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Optimizing JVM startup time is vital for many applications. This post explores various strategies, comparing their effectiveness through benchmarking a simple Netty server. Class Data Sharing (CDS) and its evolution, AppCDS, cache class data to reduce initialization overhead. By Michał Zyga.

This article then dives into the following:

• Caching class data (CDS/AppCDS) is a fundamental optimization
• AOT compilation (GraalVM) provides fast startup but has trade-offs
• Process snapshots (CRaC) can enable rapid restarts with potential for peak performance
• Project Leyden promises further improvements in startup speed
• Consider platform compatibility and application requirements when choosing a method

GraalVM leverages ahead-of-time compilation for faster startup but presents challenges with dynamic code and platform compatibility. CRaC uses process snapshots for rapid restarts, potentially maintaining peak performance but demanding code modifications and primarily supporting Linux. Project Leyden, still under development, aims to further accelerate startup by including more data in the archive. Benchmarks reveal that AppCDS offers a substantial improvement over CDS, while CRaC and GraalVM provide the fastest startup times. The best approach depends on factors like application complexity, desired performance level, and target platform. good read

Evogene and Google Cloud are accelerating life science discovery with ChemPass AI, a generative AI foundation model focused on small-molecule design. Launched in May, this collaboration dramatically reduces the time and cost associated with identifying novel drug candidates and crop protection agents. ChemPass AI’s core strength lies in its ability to simultaneously optimize multiple critical properties – potency, toxicity, stability, and bioavailability – within a single molecule generation cycle, surpassing previous approaches. By Antoine Tardif.

Unlike traditional methods that focus on trial-and-error, ChemPass utilizes transformer neural networks trained on a massive chemical dataset—estimated at 40 billion molecules—to understand complex relationships between structure and property. The model’s multi-objective optimization proactively guides the AI towards optimal design, mitigating risks associated with complex drug discovery. Recent evaluations suggest ChemPass AI exhibits notable performance and accuracy improvements, particularly regarding novelty generation. Initial tests showed that the model consistently generated molecules significantly more diverse than baseline GPT models, exhibiting a 30-40% increase in chemical space exploration. Critically, the model’s predictive accuracy for efficacy – specifically, predicting how well a molecule would interact with a target protein – increased by approximately 15% compared to existing models.

This integration extends beyond just molecule generation; it includes broader tools like MicroBoost AI, facilitating a holistic approach to chemical data analysis. The partnership strategically positions Evogene as a leader in AI-driven innovation across multiple sectors – pharmaceuticals, agriculture, and materials science. The move underscores the growing importance of AI in revolutionizing R&D—potentially impacting billions of dollars in research and development costs globally.

OpenAI is collaborating with Alphabet’s Google Cloud, a move designed to address the escalating demand for high-powered computing infrastructure required by AI models. The agreement, finalized in May, represents a substantial expansion of Google’s cloud services, signaling a broadening strategic focus beyond Microsoft’s Azure. The primary goal appears to be to meet OpenAI’s projected $10 billion annualized revenue, driven primarily by the training and operation of large language models, as well as inference tasks. This partnership isn’t simply about adding capacity; it’s a calculated move to aggressively contest Google’s market share within cloud computing and AI. It’s a dynamic where two giants are vying for control of the most important technology – AI. By CNA.

Key points in the announcement:

• Strategic Focus: Google’s strategy centers around bolstering its cloud infrastructure through a combined effort with OpenAI
• Competitive Pressure: The deal amplifies the competitive tension between Google and OpenAI in the AI space
• Cloud Hardware Advantage: Google’s TPUs are crucial to this strategic shift, positioning it as a hardware-software partner
• Ecosystem Implications: This collaboration has the potential to reshape the entire cloud computing ecosystem

The implications extend beyond just increased revenue. Google’s expansion of its own in-house chip, Tensor Processing Units (TPUs), further positions it as a key player in hardware and software convergence within the cloud space. The deal also underscores the evolving competitive landscape; ChatGPT is forcing Google to re-evaluate its approach to AI-driven cloud offerings – potentially impacting existing customer relationships and investment strategies. The long-term impact on the overall cloud market could be substantial, driving further innovation and consolidation.

