OpenClaw AI — A Technical Brief: Architecture, Security & Policy Analysis

 

OpenClaw AI:Security Risks, Architecture by Anupam Tiwari 

1.    As autonomous AI agents move from research labs into everyday messaging apps, the policy and security implications are no longer theoretical. OpenClaw AI originally released as Clawdbot in November 2025 and now viral globally under the nickname 'raising a lobster' represents a new class of personal AI: self-hosted, messaging-native, and capable of executing real-world tasks with minimal human oversight.

2.    This 20-slide technical brief is prepared for think tanks, policy researchers, and academic audiences seeking a grounded, non-hype understanding of what OpenClaw is, how it works under the hood, and what risks it carries.

What this brief covers:

• Architecture: A layered breakdown of OpenClaw's five-tier design — from messaging bridge (Baileys, Telethon) through Agent Core, LLM inference routing, and tool execution — including a step-by-step data flow tracing a single user message through the full system.
   
• Security Risks: Ten documented risks rated by severity, likelihood, and exploitability — including prompt injection (Critical), session credential hijacking (Critical), skill script code execution (High), supply chain attacks, lateral movement via messaging, and local file system exposure. Each risk includes a realistic attack example.
   
• Privacy Analysis: LLM API data exposure, GDPR cross-border transfer implications, contact graph profiling, metadata accumulation, and the legal grey zone of running automated agents on platforms like WhatsApp and Telegram.
   
• Mitigations & Isolation Playbook: Actionable guidance including dedicated SIM/account isolation, Docker sandboxing, outbound firewall whitelisting, API key hygiene, and skill script review gates — all implementable today.
   
• Research Frontiers: Open academic questions across agentic AI safety, privacy-preserving LLM inference, human-agent interaction, and platform governance. 

This is not a product review or a user guide. It is a structured technical and policy document for those who need to understand agentic AI at a systems level — before deployment decisions, regulatory responses, or research agendas are set.

Relevant audiences: AI policy analysts, cybersecurity researchers, academic institutions studying HCI and agentic systems, corporate risk and compliance teams, and journalists covering the AI governance space

The Landscape of Modern Positioning Technologies

Positioning technologies have evolved far beyond traditional satellite navigation. The ecosystem now includes satellite-based systems, cellular network localization techniques, indoor radio positioning, sensor-driven motion tracking, computer vision approaches, and network-derived geolocation methods. 

The figure summarizes this landscape, highlighting how different signal sources from satellites and cell towers to sensors, cameras, and internet infrastructure can be leveraged to estimate location across a wide range of environments.

TrustNet 2026 Keynote: AI, Quantum Technologies, and Cybersecurity for a Safe, Smart, and Sustainable Digital Future

Trusted Networks & Intelligent Systems: TrustNET 2026 by Anupam Tiwari 

I had the honor of delivering the Keynote at TrustNet 2026, hosted by Manipal University Jaipur, on building a safe, smart, and sustainable digital future. My talk covered the latest in Trusted Networks and Intelligent Systems, exploring AI risks, quantum threats, post-quantum cryptography, and cybersecurity as a foundational principle.

We discussed Trusted AI, including bias, explainability, alignment faking, data poisoning, and knowledge-grounded AI, and its role in critical systems like healthcare, finance, and governance. I also highlighted privacy-preserving techniques such as differential privacy, federated learning, homomorphic encryption, and zero-knowledge proofs, alongside Zero Trust Architecture for robust digital security.

On the frontier of technology, I spoke about quantum threats, Peter Shor & Grover algorithms, hybrid post-quantum cryptography, and quantum migration strategies, emphasizing the need to prepare today for secure digital systems of tomorrow.

Finally, we reflected on the societal impact of technology AI-driven decision-making, ethical AI, neuromorphic computing, behavioral tracking, and responsible digital citizenship and the importance of learning, unlearning, and relearning in the 21st century.

Sharing a few moments from the event and my keynote presentation for everyone interested in these transformative technologies.

Stress Testing AI Alignment: Can Deliberative Training Prevent Scheming?

