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A new Quordle puzzle appears at midnight each day for your time zone – which means that some people are always playing 'today's game' while others are playing 'yesterday's'. If you're looking for Friday's puzzle instead then click here: Quordle hints and answers for Friday, August 8 (game #1292).

Quordle was one of the original Wordle alternatives and is still going strong now more than 1,100 games later. It offers a genuine challenge, though, so read on if you need some Quordle hints today – or scroll down further for the answers.

Enjoy playing word games? You can also check out my NYT Connections today and NYT Strands today pages for hints and answers for those puzzles, while Marc's Wordle today column covers the original viral word game.

SPOILER WARNING: Information about Quordle today is below, so don't read on if you don't want to know the answers.

• The number of different vowels in Quordle today is 4*.

* Note that by vowel we mean the five standard vowels (A, E, I, O, U), not Y (which is sometimes counted as a vowel too).

• The number of Quordle answers containing a repeated letter today is 4.

• No. None of Q, Z, X or J appear among today's Quordle answers.

• The number of today's Quordle answers starting with the same letter is 0.

If you just want to know the answers at this stage, simply scroll down. If you're not ready yet then here's one more clue to make things a lot easier:

• N

• I

• E

• V

Right, the answers are below, so DO NOT SCROLL ANY FURTHER IF YOU DON'T WANT TO SEE THEM.

The answers to today's Quordle, game #1293, are…

• NOOSE
• INLET
• ELEGY
• VIRUS

I screwed up today, guessing a word with a letter that I already knew was in the wrong position.

However, as is often the case, the error actually opened up another column and allowed me to guess ELEGY – a word I would have taken longer to find without my lucky error.

The answers to today's Quordle Daily Sequence, game #1293, are…

• SPACE
• INNER
• CONIC
• KNELT

• Quordle #1292, Friday, 8 August: KNEEL, KINKY, RALPH, BOOZY
• Quordle #1291, Thursday, 7 August: PLUNK, PROXY, CURVY, PEARL
• Quordle #1290, Wednesday, 6 August: RISKY, APART, FAUNA, HANDY
• Quordle #1289, Tuesday, 5 August: ROAST, SLICK, AUDIT, BILLY
• Quordle #1288, Monday, 4 August: MACAW, SINCE, COLON, CHIRP
• Quordle #1287, Sunday, 3 August: MOTIF, LEERY, LOFTY, BURST
• Quordle #1286, Saturday, 2 August: WARTY, PUPAL, CLEAR, SLICE
• Quordle #1285, Friday, 1 August: ACTOR, MEALY, WIDTH, ADOBE
• Quordle #1284, Thursday, 31 July: STYLE, VALET, AGONY, ALLOY
• Quordle #1283, Wednesday, 30 July: DEBAR, ADMIN, FOLIO, USAGE
• Quordle #1282, Tuesday, 29 July: BATCH, TOPIC, MURKY, BUNCH
• Quordle #1281, Monday, 28 July: CANDY, TRYST, SHIRT, FORGO
• Quordle #1280, Sunday, 27 July: TRAWL, BALER, PIANO, MINCE
• Quordle #1279, Saturday, 26 July: MUDDY, SAINT, KINKY, POLAR
• Quordle #1278, Friday, 25 July: BONUS, RESIN, CEDAR, MADAM
• Quordle #1277, Thursday, 24 July: AGONY, VERVE, GLEAN, MINUS
• Quordle #1276, Wednesday, 23 July: OZONE, PENCE, ROOMY, WIDER
• Quordle #1275, Tuesday, 22 July: OPTIC, GIDDY, VOCAL, ADULT
• Quordle #1274, Monday, 21 July: KNEED, SNAIL, PINTO, FEAST

In 1967, I began my first job working with computers. The company was presciently named International Data Highways (IDH), the brainchild of my remarkable mentor, Charlie Ross. Even in the infancy of computing, Charlie was pushing the boundaries of what computer science could achieve. He envisioned a world where financial and business information would be instantly accessible via terminal. His foresight was extraordinary, especially considering the limitations of the time.

Charlie was a visionary, driven by possibilities invisible to others but clear as day to him. He created an environment where curiosity was expected and boldness rewarded. It was a heady environment for a 19-year-old to be thrown into—and Charlie believed in throwing people in the deep end. He wanted his team to be brave, empowered, and unafraid to fail. This mindset was radical in 1967 Britain, where conformity and rigid corporate hierarchies ruled.

