← All news
Product

The parts you need, not the whole dump

Reading a big file shouldn't cost you your context window. Here's what Lineman gives your model instead.

The Lineman team

Opening a big file shouldn't cost you the rest of your conversation. Yet that's the usual trade. Ask your model to read a 1,200-line module and all 1,200 lines land in the context window verbatim: function bodies you'll never look at, imports, boilerplate, the lot. The window fills, the cost climbs, and the answer you actually wanted ends up buried somewhere in the middle.

Lineman changes the shape of that read. Instead of the whole file or an arbitrary cutoff, your model gets the relevant functions and sections, the parts that bear on what you asked, without the rest of the file crowding everything out.

Truncation is the wrong fix

The instinct when a file is too big is to cut it off: read the first few hundred lines and stop. But the thing you needed might be the export at the bottom, the one function in the middle, or the type three-quarters of the way down. A blind cutoff is as likely to drop the answer as the noise.

Lineman doesn't guess by position. A secondary model reads the whole file and condenses it, so what comes back is chosen for relevance rather than for being near the top.

Context is the budget you're actually spending

Every line that enters the context window costs tokens now and leaves less room for everything that comes after. Spend a third of the window dumping one file in full and you've got less left for the next file, the test output, and the reasoning that ties them together. You also hit a compaction or a context limit sooner.

Giving your model the relevant parts instead of the verbatim dump keeps the window lean. The same conversation can touch more files, run more builds, and stay coherent for longer before it runs out of room.

What you get back

  • The functions and sections that matter to your request, rather than a positional slice of the file
  • Far fewer tokens for the same understanding. Lineman saves 40%+ on data-heavy work.
  • More room left in the window for the actual task, so long sessions stay on track

Keep the signal, drop the filler, and your model spends its context on the work instead of the scroll.

Size it on your own files

Savings depend on how much reading, building, and searching your work involves. The savings calculator on the homepage estimates the impact on your real workload, and the benchmarks page shows the measured numbers behind it. When you're ready, start a free trial. No card required.

Related

Product

How to Reduce AI Coding Spend With Log Compression in 7 Steps (2026)

Build logs and file reads account for over half of most AI coding bills. The tokens aren't reasoning about your code—they're re-reading the same data, turn after turn. AI infrastructure cost optimization starts by cutting the bytes your model never needed to see in the first place.

Product

How to Summarize Large Codebases for AI Agents in 7 Steps (2026)

Quick Guide: How to Summarize Large Codebases for AI Agents in 7 Easy Steps Parse Code with Abstract Syntax Trees: Extract meaningful structure from source files using AST parsers that preserve semantic boundaries. Chunk Code at Symbol Boundaries: Split files at function, class, and module boundaries instead of arbitrary line counts. Generate Semantic Embeddings: Convert code chunks into vector representations that capture meaning, not just syntax. Build a Vector Index for Retrieval: Store embeddings in a vector database for fast similarity search across your codebase. Implement Selective Context Retrieval: Query only the relevant code chunks based on the agent's current task. Compress Tool Output Before Context Injection: Use Lineman to automatically compress file reads and logs before they reach your AI agent's context window. Cache and Deduplicate Repeated Reads: Track what the agent has already seen to avoid re-injecting identical content. How to Help AI Agents Understand Large Repositories Without Full-Context Dumps 1. Parse Code with Abstract Syntax Trees The first step is treating code as structure, not text. AST parsers read your source files and produce a tree that represents the actual program logic: functions, classes, imports, and their relationships.

Product

9 Claude Instruction Audits for Leaner DevOps

System instructions in Claude coding agents carry a hidden cost. Every token in your CLAUDE.md file is re-sent on every single turn, whether you need it or not. For DevOps teams running automated pipelines, test triaging, and infrastructure tasks, that overhead compounds across hundreds of daily sessions.