Forem: Dilpreet Grover

Definitive Guide to AI Benchmarks: Comparing Models, Testing Your Own, and Understanding the Future

Dilpreet Grover — Mon, 03 Mar 2025 04:03:14 +0000

Artificial intelligence is transforming every industry — from customer support and healthcare to autonomous vehicles and creative tools. As AI models evolve and grow increasingly sophisticated, it becomes crucial to have standardized methods to compare their performance and capabilities. AI benchmarks serve as the “exams” that measure everything from language understanding and image recognition to advanced reasoning and safety. In this guide, we explore the evolution of these benchmarks, explain how they are built and used, and provide a comprehensive comparison of current state-of-the-art models.

Understanding AI Benchmarks

What Are They?

Imagine an exam designed not for students but for AI models. AI benchmarks are structured evaluations comprising carefully curated tasks and datasets that measure a model’s ability to:

Accurately predict outcomes or classify data
Efficiently process information with minimal latency
Robustly handle unexpected or adversarial inputs
Generalize well to new, unseen data

These metrics are quantified through standardized tasks that form the foundation of AI evaluation.

The Evolution of AI Benchmarks

Early Days

Early benchmarks, such as SPEC CPU and MNIST, provided basic performance metrics. With deep learning’s rise, tests evolved from simple image recognition tasks to complex evaluations that require models to learn and generalize from vast amounts of data. This evolution pushed the need for benchmarks that capture the multifaceted abilities of modern AI systems.

Milestone Benchmarks Today

Below is an overview of some of the key modern benchmarks:

GLUE / SuperGLUE

Domain: Natural Language Processing

GLUE was among the first comprehensive benchmarks for language understanding tasks, including sentiment analysis, natural language inference, and question answering. Its successor, SuperGLUE, was developed as models reached human-level performance on GLUE, incorporating more challenging tasks like coreference resolution and multi-hop reasoning.
Key Metrics:

Accuracy: Percentage of correct answers.
F1 Score: Balancing precision and recall.

For more details, see the GLUE Benchmark website

ImageNet

Domain: Computer Vision
ImageNet revolutionized image classification by providing millions of labeled images across thousands of categories. Its large-scale challenge spurred advancements in deep convolutional neural networks.

Key Metrics:

Top-1 Accuracy: The percentage where the model’s top prediction is correct.
Top-5 Accuracy: The percentage where the correct label appears in the top five predictions.

For further exploration, visit the ImageNet website.

COCO

Domain: Object Detection & Segmentation
COCO requires models to detect multiple objects in images, segment them, and understand their spatial relationships. This benchmark is crucial for tasks like autonomous driving where scene understanding is essential.

Key Metrics:

Mean Average Precision (mAP): Evaluates detection accuracy across multiple categories.

Learn more at the COCO Dataset website.

MMLU

Domain: Multitask Academic Knowledge

MMLU challenges models with 16,000 multiple-choice questions spanning 57 academic subjects, testing both factual recall and reasoning.

Key Metrics:

Accuracy: Compared against expert benchmarks.

For a deeper dive, visit MMLU on Wikipedia

BIG-bench

Domain: General Capabilities
BIG-bench consists of over 200 diverse tasks that assess a model’s reasoning, creativity, and problem-solving skills across various domains.

creativity, and problem-solving skills across various domains.
Key Metrics:

Composite Score: An aggregated score providing a holistic view of a model’s performance.

Access the benchmark on BIG-bench GitHub.

Humanity’s Last Exam (HLE)

Domain: Advanced Reasoning & Safety

HLE is designed for the most advanced AI models, featuring 3,000 expert-curated questions across multiple disciplines that test deep reasoning and safety-critical decisions.
Key Metrics:

Accuracy: Evaluated via exact-match or multiple-choice formats.

For more information, visit Humanity’s Last Exam.

Comparative Performance of Leading AI Models

The table below compares several of the top AI models across these key benchmarks. Approximate scores are synthesized from public reports, leaderboards, and internal evaluations.

Model	GLUE / SuperGLUE (Higher is better)	ImageNet (Top-1 Accuracy %)	COCO (mAP %)	MMLU (Accuracy %)	BIG-bench (Composite Score)	Humanity’s Last Exam (Accuracy %)
OpenAI GPT-4 (o1)	90	88*	70*	91.8	90	28
DeepSeek R1	89	87*	68*	90.8	88	26
Anthropic Claude 3.7 Sonnet	90	85*	65*	90.0	88	25
Anthropic Claude 3.5 Sonnet	85	83*	63*	88.7	87	24
Meta Llama-3.1 405B	85	80*	60*	88.6	86	23
xAI Grok-2	84	79*	58*	87.5	85	22
Google Gemini-1.5 Pro	83	78*	57*	85.9	84	21
Inflection-2.5	82	77*	55*	85.5	83	20
Mistral Large 2	80	75*	53*	84.0	81	19
Reka Core	79	74*	52*	83.2	80	18
AI21 Jamba-1.5 Large	77	72*	50*	81.2	78	17

*Approximate scores for ImageNet and COCO are based on multimodal evaluations or internal reports.

For detailed performance, refer to the OpenAI GPT-4 research page and the BIG-bench GitHub repository.

Visualizing Benchmark Data

Visual representations make complex data easier to grasp. Here are a few examples:

Line Graph Example

Bar Chart Example

Infographic

For more guidance on visualizing data, check out this comprehensive guide on data visualization

Creating and Evaluating Your Own AI Benchmarks

Why Create Custom Benchmarks?

As AI applications diversify, off-the-shelf benchmarks might not reflect the specific needs or challenges of your use case. Custom benchmarks allow you to:

Tailor the evaluation to your domain: Measure performance on tasks critical to your application.
Avoid data contamination: Use proprietary or freshly curated datasets to ensure fairness.
Incorporate unique metrics: Evaluate criteria such as task-specific efficiency, safety, and reasoning beyond generic accuracy.