AWS offers multiple services for decoupling business domains in event-driven patterns. The three main ones are EventBridge, SNS, and SQS. Use EventBridge for targeted content-based routing when you need to match complex rules. By Arpad Toth.

This blog post provides a concise comparison of three key AWS services essential for building event-driven architectures: Amazon EventBridge, Simple Notification Service (SNS), and Simple Queue Service (SQS). Each serves distinct purposes in decoupling business logic from infrastructure.

• EventBridge is ideal for complex content-based routing. It excels when you need to deliver events based on specific message criteria to targeted destinations (up to five) using rules defined on an event bus.
• SNS handles fan-out scenarios effectively. Use SNS when you need real-time, one-to-many broadcast notifications; it supports various protocols and can guarantee order for critical applications via FIFO topics.
• SQS provides buffering and reliable message handling. When downstream systems are rate-limited or require protection against spikes (e.g., protecting a third-party API), SQS queues store messages temporarily, allowing controlled processing.

The author uses an e-commerce scenario involving different order types to illustrate these points clearly. Key considerations include SNS’s fanout capabilities with filtering and EventBridge Pipes as potential alternatives for some use cases like handling AWS service events point-to-point. This comparison helps CIOs understand the trade-offs between these services, guiding architectural decisions towards appropriate routing, notification, or buffering strategies. Links to further reading are also provided. Good read!

Mark Seemann introduces an article series exploring alternative design approaches to a specific problem originally presented by Oleksii Holub. The core issue involves identifying song recommendations from data repositories containing impurities (like unreliable user activity). This series challenges the notion that some problems resist purely functional solutions, particularly those with ‘real-world’ constraints.

Key learnings:

• Problems often have multiple valid design solutions.
• Examining alternatives can reveal hidden paths to robustness.
• Functional approaches can accommodate impure data and operations through various designs.

Seemann stresses the importance of having multiple viable designs for a problem before choosing one. He sees this as crucial for informed decision-making, preventing reliance on potentially suboptimal first solutions due to language constraints or other factors (as seen in his previous “Impureim Sandwich” work). This approach allows CIOs and their technical teams to understand trade-offs better.

The series serves as a catalogue of design options, highlighting how functional programming principles can be applied flexibly even when dealing with messy data. It underscores that the original non-functional solution (the impure recommendation) is just one path among many analyzed here. The key takeaway for executives is understanding this landscape to guide architectural choices effectively. Good read!

In this summary, we explore how IBM Granite and Ollama can be integrated with HCP Vault to handle sensitive data effectively. The method involves configuring vaults to mask or tokenize data before passing it to the LLMs, ensuring that personal identifiable information (PII) is protected. By Rosemary Wang.

The article covers how to:

• Configure Vault transform secrets engine
• Apply masking or tokenization to sensitive information
• Configure a local LLM model
• Add documents to a knowledge base for RAG
• Test the knowledge base

The rise in use of large language models (LLMs) for various tasks has brought up significant concerns regarding data privacy, especially concerning sensitive information like personal identifiable information (PII). This article explores a solution to mitigate these risks by integrating IBM Granite and Ollama with HCP Vault to anonymize RAG data effectively. Nice one!

In the last five or so years, corporate responses to a challenging macroeconomic environment have been dominated by two themes: cost cuts and price increase. By Josh Vandekar, Lee Mergy, Zach Newman.

The article provides a compelling case for why focusing on identifying the right customers is paramount for long-term success in an uncertain economic environment. It highlights that cost-cutting alone does not translate into higher shareholder value, which can lead to reduced growth and profitability. Instead, businesses should adopt a more strategic approach by understanding their customers’ true needs and deriving value from them. This involves making deliberate choices about whom to serve and how to enhance the customer experience. By doing so, companies can build strong relationships with their key customers, leading to improved operational efficiency and higher returns on investment.

