2025-12-11 20:11:04
The modern LLM ecosystem offers a vast spectrum of models, each presenting distinct trade-offs in capability, cost, and latency. On one side are massive models like GPT-4 or Claude 3 Opus, which deliver exceptional reasoning and quality, but at significantly higher cost and increased response latency. On the other side are smaller, incredibly fast, and cost-efficient models like Llama-3-8B or GPT-4o Mini, which are ideal for simpler tasks.
The standard solution to leverage this diversity is LLM Routing, a mechanism that dynamically selects the most appropriate model for a given query.
The prevailing wisdom dictates building an "Intelligent Router," usually powered by a separate, smaller LLM or a sophisticated machine learning classifier (like a BERT-based model). This router's sole job is to analyze the incoming user query, predict its complexity or required output quality, and then dispatch it to the appropriate specialized model.
While sophisticated, this approach introduces fundamental architectural flaws rooted in over-engineering:
As systems scale to millions of requests, the cumulative cost of running an extra LLM inference step—even a small one—becomes prohibitive, confirming that using an LLM to decide which LLM to use is often over-engineering.
We found that the vast majority of our production workload could be successfully categorized using predictable, explicit signals rather than probabilistic reasoning. This led us to adopt the Optimizer Pattern, employing a "Dumb Router" focused entirely on speed and determinism.
The core insight is that for common, high-volume requests, basic keyword spotting and Regular Expressions (Regex) can perform the triage job instantly and deterministically. This approach operates with near-zero overhead because deterministic rule-based systems execute efficiently in constant time complexity (O(1)), guaranteeing predictability and speed.
For example, our initial production tests showed that mapping specific keywords provided accurate routing that correctly categorized 90% of cases reliably, instantly bypassing the need for a complex classification step.
The Hack: Use Regex and Keyword Spotting for instant pre-filtering:
This simple keyword match is instantaneous and deterministic, saving both inference latency and the financial cost associated with running even a small LLM classifier. This minimal overhead strategy captures nearly all the value proposition of model routing—maximizing efficiency by selecting the lightest necessary model—while incurring minimal architectural complexity.
To implement this efficient strategy while maintaining centralized control and compatibility with existing APIs, we utilized the LiteLLM Proxy. LiteLLM Proxy acts as an OpenAI-compatible gateway, serving as the single decision-making point where requests arrive before being dispatched to the actual backend models.
We configure the proxy not with intelligent classification models, but with low-latency, declarative rules that enforce immediate routing choices based on pattern matching. This allows us to benefit from the proxy's centralized management features—including cost tracking and load balancing across multiple deployments—while ensuring the initial routing decision itself remains "dumb" (instantaneous) and highly reliable.
The philosophical debate over LLM routing often pits Host A, arguing for the necessity of a sophisticated classifier for nuanced task interpretation, against Host B, arguing that a simple Keyword Switch captures 95% of the value with 0ms latency. Our production experience confirms Host B's thesis: the simplicity of the "Dumb Router" wins.
For latency-sensitive applications where milliseconds translate directly to user experience and profitability, achieving high accuracy must not come at the cost of speed. By shifting the complexity burden from probabilistic machine learning models back to deterministic logic, we achieved maximum efficiency and predictability. We embraced the architectural truth that sometimes, the most sophisticated design is the simplest one.
Ultimately, the goal of LLM routing is efficiency. Why pay a premium for over-thinking when basic pattern matching provides a reliable, instant answer? The key is knowing when to reason and when simply to switch.
An analogy for understanding this approach is sorting mail: an Intelligent Router is a dedicated postal worker who reads every letter to decide its precise destination. A Dumb Router is a simple optical sorter that instantly checks the ZIP code (the keyword) and throws the letter into the right major regional bin without opening it.
2025-12-11 20:09:40
Tailwind CSS can transform your development speed, but many developers only scratch the surface of its capabilities. This guide is for frontend developers and designers who want to unlock advanced Tailwind techniques that go beyond basic utility classes.
You'll discover how to write dramatically less code with shorthand classes, implement custom styling through arbitrary values, and create beautiful content layouts with the prose class. We'll also cover building consistent design systems and extending Tailwind's functionality with custom plugins.These proven techniques will help you build faster, write cleaner code, and create more maintainable stylesheets. Each trick includes practical examples you can start using immediately in your projects.
The size utility in Tailwind CSS provides a powerful shorthand for setting both width and height properties simultaneously. Instead of writing separate w-12 and h-12 classes, you can simply use size-12 to achieve the same result with less code. This approach is particularly useful for square elements like avatar images, icons, and buttons where maintaining equal dimensions is essential for consistent design.
Tailwind's spacing shortcuts significantly reduce the amount of classes needed for common layout patterns.The p-6 class applies padding to all sides, while px-4 and py-2 target horizontal and vertical padding respectively.Similarly, margin utilities like mx-auto center elements horizontally, and my-8 applies vertical margins. These shorthand classes eliminate the need to write individual padding-top, padding-right, padding-bottom, and padding-left declarations.
Modern Tailwind utilities often combine multiple CSS properties into single, semantic classes.The flex class not only sets display: flex but works seamlessly with related utilities like items-center for alignment and gap-4 for spacing between flex items.Complex effects like filters can be composed together using classes such as blur-sm grayscale, where Tailwind uses CSS variables to combine multiple filter functions into a single, cohesive declaration.
Tailwind CSS v3.0 and above allows you to use arbitrary values through square bracket notation to apply custom styles on the fly. You can create unique sizing by passing any value within brackets, such as h-[4rem] or w-[6rem].This works seamlessly with all units including px, rem, and em, like rounded-[5px], py-[4px], or px-[0.8rem].
For complex calculations, you can use CSS calc() functions within brackets: h-[calc(100vh-10px)].
Note: When using calc() functions, replace spaces with underscores for readability: h-[calc(100vh_-_10px)].
You can also reference custom theme values using the theme function: grid-cols-[fit-content(theme(spacing.32))].
Arbitrary values extend beyond sizing to include custom colors using hex codes directly in your classes.You can apply custom background colors with bg-[#0f355b] or text colors using text-[#e0e3e6]. This approach allows you to implement brand-specific colors or one-off design requirements without modifying your Tailwind configuration. You can combine multiple arbitrary values in a single element, such as bg-[#0f355b] text-[#e0e3e6] for complete custom styling. Custom fonts can also be referenced using the same bracket notation with font-[.custom-font].
