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

April 4, 2026 · Jessy Chan · 3 min read

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.

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April 3, 2026
The Gap Between "Ships in a Weekend" and "Earns Your Trust"
Annie Hayes · 3 min read
A notebook app looks like one of the easiest products in software: a blank page, a title field, a save button, maybe tags and search if the maker feels ambitious. That first version can be built in a weekend, which is exactly why the category keeps attracting new entrants. Yet most note apps feel flimsy after a few weeks of real use. They look polished in screenshots, but they crack under the weight of daily habits, messy thoughts, and years of stored material. The gap between easy to build and good enough to trust is where most notebook apps fall apart.
April 2, 2026
Why Going Back to the Moon Is Still Hard
Emily Henderson · 3 min read
Flying humans to the Moon is hard not because we lack the basic idea, but because every step pushes engineering to its limits with almost no margin for error. The recently launched Artemis II shows this clearly: it is only a test flight, because NASA must first prove that new systems like the Space Launch System and Orion spacecraft can safely carry humans beyond low-Earth orbit again. Despite the success of the Apollo program, this is not a simple repeat; modern missions are more complex, more integrated, and held to far stricter safety standards, meaning better technology actually introduces new risks that must be understood and tested.
April 1, 2026
How to Connect AI to Your CRM Using Structured JSON
Lillian Kim · 3 min read
AI becomes useful in CRM workflows when it stops producing vague prose and starts producing structured output your systems can act on. That is the practical bridge between AI and a CRM like Salesforce: AI reads unstructured input → returns structured JSON → your app validates it → your integration writes it into Salesforce. The same pattern works in reverse too: User asks a question → AI generates a structured query/request → your app fetches Salesforce data → AI explains the result. Salesforce’s REST API supports creating records through its sObject resources and retrieving data through query endpoints that use SOQL. Salesforce also documents composite record creation for batch-style inserts.
March 31, 2026
What Do You Need to Build a Voice AI Caller?
Jessy Chan · 3 min read
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.
March 30, 2026
What Is Threshold-Based Overage Billing?
Jessy Chan · 3 min read
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.
March 30, 2026
Is 8‑bit, 4‑bit, or 2‑bit quantization best for a 7–9B GPU LLM?
Jessy Chan · 3 min read
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).
March 29, 2026
Why Is AI Bad at PPTs?
Nina Kimes · 3 min read
A nice ppt looks simple when it is finished, yet making one is one of the messiest creative jobs a machine can face. A presentation is not just text placed on slides. It is a mix of writing, design, timing, hierarchy, audience psychology, brand taste, and storytelling. AI can help with outlines, bullet points, speaker notes, and even basic layouts, but turning all of that into a deck that feels polished, clear, and human is still very hard. The gap exists because “nice” is a fuzzy target, and presentations live in that fuzzy space.
March 28, 2026
Why Is Telegram Harder for Spammers?
Alicia Gopin · 3 min read
Telegram’s recent anti-spam changes are less about one dramatic crackdown and more about a steady redesign of how strangers, group members, and suspicious accounts interact on the app. Over the last couple of years, Telegram has added paid message gates for unknown senders, a profile check screen before you reply to a new person, stronger moderation tools for group admins, clearer labels for scam or fake accounts, and a new frozen-account appeal flow for banned users. Taken together, these updates show a platform that still wants open communication, but no longer wants open season for spam floods, impersonation, and throwaway scam accounts. ([telegram.org](https://telegram.org/blog/star-messages-gateway-2-0-and-more))
March 27, 2026
What Are the Limits of AI Coding?
Ben Larson · 3 min read
AI has rapidly evolved from a coding assistant into something that can generate entire applications, refactor legacy systems, and even debug complex issues. This has led to a growing belief that software engineering may soon be largely automated. However, despite impressive progress, there are still critical limitations that prevent AI from independently building reliable, production-grade systems. The most significant weaknesses are not in syntax or speed, but in reasoning, reliability, and long-term system integrity.
March 26, 2026
Can One AI Handle Multiple Intents at Once?
Jessy Chan · 3 min read
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?
March 25, 2026
What Is the IQ of Modern AI Like GPT-5?
Dustin Collins · 3 min read
You’ve probably seen claims that AI models now have “genius-level IQ,” but these numbers can be confusing and even misleading—so what do they actually represent, how are they measured, and should we even be using IQ as a way to understand AI intelligence?
March 24, 2026
How Can You Build an AI Agent to Log In and Export Data?
Melissa Olson · 3 min read
If your team is still manually logging into dashboards just to export CSV files, you’re spending valuable time on something that can be automated in a few dozen lines of code—this guide shows how to build a simple AI-powered agent that logs in, navigates your internal dashboard, and downloads data reliably, along with the practical considerations that separate a quick script from a production-ready system.
March 23, 2026
Build a Chess AI That Thinks Like a Human — A Beginner-Friendly Guide with ChatGPT
Emily Henderson · 3 min read
