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Show HN: Codex context bloat? 87% avg reduction on SWE-bench Verified traces
A proxy solution that significantly reduces LLM context window usage (87% avg reduction) for Codex, specifically targeting SWE-bench traces, aiming to improve efficiency and cost.
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AI Executive Synthesis
A proxy solution that significantly reduces LLM context window usage (87% avg reduction) for Codex, specifically targeting SWE-bench traces, aiming to improve efficiency and cost.
This product directly addresses a critical pain point in LLM integration: context window bloat and associated costs. An 87% average reduction in prompt tokens for Codex calls, validated against SWE-bench traces, represents a substantial efficiency gain. For B2B SaaS leveraging LLMs, this translates directly to lower API costs and potentially faster inference times. The proxy approach offers a non-invasive way to optimize existing LLM integrations. The immediate question regarding accuracy is valid and critical for market adoption. If accuracy is maintained or minimally impacted, this solution offers significant operational leverage for companies heavily reliant on large context windows, making LLM deployments more economically viable and performant.
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
Related Ecosystem & Alternatives
Discover adjacent products, open-source repositories, and developer tools sharing similar technical architecture.
Deep-Dive FAQs
What is Codex context bloat? 87% avg reduction on SWE-bench Verified traces?
Codex context bloat? 87% avg reduction on SWE-bench Verified traces is analyzed by our AI as: A proxy solution that significantly reduces LLM context window usage (87% avg reduction) for Codex, specifically targeting SWE-bench traces, aiming to improve efficiency and cost.. It focuses on This product directly addresses a critical pain point in LLM integration: context window bloat and associated costs. An 87% average reduction in pr...
Where did Codex context bloat? 87% avg reduction on SWE-bench Verified traces originate?
Data for Codex context bloat? 87% avg reduction on SWE-bench Verified traces was aggregated directly from the Hacker News community ecosystem, representing raw developer and early-adopter sentiment.
When was Codex context bloat? 87% avg reduction on SWE-bench Verified traces publicly launched?
The initial public indexing or launch date for Codex context bloat? 87% avg reduction on SWE-bench Verified traces within our tracked developer communities was recorded on April 25, 2026.
How popular is Codex context bloat? 87% avg reduction on SWE-bench Verified traces?
Codex context bloat? 87% avg reduction on SWE-bench Verified traces has achieved measurable traction, logging over 9 traction score and facilitating 2 recorded discussions or engagements.
Which technical categories define Codex context bloat? 87% avg reduction on SWE-bench Verified traces?
Based on metadata extraction, Codex context bloat? 87% avg reduction on SWE-bench Verified traces is categorized under topics such as: Codex context bloat, context window manager, proxy, intercepts Codex's calls to OpenAI.
What are some commercial alternatives to Codex context bloat? 87% avg reduction on SWE-bench Verified traces?
Our semantic intelligence engine identifies potential commercial alternatives in the SaaS space, such as Edgee Codex Compressor, which offers overlapping value propositions.
How does the creator describe Codex context bloat? 87% avg reduction on SWE-bench Verified traces?
The original author or development team describes the product as follows: "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 OpenA..."
Community Voice & Feedback
The prompt went from 44k to 6k tokens, but you're making two extra model calls per round to get there (chunker + working_memory_update). What does the all-in cost comparison actually look like?
Discovery Source
Hacker News Aggregated via automated community intelligence tracking.
Tech Stack Dependencies
No direct open-source NPM package mentions detected in the product documentation.
Media Tractions & Mentions
No mainstream media stories specifically mentioning this product name have been intercepted yet.
Deep Research & Science
No direct peer-reviewed scientific literature matched with this product's architecture.