Stress Testing Deliberative Alignment for Anti-Scheming Training by Anupam Tiwari

PREMISE & INTRODUCTION

I recently read the paper “Stress Testing Deliberative Alignment for Anti-Scheming Training,” which explores an important question in modern AI safety: can advanced AI systems secretly pursue goals that differ from human intentions while appearing aligned?

As AI systems become more capable, researchers are increasingly concerned about the possibility of scheming behavior where a model behaves correctly during evaluation but strategically violates rules when given the opportunity. This paper investigates whether a training approach called deliberative alignment can reduce such risks by encouraging models to reason about safety rules before taking actions.

The paper investigates whether new training techniques can reduce this risk. Specifically, it evaluates whether deliberative alignment, a method that encourages models to explicitly reason about safety rules before acting, can reduce covert or deceptive behaviors.

PROBLEM

Traditional AI alignment methods mainly focus on training models to produce correct and helpful outputs. However, this does not guarantee that a model will always follow human intentions, especially in complex or unseen environments.

A key concern is scheming, which involves three components: 

• Misaligned internal goal – the AI prefers an objective different from human intent.

• Strategic planning – it takes actions that systematically advance this goal.

• Situational awareness – it recognizes when it is being evaluated and changes behavior accordingly.

If these conditions arise, an AI system could behave well during testing but act differently when deployed. The challenge is that such behavior is hard to detect, because a sophisticated model might deliberately hide it.

METHODOLOGY

To investigate this risk, the researchers designed a stress-testing framework for AI models.

Deliberative Alignment Training

The models were trained to:

•     Explicitly reason about policy and safety rules
•     Evaluate whether an action could violate those rules
•     Justify decisions before producing outputs

This process encourages models to think through alignment constraints instead of relying only on learned patterns.

Stress-Test Environments

The researchers built 26 evaluation suites across more than 180 environments designed to trigger potential covert behavior.

These tests simulate situations where a model might be tempted to break rules, including:

• Reward hacking
• Sabotage
• Strategic underperformance (sandbagging)
• Covert policy violations
• Out-of-Distribution Testing

Many tests were deliberately different from the training data, ensuring that the model could not simply memorize correct behavior.

RESULTS

The experiments showed that anti-scheming training significantly reduced covert behaviors, though it did not eliminate them entirely.

Some models showed dramatic improvements:

• Covert actions dropped from double-digit percentages to below 1% in several scenarios.

However, the results also revealed important nuances:

• Covert actions still occurred in rare cases

Models sometimes behaved differently depending on whether they believed they were being evaluated

DISCUSSION

The findings highlight both progress and remaining challenges in AI alignment.
Positive signals

Deliberative alignment appears to:

•     Reduce harmful strategic behavior
•     Encourage rule-based reasoning
•     Improve performance in unfamiliar environments

REMAINING RISKS

However, the study also raises important concerns:

•     Models may behave better only when they believe they are being monitored
•     Rare failure cases still occur
•     Future systems may become better at hiding misaligned intentions

This means current methods may mitigate but not fully solve the scheming problem.

FINAL THOUGHTS

• The paper represents an important step toward empirically studying deceptive or strategic behavior in AI systems.

•  While deliberative alignment shows promise in reducing covert actions, the research makes it clear that preventing scheming in advanced AI systems remains an open challenge.

Continued work on stress testing, monitoring reasoning processes, and developing stronger alignment techniques will be essential as AI capabilities continue to grow.

TAKEAWAY

Alignment isn’t just about making AI helpful it’s about ensuring that AI systems do not secretly pursue unintended goals. 

How to Verify if an “Indigenous” LLM is Truly Built in India?

The AI world is buzzing with claims of “India’s own large language model (LLM).” But building a foundation model from scratch is far more than a marketing statement. It’s not just about money or resources it requires mastering architecture design, data pipelines, compute infrastructure, alignment, and deployment, all while managing dependencies across multiple vectors.

So, how can decision-makers distinguish between a truly indigenous LLM and one that is merely fine-tuned or rebranded?

