HarnessRouter Community Edition: the self-hosted, Apache-2.0 edition of the unified interface for agent harnesses. Run Codex, Claude Code, Hermes, PI, DSH, and more through one API, with sessions, ...

I think this is related to a known issue that’s been lingering for a while: * * * The warning looks much more like a failure in the **quiz’s own feedback grader** than a failure in your `CodeAgent`. The current quiz Space still creates its own `InferenceClient`, sends your submitted code to `Qwen/Qwen2.5-Coder-32B-Instruct`, and catches failures from that separate inference call as: > `Error generating feedback: ...` You can see that path directly in the current quiz app.py. So I would **not keep changing your exercise code just to make this particular warning disappear**. If your main goal is to continue the course, the simplest option is probably to **skip this Final Quiz for now and continue to the next section**. The course explicitly describes this quiz as ungraded and uncertified; it is meant as a learning/self-check exercise rather than a certification gate. If you want to check your answer anyway, I would use the question’s assessment criteria/reference solution as the useful signal rather than treating this connection error as evidence that your answer is wrong. Why this looks like a long-running grader-side issue (click for more details) Where the failure actually occurs (click for more details) What seems to be the current migration direction (click for more details) A practical decision tree (click for more details) So, in short: **this warning does not look like evidence that your submitted`CodeAgent` solution is wrong.** It looks closely related to the quiz grader’s long-running inference-routing problem. I would continue with the course for now, and treat fixing the grader as a separate Space/maintainer-side issue. The existing Space discussion, migration PR, and Agents Course issue #709 are probably the most useful places for future readers to follow its status.

IBM Langflow OSS versions 1.0.0 to 1.10.0 have a security weakness.

CVE-2026-61447 is a critical vulnerability in MervinPraison's PraisonAI product prior to version 1.6.78. The issue lies in the CodeAgent._execute_python() method, which executes Python code generated by the language model without AST valida

PraisonAI before 1.6.78 contains a remote code execution vulnerability in CodeAgent._execute_python() that executes LLM-generated Python code without AST validation, import restrictions, or sandbox enforcement. Attackers can influence LLM output through prompt injection to exfiltrate all environment secrets and execute arbitrary code on the host system.

PraisonAI versions prior to 1.6.78 are at risk of attackers executing malicious code on the host system.

Intelligent prompt improver hook for Claude Code. Type vibes, ship precision. - severity1/claude-code-prompt-improver

Securely run untrusted workloads in Kubernetes. Contribute to kubefence/kubefence development by creating an account on GitHub.

Semantic Intelligence for Large-Scale Engineering. Context+ is an MCP server designed for developers who demand 99% accuracy. By combining RAG, Tree-sitter AST, Spectral Clustering, and Obsidian-st...

Pre-indexed code knowledge graph for Claude Code — fewer tokens, fewer tool calls, 100% local - colbymchenry/codegraph

Complete Claude Code configuration collection - agents, skills, hooks, commands, rules, MCPs. Battle-tested configs from an Anthropic hackathon winner. - affaan-m/everything-claude-code

A light-weight and powerful meta-prompting, context engineering and spec-driven development system for Claude Code and OpenCode. - glittercowboy/get-shit-done

You 🤝 Claude Code

Learn how code execution with the Model Context Protocol enables agents to handle more tools while using fewer tokens, reducing context overhead by up to 98.7%.

こんにちは。 LayerXのバクラク事業部のAI・機械学習部でテックリードを務めております機械学習エンジニアの島越（@nt_4o54）です。 こちらはLayerX AI Agentブログリレー10日目の記事です。前回の記事はponさんによる最速でAI Agent機能をPoCするChrome Extensionsの威力でした。 今回の記事では、先日リリースを行ったAI明細仕訳の問題を題材に、データセット作成時にCodeAgentを用いた検証についてお話しします。 AI明細仕訳とは AI明細仕訳とは、請求書に記載されている表から、勘定科目などのマスタデータや過去の編集履歴を元に仕訳を自動で作成する…

考察 Agent 從 倉庫理解 → 環境配置 → 增量開發 / 代碼修復 → 項目級交付 的全鏈路能力，把「框架 × 模型」的「經濟收益」納入評測指標。