A new blog post compares AI-assisted coding to cooking steak—easy to start, hard to master—arguing that AI can't replace developer judgment, only accelerate it.

Background & Context The proliferation of AI coding assistants—from GitHub Copilot to Cursor and proprietary agentic systems—has created a narrative that software development is becoming accessible to anyone, regardless of technical depth. The promise: describe what you want, and the AI will build it. This allure has driven adoption across startups and enterprises, with teams racing to integrate AI into their pipelines. However, a recent blog post by Sydorets (posted on his personal blog) draws a sharp analogy between this workflow and cooking a steak: anyone can throw a steak in a pan, but consistently producing a perfect medium-rare requires understanding heat, timing, and technique. Similarly, AI can generate code that runs, but producing software that truly meets user needs, performs reliably, and embodies intentional design still demands human expertise. The post has resonated widely, sparking discussions about the practical limits of AI development tools and the enduring necessity of skilled engineers. The News: What Happened Exactly The blog post, titled "Software development with AI is starting to feel like cooking steak," argues that AI-assisted development is lulling developers into a false sense of competence. The author observes that we now build continuously—during commutes, between tasks, almost unconsciously—using agents, pipelines, and prompts, yet we often have little understanding of how the underlying systems work. We throw "everything at a model and hope it gives us what we imagined," without mastering the fundamental principles of software engineering. This approach yields inconsistent results: sometimes surprisingly good, other times a "charcoal with a sprig of thyme" that the AI confidently presents as a perfect dish. The author notes that these failures persist even when we invest in premium AI tools, agencies, or new frameworks, because we're essentially outsourcing the undefined problem—hoping someone else has solved it for us, but often finding that they haven't. The article's central metaphor paints AI as a "steak machine": it can follow recipes and repeat processes at scale, but it lacks the ability to understand the vision in your head. Translating that vision into requirements, constraints, and tests remains a human responsibility. Even with fine-grained feedback, the AI is bounded by its context window and the quality of the surrounding system—it cannot become a "Michelin-starred chef." The author draws a frustrating conclusion: after paying for premium AI services, you might still get the same burnt steak you'd make at home, because all restaurants (i.e., AI providers) have hired the same AI cook. Management optimizes for cost, accepting that most users won't notice mediocre quality. But for developers who care deeply about their craft, this is unacceptable. The only path forward, the post argues, is to "learn to cook"—to acquire the deep understanding of software architecture, trade-offs, and quality that AI cannot replicate. This means studying how code works, why certain patterns matter, and how to judge AI-generated output. The author encourages embracing failure as a learning tool and iterating until you can produce the desired result reliably, rather than relying on luck. Eventually, you might become skilled enough to "open your own restaurant"—meaning, you can leverage AI as a sous-chef rather than expecting it to replace your judgment. Most users won't notice the difference, but you will—because you care about what you build. This narrative is unique in its emphasis on the craft of development, rather than just productivity gains or model capabilities. It mirrors growing concerns in the industry about code quality and maintainability as AI-generated code proliferates. The post does not cite specific metrics or case studies, but its anecdotal evidence and relatable analogy have struck a chord, leading to broad discussions on Hacker News, LinkedIn, and X. Historical Parallels & Similar Incidents The "steak machine" metaphor echoes earlier moments in the AI development landscape. One direct parallel is the rise of low-code and no-code platforms in the late 2010s, such as OutSystems, Mendix, and Bubble. These tools promised to democratize software creation, allowing non-programmers to build apps by dragging and dropping. However, they quickly revealed their limitations: complex logic, scalability, and security often required hand-coded solutions. As a result, developers who embraced these platforms as a full replacement for coding found themselves hitting a glass ceiling—the platform could handle the easy 80%, but the remaining 20% needed a professional. This led to the common adage: "No-code is for building prototypes, not production software." Yet, the allure of speed and ease persists, and AI tools are merely the next evolution of this pattern. Another historical parallel is the advent of code generation tools like in the early 2000s, such as Visual Studio's