China just wrecked all of American AI. Silicon Valley is in shambles.

Scottie Drippin

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Texas private school’s use of new ‘AI tutor’ rockets student test scores to top 2% in the country​


– March 22, 2025 10:01am EDT

A Texas private school is seeing student test scores soar to new heights following the implementation of an artificial intelligence (AI) "tutor."

At Alpha School in Austin, Texas, students are placed in the classroom for two hours a day with an AI assistant, using the rest of the day to focus on skills like public speaking, financial literacy, and teamwork.

"We use an AI tutor and adaptive apps to provide a completely personalized learning experience for all of our students, and as a result our students are learning faster, they’re learning way better. In fact, our classes are in the top 2% in the country," Alpha School co-founder Mackenzie Price told "Fox & Friends."

FOX NEWS AI NEWSLETTER: 'DIGITAL TWIN' DANGER


Will A.I. make schools 'obsolete,' or does it present a new 'opportunity' for the education system? Alpha School suggests the latter.(iStock, Getty Images)

Elle Kristine, a junior at Alpha School, praised the educational institution and suggested its unique structure provides a substantial benefit over standard American learning frameworks.

"I have a lot of friends at traditional school, and every day after school and during school, they’re doing so much homework, they’re spending all this time on schoolwork, they’re so stressed out, and they’re just miserable," Kristine said during an interview with co-host Ainsley Earhardt .

The Alpha School junior revealed that she and other classmates finish their academics in three-hour blocks daily and spend the rest of their time working on "passion projects."

WHAT IS ARTIFICIAL INTELLIGENCE (AI)?

Interior of a school classroom with wooden desks and chairs.(iStock)

In her case, Kristine is working on a safe AI dating coach for teenagers .

"What 16-year-old has time for that? "So, it’s awesome," Kristine said.

Alpha School in Texas currently has a few hundred students and is expanding across the United States.

"What we’re finding is that families want this personalized education experience," Price said. It’s transforming the experience that kids have. But even more importantly, the role that teachers play."

NEW FRONTIER OF AI-POWERED ‘TEACHER-LESS’ CHARTER SCHOOLS GET MIXED REVIEWS FROM STATE OFFICIALS

Illustration of artificial intelligence(Kurt "CyberGuy" Knutsson)

In Alpha School’s structure, AI is used to create personalized academic learning, while teachers can spend their time hands-on with students and provide motivational and emotional support.

"That is really the magic in our model," Price continued.

CLICK HERE TO GET THE FOX NEWS APP

Nikolas Lanum is a reporter for Fox News Digital.
Oh cool a charter school talking out of their ass about amazing test scores with zero proof, that's never happened before. Absolute horseshyt.

Same school also working on an "A.I. dating coach" for teenagers. :francis:

You read all this shyt and it was really something you felt you needed to share, especially from foxnews.com :stopitslime: I wonder what interest they have in the destruction of the traditional school system in lieu of hare brained charters.
 

3rdWorld

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Texas private school’s use of new ‘AI tutor’ rockets student test scores to top 2% in the country​


– March 22, 2025 10:01am EDT

A Texas private school is seeing student test scores soar to new heights following the implementation of an artificial intelligence (AI) "tutor."

At Alpha School in Austin, Texas, students are placed in the classroom for two hours a day with an AI assistant, using the rest of the day to focus on skills like public speaking, financial literacy, and teamwork.

"We use an AI tutor and adaptive apps to provide a completely personalized learning experience for all of our students, and as a result our students are learning faster, they’re learning way better. In fact, our classes are in the top 2% in the country," Alpha School co-founder Mackenzie Price told "Fox & Friends."

FOX NEWS AI NEWSLETTER: 'DIGITAL TWIN' DANGER


Will A.I. make schools 'obsolete,' or does it present a new 'opportunity' for the education system? Alpha School suggests the latter.(iStock, Getty Images)

Elle Kristine, a junior at Alpha School, praised the educational institution and suggested its unique structure provides a substantial benefit over standard American learning frameworks.