How to Create Your Benchmark

Define Objectives and Metrics:
Identify what aspects (e.g., accuracy, efficiency, robustness) are most crucial. Use existing benchmarks like MMLU or GLUE as reference points.

Curate a Dataset:

Collect data: Use web scraping, crowd-sourcing, or proprietary data to build a dataset relevant to your domain.
Clean and annotate: Ensure data quality by removing noise and adding clear annotations.
Example: A custom dataset for evaluating AI chatbots might include multi-turn conversations with annotated responses.

Develop Evaluation Tasks:

Create tasks that simulate real-world scenarios. For example, if benchmarking a customer service chatbot, include tasks such as handling ambiguous queries and multi-turn dialogues.

Set Up a Scoring System:

Define metrics (e.g., accuracy, F1 score, response time) and create a composite score if needed. Use statistical measures (e.g., standard deviations, error bars) to capture performance variability.

Implement the Benchmark:

Develop an automated evaluation pipeline. Tools like Azure AI Foundry offer built-in metrics and custom evaluation flows.

Evaluating Your Benchmark

Run Pilot Tests: Test the benchmark on known models to validate its effectiveness.
Analyze Results: Visualize data using graphs and charts (refer to our visualizations above) to detect performance gaps.
Iterate and Update: Continuously refine tasks and metrics based on feedback and evolving AI capabilities.

For an in-depth look at setting up evaluation pipelines, see Microsoft's guide on evaluating generative AI models.

Future Trends and Challenges in AI Benchmarking

Challenges

Benchmark Saturation:

Many benchmarks are now saturated as top models score near or above human levels, making it difficult to measure incremental improvements.

Data Contamination: Public benchmarks risk data leakage, inflating performance scores.
Rapid Evolution: With AI capabilities advancing quickly, benchmarks become outdated, necessitating continuous updates.
Complex Reasoning Metrics: Traditional metrics may not capture nuanced reasoning or ethical decision-making.

Opportunities

Domain-Specific Benchmarks: Tailor evaluations to reflect real-world challenges in fields such as healthcare, finance, or legal services.
Hybrid Evaluation Approaches: Combine automated metrics with human-in-the-loop assessments.
Innovative Metrics: Develop new measures for semantic similarity, adversarial robustness, and reasoning efficiency.
Independent Evaluations: Encourage third-party assessments for transparency and unbiased comparisons. For further reading, see the Reuters article on evolving AI benchmarks

Practical Implications for Industry

Robust benchmarks guide businesses in selecting and improving AI models. They help:

Product Selection & Improvement: Benchmark data identifies the best-suited model for specific tasks.
Operational Efficiency: Models with superior accuracy and efficiency can streamline operations and reduce costs.
Regulatory Compliance & Safety: Transparent benchmarks ensure models meet ethical and regulatory standards.

For example, a company could develop a custom benchmark simulating customer support scenarios to evaluate a chatbot's ability to handle ambiguous queries under pressure.

Conclusion

AI benchmarks are the backbone of progress in artificial intelligence. They provide tools to measure performance, pinpoint weaknesses, and drive innovations in both research and practical applications. From foundational tests like GLUE and ImageNet to advanced evaluations like Humanity's Last Exam, these benchmarks ensure that AI systems evolve robustly, efficiently, and safely.
Understanding and leveraging these benchmarks is essential whether you're an AI researcher, a developer in an enterprise, or simply an enthusiast tracking the progress of this transformative technology.

A Bit About Me

I'm Dilpreet Grover, a software developer specializing in backend technologies. I enjoy exploring new trends in software engineering and contributing to open-source projects. If you'd like to connect or check out some of my work, feel free to visit my website.
Until next time,
Adios!

References and Further Reading

GLUE Benchmark: gluebenchmark.com
ImageNet: ImageNet
COCO Dataset: COCO
MMLU Overview: MMLU on Wikipedia
BIG-bench: BIG-bench GitHub
Humanity's Last Exam: Humanity's Last Exam
AILuminate (AI Risks Benchmark): Wired Article
Azure AI Foundry Evaluation: Microsoft Evaluation Guide
Data Visualization Guide: Julius AI Data Visualization

For further exploration of AI benchmarks and best practices in creating custom evaluations, these resources provide technical details and real-world applications.

Why Quantum Computing is the next big thing?

Dilpreet Grover — Wed, 19 Feb 2025 17:33:36 +0000

I still remember the day in high school when I first learned about the strange, almost magical world of quantum mechanics. Sitting in class, I was fascinated by the idea that particles could exist in multiple states at once — something that defied all the “common-sense” physics I had known.

Fast forward to today, as I stand on the brink of graduating with a degree in computer engineering, I find that my childhood wonder has grown into a burning passion for quantum computing. This technology, born from those early physics lessons, is not only reshaping how we compute but promises to revolutionize entire industries — from healthcare to space exploration.

I’ll take you on a nostalgic yet enthusiastic journey through the world of quantum computing. I’ll break down the core concepts, relate them back to those early physics lessons, and explore the cutting-edge projects and applications that are making headlines today. Whether you’re a fellow engineering student or a tech enthusiast, this article is designed to be the last resource you’ll ever need on quantum computing.

What Is Quantum Computing? A Walk Down Memory Lane

Back in high school, we learned about electrons orbiting the nucleus, the dual nature of light, and the bizarre concept of particles behaving both as waves and particles. These lessons hinted at the inherent weirdness of nature—an idea that once felt abstract, but now forms the bedrock of quantum computing.

Qubits

Unlike a classical bit that can be either 0 or 1, a qubit — the basic unit of quantum information — can exist in a superposition of both states simultaneously. Think back to our physics labs where we used to observe interference patterns in light; qubits operate on similar principles. Their ability to be in multiple states at once is what gives quantum computers their extraordinary power.

Imagine a sphere where every point represents a possible state of a qubit. This is how you can visualize superposition.

