AI articles
535 articles about AI, newest first.
- Why Off-the-Shelf AI Can't Understand Money (And How to Fix It) — Udi Menkes explains why LLMs give confident but wrong financial advice and how grounding real data creates trustworthy AI.
- Synthetic Personas: How LLM Agents Mimic Real Survey Respondents (And Where They Break) — Learn how LLM agents can replicate human survey answers, the hidden risks of prompt tweaks, and why a noise‑floor benchmark matters for real‑world AI decisions.
- Run 5 AI Agents at Once: Parallel Coding with Orca — Discover how Orca runs multiple AI agents simultaneously, creates varied code versions, and merges the strongest parts instantly for faster development.
- Kimi K3 vs Claude Opus 5: Open‑Source 3D World Generation Showdown — Discover how Kimi K3’s open‑source weights tackle a multi‑step 3D world task, rivaling Claude Opus 5 while revealing new developer tools.
- HyCE-RAG Explained: How AI Uses Hypergraph Diffusion to Supercharge RAG — HyCE-RAG boosts Retrieval-Augmented Generation by spreading query evidence through a hypergraph, delivering faster, more accurate AI answers.
- Never Hit a Token Limit Again: Inside Claude & Codex Token Savings — Discover why repeated input dominates token usage and how the Token Saver skill keeps your AI prompts lean and cost‑effective.
- Master ChatGPT Work: Build a Full Business Launch in Minutes — Learn how to use ChatGPT Work to create a complete business launch package — from research to website — with practical tips and prompt techniques.
- How Decagon’s Forward Deployed Engineering Powers AI Support Agents — Explore forward deployed engineering at Decagon, the AI support agent workflow, scaling challenges, and how custom work becomes self‑serve.
- Cursor’s New Plan Explained: AI Coding Editor Pricing Update — Cursor introduces a tiered pricing plan for its AI code editor, offering clear tiers, usage limits, and API access to boost developer adoption.
- How Ramp’s Forward Deployed Engineering Uses AI to Scope Smarter — Discover how Ramp’s Forward Deployed Engineers avoid blanket “yes” answers by rigorously scoping work, then use AI‑powered agents to turn intake requests into specs, blending human judgment with automation for scalable enterprise delivery.
- NotebookLM 2.0: The Ultimate AI Research Assistant You Need — Learn how NotebookLM 2.0 transforms your notes into AI‑powered docs, presentations, and data insights—no coding required.
- SHIFT: Compressing AI Reasoning into 3 Hidden Tokens — Discover how SHIFT shrinks thousand-token reasoning into three hidden vectors, enabling smarter AI search without verbose explanations.
- AI Agents Explained: What They Are & Why They Matter — Discover the basics of AI agents, how they go beyond chatbots, and why builders are seizing new opportunities.
- How AI Is Replacing Traditional Onboarding – A Quick Guide — Discover how AI automates onboarding, cuts time, and boosts efficiency in just minutes.
- Agentic Engineering Explained: Boost Your Development Workflow — Discover how agentic engineering lets AI agents automate tasks with AI agents, boosting productivity and reshaping software development.
- Debugging AI Agents at Scale: Credit Genie’s LangSmith Secrets — Credit Genie shows how LangSmith’s tracing and Insights let them turn thousands of AI agent traces into targeted tests, cutting debugging time dramatically
- API vs MCP vs A2A: How AI Agents Actually Work — Learn how APIs, RAG, MCP, and A2A fit together in AI agent architecture with a travel assistant example.
- Why LangChain Ditched Auto‑PRs for a Smarter Bug‑Fixing Inbox — LangChain’s Engine moved from noisy auto‑PRs to a clustered inbox that surfaces high‑impact bugs, cutting alert fatigue and boosting dev productivity.
- Turn Words into Perfect Flowcharts Instantly with Achify — Learn how Achify converts natural language into compact visual flowcharts, fixing AI’s spatial‑awareness gap and boosting system design.
- Why the AI Scoreboard Hype Is Misleading (And What Really Matters) — The AI scoreboard craze overlooks real evaluation methods—discover the hidden metrics that actually drive progress.
- How Topologically Regularized Side‑Paths Stop LLMs From Collapsing — Discover how a new topologically regularized side‑path technique prevents representation collapse in LLMs, boosting memory efficiency by up to 800% and improving stability.
- Why Chinese AI Model Prices Don't Tell the Whole Story — Explore the hidden costs behind DeepSeek and Kimi K3 pricing, and learn how to evaluate Chinese LLMs for real value.
- DeepSWE Benchmark: Testing LLMs on Fresh, Contamination‑Free Coding Tasks — Learn how DeepSWE creates 113 brand‑new software tasks to evaluate LLMs without training contamination, revealing true coding limits.
- 3 Claude Opus 5 Tricks You Need: Boost Productivity & Cut Costs — Learn three essential Claude Opus 5 tricks to supercharge your AI workflow, save money, and get better results fast.
- Why Data Quality Is the Hidden Engine Behind AI Breakthroughs — Explore how clean data fuels AI research, boosts investor confidence, and drives real‑world impact.
- How an AI Agent Cut 52 Support Tickets to 19 by Fixing Root Causes — Discover how an AI agent eliminated repetitive support work by targeting root causes, slashing 52 tickets to 19 and reshaping automation strategy.
- Graph Engineering Explained: The New AI Prompt Architecture Revolution — Discover how graph engineering reshapes AI workflows by turning prompt chains into structured graphs for better performance and scalability.
- Claude 5’s New Coding Rules: How AI‑Assisted Development Is Changing Fast — Explore how Claude 5 reshapes AI‑assisted coding with new interaction patterns, technical details, and real‑world impact.
- Why Your AI Needs a Company Wiki: Boost Context, Cut Token Use — Learn how a company wiki lets AI access organized knowledge, saving tokens and improving context for better responses.
- How Poolside Engineers Build a 118B AI Model with Synthetic Data & Self‑Checking Pipelines — Poolside creates a massive AI model using synthetic data pipelines that generate, police, and reuse code, while rigorously testing for hidden failures.