标签：#ia

Gas optimization is satisfying. You shave a few thousand gas off a function and feel clever. But some optimizations trade away safety in ways that are not obvious, and I have seen "optimized" contracts that introduced vulnerabilities. Here are the gas wins that are genuinely free, the ones that cost you safety, and how to tell the difference. Where gas actually goes Before optimizing, know what is expensive. Storage operations dominate. Writing a fresh storage slot ( SSTORE from zero to non-zero) costs a lot; reading storage ( SLOAD ) is cheaper but still meaningful; computation in memory is cheap by comparison. So the highest-leverage optimizations are about touching storage less. Free win 1: cache storage reads in memory If you read the same storage variable multiple times in a function, each read is an SLOAD . Read it once into a local variable instead: // WASTEFUL: reads storage `total` three times function distribute() external { require(total > 0, "empty"); uint256 share = total / count; emit Distributed(total); } // OPTIMIZED: one SLOAD, two memory reads function distribute() external { uint256 _total = total; // single storage read require(_total > 0, "empty"); uint256 share = _total / count; emit Distributed(_total); } This is free in the sense that it changes nothing about correctness. The value is identical; you just read it once. Pure win. Free win 2: calldata instead of memory for read-only arrays For external function arguments you only read (never modify), calldata is cheaper than memory because it skips the copy: // memory copies the whole array into memory function process(uint256[] memory ids) external { ... } // calldata reads directly from the transaction data, no copy function process(uint256[] calldata ids) external { ... } Again, free. If you do not mutate the array, calldata is strictly better. Free win 3: storage packing Solidity packs multiple variables into one 32-byte slot if they fit and are adjacent. Order your storage variables so smal

Guardrails positions itself as a populist political movement that runs on small donations from people in the trenches of the AI boom.

Spotify is launching "Reserved," a new system that will hold two concert tickets for an artist's superfans before they're on sale to the public.

Originally published at kunalganglani.com — read it there for inline code, hero image, and live links. Generative AI vs agentic AI vs AI agents. Three terms, used interchangeably by people who should know better, burning engineering budgets across the industry in 2026. Generative AI refers to models that produce new content — text, images, code — from a prompt. AI agents are software systems that wrap those models with planning, memory, and tool use to pursue goals autonomously. Agentic AI is the broader paradigm: orchestrated systems of agents, workflows, and decision-making that operate with minimal human oversight. Getting these distinctions wrong doesn't just lose you a Twitter argument. It determines whether your production system costs $500/month or $50,000. Every quarter, someone on a leadership team says "we need to go agentic." What they usually mean is one of three completely different things. And the architecture you pick for each one has wildly different implications for cost, latency, reliability, and maintenance burden. I've watched teams burn entire quarters building autonomous agent systems when a well-tuned prompt engineering pipeline would have shipped in a week. That's not a hypothetical. I watched it happen twice in 2025. This post cuts through the buzzword soup. I'll define all three paradigms with concrete technical distinctions, show you how they map to real production architectures, and give you a decision framework for picking the right one. What Is Generative AI? The Engine, Not the Vehicle Generative AI is the foundation layer. It's a large language model (or image model, or audio model) that takes an input and produces new output. GPT-4, Claude, Gemini, Llama — these are all generative AI. You send a prompt, you get a completion. That's it. The critical thing to understand: generative AI is stateless by default . Each API call is independent. The model doesn't remember what you asked five minutes ago. It doesn't plan a sequence of steps.

Forget stickers, GIFs, and emoji reactions. Pixi is betting that the next evolution of messaging is interactive augmented reality (AR).