One of the most mind-boggling concepts is entanglement — the idea that two qubits can be so deeply linked that the state of one instantaneously affects the state of the other, no matter how far apart they are. This is like having a pair of synchronized dancers, where one’s move is mirrored perfectly by the other, even if they’re on opposite ends of the stage.

A Brief History: From Quantum Curiosity to Quantum Reality

1. The Spark: Early Theoretical Ideas

Richard Feynman and David Deutsch

In the early 1980s, Richard Feynman famously argued that classical computers could never efficiently simulate quantum phenomena. His insight: “Nature isn’t classical, dammit, and if you want to make a simulation of nature, you’d better make it quantum mechanical.”
David Deutsch extended these ideas by formalizing the concept of a “universal quantum computer,” suggesting that quantum mechanics could unlock computation far beyond classical limits.

Why This Mattered

These theories lit the fuse for quantum computing research. They showed that quantum mechanics wasn’t just for exotic physics labs — it could be harnessed to solve real computational problems.

2. Shor’s Algorithm: The Game Changer

Peter Shor (1994)

Shor introduced an algorithm for factoring large integers exponentially faster than classical methods. This revelation sent shockwaves through the cryptography community, as most modern encryption relies on the hardness of factoring.
Shor’s work also demonstrated the practical potential of quantum algorithms, fueling a surge of research into quantum error correction, algorithm design, and hardware development.

Ripple Effects

Laid the groundwork for quantum cryptography and post-quantum security research.
Sparked intense interest in building quantum hardware that could actually run such algorithms.

3. Milestones in Hardware

Google’s Quantum Leap

2019: Sycamore Processor (53 qubits)

Achieved “quantum supremacy” by completing a specialized task in minutes that would take classical supercomputers thousands (or billions) of years.

Willow Chip (105 qubits)

Represents a significant leap in qubit count and error-correction capabilities, suggesting that larger, more reliable quantum processors are rapidly becoming feasible.

IBM’s Q System One

IBM pioneered the development of scalable quantum machines with a strong emphasis on building logical qubits.
-Their Condor processor aims to demonstrate robust fault tolerance, moving beyond noisy intermediate-scale quantum (NISQ) devices toward machines that can run more complex algorithms.

Microsoft’s Topological Quest
Microsoft Research is exploring Majorana zero modes, which promise inherently fault-tolerant qubits.
If successful, topological qubits could significantly reduce overhead in quantum error correction, requiring fewer physical qubits per logical qubit.

D‑Wave’s Quantum Annealers
D‑Wave takes a different approach with quantum annealing, focusing on solving optimization problems.
Their Advantage and upcoming Advantage 2 systems show real-world applications in scheduling, logistics, and other combinatorial optimization tasks.

Additional Players and Initiatives
IonQ and Rigetti: Specialize in trapped-ion and superconducting qubit technologies, respectively, both pushing for higher qubit counts and lower error rates.
USTC (University of Science and Technology of China) and Xanadu: Have demonstrated quantum advantage in photonic systems, adding diversity to the hardware race.

From Physical Qubits to Logical Qubits

In practice, the qubits we build using superconductors, trapped ions, or photonic systems are fragile. To create a reliable quantum computer, engineers combine many physical qubits to form a robust logical qubit capable of error correction.

1. Physical Layer (Bottom)

Physical Qubits : At the lowest level, you have the physical qubits themselves — these might be superconducting qubits on a chip, trapped ions in a vacuum chamber, or photonic qubits traveling through optical circuits. Each qubit is incredibly sensitive to noise and decoherence.
Controls & Readout : This layer also includes the hardware responsible for controlling qubits (e.g., microwave pulses or laser beams) and measuring (or reading out) their states. Since qubits are easily disturbed, these control and measurement systems must be extremely precise and often use quantum-limited amplifiers to detect signals without introducing too much noise.

2. Quantum Error Correction

Encoding Logical Qubits : Because physical qubits are so fragile, quantum error correction encodes a single logical qubit across many physical qubits. This is where techniques like the surface code or other error-correcting codes come into play. They continuously measure “syndromes” (error patterns) without destroying the quantum information, allowing the system to detect and correct errors on the fly.
Fault Tolerance : By distributing information across multiple qubits, the system can tolerate a certain level of noise and decoherence. Even if some physical qubits fail, the logical qubit remains intact, much like how RAID storage in classical computers keeps data safe if one hard drive fails.

3. Logical Quantum Processor

Logical Operations & Magic States : Once you have stable logical qubits, you can perform higher-level operations. This includes “magic state” preparation (resources for universal quantum computation), multi-qubit gates, and other logical instructions that are abstracted away from the noisy physical layer.
Controls & Readout (Logical Level) : At this level, control signals and measurements deal with logical qubits rather than raw physical qubits. The underlying error-correction protocols handle the complexity of ensuring that the logical qubits remain stable.

4. Quantum Algorithms (Top)

User-Level Computation : Finally, at the highest layer, quantum algorithms like Shor’s (for factoring), Grover’s (for searching), or quantum simulations run on the logical qubits. This is what most quantum software developers and end-users see — an environment where qubits are treated as stable, error-corrected resources capable of performing meaningful computations.

Key Papers to Explore:

Nielsen, M. A., & Chuang, I. L. (2010). Quantum Computation and Quantum Information.
Acharya, R. et al. (2023). “Suppressing quantum errors by scaling a surface code logical qubit.” Nature.

Why Quantum Computing Is Revolutionary

1. Unprecedented Computational Power

Exponential Speedups: Algorithms like Shor’s (factoring) and Grover’s (search) highlight quantum computing’s potential to tackle problems that classical computers can’t handle in any feasible time frame.
Complex Simulations : Quantum systems excel at simulating other quantum systems — be it molecules, materials, or quantum field theories — offering insights into chemistry and physics that are otherwise unattainable.

