Entroly Playground v1.0 Client-Side WASM Engine
Zero Installation · Runs 100% In-Browser · Cryptographically Auditable

Cut Context Cost. Prove Nothing Was Lost.

Entroly's 0-1 Knapsack engine selects the mathematically optimal subset of files to solve your task, emits a verifiable Merkle receipt with SHA-256 fragments, and proves what was kept, what was omitted, and why.

1

Context & Corpus Input

E-Commerce / FinTech
Payment & Rate Limit Cascade
4 files (1,840 tokens). Top-K misses rate_limiter.py; Entroly Knapsack fits all 3 critical spans.
Systems / Rust
Deadlock & Eviction Race
5 files (2,650 tokens). Knapsack preserves lock protocol and eviction without memory bloat.
Fullstack / Node
JWT Token Refresh Vulnerability
4 files (1,920 tokens). Pinpoints signature replay logic across auth middleware and session store.
1 file

Fetches top source files directly via GitHub Public API to test compression client-side.

Total Files 4
Raw Tokens 1,840
Estimated Cost $0.0055
Target Query & Budget Constraints
Target Token Budget: 450 tokens
200 450 (Default) 800 1200 2000
2

Provable Optimization & Receipts

Tokens Delivered
418 / 450
Fit in budget: 100%
Token Savings
77.3%
1,422 tokens saved
Cost Avoided
$0.0043
Sonnet / GPT-4o base
Critical Catches
3 / 3
Auth + Pay + RateLimiter
Receipt Verified · Knapsack Optimal 

          


          
          

            

              

                
VERIFIED AUDIT RECEIPT

                
rcpt_7f8a9e2d1c0b

              

              

                Schema:
                context-receipt.v1 (Merkle-CCR)
              

              

                Merkle Root Hash:
                e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855
              

              

                Original Corpus Digest:
                a8f94d12c0193e...
              

              

                Source Spans Retained:
                3 fragments across 3 files
              

              

                Reversibility Proof:
                PASS (100% bit-exact byte recovery via handle)
              


              
Selected Fragment Handles & Proofs:

              

            

          


          
          

            

              
Head-to-Head Context Selection: 450 Token Budget

              
Why Naive FIFO and Greedy Top-K fail on multi-module real-world bugs:


              

                

                  
                    

                      Strategy
                      Files Retained
                      Coverage
                      Rate Limiter Retained?
                      Token Cost
                    

                  
                  
                    

                      Raw FIFO
First files until budget cutoff		
                      checkout.py (partial)
                      1 / 4 modules (25%)
                      DROPPED
                      450 tokens
                    

                    

                      Top-K Greedy
Highest BM25 relevance score		
                      checkout.py + auth.py
                      2 / 4 modules (50%)
                      DROPPED (Budget exhausted)
                      442 tokens
                    

                    

                      ⚡ ENTROLY (Knapsack)
DP 0-1 budget optimization		
                      checkout.py + auth.py + rate_limiter.py
                      3 / 4 modules (75%)
                      RETAINED (Exact span fit)
                      418 tokens
                    

                  
                

              


              

                
💡

                

                  The Knapsack Advantage: Greedy Top-K spent 350 tokens on non-critical comments in checkout.py, leaving no budget for rate_limiter.py. The LLM produced an insecure fix missing the 429 backoff. Entroly extracted high-density fragments across all 3 necessary files, staying under budget.
                

              

            

          


          
          

            

              

                Honest Context Accounting: Every omitted span includes an inspectable reason and marginal density score.