When working with template engines like Pug, square brackets require special handling. The shorthand class syntax won't work with brackets (img.avatar.h-[127px] fails), but you can use regular attribute syntax instead: img(class='avatar h-[127px]', src='foobar.png'). You can mix shorthand with regular class attributes: img.avatar(class='h-[127px]', src='foobar.png').
Remember that Tailwind CSS doesn't support empty spaces within calc() or theme() functions, so use underscores as substitutes. Arbitrary properties can be written as [mask-type: unset] and arbitrary variants as [sm:last-child]:border-none for complete CSS control.
The Tailwind CSS Typography plugin provides prose classes that automatically apply beautiful typographic defaults to any vanilla HTML content you don't control, such as Markdown output or CMS content. Simply wrap your content with article class="prose" to instantly transform plain HTML into professionally styled text with proper spacing, font sizing, and visual hierarchy for headings, paragraphs, lists, tables, and more elements.
The prose plugin offers multiple customization options through modifier classes. You can adjust the overall typography size using variants like prose-sm, prose-lg, or prose-xl, and combine them with responsive breakpoints such as prose md:prose-lg lg:prose-xl. Choose from five gray scale themes including prose-slate, prose-zinc, and prose-stone to match your design system. For dark mode compatibility, add the prose-invert class to automatically adapt all typography colors for dark backgrounds.
Install the typography plugin by running npm install -D @tailwindcss/typography, then add @plugin "@tailwindcss/typography" to your main CSS file. For Tailwind CSS v3 projects, include require('@tailwindcss/typography') in your tailwind.config.js plugins array.Once installed, you can immediately start using prose classes throughout your project to style content-heavy sections with consistent, professional typography that requires no additional CSS customization.
Now that we've covered various Tailwind CSS optimization techniques, let's explore how to build consistent design systems through custom theme configuration.Using the @theme directive, you can define custom color variables like --color-mint-500: oklch(0.72 0.11 178) that automatically generate corresponding utility classes such as bg-mint-500, text-mint-500, and fill-mint-500. This approach creates a centralized color management system where design tokens are stored as CSS variables, enabling you to maintain brand consistency while generating utility classes on demand.
With this foundation in place, you can extend your design system beyond colors by defining comprehensive design tokens across multiple namespaces. Theme variables in the font-* namespace determine available font family utilities, while spacing-* controls padding and margin scales. By defining variables like --font-poppins: Poppins, sans-serif or custom spacing values, you create reusable design tokens that automatically generate corresponding utility classes. These tokens become regular CSS variables in your compiled output, making them accessible for custom CSS and inline styles through var(--color-mint-500) syntax.
Previously, we've seen how individual theme variables work, but the real power emerges when sharing design systems across multiple projects. By organizing your theme variables into dedicated CSS files using @theme, you can create portable design systems that maintain brand consistency. These shared theme files can be imported across different projects using standard CSS imports, or even published as NPM packages for larger organizations. This approach ensures that your brand colors, typography scales, and spacing systems remain consistent while allowing each project to extend the base theme with project-specific customizations as needed.
Now that we've explored built-in Tailwind features, let's examine how custom plugins can revolutionize your workflow by extending functionality beyond default capabilities.
Custom plugins excel at generating accessibility-focused utilities that standard Tailwind doesn't provide.Using addUtilities(), you can create high-contrast color schemes, focus indicators, and screen reader optimizations.The plugin system allows you to define complex accessibility patterns as single classes, making it effortless to maintain consistent accessible design across your entire application while keeping your HTML clean and semantic.
The addVariant() function enables creation of powerful custom variants like disabled:, first-child:, or supports-grid: that work seamlessly with existing utilities.You can register new utility styles using addUtilities() for specialized needs like custom transforms, gradients, or typography scales. These custom additions integrate perfectly with Tailwind's responsive system and pseudo-class variants, maintaining the framework's utility-first philosophy while expanding its capabilities.
Custom plugins maintain Tailwind's philosophy of keeping your CSS bundle clean and optimized. Unlike traditional CSS approaches, plugins generate only the classes you actually use through Tailwind's purging system. This means you can add hundreds of custom utilities without bloating your final CSS file, as unused classes are automatically removed during the build process, ensuring optimal performance.
Setting up dark mode in Tailwind CSS requires enabling the darkMode option in your tailwind.config.js file.By default, dark mode variants are disabled for file size considerations, so you must explicitly configure it using either the media strategy for automatic system preference detection or the class strategy for manual control.
Manual theme switching relies on JavaScript to manage the dark class on the HTML element. A robust implementation reads preferences from localStorage and uses window.matchMedia() to detect system preferences, providing three-way support for light mode, dark mode, and system preference respect.
Beyond basic dark mode, you can extend theme switching to custom brand colors by implementing data attribute selectors like [data-theme=dark] instead of classes. This approach allows for multiple theme variations while maintaining the same utility-based styling pattern that makes Tailwind's dark mode system so powerful.
Tailwind's group modifiers enable sophisticated state management by combining hover and focus interactions seamlessly. Apply the group class to parent elements and use group-hover: and group-focus: variants on children to create coordinated state changes across multiple elements simultaneously.
Beyond basic hover states, Tailwind supports advanced conditional styling through variants like group-active: and group-odd:. These modifiers allow you to create dynamic interfaces that respond intelligently to user interactions, enabling complex behavioral patterns without JavaScript while maintaining clean, declarative markup.
Create sophisticated component behaviors
Named groups using group/{name} syntax unlock nested interaction patterns for complex components. This approach enables precise targeting of specific elements within hierarchical structures, allowing you to build sophisticated UI behaviors like nested navigation menus, accordion components, and interactive cards with multiple state-dependent visual elements responding independently.
With this powerful technique in mind, the CSS clamp() function revolutionizes typography by setting minimum, preferred, and maximum values that scale fluidly across viewport sizes. Unlike traditional breakpoints that create abrupt changes, clamp() enables smooth text scaling using the syntax clamp(minimum, preferred, maximum). For example, font-size: clamp(16px, 4vw, 24px) ensures text never becomes smaller than 16px or larger than 24px while scaling naturally between these bounds.
