By Bilal Javed

Are you tired of getting mediocre responses from ChatGPT while others create magic with it? The frustration of writing prompts that lead to vague, off-target answers can be overwhelming.

Many users give up, thinking they’ll never master this powerful AI tool. But here’s the good news: With our genius prompting techniques, you’ll unlock ChatGPT’s full potential and transform your interactions into consistently amazing results.

We’ve gathered the most effective tricks and strategies that will elevate your prompting game from basic to brilliant. Let’s unlock the secrets to getting exactly what you want from ChatGPT.

1. AI CHIP TIME-SHARING NETWORKS

An innovative approach to democratizing AI computing power through collaborative resource sharing. This system would establish regional AI processing hubs where multiple businesses could schedule and utilize high-performance AI chips on a time-shared basis.

Small and medium enterprises could access expensive AI infrastructure without massive upfront investments. The network could implement dynamic pricing based on demand and processing requirements. Companies could trade or auction their unused time slots, creating a secondary market for AI computing resources.

This model could particularly benefit seasonal businesses with varying AI processing needs. The system could include redundancy across multiple locations to ensure reliability and failover capabilities. Advanced scheduling algorithms would optimize chip utilization and minimize downtime. Quality of service agreements would ensure fair access and performance standards for all participants.

2. EDGE AI WILDLIFE MONITORING

A distributed network of autonomous monitoring stations powered by renewable energy, utilizing edge AI for real-time wildlife protection. These stations would operate independently using advanced computer vision and acoustic analysis to identify species and detect unusual activities.

The system would use local processing to analyse animal behaviour patterns and identify potential threats without requiring constant connectivity. Solar-powered units would be equipped with long-term storage capabilities and efficient power management systems.

Machine learning models would be periodically updated during low-power periods to improve detection accuracy. The network could communicate with neighboring units to track animal movements across larger areas. Emergency alerts could be triggered for immediate response to critical situations like poaching attempts.

3. SLM-POWERED MEDICAL DEVICES

Integration of Small Language Models into medical devices for enhanced autonomous operation and patient care. These devices would process patient data locally, making immediate adjustments to treatment parameters based on real-time physiological changes.

The system would maintain a rolling analysis of patient patterns to predict and prevent potential health issues. Security measures would ensure data privacy while still allowing medical professionals to access critical information. The devices would use minimal power while maintaining continuous monitoring and adjustment capabilities.

Emergency protocols would be built in to handle critical situations without requiring external input. Regular model updates could be performed during routine medical checkups to improve performance. The system would maintain detailed logs of all adjustments and patient responses for medical review.

4. AGENTIC AI CITY PLANNING

Autonomous urban management systems that continuously optimize city operations through real-time data analysis and adjustment. The system would integrate data from multiple sources including traffic cameras, weather stations, and utility monitors to make informed decisions. Machine learning models would predict and respond to changing patterns in city usage throughout the day.

The platform would coordinate multiple city systems including traffic lights, public transportation, and power distribution. Emergency response protocols would be integrated to handle unexpected situations efficiently.

The system would learn from historical data to improve future planning and resource allocation. Environmental impact would be continuously monitored and factored into all decisions. Regular reports would be generated for city officials to review and adjust overall policies.

5. CROSS-COMPANY ENTERPRISE SEARCH SYNDICATES

A collaborative knowledge-sharing system that maintains privacy while leveraging collective intelligence across organizations. The platform would use advanced anonymization techniques to share valuable insights without exposing sensitive information.

Companies would define specific knowledge domains they’re willing to share and receive access to similar domains from other participants. AI algorithms would identify valuable patterns and insights across the combined knowledge base. The system would maintain strict access controls and audit trails for all information exchanges.

Regular value assessments would help companies understand the benefits of their participation. The platform would facilitate knowledge exchange while preventing competitive intelligence gathering. Automated content classification would ensure appropriate sharing boundaries are maintained.

6. AI CYBERSECURITY HONEYPOTS

Advanced decoy systems that actively engage with and analyse cyber threats using artificial intelligence. The honeypots would simulate realistic company environments to attract and study attack patterns. AI models would adapt the simulated environment based on attacker behavior to maintain believability.

The system would generate detailed threat intelligence reports for sharing with security communities. Real-time analysis would help identify new attack vectors and exploitation techniques. The platform would maintain a database of attack signatures and behavioral patterns.

