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Tips

Developer News This Week – OpenAI Token Warning, Chrome 0-Day Patch & Microsoft AI Layoffs

Here’s a look at what’s shook the software world this week.

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OpenAI Condemns “OpenAI Token” on Robinhood

Robinhood briefly listed an unofficial crypto called “OpenAI Token.” OpenAI quickly published a statement disavowing any connection and stated the tokens do not confer equity or any official connection to OpenAI.

Robinhood offered these tokens via a special purpose vehicle (SPV) to give investors indirect exposure to private OpenAI shares, but OpenAI explicitly disavowed the product and warned consumers

Moon-Lighting Debate Goes Viral

Five U.S. CEOs publicly claimed Indian engineer Soham Parekh held several full-time roles simultaneously. They called the practice “moon-lighting on steroids” but also acknowledged his technical competence.

Parekh confirmed the allegations in interviews, stating he worked up to 140 hours a week. The viral debate centres on the ethics and logistics of overemployment in remote tech roles

Claude Writes a macOS App – Zero Local IDE

Indie developer Indragie Karunaratne shipped Tap Scroll, a macOS utility fully generated by Anthropic’s Claude 3.5 model. All Swift code, tests and even the App Store screenshots were AI-authored.

Indragie’s blog post explains the journey, how he chose his tools, which are good or bad for now, and how you can leverage them to maximise the quality of your generated code output.

Microsoft Layoffs to Fund AI Push

Microsoft announced layoffs of about 9,000 workers, primarily to offset rising AI infrastructure costs and fuel its AI ambitions. The layoffs affected multiple divisions, including Xbox and other legacy areas.

Actionable steps for developers:

  • Monitor the Azure Updates and Microsoft 365 Roadmap for Copilot and Azure changes.
  • Use the Service Retirement workbook in the Azure Portal to track which services you use are scheduled for deprecation and to plan migrations accordingly.
  • If your stack depends on less-common Azure services, proactively review product lifecycle documentation and set up alerts for service retirement to avoid disruption.
  • Microsoft’s current trajectory means Copilot features will arrive faster and legacy Azure services may be retired more aggressively, so vigilance is warranted for developers on niche or older stacks.

Chrome Emergency Update

Google shipped a high-severity Stable & Extended update fixing multiple use-after-free flaws (CVE-2025-5063 et al.).

Actionable steps for developers:

Force enterprise updates via MDM.

Re-bake Docker images that embed headless Chrome/Chromium.

That’s a wrap for the developer news this week!

Categories
Community

Building for Compliance: Secure Development Practices for Fintech and Regtech Applications

In the worlds of fintech and regtech, where software must operate within frameworks dictated by financial regulators, compliance is not an afterthought; it’s a foundational principle. Developers and tech creators working in these sectors are tasked with building systems that not only perform complex financial or regulatory tasks but also adhere to evolving standards around privacy, data protection, and digital identity. Failure to meet these expectations can result in severe legal, financial, and reputational consequences.

Secure development practices must be embedded throughout the entire software development lifecycle (SDLC), from planning and coding to deployment and maintenance. These practices are not merely technical requirements; they are strategic imperatives that help ensure your applications can meet the high compliance bar set by regulators and auditors.

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Why Security Is Integral to Compliance in Fintech and Regtech

Compliance in fintech and regtech hinges on data integrity, transparency, user privacy, and the traceability of all operations. Unlike general-purpose software, applications in these fields often handle highly sensitive data — banking transactions, identity verification, financial risk modeling, or audit trails. Consequently, any security lapse can be viewed not just as a technical bug, but as a regulatory breach.

To achieve compliance, security needs to be treated as a core requirement. Security-by-design is a prerequisite for deployment, investor confidence, and customer trust.

Core Secure Development Principles for Regulated Applications

1. Shift Left on Security

The earlier security is introduced into the development lifecycle, the better. Waiting until testing or deployment stages to address vulnerabilities leads to costly rework and missed risks. Shifting security left means:

  • Performing threat modeling during the design phase
  • Identifying sensitive data flows and potential attack vectors upfront
  • Defining security requirements alongside functional ones

By involving security experts early and often, teams can reduce vulnerability windows and ensure compliance checkpoints are met continuously.

2. Adopt a Zero Trust Architecture

Zero trust assumes no system or user — internal or external — is automatically trustworthy. This model is ideal for fintech and regtech because of its rigorous access controls and audit-ready structure. Key principles include:

  • Strong identity verification: Multifactor authentication (MFA) and role-based access controls (RBAC)
  • Least privilege enforcement: Users and services should only have the access they need
  • Continuous monitoring: Real-time evaluation of access requests and data interactions

Implementing zero trust enhances your application’s ability to meet stringent compliance requirements around data access, user management, and breach containment.

3. Secure Your APIs

Fintech and regtech platforms often depend heavily on APIs for interoperability, especially with banks, government systems, or third-party vendors. Every exposed API is a potential attack surface. Ensure your APIs are:

  • Protected via OAuth 2.0 or similar authorization frameworks
  • Designed with rate limiting, input validation, and schema enforcement
  • Logged and monitored for unusual activity

Regular API penetration testing and version control can also help ensure these critical interfaces remain secure over time.

Data Handling and Storage Best Practices

Handling sensitive data — financial records, personal identification, and transaction logs — comes with its own security mandates. Here are several must-have practices:

Encrypt Everything

Encryption should be standard for data in transit and at rest. Use up-to-date, industry-approved algorithms (such as AES-256 or TLS 1.3). Avoid developing custom encryption schemes, which often fail under scrutiny.

  • Data at rest: Store encrypted data using secure key management systems (KMS)
  • Data in transit: Enforce HTTPS/TLS across all communication channels
  • Database security: Leverage column-level encryption for personally identifiable information (PII) and financial details

Log Intelligently, Not Excessively

Logging is essential for auditing and breach detection, but over-logging can create compliance risks. Sensitive information should never appear in logs.

