A tool that makes Claude Code shut up, plus a knowledge graph builder worth trying

The Lens

Caveman strips the fluff from Claude Code responses. Install it with one command, activate with /caveman, and your AI assistant drops the pleasantries, hedging, and filler words while keeping full technical accuracy. Average savings: 65% fewer output tokens. Three intensity levels: Lite keeps it professional but terse, Full drops articles and uses fragments, Ultra goes telegraphic. Code blocks, error messages, git commits, and technical terms pass through untouched. Only the natural language gets compressed. A companion tool (caveman-compress) rewrites your CLAUDE.md and memory files to cut input tokens too. Works across 40+ AI coding agents, not just Claude Code. Cursor, Copilot, Windsurf, Cline, Codex, all supported. Heavy token users will feel the difference in both speed and cost. The catch: it started as a meme (Kevin from The Office) but the benchmarks are real, backed by a 2026 arxiv paper. Ultra mode can be hard to read. And the savings are output tokens only, so your thinking/reasoning costs stay the same.

The Lens

Graphify reads your entire codebase, docs, PDFs, and even screenshots, then builds a knowledge graph you can actually navigate. It parses 19 languages via tree-sitter for code and uses an LLM for everything else. The result is an interactive HTML visualization showing how your architecture, concepts, and files connect. The first extraction pass costs real API tokens (Claude or GPT), proportional to your corpus size. After that, incremental updates via SHA256 caching mean re-runs only process changed files. The 71x token compression claim is real for subsequent queries, not the initial scan. Runs as a /graphify slash command inside Claude Code, Codex, or OpenCode. For developers onboarding to large or unfamiliar codebases: this is genuinely useful. Architecture reviews, cross-referencing code with design docs, understanding how a monorepo fits together. Exports to Neo4j, Obsidian vaults, or standalone wikis. The catch: it is a plugin, not a standalone tool. You need Claude Code or a compatible AI assistant as the runtime. Quality of inferred relationships depends on the underlying LLM, and the initial scan of a large repo is not cheap.

The Lens

OpenHarness is an open source agent framework that gives you tool-use, skills, memory, and multi-agent coordination out of the box. It ships 43 built-in tools (file ops, shell, search, web, MCP) and a plugin system for extending them. MIT licensed, Python, designed to work with any LLM provider. The architecture mirrors what you'd expect from a coding agent: an agent loop with streaming tool calls, context compression, persistent memory, and permission governance. Setup is a pip install. It's compatible with existing skill and plugin ecosystems, so you're not starting from zero on integrations. For solo developers building agent prototypes, this covers the boring infrastructure so you can focus on the agent logic. Small teams get multi-agent coordination without rolling their own orchestration layer. The catch: this is a research project from HKU, not a production-hardened framework. The ecosystem is young, documentation is thin, and you're betting on an academic team's long-term commitment. For production agent workloads, more established frameworks like LangGraph or CrewAI have deeper community support.

The Lens

RustFS is a MinIO alternative written in Rust. It speaks the S3 API, so any tool that works with AWS S3 works with RustFS. The pitch is performance: Rust's memory safety and efficiency versus MinIO's Go implementation. Self-hosting is free under Apache 2.0. You get S3-compatible API, erasure coding for data durability, distributed mode for spreading storage across nodes, and a web console for management. The catch: this is very new. It's getting a lot of attention, but attention isn't maturity. MinIO has been battle-tested in production for years. RustFS documentation is still developing, and the ecosystem of plugins, integrations, and operational knowledge is thin. If you're storing data you can't afford to lose, this is a risk. If you're experimenting with self-hosted object storage or building a non-critical pipeline, it's worth a look. But for production storage, MinIO is the safer bet today.