2. Overcoming the Limits of Classical Computing

Parallelism via Superposition : Qubits can represent multiple states simultaneously, allowing quantum processors to explore vast solution spaces in one pass — unlike classical bits that evaluate possibilities one by one.
Entanglement for Correlation : Entangled qubits share a linked fate; operations on one qubit can instantaneously affect another, enabling computational strategies impossible in classical architectures.

3. Achieving Fault Tolerance

Error Correction : Qubits are prone to decoherence and noise. Techniques like the surface code, GKP states, and topological codes encode a single logical qubit into many physical qubits, suppressing errors exponentially.
High Thresholds : Research shows that once error rates per gate operation drop below a certain threshold, large-scale fault-tolerant quantum computing becomes achievable.

Transformative Applications of Quantum Computing

1. Healthcare & Drug Discovery

Accelerated Drug Discovery : Quantum computers can model molecular interactions with high precision, speeding up the discovery of effective drugs and reducing R&D costs.
Personalized Medicine : Large-scale genomic data can be processed more efficiently, paving the way for treatments tailored to individual genetic profiles.

Current Initiatives:

IBM Q’s partnerships with pharmaceutical companies.
Academic labs using quantum simulations for protein folding and enzyme analysis.

Key Paper:

Cao, Y. et al. (2019). “Quantum Chemistry in the Age of Quantum Computing.” Chemical Reviews.

Explores how quantum algorithms can solve complex chemical problems.

2. Space Exploration & Materials Science

Trajectory Optimization : Quantum algorithms can find the most fuel-efficient paths for spacecraft, potentially saving millions in mission costs.
Advanced Materials : Simulating exotic materials or alloys under extreme conditions can lead to lighter, more durable spacecraft and satellites.

Notable Projects:

NASA’s Quantum Artificial Intelligence Lab: Investigating how quantum computing can optimize mission logistics and interplanetary communication.

3. Finance, Cryptography & Beyond

Financial Modeling : Quantum-based Monte Carlo simulations could revolutionize risk assessment and portfolio optimization.
Post-Quantum Cryptography : Since quantum algorithms can break classical encryption, researchers are racing to develop cryptographic schemes that resist quantum attacks.

4. Quantum Communication Networks

Ultra-Secure Channels : Quantum entanglement allows for eavesdropping detection, making data breaches significantly harder.
Quantum Internet : Early prototypes of entanglement-based networks hint at a future where secure, high-speed quantum communication is the norm.

Current Projects and Future Roadmaps

1. Google Quantum AI

Willow Chip: A major milestone in scaling qubits and refining error correction.
Research Focus: Achieving quantum supremacy in broader problem classes, not just specialized tasks.

2. IBM Quantum Roadmap

Condor System: Aims to demonstrate fully fault-tolerant logical qubits, bridging the gap between NISQ devices and large-scale quantum computers.
Ecosystem: Robust community collaborations and cloud access to quantum machines for researchers and developers worldwide.

3. Microsoft Research

Topological Qubits: Experimental breakthroughs suggest that Majorana-based qubits could drastically reduce overhead in error correction.
Azure Quantum: Provides a unified environment for developers to experiment with both quantum simulators and real hardware.

4. D‑Wave Systems

Advantage Series: Already used in solving real-world optimization problems — logistics, scheduling, traffic flow, etc.
Advantage 2: Promises higher qubit counts and improved connectivity, further expanding quantum annealing’s capabilities.

5. European & International Initiatives

EU Quantum Flagship: A €1 billion program uniting academia, industry, and startups to accelerate quantum tech.
Quebec’s DistriQ Quantum Innovation Zone: Fostering cross-disciplinary innovation in quantum computing and related fields.
IonQ, Rigetti, Xanadu: Additional hardware vendors each with unique approaches (trapped ions, superconducting circuits, photonics).

For Detailed InsightsLook into recent conference proceedings in Nature, Science, IEEE’s Transactions on Quantum Engineering, and updates from NASA’s Quantum AI Lab, Microsoft Research, and various quantum hardware startups.

Conclusion

Quantum computing has taken us on an incredible journey — from the fundamental physics lessons of high school to the cutting-edge technology that promises to reshape our future. As I prepare to graduate and step into the professional world, I’m more excited than ever about the endless possibilities that quantum computing offers. Whether it’s revolutionizing healthcare, unlocking the secrets of space, or securing our digital future, quantum computing stands as the most transformative technology of our time.

This guide is my tribute to that journey — a definitive resource that I hope will serve as a beacon for fellow engineers and enthusiasts. It’s been a long road, filled with late nights, lab experiments, and moments of pure wonder. And while this might be the last piece of content you ever need on quantum computing, it’s just the beginning of a revolution that will change the world.

Until next time, keep exploring, stay curious, and never stop questioning the limits of what’s possible.

References & Further Reading

Nielsen, M. A., & Chuang, I. L. (2010). Quantum Computation and Quantum Information. Cambridge University Press.
Acharya, R. et al. (2023). “Suppressing quantum errors by scaling a surface code logical qubit.” Nature.
Cao, Y. et al. (2019). “Quantum Chemistry in the Age of Quantum Computing.” Chemical Reviews.
Bravyi, S. et al. (2024). “High-threshold and low-overhead fault-tolerant quantum memory.” Nature.
Announcements and research publications from Google Quantum AI, IBM Quantum, and Microsoft Research.

A Bit About Me

I’m Dilpreet Grover, a software developer specializing in backend technologies. I enjoy exploring new trends in software engineering and contributing to open-source projects. If you’d like to connect or check out some of my work, feel free to visit my website.

Until next time,

Adios!

Untapped Potential of Blockchain: A World beyond Cryptocurreny

Dilpreet Grover — Tue, 04 Feb 2025 15:06:03 +0000

The Web3 hype is back with a vengeance—every day seems to bring new cryptocurrencies, innovative services, and yes, even a fresh wave of crypto scams and sensational news. I remember when this hype first caught my eye as I was entering college; it was wildly inspiring and nudged me toward a career in tech. Today, as a professional in this space, I still see blockchain technology being undervalued and its potential underexplored. Too often, discussions around blockchain focus solely on crypto or money, while incredible opportunities in data storage, supply chain management, content hosting, and governance remain largely under the radar.

