ai company

Figma, a Silicon Valley rising star that has evolved from a UI design tool to a global design collaboration platform, has recently made the news again! Because it is about to launch an IPO, and has reignited the spark of imagination of the design and SaaS industry for the future of the platform.
Today's article will give you a comprehensive understanding of Figma's growth process, product technology, business model and the market implications of its upcoming listing. Even if you are not a designer, you can see from Figma's evolution how a technology company uses technology and community to support the platform scale effect and gradually challenge the status of design giants such as Adobe!

In the past few years, autonomous driving technology has moved from science fiction movies to reality, especially the commercial application of unmanned taxis (Robotaxi), which is quietly changing people's imagination of transportation. From Waymo allowing ordinary people to call a taxi in Phoenix to Tesla launching Robotaxi testing in Austin, vehicles without drivers have appeared on the streets of the United States.
This is a technological revolution, and also a comprehensive test of systems, ethics, and business models. This article will help you sort out the current status of global autonomous driving development, analyze the technical routes, major players, and key challenges, and explore how it can move from experimentation to implementation. Let’s read on!

In the past, when talking about the development of AI, people often focused on the models themselves: how powerful GPT-4 is, how powerful Gemini is, how eloquent Claude is. But in fact, the data behind these models is the key asset that truly determines how well they learn and how deeply they understand. In this data race, there is a company that plays an irreplaceable role: Scale AI
Founded in 2016, Scale AI focuses on helping companies "train AI models with data". Its core business is not to develop models, but to provide large-scale, high-quality and accurately labeled data processing services. This includes data labeling from images, voice, text, to self-driving scenes. Imagine it as a coach at a training ground: not the protagonist, but it determines the success or failure of the protagonist. Many top AI models, including OpenAI, Meta, and Google, have used Scale's data services in the past.
Meta recently acquired a large stake in this low-profile but critical company, triggering an earthquake-level reaction in the entire industry: Google hastily withdrew from the cooperation, and OpenAI said it would continue to wait and see. Today's article will take you through: Why did Meta spend a lot of money to acquire Scale AI? What market signals does it represent? How will it affect the future of AI?

In the past, starting a business was a big deal that required connections, funding, and long-term planning. But now, you may only need an idea, a cup of coffee, and a set of useful AI tools to start a small project, create a business brief, or even produce an early product idea. This change in threshold is changing entrepreneurship from "something that bold people do" to "something that curious people can also start practicing."
Today's article will introduce you to Mercor, an AI recruitment startup created by a 21-year-old founder. In less than two years, they raised $100 million, with a valuation of $2 billion, and have served thousands of companies. This is not just a story of "AI + talent matching", but also an example of entrepreneurship that combines technical sensitivity, business thinking and user insights. Today, we will start from five aspects: entrepreneurial background, product design, technical highlights, market strategy and challenges, and try to answer a question: Why is Mercor so successful?

Preface: Why will "2027" become an amplified AI node?
Since 2023, the pace of progress in generative AI tools has shocked the world. From the popularity of ChatGPT to the functional superposition of GPTs, Claude, and Gemini, AI has evolved from "writing copy" to "helping you make decisions." Many people have begun to put forward more radical assumptions: Will we be able to see true AGI by 2027?
AGI, Artificial General Intelligence, means that AI will no longer just answer questions, but will be able to learn, reason, understand and plan like humans. The founders of companies such as Anthropic and OpenAI have recently stated publicly that such a goal could be achieved around 2027. This kind of talk is both exciting and scary...
As a heavy user of AI, I work with these tools and observe industry trends every day. At the same time, I deeply feel the need to use more comprehensive research to balance different viewpoints. Otherwise, I will be really anxious about the new AI research every day!
Therefore, today’s article does not attempt to predict the future, but returns to a more rational and objective perspective: starting from the arguments for/against AI 2027, understand why "AI 2027" has become the focus of the spotlight, and what mentality we should use to look at it! Let’s watch it together!

After 2024, AI tools have penetrated into every corner of our lives. From small robots that automatically reply to messages on LINE to smart assistants used by companies to generate reports and write programs, AI seems to have become a part of our work and life. As a user of at least five different AI tools every day, I am often amazed at their fluency and intelligence. At some moments, I even feel that they understand me better than I understand myself!