  • Mask or exclude credentials, tokens, or financial details
  • Encrypt log storage and restrict log access
  • Implement centralized logging solutions for audit trails

Employ Virtual Data Room Software for Critical Data Exchanges

Virtual data room software is increasingly used in regtech environments where secure document sharing and collaborative auditing are critical. These platforms enable role-based access, activity tracking, and encrypted file storage — ideal for due diligence, regulatory filings, or high-risk internal reviews.

By integrating virtual data room capabilities, developers can offer their applications a secure, auditable layer of document management that meets both security and compliance standards.

Compliance-Aware Deployment and DevOps

Modern DevOps pipelines must align with compliance and security from the ground up. Automating secure configurations and compliance validations within CI/CD workflows reduces manual errors and speeds up release cycles without sacrificing integrity. Key practices include:

  • Infrastructure as Code (IaC): Enforce secure configurations for servers, databases, and networks from version-controlled scripts
  • Container Security: Use trusted images, perform regular vulnerability scans, and isolate environments using Kubernetes or similar platforms
  • Automated Compliance Checks: Integrate tools like OpenSCAP, Chef InSpec, or custom scripts to validate configurations against compliance benchmarks such as PCI-DSS or ISO/IEC 27001

DevSecOps goes further by embedding security testing into every stage of development and deployment, ensuring your product ships with compliance in mind.

Continuous Compliance: Auditing and Monitoring in Production

Achieving compliance is not a one-time milestone; it requires continuous monitoring and adaptability. Regulatory standards change, attack methods evolve, and user behavior shifts. Your production environment must support:

  • Real-time alerting for anomalies: Implement behavior analytics and rule-based alerts
  • Audit trail generation: Capture user actions, configuration changes, and data access logs
  • Regular third-party audits: External validation not only ensures compliance but builds trust with clients and partners

Monitoring tools should also support compliance reporting formats so teams can quickly respond to inquiries or demonstrate adherence during audits.

Empowering Teams Through Secure Culture and Training

The strongest security strategy will fail without an educated and vigilant development team. Empowering developers with secure coding practices and ongoing training helps create a culture where security is second nature. Invest in:

  • Secure coding certifications or workshops (e.g., OWASP Top 10)
  • Access to vulnerability databases and patch notes
  • Code review protocols with a security lens
  • Red/blue team exercises for security response readiness

Security training must evolve alongside your application, especially as it scales or incorporates new regulatory territories.

Building Toward Compliance as a Competitive Edge

Fintech and regtech are high-stakes industries. Regulators are watching, and so are your users. Secure development is no longer simply about preventing breaches; it’s about demonstrating a mature, compliance-oriented approach to software creation. By integrating security across the SDLC, leveraging tools like virtual data room software for sensitive operations, and staying ahead of regulatory shifts, developers can build trustworthy applications that meet the moment.

Whether you’re creating tools for digital banking, automated KYC, or real-time compliance monitoring, embedding these practices into your process will ensure not just a secure product, but a resilient and compliant business.

Author bio:  Josh Duncan is Senior Vice President for Product Management at Donnelley Financial Solutions™ (DFIN) , a global financial solutions company headquartered in Chicago. He is responsible for software and technology solutions for Global Capital Markets including ActiveDisclosure, for financial and disclosure reporting, and Venue, the leading Virtual Data Room for mergers and acquisitions. Josh earned his Bachelor of Science in engineering from the University of Wisconsin and holds an MBA in marketing and finance from Kellogg School of Management at Northwestern University.

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Community

AI in DevOps: Unpacking its Impact on Developer Performance

As the landscape of software development continues to evolve at a breakneck pace, driven significantly by the rise of Generative AI tools, understanding their actual impact on our workflows is more critical than ever. Our latest “State of the Developer Nation, 29th Edition” report, Usage of AI Assistance Between DORA Performance Groups, delves into how AI tools are influencing software delivery performance, using the well-established DORA (DevOps Research and Assessment) framework.

Watch our latest meetup recording where we also discussed about this report and more here.

Since the mainstream emergence of generative AI tools like ChatGPT and GitHub Copilot, developers have rapidly adopted these technologies, promising a revolution in how we write code and solve problems. But how do these powerful tools truly affect key performance metrics like lead time, deployment frequency, time to restore service, and change failure rates? Let’s dive into the research! 

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The Nuances of AI Adoption and Performance

Our report provides fascinating insights into the relationship between AI tool usage and developer performance across different DORA metrics:

  • Lead Time for Code Changes: A Minimal Impact? Surprisingly, our research shows that AI tools have a minimal impact on the lead time for code changes—the time it takes for code to go from committed to running in production. This suggests that factors like organizational practices and streamlined processes play a far more significant role than just the speed of code creation assisted by AI. In fact, increased AI usage might even prolong the review stage due to potential quality concerns.
  • Deployment Frequency: Where AI Shines This is where AI truly seems to empower high-performing teams. Elite performers in deployment frequency (those who deploy code frequently or on demand) show significantly higher adoption of AI-assisted development tools (47% vs. 29% for low performers). They are also more likely to use AI chatbots for coding questions (47% vs. 43%). This indicates that AI tools help these teams maintain their high velocity and produce deployable code more often. Elite performers also tend to integrate AI functionality through fully managed services, leveraging external vendors for reliability and functionality.
  • Time to Restore Service: Chatbots to the Rescue? For quick recovery from unplanned outages, elite performers exhibit higher usage of AI chatbots (50% vs. 42% for low performers). AI chatbots can rapidly retrieve information, which is invaluable during critical incidents. However, the report also notes that some elite and high performers (29% and 25% respectively) choose not to use AI tools, preferring deterministic processes for rapid service restoration, and potentially avoiding the added complexity AI services can introduce.
  • Change Failure Rate: A Cautious Approach to AI Perhaps the most intriguing finding relates to change failure rates. Elite performers in this metric (those with fewer changes leading to service impairment) are less likely to use AI chatbots or AI-coding assistant tools compared to lower-performing groups. The usage of AI-assisted development tools drops to 31% among elite groups, compared to around 40% for others. This suggests that a lower reliance on AI for coding assistance is associated with fewer deployment failures. Concerns about AI-generated code being poorly understood or introducing errors are prevalent, potentially leading to increased failures if not carefully managed. Industries with a low tolerance for failure, like financial services, energy, and government, often have strong governance that discourages AI usage, and these sectors also tend to have a higher proportion of elite performers in change failure rates.