"I have a lot of friends at traditional school, and every day after school and during school, they’re doing so much homework, they’re spending all this time on schoolwork, they’re so stressed out, and they’re just miserable," Kristine said during an interview with co-host Ainsley Earhardt .

The Alpha School junior revealed that she and other classmates finish their academics in three-hour blocks daily and spend the rest of their time working on "passion projects."

WHAT IS ARTIFICIAL INTELLIGENCE (AI)?

Interior of a school classroom with wooden desks and chairs.(iStock)

In her case, Kristine is working on a safe AI dating coach for teenagers .

"What 16-year-old has time for that? "So, it’s awesome," Kristine said.

Alpha School in Texas currently has a few hundred students and is expanding across the United States.

"What we’re finding is that families want this personalized education experience," Price said. It’s transforming the experience that kids have. But even more importantly, the role that teachers play."

NEW FRONTIER OF AI-POWERED ‘TEACHER-LESS’ CHARTER SCHOOLS GET MIXED REVIEWS FROM STATE OFFICIALS

Illustration of artificial intelligence(Kurt "CyberGuy" Knutsson)

In Alpha School’s structure, AI is used to create personalized academic learning, while teachers can spend their time hands-on with students and provide motivational and emotional support.

"That is really the magic in our model," Price continued.

CLICK HERE TO GET THE FOX NEWS APP

Nikolas Lanum is a reporter for Fox News Digital.

So they didnt think for themselves :mjlol:
 

bnew

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China’s open-source embrace upends conventional wisdom around artificial intelligence​


Credit Cards

– Published Mon, Mar 24 20252:51 AM EDT

China is focusing on large language models (LLMs) in the artificial intelligence space.

Blackdovfx | Istock | Getty Images

China is embracing open-source AI models in a trend market watchers and insiders say is boosting AI adoption and innovation in the country, with some suggesting it is an ‘Android moment’ for the sector.

The open-source shifthas been spearheaded by AI startup DeepSeek, whose R1 model released earlier this year challenged American tech dominance and raised questions over Big Tech’s massive spending on large language models and data centers.

While R1 created a splash in the sector due to its performance and claims of lower costs, some analysts say the most significant impact of DeepSeek has been in catalyzing the adoption of open-source AI models.

“DeepSeek’s success proves that open-source strategies can lead to faster innovation and broad adoption,” said Wei Sun, principal analyst of artificial intelligence at Counterpoint Research, noting a large number of firms have implemented the model.

“Now, we see that R1 is actively reshaping China’s AI landscape, with large companies like Baidu moving to open source their own LLMs in a strategic response,” she added.

On March 16, Baidu released the latest version of its AI model, Ernie 4.5, as well as a new reasoning model, Ernie X1, making them free for individual users. Baidu also plans to make the Ernie 4.5 model series open-source from end-June.

Experts say that Baidu’s open-source plans represent a broader shift in China, away from a business strategy that focuses on proprietary licensing.

“Baidu has always been very supportive of its proprietary business model and was vocal against open-source, but disruptors like DeepSeek have proven that open-source models can be as competitive and reliable as proprietary ones,” Lian Jye Su, chief analyst with technology research and advisory group Omdia previously told CNBC.

Open-source vs proprietary models​


Open-source generally refers to software in which the source code is made freely available on the web for possible modification and redistribution.

AI models that call themselves open-source had existed before the emergence of DeepSeek, withMeta’s Llama andGoogle’s Gemma being prime examples in the U.S. However, some experts argue that these models aren’t really open source as their licenses restrict certain uses and modifications, and their training data sets aren’t public.

DeepSeek’s R1 is distributed under an ‘MIT License,’ which Counterpoint’s Sun describes as one of the most permissive and widely adopted open-source licenses, facilitating unrestricted use, modification and distribution, including for commercial purposes.

The DeepSeek team even held an “ Open-Source Week ” last month, which saw it release more technical details about the development of its R1 model.

While DeepSeek’s model itself is free, the start-up charges for Application Programming Interface, which enables the integration of AI models and their capabilities into other companies’ applications. However, its API charges are advertised to be far cheaper compared with OpenAI and Anthropic’s latest offerings.