Now that we understand clamp's foundation, viewport width units (vw) serve as the preferred value in fluid typography formulas. By combining viewport units with relative measurements like rem, you create responsive text that adapts seamlessly. The formula clamp(1rem, calc(0.75rem + 1vw), 1.25rem) demonstrates how viewport-based scaling maintains proportional text relationships across all screen sizes while preventing extreme sizing on very small or large displays.
Previously covered clamp techniques enable consistent readability by maintaining balanced proportions across various screen sizes. Setting maximum font sizes to no more than 2.5 times the minimum size prevents text from becoming too large on desktop displays.Testing at different zoom levels ensures WCAG 1.4.4 accessibility compliance, while combining relative units with viewport units creates typography that scales beautifully from mobile devices at 16-18px to desktop screens at 20-24px without compromising user experience.
These eight powerful Tailwind CSS techniques can transform your development workflow from good to exceptional. By implementing shorthand classes, arbitrary values, and the prose utility, you'll write cleaner, more maintainable code while reducing development time. The ability to build consistent design systems, create custom plugins, and master dynamic theme switching gives you the flexibility to handle any project requirement efficiently.
Start incorporating these tricks into your daily workflow, beginning with the simpler techniques like shorthand classes and arbitrary values, then gradually explore advanced features like custom plugins and fluid typography. With these tools at your disposal, you'll find yourself building beautiful, responsive interfaces faster than ever before while maintaining the high-quality code standards that Tailwind CSS promotes.
Disclaimer - Surprise, the images were generated by Adobe Firefly!
2025-12-11 20:05:39
REST API has long been the standard, but it has its limitations. When load increases, streaming scenarios appear, or business requires faster communication between services, REST stops being the ultimate solution. gRPC solves what REST cannot: it provides high data transfer speeds, supports streaming, and enforces a strict interaction structure. It is convenient to use in microservices, mobile, and IoT applications.
For a tester, this means one thing: knowing how to work with gRPC is shifting from a "nice-to-have" to an essential skill. If you haven't worked with this protocol yet, this article will help you understand its basics, how it differs from REST, the structure of .proto files, and most importantly - how to test gRPC services using Postman.
gRPC is a high-performance framework from Google. As a QA specialist, you don't necessarily need to dive into implementation details, but understanding how this protocol works and how to test it will definitely be useful.
To simplify, REST can be compared to paper letters sent in envelopes, while gRPC is like a phone call. Both are formats for communication between an application and a server, but with REST API, data is transferred in JSON or XML formats, which in our analogy can be equated to envelopes. This is okay and works, but not always fast or convenient.
gRPC proposes wrapping data in a binary protocol, which transmits data faster, has a smaller size, and is strictly structured. It uses Protocol Buffers (protobuf). Thanks to this, both sides participating in the information exchange know in advance what questions will be asked and what answers to expect. For a tester, this means less ambiguity and more predictability in verifications.
When we first implemented gRPC in a project, it unexpectedly turned out that Postman, by default, couldn't work with this protocol. We had to find workarounds - we tried BloomRPC, then grpcurl. In the end, we found a solution: uploading .proto files into a new beta version of Postman. It was a useful lesson - tools aren't always ready "out of the box," and it's important for a tester to have several alternatives on hand.
Why is gRPC usage increasingly common in product development? Here are the main reasons:
All of this leads to fewer misunderstandings with developers, clear rules for validation, and new testing scenarios - for example, for streaming.
Moving from testing REST to gRPC implies conceptual changes in the process. Instead of endpoints and HTTP methods - methods and messages described in a contract. For convenience, let's compare them in a table.
Next, let's look in more detail at one of gRPC's advantages over REST - the presence of streaming.
Unlike REST, which has the familiar GET/POST/PUT/DELETE, gRPC works with methods defined in a .proto file.
Four types of calls are supported:
When designing tests, it's important to consider that "one test - one request" isn't always suitable. For streaming, you need scenarios with multiple sequential messages.
But to understand which methods are available and what data can be transmitted in these calls, you need to understand how a .proto file is structured.
This is a simple text file with a .proto extension. In it, we essentially agree on what data can be sent and received, and what methods the service has.
You can think of it as an instruction or contract between the service developer and those who use it (for example, QA specialists).
Usually, the .proto file is created by the backend service developer, because they know which methods the service must support, as well as what data is accepted and returned.
A tester can also open a .proto file to understand how to correctly form a request, suggest improvements (for example, add a field or change a data type), and, if desired, write their own .proto for learning or experiments.
Before moving on to the structure of a .proto file, let's make an important clarification.
gRPC indeed transmits data in the binary Protocol Buffers format, not JSON - this is why it is more efficient than REST in terms of speed and traffic volume. However, when testing via tools like Postman, BloomRPC, or grpcurl, you will see requests and responses in a human-readable format resembling JSON. This is a visualization of the binary data for humans. The tools automatically convert the binary stream into such "pseudo-JSON" to make it easier for you to work with the fields.
In our examples, we will also use this readable format - not because gRPC uses JSON, but so you can quickly understand the message structure.
Let's look at a simple example of a .proto file:
syntax = "proto3";
service HelloService {
rpc SayHello (HelloRequest) returns (HelloResponse);
}
message HelloRequest {
string name = 1;
}
message HelloResponse {
string message = 1;
}
Syntax Specification
syntax = "proto3";
Syntax indicates which version of the Protocol Buffers language we are using.
At the time of writing, the current version is the third - proto3.
Service Definition
service HelloService {
rpc SayHello (HelloRequest) returns (HelloResponse);
}
service HelloService - declaration of a service named HelloService.
You can think of a service as a class containing methods.
rpc SayHello (...) returns (...)- this describes a remote procedure call, where:
In our example, the service has only one method, SayHello, which accepts a HelloRequest message and returns a HelloResponse.
Request Message Description
message HelloRequest {
string name = 1;
}
Message HelloRequest defines the data type for the request. Inside it:
string name = 1;
This means: the client must pass a string field name.
Example:
Response Message Description
message HelloResponse {
string message = 1;
}
Message HelloResponse defines the data type for the response. Inside:
string message = 1;
In our case, the server will return a greeting string.
Example:
All of this works as follows: the client calls the SayHello method, in the HelloRequest it sends the name field, the server receives this field, forms a response, and in the HelloResponse it returns a string with a greeting.