drag-and-drop UI builders or code wizards. These tools aimed to reduce boilerplate code, but they often produced bloated, hard-to-maintain codebases that required refactoring. Developers who relied too heavily on wizards found themselves debugging generated code that they didn't fully understand. The industry's response was a push toward clean code practices and a renewed emphasis on understanding fundamentals. Similarly, today's AI assistants often produce code that looks correct but may have hidden issues—insecure dependencies, performance bottlenecks, or architectural flaws. The lesson from both eras is that tools that abstract away complexity do not eliminate the need for expertise; they shift it to a higher level. The developer must become the chef who knows when to trust the machine and when to intervene. The difference with AI is the confidence with which it generates plausible but wrong output. Unlike a wizard which follows fixed templates, AI generates novel combinations, making it harder to spot flaws. This is reminiscent of the early days of automated translation, where systems would produce grammatically correct but semantically nonsensical sentences. Only with understanding of the source and target languages could a translator catch these errors. Similarly, today's AI coding assistants require a developer who can perform code review with a critical eye—someone who has tasted enough "burnt steak" to recognize it. These historical parallels reinforce the article's message: technology amplifies human capability but does not replace it. Every leap in abstraction—from assembly to high-level languages, from imperative to declarative, from coding to no-code, and now to AI-magic—has required a new layer of expertise to manage the complexities that the abstraction introduces. The blog post is a timely reminder that, as AI becomes ubiquitous, the demand for skilled software engineers who understand the fundamentals will not diminish; it will evolve. Those who invest in learning the craft will be able to harness AI effectively, while those who rely solely on the "steak machine" will produce mediocrity at scale. The article's call to "open your own restaurant" suggests a future where AI agents are hired as cooks, but the head chef (the developer) still defines the menu and oversees the kitchen. This vision aligns with emerging trends like "agentic development," where AI agents execute tasks within a defined framework. However, the author cautions that without human judgment, the result is still likely to be burnt steak—just faster. As we navigate this new era, the most successful developers will be those who treat AI as a tool to augment their skills, not to replace them, and who remain committed to understanding the principles that govern software quality. The steak may be easier to cook, but creating a Michelin-starred meal will always require a chef's touch.

Researchers found 50+ AI-generated CSAM ads on Meta's platforms, many linked to nudify apps, raising systemic questions about ad review and AI moderation.

Background & Context The advent of generative AI has exponentially increased the scale and sophistication of synthetic media production. While this technology enables creative and productive applications, it also empowers malicious actors to generate illegal content, including child sexual abuse material (CSAM). The ability to create photorealistic images and videos with minimal effort has created new challenges for platform moderation systems that were primarily designed to detect known hashes of illegal content, not novel AI-generated variations. Meta, as one of the largest digital advertising platforms, faces a particularly acute risk: automated ad review systems must process millions of ads daily, often relying on machine learning models that may not be fully equipped to detect AI-manipulated imagery. This incident underscores the broader industry struggle to keep pace with generative AI's misuse. The discovery of ads containing AI-generated CSAM on Meta's platforms is a critical failure that not only violates platform policies but also legal statutes globally. This news is not an isolated anomaly; it reflects a systemic vulnerability in content moderation pipelines, raising urgent questions about the efficacy of current AI screening tools and the accountability of platforms in the AI era. The News: What Happened Exactly A nine-month investigation by the Tech Transparency Project (TTP), an independent watchdog, uncovered more than 50 paid advertisements on Meta's platforms that contained explicit AI-generated child sexual abuse material (CSAM) and images of minors accompanied by sexually suggestive text. These ads were published between November 2023 and August 2024, with some reaching thousands of users across the United States, United Kingdom, and over a dozen European countries. The ads were discovered in Meta's Ad Library, a transparency tool meant to catalogue all ads, but they remained undetected for months despite Meta's claim that all ads undergo automated review. Among the most egregious examples, one video ad used a thumbnail of a child sitting