That early baptism by fire prepared me for the revolutionary journey computers would take over the next half-century. Little did I know that I had joined the most transformative industry the world had ever seen.

The first computer I worked on was the Sperry Univac 418—a massive, complex machine with a 24-bit word structure and a 12-bit addressing mechanism. It used a storage device known as a FastRand drum, which weighed two and a half tons and could store a mind-bending 100 megabytes of data. In those days, IBM dominated the industry with batch processing, while Sperry and others pushed the frontier of real-time computing.

IBM’s market share in the early 1970s was nearly 80% of the global computing market. But by the 1990s, the once-mighty firm teetered on the edge of collapse. Agile, innovative players like Digital Equipment Corporation and Data General had emerged, and IBM’s fall was swift and brutal. Watching this unfold in real time impressed upon me one core truth: this is an industry where today's titan is tomorrow’s toast.

Much of this volatility can be traced to Moore’s Law. In 1965, Gordon Moore predicted that the speed and capacity of transistors would double every 18–24 months. This exponential growth would continue to redefine the limits of possibility—not just in computing power but in how quickly innovation itself could accelerate. Two years after Moore’s prediction, I entered the industry. The rest is history—and we’re still writing it.

The innovations of the past five decades—mainframes, PCs, the internet, mobile devices—were the hors d’oeuvres. What we are now witnessing with AI is the main course. The difference lies not just in power, but in liberation. For most of my career, progress was constrained by the lack of computing power, limited storage, and slow processing speeds. Cloud computing, distributed architecture, and global broadband have obliterated those limits.

Today, we operate in a world of universal access. Storage is cheap and nearly infinite. Computing power is on demand. Barriers are gone. And with that, we have opened a new door—one that leads not to incremental improvement, but to a new industrial revolution.

This is not a wave. It’s a tsunami.

Those sitting comfortably on the beach, sipping tea and admiring the view, are about to be swept away. Those who grab a surfboard and paddle out to meet the surge head-on will experience the ride of a lifetime.

Perhaps the most stunning transformation AI brings is the democratization of knowledge. For centuries, information was locked behind paywalls—academic, professional, institutional. In some industries, guild-like structures guarded specialized knowledge jealously. AI changes that. It is the ultimate key, unlocking access for anyone with a connection and curiosity.

Professional hierarchies will flatten. Management will face new levels of transparency and accountability thanks to real-time analytics. Strategic decisions once made in echo chambers will now be data-informed and challengeable.

This will not be a time for the timid. It will favor the bold, the agile, the open-minded. Yet the true revolution lies not in how companies operate—but in how people live, learn, and grow.

Education may be the sector most radically transformed by AI. For too long, students have been shuffled through one-size-fits-all classrooms, forced to memorize facts, and judged by standardized exams. AI opens the door to personalized, adaptive learning—customized in real time to each student’s strengths, weaknesses, and interests.

Imagine a system where students are assessed continuously through participation and engagement, not just snapshots on test days. AI can identify knowledge gaps and tailor content dynamically—favoring subjects that spark passion, curiosity, and intrinsic motivation. Students will no longer be treated as production units that must progress at the same pace. They will be treated as individuals.

Parents will no longer need to buy homes in expensive school districts to access quality education. World-class learning will be accessible to everyone, everywhere. Finally, education will be democratized—just as knowledge is.

Compare this to China’s Gaokao, where every high school student takes a life-determining university entrance exam on the same day in June. Should a teenager’s future be determined by two days of testing—or by twelve years of continuous growth and learning? AI makes that question more than rhetorical. It makes a new model possible.

Beyond education, AI promises to unlock a renaissance of human creativity. By automating repetitive and menial tasks, it will free the mind to focus on imagination, problem-solving, and innovation. AI will allow people to color outside the lines again—something that corporate culture and standardized education often suppress.

Innovation will no longer come just from elite R&D labs or top universities. It will come from a billion creative minds newly empowered. AI will not just be a tool of efficiency—it will be a force of liberation.

Looking back on my 57-year journey—from machine code to machine learning—I’m reminded of how much has changed, and yet, how many principles remain the same. Vision, courage, curiosity, and resilience remain the essential traits of any innovator.

The tools may change. The pace will accelerate. But the opportunity—for impact, for growth, for transformation—has never been greater. We stand at the edge of something vast. The surf is up and the surfboards are ready.

Let’s enjoy the ride.

