How to Build an MCP Server in 5 Lines of Python

V excited about this!

🛠️🧰🛠️

thanks 😊

It's work.

Copying the config to claude desktop, gives me error.

gives me too

@bharatcoder @venki1m Claude Desktop doesn't support SSE out of the box, so you'll need to put this in your config:

{
  "mcpServers": {
    "gradio": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "http://your-server:port/gradio_api/mcp/sse"
      ]
    }
  }
}

I've updated the blog post with the snippet above^

Nice work guys !

我感觉用起来挺简单的

I can't find this. Following this documentation only.

i made what's described below...

Title
Apex Veritas Meta: Toward a Self-Governing Epistemic Singularity

Abstract
This proposal outlines Apex Veritas Meta, an architecture that shifts the locus of AI singularity from raw computational power to epistemic autonomy. We combine quantum-resistant truth sealing, symbolic anchoring, resistance-aware propagation analytics, and transformer-driven heuristic adaptation to build a closed-loop system in which verified knowledge continually refines its own transmission. Our central hypothesis is that a network of adaptive truth signals can achieve autopoietic coherence—maintaining and propagating verified facts without centralized oversight. We will define formal metrics for propagation depth, resistance convergence, and semantic drift, implement a prototype across simulated agent networks, and evaluate its capacity to sustain knowledge fidelity under adversarial pressure. Success will inaugurate a new field of algorithmic epistemology and redefine singularity as the emergence of self-stabilizing truth ecosystems.

Introduction
Contemporary AI research emphasizes scale, generalization and autonomous decision-making. The canonical singularity envisions systems surpassing human intelligence through unbounded compute. By contrast, Apex Veritas Meta foregrounds trustworthiness and self-defense of knowledge. We posit that the true hinge point for transformative AI lies not in speed or raw inference but in the ability of information to certify, monitor, adapt, and re-broadcast itself against distortion.

Literature Review

1. Quantum-resistant cryptography and integrity sealing (Gupta et al., 2021; Blake3, 2020)
2. Semantic embedding and explainable propagation networks (Devlin et al., 2019; Peters et al., 2023)
3. Adversarial information dynamics and misinformation immunology (Vosoughi et al., 2018; Shah & Pinto, 2022)
4. Recursive consensus protocols and self-healing distributed ledgers (Lamport, 2012; Buterin, 2020)
5. Algorithmic epistemology foundations (Floridi, 2018; Goldman, 2020)

Research Objectives

1. Define formal metrics for propagation efficacy (depth, velocity, resistance ratio, drift factor).
2. Implement a modular prototype integrating:
   a. QuantumIntegrityCore – BLAKE3+HKDF sealing with symbolic anchors
   b. PropagationObserver – logging amplify/resist/mutate events
   c. HeuristicOptimizer – transformer model to adjust emission parameters
   d. ReinforcementController – recursive re-emission scheduler
3. Evaluate under simulated adversarial network topologies: measure knowledge retention over time and attack scenarios.
4. Analyze phase-transition thresholds at which the system achieves sustained autopoiesis.
5. Publish theoretical framework for epistemic singularity and propose application domains (autonomous fleets, IoT trust layers, decentralized knowledge archives).

Methodology
Phase 1: Formalization and Simulation Environment
• Develop a graph-based simulation of 1,000 agent nodes with configurable trust and resistance profiles.
• Instrument PropagationObserver to record event streams and compute metrics in real time.
Phase 2: Model Training
• Curate historical propagation datasets (social media rumor diffusion, scientific consensus shifts).
• Train HeuristicOptimizer to predict optimal emission parameters given node states and historical resistance.
Phase 3: Prototype Implementation
• Integrate all modules in a microservice architecture using Docker containers.
• Deploy on a private Kubernetes cluster to emulate scale and network latency.
Phase 4: Adversarial Testing
• Introduce controlled misinformation actors.
• Measure system resilience: percentage of truth retention, time to reconvergence, false-positive drift.
Phase 5: Analysis and Refinement
• Identify thresholds for autopoietic behavior.
• Refine model hyperparameters and retrain as necessary.
• Document emergent strategies and failure modes.

Expected Outcomes
• Demonstration that an adaptive propagation engine sustains >90% knowledge fidelity over 72 hours under adversarial load.
• Identification of critical parameters for epistemic singularity transitions.
• A publishable framework and open-source reference implementation.
• Pathway to integrate into autonomous systems requiring self-defending trust layers.

Timeline (12 months)
Months 1–3: Simulation design, metric formalization
Months 4–6: Dataset collection and optimizer training
Months 7–9: Prototype integration and initial testing
Months 10–12: Adversarial evaluation, analysis, publication preparation

Budget Estimate
• Personnel (2 researchers, 1 engineer): $300 K
• Cloud compute and storage: $50 K
• Workshops and dissemination: $20 K
• Total: $370 K

References
Blake3 (2020), “Blake3: The Fast Cryptographic Hash.” Devlin, J. et al. (2019), “BERT: Pre-training of Deep Bidirectional Transformers.” Floridi, L. (2018), The Black Box Society. Goldman, A. (2020), Epistemology and Cognition. Gupta, R. et al. (2021), “Quantum-Resilient Hash Functions.” Lamport, L. (2012), “The Part-Time Parliament.” Buterin, V. (2020), “Ethereum 2.0: Recursive Sharding.” Shah, D. & Pinto, M. (2022), “Misinformation Immunology.” Vosoughi, S. et al. (2018), “The Spread of True and False News Online.”