on the floor, with text stating, "Realizing Deep Fantasies with Generation AI [sic]. There is so much more than what is shown, use your imagination." Clicking the ad revealed video clips of adults engaged in sexual acts, followed by the child's face superimposed onto the explicit content. Another ad showed a young girl reclining with her legs spread, accompanied by the text, "I can show you more." These ads were designed to promote so-called 'nudify' apps, which use AI to undress individuals in photos or swap faces into pornographic videos. The researchers also found that many ads linked to an app called MaskAI, available on Apple's App Store. MaskAI, developed by a Chinese software company, initially appeared benign upon download but contained exclusively AI-generated pornographic content and features facilitating face-swapping into sexual scenarios. Apple removed the app after being contacted by WIRED, citing its policies against nudification apps. Meta removed the ads only after WIRED reached out, with a spokesperson emphasizing that "sexual exploitation is horrific" and that the company had "removed over 36 million pieces of child sexual exploitation content last year." However, TTP director Katie Paul noted that these ads "made no effort to mask the images or hide what they were promoting," raising concerns about the effectiveness of Meta's review systems. Crucially, the ads were not just stagnant artifacts; some were actively shown to users at the time of discovery, including one that reached 2,563 accounts in Europe. Additionally, the researchers found that several ads were identical to ones Meta had previously removed for policy violations, indicating a failure to apply blocking mechanisms consistently. The advertisers behind these campaigns were often accounts with zero followers, and some were linked to Meet Social, a Chinese ad reseller that had previously been a Meta partner. Meta's ad library database lacks performance metrics for ads in the US, so the true reach may be even higher. Historical Parallels & Similar Incidents This incident is not an anomaly in Meta's history of ad moderation failures. In early July 2024, a BBC investigation revealed that Instagram had run ads promoting the sale of CSAM in India, using terms like "rape video" and linking to Telegram channels where illegal content could be purchased. In response, Meta touted its "zero tolerance" approach to child sexual exploitation. Yet within weeks, TTP discovered this new cache of AI-generated CSAM ads, indicating that Meta's enforcement measures are reactive at best, failing to proactively identify violating content before it runs. Another parallel is the persistent problem of 'nudify' ads that have plagued Meta's platforms for years. In 2023 and 2024, researcher Alexios Mantzarlis, co-founder of digital deception publication Indicator and a former trust and safety worker at Google, reported more than 25,000 ads for AI nudifiers on Meta's platforms. Meta claimed to have removed over 344,000 such ads and launched legal action against a Hong Kong company linked to nudifier platforms. However, the current incident demonstrates that these efforts have not stemmed the tide; the advertising network for such illicit tools remains robust, adapting quickly to enforcement actions. The comparison with previous incidents highlights several lessons. First, Meta's automated ad review systems, which rely heavily on machine learning classifiers, are insufficient to detect AI-generated CSAM. Unlike traditional CSAM, which can be matched against hash databases, AI-generated images are novel and may evade perceptual hashing. Second, the persistence of identical ads after removal suggests a lack of memory in the review system or the ability for advertisers to circumvent blocks by creating new accounts and domains. Third, the involvement of Chinese ad resellers like Meet Social indicates that Meta's business relationships in markets where its platforms are blocked (like China) create loopholes that malicious actors exploit. Meet Social, which at its peak published thousands of ads per day and expected over $1 billion in sales, functioned as an intermediary, but Meta failed to vet the content their resellers pushed. These historical parallels reveal a pattern: Meta's response to CSAM and nudify ads has been largely reactive, relying on reports from third parties like TTP and journalists rather than proactive detection. Despite claims of aggressive enforcement, the scale of the problem—over 50 ads running for months—demonstrates that AI-generated illegal content poses an unprecedented challenge that demands a fundamental rethink of content moderation strategies, including tighter vetting of advertisers, more sophisticated AI detection models trained on synthetic content, and robust cross-platform information sharing. The lesson for the industry is clear: without proactive and adaptive enforcement, malicious actors will continue to exploit generative AI to harm vulnerable populations with impunity.

INTERPOL's 2026 report shows AI now fuels 55% of Africa's cybercrime, with financial losses up 152% to $484M.

