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ChatGPT DOWN as ‘thousands' of baffled OpenAI users ‘unable to access chatbot' in mysterious outage

ChatGPT DOWN as ‘thousands' of baffled OpenAI users ‘unable to access chatbot' in mysterious outage

The Sun5 hours ago

CHAT GPT is down, as thousands of users report being unable to access the site.
Downdetector has received over 500 complaints over the past hour that the AI chatbot is mysteriously not working.
Earlier this month, thousands were left unable to use the chatbot after it was down for hours, leaving users with a mysterious error message.
The error message read: "A network error occurred.
"Please check your connection and try again. If this issue persists please contact us through our help center at help.openai.com."

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Network Performance Baselines That Predict Future Bottlenecks: By Scott Andery
Network Performance Baselines That Predict Future Bottlenecks: By Scott Andery

Finextra

timean hour ago

  • Finextra

Network Performance Baselines That Predict Future Bottlenecks: By Scott Andery

Your network is running fine at 9 AM. By 11 AM, everything feels sluggish. Come 2 PM, users are complaining that file transfers are crawling, and by 4 PM, someone's inevitably asking if the internet is "broken again." Sound familiar? Most IT teams treat network performance like the weather – something that just happens to them. But here's the thing: network bottlenecks don't appear out of nowhere. They follow predictable patterns, and if you know how to read those patterns, you can spot problems weeks before they actually impact your users. Understanding What Network Baselines Actually Tell You Let's start with what most people get wrong about network monitoring. They focus on the dramatic spikes – the moments when everything grinds to a halt. But the real intelligence comes from understanding what "normal" looks like during different times, different seasons, and different business cycles. A proper network baseline isn't just a single measurement. It's a collection of patterns that show you how your network behaves under various conditions. Think of it like knowing that your commute usually takes 25 minutes, but on rainy Fridays it takes 45 minutes, and during school holidays it drops to 18 minutes. The Metrics That Actually Matter for Prediction When I'm setting up proactive IT support monitoring for clients, I focus on metrics that have predictive value, not just diagnostic value. Here's what really matters: Bandwidth utilization trends over 30, 60, and 90-day periods Latency patterns during peak business hours vs. off-hours Packet loss rates under different load conditions Connection count growth as business operations expand Application-specific performance for critical business systems The key is tracking these metrics consistently enough to identify patterns, but not so obsessively that you're drowning in data that doesn't lead to actionable insights. Reading the Early Warning Signs Here's where network baseline monitoring gets interesting – and where most businesses miss opportunities for prevention. The warning signs of future bottlenecks show up in subtle changes to your baseline patterns long before users start complaining. Gradual Degradation Patterns The most dangerous network problems aren't the sudden failures – they're the gradual degradations that slowly become the "new normal" until something pushes you over the edge. I've seen companies where file transfer times slowly increased from 30 seconds to 2 minutes over six months, and nobody noticed because it happened gradually. But when you look at the baseline data, the trend is crystal clear. This is where proactive IT support becomes invaluable. Instead of waiting for users to report problems, you're identifying performance degradation trends and addressing them before they become user-facing issues. Seasonal and Cyclical Patterns Different businesses have different network usage cycles, and understanding your specific patterns is crucial for accurate predictions. For example: Accounting firms see massive spikes during tax season Manufacturing companies often have quarterly reporting periods that stress document management systems Professional services may experience increased collaboration traffic during specific project phases The goal is building baselines that account for these predictable variations, so you can distinguish between normal cyclical increases and actual capacity problems. Implementing Predictive Monitoring Systems Building a network monitoring system that actually predicts problems requires more than just installing software and hoping for the best. You need a systematic approach that captures the right data and presents it in ways that support proactive decision-making. Choosing Monitoring Points Strategically Not every network segment needs the same level of monitoring. Focus your detailed baseline tracking on: Internet gateway connections where external bandwidth limitations first appear Core switch infrastructure that handles the majority of internal traffic Server farm connections where application performance bottlenecks develop Wireless access points in high-density user areas WAN connections between office locations Setting Up Meaningful Alerts This is where a lot of monitoring systems fall apart. They either generate so many alerts that you start ignoring them, or they only alert you after problems are already impacting users. Effective proactive IT support monitoring uses graduated alerts based on baseline deviations: Trend alerts when performance metrics show concerning patterns over weeks Threshold warnings when you're approaching known capacity limits Anomaly detection for unusual patterns that don't match historical baselines Predictive alerts when current trends suggest future problems Translating Data Into Preventive Actions Having great baseline data doesn't help if you don't know how to act on it. The most valuable monitoring systems connect performance trends to specific preventive actions you can take. Capacity Planning That Actually Works Traditional capacity planning involves guessing how much your network usage will grow and buying equipment accordingly. Baseline-driven capacity planning uses your actual usage patterns to make informed predictions about future needs. For example, if your baseline data shows that bandwidth utilization increases by 15% each quarter, and you're currently at 60% capacity, you can predict that you'll hit problems in about 18 months – plenty of time to plan and budget for upgrades. Application Performance Optimization Network baselines also reveal which applications are consuming disproportionate resources and when. This intelligence allows you to: Schedule resource-intensive tasks during off-peak hours Implement traffic shaping for non-critical applications during busy periods Optimize application configurations based on actual usage patterns Plan application deployment timing to avoid creating new bottlenecks Real-World Implementation Examples Let me walk you through a couple of scenarios where baseline monitoring prevented major network problems. Case Study: The Gradual Slowdown A 75-person consulting firm was experiencing increasingly slow file access times, but nobody could pinpoint when it started or what was causing it. Their network monitoring showed everything was "green," but users were frustrated. By implementing proper baseline monitoring, we discovered that their file server response times had gradually increased by 300% over eight months. The culprit was a combination of growing file sizes and an aging storage array that was approaching its IOPS limits. Because we caught this trend early, we could plan the storage upgrade during a scheduled maintenance window instead of dealing with an emergency replacement when the system finally failed. Case Study: The Seasonal Surprise A manufacturing company experienced severe network slowdowns every quarter during their reporting periods, but each time it seemed to catch them off guard. Their proactive IT support team wasn't tracking quarterly patterns effectively. After establishing proper baselines, we could predict exactly when network stress would peak and implement temporary traffic management policies in advance. We also used the trend data to justify upgrading their WAN connections before the next major reporting cycle. Building a Sustainable Monitoring Strategy The key to successful predictive network monitoring is building systems that provide actionable intelligence without creating unsustainable administrative overhead. Start with monitoring your most critical network segments and applications. Establish baselines for normal operation during different time periods and business cycles. Then gradually expand your monitoring coverage as you develop the expertise to interpret and act on the data. Remember, the goal isn't to monitor everything perfectly – it's to monitor the right things well enough to make informed decisions about preventing future problems. Effective proactive IT support is about turning network performance data into a strategic advantage rather than just another source of technical complexity. When you can predict network bottlenecks weeks or months before they impact users, you transform from a reactive IT support team into a strategic business enabler. That's the difference between fixing problems and preventing them.

