night, still replying to messages one by one, sighing: "It's the same questions over and over. But if I don't reply, they'll go to the competitor." That's not a rare case. That's the normal state of things for thousands of businesses. We all know one thing CRM software rarely admits: customers here don't live in email. They live on WhatsApp. They ask about prices on WhatsApp, complain on WhatsApp, close deals on WhatsApp, even ask for warranty support on WhatsApp. But the teams handling all of it? They use personal phones. No records, no context, no way to help each other when one person is drowning. Hallo Zetta was born out of that. What Frustrated Us About the Existing Tools Before building our own, of course we looked. Surely someone had solved a problem this simple? Turns out what existed fell into two camps, and both were maddening. Camp one: dumb auto-reply bots. Type "hi," get a template. But the moment a customer asks something slightly off-script, the bot freezes. It actually makes customers angrier, because it feels like talking to a wall. Camp two: bloated CRMs. Loaded with features, dashboards full of charts, but WhatsApp is bolted on as one small tab. As if WhatsApp were an afterthought, not the main battlefield. For most of our customers, WhatsApp is the battlefield. Nothing fit. So we decided to build it ourselves. The Hard Part Isn't "AI Can Reply to Messages" Let me be honest about this. Bolting AI onto WhatsApp is easy. Anyone can wire GPT to a webhook and ship it overnight. If that were the whole goal, this article wouldn't need to exist. The hard part, the thing that made us rethink

How to Set Up Claude Code for a Project with Skills, Agents, Hooks, and a Secure GitHub Repository AI coding tools work best when they understand the project around the code. A fresh Claude Code session can answer questions and edit files, but it does not automatically know your architecture decisions, coding standards, security expectations, testing rules, pull request format, or operational constraints. That context needs to live somewhere predictable. This guide walks through a full Claude Code project setup using a reusable repository owned by desertfox33 : Reference repository: https://github.com/desertfox33/claude-code-project-template The goal is not just to create folders. The goal is to make Claude Code behave consistently across real project work: reviewing code, writing tests, preparing pull requests, checking security concerns, and following project-specific rules. Before using this setup in a production environment, verify current Claude Code behavior against the latest official documentation. Tooling, configuration names, and feature behavior can change. What We Are Building The repository uses this structure: claude-code-project-template/ ├── CLAUDE.md ├── CLAUDE.local.md.example ├── AGENTS.md ├── .mcp.example.json ├── .gitignore ├── SECURITY.md ├── CONTRIBUTING.md ├── .github/ │ ├── CODEOWNERS │ ├── dependabot.yml │ └── pull_request_template.md ├── .claude/ │ ├── settings.json │ ├── settings.local.json.example │ ├── rules/ │ │ ├── code-style.md │ │ ├── api-conventions.md │ │ ├── testing-standard.md │ │ └── pr.md │ ├── commands/ │ │ ├── review.md │ │ ├── deploy.md │ │ ├── scaffold.md │ │ ├── test.md │ │ └── pr.md │ ├── skills/ │ │ ├── code-review/ │ │ │ ├── SKILL.md │ │ │ └── review-checklist.md │ │ ├── testing-patterns/ │ │ │ ├── SKILL.md │ │ │ └── test-strategy.md │ │ ├── pr-description/ │ │ │ ├── SKILL.md │ │ │ └── template.md │ │ └── security-review/ │ │ └── SKILL.md │ ├── agents/ │ │ ├── security-reviewer.md │ │ ├── test-writer.md │ │ └── researc

Looking for the best AI UI design tool in 2026? I tested Flowstep, Google Stitch, Figma Make, Lovable, and Base44 with the exact same SaaS project management prompt to compare UI quality, design consistency, code generation, developer workflow, Figma integration, and overall usability. If you've searched for an AI UI design tool recently, you've probably noticed that every product claims it can turn a simple prompt into a polished interface in seconds. Landing pages are full of beautiful dashboards, glowing testimonials, and promises that you'll never have to start from a blank canvas again. The problem is that those demos rarely tell you what happens when you ask the AI design tool to generate something that looks like an actual product instead of a single screenshot. I wanted to know how these AI UI generator tools would perform on a realistic workflow. Could they keep a design system consistent across multiple screens? Would they generate layouts that developers could build on? Could they produce code that was worth keeping, or would I end up rebuilding everything from scratch anyway? Instead of trying different prompts for different tools, I decided to make things as fair as possible. I wrote one detailed prompt for a SaaS project management application and used it everywhere. The five AI design tools I tested were: Flowstep Google Stitch Figma Make Lovable Base44 They all approach AI-assisted UI generation differently, and after spending time with each one, it became clear that they're not really competing to solve the same problem. If you're trying to figure out which AI UI design tool is worth adding to your workflow in 2026, here's what I learned after putting all five through the exact same test. Why AI UI Design Tools Are Becoming Part of Every Developer's Workflow A year or two ago, most AI UI design tools were good at generating a nice-looking landing page and not much else. Today, the landscape looks very different. Some tools can generate an entire mul

Comcast subsidiary Sky plans to buy the UK's biggest broadcaster, ITV.

InfoQ has opened enrollment for a five-week AI Security & Privacy Engineering cohort for senior engineers and architects in regulated industries, focused on applying security, privacy, threat modeling, observability, and governance practices to production AI systems. By Artenisa Chatziou

👋 Hey there, Tech Enthusiasts! I'm Sarvar, a Cloud Architect who loves turning complex tech problems...