Shaping the Future Responsibly

These insights highlight that while AI offers incredible potential to boost development velocity, its impact on other crucial performance metrics is nuanced. It’s not a silver bullet, and its integration requires careful consideration. For the Developer Nation community, this means:

  • Informed Adoption: Understand where AI can truly enhance your team’s performance and where a more traditional, meticulously managed approach might be better, especially concerning code quality and reliability.
  • Continuous Learning: Stay updated on the capabilities and limitations of AI tools, and develop strategies to mitigate risks like “hallucinations” or poorly understood AI-generated code.
  • Leveraging Community: Share your experiences, challenges, and successes with AI tools within our community. By collaborating and learning from each other, we can collectively navigate the complexities of this new era.

How are you balancing AI adoption with your team’s performance goals? Share your thoughts and strategies in the comments below!

Sources:

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Community

The Role of Blockchain in Fintech: Enhancing Security and Transparency in Financial Transactions

In recent years, blockchain in fintech has gained significant attention for its potential to revolutionize the financial industry. With its ability to enhance security, transparency, and efficiency, blockchain technology is now playing a crucial role in transforming how financial transactions are processed. Whether it’s simplifying cross-border payments or enabling smart contracts, blockchain is empowering financial institutions and fintech startups to offer faster, more secure services.

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What Is Blockchain Technology?

At its core, blockchain is a distributed ledger technology (DLT) that stores data in blocks. These blocks are linked together in a chain, with each block containing a record of transactions. The key feature of blockchain is its decentralized nature—instead of relying on a central authority like a bank to validate transactions, blockchain enables peer-to-peer verification. This means transactions are verified by multiple parties across the network, making the system more secure and transparent.

In the context of fintech, blockchain has proven to be a powerful tool for improving financial transactions by offering enhanced security, greater transparency, and more streamlined operations.

How Blockchain Enhances Security in Financial Transactions

Security is one of the biggest concerns in the financial sector, especially with the increasing volume of online transactions. Traditional payment systems are vulnerable to fraud, data breaches, and cyberattacks. Blockchain, however, provides an added layer of security that makes financial transactions more resistant to tampering and fraud.

Here’s how blockchain in fintech enhances security:

  1. Immutability: Once a transaction is recorded on a blockchain, it cannot be altered or erased. This ensures that financial records are secure and tamper-proof, which is crucial for maintaining the integrity of financial data.
  2. Encryption: Each transaction is encrypted, and participants in the blockchain network are only able to access the data relevant to them. This protects sensitive financial information from unauthorized access.
  3. Decentralization: Since blockchain does not rely on a single centralized authority, the risk of a single point of failure is reduced. Transactions are verified across multiple nodes (computers), making it extremely difficult for hackers to manipulate the system.

These security features make blockchain technology ideal for use in fintech, where protecting customer data and financial assets is paramount.

The Role of Blockchain in Increasing Transparency

Another significant advantage of blockchain in fintech is its ability to increase transparency in financial transactions. Unlike traditional systems, where transactions are often opaque and difficult to audit, blockchain provides a clear and traceable record of every transaction made on the network.

Here’s how blockchain ensures transparency:

  1. Real-time Auditing: All transactions on the blockchain are recorded in real-time and are accessible to all authorized users. This enables easy auditing and tracking of funds, providing a transparent view of where money is coming from and where it’s going.
  2. Traceability: Since each block in the blockchain contains a history of all previous transactions, it’s easy to trace the origin of any transaction. This makes it harder for fraudulent activities like money laundering or illicit transfers to go unnoticed.
  3. Public Ledger: Blockchain operates on a public ledger, which means that anyone in the network can verify transactions. This level of transparency builds trust among users and reduces the possibility of fraudulent activities.

For fintech companies, this transparency is particularly valuable when dealing with complex transactions like cross-border payments, where visibility into the transaction process can reduce costs and eliminate delays.

Real-World Applications of Blockchain in Fintech

The impact of blockchain in fintech extends far beyond theoretical use. Financial institutions and fintech startups are already using blockchain to streamline their operations and improve customer experiences. Some notable applications include:

  1. Cross-Border Payments: Blockchain enables faster and cheaper cross-border payments by eliminating the need for intermediaries such as banks. Traditional international transfers often come with high fees and long processing times. Blockchain, on the other hand, enables near-instantaneous transfers with lower transaction costs.
  2. Smart Contracts: Smart contracts are self-executing contracts with the terms of the agreement directly written into code. Blockchain technology facilitates the automation of contract execution, reducing the risk of human error and ensuring transparency. In fintech, smart contracts can be used for everything from loan agreements to insurance claims, streamlining processes and reducing administrative costs.
  3. Fraud Prevention: Blockchain’s transparency and security features make it an effective tool for detecting and preventing fraud. Financial institutions can use blockchain to track and verify transactions, ensuring that all actions are legitimate and authorized.
  4. Digital Identity Verification: Blockchain can also be used to create secure, digital identities for individuals, providing a more reliable method for KYC (Know Your Customer) verification. This is especially important in fintech, where identity theft and fraudulent account creation can pose significant risks.