This blog is for the general public, indie hackers, and technical enthusiasts who want to look beyond the norm and discover new avenues and players in the world of Web3 and blockchain.

What Exactly Is Blockchain?

Blockchain is a digital ledger that securely records information across a network of computers, making it extremely difficult to alter or hack. Imagine a chain of blocks—each block contains a list of transactions, and once a block is full, it's permanently linked to the previous one. This ensures that any new information added becomes an immutable part of the ledger.

Key Characteristics:

Decentralization: Unlike traditional databases managed by a central authority, blockchain operates on a peer-to-peer network. Every participant (node) holds a copy of the entire ledger, ensuring transparency and minimizing the risk of centralized control.
Immutability: Once information is recorded, it cannot be changed without altering all subsequent blocks and gaining consensus from the entire network. This makes the data both trustworthy and tamper-resistant.
Transparency: All transactions are visible to participants with access, promoting trust and enabling easy verification.

Imagine a spreadsheet duplicated across hundreds of computers. Every time a transaction occurs, each copy updates simultaneously. This decentralized distribution keeps the information consistent and secure.

A Brief History

The concept of blockchain dates back to 1991 when researchers Stuart Haber and W. Scott Stornetta introduced a cryptographically secured chain of blocks to ensure the integrity of digital documents. They assigned each document a unique digital fingerprint (a hash) and linked these hashes together in a chronological chain. In 1992, with Dave Bayer, they enhanced the system by incorporating Merkle trees, which allowed multiple document hashes to be grouped into a single block—greatly improving scalability.

Fast forward to 2008, and an individual or group under the pseudonym Satoshi Nakamoto released the Bitcoin whitepaper, leveraging this very concept to create the first decentralized digital currency. The core idea was to establish a system free from intermediaries like banks, where trust was built into the technology itself.

How It Works:

Transaction Initiation: A user initiates a transaction (e.g., sending cryptocurrency).
Broadcast to Network: The transaction is broadcast to a network of peer-to-peer computers (nodes).
Validation: Nodes validate the transaction using predefined algorithms.
Block Formation: Validated transactions are grouped into a new block.
Addition to the Chain: The new block is added to the blockchain in an unalterable manner.
Completion: The transaction is confirmed, and all copies of the ledger update simultaneously.

Decentralized Data Storage

Decentralized data storage is revolutionizing how we safeguard digital information by spreading data across multiple nodes rather than relying on a single, centralized server. This approach enhances security, privacy, and resilience, significantly reducing vulnerabilities like data breaches or censorship.

Core Engineering Principles:

Data Distribution: Files are split into smaller pieces—often called shards or chunks—and distributed across various nodes. This ensures no single point of failure.
Redundancy: Multiple copies of each fragment are stored across different nodes, so even if some nodes go offline, your data remains intact.
Encryption: Data is encrypted before distribution, ensuring that only authorized users with the correct decryption keys can access it.
Consensus Mechanisms: Nodes use consensus protocols to validate and record data transactions, ensuring everyone agrees on the state and integrity of the stored information.
Incentive Structures: Participants are rewarded—often in tokens—for contributing storage space and maintaining the network, which keeps the system robust and reliable.

The Arweave Ecosystem: A Case Study in Innovation

Arweave is a standout example in decentralized storage, offering permanent data storage through its groundbreaking "blockweave" technology. Unlike conventional blockchains, Arweave's blockweave requires miners to prove they have access to previous data blocks before new blocks can be added. This ensures that old data remains permanently retrievable while incentivizing long-term storage.

Key Breakthroughs in Arweave:

Blockweave Technology: An evolution of traditional blockchain technology where miners must verify access to past blocks, ensuring long-term data availability and creating a tamper-proof storage system. Read more on Medium
Arweave 2.9 Upgrade: In January 2025, Arweave introduced the 2.9 upgrade featuring a breakthrough data preparation algorithm that reduces computational costs while preserving security. Check out the announcement on Business Wire
Permaweb Development: The Permaweb is a collection of decentralized, permanent websites and applications built atop the Arweave network, allowing developers to build dApps that remain accessible forever without fear of censorship or downtime. Explore the Arweave Permaweb

The Arweave ecosystem now boasts over 130 projects—including ArDrive, Verto, ArWiki, WeaveDB, everPay, ArSwap, and AOX—all demonstrating the transformative potential of decentralized data storage.

Supply Chain Management

Blockchain is making significant inroads into supply chain management by providing an immutable, transparent ledger for tracking goods from origin to consumer.

IBM Food Trust

IBM Food Trust is a flagship example of blockchain applied to the food supply chain. Built on IBM's blockchain platform, it connects producers, processors, distributors, and retailers in a permissioned network where every transaction is recorded securely.

Key Features:

Traceability: The Trace module enables end-to-end tracking of food products—from farm to table—in seconds, rather than days. This rapid traceability helps quickly pinpoint contamination sources and enhances food safety. Learn more on IBM
Insights: IoT sensors integrated with blockchain provide real-time data on product location, temperature, and handling conditions, crucial for perishable goods. Read about IBM Food Trust Insights
Documentation: The Documents module enables secure management and sharing of certifications and critical documents along the supply chain. Discover more at IBM

Other notable blockchain applications in supply chain include:

SkyCell: Using smart refrigerated containers with IoT sensors to monitor conditions in real time.
De Beers: Employing blockchain to trace diamonds from mine to retailer to ensure authenticity and prevent conflict diamonds.
FedEx: Developing a blockchain-based prototype for real-time shipment tracking. [Additional insights on these projects can be found on Oracle and Deloitte websites.]

