TideSurf: DOM compression tool reduces web agent token usage 30x, speeds TTFT 12x

What TideSurf does
TideSurf is a tool that addresses a common problem with browser use agents: they often rely on models' native multimodality over concrete page sources, and even when they use page sources, they consume excessive context tokens. The solution converts rendered DOM to a markdown-like compressed format before sending it to LLM agents.
Performance improvements
According to experiments with GitHub pages, TideSurf reduces token consumption by 32 times compared to raw DOM. The parsing process takes approximately 30ms. In specific tests with Qwen 3.5 9B (reasoning off, Q8 KV Cache quant, other configs default) on a MacBook Pro 14" Late 2021 (M1 Pro, 14C GPU, 16GB LPDDR5 Unified Memory, MacOS Tahoe 26.2) using LM Studio 0.4.7-b2 with MLX runtime:
- Tokens per second: 24.788 (raw DOM) vs 26.123 (TideSurf)
- Time to first token: 106.641s vs 8.442s (12.6x reduction)
- Generation time: 9.117s vs 6.163s
- Prompt tokens: 17,371 vs 3,312 (including tool definitions)
- Inference tokens: 226 vs 161
Tool capabilities
TideSurf includes 18 tools for LLMs to work interactively with web pages. These tools work with any model that has tool calling capabilities. The system supports both CLI and MCP (Model Context Protocol) interfaces.
Current status and availability
This is an early project at version 0.3, and the developer is seeking feedback. TideSurf is available as an npm package (@tidesurf/core), with documentation at tidesurf.org/docs and source code on GitHub.
📖 Read the full source: r/LocalLLaMA
👀 See Also

CAP: Claude Code Statusline Plugin That Installs with /plugin install
CAP (Claude Allowance Pulse) is a statusline plugin for Claude Code that installs via /plugin install without npm, curl, or jq. It displays model usage, session and weekly limits, context window usage, and session cost in the terminal.
Tokenless API Gateway Routes AI Traffic Between Models to Cut Spend in Half
Tokenless is an API gateway that dynamically routes AI agent requests turn-by-turn between models. It matches Claude Fable 5 performance at half the cost by fanning out to multiple models and selecting the best one mid-generation.

GuppyLM: A 9M Parameter LLM Built from Scratch for Educational Purposes
GuppyLM is a ~9M parameter language model trained from scratch on 60K synthetic conversations, using a vanilla transformer architecture with 6 layers, 384 hidden dimensions, and 6 attention heads. It trains in about 5 minutes on a free Colab T4 GPU and speaks with a fish personality focused on water, food, and tank life.
Claude Prototypes Real Estate Analysis App in 3 Hours Using Live Zillow Data via clawhub
A developer used Claude with the zillow-full clawhub tool to build a rental cash flow analysis app — pulling live Zillow API data, prototyping the UI around real JSON responses, and delivering a working prototype in one afternoon.