Your model isn’t
the bottleneck. You are.
Everyone’s waiting on a better model. A scaffolded weak one already beats a careless strong one. Dash indexes your repo, strips the noise, and gives your agent only what matters.
Private beta · free for solo devs at launch
Runs inside every MCP-compatible agent
The gap was never the model.
It was everything you fed it.
Same weights. Same question. Wildly different answers — because one run got a curated window and the other got a junk drawer. Dash is the difference between the two.
Context, not capacity
A frontier model reading forty irrelevant files loses to a small one reading the right three. Dash decides which three.
Tokens are a quality metric
Accuracy degrades as the window fills. Cutting tokens isn’t a billing optimisation. It’s an accuracy one.
Scaffolding compounds
Caching, filters, compression, routing. Each is worth 40–90% alone. Stacked in order, they’re worth 85% of your bill.
324k → 48k tokens.
Same task. Same result.
We ran a full 50-turn agentic coding task on a real codebase — with and without Dash’s four-layer stack.
97.9%
noise filtered
out of 12.4M repo tokens
90.6%
RAG retrieval
vs. naive file reads
85.9%
CLI compressed
git status, tests, grep
85.1%
total saved
full 50-turn session
full session reduction
85.1%
324,753 → 48,233 tokens across a 50-turn task
Context filtering
97.9%
RAG retrieval
90.6%
CLI compression
85.9%
Prompt caching
~81%
targeted retrieval
One task with Dash vs. naive full-file reads:
naive: 43,503 tokens
dash: 4,108 tokens
CLI output compression
git status2,100 → 37882%cargo test8,400 → 84090%grep -rn3,200 → 70478%Filter. Compress.
Tighten. Route.
Token waste comes from four predictable places. Dash attacks them in order — each layer stacks on the last.
Control what enters context
The biggest wins come from keeping junk out entirely.
Prompt caching
Cache system prompts, tool defs & CLAUDE.md — pay once per 5-min window.
.claudeignore / context filters
Exclude node_modules, logs, build artifacts from agent visibility.
RAG / codebase indexing
Agent searches a semantic index instead of reading raw files — retrieves only what’s relevant.
CLI output compression (RTK)
Compress shell output before it enters the context window.
Compress what’s in context
Compound gains after filtering.
/compact & context summaries
Summarize & prune accumulated history. Auto-triggered at ~95% context fill.
Tool output aggregation
Expose summarized MCP tools — the server does the work, not your context window.
Compact data formats
Replace verbose JSON with MessagePack, TOON, or CSV. JSON wastes 40–70% of tokens.
Tighten model outputs
Underrated — every output token becomes future context.
Output verbosity control (Caveman)
Strip filler, pleasantries, and over-explanation. Dense answers = less future context.
Structured output formats
Request JSON/XML with strict schemas — prevents verbose markdown explanations.
Token budget headers
Pass max_tokens and budget hints to cap reasoning tokens.
Route smarter
Architectural decisions that cut cost at the router level.
Model routing (RouteLLM)
Simple tasks to cheap models, complex ones to frontier — automatically.
Scoped sessions
Use /clear between unrelated tasks. Focused sessions cost ~67% less than sprawling ones.
Pre-downloaded data patterns
Fetch files before the agent loop starts; local reads instead of API calls.
The token-saving stack.
Dash integrates with the best open-source tool for each layer — or works standalone. Take what you need.
RTK
39.5kCLI output compression. Strips noise from git status, test runs, grep results — 60–91% token savings, zero config.
Learn moreLLMLingua
4.8kMicrosoft Research. Small model drops non-essential tokens from prompts — up to 20× compression with <2% quality loss.
Learn moreCaveman
Claude Code skill. Strips filler, pleasantries, and over-explanation — 50–75% output token reduction.
Learn moreHeadroom
Context Optimization Layer. CacheAligner for stable prefixes, SmartCrusher for compression, CodeCompressor for agentic traces.
Learn moreRouteLLM
3.2kICLR 2025. Routes simple tasks to cheap models, complex to frontier — 85% cost reduction at 95% of GPT-4 quality.
Learn moreTokless
Unified CLI installer for the full token-saving plugin stack — RTK, Caveman, CodeGraph, Context-Mode — one command.
Learn moreHabits that cost nothing.
Prompt caching
~81%Every session re-sends the full history. With caching, KV tensors for static content are computed once. 81% cost reduction on stable-prefix turns.
/compact & /clear
~67%Compact after each work phase, clear between tasks. Quality degrades as the context fills — token management is a quality problem.
Expose aggregation tools
variesInstead of get_all_records, expose get_summary_by_region. The server does the work in milliseconds; the context window stays lean.
Free forever for solo.
Team when you scale.
Unlimited projects, unlimited symbols, all 25 MCP tools — free. Add team sharing, cloud sync, and admin controls when you need them.
Solo
$0forever
Join the waitlistTeam
Most popular$30/dev/mo
Join the waitlistStop upgrading.
Start scaffolding.
Dash indexes your codebase once, keeps it current, and gives your agents exactly what they need — not the entire repo.