OpenAI and Anthropic also generate revenue by charging individual users and enterprises to access some of their models. These models are considered to be ‘closed-source,’ as their datasets, and algorithms are not open for public access.

China opens up​


In addition to Baidu, other Chinese tech giants such asAlibaba GroupandTencenthave increasingly been providing their AI offerings for free and are making more models open source.

For example, Alibaba Cloud said last month it was open-sourcing its AI models for video generation , while Tencent released five new open-source models earlier this month with the ability to convert text and images into 3D visuals.

Smaller players are also furthering the trend. ManusAI, a Chinese AI firm that recently unveiled an AI agent that claims to outperform OpenAI’s Deep Research, has said it would shift towards open source.

“This wouldn’t be possible without the amazing open-source community, which is why we’re committed to giving back” co-founder Ji Yichao said in a product demo video . “ManusAI operates as a multi-agent system powered by several distinct models, so later this year, we’re going to open source some of these models,” he added.

Zhipu AI, one of the country’s leading AI startups, this month announced on WeChat that 2025 would be “the year of open source.”

Ray Wang, principal analyst and founder of Constellation Research, told CNBC that companies have been compelled to make these moves following the emergence of DeepSeek.

“With DeepSeek free, it’s impossible for any other Chinese competitors to charge for the same thing. They have to move to open-source business models in order to compete,” said Wang.

AI scholar and entrepreneur Kai-Fu Lee also believes this dynamic will impact OpenAI, noting in a recent social media post that it would be difficult for the company to justify its pricing when the competition is “free and formidable.”

“The biggest revelation from DeepSeek is that open-source has won,” said Lee, whose Chinese startup 01.AI has built an LLM platform for enterprises seeking to use DeepSeek.

U.S.-China competition​


OpenAI — which started the AI frenzy when it released its ChatGPT bot in November 2022— has not signaled that it plans to shift from its proprietary business model. The company which started as a nonprofit in 2015 is moving towards towards a for-profit structure.

Sun says that OpenAI and DeepSeek both represent very different ends of the AI space.She adds thatthe sector could continue to see division between open-source players that innovate off one another and closed-source companies that have come under pressure to maintain high-cost cutting-edge models.

The open-source trend has put in to question the massive funds raised by companies such as OpenAI. Microsoft has invested $13 billion into the company.It is in talks to raise up to $40 billion in a funding round that would lift its valuation to as high as $340 billion, CNBC confirmed at the end of January.

In September, CNBC confirmed the company expects about $5 billion in losses, with revenue pegged at $3.7 billion revenue. OpenAI CFO Sarah Friar, has also said that $11 billion in revenue is “ definitely in the realm of possibility ” for the company this year.

On the other hand, Chinese companies have chosen the open-source route as they compete with the more proprietary approach of U.S. firms, said Constellation Research’s Wang. “They are hoping for faster adoption than the closed models of the U.S.,” he added.

Speaking to CNBC’s “ Street Signs Asia ” on Wednesday, Tim Wang, managing partner of tech-focused hedge fund Monolith Management, said that models from companies such as DeepSeek have been “great enablers and multipliers in China,” demonstrating how things can be done with more limited resources.

According to Wang, open-source models have pushed down costs, opening doors for product innovation — something he says Chinese companies historically have been very good at.

He calls the development the “Android moment,” referring to when Google’s Android made its operating system source code freely available , fostering innovation and development in the non-Apple app ecosystem.

“We used to think China was 12 to 24 months behind [the U.S.] in AI and now we think that’s probably three to six months,” said Wang.

However, other experts have downplayed the idea that open-source AI should be seen through the lens of China and U.S. competition. In fact, several U.S. companies have integrated and benefited from DeepSeek’s R1.

“I think the so-called DeepSeek moment is not about whether China has better AI than the U.S. or vice versa. It’s really about the power of open-source,” Alibaba Group Chairperson Joe Tsai told CNBC’s CONVERGE conference in Singapore earlier this month.

Tsai added that open-source models give the power of AI to everyone from small entrepreneurs to large corporations, which will lead to more development, innovation and a proliferation of AI applications.

