This article explains why the Web UI becomes a bottleneck for automation, introduces the DeepSeek Harness Python SDK, walks through installation, credential setup, running built‑in and custom agent scripts, and highlights key configuration and safety considerations for programmatic agent orchestration.
The new Skills v1.2 release adds Claude Code plugin support, revamps the grilling workflow to cut interaction rounds, and introduces three practical new skills—/wizard, /to-questionnaire, and /wait-what—while refactoring existing ones, offering a focused solution to the maintainability problems of AI‑generated code.
The article critiques the uncontrolled “blind rail” AI coding loops that generate massive PRs, explains why control theory‑based feedback loops are essential, and details a concrete Effect‑TS migration case that demonstrates a repeatable, low‑risk engineering pattern for AI‑assisted code evolution.
The SGLang team turned their benchmarking, profiling, CUDA kernel tuning, and production‑issue triage know‑how into reusable agent skills, merging three KDA‑Pilot PRs that delivered up to 2.75× kernel acceleration, a 71.4% throughput boost for Qwen3‑Next and a TTFT reduction from 456 ms to 168 ms, while outlining a repeatable workflow and practical rules for large‑scale performance engineering.
The article traces the rapid evolution of AI terminology—from Prompt Engineering to Context Engineering, Harness, and finally Loop Engineering—explains what a loop is, breaks down its five essential components plus persistent memory, shows a concrete daily‑triage loop, and warns of new pitfalls such as validation, comprehension debt, and cognitive surrender.
The article analyzes recent AI updates—from DeepSeek's DSpark inference boost and FlashAttention‑4's kernel redesign to Codex UI tweaks and design‑mode tools—arguing that the competition is shifting from answering questions to actually completing tasks, and it highlights three layers of progress, evaluation challenges, and the practical questions we must now ask of AI agents.
The article details how Alibaba’s production‑grade Verify‑Data Agent Skill replaces manual, multi‑SQL data validation with a single natural‑language command, automating table discovery, SQL generation, execution, and review‑level reporting, achieving up to 30‑minute turnaround, comprehensive coverage, and robust risk controls for big‑data pipelines.
Tabbit 1.0 launches as a free AI‑native browser that embeds large‑model agents to automate web tasks, integrate documents, and generate content, with a free tier covering 1,000 dialogues, 50 image generations and 10 agent runs per week, plus a paid professional edition and a new mobile app.
The article reviews the open‑source AI workbench Odysseus, explaining its self‑hosted ChatGPT‑like UI, modular features such as Cookbook, Agent and Deep Research, deployment steps with Docker, hardware constraints, community reactions, and why it attracted over 50 K GitHub stars in just five days.
Qwen3.7-Plus is a multimodal large‑model that unifies vision and language, delivers top‑5 global Vision Arena rankings, excels on a wide range of pure‑text, visual‑reasoning, and video benchmarks, and powers autonomous agents that perceive screens, generate code, and complete complex GUI/CLI workflows end‑to‑end.
This article explains a structured workflow that uses git worktree to run multiple independent worktrees for parallel agent execution, detailing the three‑step process, bottlenecks, and a bash orchestration script for managing front‑end and API worktrees in a multi‑repo environment.
This article examines how a major internet platform leverages large‑model AI—through multimodal fusion, script‑recognition fine‑tuning, and an automatic model‑iteration framework—to overcome precision, adversarial, and cross‑scenario challenges in fraud detection, and how agent‑based tools further boost operational efficiency.
Product managers fearing AI should stop chasing new models and instead focus on how to embed homogeneous AI into unique user scenarios, leveraging proprietary data, agent capabilities, and friction‑free interactions to create real competitive moats.
