Build an LLM Tokenizer and Attention from Scratch in TypeScript

Friendly Fraud Prevention for Developers: What to Log, Block and Automate

Friendly fraud, where a real customer disputes a legitimate charge, accounts for around 20 to 30% of fraudulent disputes per Visa and 75% of online fraud per Mastercard. Winning disputes requires logging IP address, device ID, account ID and shipping address at checkout and keeping them for at least 13 months. Visa's CE3.0 rules require two undisputed prior purchases on the same card from 120 to 365 days earlier sharing data points, while Mastercard's First-Party Trust needs device, delivery and identity factors and a lookup service responding within 2.5 seconds. The piece walks through Node.js code for checkout logging, risk scoring, a pre-fulfillment hold gate, pre-dispute order lookup endpoints, webhook-driven refunds and evidence automation, plus a comparison of building in-house versus using tools like Stripe Smart Disputes or Chargeflow.

RAG vs. Vectorless RAG, clearly explained!

...explained visually (with tradeoffs in production).

DeepSeek Super-Agent Architecture Explained

How DeepSeek Harness's Super-agent works: a Router, isolated Workers, event triggers and a Watchdog let one AI agent run many tasks. Diagrams inside.

The End of AI Frontiers

Large language models remain far less efficient than biological brains at learning, representing knowledge, and planning, the post argues. A child hears roughly one token per second, reaching a billion tokens only after 31 years, while GPT-4-class models reportedly use four orders of magnitude more data. The Phi model series, trained on 10 trillion tokens, still trails GPT-4 along most axes. A Portia spider plans prey-catching behavior with a brain a million times smaller than a human's, and squirrels recall cached food after months, suggesting that existing models may be far less representationally and memory efficient.

How to Find and Fix Common Technical SEO Issues on Your Website

Let's say you've built a website. You've invested in the design, bought a domain, and so on. But a website isn't only what you see on the screen. A lot happens behind the scenes. You have to understan

When the Political Shift Hits the Cluster: What the 2026 Midterms Mean for AI Infra and Code

If you’ve been watching the tech sector and macroeconomic feeds over the last 48 hours, you’ve... Tagged with discuss, ai, webdev, programming.

Prompt Management in TypeScript: LangSmith vs Git vs 3 Open-Source Registries

Four prompt registries, LangSmith, Langfuse, Phoenix and AcruxCore, were tested against Git files on one TypeScript app with a single prompt containing a condition and a loop. All four can change a running app's prompt, roll it back, and split production and staging. The differences are in template syntax, dependency size and change latency. LangSmith pulled in 23 packages and 70MB via LangChain and took 300 seconds to reflect a change. Langfuse needed just 5 packages and 8MB with a 62-second cache. Phoenix has no template logic and no cache (0-second latency) but installed 57 packages and 90MB. AcruxCore supports Jinja2 server-side rendering with only 3 packages and 12MB, but every new variable set triggers a network call. Phoenix and Langfuse left the Jinja2 tags in their output with no error.

Build Your Own Reasoning Model: A Full Video Course

Khóa học video kéo dài 11 giờ tập trung vào việc xây dựng model reasoning với Python và PyTorch. Nội dung bao gồm các kỹ thuật advanced như evaluation, inference-time scaling, reinforcement learning và distillation. Qua khóa học, bạn sẽ học cách tối ưu hiệu suất của các reasoning model hiện đại. Các bài thực hành code chi tiết giúp hiểu sâu về quy trình training và fine-tuning. Giá trị lớn nhất nằm ở việc kết hợp lý thuyết với thực hành để xây dựng được hệ thống AI có khả năng reasoning mạnh mẽ.

Lập trình viên muốn nâng cấp kiến thức về mô hình học máy từ cơ bản đến ứng dụng thực tế—đặc biệt là trong scaling mô hình, reinforcement learning, và distillation—để áp dụng ngay trong dự án AI của mình với Python và PyTorch.