If you know how to read and understand .proto files, you can easily grasp the data structure and suggest improvements to the API even at the design stage.
And now, it's logical to move on to the next question: what else does the service return besides useful data? Here we'll talk about gRPC status codes.
In gRPC, every response (even a successful one) is accompanied by a status code. These are not HTTP 200/404/500, but their own set of codes defined in the gRPC library. In total, gRPC has 17 predefined status codes, from 0 to 16, but we will consider only the most basic and commonly used ones.
Main gRPC Status Codes
It's important not only to verify the correctness of the data in the response but also to ensure the service correctly returns status codes in various scenarios.
Previously, Postman could only work with REST, but now - also with gRPC.
How it works:
For example, for a simple service:
A request in Postman will contain the name field, and in response a greeting message will arrive.
Let's look at an example.
By mastering gRPC, you will expand your arsenal: you'll be able to test not only REST services but also more modern microservice systems. And that means - you'll become a more in-demand specialist.
Author: Vasil Khamidullin
2025-12-11 20:02:15
Will Netflix destroy Warner Brothers?
It’s being reported that Warner Bros. CEO David Zaslav has struck an $80 billion-plus deal to sell the studio’s entire movie, TV and gaming catalog to Netflix—think Superman, Batman, Wonder Woman, Harry Potter, Lord of the Rings, Game of Thrones, The Matrix and everything in between.
With Netflix poised to own DC Comics, Dune, Mad Max, Looney Tunes and more, this mega-acquisition could rewrite the rules of streaming. Plus, Paramount’s David Ellison is making one last play to swoop in—so buckle up for a wild ride. Tune into The Weekly Planet podcast for the deep dive.
Watch on YouTube
2025-12-11 20:00:00
The finance worker's video call seemed perfectly normal at first. Colleagues from across the company had dialled in for an urgent meeting, including the chief financial officer. The familiar voices discussed routine business matters, the video quality was crisp, and the participants' mannerisms felt authentic. Then came the request: transfer $25 million immediately. What the employee at Arup, the global engineering consultancy, couldn't see was that every single person on that call, save for himself, was a deepfake—sophisticated AI-generated replicas that had fooled both human intuition and the company's security protocols.
This isn't science fiction. This happened in Hong Kong in February 2024, when an Arup employee authorised 15 transfers totalling $25.6 million before discovering the deception. The sophisticated attack combined multiple AI technologies—voice cloning that replicated familiar speech patterns, facial synthesis that captured subtle expressions, and behavioural modelling that mimicked individual mannerisms—creating a convincing corporate scenario that bypassed both technological security measures and human intuition.
The Hong Kong incident represents more than just an expensive fraud. It's a glimpse into a future where artificial intelligence has fundamentally altered the landscape of financial manipulation, creating new attack vectors that exploit both technological vulnerabilities and human psychology with unprecedented precision. As AI systems become more sophisticated and accessible, they're not just changing how we manage money—they're revolutionising how criminals steal it.
“The data we're releasing today shows that scammers' tactics are constantly evolving,” warns Christopher Mufarrige, Director of the Federal Trade Commission's Bureau of Consumer Protection. “The FTC is monitoring those trends closely and working hard to protect the American people from fraud.” But monitoring may not be enough. In 2024 alone, consumers lost more than $12.5 billion to fraud—a 25% increase over the previous year—with synthetic identity fraud alone surging by 18% and AI-driven fraud now accounting for 42.5% of all detected fraud attempts.
The traditional image of financial fraud—perhaps a poorly-written email from a supposed Nigerian prince—feels quaint compared to today's AI-powered operations. Modern financial manipulation leverages machine learning algorithms that can analyse vast datasets to identify vulnerable targets, craft personalised attack vectors, and execute sophisticated social engineering campaigns at scale.
Consider the mechanics of contemporary AI fraud. Machine learning models can scrape social media profiles, purchase histories, and public records to build detailed psychological profiles of potential victims. These profiles inform personalised phishing campaigns that reference specific details about targets' lives, financial situations, and emotional states. Voice cloning technology, which once required hours of audio samples, now needs just a few seconds of speech to generate convincing impersonations of family members, colleagues, or trusted advisors.
Deloitte's research reveals the scale of this evolution: their 2024 polling found that 25.9% of executives reported their organisations had experienced deepfake incidents targeting financial and accounting data in the preceding 12 months. More alarming still, the firm's Centre for Financial Services predicts that generative AI could enable fraud losses to reach $40 billion in the United States by 2027, up from $12.3 billion in 2023—representing a compound annual growth rate of 32%.
The sophistication gap between attackers and defenders is widening rapidly. While financial institutions invest heavily in fraud detection systems, criminals have access to many of the same AI tools and techniques. “AI models today require only a few seconds of voice recording to generate highly convincing voice clones freely or at a very low cost,” according to cybersecurity researchers studying deepfake vishing attacks. “These scams are highly deceptive due to the hyper-realistic nature of the cloned voice and the emotional familiarity it creates.”
AI's most insidious capability in financial manipulation isn't technical—it's psychological. Modern algorithms excel at identifying and exploiting cognitive biases, emotional vulnerabilities, and decision-making patterns that humans barely recognise in themselves. This represents a fundamental shift from traditional fraud, which relied on generic psychological tricks, to personalised manipulation engines that adapt their approaches based on individual responses.
Research from the Ontario Securities Commission's September 2024 analysis identified several concerning AI-enabled manipulation techniques already deployed against investors. These include AI-generated promotional videos featuring testimonials from “respected industry experts,” sophisticated editing of investment posts to fix grammar and formatting while making content more persuasive, and algorithms that promise unrealistic returns while employing scarcity tactics and generalised statements designed to bypass critical thinking.
The manipulation often extends beyond obvious scams into subtler forms of algorithmic persuasion. As researchers studying AI's darker applications note: “Manipulation can take many forms: the exploitation of human biases detected by AI algorithms, personalised addictive strategies for consumption of goods, or taking advantage of the emotionally vulnerable state of individuals.”
This personalisation operates at unprecedented scale and precision. AI systems can identify when individuals are most likely to make impulsive financial decisions—perhaps late at night, after receiving bad news, or during periods of financial stress—and time their interventions accordingly. They can craft messages that exploit specific psychological triggers, from fear of missing out to social proof mechanisms that suggest “people like you” are making particular investment decisions.