Trump's Bezos wedding snub: How Amazon founder and bride Lauren Sanchez failed to cash in on Elon Musk fallout
Trump's Bezos wedding snub: How Amazon founder and bride Lauren Sanchez failed to cash in on Elon Musk fallout

Daily Mail​

timean hour ago

  • Daily Mail​

Trump's Bezos wedding snub: How Amazon founder and bride Lauren Sanchez failed to cash in on Elon Musk fallout

Jeff Bezos is on maneuvers to try and replace Elon Musk as Donald Trump 's preferred billionaire - but the president is playing it cool, it is claimed. The pair have spoken at least twice this month, with Trump even having received a visit from the CEO of Bezos' astronautics company Blue Origin, The Wall Street Journal reports. Bezos is getting married to fiancée Lauren Sanchez in Venice Friday evening. The lovebirds invited President Trump to the wedding, but he turned them down. Trump cried 'scheduling conflicts,' but it has been speculated that he may be enjoying serving up some ice-cold revenge for an earlier Bezos move. The president reportedly phoned up Bezos in a fury in April after learning Amazon planned to display how much Trump's tariffs had added to the cost of every item sold on its website.. Daily Mail has approached the White House for comment. There was one strong Trump connection at the Venice nuptials. First Daughter Ivanka Trump and her husband Jared Kushner are among the A-listers attending the Amazon founder and Lauren Sanchez 's lavish $20 million wedding in Venice this weekend. Trump and Musk's friendship exploded at the start of this month after the pair locked horns over the president's 'Big Beautiful Bill', which the Tesla billionaire claims will batter the economy - while also impacting his businesses. Musk turned on Trump in a stunning series of X posts, linking him to Jeffrey Epstein and digging up old tweets that warn against such expenditure. The president furiously hit back, threatening to rip away billions in government contracts and subsidies to the former DOGE leader's businesses. Musk then threatened to 'immediately' remove SpaceX's Dragon spacecraft from service, despite it being essential for America's space initiatives. Trump's former First Buddy ultimately admitted he had gone 'too far', but Bezos - who has long clashed with Musk over the race to space - seemingly saw his in. He reached reached out to Trump just days after the public blow-up with Musk. Trump has told Bezos, who attended his inauguration in January, that he wants to send a crew to the moon during his Oval Office term, sources told WSJ. Federal space exploration contacts handed to Musk's SpaceX have been credited with making him the world's richest man with a $412 billion fortune. Bezos almost certainly realizes that befriending Trump may increase the chances of Blue Origin being handed some of the lucrative contracts, which could help swell his $227 billion fortune that makes him the world's third-richest man. Blue Origin CEO Dave Limp also met with White House Chief of Susie Wiles in mid-June, the insiders added. The apparent schmoozing goes beyond Bezos, with his new bride having seemingly tried to inch her way into the Trump family's inner circle. Sanchez struck up a friendship with Ivanka in June last year after they both attended Kim Kardashian's birthday party in Beverly Hills. They're now neighbors on the exclusive Indian Creek island in Miami. Bezos and Sanchez own three of the 40 lots in the gated compound, worth $237 million, and announced in 2023 they were abandoning Seattle and making Miami their permanent home. Sanchez is also friends with Donald Trump Jr.'s girlfriend Bettina Anderson. Anderson lavished praise on Sanchez after her much-mocked space jaunt with Katy Perry, calling her 'my incredible friend'. The familial ties are even inter-generational with Sanchez's son Evan Whitesell slated start at the University of Miami alongside Don Jr's daughter Kai Trump this fall.