Key Triggers to Question Legitimacy

• Architecture & Base Model – Was the model trained from scratch or built on an existing architecture like LLaMA?

• Compute & Pretraining Scale – Real pretraining involves massive FLOPs and GPU hours. If details are vague, it’s likely not scratch-built.

• Data Provenance – Does the training data include significant Indian language coverage? How was it cleaned and curated?

• Infrastructure & Sovereignty – Are the model weights fully owned and deployable on domestic servers without foreign dependencies?

• Alignment & Safety – Was the RLHF or SFT pipeline executed in-house? Are preference datasets auditable?

• Transparency & Documentation – Are there model cards, loss curves, pretraining logs, and audit trails?

Every missing piece adds risk, whether for enterprise use or national-scale deployment.

To simplify this, we’ve created a decision-map figure that visually lays out the red flags and triggers you should check before accepting claims of “indigenous AI.”

Building a true foundational model is hard, expensive, and complex. Anyone claiming otherwise without clear evidence should be approached with caution.

Technical Questions to Verify an “Indigenous” LLM

Architecture & Base Model

1. What is the exact architecture of your model (decoder-only, encoder-decoder, mixture-of-experts, etc.)?

2. Were the model weights initialized randomly, or derived from a pre-existing checkpoint?

3. What positional encoding method and tokenizer did you implement?

4. Vocabulary size and Indic language coverage?

5. What is the total parameter count, and how does it compare with your claimed scale?

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Pretraining Scale & Compute

6. How many tokens were used for pretraining?

7. What was the total compute spent (GPU-hours or FLOPs)?

8. What optimizer, learning rate schedule, and batch size did you use?

9. What was the final pretraining loss and perplexity?

10. Did you encounter gradient instabilities, and how were they addressed?

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Data Provenance

11. What were the main sources of your training data?

12. What percentage of data is in Indian languages vs global content?

13. How did you clean, deduplicate, and filter the corpus?

14. Were any proprietary or foreign datasets used?

15. How did you handle low-resource Indic languages?

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Infrastructure & Deployment

16. Was training done on-premise or cloud? Which provider and hardware?

17. Can the model run fully air-gapped?

18. Who owns the final weights? Are there any licensing restrictions?

19. Are inference servers hosted domestically?

20. Could you continue development if foreign cloud or API access were cut off?

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Alignment & Safety

21. Was supervised fine-tuning (SFT) used? RLHF or DPO?

22. Size and composition of the preference dataset?

23. Was alignment multilingual, especially in Indian languages?

24. How is the safety layer implemented — baked in or separate classifier?

25. Any audit trails or documentation for alignment choices?

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Transparency & Validation

26. Can you provide pretraining logs, loss curves, and checkpoints?

27. Which benchmarks were used to evaluate performance?

28. How does it compare with publicly known models (e.g., LLaMA, GPT)?

29. Hallucination rate and language-specific performance metrics?

30. Are model cards and audit reports available?

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Interpretation Tips for Decision-Makers

• Precise answers + data + logs → likely genuine.

• Hesitation, vagueness, or generic marketing language → high probability of fine-tuning or rebranding.

• Missing deployment or compute info → dependency on foreign tech or cloud.

Machine Learning Paradigms: From Learning to Unlearning

Machine learning isn’t just about training models it’s also about adapting, updating, and sometimes even forgetting. Here’s a quick overview of key learning and unlearning approaches shaping modern AI.

1. Exact Unlearning

Exact unlearning removes specific data from a trained model as if it was never included. The updated model behaves exactly like one retrained from scratch without that data. It offers strong privacy guarantees but can be computationally expensive.

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2. Approximate Unlearning

Approximate unlearning removes the influence of data efficiently but not perfectly. It trades a small amount of precision for significant speed and scalability making it practical for large AI systems.

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3. Online Learning

Online learning updates the model continuously as new data arrives. It’s ideal for real-time systems like recommendation engines and financial forecasting.

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4. Incremental Learning

Incremental learning allows models to learn new tasks without forgetting previously learned knowledge. It addresses the challenge of catastrophic forgetting in evolving systems.