Key Learnings:

• Cost-cutting alone is ineffective in driving long-term growth
• Identifying “win-win” customers is crucial for creating value for both businesses and customers
• Tailoring strategies to specific customer segments can enhance operational efficiency and profitability
• Companies should focus on building strong relationships with their most valuable customers to ensure sustained success

The trouble is, we’ve found that cost-cutting is ineffective in isolation, and consumers are showing that they’re increasingly unable or unwilling to pay higher prices, or will purchase less or make alternative product choices. Whether this period will ultimately be viewed as an anomaly or as the beginning of a new era characterized by near-constant disruption and accelerated innovation remains uncertain. However, what is clear is that this “new normal” shows no signs of going away anytime soon. Good read!

This blog helps you to build simple reusable button components and you will know about how to write CSS in the Js library. And how to access the props to your component and change the styles based on props. By Surendra Reddy S.

Key learnings in the article:

• Emotion is a high-performance library for styling JavaScript frameworks like React.
• It enables dynamic and predictable style compositions using CSS-in-JavaScript technique
• Benefits include improved code readability, better maintainability, and enhanced performance
• Emotion facilitates the creation of reusable UI elements that adapt seamlessly within applications

Emotion CSS is a cutting-edge library that integrates seamlessly with JavaScript frameworks like React to provide powerful styling capabilities. It allows developers to style components dynamically and predictably using CSS-in-JavaScript techniques. This approach has several benefits, including improved code readability, better maintainability, and enhanced performance. By embedding styles directly into components based on props or states, Emotion facilitates the creation of reusable and flexible UI elements that adapt effortlessly within applications. Good read!

Coinbase, America’s largest cryptocurrency exchange, received an unsolicited email from an unknown threat actor on May 11, 2025. They claimed to possess sensitive information about its customers and demanded a ransom of $20 million. By Dilip Kumar Patairya.

In May 2025, Coinbase was breached via an unsolicited email alleging possession of customer data. Attackers recruited overseas Indian customer service agents to exfiltrate sensitive information gradually. A 20M ransom demand on May 11 led to increased scrutiny. By May 21,attackers transferred 42.5M between Bitcoin and Ethereum using THORChain.

Coinbase’s comprehensive response included:

• A $20M reward fund for actionable intelligence leading to arrests
• Full reimbursement commitments (up to $400M in estimated costs) paired with one-year credit monitoring and identity restoration services
• Enhanced account security requiring multi-factor verification for large withdrawals, coupled with scam-awareness prompts
• Expansion of a U.S.-based support hub fortified with rigorous security protocols across all operations
• Transparent collaboration with federal and international law enforcement, culminating in internal terminations and criminal referrals of involved insiders

Coinbase’s internal security team detected anomalies, terminated complicit employees, and publicly denied the ransom in an SEC filing. The breach impacted 69,461 accounts, exposing names, emails, masked financial identifiers, and transaction histories—but not private keys or wallet access. In the wake of large-scale data breaches of crypto platforms, you should take proactive steps to protect yourself from social engineering attacks - read article for a good advice on this last point. Interesting read!

In 2014, crypto advertising barely existed. The term “Web3” hadn’t been coined, Facebook banned crypto ads and startups promoting their tokens were mostly confined to forums and niche publications. But for Bitmedia founder Matvii Diadkov, the opportunity was obvious. By Martin Young.

Bitmedia now uses AI for fraud detection, creative analysis, and predictive bidding, aiming to let autonomous agents manage campaigns using real-time blockchain data. Compliance is handled via geo-targeted moderation systems to adhere to regional regulations. A DeFi client saw a 34% drop in cost-per-acquisition and 3.5x user retention by targeting wallet segments active on DEXs or staking.

Some key larning in this article:

• Bitmedia revolutionized Web3 marketing through AI and blockchain data
• Compliance requires adaptive moderation across jurisdictions
• Onchain analytics drive precise user targeting and retention
• Continuous optimization ensures sustainable campaign success
• Stealth innovation may redefine crypto advertising transparency and scalability

Long-term success stems from daily optimization of creatives, targeting, and bidding using blockchain analytics. Diadkov also launched Chainers.io, an NFT game, blending marketing insights with gaming experience. Future plans include a groundbreaking stealth tech that redefines crypto-ad intersections via blockchain transparency. Good read!