Codex context bloat? 87% avg reduction on SWE-bench Verified traces
If you had to build a context window manager in 24h, would you stick to the existing model or come up with something better? Here's what I did: 1. Built a proxy that intercepts Codex's calls to OpenAI and rewrites them on the fly. 2. Replayed 3,807 rounds of SWE-bench Verified traces through it: avg prompt 44k → 6k tokens (-87%). 3. Posted it to HN to get the next reduction applied to my confidence interval — starting with the inevitable "How about accuracy?" npx -y pando-proxy · github.com/human-software-us/pando-proxy
Codex context bloat? 87% avg reduction on SWE-bench Verified traces launched on April 24, 2026 with 10 votes, #1,251 of 21,248 launches that month and more than 90% of that year's launches. The site was not reachable when checked in September 2026.
In plain words
Pando-proxy is a context window manager that acts as a proxy intercepting API calls to compress prompts sent to language models. It rewrites requests on the fly to reduce token usage while maintaining functionality, tested on software engineering benchmarks. The tool is for developers working with large language models who need to optimize token consumption and reduce API costs without sacrificing performance.
written from the facts on this page · September 2026
Does a similar job
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Context GatewayMar 2026 · github.com · ▲220We built an open-source proxy that sits between coding agents (Claude Code, OpenClaw, etc.) and the LLM, compressing tool outputs before they enter the context window. Demo: https://www.youtube.com/watch?v=-vFZ6MPrwjw#t=9s. Motivation: Agents are terrible at managing context. A single file read or grep can dump thousands of tokens into the window, most of it noise. This isn't just expensive — it actively degrades quality. Long-context benchmarks consistently show steep accuracy drops as context grows (OpenAI's GPT-5.4 eval goes from 97.2% at 32k to 36.6% at 1M…
Spall – WASM-based profiler for JavaScript and C and Odin2023 · gravitymoth.com · ▲83[I'm the author] Spall is a web-accessible profiler that I made to help my web-dev friends load gigabyte+ JSON traces without lunch-break-long load times. Recently, Spall got experimental support for auto-tracing with binary traces (along with an in-progress native-port, to give it more memory headroom), which was used to help track down and fix some hard-to-spot lock contention issues in the Odin-language compiler. I demoed it at the Handmade Seattle conference in October, https://guide.handmade-seattle.com/c/2022/spall/, with a head-to-head against Perfetto,…
- BAButter, a muscle memory cache for LLMs2025 · docs.butter.dev · ▲23
Hi HN, Erik here. Today we launch Butter, an OpenAI-compatible API proxy that caches LLM generations and serves them deterministically on revisit. Since April, we’ve been working on this concept of “muscle memory,” or deterministic replay, for agent systems performing automations. You may recall our first post in May, launching a python package called Muscle Mem: https://news.ycombinator.com/item?id=43988381 Since then, the product has evolved entirely, now taking the form of an LLM Proxy. For a deep dive into this process, check out:…
Context Mode – 315 KB of MCP output becomes 5.4 KB in Claude CodeFeb 2026 · github.com · ▲84Every MCP tool call dumps raw data into Claude Code's 200K context window. A Playwright snapshot costs 56 KB, 20 GitHub issues cost 59 KB. After 30 minutes, 40% of your context is gone. I built an MCP server that sits between Claude Code and these outputs. It processes them in sandboxes and only returns summaries. 315 KB becomes 5.4 KB. It supports 10 language runtimes, SQLite FTS5 with BM25 ranking for search, and batch execution. Session time before slowdown goes from ~30 min to ~3 hours. MIT licensed, single command install: /plugin marketplace add mksglu/claude-context-mode…
- ATA tool to benchmark LLM APIs (OpenAI, Claude, local/self-hosted)2025 · llmapitest.com · ▲55
I recently built a small open-source tool to benchmark different LLM API endpoints — including OpenAI, Claude, and self-hosted models (like llama.cpp). It runs a configurable number of test requests and reports two key metrics: • First-token latency (ms): How long it takes for the first token to appear • Output speed (tokens/sec): Overall output fluency Demo: https://llmapitest.com/ Code: https://github.com/qjr87/llm-api-test The goal is to provide a simple, visual, and reproducible way to evaluate performance across different LLM providers, including…
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Source: Show HN post ↗
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