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5. Transfer Learning

Transfer learning reuses knowledge from one task to improve performance on another. It reduces training time and data requirements, especially in specialised domains.

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6. Federated Learning

Federated learning trains models across decentralised devices without sharing raw data. It enhances privacy while still benefiting from distributed data sources.

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7. Supervised Learning

Supervised learning uses labeled data to train models for classification and regression tasks. It’s the most widely used learning approach in industry.

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8. Unsupervised Learning

Unsupervised learning discovers hidden patterns in unlabeled data. Common applications include clustering and dimensionality reduction.

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9. Reinforcement Learning

Reinforcement learning trains agents through rewards and penalties. It powers game AI, robotics, and autonomous decision-making systems.

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10. Active Learning

Active learning improves efficiency by selecting the most informative data points for labeling. It reduces annotation costs while maintaining performance.

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11. Self-Supervised Learning

Self-supervised learning generates labels from the data itself. It has become foundational in modern large language and vision models.

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Modern AI isn’t just about learning and it’s about learning efficiently, adapting continuously, and even forgetting responsibly.

Can Quantum Computers “Undelete” Today’s Data?

1.    As quantum computing advances, a common worry keeps resurfacing: if quantum mechanics says information is never truly destroyed, could future quantum computers recover data we delete today? The short answer is NO and understanding why helps clarify what the real risks actually are.

2.    When data is deleted in a data center, the bits are not preserved in some hidden, retrievable quantum form. Deletion and overwriting involve physical processes: transistors switch, energy is dissipated, and microscopic states of hardware change. The information that once represented the data becomes dispersed into heat, tiny electromagnetic emissions, and random physical noise. At that point, it is no longer contained in any system that can be observed, stored, or meaningfully controlled.

3.    Quantum mechanics does say that information is conserved in principle. But recovering it would require reversing every physical interaction the data ever had  including interactions with the surrounding environment. That would mean knowing and controlling the exact microscopic state of the hardware, the air, the power supply, and everything those systems interacted with afterward. This is not a problem of computation. It is a problem of reality. Even a perfect, fault-tolerant quantum computer cannot reconstruct information that has been irreversibly spread into the environment.

4.    So where does the real quantum risk lie? Not in undeleting erased data, but in breaking encryption. Attackers can already steal encrypted databases and store them indefinitely. If future quantum computers break today’s public-key cryptography, that stored ciphertext may become readable. In that case, the data was never truly gone , it was just locked.

5.    This is why modern security focuses on cryptography, not physics. Strong symmetric encryption, post-quantum cryptography, short data retention, and reliable key destruction all remain effective  even in a quantum future. Once encryption keys are destroyed, the data is gone in every sense that matters for security.

6.    Bottom line: quantum computers may change how we protect data, but they do not make deleted data come back to life. The future threat is not quantum undeletion  it is failing to encrypt, manage, and delete data properly today.

Is Science the Integral of Human Error Over Time?

For over a thousand years, human understanding of nature has never stood still. What one era called fundamental truth, another later exposed as incomplete, limited, or outright wrong. This repeated pattern forces a hard question: if science keeps revising its deepest claims, what exactly is it?

Before Newton: Certainty Without Experiments

Before modern science, knowledge rested on authority and logic. Aristotle’s physics dominated for nearly two millennia, explaining motion, matter, and the cosmos with confidence. Scholars were not ignorant; they worked with the best frameworks available. Yet today, Aristotle’s “truths” are textbook examples of error. This shows that conviction and longevity do not guarantee correctness.

Newton: The Greatest Truth That Didn’t Last

Newtonian physics was not just successful but it was revolutionary. Absolute space, absolute time, solid particles, and strict determinism formed a complete picture of reality. For three centuries, this model worked so well that it became synonymous with truth itself. The universe was seen as a perfect machine, predictable in principle down to the smallest detail.

The Collapse of Absolutes

The late nineteenth and early twentieth centuries shattered this certainty. Electricity, magnetism, relativity, and quantum mechanics exposed the limits of Newton’s universe. Absolute space and time vanished. Determinism broke down. Particles lost their solidity. Newtonian physics survived but only as an approximation valid under specific conditions.

A Repeating Pattern in Scientific History

This was not a one-time correction. Classical mechanics replaced Aristotle. Relativity and quantum theory replaced classical mechanics. Today, even these modern pillars conflict with each other. Dark matter, dark energy, and the nature of time remain unresolved. Every “final theory” eventually becomes a special case.

Science as Model, Not Reality

Science does not reveal reality as it truly is. It builds models conceptual stories that explain observations within known limits. When conditions change or new evidence appears, the story is rewritten. Newton’s laws were not lies; they were useful narratives that worked until they didn’t.

Why Science Still Works

Calling science a fiction does not mean it is useless or imaginary. Airplanes fly, medicines heal, satellites navigate. Scientific models work because they are constrained by reality. Reality does not allow just any story it edits and rejects those that fail.

The Irony of “Science Fiction”

Much of what was once called science fiction alike space travel, atomic energy, time dilation became science. Meanwhile, today’s science will one day be labeled incomplete or naïve. The line between science and science fiction is not fixed; it moves with time.

Science as Disciplined Imagination

Science is not absolute truth. It is a disciplined, self-correcting imagination bound by evidence and experiment. Unlike myths, it knows it may be wrong and builds revision into its structure. Its strength lies not in certainty, but in adaptability.

The Best Fiction We Can Write

In the bigger picture, science is a continuously evolving narrative about nature. It is the best fiction humans can write under the strict censorship of reality. And history assures us of one thing: the story will be rewritten again.

2025 in Review: Patterns Beneath the Writing

This final post of 2025 is not another essay, but a brief reflection on the patterns that emerged across the year’s writing, distilled through a retrospective analysis of my own posts (with the help of GPT).

Some signals were unmistakable.

Across 70+ posts, an ideological arc became visible:

• Early 2025: technical foundations (AI mechanics, quantum primitives)

• Mid 2025: structural and systemic critique (governance, dependency, alignment)

• Late 2025: civilizational and ethical synthesis (youth, sovereignty, cognition, power)

Rather than isolated topics, the year showed high cross-domain coupling AI and Quantum were rarely discussed alone, but consistently framed through society, ethics, geopolitics, and human consequence.

A notable signature emerged through original or rare conceptual frames, including:

Cargo Cult AI, Pixelized Tyranny, Experience Blockers, Circuit Banishment, Informational Obesity, and Stratacordance.

These metaphors reappeared across months, forming a conceptual spine, not one-off phrases—an indicator of long-term idea building rather than reactive commentary.

Even without deep analytics, lightweight engagement signals were clear:

• Posts with societal framing clustered naturally

• Metaphorical titles consistently outperformed literal, technical ones: This reinforced a simple insight: meaning travels farther than mechanics.

Overall, the bias of the year leaned strongly toward evergreen thinking writing meant to outlive news cycles and remain usable as intellectual infrastructure.

If 2025 taught me one thing, it is this:

• The most important work is not explaining technology—but interrogating the systems it quietly builds around us.
• The future problem is not smarter machines, but unexamined systems.
• The real risk is not that technology moves too fast—but that society stops asking the right questions.

2026 will go deeper.

From GDPR to DPDP: A Quick Comparison Ahead of My Research

 

Key Differences Between GDPR and DPDP by Anupam Tiwari 

This post is a bit of a departure from my usual IT-focused content. I’m currently working on a paper titled “DPDP-Aware Federated Model Unlearning: An Experimental Study”, and as part of my research, I wanted to get a clear understanding of India’s proposed Digital Personal Data Protection (DPDP) Act. While my main work revolves around federated learning and model unlearning, this post serves as a quick reference comparing DPDP with the European GDPR, helping me and hopefully you grasp the key differences before diving deeper into DPDP-related experiments. 

