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Article URL: https://www.wsj.com/business/big-companies-are-starting-to-hire-again-defying-predictions-of-ai-wipeout-f4974e99

Comments URL: https://news.ycombinator.com/item?id=49100549

Points: 6

# Comments: 1

Article URL: https://www.theverge.com/tech/972063/apple-upgrade-program-no-restricted-mode

Comments URL: https://news.ycombinator.com/item?id=49100531

Points: 2

# Comments: 1

Article URL: https://www.scientificamerican.com/article/rescue-mission-to-save-nasas-swift-space-telescope-is-imperiled-in-orbit/

Comments URL: https://news.ycombinator.com/item?id=49100530

Points: 2

# Comments: 0

Article URL: https://www.reuters.com/business/openais-rogue-agent-compromised-an-account-second-tech-firm-sources-say-2026-07-28/

Comments URL: https://news.ycombinator.com/item?id=49100529

Points: 2

# Comments: 0

Article URL: https://twitter.com/forloopcodes/status/2081089016250949651

Comments URL: https://news.ycombinator.com/item?id=49100520

Points: 1

# Comments: 0

Article URL: https://unui.cc/

Comments URL: https://news.ycombinator.com/item?id=49100491

Points: 3

# Comments: 0

Article URL: https://github.com/mindcloud-inc/universal-api-reference

Comments URL: https://news.ycombinator.com/item?id=49100483

Points: 3

# Comments: 0

Article URL: https://huggingface.co/datasets/neuportal/neuportal-sealed-crypto-forecasts

Comments URL: https://news.ycombinator.com/item?id=49100476

Points: 2

# Comments: 0

Article URL: https://github.com/kirandas-reload/tiyov

Comments URL: https://news.ycombinator.com/item?id=49100470

Points: 1

# Comments: 0

Article URL: https://www.nature.com/articles/d41586-026-02335-5

Comments URL: https://news.ycombinator.com/item?id=49100424

Points: 1

# Comments: 0

Article URL: https://www.treygoff.com/stack

Comments URL: https://news.ycombinator.com/item?id=49100419

Points: 1

# Comments: 0

Article URL: https://dpa-international.com/general-news/urn:newsml:dpa.com:20090101:260729-930-457184/

Comments URL: https://news.ycombinator.com/item?id=49100411

Points: 1

# Comments: 0

Article URL: https://moddogbot.com/blog/intel-arc-pro-b70-postmortem

Comments URL: https://news.ycombinator.com/item?id=49100410

Points: 2

# Comments: 0

Article URL: https://noelo.org/blog/kuna-release/

Comments URL: https://news.ycombinator.com/item?id=49100387

Points: 2

# Comments: 1

A whole industry has sprung up around selling TikTok “growth.”

Cheap views by the hundred, pre-made ad accounts, and polished sales pages promising a repeatable path to serious revenue.

None of it is officially sanctioned by TikTok, and depending on what you’re buying, you could end up wasting money, losing your account, or handing your login details to scammers.

Scam 1: Sites selling cheap likes and engagement

Sites selling bulk engagement all look remarkably similar.

They offer small bundles of views, likes, or followers for a few pounds, usually alongside identical packages for YouTube, Instagram, and other platforms.

The sales pitch is almost always the same: “100% real profiles,” “no bots, no click farms,” and “completely safe.”

Those claims are worth reading carefully because they’re addressing the biggest concern buyers already have.

At this price point, bulk engagement is usually generated through bots, click farms, or other artificial means—the very thing these sites insist they don’t use.

Even if your engagement numbers increase initially, TikTok’s fraud detection systems can remove artificial engagement, and accounts that repeatedly use these services risk being flagged or restricted.

Scam 2: The “aged” ad account marketplace

Another common offer is bulk TikTok Ads accounts sold as “aged” or “trusted,” often bundled with a replacement guarantee if an account stops working. The pitch is that you skip the hassle of setting up and verifying a new advertising account.

The problem is that you don’t know how those accounts were created. Many are built using stolen or synthetic identities, compromised payment details, or other deceptive methods. Buying one means inheriting that history—and the very real risk that TikTok detects it and suspends the account, along with any campaigns or ad budget attached to it. A replacement guarantee won’t help if your advertising is suddenly brought to a halt.

Scam 3: The growth framework

A third type of offer is less obviously a scam and more of a marketing funnel.

