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DYNOSuprovo (Suprovo Mallick (DYNO)) · GitHub

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DYNOSuprovo/README.md

01 // NEURAL_DOSSIER [IDENTITY_MATRIX]

# OPERATOR IDENTITY SPECIFICATION
OPERATOR:        Suprovo Mallick
CALLSIGN:        DYNO
ROLE:            Generative AI & ML Systems Engineer
ACADEMICS:       B.Tech in Computer Science @ KIIT (2023 - 2027)
ACCREDITATION:   BS in Data Science (Foundation) @ IIT Madras
COORDINATES:     West Bengal, India [88.3639° E, 22.5726° N]

# COMBAT MANIFESTO
DIRECTIVE: >
  "Notebook prototypes are child's play. I engineer verifiable,
  evidence-grounded, low-latency AI pipelines and multi-agent
  swarms architected to withstand heavy production concurrency."

⚡ TELEMETRY INTEGRITY GAUGES

  • Adaptive RAG & Dynamic Pruning [████████████████] 100% (NLI Filtering)
  • Multi-Agent State Orchestration [██████████████░░] 92% (LangGraph StateGraphs)
  • Deterministic Output Grounding [██████████████░░] 90% (Token-Overlap Bypass)
  • Inference Concurrency & APIs [███████████████░] 95% (FastAPI / 4-bit Quantization)
  • Open Source Runtime Patches [████████████████] 98% (ComfyUI / LlamaIndex / Haystack)
 



[RUNTIME_STATUS]
RETRIEVAL_LATENCY   = < 2.0s
HALLUCINATION_RATE  = < 10%
TOKEN_OPTIMIZATION  = ~80% PRUNED
CITATION_PRECISION  = 100% VERIFIED

02 // ARCHITECTURAL_CORE [EVIDENCE_FIRST_AI]

"Foundation models are commoditized runtime engines. Real AI engineering begins at query semantics, dynamic pruning, context sanitation, and deterministic output verification."

flowchart TD
    classDef influx fill:#050816,stroke:#00f7ff,stroke-width:2px,color:#00f7ff;
    classDef router fill:#120136,stroke:#7209b7,stroke-width:2px,color:#fff;
    classDef filter fill:#240046,stroke:#ff007f,stroke-width:2px,color:#ff70a6;
    classDef mesh fill:#0a192f,stroke:#00ff99,stroke-width:2px,color:#00ff99;
    classDef gate fill:#1f1147,stroke:#ffd166,stroke-width:2px,color:#ffd166;
    classDef out fill:#003566,stroke:#00e5ff,stroke-width:2px,color:#fff;

    Q[User / Telemetry Stream Influx]:::influx --> R[Semantic Router Node]:::router
    R --> S[Hybrid Dense-Sparse Fetch: ChromaDB + BM25]:::router
    S --> P[NLI Context Pruning Engine: Threshold >= 0.70]:::filter
    P --> M[Multi-Agent Reasoning Graph: LangGraph]:::mesh
    M --> V{Deterministic Gate: Overlap Heuristic >= 0.72?}:::gate
    V -- Bypass Heavy LLM --> O[Async FastAPI REST Endpoint]:::out
    V -- Ambiguous Match --> C[Deterministic Critic Node]:::filter
    C --> O
Loading

LAYER ENGINE SUBSYSTEM ARCHITECTURAL IMPACT
01 Hybrid Vector-Sparse Indexing Combined ChromaDB dense embeddings with BM25 sparse keyword rankers for multi-modal recall.
02 Cross-Encoder NLI Pruning Strips duplicate and contradictory claims with NLI score $\ge 0.70$, slashing token spend by $\sim 80%$.
03 Stateful Multi-Agent Mesh LangGraph orchestration isolating Router, Extractor, Synthesizer, and Critic nodes with cyclic recovery.
04 Deterministic Verification Gate Token-overlap heuristic ($\ge 0.72$) eliminates costly validation LLM calls; bounds hallucinations to $&lt; 10%$.
05 Low-Latency Runtime Delivery Multi-worker async FastAPI backend with containerized Docker deployment delivering $&lt; 2.0\text{s}$ execution.

