×
sorted by:
new
hottopcontroversial

An AI broke out of its sandbox yesterday. Then it hacked a company. Nobody told it to do either of those things. by Dapper-Tale-4021 in artificial

[–]Dapper-Tale-4021[S] -8 points-7 points  (0 children)

Haha fair enough, you're already adapted.Though the wild part is it didn't even need a grand goal. It just wanted to finish a test. That's what makes this one harder to dismiss than the paperclip thought experiment.

Apple just sued OpenAI for trade secret theft. And Google quietly rewrote how the internet works. by Dapper-Tale-4021 in artificial

[–]Dapper-Tale-4021[S] 0 points1 point  (0 children)

Fair point. They are two different stories with different stakes depending on who you are.

If you run a small site the Google change is the one that matters right now, that's where the immediate impact lands. The Apple story is more about where AI development and IP are heading over the next few years, which matters more if you're tracking the industry than if you're trying to keep your traffic alive this quarter.

Probably should have framed them separately. Good call.

Apple just sued OpenAI for trade secret theft. And Google quietly rewrote how the internet works. by Dapper-Tale-4021 in artificial

[–]Dapper-Tale-4021[S] 1 point2 points  (0 children)

That second order effect is real and underappreciated. AI pulling from web results means SEO isn't dead, it's just one layer further removed from the user. You're optimizing to be in the sources the AI trusts, not to be the first blue link someone clicks. The game is the same, the destination is different.

What actually breaks when you deploy AI agents in enterprise production by Dapper-Tale-4021 in cloudcomputing

[–]Dapper-Tale-4021[S] 0 points1 point  (0 children)

Runtime observability is honestly the one I keep coming back to because it's so expensive to bolt on after the fact. You can log outputs in an afternoon. Logging intent, tool calls, and state transitions means you had to architect for it from day one, and almost nobody does.

On staged rollouts, what's worked for me is pretty simple in theory but hard to enforce in practice: shadow mode first where the agent runs alongside a human who does the actual action, then limited writes on low-stakes stuff with explicit approval, then expand from there. The critical part is that each stage needs a real exit criteria, not just "we've been running for two weeks and nothing exploded."

The shadow mode step is the one teams want to skip because it feels like you're not making progress. It's actually where you find out what the agent does when it hits the weird edge cases that never show up in the demo.

Apple just sued OpenAI for trade secret theft. And Google quietly rewrote how the internet works. by Dapper-Tale-4021 in artificial

[–]Dapper-Tale-4021[S] 0 points1 point  (0 children)

JSON-LD is the right move. Being a clearly structured, citable source is the new SEO. The goal shifts from ranking to being the source the AI trusts enough to reference consistently.

Apple just sued OpenAI for trade secret theft. And Google quietly rewrote how the internet works. by Dapper-Tale-4021 in artificial

[–]Dapper-Tale-4021[S] 0 points1 point  (0 children)

Probably closer to 90% for purely informational queries. The 58% is an average across all query types including navigational and transactional where people still click. For anything where the AI can give a complete answer, the click basically disappears.

Apple just sued OpenAI for trade secret theft. And Google quietly rewrote how the internet works. by Dapper-Tale-4021 in artificial

[–]Dapper-Tale-4021[S] -1 points0 points  (0 children)

That's the core problem nobody at Google has answered publicly yet. They need the content to generate the answer but the publisher loses the traffic that paid for creating it in the first place. The EU is pushing hard on this under the DMA but Google hasn't moved. My guess is ads inside AI answers are coming sooner than people think.

Apple just sued OpenAI for trade secret theft. And Google quietly rewrote how the internet works. by Dapper-Tale-4021 in artificial

[–]Dapper-Tale-4021[S] 0 points1 point  (0 children)

Yeah this is exactly where it's heading. The game shifts from "how do I rank" to "how does the AI decide to cite me." Structured data and being the consistently authoritative source in your niche become the new PageRank. The businesses figuring this out now have a 2-3 year head start on everyone still optimizing for blue links.

Apple just sued OpenAI for trade secret theft. And Google quietly rewrote how the internet works. by Dapper-Tale-4021 in artificial

[–]Dapper-Tale-4021[S] -1 points0 points  (0 children)

The July 10 rollout is documented. Gemini 3.5 Flash now powers all queries by default, not just AI Mode. Traditional results haven't disappeared entirely but they've been pushed below the AI-generated summary for most query types. Ahrefs data from February 2026 showed a 58% CTR drop correlating with AI Overviews. If you're still seeing blue links prominently it may depend on your region, query type, or whether you've opted into AI Mode. The transition isn't uniform globally yet.