Slick landing pages—often hosted on free platforms and paired with an embedded video—promise a “proven blueprint” for turning TikTok into a major source of income, usually backed by impressive but unverifiable claims about past clients.

The immediate goal is usually to collect your email address, and sometimes your phone number, before revealing what’s actually for sale. That might be a paid course, a “done-for-you” management service, or a request for direct access to your TikTok Shop or Ads account.

What happens next varies, but the common thread is the same: you’re being asked to trust an unverified third party with your business, your money, or your account.

What you’re really signing up for

Not every TikTok marketing service is a scam. But if someone’s offering thousands of views for a few pounds, bulk “aged” ad accounts, or guaranteed growth, you’re in a very different part of the market.

These services promise shortcuts. What they often deliver is fake engagement, accounts with questionable histories, or requests for access to your own account.

At best, you’ve wasted your money on engagement TikTok later strips away. At worst, you’re buying an account built on stolen information or giving an untrusted third party full access to your own.

Our advice

• Don’t pay for views, likes, or followers. Artificial engagement isn’t real growth and can put your account at risk under TikTok’s rules.
• Never share your TikTok username and password with a “boosting” service, regardless of how it’s presented.
• Don’t buy or sell TikTok Ads or Business accounts outside TikTok’s own account creation process.
• Treat “guaranteed revenue” frameworks and courses like any other business opportunity: they’re sales pages first, educational content second.

None of this is unique to TikTok. The platform’s explosive growth has simply given a familiar ecosystem of low-effort scams a new audience.

Scammers don’t need to hack you. They just need you to click once. 

Malwarebytes Identity Theft Protection catches suspicious activity before it becomes a problem.

The energy regulator wants to fast-track viable projects and deter speculative projects that request grid connectivity

As organizations move beyond AI experimentation, success will depend on how effectively they combine intelligence and trust. The same systems that amplify knowledge, accelerate decisions, and unlock new outcomes must also protect data, govern AI, and build resilience. In this next phase of transformation, security is not separate from innovation—it is an enabler that helps make responsible innovation possible at a faster pace. That starts with asking better questions—the kind that help organizations turn intelligence into action and trust into a foundation for progress. 

AI is changing how security decisions are made. Defenders now have access to more signals, insights, and analytical power than ever before. But better security does not start with more information. It starts with asking the right questions: What are we trying to protect? What risks matter most? What conditions need to be true? And what decisions do we need to make with confidence? 

That clarity matters because security is shaped by more than technology. The challenges organizations face rarely exist in isolation. They emerge across people, processes, technology, data, identities, and governance. Understanding those connections is what allows security teams to use platforms, AI, and automation to make better decisions under real-world conditions. 

Connect with Microsoft Security at Black Hat USA 2026 Security as a systems challenge 

Security has never been a single-layer challenge. Vulnerabilities can emerge across code, data, identities, and integrations, while exposure is often created at the intersections between them. Designing for security requires a systems mindset—understanding how these elements work together, where failure can occur, and what safeguards are needed so no single layer carries the burden alone. That is why defense in depth remains essential: layered controls, ongoing monitoring, mitigations, and risk management across the AI lifecycle help organizations reduce exposure while continuing to adapt. 

This is especially important as AI becomes more embedded in how organizations operate. AI can help teams analyze vast amounts of information, identify patterns, and surface recommendations at a scale that was previously unthinkable. Those AI outputs still require oversight, governance, and human judgment, with clear accountability for how AI-generated insights are validated and used. But insight only creates value when it is grounded in the right context and connected to action. 

AI-generated insights still require validation, oversight, and resilience planning because AI systems can produce incomplete or inaccurate outputs.

Clarity creates better decisions 

The most important security decisions start with a clear view of the risk, the level of control or visibility required, and the outcome the system is designed to achieve. When we optimize for capability over context, we miss how security decisions are actually made: through signals, expertise, validation, and judgment. This becomes even more important as AI expands what is possible. Better analysis can surface more insights, but better decisions still depend on understanding what matters most and applying the right context. That matters most when conditions are changing quickly, and teams need to act before every answer is certain. 

Threat intelligence offers a useful example. Defenders operate in environments defined by ambiguity, incomplete information, and rapidly changing conditions. Success rarely comes from a single source or signal. It comes from combining multiple forms of intelligence, applying expertise, validating assumptions, and connecting insights in ways that strengthen assurance.  