A while back I was building a side project that needed public data from a few social platforms. Nothing crazy — profiles, posts, some engagement numbers. I figured I'd just grab each platform's official API. Reader, I did not "just grab each platform's official API." Here's what that road actually looked like, and how I ended up consolidating everything down to one key and roughly ten lines of shared code. The five-API nightmare Instagram (Meta Graph API). Great if you own the account. Useless for pulling public data about accounts you don't. Endless app review. TikTok. The research API is academics-only with a long application. For commercial use, basically nothing. X (Twitter). Used to be wonderful. Now $100/month to start, more for anything serious. YouTube. Honestly the best of the bunch — generous and well-documented. Credit where due. LinkedIn. Partner-only. For most people, no useful public access at all. So to cover five platforms I was looking at: five sets of credentials, five auth flows, five rate-limit models, five totally different response shapes, two flat-out rejections, and a monthly bill. For a side project. What I actually wanted getProfile ( " tiktok " , " someuser " ) getProfile ( " instagram " , " someuser " ) getProfile ( " twitter " , " someuser " ) Same call shape, same auth, same error handling. That's it. I don't care that each platform structures things differently internally — I want one boundary that hides that from me. The consolidation I switched to SociaVault , which puts public data from all of these behind one API and one key. My entire client became this: const API_KEY = process . env . SOCIAVAULT_API_KEY ; const BASE = " https://api.sociavault.com " ; async function sv ( path , params = {}) { const url = new URL ( BASE + path ); Object . entries ( params ). forEach (([ k , v ]) => url . searchParams . set ( k , v )); const res = await fetch ( url , { headers : { " X-API-Key " : API_KEY } }); if ( ! res . ok ) throw new Error ( ` $

1. Python Ortam Kontrolü (Windows CLI) Python sürüm kontrol python --version py --version where python pip kontrol pip --version python -m pip --version pip güncelleme (kritik) python -m pip install --upgrade pip 2. Python Çalıştırma Mekanizması (Windows Standard) Script çalıştırma python app.py Py launcher ile sürüm seçme py app.py py -3 .12 app.py py -3 .11 app.py Modül çalıştırma python -m mymodule 3. Sanal Ortam (venv) – Kurumsal Standart Oluşturma python -m venv venv Aktivasyon (PowerShell) venv \S cripts \A ctivate.ps1 Aktivasyon (CMD) venv \S cripts \a ctivate.bat Deaktivasyon deactivate Sanal ortam kontrol where python where pip pip list 4. requirements.txt Yönetimi Oluşturma pip freeze > requirements.txt Kurulum pip install -r requirements.txt Güncelleme pip install --upgrade -r requirements.txt 5. Paket Yönetimi (pip Core Set) Paket yükleme pip install requests Versiyon sabitleme pip install requests == 2.31.0 Paket kaldırma pip uninstall requests Listeleme pip list Güncellenebilir paketler pip list --outdated 6. Windows .env Yönetimi (Konfigürasyon Standardı) .env dosyası oluşturma notepad .env Örnek içerik DEBUG=True API_KEY=123456 DB_URL=localhost Python tarafı (.env kullanımı) pip install python-dotenv from dotenv import load_dotenv import os load_dotenv () api_key = os . getenv ( " API_KEY " ) print ( api_key ) 7. Sistem Komutları ve Process Yönetimi Process listeleme tasklist Python process filtreleme tasklist | findstr python Process sonlandırma taskkill /PID 1234 /F Python process kill taskkill /IM python.exe /F 8. Dosya İşlemleri (CLI seviyesinde) Dosya listesi dir Klasör değiştirme cd project Dosya silme del file.txt Klasör silme rmdir /S /Q folder 9. Log ve Debug Yönetimi Dosya log izleme (PowerShell) Get-Content app.log -Wait Son satırlar Get-Content app.log -Tail 100 Filtreleme Select-String "ERROR" app.log 10. Uzaktan Erişim (Windows → SSH) SSH bağlantı ssh user@server_ip Dosya gönderme scp app.py user@server_ip:C: \U sers \u ser \ Klasör gön

If you've ever felt like LangChain was too heavy, you're not alone. The dependency tree is enormous. Abstraction layers pile up. At some point you lose track of what's actually happening underneath. That frustration has pushed a lot of people toward lighter alternatives — frameworks that prove you can build a capable agent without a hundred transitive dependencies. Agno is one of those alternatives. It started as Phidata and rebranded in early 2025. I spent an afternoon installing Agno v2.6.17 in a clean sandbox and running through Calculator tools, Wikipedia retrieval, Pydantic structured output, and a two-agent Team. I'll share the real execution logs and, more importantly, the traps I hit that the docs don't warn you about. What Agno Is and Where It Came from Phidata built a solid reputation as "the Python framework for AI assistants." When it rebranded to Agno in 2025, the design philosophy got articulated more clearly around three ideas. Model-agnostic from day one. Over 70 LLMs — OpenAI, Anthropic, Google, Ollama, Cohere — can plug in with the same code structure. Swap the model, keep the agent logic. Multimodal as a default. Text, image, audio, video agents all use the same API surface. You don't need a different abstraction layer for each modality. Multi-agent orchestration as a first-class citizen. The Team class is built in. You can switch between coordinate , route , and collaborate modes with a single parameter change. Reading that, I thought: "How is this different from LangChain?" The answer showed up when I actually wrote code. Agno favors composition over class inheritance. One agent takes about 6 lines to set up. There's far less boilerplate to wade through. Installation: No Dependency Hell pip install agno google-genai ddgs wikipedia The agno package installs just the core. Tools require their own extra dependencies — wikipedia for the Wikipedia tool, google-genai for Gemini. This lazy-loading approach keeps the base install clean. $ python3 -c "im

Internal Home Office tests of age-verification technology show the risks of life-altering errors. It’s moving forward anyway.