Benefits of Blockchain for Banks and Fintech Startups

For both traditional banks and emerging fintech startups, adopting blockchain technology can offer several benefits:

  1. Cost Reduction: Blockchain reduces the need for intermediaries and manual processes, leading to lower transaction fees and operational costs.
  2. Faster Transactions: By cutting out intermediaries and automating processes like payment verification, blockchain enables faster transaction times, improving customer satisfaction.
  3. Better Customer Experience: With more transparent and secure transactions, blockchain enhances customer trust and loyalty, which is essential for maintaining a competitive edge in the fintech space.
  4. Regulatory Compliance: Blockchain makes it easier for fintech companies to comply with regulations by providing an immutable and transparent record of all transactions.

Challenges and Considerations for Implementing Blockchain in Fintech

While blockchain offers many advantages, it’s not without its challenges. Some key hurdles include:

  • Scalability: Blockchain networks can struggle with handling large volumes of transactions at high speeds, which can be a limitation for financial institutions that process millions of transactions daily.
  • Regulatory Uncertainty: The regulatory environment for blockchain in fintech is still evolving, and compliance with existing laws can be complex.
  • Integration with Legacy Systems: Many financial institutions still rely on legacy systems, and integrating blockchain technology with these outdated infrastructures can be a complex and costly process.

Conclusion

Blockchain in fintech is undeniably a game-changer, offering enhanced security, transparency, and efficiency in financial transactions. As the technology continues to evolve, its applications will expand, bringing even more benefits to both financial institutions and customers.

For fintech startups and banks looking to innovate and improve their operations, integrating blockchain technology is no longer just an option—it’s a strategic move that can lead to significant improvements in performance, customer satisfaction, and market competitiveness.

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News and Resources

Developer News This Week – Firefox 140 Critical Patch & GitHub Copilot Coding Agent (June 27 2025)

If your week was a blur of stand-ups and sprint reviews, we’ve got you covered with this week’s essential updates for developers, sysadmins and security teams. Grab a coffee, skim the highlights and keep your stack one step ahead.

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Firefox 140 – Critical CVEs Squashed

Mozilla has released Firefox 140, addressing several high-impact vulnerabilities. Notable fixes include:

  • CVE-2025-6424: Use-after-free in FontFaceSet, potentially exploitable for crashes or code execution.
  • CVE-2025-6425: Persistent UUID exposure via the WebCompat extension.
  • CVE-2025-6426: Missing executable warning on macOS.
  • Additional issues affecting Android and developer tools.

Action: Update Firefox to version 140 as soon as possible to mitigate these risks.

Rust 1.88.0: Naked Functions & Smarter Syntax

Rust 1.88.0 is now stable, introducing:

  • Naked Functions: Full control over function assembly, ideal for low-level and embedded development.
  • Let Chains: More ergonomic conditional logic with let statements inside if and while conditions, available in the Rust 2024 edition.

These features improve both performance tuning and code clarity for advanced Rust users.

GitHub Copilot “Coding Agent” Public Preview

GitHub Copilot’s new “coding agent” is now in public preview for Copilot Pro users. This agent can offload multi-step coding tasks directly within VS Code or Visual Studio, streamlining complex workflows and boosting productivity.

Node.js v24 & v22: Security Releases Out – Update Images

Security updates are available for Node.js versions 24.x, 23.x, 22.x, and 20.x. The most critical fix addresses a vulnerability in async cryptographic operations (CVE-2025-23166) that could allow remote process crashes. All users tracking Current or LTS should update their images immediately to stay protected.

NVIDIA AIStore: Kubernetes Privilege Escalation Patch

A new patch is available for NVIDIA AIStore on Kubernetes, addressing CVE-2025-23260. This vulnerability allowed users to gain elevated cluster access via incorrect privilege assignment in the AIS Operator’s ServiceAccount. Update your AIStore containers to close this privilege escalation risk.

Copilot Chat: Improved Attachments & Context

GitHub Copilot Chat now supports larger context windows and improved attachment handling in public preview. These enhancements make it easier to reference and discuss code, files, and issues within your team.

Stay secure and productive – update your tools and dependencies today!

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Community Tips

Best Practices for Integrating External Data APIs Into Your Application 

One of the most important aspects of developing current applications is integrating external data APIs (Application Programming Interface). APIs are the links between your app and outside services, allowing you to access everything from social network feeds and user behavior to geographical information and financial insights.

Leveraging these integrations enables you to deliver richer functionality, faster performance, and better user experiences without reinventing the wheel.

However, seamless API integration requires more than just tying the dots together. Inadequate implementation can result in frustrating downtime, bad app performance, or security threats. Developers must thus approach integration with a solid basis and a well-defined plan.

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1. Know the API inside and out

Make sure you understand the API you’re working with before you start writing code.  Learn about its needed headers, rate restrictions, authentication procedures, data formats, and endpoints by carefully reading its documentation.  Keep an eye out for versioning support and how it manages problems.

Among the main factors to consider is the question of the API being well-designed and developer-friendly. A high-quality, well-designed API tends to be predictable, consistent, and well-documented, which makes the integration process less painful and surprises less likely to happen.

Understanding these characteristics early on helps developers choose APIs that support long-term stability and ease of use.

2. Implement security from the start

Security of the API should not be an afterthought. External APIs make your application accessible to new data flows and services, and it is paramount to ensure that interactions are secure from the outset.

Authenticate using industry-standard techniques, including signed tokens, OAuth 2.0, and API keys. Credentials should never be kept in public repositories or stored in your frontend code. Make sure that all data is served over HTTPS to avoid snooping or data alteration.

Just as important is input validation. Don’t assume the data from an external API is safe. Always sanitize and verify it before passing it to your system. This mindset of cautious trust helps protect your app and your users from potential threats.

3. Build for resilience

No API is immune to failure. Whether it’s a timeout, a rate limit hit, or a temporary outage, your application must be prepared to adapt without breaking.

Start with solid timeout and retry strategies. When an API doesn’t respond quickly, your system should know when to try again or move on. Techniques like exponential backoff (gradually increasing wait time between retries) can reduce the strain on both systems.

Additionally, consider fallback solutions. For example, if the live data is unavailable, you might display cached information or a user-friendly message. Finally, log errors in a clear and searchable format so you can track recurring issues and fix them proactively.

4. Stay within rate limits and service constraints

Most APIs come with usage limits to protect their performance and prevent misuse. Ignoring these limits can lead to throttling, delayed responses, or even a complete block of your access.

To prevent such problems, familiarize yourself with your request quotas well in advance and build your app around them. Batching requests or, if practical, utilizing server-side aggregation can help you avoid making too many calls.  It is essential to use queuing and throttling techniques if your app polls for data on a regular basis, such as when tracking real-time market data.

This is especially relevant for high-frequency data tools like equity trackers for hedge funds and asset managers, which help them monitor company-level trends. When consuming APIs that power these kinds of services, managing rate limits becomes a matter of performance and reliability.

5. Design for modularity and maintainability

As your application grows, so will the number of APIs you depend on. A modular design will help keep your codebase organized and maintainable.

Place the API logic in a separate service layer or module to keep it apart from the main body of your application code.  This makes testing, updating, or replacing APIs easier later on.  To store keys and endpoints, use environment variables rather than hardcoded values, which are insecure and hard to manage.

Furthermore, document how each API is integrated by including any quirks or special formatting required. This level of internal transparency helps future developers understand the system and onboard quickly.

6. Monitor, log, and evolve your integration

The work doesn’t stop when your integration goes live. APIs change over time as endpoints are deprecated, limits are updated, and features are added. Constant observation makes sure you’re prepared for any issues that may arise.

Track uptime, error rates, and response times with monitoring tools.  Create notifications for persistent problems or unexpected increases in rejected requests.  By examining these patterns, you can find areas where your integration is lacking and improve performance.

Subscribe to the API provider’s update channels to stay in the loop. Staying engaged ensures that your application remains compatible and competitive.

Conclusion

External APIs are powerful enablers of modern app development. They can power up your application, linking it to services and data streams that you would be hard-pressed or unable to create by yourself. With great power, however, comes great responsibility.

With the best practices listed above, you will be able to combine external data with intent and accuracy. You can enrich your app with location data, scale with cloud services, or both, and the considerate use of APIs will make you move faster, remain agile, and provide better experiences.

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Community

9 Questions to Ask Before You Integrate an Embedded Analytics Solution With Your App

Embedded analytics have evolved from a nice-to-have feature to a board-level requirement. By allowing executives to access data insights within the working environments where they already spend their time, embedded analytics is fast becoming a necessity for steering mission-critical decisions.  

“To make business analytics more accessible and relevant, organizations have been striving heavily to put analytics in the context of their business applications and workflows rather than treat it as a separate set of tools and functions running in its own sandbox,” says Avi Perez, Chief Technical Officer of Pyramid Analytics.  

Perez cautions that organizations need to evaluate their needs to ensure a good fit. “The process of embedding analytics is now a top-tier demand, and it comes with its own set of problems and complexities,” he adds.  

To address these concerns, here are nine questions for organizations evaluating their options to ensure success in mission-critical applications of these technologies. 

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1. Who are the primary users of the embedded analytics features?

Identify whether the audience is internal staff, end-customers, or both, to clarify the design persona. In addition, here’s where multi-tenancy will play an important part, which involves protecting data across both internal and external users – a challenge that might not be considered in use cases that are solely internal. 

Each of these factors carries distinct UX requirements. Operations teams require granular tables, while clients prefer concise KPIs and guided narratives. Map these personas to data literacy levels, device preferences, and time-to-insights expectations before even writing a single line of code or considering a solution to embed into your platform. You can validate these assumptions with user interviews or analytics from your existing dashboards. 

A recent study by Dresner Advisory Services points to differing priorities according to organizational role. Data science, sales and marketing, and information technology departments each had their priorities and needs: contextual insights for internal users for data scientists, external-facing objectives for sales and marketing, and efficiency for IT. 

2. What decisions will analytics features empower users to make?

How will your embedded analytics deployment be useful to the people who engage with it? According to a report by Aberdeen, 37% of business leaders say more data is available than can be used for meaningful analysis. 

Start with the operational aspects, according to user needs. Are you spotting supply chain delays, identifying churn risk, and the like? Then, translate these into concrete metrics, filters, and visualizations. Try to avoid feature creep, or tagging every requested chart just to address all outcomes. You can differentiate between essential and nice-to-have visualizations, which can be turned on once the organization has proven the advantage of adopting embedded analytics. 

3. Should we build our own solution or use a third-party embedded analytics platform?

Building in-house maximizes control, but will involve higher initial expenditure and might result in higher maintenance costs, too. According to Full Scale, third-party tools can ensure faster time-to-market than building from scratch, from a year to as quickly as one quarter in deployment. However, vendor lock-in and per-seat pricing can also be costly as usage scales. A build approach can save 35% in the long term, but a buy approach can save 45% in the short term. 

To address this, prototype a slim build of your hardest-to-model visualization. If you cannot deliver a maintainable version in just a few agile sprints, an off-the-shelf solution may be justified, with your own practical customizations. It may not be that simple, however. Other considerations apart from cost and time-to-market can include security, integration, scalability, and technology, as part of the total impact of a build vis-a-vis buy decision.

4. What data sources and pipelines need to be integrated?

According to a study by IDC, businesses face an average of 12% revenue loss due to data fragmentation and silos. Analytics can amplify bad data as it can the good, possibly resulting in garbage in, garbage out outcomes. 