Background & Context The digital transformation of Africa has been rapid and profound. With over 1.1 billion mobile subscribers in 2025, the continent has leapfrogged traditional infrastructure, embracing mobile money, digital banking, and online services. This digital economy boom, however, has created a fertile ground for cybercriminals, who are now leveraging artificial intelligence to scale and refine their attacks. INTERPOL's African Cyberthreat Assessment Report 2026, released on August 4, 2026, provides a stark picture: AI is no longer a niche tool but a mainstream enabler of cybercrime across the continent. This development matters globally because Africa's digital ecosystem is deeply interconnected with the rest of the world. Cybercriminal groups based in Africa are targeting victims in Europe and North America, and the tactics they employ are becoming templates for attacks elsewhere. The report's findings signal a shift in the global cyber threat landscape, where AI-driven attacks are becoming the norm rather than the exception. The News: What Happened Exactly According to the INTERPOL African Cyberthreat Assessment Report 2026, 55% of reported cybercrime cases in Africa now involve the use of artificial intelligence. This figure, based on data from 36 African countries, highlights a dramatic evolution in the sophistication and scale of cyberattacks. The 40-page assessment outlines how AI is being deployed across various attack vectors, from social engineering to identity fraud, making attacks faster, more convincing, and harder to detect. Online scams remain the most prevalent form of cybercrime in Africa, and AI has turbocharged their effectiveness. Cybercriminals are combining AI with social media platforms and mobile money services to target victims with unprecedented precision. The financial impact has been severe: reported losses climbed from $192 million in 2024 to $484 million in 2025, a 152% increase. INTERPOL attributes this spike to AI-powered fraud, stolen login credentials, and sophisticated social engineering campaigns. The report also reveals a widespread presence of scam centers across the continent, with 72% of surveyed countries identifying such centers within their borders, particularly in West and Southern Africa. These operations function like legitimate businesses, often using AI to automate phishing attempts, generate deepfake content, and manage large-scale fraud campaigns. Regional variations in cybercrime patterns underscore the adaptability of criminal networks. In East Africa, mobile money fraud and ransomware attacks on critical infrastructure are the primary threats. West and Central Africa are hotspots for business email compromise (BEC) and romance scams, affecting both individuals and corporations. Meanwhile, Southern Africa's advanced digital infrastructure has made it a target for international cybercriminal networks seeking maximum disruption. AI's role extends to more insidious forms of crime. Deepfake technology and AI-generated content are increasingly used in digital sextortion and online harassment. INTERPOL's technology partner, TrendAI, detected around 600,000 sextortion cases linked to these tactics. The report notes a sharp rise in BEC scams, where AI crafts hyper-realistic emails impersonating trusted contacts. Some African criminal groups have even targeted businesses and individuals in Europe and North America, using infrastructure spread across multiple countries to evade detection. A particularly troubling trend is the emergence of synthetic identities. Rather than merely stealing personal information, cybercriminals now combine genuine data with fabricated details to create entirely new digital personas. These fake profiles have been used to open bank accounts, obtain mobile loans, and register SIM cards, often bypassing biometric verification systems. This represents a sophisticated evolution of identity fraud, exploiting gaps in verification processes. The report emphasizes that weak coordination between banks, telecom companies, and law enforcement is hampering countermeasures. The absence of real-time information sharing allows criminals to move stolen funds swiftly across jurisdictions before authorities can respond. Moreover, many African law enforcement agencies are ill-prepared to handle AI-driven threats, despite the rapid adoption of AI by criminal networks. Despite these challenges, there are signs of progress. In 2025, 17 African countries introduced or updated cybercrime legislation, and Senegal launched an online reporting platform for child-related offenses. Joint international operations—Operation Serengeti 2.0, Operation Contender 3.0, Operation Sentinel, and Operation Red Card 2.0—resulted in over 1,500 arrests, the seizure of hundreds of electronic devices, and the recovery of more than $100 million linked to cybercrime. Historical Parallels & Similar Incidents The current AI-driven cybercrime surge in Africa draws eerie parallels to the early 2010s when cybercriminals in other regions began leveraging then-emerging technologies like botnets and ransomware-as-a-service. One notable parallel is the 2016 Bangladesh Bank heist, where cybercriminals exploited the SWIFT banking network to steal $81 million. While not AI-driven, the heist demonstrated how organized criminal groups could target financial systems across borders, exploiting vulnerabilities in coordination and security