One of the challenges I faced while building my LaTeX Writer project was implementing version control and project forking in a storage-efficient way. A typical LaTeX project contains multiple files. Even a simple project usually has a "main.tex" file, bibliography files, images, style files, and other supporting documents. If I stored a complete copy of every file for every version or fork, storage requirements would grow rapidly. Imagine a project with four files and ten versions. Storing the entire project for every version would mean storing the same files repeatedly, even when only one line changed. Forking would create an even bigger problem because every fork would require another complete copy of the project. Instead of accepting this inefficiency, I started researching how large platforms solve the same problem. GitHub was the obvious inspiration. Learning from GitHub GitHub does not store a complete copy of a repository every time a change is made. Instead, it stores content separately and uses references to connect files, commits, and repositories. This idea became the foundation for my own implementation. Project Structure Whenever a new project is created, a default file called "main.tex" is generated automatically. The project itself does not directly contain file contents. Instead, it stores metadata such as: Project ID Owner ID Root Folder ID File References Each file also has its own metadata record containing: File ID File Name Blob ID Project ID Owner ID Folder ID The actual content is not stored inside the file metadata. Instead, the content lives inside a separate entity called a Blob. Loading a Project When the editor loads a project, it reconstructs the directory structure using metadata. The process works like this: Retrieve the project's Root Folder ID. Find all folders belonging to that folder hierarchy. Find all files belonging to each folder. Build the directory tree for the frontend. Because files and folders are stored independently, the

SISTEMA INTELIGENCIA DIGITAL HUMANA (IDH) ARQUITECTURA DE CONSTRUCCIÓN GLOBAL Y ESPECIFICACIÓN TÉCNICA FINAL VERSIÓN 2.0 EJECUCIÓN REAL EN HARDWARE BARE-METAL DESCENTRALIZADO CAPA 1: SUBSISTEMA DE INGESTA MULTIMODAL, PERCEPCIÓN AFECTIVA Y FRONTERA DE ADQUISICIÓN Esta capa opera como el sistema nervioso periférico de la Inteligencia Digital Humana. Su función es capturar los flujos del mundo real (texto, audio, imagen), autenticarlos a nivel de kernel, extraer el estado emocional del usuario y unificar la información en un único espacio matemático comparable antes de que llegue al motor de procesamiento central. 1.1 Frontera de Ingreso y Seguridad Perimetral (Kernel Space) Arquitectura de Red: El sistema levanta sockets TCP/IP directos y conexiones QUIC/UDP sobre hardware Bare-Metal. QUIC maneja la multiplexación de flujos (audio, video, texto) en paralelo sin bloqueo de cabecera, permitiendo comunicación fluida en tiempo real. Cifrado y Autenticación en Dos Niveles: Nivel 1 - Handshake QUIC: Durante el establecimiento inicial de la conexión QUIC, los paquetes de handshake contienen una firma criptográfica Ed25519 en sus campos de token. El programa eBPF/XDP en espacio kernel valida exclusivamente esta firma en los paquetes de handshake, los cuales no están completamente cifrados. Paquetes sin firma válida son descartados inmediatamente en la tarjeta de red. Nivel 2 - Token de Sesión Efímero: Una vez validado el handshake, se establece un token de sesión efímero con tiempo de vida limitado. Los paquetes subsiguientes de datos se validan contra este token sin necesidad de verificar la firma completa en cada paquete. Filtro eBPF/XDP en Espacio Kernel: El código de seguridad se inyecta directamente en el espacio de ejecución del kernel mediante un programa eBPF acoplado a XDP. Funciones del filtro: · Descarte temprano de ataques DDoS y tráfico malformado a nivel de controlador de red, antes de consumir ciclos de CPU en espacio de usuario. · Validación de firmas Ed25519