List all tables, APIs, and flat files that feed your app’s core workflows to create an integration inventory. Check whether data is siloed in legacy solutions or trapped in silos that lack export APIs. If batch ETL adds hours of delay, consider event-streaming or change data capture to maintain freshness, to ensure the architecture aligns with real-time demands. However, you would have to budget time for data-quality rules to account for latencies. 

5. Can our existing app architecture support this integration? 

Audit frontend frameworks for component reuse. React or Angular apps can host embedded dashboards through iframes or component libraries. In contrast, vanilla JSP may need heavy refactoring. Measure current API response times and memory headroom.  

Visual queries often multiply backend load when filters stack. If you run microservices, isolate the analytics engine to limit load in your main platform during spikes to add resilience.

6. How will we handle user-level data security and access control in a multi-tenant environment?

According to Verizon’s recent Data Breach Investigations Report, misdelivery, misconfiguration, and publishing errors are the top reasons for security compromises. Meanwhile, privilege misuse also accounts for 10% of such security breaches. 

Assume multi-tenancy by default. B2B customers increasingly expect a single logical instance with tenant isolation, aligning with SaaS norms. Implement attribute-based or row-level security so that users only see rows tagged with their tenant ID or role. This enforces the concept of least privilege. You can also automate policy tests in CI to avoid regressions, ensuring access control is continuously implemented in the development cycle. 

7. What visual and interaction experiences do our users expect?

Dashboards are a staple in business environments. Downloadable CSV or PDF reports are non-negotiable for finance and audit teams to meet their compliance needs. Include in-context tooltips and “why it matters” annotation layers, as contextual analytics improves feature adoption.  

Mobile-first loading and pinch-to-zoom charts are essential if your app sees at least 30 percent mobile traffic. Test for load speeds – according to Google, 53% of visits are abandoned if a mobile site or app takes longer than three seconds to load. 

8. How scalable and performant does our analytics need to be?

Model best-case and worst-case workloads. If concurrent query volume doubles during month-end, for instance, the data analytics dashboard needs to be able to handle these peaks.  

Plan for horizontal scaling – columnar warehouses, result caching, and async rendering can cut lag from seconds to milliseconds to keep the UX snappy. Measure service-level objectives against render time and query cost to avoid surprise cloud utilization spikes, which can have an impact on your organization’s budget. 

9. How will analytics be maintained and updated post-integration?

Without ownership, projects tend to get abandoned beyond proof-of-concept. Gartner predicts that at least 30% of generative AI projects will be abandoned at this stage. 

Define ownership upfront. Product owns the roadmap, engineering owns the pipelines, and data teams own the semantic models. This avoids orphaned dashboards. Schedule quarterly schema reviews. Feature rollouts often require new measures or dimensions. Automate regression tests on visuals so version bumps in libraries don’t break embedded widgets. Finally, publish a changelog or in-app banner when KPIs change. Nothing erodes stakeholder trust faster than silent metric shifts.

The Takeaway

Embedding analytics can unlock new revenue, reduce churn, and help users make data-driven calls without leaving your app. Yet every benefit stems from clear answers to the questions above. Start small: pilot with one persona, one decision flow, and one well-governed dataset. Measure adoption, iterate on UX, and only then expand to additional tenants and use cases, to ensure disciplined scaling. By treating embedded analytics as a product, not a project, you’ll turn data into a durable competitive advantage rather than a perpetual backlog item. 

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How Today’s Developers Are Using Web Data to Train AI Models

Even though we’re only two or so years into AI’s mainstream adoption, today we’re seeing something of an arms race in the enterprise world, with many companies rushing to develop the best AI model for the needs of their users. 

For developers, this means building, training, and fine-tuning AI models so that they meet their company’s business objectives. As well as requiring a lot of time, AI model development demands large amounts of training data, and developers prefer to acquire it from the open web. 

Data for AI 2025, a new report from Bright Data, found that 65% of organizations use public web content as their primary source for AI training data, and 38% of companies already consume over 1 petabyte of public web data each year. Apparently, developers are seeing the advantages of using dynamic, real-time data streams, which are continuously updated and customized. 

What’s more, demand for public web data is growing rapidly. According to the Bright Data survey, information needs are expected to grow by 33% and budgets for data acquisition to increase by 85% in the next year. The report maps the growing importance of web data in AI engineering workflows, and how developers are drawing on it to maximize model reliability. 

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Improving Model Accuracy

As organizations increasingly rely on AI insights for both operational and strategic decision-making, accuracy is crucial. AI models play important roles in tasks such as assessing applicants for insurance or managing quality control in manufacturing, which don’t allow much margin for error. AI-driven market intelligence also requires accurate models fed the most recent information, and is one of the top use cases cited by participants in the survey. 

Training models to recognize patterns, apply rules to previously unseen examples, and avoid overfitting, demands vast amounts of data, which needs to be fresh to be relevant to real-world use cases. Most traditional data sources are outdated, limited in size, and/or insufficiently diverse, but web datasets are enormous and constantly updated.

When asked about the main benefits of public web data, 57% said improving AI model accuracy and relevance. Over two-thirds of respondents use public web data as their primary source for real-time, connected data.

Optimizing Model Performance

Enterprises seeking the best AI model are looking not only for accuracy but also for model performance, which includes speed, efficiency, and lean use of resources. Developers are well aware that performance optimization relies at least as much on data as on model improvements, with 92% agreeing that real-time, dynamic data is critical to maximizing AI model performance.

When asked about the source of their competitive edge in AI, 53% said advances in AI model development and optimization, and the same number pointed to higher quality data. Reliable, fresh, dynamic data fits models to make better, faster predictions without increased compute resources. 

Finding that data can be challenging, which is why 71% of respondents say data quality will be the top competitive differentiator in AI over the next two years. Live web data is the only way for developers to get hold of quality data in the quantities they need.

