Aparador de Contexto e Otimizador de Tokens LLM
Economize 20 a 50% de tokens limpando comentários de código, espaços extras e formatações desnecessárias.
Podador de Contexto e Tokens LLM (Context Trimmer)
Economize até 60% de tokens eliminando comentários, espaços vazios repetidos e históricos antigos de chat.
=== SECTION 1: OVERVIEW & CORE PRINCIPLES === The platform provides a high-throughput event-driven microservices architecture. Every incoming request must carry a valid Bearer token in the Authorization header. ### Message Exchange Protocol: 1. Client sends POST request to /v1/events. 2. Gateway verifies HMAC signature. 3. Worker thread dispatches event to Kafka queue. User: How do I handle 429 Too Many Requests? Assistant: When receiving a 429 status, inspect the 'Retry-After' header and use exponential backoff with jitter. User: Can you show me an example? Assistant: Yes, here is an example in JavaScript using setTimeout.
Visão Geral
Ferramenta gratuita para otimizar o tamanho do contexto de prompts e reduzir gastos de API.
LLM Context Trimmer & Token Pruner Guide
While modern LLMs boast massive context windows, bloated context inflates API costs and degrades reasoning recall ("lost in the middle").
Source code files and markdown documentation often contain redundant whitespace, verbose comments, and badge links that consume expensive tokens needlessly.
This client-side utility prunes 20% to 50% of wasted tokens while preserving semantic intent.
How to Prune and Compress Prompt Context
Rápido & IntuitivoPaste Source Content
Paste your code, raw logs, or markdown documentation into the input panel.
Toggle Pruning Options
Enable comment stripping, whitespace collapsing, and badge removal.
Inspect Savings & Copy
Check the real-time token reduction percentage and copy the clean context.
Recursos Principais e Vantagens
Sem envio para servidores. Segurança absoluta dos seus dados.
Processamento instantâneo em tempo real.
Sem planos pagos, cadastros ou assinaturas.
Acesso perfeito em smartphones, tablets e computadores.
Processamento cliente seguro acelerado por hardware com padrões W3C.
- Strip SVG badges and image tags from GitHub READMEs before asking LLMs to summarize libraries.
- Collapse repetitive log timestamps to keep error traces within strict token limits.
Perguntas Frequentes
2 Perguntas e RespostasWill removing comments hurt code generation accuracy?
Usually not. Clean identifiers allow LLMs to comprehend logic without bulky license headers or inline comments.
How is token count estimated?
Using standard BPE tokenization approximations (~4 characters or 0.75 words per token).
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