Automated response systems would engage with attackers to gather maximum intelligence. The collected data would be used to improve security measures across participating organizations.

7. EDGE AI MENTAL HEALTH MONITORING

Personal devices that provide continuous, private mental health support through local AI processing. The system would analyze multiple data points including voice patterns, sleep quality, and activity levels. Privacy-focused design would ensure all sensitive data remains on the device.

The AI would learn individual baseline patterns to identify meaningful deviations. Regular wellness reports would be generated for personal review or sharing with healthcare providers.

The system would include proactive intervention suggestions based on detected patterns. Emergency support protocols would be activated if critical situations are detected. The platform would adapt to individual preferences and response patterns over time.

8. SLM-ENHANCED INDUSTRIAL EQUIPMENT

Manufacturing equipment with integrated Small Language Models for natural interaction and diagnostics. The systems would enable voice-controlled operation and maintenance procedures. Real-time problem diagnosis would reduce downtime and maintenance costs.

The equipment would maintain detailed operational logs with natural language descriptions. Integration with existing industrial systems would enhance overall factory intelligence. The models would learn from operational patterns to predict potential issues.

Voice interfaces would support multiple languages for international deployment. The system would include safety protocols and emergency voice commands.

9. AGENTIC AI LEGAL COMPLIANCE

Autonomous systems for maintaining regulatory compliance across multiple jurisdictions and industries. The platform would continuously monitor regulatory changes and assess their impact on operations. AI agents would suggest and implement compliance adjustments in real-time.

The system would maintain comprehensive audit trails of all compliance-related changes. Risk assessment algorithms would prioritize compliance actions based on potential impact. Regular compliance reports would be generated for management review.

The platform would integrate with existing business systems to ensure comprehensive coverage. Predictive analytics would help anticipate future regulatory changes.

10. HYBRID SEARCH ECOSYSTEMS

Integrated search platforms combining multiple data sources and AI models for comprehensive information retrieval. The system would automatically select the most appropriate search method based on query type. Different AI models would be deployed based on the specific requirements of each search domain.

The platform would maintain context awareness across multiple search sessions. Results would be presented in a unified interface while maintaining source attribution. The system would learn from user interactions to improve future search relevance.

Privacy controls would manage access to different information sources. Integration capabilities would allow for easy addition of new data sources.

TIPS FOR IMPLEMENTATION:

Start with small-scale pilot programs to test concepts
Focus on data privacy and security from the beginning
Ensure scalability in system design
Maintain human oversight and intervention capabilities
Regularly assess and update AI models
Document all processes and decisions thoroughly
Build in robust error handling and failover systems
Establish clear metrics for success measurement

FINAL THOUGHTS

These AI innovations represent a significant shift in how we approach complex problems across various industries. Success in implementing these systems will require careful balance between automation and human oversight. Organizations should focus on building foundational capabilities while maintaining flexibility for future adaptations.

The key to successful implementation lies in understanding both the technical capabilities and practical limitations of these systems. Regular assessment and adjustment of these systems will be crucial for long-term success. It’s important to maintain ethical considerations throughout the development and deployment process.

These technologies will continue to evolve, requiring organizations to stay adaptable and forward-thinking. The future of AI implementation will likely involve combinations of these approaches to create more comprehensive solutions.

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By Bilal Javed

Sourced from Corex Gaming

By Nicholas Leighton Edited by Chelsea Brown

Want to go viral and boost your brand’s visibility? Learn how to spot emerging trends early, create original content that resonates and master the art of trendjacking to drive massive engagement on social media.

By Nicholas Leighton 

Edited by Chelsea Brown

Sourced from Entrepreneur

By Jodie Cook

Leading a team means making tough calls. But if you’re prioritizing being liked over being respected, you’re doing everyone a disservice. When you avoid conflict and bend over backwards to keep people happy, you create bigger problems down the line. Your star players get frustrated and your business stalls. Your clients walk all over you.

Stop trying to make everyone happy. It’s not possible if you’re serious about success. Dora Vanourek, founder, executive coach and number one LinkedIn creator in Canada, shared powerful insights about people-pleasing in leadership. She spotted the patterns so we can learn

Here’s how to see if you’re guilty of being too nice in business, and what to do instead.