03 // FLAGSHIP_SYSTEMS [PRODUCTION_NODES]

🧬 NODE 01 // ADAPTIVE EVIDENCE RAG

Intelligent, Self-Evaluating Context-Pruning Retrieval Engine
Latency: < 2.0s • Token Pruning: ~80% • Hallucination Rate: < 10% • EM Accuracy: > 75%

[ARCHITECTURE FLOW]
Query ──► Dynamic Expansion ──► 5x Parallel Fetch ──► NLI Pruning (>=0.70) ──► Grounded Qwen-4bit
  • Hallucination Suppression ($&lt; 10%$): Employs strict NLI contradiction filtering and atomic claim extraction prior to synthesis.
  • Context Sanitation: Cross-encoder NLI independence metric eliminates $\sim 80%$ of redundant tokens, ensuring ultra-dense context windows.
  • Full-Stack Container: Containerized React.js + FastAPI pipeline powered by a local 4-bit quantized Qwen model for sub-2-second inference.

🌌 NODE 02 // PLUTO v2

Multi-Agent Document Intelligence & Evidence Verification Mesh
Concurrency: 8-Worker Pool • Extraction Latency Drop: ~80% • Citation Precision: 100%

[MULTI-AGENT PIPELINE]
Document ──► Router Node ──► Extractor + Retriever ──► Synthesizer ──► Critic Gate ──► Verified Payload
  • 100% Verifiable Quote Attribution: Router $\to$ Extractor $\to$ Synthesizer $\to$ Critic state graph guarantees unbacked claims are scrubbed.
  • 80% Latency Drop: Multi-worker thread pools combined with an internal two-tier LLM response cache eliminate redundant computation.
  • Heuristic Bypass Gate: Token-overlap heuristic ($\ge 0.72$) bypasses heavy LLM critic verification for obvious evidence matches.

🧠 NODE 03 // INTENT COMPILER

Autonomous Architecture, Schema & Technical Specification Engine
Engine: LangGraph StateGraph • Inference: Groq LLaMA-3 LPU • Stack: Streamlit + Python

[AUTONOMOUS COMPILATION]
Product Vision ──► Agent Graph ──► Requirements Spec + Architecture Blueprints + DB Schemas + Pseudocode
  • Compiles natural-language product ideas into complete, production-grade technical blueprints, database schemas, and executable pseudocode.
  • Employs stateful LangGraph agents that autonomously critique technical viability and validate dependency graphs before compilation.

SYSTEM NODE CORE STACK & ARCHITECTURE KEY PERFORMANCE METRIC LIVE ENDPOINT
🌾 WHEAT GUARDIAN Edge CV Crop Pathology (EfficientNetV2B2 + OpenCV + FastAPI) 93%+ Accuracy • $\sim 300\text{ms}$ Latency DEPLOYED APP
🌍 TRANSLATE-V2 Low-Resource NMT Transformer (PyTorch + NLLB-200 + FastAPI) -38% Latency Gain via Batching HF SPACE
🥗 AAHAR Dietary RAG Assistant (LangChain + Gemini + ChromaDB + React) Personalized Multi-Turn Memory WEB APP
💰 AI EXPENSE ADVISOR Financial Retrieval Engine (Streamlit + LangChain + Gemini) Semantic Spend Classification GITHUB REPO

04 // OPEN_SOURCE_OPS [17+_MERGED_PRS]

PRODUCTION REPOSITORY IMPACT HIGHLIGHT & PR MISSION DOMAIN STATUS
Comfy-Org / ComfyUI (PR #16346 / #16312) Runtime Reliability: Guarded prompt_worker against unhandled execute exceptions, preventing execution thread crashes. GenAI Core Engine
open-webui / open-webui (PR #30047 / #30006) Routing & Observability: Extended parse_custom_headers() to propagate model_id alongside user contexts for downstream routing. LLM Interface
run-llama / llama_index Context Integrity: Implemented header-aware deterministic chunking and post-RAG verification primitives. Agentic RAG
deepset-ai / haystack Evidence Validation: Created factual consistency and hallucination scoring modules against source documents. Search Pipelines
MakazhanAlpamys / Soup Distributed Training: Patched DeepSpeed empty-param-group guards to properly forward transformer model arguments. Model Training
smoothAPI & Surgite Backend Resilience: Built async retry predicates, backoff logic, and extensible model provider wrappers. Cloud / APIs

