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The author built an "EQ plugin" with Claude Opus 5.5 and used UUMit to turn a local service into a team link

The author used Cursor together with Claude Opus 5.5 to build a zero-dependency local web tool called "EQ plugin," which rewrites emotionally charged drafts according to the four elements of Nonviolent Communication, and includes seven types of communication targets and three tone levels: gentle, firm, and direct. The tool runs on the local machine at 127.0.0.1:8787, and the UUMit client generates a fixed HTTPS link to distribute to colleagues, with no need to buy a server or file for registration, and permissions can be granted and revoked per person. The article also provides an 800-character limit per input, default rate limits of 10 times per minute and 200 times per day, as well as the source code and System Prompt for two GitHub repositories.

Pydantic compares code mode and tool calling across 5,276 runs

Michael Pfaffenberger of Pydantic used 5 small Pydantic AI applications and 8 models to conduct 5,276 scored runs, comparing code mode with direct tool calling. On long tasks, code mode reduced average cost by 58%, latency by 51%, and increased success rate by 16 percentage points; however, for short tasks with 5 items, 3 out of 5 tasks were instead 21% to 38% more expensive. The author argues that models are better at writing loops than at acting as the loop, and the gap widens as tasks get longer.

Coding Agent Leaderboard Upgrade v2.0: Claude Opus 5.5 Leads at 61.7

Artificial Analysis released Coding Agent Index v2.0, replacing DeepSWE with Cognition's FrontierCode v1.1 and tightening repository Q&A scoring; the leaderboard evaluates model-plus-harness combinations. Currently first is Claude Code + Claude Opus 5.5 (max) at 61.7, followed by Claude Code + Sonnet 5.5 at 59.3 and Muse Code + Muse Wonder Full at 58.9; the first OpenAI combination, Codex + GPT-6 Astra, is at 54.7. For the same 21 configurations, the mean across the old and new leaderboards fell from 54.8 to 47.4, with 20 declining, mainly because FrontierCode's mean is about 45.1%, far below DeepSWE's 65.6%.

Stanford CS 329Z Lecture 9 Outlines the Selection of Agent System Optimization Methods

Lecture 9 of Stanford's CS 329Z course presents a selection diagram for Agent system optimization: the prerequisite for optimization is having data, a system, and evaluation metrics all in place; generally, context optimization (prompts and few-shots) is done first because it is cheap, fast, and sample-efficient; when context optimization is insufficient, or when you want to distill capabilities into a small model, or when prompts grow longer and longer, then you move on to changing the weights. The lecture notes also review reflective prompt optimization methods such as OPRO, MIPROv2, TextGrad, and GEPA; weight optimization paths such as SFT, RL (PPO vs. GRPO), and on-policy distillation; and approaches that let agents self-optimize, such as the Darwin Gödel Machine, Meta-Harness, and karpathy's autoresearch. They also point out that agents are already decent optimizers, making evaluation metrics the link that most needs to be controlled.

Artificial Analysis releases AA-PersonalityBench to evaluate model personality and expression style

Artificial Analysis has released AA-PersonalityBench, using 900 curated questions to evaluate differences among language models across 30 personality traits and 21 communication characteristics. Personality traits are scored by LLM judges through pairwise comparisons and aggregated into eight indices: expressiveness, organization, informativeness, transparency, agreeableness, assertiveness, imaginativeness, and Slop; communication characteristics are computed programmatically across four categories of metrics: length, structure, punctuation, and emphasis. Results show that Claude Fable 5.1 stands out on multiple indices including expressiveness, informativeness, transparency, agreeableness, and assertiveness, while Qwen3.8 27B averages about 1,030 words per response, nearly four times the 270 words of GPT-6 Luna.

English translation is being prepared.

English translation is being prepared.

TinyFish Makes Search Free: Breaking Down the Real Cost of AI Agents Reading Web Pages

TinyFish announced that, within limits, it will waive retrieval fees for Search and Fetch: Search is limited to 30 requests per minute and 500 per hour, while Fetch is limited to 150 URLs per minute and 1,000 per day. The article notes that free retrieval does not eliminate the inference costs of downstream models; the same article of about 4,300 characters returns an estimated 1,184 to 42,774 tokens across different providers, and three pages of unfiltered content can fill a 128K context window. In the September 28, 2026 Artificial Analysis snapshot, TinyFish Search scored 71 at $9.30 per 1,000 tasks, the lowest total cost among the displayed configurations but also the lowest score, with about 59 seconds per task.

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OpenWiki hands-on: building repository memory for coding agents

heise online tested LangChain's CLI tool OpenWiki, which analyzes a codebase and generates a linked local Markdown knowledge base, then updates it based on new diffs and commits in Git history, and makes the content retrievable through agent prompts and MCP tools. The article notes that AGENTS.md or CLAUDE.md are suitable for short rules such as install commands, tests, style conventions, and security boundaries, and in a monorepo can also be nested in subdirectory files, but they struggle to serve as a complete knowledge model for large, rapidly changing repositories.

English translation is being prepared.

English translation is being prepared.

English translation is being prepared.

English translation is being prepared.

English translation is being prepared.

English translation is being prepared.

English translation is being prepared.