Enabling Real-Time Decision-Making

Developers are under rising pressure to produce models that deliver real-time outcomes, whether for decision-making such as medical diagnoses; predictions like evaluating loan applications; or reasoning as part of an agentic AI system. 

Producing real-time responses while preserving accuracy requires feeding AI models a constant diet of context-rich data that’s as close to real time as possible. 

Only public web data can deliver quality data at this kind of speed, which would be why 96% of organizations indicated that they collect real-time web data for inference.

Scaling Up AI Capabilities

As organizations grow, they have to scale up AI capabilities to efficiently handle growing numbers of users, tasks, and datasets. 

Scalability is vital for consistent performance, cost-effectiveness, and business growth, but scaling up models to handle more queries, more quickly, requires more diverse, relevant data. 

Without scalable data sources, AI models can’t adapt to the rising demands placed upon them. Only web data is an immediately scalable source of flexible, fresh, and instantly available information. The report found that 52% of participants see scaling AI capabilities as one of the main benefits of public web data. 

Acquiring Diverse Data

It’s not enough for training data to be plentiful and up-to-date; it also needs to be diverse. When AI models are fed on diverse data, they produce more accurate predictions, fewer mistakes, and more trustworthy AI systems. 

Web data encompasses many types of content media, including text, video, and audio. Some 92% of organizations turn to vendor partnerships to improve data variety, and their desire for data is wide-ranging. 

While 80% of all businesses collect textual training sets, 73.6% also gather images; 65% video; and 60% audio. Compared to enterprises and small businesses, startups consume the greatest range of data types, with more than 70% saying they collect image, video, audio, and text. 

Advancing Personalization and Automation

Personalization tailors AI outputs to individual user needs, which is especially important for customer-facing digital products that incorporate AI. 

Bringing in automation makes the models more efficient, enabling them to adjust automatically to diverse users and contexts without manual adjustments and corrections. These twin goals were cited as the main benefits of public web data by 49% of survey participants.

Web data empowers developers to ramp up both personalization and automation by connecting them with the diverse real-world information that they need. Updated, relevant data about user behavior, trends, and preferences allows AI models to make smarter, self-improving responses that are relevant to each use case, with minimal manual input. 

Public Web Data Is AI Developers’ New Must-Have

As developers work hard to produce AI models that meet rapidly evolving business needs, public web data has become indispensable. Bright Data’s survey underlines that web data has become their best source of real-time, reliable, relevant, and diverse data, giving developers the training sets they need for fine-tuning, scaling, and generally preparing models for any requirement. 

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Pioneering the Future: How Developers are Shaping Generative AI

Our mission at Developer Nation is to “Enable developers and tech creators worldwide to shape the future of Technology by leveraging the power of Research and Community”. In line with this, we’re diving into the exciting world of Generative AI with insights from our latest “State of the Developer Nation, 29th Edition” report: The Developers Behind Generative AI Applications. This report, based on a global survey of over 10,000 developers, sheds light on who is building with Generative AI and why. 

Generative AI is rapidly becoming a cornerstone of the modern software ecosystem, redefining what our applications can do and introducing new considerations for innovation. The good news? Developers are at the forefront of this transformation.

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The Rise of Generative AI in Applications

Our research reveals that 20% of developers worldwide are already incorporating generative AI functionality into their applications. This includes capabilities like generating text, images, audio, video, and even code. While this is a significant number, it also means there’s vast potential for more developers to engage with this transformative technology. 

So, who are these pioneers and what drives their adoption of Generative AI?

Professional Status Matters

  • Professionals Lead the Way: Professional developers are approximately twice as likely as their counterparts to integrate generative AI into their applications (22% vs. 11%). This difference highlights the impact of workplace context. Professionals often benefit from better resources, technical support, and access to advanced infrastructure, enabling them to incorporate complex technologies more effectively. They are also driven by real-world business needs and the pressure to deliver feature-rich applications. 
  • Students and Hobbyists Face Hurdles: In contrast, hobbyists and students may have lower adoption rates due to limited access to training, fewer financial resources, or less exposure to cutting-edge tools. 

Experience Plays a Role

  • Mid-Career Developers at the Forefront: Developers with 6-10 years of experience are the most active in adding generative AI to their applications (26%), closely followed by those with 3-5 years of experience (23%). These mid-career developers are uniquely positioned, often trusted with innovative features. 
  • Early and Senior Career Trends: Beginners (less than one year of experience) are the least likely to build with generative AI (11%), often focusing on core skill development in simpler projects. Interestingly, there’s a slight drop in adoption among senior developers (over 10 years of experience) at 17%. This could be due to established workflows, where they might delegate innovative tasks or leverage their deep expertise in other critical areas of a project. 

Regional and Company Size Dynamics

  • North America Leads Globally: North America stands out with the highest integration rate of generative AI (27%), reflecting its concentrated tech industry, significant venture capital, and sophisticated technology ecosystems. Regions like Eastern Europe (11%) and South America (12%) show lower adoption. 
  • Midsize Companies Are Agile Innovators: Among professional developers, midsize companies (101-1,000 employees) lead in generative AI adoption at 29%. These companies strike a balance, possessing sufficient resources and expertise while remaining agile enough for rapid innovation. Freelancers and very small companies (2-20 employees) show lower rates (13% and 16% respectively) due to resource and financial constraints. Even large enterprises (>1,000 employees) have a slightly lower adoption rate (24%) compared to midsize firms, often navigating complex ecosystems, legacy systems, and regulatory considerations. 

Your Role in the Generative AI Revolution

The insights from this report reinforce that shaping the future of technology is a collaborative effort. Whether you’re a student experimenting with your first AI model, a mid-career professional integrating AI into enterprise solutions, or a seasoned expert guiding strategic decisions, your engagement is vital.