protocols. However, the scale and sophistication of AI-enabled attacks today far exceed those earlier incidents, as AI allows for automated, adaptive, and highly personalized attacks that were impossible before. More recently, the 2023 deepfake fraud in China (or the Hong Kong finance worker scam, where AI voice cloning was used to impersonate a director and authorize a $35 million transfer) highlighted the real-world impact of AI in cybercrime. That incident, though isolated, showcased how AI could be used to bypass human verification. The INTERPOL report indicates that such tactics are now being industrialized across Africa, with deepfakes and synthetic identities becoming standard tools. The shift from opportunistic attacks to systematic, AI-powered fraud represents a significant evolution in the cyber threat landscape. The comparison with the Bangladesh Bank heist is instructive because it underscores the importance of international cooperation and information sharing. In the aftermath of that heist, banks worldwide improved their security protocols, but the lack of real-time collaboration between institutions allowed the attackers to succeed. The INTERPOL report echoes this concern, noting that Africa's financial systems are exposed due to weak coordination between banks, telecom companies, and law enforcement. The lesson is clear: without robust cooperation and AI-enabled defense mechanisms, cybercriminals will continue to exploit these gaps. Furthermore, the rise of synthetic identities in Africa mirrors the synthetic identity fraud epidemic in the United States during the 2010s, which cost lenders billions. In both cases, fraudsters combined real and fake personal information to create new identities that evade traditional verification. However, AI has amplified the problem, enabling attackers to generate synthetic identities at scale and automate their use across multiple financial platforms. The lesson is that as digital economies grow, so do the opportunities for AI-powered fraud, and proactive measures must be taken to secure identity verification systems. The INTERPOL report's findings represent a wake-up call for global cybersecurity. As AI continues to evolve, the battle against cybercrime will increasingly become an AI-versus-AI contest. The response must be equally sophisticated, with real-time threat intelligence sharing, AI-driven detection systems, and robust legal frameworks. Africa's experience may serve as a cautionary tale for other regions undergoing rapid digital transformation. By learning from these parallels and investing in collaborative defense, authorities can hope to stay one step ahead of AI-powered criminals.

A developer's viral post argues AI-generated images in personal blogs signal potential LLM-written content, eroding trust.

Background & Context The proliferation of generative AI has democratized content creation, enabling anyone to produce text, images, and code at scale. However, this democratization has a dark side: a growing crisis of authenticity. As large language models (LLMs) and image generators like DALL-E and Stable Diffusion become ubiquitous, audiences—especially in technical communities—are becoming increasingly vigilant about detecting AI-generated content. The latest flashpoint comes from a software engineer's personal blog post that ignited a widespread discussion about the role of AI-generated images in independent publishing. The post strikes a nerve because it touches on the subtle signals that distinguish authentic human expression from AI-assisted production. For developers and technical writers, this is not just an aesthetic debate; it's about the fundamental trust that underpins knowledge sharing in open-source and indie tech communities. The issue is that AI-generated images have become a shorthand for potential AI-written text. When a reader sees a generic, overly polished AI illustration in an individual's blog, it triggers a suspicion that the accompanying prose may also be AI-generated, even if it isn't. This association is not unfounded—many content farms use AI for both text and images. But for legitimate independent writers, the presence of such images can undermine perceived authenticity and reduce reader engagement. This has broader implications for the AI industry: it highlights the need for transparency and the importance of human-centered content in an AI-saturated world. The News: What Happened Exactly A developer, known as nelson.cloud, published a candid blog post titled "AI-Generated Images Discourage Me from Reading Your Blog." The post is a personal manifesto against the use of AI-generated images in personal blogs. The author expresses a "growing hatred" for these images, stating that they make him wonder if the text in the blog posts is also AI-generated to some extent. He explicitly distinguishes between corporate blogs, where he expects such practices, and indie blogs, where he finds it "disappointing." The core of his argument is that AI images are a trust signal that devalues the human effort behind the content. The author goes further to state a preference: "I'd rather see a shitty Microsoft Paint drawing as opposed to some AI image." This provocative statement underscores his desire for flawed, human-made visuals over polished, AI-generated ones because even a crude hand-drawn image proves a human was involved. He acknowledges his own blog may be roastable, but emphasizes