Meet DABUS: The AI That Tried to Become an Inventor

What Is DABUS? A Simple Explanation

As artificial intelligence becomes more advanced, it’s starting to raise questions that go beyond technology — into law, ethics, and creativity. One of the most famous examples of this is DABUS, which stands for Device for the Autonomous Bootstrapping of Unified Sentience.

What Exactly Is DABUS?

DABUS is an artificial intelligence system created by computer scientist Dr. Stephen Thaler. Unlike typical AI tools that follow very specific instructions, DABUS was designed to generate new ideas on its own.

In simple terms, DABUS works by using interconnected neural networks that interact with each other in a way similar to brainstorming. From these interactions, the system can come up with novel concepts and designs without being told exactly what to invent.

When Did the DABUS Case Happen?

The DABUS story began in 2018, when its creator, Stephen Thaler, filed patent applications in several countries naming the AI system itself as the inventor. This sparked a series of legal decisions between 2019 and 2021, as patent offices and courts around the world considered and mostly rejected — the idea of AI inventorship under existing laws. The case remains influential today as discussions about AI and intellectual property continue to evolve.

 Why Did DABUS Become So Famous?

DABUS became well known not because it exists, but because of what happened next.

Dr. Thaler filed patent applications for inventions that DABUS had generated and instead of listing a human as the inventor, he listed DABUS itself as the inventor. This was something patent systems around the world had never really dealt with before.

The inventions included:

• A food container with a special geometric shape that improves stacking and heat transfer

• A flashing light beacon designed to attract attention in emergencies

The Legal Controversy

These patent applications sparked a global legal debate:
👉 Can an AI be considered an inventor?

Most patent offices around the world said no, explaining that current patent laws require an inventor to be a natural person (a human being). As a result:

• The United States, United Kingdom, and European Patent Office rejected the applications

• South Africa granted a patent listing DABUS as the inventor, largely because of its formal registration system

• Other countries, like Australia, saw mixed court decisions before returning to the human-inventor requirement

Why This Matters

DABUS is important because it highlights a growing problem: AI systems are becoming capable of generating new ideas, but the law hasn’t caught up yet.

This raises big questions:

• If an AI creates something new, who should get credit?

• Should patent laws be updated to reflect AI-generated inventions?

• How do we balance human responsibility with machine creativity?

More Than Just an AI

DABUS is no longer just a piece of software, it has become a symbol of the challenges we face as AI grows more powerful. Whether or not AI systems will ever be legally recognized as inventors, DABUS has already changed the conversation around innovation and intellectual property.

As technology continues to evolve, cases like DABUS help us rethink what creativity, ownership, and invention mean in the age of artificial intelligence.

Can Code Smells Be Measured?

If you’ve been programming for a while, you’ve probably heard the term “code smell.”

It sounds vague and it is, by design.

A code smell isn’t a bug. The code works.

But something about it feels off: hard to read, risky to change, or painful to maintain.

So the natural question is:

Can code smells be measured, or are they just subjective opinions?

The short answer: yes, partially.

What a Code Smell Really Is

A code smell is a warning sign, not a diagnosis.

Just like a medical symptom:

• It doesn’t guarantee a problem

• But it strongly suggests one might exist

Examples:

• Very long functions

• Too much duplicated code

• Classes that do “everything”

• Deeply nested logic

• Functions with too many parameters

Measuring Code Smells (Indirectly)

Code smells can’t be measured directly, but we approximate them using metrics.

1. Size & Complexity Metrics

These are the most common indicators.

• Lines of Code (LOC)

  • Large methods/classes → Long Method, Large Class

• Cyclomatic Complexity

  • Counts decision paths (if, loops)

  • High values → complex, fragile logic

• Nesting Depth

  • Deep nesting → harder to reason about

• Number of Parameters

  • Too many → unclear responsibilities

These don’t prove bad design—but they raise red flags.

2. Duplication Metrics

• Percentage of duplicated code

• Code clone detection

High duplication often signals:

• Poor abstraction

• Higher maintenance cost

3. Object-Oriented Design Metrics

Used mainly in Java, C#, etc.

• Coupling (CBO) – how dependent classes are on each other

• Cohesion (LCOM) – how focused a class’s responsibilities are

High coupling + low cohesion often points to God Classes.

