Hexum — 85% fewer tokens to the model
Save token costs and remove code spaghetti
Hexum — 85% fewer tokens to the model launched on August 29, 2026 with 1 votes, #11,494 of 23,274 launches that month.
What it does
Hexum cuts up to 85% of token usage without losing context, precision, or code quality. 📉 Slash Token & API Costs: Compress codebases and large prompts efficiently. 🧹 Eliminate Code Spaghetti: Clean and structure context so models generate cleaner, more accurate outputs on the first try. 🔌 Seamless Integration: Drop it into your existing AI workflows and pipelines in minutes. Stop paying LLMs to process redundant boilerplate. Ship faster, spend less, and get sharper completions with Hexum.
Your agent keeps the editor. Hexum keeps the bill, the architecture, and the loop. Five focused products, 12 tools and one MCP key for coding agents.
Oversized prompts. Broken boundaries. Endless retries. Give your coding agent five guardrails that work inside the editor you already love. Pick a problem. See what changes. Each product handles a different moment in your workflow. Try the sample repo or choose source files from your project. See how much context Hexum trims before connecting your agent. Drop source from a real folder. Hexum folds what the model does not need this turn. Up to 8 files, 96KiB. Nothing is stored. This playground is not the MCP. Connect Hexum once in your editor’s MCP settings. Start on the free starter window, then choose a plan if you need more. Start without a card Personal keys include a monthly starter…from hexum.dev
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CodeBurn – Analyze Claude Code token usage by taskApr 2026 · github.com · ▲112Built this after realizing I was spending ~$1400/week on Claude Code with almost no visibility into what was actually consuming tokens. Tools like ccusage give a cost breakdown per model and per day, but I wanted to understand usage at the task level. CodeBurn reads the JSONL session transcripts that Claude Code stores locally (~/.claude/projects/) and classifies each turn into 13 categories based on tool usage patterns (no LLM calls involved). One surprising result: about 56% of my spend was on conversation turns with no tool usage. Actual coding (edits/writes) was…
Librarian – Cut token costs by up to 85% for LangGraph and OpenClawFeb 2026 · uselibrarian.dev · ▲8Hi HN, I'm building Librarian (https://uselibrarian.dev/), an open-source (MIT) context management tool that stops AI agents from burning tokens by blindly re-reading their entire conversation history on every turn. The Problem: If you're building agentic loops in frameworks like LangGraph or OpenClaw, you hit two walls fast: Financial Cost: Token usage scales quadratically over long conversations. Passing the whole history every time gets incredibly expensive. Context Rot: As the context window fills up, the LLM suffers from the "Lost in the Middle" effect. Response latency…
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VexpMar 2026 · vexp.dev · ▲14I built vexp because AI coding agents have two expensive problems: they waste tokens reading irrelevant code, and they forget everything between sessions. The token problem: agents read entire files linearly to build context. On a medium TypeScript project, a single query was consuming ~18k tokens — most of it irrelevant. vexp builds a dependency graph from the AST (who calls what, who imports what, what types flow where) and serves only the relevant subgraph as a token-budgeted capsule. ~2.4k tokens instead of ~18k, with better response quality because the context is precise. The memory…
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