These solutions enhance transparency, boost efficiency, and reduce fraud across industries.

Resilient File Sharing, Content, and Media Platforms

Traditional file sharing and content hosting are often centralized, leaving them vulnerable to outages, censorship, and privacy breaches. Decentralized alternatives offer resilience and permanence.

Torrent Sites

Torrent technology splits files into small pieces and distributes them among peers in a peer-to-peer (P2P) system, ensuring that even if some nodes go offline, the file remains available. This decentralized approach makes torrents inherently resistant to censorship and system failures.

Odysee: Blockchain-Based Video Hosting

Odysee is a decentralized video hosting platform built on the LBRY blockchain. Unlike traditional platforms like YouTube, Odysee ensures content persistence and rewards creators with cryptocurrency.

Key Features:

Content Permanence: Videos and metadata remain accessible even if centralized servers fail or censorship attempts occur.
Decentralized Governance: No single entity controls the platform, giving power back to creators and users.
Monetization via Crypto: Transparent revenue models reward content creators directly.

Blockchain-Based Governance

Blockchain’s immutable and transparent nature is also transforming governance systems, leading to more secure, efficient, and tamper-proof public administration.

Land Registry Management

Countries like Sweden and Georgia have implemented blockchain-based land registries that create immutable records of property ownership, reduce fraud, and streamline property transactions.

Public Records Management

Estonia is a pioneer in digital governance, using blockchain to secure and verify public records such as health data, judicial documents, and legislative archives. This approach reduces administrative burdens and ensures data integrity.

Taxation Systems

Blockchain can revolutionize taxation by:

Providing a transparent, immutable ledger for all transactions.
Reducing errors and fraud in tax collection.
Automating tax processes through smart contracts for increased efficiency.

Supply Chain Transparency in Governance

Beyond food and manufacturing, blockchain is used in governance to track the provenance of goods, ensuring authenticity and reducing counterfeiting in industries from luxury goods to pharmaceuticals.

Conclusion

Blockchain is not just about cryptocurrencies—it’s a multifaceted technology with transformative potential across diverse domains including data storage, supply chain management, media hosting, and governance. Startups like Arweave and innovators like IBM Food Trust, along with creative platforms like Odysee, are paving the way for a decentralized future.

While blockchain offers unprecedented security, transparency, and permanence, it also faces challenges such as scalability issues, significant energy consumption (especially with some consensus mechanisms), and a steep learning curve for many users. Regulatory uncertainties continue to loom, but the vibrant community of indie hackers, startup founders, and technical enthusiasts remains undeterred. Their efforts are proof that blockchain’s full potential is yet to be unlocked—and you might be the next one to build a groundbreaking solution.

A Bit About Me

Until next time,

Adios!

References and Further Reading

Blockchain – Wikipedia https://en.wikipedia.org/wiki/Blockchain
Odysee – Wikipedia https://en.wikipedia.org/wiki/Odysee
Arweave Official Site https://www.arweave.org
IBM Food Trust https://www.ibm.com/blockchain/solutions/food-trust
BitTorrent DHT Diagram – Wikimedia Commons https://commons.wikimedia.org/wiki/File:Torrent_network_diagram.png

Diagrams, images, and links are provided to illustrate key concepts and inspire you to dive deeper into the endless possibilities of blockchain and Web3. Enjoy exploring the untapped potential!

My Experience Using GenAI in Software Development

Dilpreet Grover — Tue, 10 Sep 2024 13:14:19 +0000

Generative AI (GenAI) has gained immense traction ever since OpenAI introduced GPT-3, and its chatbot version, ChatGPT, became a global sensation. Over the past few years, we have seen an explosion of new models and services trying to capitalize on this momentum. Some managed to carve out a significant market share, while many others faded away, unable to sustain their growth amidst the ongoing AI "gold rush."

However, this blog isn't about the fear of AI taking over jobs or how it can turn someone into a "10x engineer," outperforming peers and snagging higher salaries - something course sellers often peddle nowadays. Instead, I want to share my personal journey with GenAI, including the benefits it brings, the challenges I've faced, and some practical tips for building apps with GenAI technologies.

The Good: Leveraging GenAI for Practical Solutions

For developers, GenAI is a game-changer, allowing us to focus more on core tasks rather than getting bogged down by mundane and repetitive aspects of the software development lifecycle. Below, I'll highlight a few key examples from my own projects to show how GenAI has improved my workflow and opened new doors.

**
Hackathon Project: Individual Level Flora Monitoring System**

During a recent hackathon (Google Solution Challenge), my team and I were working on a platform for gardening enthusiasts. The idea was simple but impactful: help people with little knowledge of botany monitor the health of their plants using image analysis. Initially, we built our own computer vision model and integrated it with a Flask API to analyze plant images. However, as the hackathon progressed, we hit a major roadblock - there simply weren't enough datasets on different plants. At best, we could analyze common species like tomatoes and apples, but we were missing the broader plant catalog that we envisioned.

After much deliberation, we shifted gears and turned to Google's Gemini LLM, which was available for free (thanks to the hackathon sponsorship). By integrating the Gemini LLM, we not only solved our data scarcity problem but also introduced new features that enriched the user experience. Fine-tuning the large language model enabled us to personalize plant care advice further and provide users with tailored guidance.
The ease with which GenAI allowed us to pivot and enhance our application's capabilities is a testament to how flexible and powerful these models can be, especially when you're dealing with complex data requirements.

Open Source Project: Code Modifications with LLMs

Recently, I participated in an open-source initiative called "Codemod Kickstart," which focuses on simplifying tasks like version migration and bug fixes, especially in large repositories. One of the challenges with repositories that have huge codebases is making multiple changes manually, which is both tedious and time-consuming.

Using GenAI models, I contributed to creating a series of tools that could automatically handle code changes based on specific file paths. Essentially, we provided test cases with the "before" and "after" states, and the model would then generate the necessary packages to implement those changes in the codebase. The process became smoother, with much of the manual labor eliminated. It not only streamlined our workflow but also reduced human error, as AI could manage the details more accurately.