— CNBC’s Evelyn Cheng contributed to this report
 

bnew

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Oh cool a charter school talking out of their ass about amazing test scores with zero proof, that's never happened before. Absolute horseshyt.

Same school also working on an "A.I. dating coach" for teenagers. :francis:

You read all this shyt and it was really something you felt you needed to share, especially from foxnews.com :stopitslime: I wonder what interest they have in the destruction of the traditional school system in lieu of hare brained charters.


i don't have any interest in the destruction of public education. I think A.I can help students to learn by augmenting existing methods. a personalized tutor for everyone is the future.

I'm not a fan of charter schools at all.
 

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DeepSeek is even more efficient than Nvidia, says analyst, and the industry could copy them​


But it lacks a fluid user experience

Hassam Nasir


Last Updated on March 31, 2025

DeepSeek is even more efficient than Nvidia, says analyst, and the industry could copy them


PC Guide is reader-supported. When you buy through links on our site, we may earn an affiliate commission. Read More

When DeepSeek first launched, it made a big impact in the AI market, largely due to its low computational requirements. But even more impressive was the fact that, despite needing so little power, it managed to outperform AI models from tech giants like OpenAI. Fast forward to today, and we are still uncovering just how efficient DeepSeek really is and whether this efficiency comes with trade-offs or if DeepSeek has simply cracked the code.

These questions stem from a recent analysis highlighting that DeepSeek serves tens of millions of daily active users (DAU) with just 2,000 GPUs. This is an astonishing feat compared to competitors like OpenAI and xAI, which rely on vastly larger GPU clusters. For instance, xAI’s latest Grok 3 AI model is powered by Colossus, a supercomputer equipped with 200,000 Nvidia GPUs.



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Nvidia follows DeepSeek’s optimization methods​


According to the analysis, DeepSeek’s efficiency shows that a single H20 node (8 GPUs) can serve about 600 users. This means that while a service like WeChat would traditionally require around 400,000 GPUs to support 40 million concurrent users at 20 TPS per user, DeepSeek’s optimizations reduce this need to around 100,000–200,000 GPUs by operating at 10 TPS per user.

“But DeepSeek has had even fewer GPUs from the very beginning, and they even had to use downgraded GPUs like A800/H20. However, they can squeeze the performance of the existing GPUs to the extreme, and their optimizations are even more effective than the official optimizations provided by NVIDIA.”

Source: Wukong, Substack

The report notes that “DeepSeek’s underlying infrastructure optimization capabilities are the most underestimated. And it can be copied by the industry.”

On top of that, unlike major tech companies that scale with high-end GPUs, the research reveals that DeepSeek has relied on downgraded GPUs like A800 and H20 from the start. Yet, despite this constraint, it has pushed hardware performance to the extreme, surpassing even NVIDIA's own optimizations. As a result, NVIDIA engineers have shared that the company is now working to integrate DeepSeek's optimization methods.



But unfortunately, there’s a tradeoff​


Now, since DeepSeek reportedly serves tens of millions of DAUs with just 2,000 GPUs, a fraction of what other AI services require, this suggests that DeepSeek prioritizes efficiency over user experience. Unlike mainstream AI chatbots, which allocate more computing resources for lower latency and faster responses, DeepSeek users often have to wait longer for replies.

That said, DeepSeek’s success proves that better software optimization can achieve similar results with far fewer resources, unlike most large companies that focus on expanding GPU clusters. If more companies follow this approach, the AI industry could shift toward lower costs, greater accessibility, and broader adoption. However, the Jevons Paradox suggests that as computing power becomes cheaper, demand for AI applications could surge, potentially increasing the need for GPUs in the long run.
 

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What will DeepSeek Release Next​


and implications for the capex story​


Wukong

Mar 30, 2025

To put it simply:

  1. The second inference model R2, between April and May. Based on the previous base model V3
  2. Sometime in the second half of 2025, the next base model V4

R2 is developed by simply conducting more Reinforcement Learning. This is exactly what the DeepSeek team mentioned on Twitter in February: "RL is still in its early stages, and we will see'significant progress' this year."