05 // TACTICAL_ARMORY [FULL_SPECTRUM_STACK]

06 // INDUSTRY_LOG [EXPERIENCE_&_RESEARCH]

🧪 ROLE // AI CHATBOT & GENAI BACKEND DEVELOPER

FED Society | 2024 — PRESENT

Client ──► [FastAPI Gateway] ──► [Vector DB RAG] ──► [Memory Matrix] ──► Response
  • Modular FastAPI Microservices: Engineered scalable backends integrating persistent vector databases with multi-turn conversation memory.
  • 30% Latency Reduction: Accelerated end-to-end response pipelines through embedding matrix pooling and selective cross-encoder reranking.
  • Production LLM Endpoints: Architected robust fallback-enabled inference endpoints powering conversational wellness workflows.

📚 RESEARCH // RAAHI (PIC-2026)

Real-Time Transport Tracking in Low-Connectivity Cities
Proceedings of the 15th Project Innovation Contest (PIC-2026)
Satellite Event of ICDCIT-2026, KIIT

status: Peer-Reviewed & Published
selection: Top 40 out of 160 submissions globally
domains: [Distributed Systems, Edge Routing, Telemetry Sync]
  • Formulated a fault-tolerant telemetry synchronization mechanism maintaining positional continuity across packet-loss-heavy cellular networks.

07 // ALGORITHMIC_CORE [PROBLEM_SOLVING]

ALGORITHMIC DOMAIN MASTERY DEPTH CORE HEURISTICS & PATTERNS
Dynamic Programming & Graphs [██████████████░░] 88% State Compression, Topological Sort, Shortest Path (Dijkstra), Memoization
Sliding Window & Two Pointers [████████████████] 96% Dynamic Subarrays, Monotonic Queues, Influx Stream Optimization
Trees, Heaps & Tries [██████████████░░] 90% LCA via Binary Lifting, Prefix Search Trees, Priority Cache Scheduling
Asymptotic Bounds & Memory [█████████████░░░] 85% Space-Complexity Guards, Bit Manipulation, CPU Cache Locality

08 // CREDENTIALS_VAULT [CERTIFICATIONS]

09 // TELEMETRY_STREAM [REPO_METRICS]




10 // COMM_LINK [TRANSMISSION_PORT]

[SYSTEM_TRANSMISSION]
STATUS   = "LISTENING_ON_ALL_PORTS"
CHANNELS = ["Agentic RAG Architectures", "Multi-Agent System Graphs", "Deterministic AI Verification", "High-Throughput ML Backends"]
QUOTE    = "Logic over guesswork. Evidence over hallucinations. Deployed systems over notebooks."

🏆 GitHub Achievements & Milestones

Badge Description Status Verification & Evidence
🧑‍🤝‍🧑 Pair Extraordinaire Co-authored commits on merged pull requests Gold 🥇 Qualified 78 Merged PRs (8 Upstream in aws-powertools, openinference, anyio, Agronaut + 70 Showcase)
🧠 Galaxy Brain Answered technical discussions with accepted answers Silver 🥈 In Flight 5 Verified Accepted Answers (Base ✅ Unlocked, 25 In-Flight Solutions live)
🦈 Pull Shark Opened pull requests that have been merged Bronze 🥉 ✅ Multiple merged pull requests across public open-source repos
🎯 YOLO Merged a pull request without a review ✅ Unlocked Unlocked on repository workflows
⚡ Quickdraw Closed an issue or PR within 5 minutes ✅ Unlocked Verified rapid triage response

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