The emotional manipulation component represents perhaps the most troubling development. Steve Beauchamp, an 82-year-old retiree, told The New York Times that he drained his retirement fund and invested $690,000 in scam schemes over several weeks, influenced by deepfake videos purporting to show Elon Musk promoting investment opportunities. Similarly, a French woman lost nearly $1 million to scammers using AI-generated content to impersonate Brad Pitt, demonstrating how deepfake technology can exploit parasocial relationships and emotional vulnerabilities.
The financial services industry's embrace of AI extends far beyond fraud detection and into the realm of investment advice, creating new opportunities for manipulation that blur the lines between legitimate algorithmic guidance and predatory practices. Robo-advisers, which manage over $8 billion in assets as of 2024 and are projected to reach $33.38 billion by 2030, represent both a democratisation of financial advice and a potential vector for systematic bias and manipulation.
The robo-advisor market's explosive growth—characterised by a compound annual growth rate of 26.71%—has created competitive pressures that may incentivise platforms to prioritise engagement and revenue generation over genuine fiduciary duty. Unlike human advisers, who are subject to regulatory oversight and professional ethical standards, AI-driven platforms operate in a regulatory grey area where the traditional rules of financial advice haven't been fully adapted to algorithmic decision-making.
“Every robo-adviser provider uses a unique algorithm created by individuals, which means the technology cannot be completely free from human affect, cognition, or opinion,” researchers studying robo-advisory systems observe. “Therefore, despite the sophisticated processing power of robo-advisers, any recommendations they make may still carry biases from the data itself.” This inherent bias becomes problematic when algorithms are trained on historical data that reflects past discrimination or when they optimise for metrics that don't align with client interests.
The Consumer Financial Protection Bureau has identified concerning evidence of such misalignment. As CFPB Director Rohit Chopra noted, the Bureau has seen “concerning evidence that some companies offering comparison-shopping tools to help consumers pick credit cards and other products may be providing users with manipulated results fuelled by undisclosed kickbacks.” The CFPB recently issued guidance warning that the use of dark patterns and manipulated results in comparison tools may violate federal law.
This manipulation extends beyond simple kickback schemes into more subtle forms of algorithmic steering. AI systems can be programmed to nudge users towards higher-fee products, riskier investments that generate more commission revenue, or financial products that serve the platform's business interests rather than the client's financial goals. The opacity of these algorithms makes such manipulation difficult to detect, as clients cannot easily audit the decision-making processes that generate their personalised recommendations.
The deployment of AI in financial markets has created new opportunities for market manipulation that operate at speeds and scales impossible for human traders. While regulators have historically focused on traditional forms of market abuse—insider trading, pump-and-dump schemes, and coordination among human actors—algorithmic market manipulation presents entirely new challenges for oversight and enforcement.
High-frequency trading algorithms can process market information and execute trades in microseconds, creating opportunities for sophisticated manipulation strategies that exploit tiny price movements across multiple markets simultaneously. These systems can engage in techniques like spoofing—placing and quickly cancelling orders to create false impressions of market demand—or layering, where algorithms create artificial depth in order books to influence other traders' decisions.
The prospect of widespread adoption of advanced AI models in financial markets, particularly those based on reinforcement learning and deep learning techniques, has raised significant concerns among regulators. As financial services legal experts note, “requiring algorithms to report cases of market manipulation by other algorithms could trigger an adversarial learning dynamic where AI-based trading algorithms may learn from each other's techniques and evolve strategies to obfuscate their goals.”
This adversarial dynamic represents a fundamental challenge for market oversight. Traditional regulatory approaches assume that manipulation strategies can be identified, documented, and prevented through rules and enforcement. But AI systems that continuously learn and adapt may develop manipulation techniques that regulators haven't anticipated, or that evolve faster than regulatory responses can keep pace.
The Securities and Exchange Commission has begun to address these concerns through enforcement actions and policy guidance. In March 2024, the SEC announced its first “AI washing” enforcement cases, targeting firms that made false or misleading statements about their use of artificial intelligence. SEC Enforcement Director Gurbir Grewal stated: “As more and more investors consider using AI tools in making their investment decisions or deciding to invest in companies claiming to harness its transformational power, we are committed to protecting them against those engaged in 'AI washing.'”
The democratisation of deepfake technology has transformed synthetic media from a niche research area into a mainstream tool for financial fraud. What once required Hollywood-level production budgets and technical expertise can now be accomplished with consumer-grade hardware and freely available software, creating a new category of financial crime that leverages our fundamental trust in audio-visual evidence.
The capabilities of modern deepfake technology extend far beyond simple video manipulation. AI systems can now generate convincing synthetic media across multiple modalities simultaneously—combining fake video, cloned audio, and even synthetic biometric data to create comprehensive false identities. These synthetic personas can be used to open bank accounts, apply for loans, conduct fraudulent investment seminars, or impersonate trusted financial advisers in video calls.
The financial industry has been particularly vulnerable to these attacks because it relies heavily on identity verification processes that weren't designed to detect synthetic media. Traditional “know your customer” procedures typically involve document verification and perhaps a video call—both of which can be compromised by sophisticated deepfake technology. Financial institutions are scrambling to develop new verification methods that can distinguish between genuine and synthetic identity evidence.
Recent case studies illustrate the scale of this challenge. Beyond the Hong Kong incident, 2024 has seen numerous high-profile deepfake frauds targeting both individual investors and financial institutions. Cyber threats and fraud scams drove record monetary losses of over $16.6 billion in 2024, representing a 33% increase over the previous year, with deepfake-enabled fraud playing an increasingly significant role.
The technology's evolution continues to outpace defensive measures. Document manipulation through AI is increasing rapidly, and even biometric verification systems are “gradually falling victim to this trend,” according to cybersecurity researchers. The Financial Crimes Enforcement Network (FinCEN) issued Alert FIN-2024-Alert004 to help financial institutions identify fraud schemes using deepfake media created with generative AI, acknowledging that traditional fraud detection methods are insufficient against these new attacks.
Perhaps the most insidious form of AI-enabled financial manipulation operates not through overt fraud but through systematic discrimination that perpetuates and amplifies existing inequities in the financial system. This phenomenon, termed “digital redlining” by regulators, uses AI algorithms to deny or limit financial services to specific communities while maintaining a veneer of algorithmic objectivity.