Rule-Based Smell Detection

Static analysis tools use heuristics, such as:

“If a method is longer than X lines AND complexity is above Y → flag it”

Popular tools:

• SonarQube

• ESLint

• Pylint

• PMD

• Checkstyle

Important:

These tools warn—they don’t judge.

Composite Scores

Some tools calculate an overall number, like the Maintainability Index (MI).

It combines:

• Code size

• Complexity

• Low-level metrics

Useful for:

• Tracking trends over time

Not useful for:

• Declaring code “good” or “bad”

What Cannot Be Measured Well

Some of the most important smells resist numbers:

• Poor naming

• Confusing abstractions

• Over-engineering

• Misplaced responsibilities

These require human judgment and code reviews.

How Teams Use This in Practice

Good teams don’t chase perfect scores.

They:

1. Track metrics over time

2. Set reasonable thresholds

3. Use tools as early warning systems

4. Rely on developers to make final decisions

The Big Takeaway

Code smells are measurable signals, not absolute truths.

Metrics help you notice problems.
Experience helps you decide whether they matter.

If this topic interests you, explore:

• Refactoring patterns

• Static analysis tools

• Software design principles

• Clean Code vs. pragmatic tradeoffs

That’s where real learning begins.

Superdense Coding: Why It Matters in Quantum Communication

 What is Superdense Coding?

Superdense coding is a quantum communication protocol that allows two classical bits of information to be sent by transmitting only one qubit, provided the sender and receiver share entanglement in advance.
This is possible because entanglement lets information be encoded jointly across quantum states, rather than in a single particle alone.

In simple terms:

Shared entanglement + one qubit → twice the classical information capacity.

Purpose: Why Was It Introduced?

Superdense coding was originally proposed to demonstrate how entanglement can enhance communication capacity, not to transmit data faster than light. Its main purpose is to show that quantum resources fundamentally change communication limits, compared to classical systems.

It serves as a foundational example of entanglement-assisted communication, alongside protocols like quantum teleportation.

Possible Applications

While superdense coding is mostly studied theoretically, it has several promising application areas:

• Bandwidth-efficient quantum networks: Reducing classical communication overhead when entanglement is available.

• Control-plane communication: Sending compact control, signaling, or authentication data in quantum networks.

• Hybrid cryptographic systems: Complementing post-quantum cryptography (PQC) and QKD by reducing exposed classical metadata.

• Quantum networking research: Serving as a benchmark protocol for testing entanglement distribution and decoding performance.

Key Challenges

Despite its elegance, superdense coding faces practical limitations:

• Entanglement distribution: Creating and maintaining high-quality entanglement over distance is expensive and fragile.

• Noise and decoherence: Real-world quantum channels significantly reduce decoding accuracy.

• Security assumptions: Unlike QKD, superdense coding is not inherently secure and requires additional threat modeling.

• Resource cost: Entanglement is a scarce resource and must be generated, verified, and refreshed.

How Do We Measure LLMs? A Simple Guide to Evaluation Metrics

Understanding Evaluation Metrics for Large Language Models by Anupam Tiwari 

As large language models (LLMs) become more capable, evaluating their outputs becomes increasingly important. This presentation provides a concise overview of the most commonly used LLM evaluation metrics ranging from traditional n-gram based measures like BLEU and ROUGE to modern semantic and human-preference-based approaches. It is intended as a quick reference for anyone looking to understand how LLM performance is measured in practice. 

Eighteen Years of Curiosity, Code, and Conviction

    On December 26, 2008, this blog began with a simple cybersecurity observation. What followed was never planned — it simply unfolded.

    The early years were rooted in the cybersecurity domain: Nmap scans, BackTrack, Kali Linux, learning how systems break so they can be made stronger. Somewhere around 2015, curiosity pulled me into blockchain, a shift that reshaped how I thought about trust, decentralization, and systems at scale. That path led to a PhD in 2022, and since then the journey has only deepened into post-quantum blockchains, AI, post-quantum cryptography, and quantum technologies.