Thanks for reading! Subscribe for free to receive new posts and support my work.





As the RL data increases, the model's ability to solve complex reasoning tasks will continue to improve steadily, and some complex behavioral capabilities will emerge spontaneously, such as "reflection" and "exploring different methods."

Then, what can we expect from the next base model V4?

NSA. NSA is a research work that Liang Wenfeng personally participated in and signed name on this paper. On the same day the paper was published, xAI launched Grok3 trained with 200,000 GPUs. Is it just a coincidental timing? Because the core of the paper is to effectively reduce the computational cost of long-context attention and further lower the cost of model training.

The MLA work in May last year focused on compressing the latent space, while NSA focuses on compressing the sequence length. This paper was published in February, which means that NSA has not been applied to V3 and R1 yet. In the subsequent training of DeepSeek, if NSA is integrated, we can expect a significant improvement of the base model. To quote from the paper: "The model pre-trained with NSA outperforms the full-attention model." And this performance improvement does not come from the benefits of the traditional Chinchilla scaling law but from optimization of GPU usage.

At the end of DeepSeek's Open Source Week in early March, the most eye-catching news was: using only 2,000 GPUs to serve DeepSeek's tens of millions of Daily Active Users (DAU). This caused a great stir in China at that time. After intense discussions, it was finally found that this was indeed not a typical service. For services like ChatGPT, they need to allocate several times or even dozens of times more computing power to meet various constraints, such as different latency requirements, time to first token, etc. And we calculated the concurrency disclosed by DeepSeek. According to the announced average inference output rate of 20-22 tps, the average number of concurrent requests is calculated as 168B / 24 * 3600 / 21 = 92,600 (per second), and the ratio of concurrency to DAU is 0.386%.

In simple terms, most users' experience is just:waiting.

Because they position themselves as a research lab and do not regard serving end-users as their top priority. However, the optimization results of DeepSeek are still very astonishing. Most importantly, these optimizations can be reused by large companies.

Later, during the GTC, we interviewed NVIDIA engineers, who told us that NVIDIA is also trying to integrate the optimization methods developed by DeepSeek. For example, DeepSeek has achieved a 10-fold efficiency improvement on its own, but for other CSP, the actual optimization effect they can achieve may be around 5-6 times. How can we quantify it ultimately? We also consulted our friends at AliCloud, who helped us calculate how many GPUs would be approximately needed if China's current super APP integrated AI Chatbots.

Assuming that the performance of a single GPU is calculated at the lower limit of 2000 TPS, a single machine with 8 GPUs can achieve 16,000 TPS. At a rate of 20 TPS per user, a single H20 node(8-cards) can probably serve about 800 users. Considering some additional overheads of the Prefill nodes in the PD separation, it is technically feasible to estimate that a single machine with 8 H20 GPUs can serve 600 users.

Taking the billion user super APP WeChat as an example, if we estimate that the average usage time of a single WeChat user is 60 minutes, the number of concurrent active users is estimated to be about 40 million. Calculating at 800 users per machine, we would need about 50,000 machines, which means 400,000 GPUs. But in fact, if we further relax the requirement to 10 Tokens/s and consider some Poisson arrival and user usage frequency situations, about 100,000 to 200,000 H20 GPUs would be sufficient.

(Note: The above calculation only considers the current form of Chatbot and not the agent applications where the token consumption may be several orders of magnitude higher.)

What does this essentially indicate?

DeepSeek's underlying infrastructure optimization capabilities are the most underestimated.
And it can be copied by the industry.

In the past, as quantitative fund, extreme pursuit of low latency made DeepSeek very familiar with GPU usage. But on the other hand, hyperscalers that have just entered the GenAI field with abundant financial resources and computing power. Their first thought is to pursue AGI. Cost reduction or efficiency improvement is not on their to do list yet. But DeepSeek has had even fewer GPUs from the very beginning, and they even had to use downgraded GPUs like A800/H20. However, they can squeeze the performance of the existing GPUs to the extreme, and their optimizations are even more effective than the official optimizations provided by NVIDIA.