Leadership isn’t a popularity contest

Your job isn’t to be your team member’s friend or your client’s therapist. Your role is to build something meaningful and guide your people to success. When you focus on being liked, you create a weak culture where standards slip and top performers leave.

Too many founders fall into this trap. They sugar coat feedback, avoid tough conversations, and let small issues grow into big problems. They make concessions to avoid rocking the boat. But real leadership means doing what’s right, not what’s easy.

How to spot if you’re too focused on being liked

You never protect your team’s boundaries

You might be too accommodating with your clients. When every request is “urgent” and every favour “just this once,” you’re teaching others to ignore your team’s processes. A sign you’re making this mistake? “You accept every urgent request, overwhelming your team,” says Vanourek. Your people need you to defend their time and energy.

Stop letting other leaders bypass your systems. Make them follow proper channels. Your team will respect you more for protecting their workflow.

You reward the squeaky wheels

Playing favourites to keep complainers happy is a recipe for disaster. You end up overloading your reliable people while placating the difficult ones. Vanourek notes this means “rewarding complainers while overloading reliable people.”

Make decisions based on performance, not who might get upset. Your best people notice when you give special treatment to those who don’t deserve it.

You avoid sharing bad news

Think you’re being kind by holding back tough updates? Think again. “Share news promptly with empathy – they need time to plan,” Vanourek advises. Your team needs to know what’s happening, good or bad. Those situations that directly affect their work. The less-than-ideal calls you had to make. They’d rather know the score than be left anxiously waiting.

Give people time to process changes and adapt their plans. Share the right information with the right people at the right time. Hiding details can create uncertainty and kill trust.

You let problems fester

Small issues become big headaches when you’re scared to rock the boat. “Be transparent and share any problem within a day,” says Vanourek. Worrying about upsetting your team means problems multiply unchecked. What began as an issue with one team member infects the entire team.

Face challenges head-on when they appear. Your quick action prevents bigger explosions later, and stops you losing sleep worrying what to say.

You make decisions by committee

If you’re constantly seeking consensus and trying to keep everyone happy, you’re not leading. “Prioritize business needs, not who might get upset,” Vanourek emphasizes. Your role is to chart the course, not take a vote.

Make clear calls based on what’s right for the business. People respect decisive leadership more than endless consultation. Not everything is up for discussion.

You tolerate poor behaviour from top performers

When star players break rules without consequences, your culture crumbles. “Have the hard conversation now, before good people leave,” warns Vanourek. Watching top performers get away with toxic behaviour drives away your other awesome team members.

Set clear standards and enforce them equally. No one should be above the rules, no matter their results. Make your business a no diva zone.

Your feedback lacks punch

Having serious conversations in casual settings sends the wrong message. Stop diluting tough feedback to make it more palatable. Your team needs clear direction, not vague suggestions.

Give specific examples and concrete action steps. Don’t be afraid to share honest feedback. Speak with love but be firm. Take your delivery seriously to make your message heard.

Lead with purpose not popularity

Strong leadership means doing what’s necessary, even when it’s uncomfortable. Your business needs you to make tough decisions, have hard conversations, and maintain high standards. Stop worrying about being liked and start focusing on being respected.

The best leaders build strong teams through clear boundaries and consistent expectations. Make decisions that serve your business, not your need for approval.

Feature Image Credit: Getty

By Jodie Cook

Follow me on LinkedIn. Check out my website.

Sourced from Forbes

By Marisa Cramer

In the last few years, there’s been a noticeable shift in the way that people interact with their online persona. I think this comes as a side effect of more people sharing more of their lives online as social media becomes more ingrained in our daily lives. Just last week, I found myself following in the footsteps of my friend from high school and making a second account just for friends, a sort of call back to the Finsta phenomenon of the 2010s.

For those who aren’t familiar with the idea of a finsta, it was a second account that was typically private and inevitably messier than your main account. It consisted of things like “Comment a number and I’ll post that photo from my camera roll” or pictures with Snapchat filters on from a girly sleepover. Ultimately, it was pretty wholesome and cute, but as we got older, I remember second accounts used in ways that ranged from online bullying to records of people doing things they didn’t really want in their digital footprint. In high school, teenagers usually try to hide things from their parents. Overall, it was a pretty strange moment in time that coincided with Gen Z’s most awkward years. We were the first iPad baby generation, giving us technological literacy that interacted with the already intense social dynamics of our teens and tweens. The relic of Finsta resembles the desire in young adults to experience autonomy on the internet and have interactions sheltered from parents and teachers.

In the years since this era, influencers were elevated to the forefront of social media by users who wanted relatable content. Our feeds slowly shifted away from plain corporate ads to corporate ads disguised as advice from your favourite influencer. Thanks to many unfortunate circumstances of false advertising and influencer scams, there are Instagram policies about disclosing sponsored content. However, in the last few years, there’s been a shift toward the micro-influencer. Someone on a smaller scale of a few thousand to even tens of thousands of followers if their content is sort of niche. Not necessarily specialized but catered towards a certain demographic of Instagram and TikTok users. I’m sure we all have people we follow who aren’t necessarily famous but have a rich online community surrounding their content.

This past weekend, I created an account sort of resembling a finsta. When I told my roommate I’d created a second account to post whatever I wanted on, she referred to it as a Finsta. For me, a Finsta is isolated in that period, but maybe I’m being too picky. A few people I know use their main or secondary accounts to reconnect with the people they know. I’m not on TikTok, so I can’t attest to how users on that platform are engaging on a micro level. I like Instagram’s format for connecting with those in my real life more than TikTok’s, but that’s also a personal preference. I’m feeling this shift towards using Instagram for two separate things: bathing information from the outside and interacting with those you know in real life. On my second account, I can post whatever I’m up to daily and keep those in my life updated in a way. But also, I can go on reels and see people I’d never see in real life teach me a new craft. I think Instagram’s prior attempt to isolate these things isn’t what users want. And by making secondary accounts or utilizing Close Friends on the platform, you can use it in a more personal way that facilitates connection more than parasocial online relationships. I kind of wish everyone on my feed would start posting like a micro-influencer, like making a vlog of your day or posting a recipe. It’s so fun connecting with people in the community when I share snippets of my life online. It’s beautiful to connect on that small scale, and it balances out the cognitive dissonance from watching more famous people’s content. Because as we’ve come to see, a lot of people are completely fabricating their lives. Beyond that, even the person with a few thousand followers and who lives 300 miles away is diluting their real experience for privacy. Which we all do, even on a personal level. But it’s more real than anyone you don’t know, and balancing out your online diet is important! I encourage you to try out posting like you have free will, even if you have to narrow the audience down. Or better yet, make a big group chat with your friends where you all do it. There are so many ways to use these platforms to facilitate connection with people you know, not just people far away!

Feature Image Credit: Instagram / @emmachamberlain

By Marisa Cramer

Sourced from Her Campus

By Hunter Schwarz

Messenger once allowed users to chat across platforms, but now Meta is emphasizing the app’s ties to Facebook.

The instant messaging app dropped the multicolour gradient used in its previous logo for a solid blue that matches the shade used by Meta’s flagship app. Some small, subtle refinements were also made to the lightning-bolt shape inside the Messenger logo’s word-bubble mark. Secondary versions of the logo appear in black or white.

“We often refine our designs to enhance the look and feel of our products,” a Meta spokesperson tells Fast Company. “In this spirit, you’ll find that we’ve updated the Messenger colour palette.”

Online, some suggested the change was made because of Meta CEO Mark Zuckerberg’s comment that his company needed more “masculine energy,” or his hopes to get back to the feeling of “OG Facebook.” Regardless, the colour change just so happens to be a delayed reflection of the app’s diminished cross-platform communications capabilities.

Messenger was originally known as Facebook Chat, but Facebook spun its instant messaging services into a stand-alone app in 2014. The light-to-dark-blue gradient of the Messenger logo when it launched matched the gradient of Facebook’s logo at the time. In 2020, Messenger rebranded to the multicolour gradient that it used up until last month.

The Messenger app’s colourful gradient went from blue to purple to orange and pink, colours that seemed to suggest a bridge from Facebook to Instagram; for a time, Messenger did allow users to chat across both platforms. That integration came as some speculated that Meta’s portfolio of apps were more tightly integrating to avoid being broken up. But by 2023, Meta killed Messenger’s cross-platform instant messaging capabilities. That change came just as Meta was arguing that certain European antitrust rules didn’t apply to Messenger because it was a Facebook feature instead of a stand-alone messaging app.

Though Meta eventually did announce last year it would acquiesce to the EU’s Digital Markets Act and open up Messenger as well as its other messaging app, WhatsApp, to third-party chats for users in the European Union, the new Messenger logo suggests the company is still set on linking Messenger to Facebook.

“Messenger is a messaging app from Facebook,” Messenger’s brand guide says. That connection is now made crystal clear with colour.

By Hunter Schwarz

Sourced from Fast Company

By Julian Horsey

By Julian Horsey

Sourced from Geeky Gadgets

By David Henkin

The age of AI is here and is rewriting the rules of innovation. Innovation has been the backbone of human progress by transforming industries, revolutionizing businesses and improving lives.

We are not just talking about incremental improvements. We are witnessing a paradigm shift, a fundamental change in how we conceive, develop and deploy new ideas. From enhancing product development and streamlining workflows to personalizing customer experiences and enhancing decision-making, AI is fundamentally changing how we innovate.

The shift is not just about efficiency; it is about expanding the boundaries of what is now possible. AI’s transformation of everything from business intelligence to healthcare are top-level indicators of a key underlying factor — that AI is forever altering innovation across practically every industry and at every level.

Through a wide range of activities, AI is serving to improve humankind’s own innovative capabilities by acting as a powerful augmentative tool.

Innovation, AI And Data

There is no denying the importance of reliable data in driving meaningful innovation that solves problems and creates worthwhile solutions. Despite this, many individuals and organizations struggle to make use of the data that is available to them — partly because the sheer scope of that information can quickly become overwhelming.

AI has proven to be a powerful tool in driving smarter innovation efforts by helping people identify patterns and trends that might otherwise get lost in a sea of data. As Tim Stobierski writes for The Harvard Business School, “While intuition can provide a hunch or spark that starts you down a particular path, it’s through data that you verify, understand, and quantify.

Highly data-driven organizations are three times more likely to report significant improvements in decision-making compared to those who rely less on data.”

Several seaports have begun investing in AI to help with everything from potential port disruptions to creating digital versions of ports to test servicing options for autonomous ships. The machine learning and data collection enabled by these AI applications are helping seaports drive innovative efforts that will help improve future efficiency.

AI-powered decision-making tools such as IBM Watson and Tableau AI are equipping leaders and employees at every level with deeper insights, enabling data-driven strategies that were previously unattainable.

Shifting Investment Strategies

AI’s rise to prominence has also forever altered innovation through the simple fact that many companies and even countries are increasingly shifting their innovation efforts toward artificial intelligence.

In an interview with A&S Media East, Lario Lovric, CEO of Aldra Alamen Security Services explained, “Recent market studies reveal that governments of prosperous Gulf oil-producing countries, including the UAE, Saudi Arabia, and Qatar, allocate substantial funds – equivalent to, if not more than, certain European nations – for the advancement of AI-related technologies. This unequivocally underscores AI’s role in shaping futuristic security. Furthermore, the UAE was the first nation in the region to adopt a National AI Foundation strategy in 2017, and it holds the distinction of being the world’s inaugural Ministry for Artificial Intelligence.”

From a technology perspective, this shift in focus means that artificial intelligence is increasingly viewed as the most important driver of innovation. By making AI the key point of emphasis, expectations increase that future innovations and developments in most spaces will be largely derived from AI. This will include the development of more advanced AI tools or finding new use cases for existing AI applications.

Rapid Testing Of Ideas

Another key area where AI is reshaping innovation is through its capabilities to help individuals rapidly prototype their ideas. The Arizona State University Artificial Intelligence Cloud Innovation Center reports that AI-powered rapid prototyping has been used in 80% of its projects since its launch. Utilizing AI has reduced the cycle time to complete a project from six to eight months to just four to six weeks.

AI’s ability to test concepts faster and more efficiently proved vital in project development, allowing innovative solutions — such as a bot designed to help travellers determine if they could make a flight on time or a hospital tool for querying medical documents — to reach usability much faster.

Similarly, predictive modelling through AI algorithms is also reshaping innovation efforts, by using data to predict future trends and outcomes that could stem from a particular decision. This added guidance allows for organizations to test multiple possible solutions in a much more effective manner, fuelling better innovative outcomes.

Idea Generation

Of course, no conversation about AI and innovation can ignore generative design. Generative design often helps lay the groundwork during the brainstorming and drafting phases of a project, serving as a springboard for additional ideation and innovation. Key examples include:

• Toyota Japan used generative AI to help redesign its seat frames in a way that fit the necessary size and weight parameters, while simultaneously increasing the level of safety and comfort provided by the frames.
• AI is widely used in marketing and content creation efforts, helping to generate multiple versions of emails and other content, or to create “rough draft” visuals that are then reworked by human professionals. Video game maker Capcom recently highlighted its use of AI to generate visual concepts and proposals for in-game objects to improve game development speed.
• An analysis from the Harvard Business Review noted that generative AI can “challenge expertise bias” and “inspire designers to think beyond their preconceptions of what is possible or desirable in a product in terms of both form and function. This approach can lead to solutions that humans might never have imagined using a traditional approach, where the functions are determined first and the form is then designed to accommodate them.”

The Long-Term View

The most profound impact of AI on innovation may be how it changes our conception of what is possible. When we can explore vast solution spaces quickly and cheaply, when we can simulate and test ideas with unprecedented fidelity and when we can collaborate with AI systems that amplify our creativity — we enter a new era of innovation potential.

The organizations and individuals who thrive in this new era will not be those who simply adopt AI tools, but those who reimagine their entire approach to innovation. The future belongs to those who can effectively combine human creativity and judgment with AI’s computational power and pattern recognition.

The innovation landscape has been forever altered. The question is not whether to embrace AI-driven innovation, but how quickly and effectively you can adapt to this new reality.

Feature Image Credit: Getty

By David Henkin

Follow me on LinkedIn. Check out my website or some of my other work.

David Henkin helps organizations and individuals innovate and grow.

Sourced from Forbes

By Aytekin Tank

Just a week after launching its R1 artificial intelligence model, DeepSeek took the title for most downloaded free app in the United States. Curious about this new AI disruptor, I decided to test it against ChatGPT. I gave both models the same prompt:

“Can you please write an intro to an article comparing ChatGPT and DeepSeek? It should be conversational, punchy, and timely.”

The results were surprising. A regular ChatGPT user, I found myself leaning toward DeepSeek’s introduction. The headline alone felt slightly more compelling.

ChatGPT vs. DeepSeek: The AI Showdown You Didn’t Know You Needed

Versus ChatGPT’s:

ChatGPT vs. DeepSeek: Which AI Model Wins in 2024?

ChatGPT’s version was more straightforward but lost points for incorrectly stating ‘2024’—I ran this mini-test in early February 2025. DeepSeek, on the other hand, leaned into trendy jargon, yet its response felt like a headline I’d actually want to click.

No matter how you phrase the question, the answer is clear: their strengths are comparable and there is no AI winner yet. As CEO of Jotform, I’m always researching the latest AI tools and new ways to automate my busywork. Here are the relative strengths of the two AI models dominating today’s headlines.

Writing: Creativity vs. Precision

As of 2023, employees reported using ChatGPT primarily for writing, copywriting, and content creation—second only to coding. Generating creative, engaging content is one of ChatGPT’s strengths. What’s more, you can even train ChatGPT to write in your voice by sharing excerpts of your writing and guiding the LLM on which facets of your writing to channel. For example, if you want it to generate content reflecting your humour and wit, but not your more formal tone, a simple prompt is all you need. It’s truly impressive.

While DeepSeek isn’t a bad option for writing, I’ve found ChatGPT to have a bit more sophistication and finesse—the kind of writing you’d expect from a reputable lifestyle publication. It makes sense. While OpenAI trains ChatGPT using human feedback, DeepSeek relies solely on computer-driven reinforcement. This approach might give it an edge in technical subjects, but when it comes to areas requiring nuance and subjectivity—where answers aren’t black or white—it is less refined.

Research: Synthesis vs. Rigor

Large language models can be powerful research tools, helping you identify and synthesize relevant information in an instant. It’s no secret, however, that tools like ChatGPT hallucinate sometimes—in other words, they make things up. Usually, this happens when the information you’re seeking is beyond its training scope. LLMs tend to be people pleasers—they’d rather generate a coherent response than admit they don’t know the answer to something. That’s why the cardinal rule of using LLMs for research is to always verify sources and locate the relevant information within them.

ChatGPT and DeepSeek have unique strengths when it comes to research. Being the more creative, nuanced idea synthesizer, ChatGPT is my choice for research tasks requiring interpretation or subjective reasoning. For example, if you want the LLM to locate a historical fact and explain its significance in a larger context.

To demonstrate, I used the following prompt with ChatGPT:

“Briefly, can you explain how the fall of the Roman Empire influenced modern governance? Please link to sources.”

It provided a comprehensive answer that stuck to the original question. It included direct links to sources after each paragraph. ChatGPT concluded, “In summary, the fall of the Roman Empire prompted a shift from centralized imperial rule to decentralized governance structures, laying foundational elements for contemporary political systems.” DeepSeek, on the other hand, offered an organized answer, broken down into four points. It did not, however, stick to the original question. For example, it provided insight into how Roman law (rather than the downfall) influenced the modern civil law system. Then, it listed a few sources—books like Edward Gibbon’s The History of the Decline and Fall of the Roman Empire. Don’t get me wrong, I love reading paperbacks, but for my daily research relating to work, I tend to stick to digital sources.

DeepSeek, however, might be a smart choice for objective data, structured computations, or technical accuracy. For instance, I asked both models to break down the Pythagorean theorem. Both models offered thorough answers, but DeepSeek included an illustrative visual representation. For users looking for clarity, precision, and supplemental visuals in their research, DeepSeek could offer a distinct advantage.

Idea Generation: Who’s the Better Brainstorming Partner?

When working with an LLM, it’s crucial not to delegate your creativity entirely. AI can be a powerful tool to kickstart ideas, expand your pool of possibilities, and handle the parts of the creative process you might not enjoy—like headline writing or social media captions. However, it’s important to maintain control. AI should free up time for your best thinking, not replace it.

With that in mind, both ChatGPT and DeepSeek have proven valuable for idea generation. Since ChatGPT is often associated with creative tasks, while DeepSeek has a reputation for more technical prowess, I tested both with the following prompt:

“Please generate a list of article ideas about the AI race.”

I wanted to see how each model would interpret the vagueness of the prompt—which “race” it would focus on (competition between models? Between countries?)—as well as how it handled the lack of criteria (e.g., SEO optimization, authoritative tone).

ChatGPT generated its reply a few seconds faster, but DeepSeek’s response was more in-depth—producing 24 ideas compared to ChatGPT’s 20, and organizing them into eight categories (e.g., “Cultural and Philosophical Perspectives” and “Ethical and Societal Implications”) versus ChatGPT’s four. Both models generated thoughtful ideas that reflected today’s narratives surrounding AI across multiple dimensions.

While ChatGPT’s suggestions were sophisticated and aligned with the current media landscape, DeepSeek remains a strong contender. If your work relies more on structured insights than imagination or creativity, DeepSeek could be a valuable addition to your toolkit.

Coding: A New Open-Source Power Player?

Investor and entrepreneur Marc Andreessen called DeepSeek R1 “one of the most amazing and impressive breakthroughs I’ve ever seen — and as open source, a profound gift to the world.”

So, what do developers think?

While it depends on the goal and situation, the consensus seems to be that DeepSeek’s R1 is a great option. Some are saying it’s the best model at the moment. At the very least, it’s the first open-source model to compete with the current juggernaut leaders like OpenAI and Google. For developers looking for transparency, flexibility, and high performance in their coding projects, DeepSeek’s open-source model is an excellent choice.

Models like ChatGPT and DeepSeek are evolving and becoming more sophisticated by the day. Meanwhile, new players like Alibaba Cloud, Zhipu, Moonshot AI, and ByteDance are entering the ring. For the average AI user, navigating this increasingly crowded market might feel overwhelming—but ultimately, more competition means better, more accessible tools. If DeepSeek’s arrival has proven anything, it’s that as AI advances, it also becomes more user-friendly. So experiment, explore, and stay tuned.

Feature Image Credit: GREG BAKER/AFP via Getty Images. AFP via Getty Images

By Aytekin Tank

Follow me on Twitter or LinkedIn. Check out my website. Contributor. Aytekin Tank is the founder and CEO of Jotform.

Sourced from Forbes

By Abhimanyu Ghoshal

Since 2012, UK-based Power Roll has been working on a way to print low-cost solar film to generate clean energy from sunlight. It’s now one crucial step closer to manufacturing its lightweight, apply-anywhere film, with a new design for its perovskite solar cells that should make make production cheap and scalable.

Power Roll has been focusing on embossing ‘microgroove structures’ into a plastic substrate, similar to a “hologram” on a credit card. A single square meter has 500,000 microgroove structures, and these are coated with conductive materials and photo-active ink. Layers of encapsulation film keep the printed rolls stable and enhance their durability.

This is all carried out using what’s called roll-to-roll processing, where you coat or emboss an entire length of a material that is fed continuously from one roller on to another. It’s inexpensive and efficient. In addition, Power Roll is going with abundant perovskite as a key material in its solar cells to absorb sunlight and convert it into electricity.

The company has already been at this for a while and has loads of patents to prove it, so what’s new?

Essentially, the team, in collaboration with researchers at the University of Sheffield, has devised a new microgroove structure with a back-contact format – with all the electrical contacts of the solar cell on the back rather than in front. This not only allows for more efficient energy generation, but also makes it cheaper and simpler to produce the solar cells.

The new design also increased the number of grooves in each component of the back-contact perovskite solar cell, from 16 to 362. That improved power conversion efficiencies (PCEs) by up to 12.8%.

ACS Applied Energy Materials

The group also used a Hard X-ray nanoprobe microscope to check the structure and composition of the solar cells for defects. The detailed images helped identify issues like empty spaces within the semiconductor material.

This back-contact design of the solar cell allows the perovskite to directly absorb the light without it first passing through a transparent conductive oxide (TCO) layer. That negates the need for TCOs incorporating rare and expensive materials like indium, and as such, brings down the cost of production.

The big deal about its product is that this solar film is awfully light and easy to apply. That means you can install it on all kinds of surfaces including non-loading bearing rooftops, transport it to remote areas that need accessible options for generating electricity, and hopefully, blanket many currently unproductive buildings and spaces with it so you’ve got many more sources of clean energy to supply the grid. Power Roll has a neat visualization of this optimistic vision in its promo video below.

With that, Power Roll is set to scale up manufacturing of its solar cells sharpish. The company and its collaborators at Sheffield have published these advancements in the production process in Applied Energy Materials. The team working on this will move on to explore the use of X-ray microscopy in better understanding how its film will operate and what sort of stability it will exhibit.

Last October, Power Roll raised US$5.4 million in a funding round to grow its fully operational manufacturing plant’s production capacity. It’s also expecting to produce enough of the solar film to generate 1 GW of electricity in the near future.

Source: Power Roll

Feature Image Credit: Power Roll / The University of Sheffield

By Abhimanyu Ghoshal

Sourced from New Atlas

By Laura Pippig and Joel Lee

France-based AI chatbot Le Chat is currently the fastest of them all, and it can now generate images.

With all the news about ChatGPT, Copilot, and DeepSeek, you might think that the US and China are the two main players at the head of the AI pack, with everyone else lagging behind. But that’s not necessarily true.

Some European alternatives are making waves. For example, last week’s update for the France-based AI chatbot Le Chat integrated an image generator based on a German AI model from the company Black Forest Labs. That’s Franco-German collaboration par excellence.

According to Mistral, the company behind Le Chat, this AI chatbot is the fastest in the world, capable of generating up to 1,100 tokens per second. (That’s about 13 times faster than ChatGPT’s 85 tokens per second.) And not only does the AI respond quickly to prompts, it also draws on in-depth knowledge from a wide variety of sources for robust answers.

According to the update blog post, it’s now possible to generate photorealistic images using Le Chat. Fittingly, a prompt with a cat drinking tea was shown as an example.

Le Chat is a free app for Android and iOS, but you can also try out the AI tool directly in your browser. Apparently, the maximum permitted requests are limited at a certain point, at which point you’ll have to register. Later, you may even need to subscribe at $14.99 per month, but this is explicitly aimed at power users.

In general, the European chatbot makes a good impression. It responds quickly and precisely and can also explain complex issues. Le Chat could soon establish itself as an alternative to the current top dogs.

Feature Image Credit: Owlie Productions / Shutterstock.com

By Laura Pippig and Joel Lee

Sourced from PCWorld