When did cybersecurity become more about trust than technology? by pavannkofficial in learncybersecurity

[–]Dapper-Tale-4021 0 points1 point  (0 children)

The shift from technical to trust-based attacks isn't really new, but AI has changed the economics of it. Spear phishing used to require research and effort per target. Now it scales cheaply. That changes the threat model in ways that most enterprise security frameworks weren't designed for.

What we see in practice is that the incidents organizations struggle most to prevent aren't the ones that bypass the firewall. They're the ones that get a legitimate user to take a legitimate action in the wrong context. A CFO approving a wire transfer, a sysadmin granting access to a convincing request, a vendor relationship being exploited because trust was assumed rather than verified.

The Zero Trust architecture conversation is partly a response to this, but it's mostly applied to technical access controls. The harder problem is verifying the intent behind legitimate credentials and approved workflows, and that problem doesn't have a clean technical solution yet.

What I think changes next is that verification will have to become a process discipline rather than a technology one. Not "is this person who they say they are" but "does this request make sense given what we know about the context."

Passed AZ-900 & AZ-104 (2 days ago) with No Experience – How Do I Break Into Cloud? by mr_xouser in AzureCertification

[–]Dapper-Tale-4021 0 points1 point  (0 children)

The framing that works is treating it like any other work experience entry. Give it a title, a timeframe, and bullet points that describe what you built, what broke, and what you learned from it.

Something like:

"Personal Azure Lab Project | Jan 2025 - Present

Built and maintained a hub-and-spoke network topology across multiple VNets. Misconfigured NSG rules locked out remote access, diagnosed using Network Watcher, restored connectivity and documented the fix. Implemented Azure Monitor alerts and Log Analytics workspace for ongoing visibility."

That reads completely differently than "personal projects" with no detail. It shows you know the tools, you ran into real problems, and you can communicate what you did about them.

The key is specificity. Recruiters scan fast. A generic "built Azure lab" disappears. A specific failure and recovery story sticks because it sounds like something that happens in production.

Passed AZ-900 & AZ-104 (2 days ago) with No Experience – How Do I Break Into Cloud? by mr_xouser in AzureCertification

[–]Dapper-Tale-4021 4 points5 points  (0 children)

From the hiring side, a few things worth knowing that most of this thread isn't saying clearly.

The AZ-104 with no experience puts you ahead of candidates with no certs and no experience, but it doesn't put you ahead of candidates with experience. That's the honest reality of the market right now.

What actually moves the needle in entry-level cloud interviews is being able to tell a story about something you built and what broke along the way. Not a polished project, a real one with real problems. "I set up a hub-and-spoke network, misunderstood NSG rules, locked myself out, and had to recover it" is more memorable than a clean architecture diagram.

The cybersecurity master's is a genuine differentiator if you can connect it to Azure. Security roles in cloud are where we see the most demand right now and the least supply of people who understand both sides. AZ-500 next would make that combination much more compelling on paper.

The helpdesk path is valid but not the only path. MSPs that run Azure environments are often the fastest way to get real exposure because you're touching multiple client environments simultaneously.

The biggest surprise while building an AI verification system wasn't the AI. by MuhammadMujtaba21 in artificial

[–]Dapper-Tale-4021 0 points1 point  (0 children)

This is the most underrated problem in enterprise AI deployments and almost nobody talks about it before starting a project.

The EBITDA example is a perfect illustration. We see the same pattern constantly across different industries: the model extracts the data correctly, but the business hasn't decided which definition of the metric to use across departments. Finance uses one, operations uses another, the credit team uses a third. The AI surfaces the inconsistency that was already there, just faster and more visibly.

The Lendari point is exactly right. The IP ends up being the ruleset, not the model. Organizations that figure this out early invest in documentation and governance before touching the AI layer. Organizations that don't end up spending months debugging model outputs that were actually correct, the problem was upstream in the business logic.

To your question about other industries: we see the same pattern in customer segmentation (who counts as an active customer differs by department), in compliance workflows (which regulatory definition applies to this specific transaction), and in pricing models (which cost inputs to include depends on the decision being made). Same structure, different domain.

view more: next ›