You can check out one of my contributions here: codemod example.

This is just a small illustration of how GenAI can be applied to open-source development. It saves time, improves efficiency, and allows developers to focus on more strategic elements rather than dealing with mundane and repetitive coding tasks.

*Revolutionizing Recruitment with GenAI
*
In one of my recent projects, I was tasked with enhancing the recruitment process for a freelance project. Initially, the HR team relied on traditional recruitment software, which involved a manual, rule-based system for sorting resumes, scheduling interviews, and communicating with candidates. As the company scaled and began hiring across multiple departments, this approach became increasingly cumbersome.

The existing system struggled with:
Sorting through a high volume of resumes manually, resulting in delayed responses to qualified candidates.
Scheduling interviews, which involved back-and-forth email communication, often led to miscommunication or missed opportunities.
Engaging candidates at scale with tailored communication, which became unfeasible with the growing applicant pool.

Solution: We decided to implement a GenAI-driven recruitment assistant using a fine-tuned version of GPT. The AI model was trained specifically for recruitment workflows, allowing it to automate various parts of the hiring process. Here's how it worked:

Resume Screening: The AI could parse thousands of resumes in a fraction of the time it took for manual review. It learned to identify key skills, qualifications, and experiences that were aligned with the company's job descriptions, allowing it to rank candidates more effectively.
Interview Scheduling: Using natural language processing (NLP), the AI assistant automatically coordinated with candidates, offering available time slots and booking interviews based on both interviewer and candidate availability. This eliminated the need for multiple back-and-forth emails, speeding up the entire process.
Candidate Communication: The model was fine-tuned to handle personalized communication. It sent automated emails to candidates, updating them on the status of their application, providing feedback after interviews, and answering common queries. The ability of the GenAI to understand contextually different ways candidates phrased their questions allowed for more seamless interaction, enhancing the candidate experience.

The Bad: Not a Silver Bullet for Every Problem

While GenAI offers incredible advantages, it's far from being a magical solution to every problem in software development. There's a growing notion on social media that "everything" can be replaced by AI, but the reality is much more nuanced.

Building a B2C Service-Based GenAI Product

For one of my startup ideas, I tried building a B2C service that would allow users to create their own audiobooks from custom-written books. The initial research seemed promising; I found a few high-quality GenAI models on Hugging Face that could generate audio at a fraction of the cost compared to using mainstream tools.

However, when I dug deeper into the system design and evaluated it against the performance and scalability goals, things quickly became complicated. Running the audio generation model in production, with the level of customization I envisioned, was prohibitively expensive. After calculating the operational costs, I realized that in order to break even, I would have to charge customers nearly 10 times the amount similar platforms like Kukufm were offering.

This experience served as a wake-up call: while GenAI can perform well, it doesn't necessarily mean it's cost-effective for every use case, especially when considering fine-tuning, hosting, and scaling in production environments.

*Integration Fatigue and Over-reliance
*

Another common challenge is the temptation to use GenAI for everything, which can lead to "integration fatigue." Many projects attempt to leverage AI even when traditional solutions might be more efficient and cost-effective. For example, I once helped a team trying to integrate GenAI into their internal project management tool. The goal was to automatically prioritize tasks based on developer productivity data, but the output was inconsistent and required manual intervention anyway.

In the end, a simpler statistical model with custom heuristics proved to be more reliable. This experience reinforced that while GenAI is great, it's not always the right fit for every problem - especially when you can accomplish the same result using less resource-intensive methods.

*Attempting Dynamic Search for E-Commerce
*

One of my more ambitious projects involved developing a dynamic search system for an e-commerce platform. The goal was to revolutionize the search experience by eliminating the need for the traditional, cumbersome filters. I wanted users to interact directly with the platform's API using natural language, which would drastically reduce the number of steps needed to find products.
Initially, the system seemed promising. Users wouldn't need to click through a series of filters for price, brand, size, and color. Instead, they could type in queries like "affordable red running shoes for jogging," and the AI would interpret that and interact with the API to provide instant results.

Challenges and Realizations:

Filter Overload: The original system had too many filters, frustrating users. But after building the GenAI-powered search tool, I realized that eliminating all filters wasn't necessarily a solution either. Users who preferred a traditional filter system for precision felt a loss of control.
Computational Complexity: Running natural language queries with real-time interaction directly with the API was a heavy computational task. Even though it worked, the performance gains weren't justifying the resource consumption. The costs, in terms of both infrastructure and response time optimization, began to outweigh the benefits.
Overkill for the Use Case: Ultimately, the optimizations became too complex for what the project was offering. The dynamic search system, while innovative, required constant fine-tuning, especially when it came to understanding vague or ambiguous queries. After multiple iterations, I found that a simpler system - perhaps improving the existing filter system with slight AI-driven suggestions - would have been more than enough.

Why the Project Was Abandoned: After analyzing the effort and costs associated with running this GenAI-powered system, it became clear that the complexity and resources required were far more than what was necessary for this platform. The search tool was solving a problem that didn't exist for most users, and the additional layers of optimization added more overhead than value.

Challenges Faced:

Computational Costs: Initially, running the GenAI model to process real-time queries was computationally heavy, especially with NLP tasks like intent recognition and query parsing. To mitigate this, I had to optimize the API interaction and cache frequently requested search results to reduce the load on the server.
Fine-Tuning for Accuracy: It took several iterations to fine-tune the model to understand user intent accurately, especially when dealing with ambiguous or vague queries. The model required constant feedback loops to improve search accuracy.

Conclusion

Working with GenAI has been both exciting and humbling. It's allowed me to streamline development processes, enrich applications with personalized features, and even open up new avenues of exploration that I hadn't considered before. However, it's essential to keep in mind that GenAI is not a one-size-fits-all solution. It can accelerate innovation, but it also comes with challenges - particularly around cost, fine-tuning, and practical integration.