CFPB Director Rohit Chopra has made combating digital redlining a priority, noting that these systems are “disguised through so-called neutral algorithms, but they are built like any other AI system—by scraping data that may reinforce the biases that have long existed.” The challenge lies in the subtlety of algorithmic discrimination: unlike overt redlining practices of the past, digital redlining can be embedded in complex machine learning models that are difficult to audit and understand.
These discriminatory algorithms manifest in various financial services, from credit scoring and loan approval to insurance pricing and investment recommendations. AI systems trained on historical data inevitably inherit the biases present in that data, potentially excluding qualified applicants based on factors that correlate with race, gender, age, or socioeconomic status. The opacity of many AI systems makes this discrimination difficult to detect and challenge, as affected individuals may never know why they were denied services or offered inferior terms.
The scale of potential impact is enormous. As AI-driven decision-making becomes more prevalent in financial services, discriminatory algorithms could systematically exclude entire communities from economic opportunities, perpetuating cycles of financial inequality. Unlike human discrimination, which operates on an individual level, algorithmic discrimination can affect thousands or millions of people simultaneously through automated systems.
Regulators are beginning to address these concerns through new guidance and enforcement actions. The CFPB has proposed rules to ensure that algorithmic and AI-driven appraisals are fair, while state-level initiatives like Colorado's Senate Bill 24-205 require financial institutions to disclose how AI-driven lending decisions are made, including the data sources and performance evaluation methods used.
The regulatory landscape for AI in financial services is evolving rapidly across jurisdictions, with different approaches emerging on either side of the Atlantic. The European Union implemented its comprehensive AI Act on 1 August 2024, creating the world's first legal framework specifically governing AI systems, while the UK has adopted a principles-based, sector-specific approach that prioritises innovation alongside safety.
The Consumer Financial Protection Bureau has taken an aggressive stance, with Director Chopra emphasising that “there is no 'fancy new technology' carveout to existing laws.” The CFPB's position is that firms must comply with consumer financial protection laws when adopting emerging technology, and if they cannot manage new technology in a lawful way, they should not use it. This approach prioritises consumer protection over innovation, potentially creating friction between regulatory compliance and technological advancement.
The Securities and Exchange Commission has similarly signalled its intent to apply existing securities laws to AI-enabled activities while developing new guidance for emerging use cases. The SEC's March 2024 enforcement actions against “AI washing”—where firms make false or misleading statements about their AI capabilities—demonstrate regulators' willingness to take enforcement action even as they develop comprehensive policy frameworks.
Federal agencies are coordinating their responses across borders as well as domestically. The Federal Trade Commission has updated its telemarketing rules to address AI-enabled robocalls and launched a Voice Cloning Challenge to promote development of technologies that can detect misuse of voice cloning software. The Treasury Department has implemented machine learning systems that prevented and recovered over $4 billion in fraud during fiscal year 2024, showing how AI can be used defensively as well as offensively. Internationally, the UK, EU, and US recently signed the Council of Europe Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law—the world's first international treaty governing the safe use of AI.
However, regulatory responses face several fundamental challenges. AI systems can evolve and adapt more quickly than regulatory processes, potentially making rules obsolete before they take effect. The global nature of AI development means that regulatory arbitrage—where firms move operations to jurisdictions with more favourable rules—becomes a significant concern. Additionally, the technical complexity of AI systems makes it difficult for regulators to develop expertise and enforcement capabilities that match the sophistication of the technologies they're attempting to oversee.
Individual consumers face an asymmetric battle against AI-powered financial manipulation, but several practical strategies can significantly improve personal security. The key lies in understanding that AI-enabled attacks often exploit the same psychological and technical vulnerabilities as traditional fraud, but with greater sophistication and personalisation.
The first line of defence involves developing healthy scepticism about unsolicited financial opportunities, regardless of how legitimate they appear. AI-generated content can be extraordinarily convincing, incorporating personal details gleaned from social media and public records to create compelling narratives. Individuals should establish verification protocols for any unexpected financial communications, including independently confirming the identity of supposed colleagues, advisors, or family members who request money transfers or financial information.
Voice verification presents particular challenges in an era of sophisticated voice cloning. Security experts recommend establishing code words or phrases with family members that can be used to verify identity during suspicious phone calls. Additionally, individuals should be wary of urgent requests for financial action, as legitimate emergencies rarely require immediate wire transfers or cryptocurrency payments.
Digital hygiene practices become crucial in an AI-enabled threat environment. This includes limiting personal information shared on social media (criminals can use as little as a few social media posts to build convincing deepfakes), regularly reviewing privacy settings on all online accounts, using strong, unique passwords with two-factor authentication, and being cautious about public Wi-Fi networks where financial transactions might be monitored. AI systems often build profiles by aggregating information from multiple sources, so reducing the available data points can significantly decrease vulnerability to targeted attacks. Consider conducting regular 'digital audits' of your online presence to understand what information is publicly available.
Financial institutions and service providers should be evaluated based on their AI governance practices and transparency. Under new regulations like the EU's AI Act, which entered force in August 2024, institutions using high-risk AI systems for credit decisions must provide transparency about their AI processes. Consumers should ask direct questions: How does AI influence decisions affecting my account? What data feeds into these systems? How can I contest or appeal algorithmic decisions? What protections exist against bias? Institutions that cannot provide clear answers about their AI governance—particularly regarding the five key principles of safety, transparency, fairness, accountability, and contestability—may present greater risks.
Multi-factor authentication and biometric security measures provide additional protection layers, but consumers should understand their limitations. As deepfake technology advances—with fraud cases surging 1,740% between 2022 and 2023—even video calls and biometric data may be compromised, requiring additional verification methods. Consider establishing 'authentication codes' with family members and trusted contacts that can be used to verify identity during suspicious communications. The principle of 'trust but verify' becomes particularly important when AI systems can generate convincing false evidence, including synthetic documents and identification materials.
The battle between AI-enabled fraud and AI-powered defence systems represents one of the most sophisticated technological arms races in modern cybersecurity. Financial institutions are fighting fire with fire, deploying machine learning algorithms that can process millions of transactions per second, looking for patterns that human analysts would never detect. As attack methods become more advanced, detection systems must evolve to match their sophistication, creating a continuous cycle of technological advancement that benefits both attackers and defenders.