What if your chess app didn’t try to calculate every possible move like old-school engines, but instead played more like a human—using intuition, simple plans, and a bit of “thinking”? The good news is: with today’s tools like ChatGPT and open-source libraries, even a high school student can build a chess AI that feels intelligent without needing massive computing power or years of research.
March 21, 2026
Which regex turns plain-text URLs into clickable HTML links?
Nina Kimes · 3 min read
Turning plain-text links into clickable HTML anchors is one of those tasks that looks simple until you run into punctuation, parentheses, query strings, and edge cases like already-linked URLs. Regex can help a lot, but the “best” pattern depends on what you consider a valid URL, what inputs you expect, and how safe you need the output to be.
March 20, 2026
Why Are GPUs Still the Top Choice for AI Training Over NPUs?
Dustin Collins · 3 min read
AI training is mostly a story of moving and multiplying huge matrices, again and again, across weeks of iteration and constant model changes. In that world, GPUs continue to win mindshare and budgets even as NPUs (neural processing units) improve quickly. The reason isn’t that NPUs are “bad” or that GPUs are “perfect”; it’s that training demands a mix of flexibility, mature software, memory scale, and predictable performance across many model types—areas where GPUs have built a long lead.
March 19, 2026
Can You Train an AI Stock Trader on Historical Market Data?
Lillian Kim · 3 min read
Training an AI stock trader on historical market data is possible, and people do it every day in research labs, hedge funds, and personal projects—but “possible” is not the same as “profitable” or “reliable.” Historical prices, volumes, order book snapshots, and related indicators can teach models to detect patterns and make predictions or trading decisions. The real question is whether those learned patterns will hold up when the market changes, costs are applied, and risk is measured in real money.
March 18, 2026
Why Is It So Hard for AI to Search and Find the Right Number in Excel?
Katherine Holland · 3 min read
If you've ever asked an AI assistant to look up a value in your spreadsheet and gotten back a confidently wrong answer, you're not imagining things — and it's not a random glitch. AI feels dumb with Excel because, under the hood, it is not doing cell-by-cell lookup the way Excel does. Instead, it is doing fuzzy pattern matching over text, which is great for language but terrible for finding exact numbers in specific rows and columns. That fundamental mismatch is exactly why AI keeps mixing and matching data that you, as a human, can see is obviously separate.
March 17, 2026
Why is oil so critical in industrial production?
Dustin Collins · 3 min read
Oil sits quietly behind much of modern industry, not just as a fuel but as a material that keeps machines moving, temperatures controlled, surfaces protected, and countless products possible. From heavy manufacturing floors to precision assembly lines, oil supports reliability, output, and product quality in ways that are easy to overlook until something grinds to a halt.
March 17, 2026
How AI Coding Solves Real Problems
Lillian Kim · 5 min read
Large language models are often treated like writing assistants, but one of their most useful abilities is coding. When you ask AI to write code that produces a result, you move beyond getting a one-time answer and start building a tool that can solve the same problem again and again. That is what makes AI coding so practical. Many everyday tasks are not just writing tasks. They are formatting tasks, data tasks, automation tasks, and workflow tasks. Instead of asking AI to only “help,” you can ask it to build the thing that does the work.
March 16, 2026
Why Box-Lifting Humanoids Are Hard
Lillian Kim · 3 min read
At first glance, this problem seems like it should already be solved. Vision models can detect boxes. Speech systems can understand simple commands. Robots can run, jump, dance, and even play the piano in carefully staged demos. So why is pick up that box still such a hard problem for humanoids in factories? The answer is that the hard part is not object detection. The hard part is integrating perception, language, manipulation, balance, safety, and error recovery into one system that works reliably in the real world. In robotics terms, the challenge is not a single model. It is the full stack.
March 16, 2026
What Can OpenClaw Do?
Nina Kimes · 3 min read
If you are asking about the current OpenClaw app in its official docs, it is a self-hosted gateway that runs on your own machine or server and connects chat apps to an AI agent. That means, after installation, it is not just another local program sitting in a folder. It can become a message-driven assistant you talk to through services like WhatsApp, Telegram, Discord, iMessage, and other channels, while keeping the main gateway under your control. In plain terms, OpenClaw can turn your computer into the home base for an AI helper that follows you across the chat apps you already use.
March 15, 2026
Which AI chips lead now?
Ben Larson · 3 min read
The answer to “what is the most powerful AI chip?” depends on what kind of power you care about. One class leads in giant GPU servers for training and reasoning, another wins on memory-heavy accelerator design, another is the biggest single piece of AI silicon ever sold, and a newer workstation card brings serious AI work much closer to a normal desk. That means the best chip is not one universal winner. It is the one that fits the way you plan to run models, store weights, cool the hardware, and pay the power bill. ([nvidia.com](https://www.nvidia.com/en-us/data-center/dgx-b300/?utm_source=openai))
March 14, 2026
How Should You Chunk Documents for AI?
Lillian Kim · 3 min read
Document chunking sounds simple until you try to build it for a real AI system. Split text too aggressively and the model loses context. Make chunks too large and retrieval gets noisy, slow, and expensive. Good chunking sits in the middle: small enough to keep results precise, large enough to preserve meaning. If you want better search, cleaner summaries, and stronger question answering, chunking deserves careful thought from the start.