Claude/Gemini/Codex 10-100x faster with pandō
AgentHi HN,I'm George Ciobanu (https://www.linkedin.com/in/georgeciobanunyc). I built pandō ('CAD for code') because I got tired of watching AI agents burn tokens, take forever, and still get it wrong.Here's (one reason) why this happens: AI agents read and edit co
- Best for
- prompt compression and optimization for llm inference, multi-provider llm abstraction with transparent compression routing, code-aware prompt structuring and context selection
- Type
- Agent
- Score
- 34/100
- Best alternative
- LangChain
Capabilities5 decomposed
prompt compression and optimization for llm inference
Medium confidencePandō compresses prompts and context before sending to LLMs (Claude, Gemini, Codex) using a proprietary compression algorithm that reduces token count while preserving semantic meaning. This works by identifying and removing redundant information, collapsing repetitive patterns, and applying lossless compression techniques to the input prompt. The compressed prompt is then sent to the target LLM API, reducing both latency and cost proportional to the compression ratio achieved.
Applies CAD (Computer-Aided Design) principles to code prompts — treating prompt structure as a designable artifact that can be optimized for compression without semantic loss, rather than treating prompts as opaque text strings
Claims 10-100x speedup over direct LLM calls by compressing prompts before transmission, whereas standard LLM APIs process full context unoptimized
multi-provider llm abstraction with transparent compression routing
Medium confidencePandō provides a unified interface that accepts prompts and routes them to Claude, Gemini, or Codex while automatically applying compression before transmission. The abstraction layer handles provider-specific API differences (authentication, request/response formats, rate limiting) and transparently applies compression optimization. This allows developers to switch between LLM providers or use multiple providers without changing application code, while benefiting from compression on all providers.
Combines provider abstraction with automatic compression — most multi-provider frameworks (LangChain, LiteLLM) handle routing but don't optimize prompts, whereas Pandō compresses before routing to reduce costs across all providers simultaneously
More efficient than LangChain or LiteLLM for cost optimization because it compresses prompts before sending to any provider, whereas those frameworks send full context unoptimized
code-aware prompt structuring and context selection
Medium confidencePandō applies CAD (Computer-Aided Design) principles to code prompts by parsing code structure (AST-level or semantic understanding) and intelligently selecting which parts of a codebase are relevant to include in the prompt. Rather than including entire files or arbitrary context windows, it identifies dependencies, related functions, and relevant patterns, then structures the prompt to emphasize important code while compressing boilerplate and repetitive patterns. This enables more effective code generation with smaller context windows.
Treats code prompts as designable artifacts (CAD metaphor) that can be optimized for both compression and relevance — uses semantic code understanding to select context rather than naive token-counting or file-based selection like most code generation tools
More intelligent than Copilot's context selection because it understands code structure and dependencies rather than using simple recency/frequency heuristics, enabling better generations with smaller context
batch prompt compression and cost estimation
Medium confidencePandō provides batch processing capabilities that compress multiple prompts in parallel and estimate the cost savings and latency improvements before sending to LLMs. The system analyzes a batch of prompts, applies compression to each, calculates compression ratios, and projects API costs and response times. This enables developers to understand the impact of compression on their workload and make informed decisions about which prompts to optimize.
Provides pre-execution cost/latency estimation for compressed prompts — most LLM tools only show costs after API calls, whereas Pandō estimates impact before committing resources
More transparent than direct LLM API usage because it shows compression impact and cost savings upfront, enabling informed optimization decisions
streaming response decompression and reconstruction
Medium confidencePandō handles streaming LLM responses from compressed prompts by decompressing and reconstructing the output in real-time as tokens arrive. The system maintains state about the compression context used for the original prompt and applies inverse transformations to the streamed response, ensuring that code generation and other outputs are properly reconstructed even when using streaming APIs. This enables low-latency streaming interactions while maintaining compression benefits.
Applies compression to streaming responses by maintaining decompression state across token boundaries — most streaming implementations don't compress because stateless token-by-token processing makes compression difficult
Enables streaming with compression benefits, whereas standard streaming APIs send uncompressed tokens, resulting in higher latency and cost for the same quality
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
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Semantic Kernel Python SDK
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Streamline AI prompt creation, enhance user...
Best For
- ✓Teams using Claude/Gemini/Codex APIs at scale with large codebases
- ✓Developers optimizing for cost and latency in production LLM pipelines
- ✓Solo developers working on token-budget-constrained projects
- ✓Teams evaluating multiple LLM providers for production use
- ✓Developers building LLM applications that need provider flexibility
- ✓Cost-conscious teams wanting to optimize across multiple APIs
- ✓Developers working on large codebases (>100k LOC) with limited context windows
- ✓Teams using code generation to maintain consistency across large projects
Known Limitations
- ⚠Compression effectiveness varies by content type — highly structured code compresses better than prose
- ⚠Unknown whether compression introduces latency overhead that offsets API call speedup
- ⚠No visibility into compression algorithm details — black-box approach limits debugging
- ⚠Requires integration with specific LLM providers (Claude, Gemini, Codex); not universal
- ⚠Abstraction adds latency overhead for request/response translation
- ⚠Provider-specific features (vision, function calling, streaming) may not be uniformly supported
Requirements
Input / Output
UnfragileRank
UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.
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Show HN: Claude/Gemini/Codex 10-100x faster with pandō (CAD for code)
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