Why We're Watching AI Carefully Before We Commit
Why We're Watching AI Carefully Before We Commit

Business News Wales

time2 hours ago

  • Business News Wales

Why We're Watching AI Carefully Before We Commit

We haven't yet adopted AI in any widespread way across our HR practices at the University of the Arts London. But like many others in the sector, I'm watching its development with interest – and with a fair amount of hope for what it could help us achieve. While we're not using it now, we are thinking carefully about how it might fit into our future resourcing strategy. One thing I'm clear on is that individuality still matters. When candidates apply for roles with us, we encourage them to write in their own tone of voice. We want to see who they are, not just how well they can mimic a job description. That's part of why we've stuck with human decision-making at every stage so far, even when managing several hundred applications for a single vacancy. That approach isn't without its challenges, but we've tried to manage it practically – for example, encouraging hiring managers to shortlist gradually rather than waiting until a role closes. That said, I recognise the potential of AI to improve some of the more routine elements of recruitment. Our applicant tracking system provider is developing a new tool aligned to skills-based hiring, which we already use through our competency framework. If it works as well as the early version I've seen, it could help us manage volume and complexity without losing the human element. I don't see AI making final decisions for us but I can see it offering a helpful starting point. It might rank applications, surface potential matches, or highlight gaps to explore. The final judgment should still rest with people, but I'm open to tools that make that process more focused. Beyond recruitment, I'm interested in AI's potential to support decision-making more broadly. Sometimes in HR, particularly when dealing with complaints or complex situations, it's useful to have something to help sense-check your thinking. Not to take over the process, but to confirm that the route you're taking is sound, or to suggest questions you haven't yet considered. I've already used AI tools in that way – not with confidential data, but to help frame responses or understand an issue more clearly when time is short and the pressure is high. Like many professionals, I juggle a lot of priorities. There are always several projects, competing deadlines, and daily operational tasks. AI can play a role in helping me manage that. For example, it's already helped me to create structured plans and summaries more quickly, freeing up headspace for more strategic work. And that's where I think its real value lies – not in replacing HR roles, but in supporting us to focus more on the parts of our jobs that add long-term value. Workforce planning is another area where I see potential. With the right data, AI could help identify where future risks lie, whether that's retention issues, capacity challenges, or the impact of organisational change. I've seen examples in previous roles where predictive analysis gave useful insight. When an organisation moved offices, we were able to forecast how many people might leave as a result of a longer commute. That kind of visibility is valuable. If tools can help us plan redeployment, identify skills gaps, or prepare for change in a more proactive way, that's a step forward. There's also a possible benefit for individuals themselves. If you had access to a tool that monitored patterns in your organisation's performance and market conditions, you might be able to assess your own job security more realistically. Having gone through several rounds of redundancy in my career, I can see how helpful that kind of insight might be, offering early warning signs and helping people to plan ahead. That said, I do have concerns. I don't want AI to dilute the human element of employee experience. We talk a lot about people-centric HR, and that has to remain the focus. There's a risk that, in automating too much, we lose some of the empathy and individual attention that people need, especially during periods of stress or poor mental health. I've experienced those challenges myself, and while AI might play a useful role in guiding someone through a difficult time, it can't replace genuine human support. The same goes for wellbeing tools more broadly. If done well, they might become part of a wider package, but they can't be the whole answer. Looking ahead, I think we'll see a mix of outcomes. Some roles may change. Some tasks may be automated. But HR is a resilient profession, and I believe we'll adapt. AI won't replace us, but those who know how to use it effectively will likely be better placed to succeed. For me, the question isn't whether AI is coming into HR – it's how we make sure it's used well. If we get that right, it can be a powerful tool that empowers our work, rather than replacing it. Listen to Mark discuss this and more in episode one of the DeeplearnHS podcast series, AI in Hiring & Workforce Strategy, here

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