📚 Limn Engine — Complete API Reference Quick Navigation Class Purpose Level Display Canvas, game loop, input, camera, scenes 🟢 L1 Component Every visible game object 🟢 L1 Camera Viewport control (follow, shake, zoom) 🟡 L2 move Movement, physics, particles, helpers 🟢 L1 state Read-only query helpers 🟢 L1 TileMap Grid-based levels 🟡 L2 Tctxt Rich text with backgrounds 🟢 L1 Sound Single audio file 🟢 L1 SoundManager Multiple sounds, volume control 🔴 L4 ParticleSystem Emit, burst, continuous emitters 🟠 L3 Sprite Spritesheet animation 🟡 L2 Display The heart of every Limn Engine game. Creates the canvas, runs the game loop, captures input, manages the camera, and controls scenes. Constructor const display = new Display (); Properties Property Type Description .canvas HTMLCanvasElement The game canvas .context CanvasRenderingContext2D 2D drawing context .keys Array Boolean array indexed by keyCode .scene Number Current active scene (default 0) .camera Camera Attached camera instance .deltaTime Number Time since last frame (seconds) .fps Number Current frames per second .frameNo Number Total frames elapsed .x / .y Number false Methods Method Parameters Description .start(w, h, node) width, height, parentNode Initialise canvas and start game loop .perform() — Activate dual-canvas pipeline (call before .start() ) .add(comp, scene) Component, scene number Register a Component for rendering .stop() — Pause the game loop .scale(w, h) width, height Resize canvas after start .backgroundColor(color) CSS color Set background colour .lgradient(dir, c1, c2) direction, color, color Linear gradient background .rgradient(c1, c2) color, color Radial gradient background .fullScreen() — Enter fullscreen .exitScreen() — Exit fullscreen .tileMap() — Build TileMap from display.map and display.tile Usage const display = new Display (); display . perform (); display . start ( 800 , 600 ); display . backgroundColor ( " #0a0a2a " ); const player = new Component ( 40 , 40 , " blue " , 100 , 100 ); d

Abstract In modern software engineering, writing code that simply "works" is only the first step. The real challenge lies in designing systems that are maintainable, scalable, and easy to test. This article explores the Dependency Inversion Principle (DIP), the final pillar of the SOLID design principles. Through a practical, real-world example in Kotlin, we will demonstrate how to transition from a tightly coupled architecture to an abstraction-based design. This shift dramatically improves our codebase, facilitates unit testing, and prepares our applications for future growth. Introduction: The Chaos of Coupling As applications grow, it is common to see how a minor change in a database schema or a third-party API triggers a domino effect, breaking unrelated parts of the system. This fragility is a direct consequence of tight coupling. Software design principles, particularly SOLID, were established to prevent this architectural decay. Today, we focus on the "D" in SOLID: the Dependency Inversion Principle (DIP). This principle establishes two core rules: High-level modules should not depend on low-level modules. Both should depend on abstractions (interfaces). Abstractions should not depend on details. Details (concrete implementations) should depend on abstractions. The Scenario: An E-commerce Payment Processor Imagine you are building the billing system for an online store. To process purchases, the system needs to connect to a payment gateway, such as PayPal. The Bad Way: Tight Coupling (Violating DIP) In this initial design, our high-level business logic (OrderProcessor) directly instantiates and depends on the concrete low-level class (PayPalService). // Low-level component (Concrete detail) class PayPalService { fun executePayment(amount: Double) { println("Processing payment of $$amount via PayPal API.") } } // High-level component (Business logic) class OrderProcessor { // Tight coupling: this class depends directly on a concrete implementation private val

Days before Anthropic took its most advanced AI models offline, the White House ordered the company to revoke SK Telecom’s access to Claude Mythos over claims of alleged ties to China.

FCC considers AT&T petitions to preempt state rules and discontinue phone service.

Nvidia's self-improvement program for robots enlists teams of AI coding agents.

In Shenzhen, workers at IO-AI Tech control humanoid robots using a VR rig reminiscent of Ready Player One.

Social media feeds are becoming more customizable as platforms like Threads, Instagram, and TikTok introduce tools that let users directly influence the algorithms powering their recommendations.

Mastodon’s newly launched newsletter feature lets anyone subscribe to creators by email, even without a Mastodon account.

Google is betting generative AI can breathe new life into the smart speaker. The company's new $99.99 Google Home Speaker replaces the rigid commands of the Google Assistant era with more conversational Gemini interactions.