Current detection technologies focus on identifying synthetic media through multiple sophisticated approaches. These include pixel-level analysis that examines compression artefacts and temporal inconsistencies in video frames, audio frequency analysis that detects telltale signs of voice synthesis in spectral patterns, and advanced Long Short-Term Memory (LSTM) AI models that can identify behavioural anomalies in real-time. American Express improved fraud detection by 6% using these LSTM models, while PayPal achieved a 10% improvement in real-time detection. However, each advance in detection capabilities is matched by improvements in generation technology, creating a perpetual technological competition where deepfake fraud cases surged 1,740% in North America between 2022 and 2023.
Machine learning systems designed to detect AI-generated content face several fundamental challenges. Training these systems requires access to large datasets of both genuine and synthetic media, but the synthetic examples must be representative of current attack methods to be effective. As generation technology improves, detection systems must be continuously retrained on new examples, creating significant ongoing costs and technical challenges.
The detection problem becomes more complex when considering adversarial machine learning, where generation systems are specifically trained to fool detection algorithms. This creates a dynamic where attackers can test their synthetic content against known detection methods and refine their techniques to evade identification. The result is an escalating technological competition where both sides continuously improve their capabilities.
Financial institutions are investing heavily in AI-powered fraud detection systems, with 74% already using AI for financial-crime detection and 73% for fraud detection. These systems analyse transaction patterns, communication metadata, and behavioural signals to identify potential manipulation attempts, processing vast amounts of data in real-time to spot suspicious patterns that might indicate AI-generated content or coordinated manipulation campaigns. The integration of multi-contextual, real-time data at massive scale has proven particularly effective, as synthetic accounts leave digital footprints that sophisticated detection algorithms can identify. However, these systems generate false positives that can interfere with legitimate transactions, and an estimated 85-95% of potential synthetic identities still escape detection by traditional fraud models.
The integration of detection systems into consumer-facing applications remains challenging. While sophisticated detection technology exists in laboratory settings, implementing it in mobile apps, web browsers, and communication platforms requires significant computational resources and may impact user experience. The trade-offs between security, performance, and usability continue to shape the development of consumer-oriented protection tools.
The evolution of AI technology suggests several emerging threat vectors that will likely reshape financial manipulation in the coming years. Understanding these potential developments is crucial for developing proactive defence strategies rather than reactive responses to new attack methods.
Multimodal AI systems that can generate convincing synthetic content across text, audio, video, and even physiological data simultaneously represent the next frontier in deepfake technology. These systems could create comprehensive false identities that extend beyond simple impersonation to include synthetic medical records, employment histories, and financial documentation. The implications for identity verification and fraud prevention are profound.
Large language models are becoming increasingly capable of conducting sophisticated social engineering attacks through extended conversations. These AI systems can maintain consistent personas across multiple interactions, build rapport with targets over time, and adapt their persuasion strategies based on individual responses. Unlike current scam operations that rely on human operators, AI-driven social engineering can operate at unlimited scale while maintaining high levels of personalisation.
The integration of AI with Internet of Things (IoT) devices and smart home technology creates new opportunities for financial manipulation through environmental context awareness. AI systems could potentially access information about individuals' daily routines, emotional states, and financial behaviours through connected devices, enabling highly targeted manipulation attempts that exploit real-time personal circumstances.
Quantum computing represents a more immediate threat than many realise. The Global Risk Institute's 2024 Quantum Threat Timeline Report estimates that within 5-15 years, cryptographically relevant quantum computers could break standard encryptions in under 24 hours. By the early 2030s, quantum systems may bypass widely used public key infrastructure algorithms like RSA and ECC, rendering current financial encryption ineffective. The US government has set a deadline of 2035 for full migration to post-quantum cryptography, but the Department of Homeland Security describes a shorter transition ending by 2030. Compounding the urgency, malicious actors are already employing 'harvest now, decrypt later' strategies, collecting encrypted financial data today to decrypt when quantum computers become available.
The emergence of AI-as-a-Service platforms makes sophisticated manipulation tools accessible to less technically sophisticated criminals. These platforms could eventually offer “manipulation-as-a-service” capabilities that allow individuals with limited technical skills to conduct sophisticated AI-powered financial fraud, dramatically expanding the pool of potential attackers.
The challenge of regulating AI in financial services requires fundamentally new approaches that can adapt to rapidly evolving technology while maintaining consumer protection standards. Traditional regulatory models, based on fixed rules and periodic updates, are proving insufficient for the dynamic nature of AI systems.
Regulatory sandboxes represent one innovative approach, allowing financial institutions to test AI applications under relaxed regulatory requirements while providing regulators with opportunities to understand new technologies before comprehensive rules are developed. These controlled environments can help identify potential risks and benefits of new AI applications while maintaining consumer protections.
Algorithmic auditing requirements are emerging as a key regulatory tool. Rather than attempting to regulate AI outcomes through fixed rules, these approaches require financial institutions to regularly test their AI systems for bias, discrimination, and manipulation potential. This creates ongoing compliance obligations that can adapt to evolving AI capabilities while maintaining accountability.
Real-time monitoring systems that can detect AI-enabled manipulation as it occurs represent another frontier in regulatory innovation. These systems would combine traditional transaction monitoring with AI-powered detection of synthetic media, coordinated manipulation campaigns, and anomalous behavioural patterns. The challenge lies in developing systems that can operate at the speed and scale of modern financial markets while avoiding false positives that disrupt legitimate activities.
International coordination becomes crucial as AI-enabled financial manipulation crosses borders and jurisdictions. Regulatory agencies are beginning to develop frameworks for information sharing, joint enforcement actions, and coordinated policy development. The challenge lies in balancing national regulatory sovereignty with the need for consistent global standards that prevent regulatory arbitrage.
The development of industry standards and best practices, coordinated by regulatory agencies but implemented by industry associations, may provide more flexible governance mechanisms than traditional top-down regulation. These approaches can evolve more quickly than formal regulatory processes while maintaining industry-wide consistency in AI governance practices.
The future of financial consumer protection in an AI-powered world demands nothing less than a fundamental reimagining of how we secure our economic infrastructure. The convergence of AI manipulation, quantum computing threats, and increasingly sophisticated deepfake technology creates challenges that no single institution, regulation, or technology can address alone. Success requires unprecedented coordination across technological, regulatory, industry, and educational domains.
