Do you know how Instant vs Thinking vs Auto mode works in ChatGPT? And what the actual difference is?👇 These aren’t different models. They are different reasoning modes for the same model. Here’s what actually changes when you switch modes: 1. Instant Mode (Fast): Fast mode answers as quickly as possible. What happens internally: Minimal internal reasoning No internal chain-of-thought Small compute budget per token Good for: simple questions, summaries, translations, quick chat 2. Thinking Mode: This mode changes how long the model is allowed to think before answering. Internally, the model: Generates hidden reasoning steps Tries multiple small reasoning paths Evaluates which answer is most consistent Uses more compute before replying Good for: math, coding, logic problems, multi-step tasks 3. Auto Mode: Auto mode chooses dynamically based on your question. The model: Checks how complex the task is Predicts whether fast mode is enough Switches to deeper reasoning only if needed This saves time on easy tasks and boosts accuracy on hard ones. So the model architecture stays the same, only the inference strategy changes.
Yeah, I’ve been seeing more replies that read like polished, template-style LLM outputs—overly neutral tone, generic phrasing, and lots of “great question” energy. It definitely changes how discussions feel. Next, if you want, I can generate you a short checklist...
How does AI create images or ideas that never existed? Not by remembering everything. AI reduces the data into a small, meaningful code a compressed space where similar things stay close and different things spread apart. That space is called the latent space. The carousel below...
Here’s a simple breakdown of how a basic RAG pipeline actually works.👇 You start by breaking long documents into smaller, focused chunks and converting each chunk into an embedding vector. These embeddings capture semantic meaning which lets the system understand what each piece...
Taking the leap into entrepreneurship doesn’t have to be reckless. Shaw Talebi’s story is the blueprint for doing it safely. He gave himself oxygen: 12 months of runway. That alone turned a “risky jump” into a reversible experiment. Worst-case scenario? He...

The video tackles a practical question many aspiring founders face: how to dip a toe into entrepreneurship without jeopardizing financial stability. Using the experience of Shah Talibi as a case study, the presenter outlines a step‑by‑step framework that hinges on...
If you’re learning Python and building projects follow this simple workflow: Plan → Write → Test → Debug (+ Code with AI) This carousel breaks down the workflow beginners should use to build projects. If you want to learn Python + AI through hands-on...

The video announces Kimi’s newest offering – a command‑line interface (CLI) agent that brings AI‑driven coding assistance directly into the developer’s terminal. Positioned as a competitor to established tools like Cloud Code, Gemini and OpenAI’s offerings, the Kimi CLI aims...

The video explores the realities of transitioning from a traditional AI role within a large corporation to running an independent AI consultancy, using Shah Talebbi’s journey from a data‑scientist at Toyota to founder of an AI education community as a...
Getty Images sued Stable Diffusion (Stability AI) in the UK and many expected this case to finally answer a big question: "Is training AI on copyrighted data illegal?" But the outcome surprised everyone. Getty claimed millions of their photos were used during training and...
Everyone asks how to ship faster as an AI engineer. @ShawhinT nailed the answer. He’s a former senior data scientist turned entrepreneur and one of the most efficient AI builders I know — the guy is literally shipping full products,...

The video explores a streamlined workflow for AI engineers aiming to ship products at maximum speed, featuring Shah Terebi’s personal methodology. Terebi, a former senior data scientist turned AI educator, outlines how he leverages a combination of voice‑driven ChatGPT sessions,...
Proudly repping that Gemini merch. Thanks for sending that @googleaidevs @GoogleAI ! Gemini 3 and all suites of models including nano banana 2.5 are a clear step forward and we use it in most of our projects and courses. Honestly, amazing progress...
We keep talking about “open-source models" But honestly, that’s not where most of the real momentum is right now. What’s actually shaping the ecosystem today are "open models" Especially the ones released as open weights. Not fully open-source. Not fully closed either. Just open enough...
Everyone’s out here upgrading their setups today. New laptops, new GPUs, new toys. But the truth is simple: the real leverage isn’t the machine. It’s whether you actually know how to build and deploy AI on it. So if you’re upgrading...
If you’ve been wanting to jump from using AI tools to actually building real AI applications, this is your moment. The Black Friday window just opened for the Towards AI Academy, and it’s the lowest price we offer all year....
In this brief Black Friday announcement, Louis‑François Bouchard promotes a 40% discount on all AI engineering courses, highlighting the Full‑Stack AI Engineering program dropping from $349 to $209 as the flagship offer. He outlines how the course equips learners with...
Your first question to an AI model takes a moment… But the next ones appear almost instantly, there’s a simple reason behind it. The model keeps a small snapshot of the work it already did. This is called the "KV Cache". When an AI...
Ever wondered how AI “remembers” your question… without having memory? 🤔 Every time you chat with an LLM, it somehow knows what you said before. But here’s the secret: It doesn’t remember your words. It understands meaning through something called embeddings. Embeddings are how machines...

Another cool milestone to share: My book Building LLMs for Production just got translated into Simplified Chinese!! …and I (again) can’t really proofread it. 😅 Still, it feels incredible. Posts & Telecom Press reached out last year asking to translate the book, and...

Since my review of the book actually made it inside, I'll just share it here: This book is an excellent starting point for beginners looking to understand the essential history and foundational concepts of machine learning. With well-structured code sections...

The video centers on the contentious role of synthetic data in training large language models (LLMs) and vision‑language models (VLMs), featuring Leticia, a newly minted PhD who specializes in these areas. She weighs the benefits and drawbacks of generating artificial...
Synthetic data might be the most misunderstood topic in AI right now. Is it a cheat code for training better models or a trap that slowly collapses model diversity? Here's what @AICoffeeBreak, one of the sharpest minds in VLMs and...
What if your vision language model isn’t actually seeing… but mostly guessing from text? 👀 @AICoffeeBreak explains it perfectly: when VLMs rely too heavily on text, they start hallucinating answers based on the most common phrasing in their training data instead...

The video highlights a growing concern in the field of vision‑language models (VLMs): they tend to lean heavily on textual cues at the expense of visual grounding, leading to what researchers call "text‑driven hallucinations." Leticia, a recent PhD graduate specializing...

𝗬𝗼𝘂 𝘄𝗼𝗻’𝘁 𝗯𝘂𝗶𝗹𝗱 𝗮 𝗰𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝗔𝗜 𝗶𝗳 𝘆𝗼𝘂 𝗸𝗲𝗲𝗽 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗹𝗶𝗸𝗲 𝗶𝘁’𝘀 𝟮𝟬𝟮𝟬. A lot of people ask me if it’s still worth doing a Master’s to learn 𝘼𝙄. Honestly, for most people, it’s not about the degree anymore. It’s about staying...
Many people still believe you need to publish research to succeed in AI. That might’ve been true a few years ago, but things have changed 👇 I’ve done research during my Master’s and PhD. Most of my work never made it to...