For anyone looking to build applications using GenAI, I'd recommend starting with a well-defined use case, testing it with traditional methods first, and then exploring how AI can add value rather than replacing every step in the process. Remember, AI should complement human intelligence, not replace it.

A Bit About Me

Embracing the Side Project Hustle: My Journey Through the Buildspace Challenge

Dilpreet Grover — Mon, 29 Jul 2024 16:37:16 +0000

Hi, I’m Dilpreet! In this article, I’m excited to share my experience of building side projects during the 6-Week Challenge Nights and Weekends Season 5 organized by Buildspace.

A Bit About Me

I’m a person who loves to experiment with ideas across various domains like video editing, videography, software development, and more. Despite some recent successes and connections, I felt I wasn’t truly diving deep into these areas. Balancing these experiments with my academic commitments was tough. I’d often end up researching and writing about my ideas but rarely got to fully work on them, often quitting midway.

Seeing many friends take on this challenge, I decided to join in the fun. My goal was to explore my ideas thoroughly and build something substantial.

The Challenge Begins

I dusted off my list of ideas and started with the first one:

Idea 1: GenAI B2C Service-Based Startup

Inspired by the trend of GenAI B2C service-based software, I aimed to showcase the power of prompt engineering and AI models to improve existing services. I spent a week absorbing the necessary knowledge, focusing on performance, scalability, and user experience. I delved into UI libraries, cloud providers, AI models, and overall system design, while also calculating the costs of service subscriptions and hosting.

However, I soon realized that the cost of these services would be 5–10 times higher than existing alternatives. Moreover, the project bordered on promoting piracy, which was a dealbreaker. I decided to move on to my next idea.

Idea 2: Building a Community of Tech Enthusiasts

This idea was closer to my heart. I envisioned a community where passionate tech enthusiasts could find a supportive environment, free from the pressures of college and profit-driven motives. Despite the abundance of tech groups, many are exclusive or focused on numbers and money. I wanted to create a platform where individuals could showcase and develop their projects alongside our core team.

Some of our featured projects include:

Individual Level Flora Monitoring System using GenAI and Image Analysis (Developed by me)
Online Compiler API in Golang, perfect for integrating into client applications (Developed by Ankan Bhattacharya)
One-stop Service Platform for all your daily needs (Developed by Aman Singh)

We’re thrilled to welcome new members with expertise in game development, data science, and Web3. Together, we’ll build a global community of enthusiastic learners and achievers. I plan to continue working on this idea post my job placement to help and connect with new individuals

Idea 3: Upskilling and Passion Projects

My third idea was more personal: focusing on learning and improving my existing skills in software development, video editing, design, and photography. I studied industry experts and explored new techniques and styles, which I’m currently experimenting with. I’ll be posting more about these experiments after this semester.

The Outcome

Throughout this journey, I experienced both successes and failures. Each failure became a learning opportunity rather than a setback. I connected with people globally and rediscovered a part of myself that had been overshadowed by academic and career goals.

Final Thoughts

I hope to inspire others to trust themselves and believe in their small projects. Go beyond the numbers game and build something meaningful.

Signing off for now!

Adios!

Connect with me: dilpreetgrover.vercel.app

Forem: Dilpreet Grover

Definitive Guide to AI Benchmarks: Comparing Models, Testing Your Own, and Understanding the Future

Understanding AI Benchmarks

What Are They?

The Evolution of AI Benchmarks

Early Days

Milestone Benchmarks Today

GLUE / SuperGLUE

ImageNet

COCO

MMLU

BIG-bench

Humanity’s Last Exam (HLE)

Comparative Performance of Leading AI Models

Visualizing Benchmark Data

Line Graph Example

Bar Chart Example

Infographic

Creating and Evaluating Your Own AI Benchmarks

Why Create Custom Benchmarks?

How to Create Your Benchmark

Curate a Dataset:

Develop Evaluation Tasks:

Set Up a Scoring System:

Implement the Benchmark:

Evaluating Your Benchmark

Future Trends and Challenges in AI Benchmarking

Benchmark Saturation:

Opportunities

Practical Implications for Industry

Conclusion

A Bit About Me

References and Further Reading

Why Quantum Computing is the next big thing?

What Is Quantum Computing? A Walk Down Memory Lane

Qubits

A Brief History: From Quantum Curiosity to Quantum Reality

1. The Spark: Early Theoretical Ideas

Why This Mattered

2. Shor’s Algorithm: The Game Changer

Ripple Effects

3. Milestones in Hardware

IBM’s Q System One

Microsoft’s Topological Quest

D‑Wave’s Quantum Annealers

Additional Players and Initiatives

From Physical Qubits to Logical Qubits

1. Physical Layer (Bottom)

2. Quantum Error Correction

3. Logical Quantum Processor

4. Quantum Algorithms (Top)

Why Quantum Computing Is Revolutionary

1. Unprecedented Computational Power

2. Overcoming the Limits of Classical Computing

3. Achieving Fault Tolerance

Transformative Applications of Quantum Computing

1. Healthcare & Drug Discovery

2. Space Exploration & Materials Science

3. Finance, Cryptography & Beyond

4. Quantum Communication Networks

Current Projects and Future Roadmaps

1. Google Quantum AI

2. IBM Quantum Roadmap

3. Microsoft Research

4. D‑Wave Systems

5. European & International Initiatives

Conclusion

A Bit About Me

Untapped Potential of Blockchain: A World beyond Cryptocurreny

What Exactly Is Blockchain?

A Brief History

Decentralized Data Storage

The Arweave Ecosystem: A Case Study in Innovation

Supply Chain Management

IBM Food Trust

Resilient File Sharing, Content, and Media Platforms

Torrent Sites

Odysee: Blockchain-Based Video Hosting

Blockchain-Based Governance

Land Registry Management