The lesson extends beyond threat intelligence. Different security objectives require different combinations of signals, analysis, and human judgment. Resilient decisions come from bringing those elements together thoughtfully, rather than relying on a single source of truth or assuming technology alone can provide the answer. 

Designing for better outcomes  

As AI becomes more embedded in security operations, the quality of our outcomes depends on how clearly we define the objectives we are trying to achieve. Security leaders create the most value when they identify the risks that matter most, the conditions that need to be true, and the systems required to support better decisions. 

Then we design for those outcomes through the right mix of controls, safeguards, and decision-making processes. This shows up not just in architecture, but in how teams establish guardrails, validate assumptions, and respond to the unexpected. The aim is not to make security harder for defenders. It is to make the work easier to execute, supported by platforms, tooling, and AI that help deliver greater speed, accuracy, and confidence. 

The systems we are building today do not exist in isolation. They interact with people, shape decisions, and operate at a scale that can amplify both strengths and weaknesses. Our responsibility extends beyond technology choices. We have to help organizations design systems they can understand, govern, and rely on with confidence as complexity grows. 

Trust is not something we can take for granted, and that does not change in the era of AI. It is built through deliberate choices: the controls we establish, the visibility we create, the assumptions we validate, and the safeguards we put in place. As AI becomes more embedded in how organizations operate, security leaders have a responsibility to help build confidence in the systems people rely on every day. 

Building trustworthy AI systems requires governance, security, privacy protections, transparency, and accountability across the full technology stack, aligned to responsible AI principles and standards.

The risk is not simply that we choose the wrong tool, model, or platform. The greater risk is believing that one answer can solve a complex, evolving problem. AI can help teams make sense of complexity, but it does not eliminate the need for judgment. If anything, it raises the importance of defining the right outcomes and designing systems that make the right actions easier to take. 

Better security starts with better questions, and with the clarity to act on them. The organizations that succeed will apply AI thoughtfully, define outcomes clearly, and combine analytical power with the expertise, judgment, and adaptability needed to build more resilient systems in the age of AI. 

Explore Microsoft Security solutions

To learn more about Microsoft Security solutions, visit our website. Bookmark the Security blog to keep up with our expert coverage on security matters. Also, follow us on LinkedIn (Microsoft Security) and X (@MSFTSecurity) for the latest news and updates on cybersecurity.

The post ​​Better security starts with better questions appeared first on Microsoft Security Blog.

I'm a director/exec, not a software engineer. Background's mostly data analytics, SQL and Excel day to day, some R and PostgreSQL. Years ago I could hack together basic HTML/CSS/PHP too.

Got into AI early and now use ChatGPT and Claude constantly. With their help I've built a handful of web apps, some Python scripts and desktop tools, and an Android app on Supabase with real multiple users.

So I can build things. But I'm very aware there are big holes underneath it. I can usually get something working without really knowing why it works, whether it's actually well built, or what's going to break down the road once it gets more complex.

Feels like it's time to go back to fundamentals. Michael Jordan still worked on his footwork, that kind of thing.

What I'm hoping to get to eventually:

* build small useful apps, windows/mac/android/ios/web

* automate chunks of my own and my team's work

* maybe build a simple SaaS at some point

* generally not get left behind, open to this reshaping my career or spinning into a side thing

Was hoping for something structured, starting from prompt/context engineering and working into real programming fundamentals, APIs, databases, architecture, security, testing, deployment. Problem is anything I find is stale within months given how fast this moves.

Not looking for free necessarily, and definitely not looking for "learn to code in a weekend." Willing to put in real time.

For people who've gone down this road already: what's actually worth learning properly, what's fine to just learn enough to supervise, and what can honestly just stay AI's job?

P.S.: Yes I polished/refined this post using AI, but I first wrote it all myself (the original was twice as long).

Comments URL: https://news.ycombinator.com/item?id=49098829

Points: 1

# Comments: 0

Article URL: https://www.argmin.net/p/open-and-shut

Comments URL: https://news.ycombinator.com/item?id=49098826

Points: 1

# Comments: 0

Article URL: https://web-ai-sdk.dev/playground/

Comments URL: https://news.ycombinator.com/item?id=49098813

Points: 1

# Comments: 1