← 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 Build an LLM Spend Audit by Department in 7 Steps (2026)

Quick Guide: How to Build an LLM Spend Audit by Department in 7 Steps Define your cost attribution dimensions — Decide whether you'll track by department, team, repository, or individual developer. Set up token telemetry collection — Install an SDK wrapper or proxy that captures every LLM API call with metadata. Configure department tagging — Map each request to a department using git context, API keys, or manual labels. Aggregate costs by time window — Build hourly, daily, and monthly rollups so you can spot trends and spikes. Add prompt compression middleware — Use Lineman to cut token costs by 40%+ while maintaining output quality. Create budget thresholds and alerts — Set per-department limits that notify you before overspending. Export to your FinOps dashboard — Push data to CloudWatch, Prometheus, or your existing observability stack. How to Build a Department-Level LLM Spend Audit 1. Define Your Cost Attribution Dimensions Start by mapping how your organization actually uses LLM APIs. Most engineering teams find that repository-level attribution gives the clearest picture of spend.

Product

How Source Code Context Reduction Cuts LLM Spend

If you manage AI coding tools at scale, you've noticed the bill grows faster than usage does. The root cause: LLM API cost optimization starts with understanding where your tokens actually go. Most of the cost comes from context compounding and verbose tool output, not reasoning.

Product

How to Cut Code Boilerplate Tokens in AI Coding in 7 Steps (2026)

Your AI coding assistant burns tokens on repetitive code boilerplate, file reads, and build logs before it even starts reasoning about your task. Lineman cuts that overhead by 40%+ while maintaining output quality. This guide walks you through seven steps to identify and reduce the redundant context that inflates your token bill.