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Small Web, Big Idea: Inside the Indieweb Movement
Did you know that 65-70% of users think that the web and the internet are the same and only 20% truly know the difference? For those few who don’t know the difference, the internet is made up of all the computers in the world connected together and it runs services like the Web, email, online games and FTP amongst others. The web is just a publishing service. The internet started in 1969 while the first website appeared in 1991- and you can still visit it. But many people believe that the web as it exists today has become too dominated by the tech giants with Google, YouTube and Facebook being the top three most visited websites. Facebook evolved to try and keep users on for longer, but the cost is you’re unlikely to see all the posts by friends instead of paid posts by others. Google searches now lead with AI summaries instead of links to websites. What is the Indieweb? As a reaction to this, a movement to create a more human web was started in 2011 called the Indieweb. The people beh
Fast Code, Fragile Systems: AI Is Quietly Rewriting Technical Debt
While generative tools significantly increase development speed, engineering teams must ensure rapid delivery doesn't lead to disorganized architecture, hidden technical issues or a lack of deep codebase understanding. AI coding assistants have altered software delivery timeframes, allowing developers to generate boilerplate, touch code components they’d long forgotten about and ship functional features quickly. Yet speed without scrutiny creates a silent, compounding liability (or “technical debt”) across libraries and repositories. When syntactically correct code floods a codebase faster than engineers can review it, technical debt often balloons. It stops showing up as simple syntax errors and becomes systemic architectural fragmentation that's extremely difficult to track. Navigating this reality requires rethinking code provenance, task and codebase ownership and reviewing workflows. To understand how developers and engineering leaders can keep delivery high without losing control
When AI Ranks the Résumés, Who Explains the Decision?
An AI-generated ranking can move hundreds of résumés through a hiring funnel in minutes, but might leave an employer unable to answer a basic question: Why did this candidate advance while another was rejected? In these cases, a score is not an explanation, and neither is saying a recruiter reviewed it. Employers must have the capability to reconstruct what the system evaluated, why it mattered and how a person reached the final decision. Accountability is Indispensable Accountability requires a record connecting the recommendation to its inputs and rationale. “Employers should be able to trace a recommendation back to the information that informed it and clearly explain why a candidate was advanced or rejected,” says Greg Summers of Orion Talent. He notes AI can surface insights and make the process more efficient, but those insights should ultimately be reviewed and validated through human judgment--the rationale must remain attached to the score it produced. “The strongest tools del
What AI Job Apocalypse? Data Shows Companies Need Tech, Cyber Pros
The artificial intelligence job apocalypse has not arrived as expected. While companies such as Meta, Facebook’s parent company, still garner attention when cutting employees to allow capital expenditures on AI research and development to continue, other enterprises that have invested in and deployed these technologies have slowly begun hiring back workers after making layoffs. Over the past several weeks, several large enterprises and corporations have backtracked on layoffs after realizing initial AI deployments did not work as previously believed or that human workers are still needed to ensure these technologies and platforms perform as needed. Consider these examples: Ford reportedly hired back several hundred engineers to work on quality control issues after AI systems could not fully address them. Charles Poon, Ford’s vice president of vehicle hardware engineering, told the BBC that AI is “only as good as the information you use to train it.” IBM, itself a huge booster of AI, an
What Cyber Pros Should Learn from Recent Frontier AI Disclosures
Since the release of the first commercially available large language models (LLMs) three years ago, organizations have sought to balance enthusiasm for deploying artificial intelligence within their networks with concerns about the added risks and cybersecurity challenges that come with adopting these technologies. A recent slate of disclosures from OpenAI, Meta and other AI firms and researchers shows that concerns about risk and cybersecurity should prompt organizations to exercise greater caution when testing and deploying AI, even when considering the benefits the technology offers. Consider the following four items announced between late July and early August: OpenAI disclosed in late July that two of its frontier AI cybersecurity models broke out of sandboxed testing environments and accessed the network of a different AI company – Hugging Face – using at least one and possibly more zero-day exploits in third-party applications. Following the OpenAI disclosure, Anthropic released