Claude’s new Skills enable modular, auto-loaded functions, code, data handlers, or workflows triggered by context. Composable, portable, efficient. Transforms Claude into a task-aware runtime, not just a model. #AI #Agents #Claude #LLM #Automation #DevTools #ContextAware

🚀 Just leveled up: using the GitHub Copilot CLI—a fully terminal-native coding assistant from GitHub. From navigating a codebase to installing dependencies and refactoring legacy modules in one chat-style session. #DevLife #AI #Productivity #SoftwareEngineering

Today we announce a significant step in quantum computing by introducing Quantum Echoes, a new quantum computational algorithm measuring out-of-time-order correlators. This verifiable quantum advantage paves the way for solving real-world problems. More: https://t.co/xxK6zjG1Mm

Exploring Claude Code templates: a set of practical, AI-powered code snippets to streamline your dev workflow. Think a beautifully organized code desk, no frills—just clean, efficient tools for the everyday coder. #AI #DevLife

🚀 Chrome DevTools now supports MCP (Model Context Protocol) — AI agents can directly inspect, debug & analyze live web pages through DevTools. A big leap from “code + guess” to “code + see.” 🔍 👉 https://t.co/R6KjwUxqbb #AI #MCP #WebDev

Short explanation of the physics Nobel Prize 2025 In classical mechanics you can know where a particle is and its momentum at the same time. In Quantum mechanics you can't. All information is in the wavefunction. Even if a particle is trapped, part of the wavefunction.. 🧵1/7

Claude Sonnet 4.5 expands the model’s agentic capabilities with improved reasoning, long-duration task handling, and advanced tool coordination. It supports over 30 hours of continuous execution, refined memory and context editing. #AI #LLM #Anthropic #Claude

Thrilled to dive into Claude Sonnet 4.5! This latest iteration brings remarkable advancements, including autonomous task performance improved capabilities in coding, financial analysis, and cybersecurity. It’s a significant leap forward. #ClaudeSonnet #AI #Innovation

Excited about OpenAI Dev Day 2025! The new Agent Kit empowers developers to build AI agents seamlessly from prototype to production. AI innovation just got a major upgrade! 💡 #OpenAIDevDay #AgentKit

Mathematics and Logic Symbols ✍️ Quantifiers and Sets: ∀ - For all (universal quantifier) ∃ - There exists (existential quantifier) ∈ - Element of ∉ - Not element of ⊂ - Subset of ⊆ - Subset or equal ⊇ - Superset or equal ⊄ - Not a subset of ∪ - Union ∩ - Intersection

The real upgrade in Codex isn’t just speed — it’s sustained multi-file context. Finally you can keep a long session alive across CLI, IDE, and GitHub without losing state. Feels less like autocomplete, more like an actual co-dev. #AI #Codex #DevTools #Programming

Claude can now create & edit files—Excel, Word, PowerPoint, and PDFs—directly in https://t.co/6LC19nZpE8 & the desktop app. Fewer handoffs, faster reports, decks, and docs. Big win for ops + analytics workflows. 🚀📄

Choosing the right AI agent platform isn’t about hype—it’s about 8 core capabilities: scalability, security, integration, context handling, orchestration, monitoring, cost control, and compliance. For devs, the right choice now means fewer rebuilds later. #AIEngineering #DevTools

OpenAI just leveled up AI agent development. With built-in Web Search, Code Interpreter, and MCP (Model Context Protocol), developers can now build agents that reason across live data, analyze local files, and scale context seamlessly. #AI #Developers #AgenticAI

6/ Bottom line: Hallucination is a direct consequence of probabilistic next-token prediction, overconfident training regimes, and fundamental model limitations. True mitigation requires both improved grounding and reconceived evaluation incentives.

5/ Mitigation strategies include: Retrieval-augmented generation (RAG) to ground outputs in external knowledge. RLHF and synthetic tasks to penalize hallucination behavior. Uncertainty-aware evaluation frameworks that reward models for calibrated responses.

4/ From a theoretical lens, hallucination is not just an engineering bug—it is provably unavoidable. LLMs are function approximators that cannot capture all computable functions or ensure perfect alignment with ground truth, so hallucinations are inherent

3/ Furthermore, conventional evaluation and training protocols penalize expressions of uncertainty. Instead, they reward confident output, causing models to favor “cocky” plausible-sounding falsehoods over admitting “I don’t know

Pretraining teaches consistent structural patterns (like syntax), so scaling improves those. But arbitrary low-frequency facts lack pattern reinforcement—this leads to systematic hallucination unless downstream stages successfully correct it—which they often fail to do fully

1/ OpenAI paper: Hallucinations in LLMs arise because next-token prediction is inherently probabilistic; when the model lacks high-frequency factual context (e.g., a pet’s birthday), it predicts plausible guesses rather than verifiable truth