64 related articles

Robynn AI is a self-learning website operations tool that uses intelligent auditing, natural language editing, and data-driven auto-rollback to solve post-launch decay issues like broken links and ranking drops.

Webhound is a research engine for AI agents that controls research depth via dollar budgets, outputting cited traceable reports with MCP protocol and API integration.

Webhound is a research engine for AI agents that controls research depth via dollar budgets, delivering cited traceable reports with MCP protocol and API integration.

Exploring a mathematically precise definition of "exception edges" in TSP, using closure problem theory to identify critical non-local edges that determine optimal solutions, providing verifiable structural priors for RL and NCO solvers.

Via 1.0 is an AI-powered smart scheduling tool that supports one-click brain dumps, auto-generates actionable schedules, and dynamically adjusts tasks in real-time to help professionals reduce decision fatigue and focus on high-priority work.

How AI coding agents are transforming decompiler development. Using the Kuna project as a case study, exploring AI-assisted iteration, generate-verify loops, and the lowering barriers to complex system tool development.

Deep dive into how Transformer² uses a unified Transformer architecture to integrate robot morphology design and motion control into one model, enabling task-driven end-to-end co-design for embodied AI.

Deep dive into the PIRL reinforcement learning framework: how to smoothly transition from open-loop exploration to closed-loop RL, mitigating the exploration-exploitation dilemma and improving sample efficiency.

Why AI research automation looks more like data cleaning than inventing the Transformer. Exploring the value of automating 60%-80% of repetitive research work and how human-AI collaboration reshapes the research paradigm.

AI research automation will look more like data cleaning than inventing the Transformer. Explore how automating 60%-80% of repetitive research work reshapes the AI research paradigm.

Hands-on test of LibTV's AI Agent: from script and storyboarding to video compositing, one person completes an animated short in a day. Full breakdown of the Skill library, node workflow, and Story Board features.

A tiny 14-byte AI brain attempts to solve a 2D maze, exploring the limits of information compression and intelligence. Discover evolutionary algorithms, memory constraints, and the value of minimal AI.

How Pinterest engineers built Medic for Apache Spark — a multi-agent auto-diagnosis tool — covering the evolution from a single ReAct agent, observability, log denoising, and end-to-end testing.

An in-depth look at using AI LLMs to audit Cloudflare's open-source cryptographic library CIRCL, covering constant-time detection, side-channel vulnerabilities, and human-AI collaboration.

AI code getting messier with edits? The root cause isn't weak model capability but a lack of context and process. A deep dive into Matt Pocock's Skills v1.1: grilling, vertical-slice tickets, TDD, and WebFinder.

Perplexity users aren't demanding unlimited quotas — they want to know how close they are to the limit. This piece explores why usage transparency is a low-cost, high-trust win for AI subscription products.

DecisionRL is an open-source RL library for operational decision-making, with six built-in scenarios (inventory, pricing, energy), OR baselines, and support for DQN, PPO, SAC, and more.
decisionrl: An Open-Source Reinforceme…
decisionrl is an open-source RL library for operational decisions, with built-in environments for inventory, pricing, supply chain, and OR baselines for comparison. Built on PyTorch, MIT licensed.
Deep Dive into AI Agent Skill Design: …
A deep dive into Skill design philosophy from Anthropic's Claude Code team and Perplexity's Agent team, covering the Tax Test, Gotchas Flywheel, progressive disclosure, and Eval-First practices for building high-quality AI Agent skill systems.
CUDA Kernel Fusion: A Practical Guide …
Learn how CUDA Kernel Fusion merges multiple GPU kernels to reduce global memory traffic and launch overhead, with real-world examples from AI inference and deep learning.