But because of this, a sense of unease begins to emerge - do we really understand how these AIs reach their conclusions? Whenever I see AI complete an almost flawless report, a question inevitably arises in my mind: Does it truly understand these results, or is it just a coincidence?

If I were to use a picture to describe today's AI, it would probably be: it is like a strange plant that can grow on its own. We see it blooming beautiful flowers and bearing attractive fruits, but when we pick up a magnifying glass, we find that we have no idea how its roots, stems, and leaves interact with each other.

A study recently published by Anthropic is an attempt to open this black box. They used a nearly biologist-like approach to analyze the internal operating mechanisms of large language models such as Claude 3.5. Instead of just looking at inputs and outputs, we can observe cells and trace neurons, and try to answer the question: "What is each cell of this strange plant doing?"

If AI really enters sensitive fields such as medicine, law, and finance in the future, we cannot just look at the performance results, but must truly understand whether its reasoning process is reliable, safe, and controllable. Today, let’s explore how the AI brain works through Anthropic’s research!

When you use ChatGPT for the first time, you may be surprised by its response speed, language capabilities, and amount of information. It is like an all-knowing and all-powerful online encyclopedia assistant that can write articles, modify resumes, generate marketing copy, and even write a piece of code. For many people, such tools are enough to change their work habits and lifestyle.
But if you are an entrepreneur, PM or freelancer, you will soon find that although ChatGPT can help you "make things", it cannot "complete the task". You have to direct every step yourself, as if you were working with a very smart but passive assistant. At this time, the concept of AI Agent emerged.
AI Agent is not a simple chatbot, but an intelligent system that can actively understand goals, plan task processes, and perform multi-step actions. You just need to tell it "I want to increase the conversion rate of the website", and it will automatically help you analyze website problems, make copywriting suggestions, perform A/B testing, and finally report the results. Such capabilities not only subvert our expectations of AI, but also mark the starting point for the next wave of AI revolution.
Today’s article will take you from the most basic definition to an in-depth understanding of what AI Agent is, what it can do, what are the representative tools and frameworks, and why it is the new trend that deserves your attention after ChatGPT. Let’s read on!

When a small or medium-sized enterprise begins to expand, the first problem it faces is usually not the market or product, but a lack of staff. Imagine that you are the person in charge of this company today. You may need to perform three duties: replying to customers, writing copy, and handling customer reviews at the same time.
And in the midst of these tedious but important tasks, you start hearing about a new helper that could change the way you do your job: Artificial Intelligence, or more specifically: Large Language Models (LLMs).

Artificial intelligence (AI) is rapidly changing our world. Whether it is chatbots, voice assistants, or self-driving vehicles, they all rely on powerful AI training and reasoning technologies. But not all AI models are trained the same, with some companies choosing to use state-of-the-art hardware while others try to achieve similar results with fewer resources.
DeepSeek, OpenAI, and Anthropic are the three major competitors in the AI field, and each company has different training strategies. DeepSeek chose to use the older but less expensive A100 GPU, OpenAI relied on the latest NVIDIA H100, and Anthropic relied on Google TPU to optimize AI training. This article will delve into the strategies of these three companies in AI training and reasoning, and analyze their impact on the AI industry. Let’s read on!

Who is DeepSeek?
DeepSeek is a startup from China that recently developed an AI model that competes with OpenAI GPT-4 at a very low cost. DeepSeek's success is mainly attributed to its innovative AI training method, which breaks through the high cost of AI training through a combination of older NVIDIA A100 GPUs and low-cost chips.
NVIDIA’s Role
NVIDIA is a leader in the global AI hardware market, and its high-end GPUs are widely used in AI training and inference. In the past, NVIDIA was the core supplier of almost all large-scale AI projects, and companies had to rely on their expensive GPUs to train AI models.
DeepSeek and NVIDIA
DeepSeek currently still relies on NVIDIA's hardware to run AI models, but DeepSeek has proved that AI training does not necessarily require the use of NVIDIA's latest H100 or Blackwell architecture GPUs, which has also made the market begin to rethink the necessity of NVIDIA's high-end hardware.