that readers at least know they're getting "the thoughts of a real human being and not some LLM." This directly ties the use of AI images to the perception of the text's authenticity. The post then pivots to related personal experiences that reinforce his stance. He reveals that he used AI to contribute to an open-source project, and while the code was merged, he "didn't learn anything and felt bad as an engineer." This anecdote illustrates that even when AI output is acceptable, it can lead to a hollow feeling and a lack of personal growth— a sentiment that resonates with many engineers who value the learning process over the end result. He also mentions reasons for not renewing his Proton subscription (a privacy-focused email service) and a commentary on how most people work for money (a separate topic), which seem to be additional posts on his blog but are cited as context for his overall perspective on authenticity and value. The central message is clear: AI-generated images in personal blogs erode trust and discourage readership, and the author urges individuals to avoid them. The post quickly gained traction on social platforms like Hacker News and Reddit, sparking a debate that split into two camps: those who agree and shared similar experiences of distrust, and those who argue that AI images are just tools and the content quality matters more. However, the author's point cuts deeper: it's not about the image quality but about the signal it sends. In an era where AI can generate convincing text, visual cues become the first line of authenticity verification. The news here is not just one person's opinion, but a growing sentiment that could influence how independent writers approach their content creation workflow. Historical Parallels & Similar Incidents This isn't the first time a visual cue has triggered a crisis of authenticity in tech media. A notable parallel is the reaction to the use of stock photos in tech blogs in the early 2010s. Many developers voiced similar frustrations, arguing that generic stock photos of people shaking hands or typing on laptops made blogs feel impersonal and corporate. Sites like TechCrunch and Mashable were criticized for using such images, and some indie bloggers proudly eschewed them in favor of screenshots or custom illustrations. The underlying concern was the same: stock photos were a shortcut that signaled a lack of genuine effort or originality. Over time, the community shifted towards more authentic visuals like diagrams, hand-drawn sketches, or even photos from the author's desk. This historical incident shows that the core issue—visual authenticity as a proxy for textual authenticity—is not new, but AI has intensified it by making the visuals themselves generative. Another more recent parallel is the 2023 controversy surrounding the use of AI-generated art in open-source projects and documentation. For instance, when Mozilla released a blog post with AI-generated images, it faced backlash from contributors who felt it contradicted the organization's commitment to human creativity. Similarly, in academic publishing, there have been papers revealed to contain AI-generated figures that misrepresented data, leading to retractions. These incidents have led to the establishment of guidelines requiring authors to disclose AI involvement in content creation. The current news is a bottom-up movement from individual developers, not institutions. The contrast is that while corporations and institutions are slowly adopting AI transparency guidelines, individuals are voting with their attention, like the author, by refusing to read blogs that use AI images. This could pressure indie writers to adopt more human-centric visuals, or conversely, it could accelerate the development of AI-detection tools for images to distinguish synthetic from human-created content. Lessons drawn from these parallels indicate that trust is hard to build and easy to lose. Once a reader doubts the authenticity of content, they may disengage entirely. The lesson for AI developers is that the ethical deployment of generative AI must consider not only the output quality but also the social signals it carries. For content creators, the lesson is to prioritize transparency and demonstrable human input, even if it means using less polished visuals. The current news is a reminder that AI tools should augment human expression, not replace the subtle cues that build community and trust in the tech world. As AI detection becomes more sophisticated, it's likely that the backlash will lead to new norms, such as mandatory labeling of AI-generated images, similar to the labeling required for deepfake videos. In conclusion, the blog post is a microcosm of a larger tension between efficiency and authenticity in the age of AI. It highlights the need for the AI industry to consider the social impact of its tools, and for content consumers to remain vigilant. The author's preference for a "shitty Microsoft Paint drawing" over an AI image is a powerful call for human imperfection as a badge of credibility. As AI becomes more integrated into our workflows, we must find ways to preserve the signals that connect us on a human level—even if it means embracing the flaws that make us distinctly human. --- This article is based on the original blog post by nelson.cloud. The source can be found at: https://nelson.cloud/ai-generated-images-discourage-me-from-reading-your-blog/