Financial institutions must invest not just in AI-powered fraud detection but in comprehensive AI governance frameworks that address bias, transparency, and accountability throughout their AI systems. This includes regular algorithmic auditing, clear documentation of AI decision-making processes, and mechanisms for consumers to understand and contest AI-driven decisions that affect their financial lives.
Regulatory agencies need to develop new forms of expertise and enforcement capabilities that match the sophistication of AI systems. This may require hiring technical specialists, investing in AI-powered regulatory tools, and developing new forms of collaboration with academic researchers and industry experts. Regulators must also balance innovation incentives with consumer protection, ensuring that legitimate AI applications can flourish while preventing abuse.
Industry collaboration through information sharing, joint research initiatives, and coordinated response to emerging threats can help level the playing field between attackers and defenders. Financial institutions, technology companies, and cybersecurity firms must work together to identify new threat vectors, develop countermeasures, and share intelligence about attack methods and defensive strategies.
Consumer education remains crucial but must evolve beyond traditional financial literacy to include AI literacy—helping individuals understand how AI systems work, what their limitations are, and how they can be manipulated or misused. This education must be ongoing and adaptive, as the threat landscape continuously evolves.
The path forward requires acknowledging that AI-enabled financial manipulation represents a fundamental paradigm shift in the threat landscape. We are moving from an era of static, rule-based security systems designed for human-scale threats to a dynamic environment where attacks adapt in real-time, learn from defensive measures, and personalise their approaches based on individual psychological profiles. The traditional assumption that humans can spot deception no longer holds when faced with AI that can perfectly replicate voices, faces, and behaviours of trusted individuals.
Success will require embracing the same technological capabilities that enable these attacks—using AI to defend against AI, developing adaptive systems that can evolve with emerging threats, and creating governance frameworks that balance innovation with protection. The stakes are high: failure to adapt could undermine trust in financial systems at a time when digital transformation is accelerating across all aspects of economic life.
The $25.6 million deepfake incident at Arup in Hong Kong was not an isolated anomaly—it was the opening salvo in a new era of financial warfare. As we stand at this technological inflection point, we face a stark choice: we can proactively build the defensive infrastructure, regulatory frameworks, and consumer protections needed to harness AI's benefits while mitigating its risks, or we can remain reactive, constantly playing catch-up with increasingly sophisticated attacks that threaten to undermine the very foundation of financial trust.
The technology exists to detect synthetic media, identify manipulation patterns, and protect consumers from AI-enabled fraud. What's needed now is the collective will to implement these solutions at scale, the regulatory wisdom to balance innovation with protection, and the public awareness to recognise and resist these new forms of manipulation. The future of finance—and our economic security—depends on the decisions we make today.
In a world where seeing is no longer believing, where voices can be cloned from seconds of audio, and where algorithms can exploit our deepest psychological vulnerabilities, our only defence is a combination of technological sophistication, regulatory vigilance, and informed scepticism. The question isn't whether AI will transform financial services—it's whether that transformation will serve human flourishing or enable unprecedented exploitation. The choice remains ours, but the window for action is closing with each passing day.
Ontario Securities Commission. “Artificial Intelligence and Retail Investing: Scams and Effective Countermeasures.” September 2024.
Consumer Financial Protection Bureau. “CFPB Comment on Request for Information on Uses, Opportunities, and Risks of Artificial Intelligence in the Financial Services Sector.” August 2024.
Federal Trade Commission. “New FTC Data Show a Big Jump in Reported Losses to Fraud to $12.5 Billion in 2024.” March 2025.
Securities and Exchange Commission. “SEC Charges Two Investment Advisers with Making False and Misleading Statements About Their Use of Artificial Intelligence.” March 18, 2024.
Deloitte. “Deepfake Banking and AI Fraud Risk.” 2024.
Incode. “Top 5 Cases of AI Deepfake Fraud From 2024 Exposed.” 2024.
Financial Crimes Enforcement Network. “Alert FIN-2024-Alert004.” 2024.
Tim Green UK-based Systems Theorist & Independent Technology Writer
Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.
His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.
ORCID: 0009-0002-0156-9795
Email: [email protected]
2025-12-11 19:55:17
For the last few weeks I’ve been thinking seriously about building my own programming language. Not a big general-purpose language, not a Python replacement, and definitely not something with heavy ambitions. I just wanted to create something small, useful, and focused.
That’s where Dusty comes in.
Dusty is a lightweight DSL (domain-specific language) designed only for ETL tasks and research data cleaning. Nothing more. No huge ecosystem, no package manager, no frameworks. The entire goal is simple:
turn messy CSV/JSON cleaning work into short, readable scripts.
I’m starting with problems I’ve personally faced. Whenever I work on research data or hackathon datasets, I end up writing the same pattern again and again:
load CSV
filter rows
fix missing values
rename some fields
join with another file
export the cleaned result
Python works, but the scripts get ugly fast. Pandas is powerful, but not great for small tasks. SQL is good for structured tables but not for irregular CSVs. Most ETL tools are built for companies, not students or indie developers.
So Dusty focuses on the middle ground:
simple data transformations without the overhead.
A Dusty script looks like this:
source users = csv("users.csv")
transform adults = users
| filter(r -> int(r.age) >= 18)
| map(r -> { id: r.id, name: r.name })
save adults to csv("clean_adults.csv")
Readable.
No imports.
No boilerplate.
Just the data flow.
Dusty will support the essential ETL operations:sourcefiltermapselect / renamejoinaggregatesave
That’s enough to clean real datasets used in labs, projects, and university research.
How I’m building it
This is my first language project, so I’m keeping things practical:
The Dusty interpreter is written in Python (not related to Dusty syntax at all).
Dusty code will live in .dusty files.
Users run it with a simple CLI like:
dusty run main.dsty
My plan is to finish Dusty v0.1 with:
a working parser
CSV support
filter/map
save
a couple of example pipelines
basic documentation
I’m not adding a package manager, modules, or big features yet. Dusty V0.1 should be small enough that anyone can understand the whole project in one sitting.
Why I’m writing this publicly
I’ve noticed something: when you build in silence, you get lost. When you build in public, even quietly, you naturally stay accountable. So this weekly blog is just a way to share the progress, mistakes, and insights along the journey of creating a tiny DSL from scratch.
No big promises.
No hype.
Just consistent work.