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This article was produced as part of TechRadarPro's Expert Insights channel where we feature the best and brightest minds in the technology industry today. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro

As the adoption of agentic AI accelerates across industries, enterprise leaders face a crucial challenge: preparing today’s knowledge and systems for tomorrow’s AI-powered customer service.

Integrating distributed knowledge, ensuring information accuracy, and architecting AI agents are not just technical exercises—they are strategic imperatives for any organization seeking to stay ahead in the age of generative and agentic AI.

This article provides five key steps to future-proofing an enterprise for the agentic AI era.

At the heart of any effective agentic AI system lies one asset: organizational knowledge. Yet, as enterprises have grown, so too has the sprawl of knowledge—scattered across departments, tools, and formats. The first—and arguably the most difficult—step is consolidating this knowledge into one unified, accurate, and accessible source.

It’s not a one-off job. It’s not about ingesting that information just once and then having it distributed to customers, for example, when we're talking about customer service. It is especially relevant to keep it up to date and think of knowledge as something that needs to be maintained.

Key actions for enterprises:

Aggregate and validate: employ technologies like Azure AI Search to unify data, but ensure each piece of information is verified and free of conflicts or outdated content.

Assign ownership: make subject matter experts responsible for ongoing accuracy. Knowledge must be continuously maintained, not simply imported.

Automate where possible: use tools to automatically detect ambiguity, outdated data, and discrepancies, but always have clear human accountability.

Even the highest-quality knowledge is useless if it’s trapped in silos or lost in translation between systems. Technical integration is a prerequisite for agentic AI success. Leaders should focus on both aggregation and real-time synchronization across all knowledge repositories, ensuring seamless interoperability with AI agents.

Best practices:

Synchronize changes in real time: any updates to knowledge should be reflected instantly across all systems feeding the AI.

Prepare for multimodality: enterprises must handle diverse file types—text, images within PDFs, and even external references—that affect the factual reliability of responses.

Design for compatibility: ensure integration mechanisms work not only for aggregation but for active use by AI systems, reducing friction between legacy and modern platforms.

Agentic AI thrives not on sheer volume of knowledge, but on the precision of its retrieval and application. This requires a clear strategy for domain separation—defining specific knowledge “buckets”—and robust quality assurance processes.

With agentic AI systems, it is better to split the domains into respective buckets... define the different domains that need to be handled and make sure that specialists are available for respective knowledge retrieval. Proper quality assurance with, for example, human-in-the-loop approaches, is essential.

What this means:

Define and limit scope: by narrowing knowledge domains, organizations make quality control manageable while improving retrieval accuracy.

Human oversight: subject matter experts should be involved in reviewing responses, especially when AI interacts with ambiguous or complex queries.

Smart conversational design: build agentic systems that clarify user requests, routing queries to the correct knowledge base.

Contrary to popular belief, the primary challenge in deploying agentic AI is no longer traditional “training” of models. Instead, it is about providing the right context, curated and orchestrated by a new breed of professionals: agent architects and prompt engineers.

What’s changing:

Shift from ML training to context engineering: it’s less about fine-tuning models, and more about architecting the context and guidance that AI agents need to perform.

Emergence of prompt engineering: there’s a growing need for experts who can design effective prompts, stay abreast of changing base model standards, and translate business needs into actionable guidance for AI agents.

Use case definition: effective AI agents require clear use case data, drawn from real customer pain points—not misleading conversational analytics from outdated IVR systems.

Many enterprises remain shackled by legacy infrastructure. Successful AI transformation demands not only technical modernization but also alignment with open standards that enable agent-to-agent collaboration and cross-system compatibility.

Key actions for enterprises:

Assess and modernize: identify which legacy systems hinder integration and prioritize making them accessible.

Adopt open standards: invest in emerging protocols that facilitate inter-agent communication, collaboration, and future expansion.

For those steering enterprises toward agentic AI, preparation means far more than adopting the latest model or platform. It demands a holistic strategy: consolidating and maintaining accurate knowledge, breaking down technical silos, orchestrating precise retrieval, and embracing the new disciplines of context and prompt engineering. By acting now, leaders can ensure that tomorrow’s AI agents not only deliver on their promise, but do so with the precision, reliability, and agility that today’s customers—and tomorrow’s enterprises—will demand.

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This article was produced as part of TechRadarPro's Expert Insights channel where we feature the best and brightest minds in the technology industry today. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro