Jessy Chan

Why Fake News Is Still Hard for AI to Catch

Fake news spreads with brutal speed. A false claim can bounce across social platforms, group chats, video clips, and comment sections long before a reporter, editor, or public official has time to respond. That raises a big question: can AI detect and filter out fake news? The short answer is yes, to a point. AI can spot patterns, flag suspicious claims, compare sources, and help platforms reduce the reach of misleading content. Still, it is not a magic shield. Fake news shifts shape quickly, copies the style of real reporting, and often plays on emotion rather than facts alone. AI works best as a sharp tool guided by human judgment, not as a final judge that decides truth all on its own.

Hackathons — Innovation Labs or Just Pizza Parties?

Hackathons have become a staple of the modern tech ecosystem, promising bursts of creativity, rapid prototyping, and the thrill of building something from scratch in a matter of hours. At first glance, they appear to be high-energy innovation labs where the next big idea might emerge overnight. But for many participants, the reality can feel quite different—less like a focused sprint toward meaningful outcomes and more like a loosely organized social event fueled by free food and good intentions. This contrast raises an important question: are hackathons truly engines of innovation, or have they evolved into little more than glorified pizza parties?

Where Support Meets People: Mainstream Customer Service Channels

Customer support is no longer tied to a single desk, phone line, or inbox. People now expect help through the channel that feels most convenient in the moment, whether they are calling about a billing issue, sending a chat message during checkout, or posting a complaint on social media. For businesses, this means support is not just about solving problems but also about choosing the right places to meet customers, respond quickly, and create a smooth experience across every touchpoint.

Beyond the Teraflops: How the AI Chip in a Tesla Model 3 is Rewiring the Future of Driving

When the Tesla Model 3 first hit the streets, it was praised for its minimalist interior and electric range. Today, however, the conversation has shifted entirely to what is hidden under the hood—not the motor, but the AI computer. In the latest iterations of the Model 3, the Full Self-Driving Computer (Hardware 4) is the real engine of the vehicle. While competitors are obsessed with throwing massive numbers like 2000 TOPS onto spec sheets, Tesla is playing a different game. They are betting on extreme energy efficiency, rapid iteration cycles, and a revolutionary chip architecture that aims to do more with less. Here is a look at how powerful the AI chip in a Tesla Model 3 really is, and why raw numbers might not tell the whole story.

What Do You Need to Build a Voice AI Caller?

A proactive voice AI system sounds simple on paper: an AI places a call, speaks with a natural voice, shares a message, answers basic questions, and moves the conversation toward a goal. In practice, it takes much more than a voice model and a phone number list. You need calling infrastructure, speech tools, conversation logic, safety controls, consent rules, testing workflows, and a clear business purpose. If you want a voice AI that can call people with AI-generated voice messages, the real job is building a reliable calling product, not just a talking bot.

Is 8‑bit, 4‑bit, or 2‑bit quantization best for a 7–9B GPU LLM?

Running a 7–9B “fast small LLM” on a consumer GPU often comes down to one blunt constraint: how many gigabytes of VRAM you have, and how much latency you can tolerate before the model stops feeling “fast.” Weight quantization—storing model weights in fewer bits—seems like an easy win, but the real trade‑off isn’t just “lower bits = better.” Latency, memory, and benchmark accuracy move in different directions depending on quantization level, kernel quality, and where your workload spends time (prefill vs decode).

What Is Threshold-Based Overage Billing?

Threshold-based overage billing is a payment method used when a customer goes past the amount included in a plan, but instead of charging every tiny extra unit right away, the system waits until usage crosses a set billing point, called a threshold. This method is common in software, cloud services, messaging platforms, data tools, and subscription products that mix fixed plans with variable usage. For customers, it can make bills feel more organized and less noisy. For businesses, it can reduce the number of very small charges and create a cleaner billing process.

Can One AI Handle Multiple Intents at Once?

Users don’t think in neatly separated commands—they ask, compare, decide, and act all in a single breath. A modern AI agent must keep up, unpacking layered requests like “summarize this report, highlight risks, and tell me if it’s worth investing in” without dropping the thread. The challenge isn’t just understanding language—it’s orchestrating multiple goals, each requiring different reasoning paths or tools, and then weaving everything back into a clear, useful response. So how do we actually build systems that can do this reliably, without handing over too much control to the model itself?

What Does a Machine Learning Algorithm Look Like?

If you are new to machine learning, it is normal to picture something mysterious: long code, hard math, and strange symbols on a whiteboard. The truth is much simpler. A machine learning algorithm often looks like a pattern finder. Sometimes it can be written as a short math formula. Sometimes it looks more like a list of rules or steps. In many cases, it is both: a formula plus a method for adjusting the numbers inside that formula until the predictions get better.

What does a modern pre-training data pipeline look like?

Pre-training looks less like “download the internet and train” and more like an industrial process: many stages that progressively turn noisy web-scale text into a shaped mixture of tokens that matches a training plan. Modern pipelines spend serious effort on deduplication, filtering, and curriculum-style mixing because model quality depends on what you feed it, not just how much you feed it.

What Is an NPU? A Simple Guide to the AI Processor in Modern Devices

You’ve probably started seeing laptops and phones advertised as “AI PCs” or “AI-ready devices.” The reason isn’t just software — it’s a new chip inside them called the NPU (Neural Processing Unit). Unlike a CPU that runs programs or a GPU that handles graphics, an NPU is designed specifically to run artificial intelligence directly on your device. It enables live translation, video call background blur, smart photo search, voice assistants, and even offline AI writing tools — all without sending your data to the cloud.

How can AI improve enterprise reporting?

Enterprise reporting often suffers from the same recurring issues: inconsistent definitions, manual data prep, slow cycles, and dashboards that look polished but don’t answer the questions leaders actually have. AI can help—but only if it’s applied to the right parts of the reporting workflow and supported by solid data practices.

What Is a Watt?

A watt is a simple idea with a big impact: it tells you how quickly energy is being used or produced. You see watts on light bulbs, phone chargers, speakers, kettles, solar panels, and electric motors. Knowing what watts mean helps you compare devices, estimate electricity costs, and pick the right power supply.

Can AI Be Programmed Into Stock Trading?

Stock trading is full of patterns, noise, and time pressure. That mix makes it a strong match for AI: machines that can learn from data, react quickly, and follow rules without fatigue. Programming AI into trading is not a single trick—it’s a workflow that turns market data into decisions, then turns decisions into controlled orders.

How Does a Transmitter Turn Data Into Light?

Light-based transmitters are the workhorses of fiber-optic communication. They take electrical data from a device and convert it into carefully controlled flashes of light that can travel long distances through optical fiber with low loss and low interference. This article explains the main stages of that conversion, from bits to photons, and what must go right for the link to perform well.

How does fiber optic cable work?

Fiber optic cable carries data as light rather than electrical signals. That simple change is why fiber can move enormous amounts of information over long distances with low loss and high reliability. Instead of pushing electrons through metal, fiber guides pulses of light through a thin strand of glass or plastic, keeping the signal clean and stable even across many kilometers.

The Agentic Era Begins: NVIDIA’s Vera Rubin and the Future of AI Supercomputing

In the high-stakes race for artificial intelligence dominance, the finish line keeps moving. Just as the world began to acclimate to the power of NVIDIA’s Blackwell chips, the tech giant has once again reset the standard. At CES 2026, NVIDIA unveiled its latest marvel: the Vera Rubin platform. Named after the pioneering astronomer who provided the first evidence of dark matter, this new system is not merely a faster chip—it is a re-imagining of the AI Supercomputer designed to power a new generation of autonomous, reasoning AI.

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