By understanding these trends, we can better equip ourselves and our community to leverage Generative AI, drive innovation, and continue to build the future of technology.

What are your experiences with Generative AI? Are you building with it? What challenges or opportunities have you encountered? Share your thoughts in the comments below!

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AI-Powered Fintech: Smarter, Faster, Future-Ready

Have you noticed how effortlessly apps like PayPal, Klarna, or Robinhood seem to “know” what you need—even before you do?

That’s not magic. It’s artificial intelligence quietly working behind the scenes, shaping how financial technology companies interact with users, approve loans, detect fraud, and more. The impact of AI in fintech is not just growing—it’s redefining the rules of the industry.

As someone deeply involved in the fintech space, you’ve likely heard the buzz. But beyond the hype, AI is delivering measurable improvements in speed, personalization, and risk management. And as we head deeper into 2025, it’s becoming clear: those who understand and integrate this technology early will have the advantage.

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The Evolution of Fintech and Role of AI

The term “fintech” emerged as a buzzword in the early 2010s. What started as digitized banking and mobile payments has now evolved into a sprawling ecosystem of apps, platforms, and infrastructure serving every niche of financial activity—from peer-to-peer lending to wealth management.

In the last decade, the sector witnessed rapid automation, increased reliance on big data, and the rise of customer-first experiences. However, as the sheer volume of data exploded and consumer expectations grew, traditional automation hit its limits.

Artificial Intelligence is now at the core of next-gen financial services. Unlike traditional software, AI systems can learn, adapt, and improve with each data point. This makes them ideal for complex, data-driven environments like finance—where speed, accuracy, and personalization matter more than ever.

Top Applications of AI in Fintech (2025 Outlook)

AI is not just another tech upgrade—it’s the new operating system of modern finance. Here are the top applications gaining traction in 2025:

1. Fraud Detection & Risk Analysis

Traditional rule-based fraud systems often miss anomalies or flag false positives. AI, particularly machine learning models, can analyze millions of transactions in real-time and identify patterns that suggest fraud—instantly and accurately.

2. Personalized Financial Services

From budgeting apps to digital banks, AI enables hyper-personalized insights tailored to a user’s spending habits, goals, and behaviors. AI-driven chatbots and recommendation engines create experiences that feel truly one-to-one.

3. Credit Scoring and Underwriting

Legacy credit scoring models often exclude borrowers with thin files. AI evaluates alternative data – such as transaction history, social media signals, and mobile behavior – to deliver fairer and more inclusive underwriting decisions.

4. Robo-Advisors and Wealth Management

Platforms like Betterment and Wealthfront use AI to manage portfolios, rebalance allocations, and optimize for tax efficiency – all without human intervention. In 2025, expect robo-advisors to get smarter and even more human-like in their decision-making.

5. Predictive Analytics in Loan Origination

AI helps lenders forecast repayment behavior by analyzing thousands of variables across multiple dimensions. This ensures better risk-adjusted decisions, improving both approval rates and portfolio quality.

Benefits of AI-Driven Financial Technologies

AI brings more than just automation—it brings intelligence. Here are some of the most significant benefits of AI in fintech:

  • Speed & Scalability: Processes that once took days—like identity verification or underwriting—now take seconds.
  • Accuracy & Cost-Efficiency: AI reduces human error and operational costs by streamlining repetitive tasks.
  • Smarter Decision-Making: AI uncovers hidden insights from massive datasets that would be impossible to detect manually.
  • Real-Time Insights: Whether it’s flagging suspicious activity or optimizing a stock portfolio, AI delivers intelligence when it’s needed most.

These capabilities don’t just boost productivity—they create entirely new financial services.

Challenges and Ethical Considerations

With great power comes great responsibility. While AI enhances fintech, it also introduces new complexities.

1. Data Privacy Concerns

AI thrives on data, but collecting and processing sensitive financial information raises legitimate privacy questions. Companies must ensure GDPR and other compliance frameworks are respected.

2. Algorithmic Bias

AI systems can unintentionally reinforce societal biases—especially in lending and hiring. Transparent, explainable AI (XAI) models are essential to address this.

3. Regulatory Hurdles

Financial regulators are still catching up to the pace of AI innovation. Fintechs must navigate an evolving legal landscape while ensuring ethical and compliant AI use.

The Future of AI in Fintech

Looking ahead, we’re just scratching the surface of what AI in fintech can achieve.

  • Explainable AI (XAI): Regulators and customers alike want transparency. XAI will make AI-driven decisions more interpretable.
  • AI + Blockchain: The convergence of AI and decentralized finance (DeFi) can power smart contracts that self-optimize.
  • Conversational Banking: AI chatbots will evolve into sophisticated virtual assistants capable of managing finances, investments, and more with human-like fluency.

According to Deloitte, financial institutions that adopt AI early stand to gain the most in terms of market share and customer trust.

Case Studies: Leading AI Fintech Innovators

1. Upstart

Using AI and non-traditional data, Upstart improves access to credit and outperforms legacy FICO-based models. It has processed over $35 billion in loans with significantly lower default rates.

2. Zest AI

Zest’s AI-powered underwriting tools help lenders make better credit decisions, particularly for underserved demographics. It enables fair lending practices while reducing risk.

3. Klarna

The Swedish fintech giant leverages AI for personalized marketing, fraud detection, and customer service. AI is the backbone of Klarna’s “buy now, pay later” model.

Conclusion: The Time to Act Is Now

The adoption of AI in fintech is not just a technological upgrade—it’s a business imperative. It offers a unique blend of precision, personalization, and predictive power that traditional systems simply cannot match.

For fintech leaders, the message is clear: those who leverage AI smartly will lead the next wave of innovation—and those who don’t risk being left behind.

If you’re ready to embrace the AI-powered future, start by exploring AI-driven tools that align with your growth goals and customer expectations. Because in the future of finance, smart is the new standard.