It also shows that if everyone learns DeepSeek's optimization methods, the existing GPUs can still release more computing power. At the same time, on the opposite, everyone knows Jevons paradox. (Hopefully), more computing power capacity and lower costs are expected to drive more application types, such as Agents.
 

bnew

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1/12
@JafarNajafov
DeepSeek is literally the most powerful AI chatbot in the world and it's free.

You can automate all your tasks using it.

Here are 8 prompts that will help you automate tedious tasks:



GnwZfAGacAA8v2t.jpg


2/12
@JafarNajafov
1/ Business Model Validation

Prompt:

"Act as a startup advisor & evaluate my business idea: {briefly describe your idea}. Analyze market potential, customer demand & key risks. Provide a score (1-10) w/ reasoning, suggest improvements & a go-to-market strategy. Prioritize realistic, data-backed insights."



https://video.twimg.com/amplify_video/1908428378979655681/vid/avc1/1384x720/it-HjXvP9oZRfB9w.mp4

3/12
@JafarNajafov
2/ AI Sales Strategy

Prompt:

"Help optimize my sales strategy for {industry/business type}. Provide a tailored approach using AI-driven lead generation, customer segmentation & automated follow-ups. Suggest specific tools, outreach methods & A/B test ideas to improve conversion rates."



https://video.twimg.com/amplify_video/1908428428224913408/vid/avc1/1392x720/PC4_pyJT4JIwNRat.mp4

4/12
@JafarNajafov
3/ Productivity Hacks for Entrepreneurs

Prompt:

"As an AI efficiency coach, recommend 10 productivity hacks specifically for entrepreneurs in {industry}. Include AI automation tools, workflow improvements & delegation strategies. Rank them by impact & effort level for easy implementation."



GnwZnLAboAAUjWF.jpg


5/12
@JafarNajafov
4/ Investor Pitch Refinement

Prompt:

"Review my startup pitch: {insert key pitch points}. Improve clarity, impact & persuasion. Provide a revised version w/ a strong hook, data-backed validation & compelling call-to-action. Suggest ways to make it more investor-friendly & engaging."



GnwZoHBbYAE_GIk.jpg


6/12
@JafarNajafov
5/ AI Content Marketing

Prompt:

"Develop a content marketing strategy for {business type}. Outline a 3-month plan w/ blog topics, social media posts & email campaigns. Suggest AI tools for content creation, SEO optimization & audience engagement. Prioritize growth-driven tactics."



GnwZpDUa4AAaCs0.jpg


7/12
@JafarNajafov
6/ Automating Business Operations

Prompt:

"Suggest automation tools for {specific business process} in my {industry}. Identify AI-driven solutions for efficiency, cost reduction & scalability. Provide implementation steps & potential challenges. Prioritize low-cost & high-impact options."



8/12
@JafarNajafov
7/ Competitive Analysis for Startups

Prompt:

"Act as a market analyst & compare my startup {business name} vs. {top competitors}. Identify strengths, weaknesses & unique selling points. Provide a SWOT analysis & strategies to differentiate in the market. Suggest AI tools for ongoing competitive research."



9/12
@JafarNajafov
8/ AI Decision Making

Prompt:

"Help me make a data-driven decision: {describe your dilemma}. Analyze risks, benefits & provide a logical framework to evaluate options. Suggest AI-powered analytics tools to enhance decision-making in {specific business area}."



10/12
@hasantoxr
I've been using DeepSeek for months now.

The tool works.
Coding. content. and automation.

It can do it



11/12
@thisguyknowsai
Social media content creation with DeepSeek is underrataed.



12/12
@ashok_hey
The DeepSeek chatbot seems to be a game-changer for entrepreneurs looking to streamline their tasks and enhance their business strategies.




To post tweets in this format, more info here: https://www.thecoli.com/threads/tips-and-tricks-for-posting-the-coli-megathread.984734/post-52211196
 

bnew

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Deepseek Has a New Updated Model that Is Wowing Coders​


written by Ravi Teja KNTS Published: March 27, 2025 0 comment

DeepSeek has just dropped an upgraded version of its already impressive V3 model—and it’s got developers talking. This Chinese AI startup released the V3 and R1 models earlier this year, and they immediately grabbed attention by offering performance that rivals top-tier models from OpenAI and Google—completely open-source and free.

DeepSeek-Install-Locally-1024x640.webp


Now, they are back at it again with the updated version of the V3 model – DeepSeek-V3-0324. This is already generating buzz for writing hundreds of lines of code without breaking a sweat.

Let’s break it down.

Table of Contents




What’s New in DeepSeek-V3-0324?​


The big change here is power. The parameter count jumped from 671 billion to 685 billion, giving it more capacity while still using the efficient Mixture-of-Experts (MoE) architecture. Only 37 billion parameters activate per task, so it’s smart with how it uses resources.

They also switched to the MIT license, which is developer-friendly and makes integration much easier.

Benchmarks also show strong gains:

  • MMLU-Pro: 75.9 → 81.2 (+5.3)
  • GPQA: 59.1 → 68.4 (+9.3)
  • AIME: 39.6 → 59.4 (+19.8)
  • LiveCodeBench: 39.2 → 49.2 (+10.0)

DeepSeek-Benchmarks-1024x486.webp


This isn’t just benchmark fluff, either. Here are the changes that you will notice when using the new model.



What You’ll Notice When Using It​


  • It’s much better at solving math problems. You’ll see a clear boost when you give it reasoning-heavy tasks, especially complex ones like AIME-style questions.
  • It doesn’t choke on long code generations anymore. You can ask it to write full websites or applications, and it’ll handle 700+ lines of code in one go without crashing.
  • The code it generates for websites now looks cleaner and more polished. If you’re into front-end work, the HTML and CSS it spits out will feel much closer to something you’d deploy.
  • If you’re working with Chinese content, you’ll notice the writing feels more natural and better structured. Medium to long articles, especially, show better tone and flow.
  • Conversations are smoother now. It remembers what you said earlier in the chat and responds with more relevant replies, even across multiple turns.
  • Translation and search tasks are also sharper, especially when switching between Chinese and English. The answers feel more complete and less generic.
  • It’s more accurate when generating code that involves function calls. So if you’re using it to write Python, JavaScript, or anything else that requires precise logic—it’ll mess up less often.



Then How It Performs?​


People have tested it—and the results are impressive.

Petri Kuittinen, a Finnish lecturer, got it to generate a fully responsive landing page for an AI company—958 lines of working code. Jasper Zhang, a Math Olympiad gold medalist, gave it a 2025 AIME problem. It solved it flawlessly.

DeepSeek-example-1024x577.webp


Apple’s Awni Hannun ran it on a 512GB M3 Ultra Mac. The speed was around 20+ tokens per second, but the peak memory usage was just 381GB, which is solid for a model this size.

We tested it too.

When we asked it to create a Python web app using Flask, including login functionality and hashed password security, it generated the code. To my surprise, it worked, too.

DeepSeek-updated-code-1024x682.webp


We tried the same on ChatGPT and Gemini. ChatGPT kept restarting the output. Gemini managed to finish it after a few tries, but the code was incomplete and didn’t work without serious fixing.



How to Access the Latest DeepSeek V3?​


You can directly access the V3 from the DeepSeek website and the mobile app. By default, it uses the new DeepSeek-V3-0324 model. So you can just hop on and try the new model right away.

DeepSeek-updated-1024x566.webp


Developers can integrate DeepSeek into their applications and websites by using the API, which costs the same. You can use the same API endpoint (model=deepseek-chat)

To download and run the model locally, you can do it from the HuggingFace platform.



What’s Next?​


Rumors point to an upcoming R2 reasoning model—possibly even sooner than expected. And based on how good V3-0324 is, R2 could make an even bigger splash.

However, not everyone’s thrilled. With its rising influence, DeepSeek is under U.S. government scrutiny over national security and data privacy. There’s talk of banning its apps from official devices. Still, DeepSeek-V3-0324 is proving that open-source AI can be powerful, practical, and cost-effective. If you’re a coder, builder, or just curious about what’s next in AI, you should try it for yourself.
 
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