Light Reach

Forward deployed engineers used to be the activity for firms chasing million dollar contracts. Did AI just raise the bar on what's expected of software engineers?

@MHusnainAli73 @MHusnainAli73 another great one is compress.lightreach.io...we built an entire agent harness backend so that we can use almost any model at a lower cost without sacrificing quality. Also provides complete visibility at the prompt layer

Our LLM API bill was $12K/month. Added 3 caching layers → dropped to $3,200. 73% COST REDUCTION. Zero accuracy loss. What's your biggest LLM cost driver right now? #LLM #AI

@CiprianiRanieri We are lowering costs and increasing observability with cursor and claude code usage by living at the middleware/router layer. Showing users exactly what prompts and retry logic are being sent to the LLM: compress.lightreach.io

What are you building? Drop it. I'm curious and I have time

@suni_code compress.lightreach.io

Drop your project URL Let’s drive some traffic

@mfranz_on @mfranz_on we also noticed a more important key to this puzzle working on compress.lightreach.io ....Issue is that the LLM IDEs run significant retry/harness logic in the background that our tool exposes to you

Problem: AI coding agents (e.g. Claude Code, Cursor, Copilot) spend a significant portion of their token budget on file reads. When exploring an unfamiliar codebase, the typical pattern is: 1. Read a file in full to understand what it contains 2. Decide whether it is relevant 3. Repeat for N files until the answer is found The inefficiency: the agent reads the entire file before knowing it needs only a fraction of it, or before knowing it doesn't need it at all. On a medium codebase (Flask, ~25 files, ~50k tokens of source), reading everything to answer a specific question costs between 9k and 50k tokens depending on how many files are relevant. Solution (maybe): TOC-First Access: instead of reading the entire file, the agent first reads a Table of Contents generated from the file's AST. The TOC contains: - All class names and their public methods (with line numbers) - All top-level function signatures - Module-level imports - Docstrings (first line only) The TOC is produced statically from the AST no LLM, no inference, instant. It compresses files by ~86% on average (e.g. app .py: 9,090 → 702 tokens). The agent reads all TOCs first (~7k tokens for all of Flask), identifies which files are relevant, then reads only those in full. Some benchmarks made with Flask codebase.