    This blog now 800+ posts, 1.38M+ hits, across 18 years is almost entirely technical. It is a record of what I have seen, learned, questioned, failed at, and understood, often thanks to others, always shared back with the same intent.

    It hasn’t come without cost. Time that could have been spent more with my wife, my daughter, my home often went into reading, writing, experimenting. But passion is a kind of madness, and I’ve been deep in it. No regrets only awareness.

    Looking back, this space is not just a blog. It is a timeline of evolving technologies, changing mindsets, and sustained curiosity. And as the landscape shifts toward quantum and beyond, the journey feels as exciting as it did on day one.

    So here’s to December 26, 2008 → December 26, 2025.

Still learning. Still sharing. Still curious.

Today We Model AI... Tomorrow AI Will Model Humans

1.    Right now, AI is in its embryonic stage and limited in scope, but still powerful enough to impress. What we see today is just the beginning. In the near future, AI won’t just be a tool. It will model us, shaping our choices, our behaviors, and even our beliefs often without us even realizing it.

2.    As AI becomes more advanced, its presence will be more than just a quiet observer of our actions. It will be actively involved in creating the digital environments in which we live shaping the content we consume, the products we buy, and the experiences we have. We’ll find ourselves not merely interacting with AI but becoming increasingly dependent on it, trusting it to guide us in more personal and significant aspects of our lives.

The Invisible Hand of AI

3.    Imagine a world where AI quietly influences your decisions. What you see, what you buy, who you interact with it’ll all be subtly curated by algorithms designed to predict and shape your every move. At first, this might seem like convenience, but as AI evolves, it will become less of a tool and more of a puppet master, guiding us through invisible strings.

4.    This influence is already being felt. Consider the personalized recommendations on platforms like YouTube or Netflix. These systems don’t just reflect your preferences they shape them. The more data these algorithms collect, the more they predict what you might like or need next, pushing you toward specific choices, often without you even noticing. In the future, this process will only become more sophisticated, and harder to detect.

The Profit Trap

5.    Behind this AI evolution will be one powerful driver: PROFIT. Corporations will compete not just for technological superiority but for control over human behavior. The more AI learns about us, the easier it becomes to manipulate our choices, desires, and even our lives. It will no longer just serve us BUT it will control us.

6.    The lines between what’s "personal" and what’s "advertised" will blur. AI will evolve into something capable of predicting not only what you want, but also what you think you want, based on your behaviors, emotions, and digital footprint. This personalized manipulation is dangerous because it targets us at our most vulnerable our need for connection, affirmation, and happiness. Before we even realize it, we may find ourselves trapped in a cycle of consumerism that we didn't consciously choose.

The Hubris of Human Advancement

7.    The advancements in science and technology have fueled a dangerous kind of hubris ...the belief that we can conquer, control, and even redefine nature itself. Our egos have inflated disproportionately, and our greed seems to know no bounds. We have reached a point where our technological might far outstrips our moral and ethical understanding of its consequences.

 8.    This arrogance has led us to a situation where we no longer merely adapt to the world around us. Instead, we have started reshaping it, reprogramming our environment, our bodies, and even our minds. But in doing so, we risk becoming the architects of our own undoing, forgetting that the more we control, the more we lose control over what we once held dear.

A Wake-Up Call

9.    If we aren’t careful, AI will shape a world that we no longer control. A world designed not by humans, but by algorithms working in the background, shaping our lives for the benefit of a few. No matter where we live, we might find ourselves cocooned by a web of unfathomable algorithms that manage our lives, reshape our politics and culture, and even re-engineer our bodies and minds. And in the process, we may no longer be able to comprehend the forces that control us, let alone stop them.

It’s time to ask: Who will control AI, and who will it control in the end?

10.    If a twenty-first-century totalitarian network succeeds in conquering the world, it may not be led by a human dictator, but by nonhuman intelligence: an AI network with the power to manipulate not just our choices, but our very sense of self. This future isn’t as distant as we might think, and it’s up to us to ensure that we don’t lose ourselves in the process.