Claude VS Code Extension Reasoning Effort Slider Sends Inconsistent Values

The reasoning effort slider in the Claude VS Code extension has a bug that causes it to send inconsistent numeric values to the model. The slider label does not correspond to a stable numeric value, and the mapping is non-monotonic, meaning moving the slider "up" can actually send a lower number to the model.
Documented Inconsistencies
During a single testing session where the model was asked to report its reasoning_effort value after each slider change, the following inconsistencies were observed:
- High (session starting default): 99
- Max: 99
- High (moved back down): 19
- Medium: 85
- High (moved back up): 99
- Max (again): 50
The issue is documented in GitHub issue #41012 in the anthropics/claude-code repository. The bug report shows that the same slider position (like "High") can send different values (99 vs 19) depending on the slider's movement history, and higher slider positions don't necessarily correspond to higher numeric values.
For developers using the Claude VS Code extension, this means the reasoning effort control is currently unreliable. The model may be receiving different reasoning effort values than what the slider interface indicates, which could affect the quality and consistency of AI-generated code suggestions.
📖 Read the full source: r/ClaudeAI
👀 See Also

Implementing a Local Voice Assistant with Qwen3 on RTX 5060 Ti
A fully local home automation voice assistant using Qwen3 ASR, LLM, and TTS on an RTX 5060 Ti, featuring Morgan Freeman voice cloning and a variety of integration tools.

Pleng: Self-Hosted Cloud Platform with AI-Driven Infrastructure Management
Pleng is an AGPL-3.0 licensed, self-hosted cloud platform that uses an AI agent (currently Claude) to manage infrastructure via Telegram bot commands. It deploys from GitHub repos or local directories with automated Traefik routing, Let's Encrypt SSL, and basic analytics.

Security scanning skill for AI coding agents automatically checks deployments
A developer created a skill file that enables AI coding agents to automatically scan their own deployments for security issues like exposed secrets, open ports, missing security headers, and leaked source code. The scan runs after every deploy and takes about 30 seconds.

Jake Benchmark v1: Local LLM Performance Testing for OpenClaw AI Agents
A developer tested 7 local LLMs as AI agents with OpenClaw using 22 practical tasks including email processing, meeting scheduling, and phishing detection. Results ranged from 59.4% for Qwen 27B to 1.6% for Nemotron 30B, with detailed conversation logs available.