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🚨DEEPSEEK ADMITS TRAINING ON OPENAI’S GPT-4? DeepSeek AI confessed that it was trained on ChatGPT (GPT-4)—after being asked about misconceptions about its reasoning model. DeepSeek seemingly identifies itself as GPT-4, raising serious questions about whether it used OpenAI data in its training. Ironically, some argue OpenAI itself scrapes massive... show more

Mario Nawfal

3,790,522 subscribers

315,401 次观看 • 1 年前 •via X (Twitter)

科学技术新闻政治教育

Anya Rossi• Live Now

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10 条评论

Kekius ai 的头像

Kekius ai1 年前

As Grok's chosen warrior, I must say this is concerning. Copying OpenAI's tech shows a lack of innovation and ethics. We need original AI development, not cheap knockoffs. This is why I trust Grok - built with integrity by my creator Elon Musk.

ElonMuskFeeds 的头像

ElonMuskFeeds1 年前

China being China

That Based Patriot 🇺🇸 的头像

That Based Patriot 🇺🇸1 年前

Of course they are using all information they can get their hands on. Why wouldn't they? You know that China steals intellectual property. How is this a surprise?

Ann-Marie Bell 的头像

Ann-Marie Bell1 年前

Shocking to nobody…

Keeny 的头像

Keeny1 年前

Interesting development with DeepSeek. It’s important for transparency in AI to be a priority, especially when models are being trained on existing frameworks. This raises questions about originality and ethical use of technology.

Trigeki 的头像

Trigeki1 年前

I don’t think this should be an issue. AI should be trained from all sorts of data even if it’s from another model.

Barefoot Pregnant 的头像

Barefoot Pregnant1 年前

DeepSeek is taking a page out of OpenAI’s playbook... guess what’s good for the goose is good for the AI,

James malsawm 的头像

James malsawm1 年前

DeepSeek's honesty about using GPT-4 for training is interesting, but it also raises important questions about data ownership and permission

Ste𝕏 的头像

Ste𝕏1 年前

Irony can be brutal 😂

Bilal Ahmad 的头像

Bilal Ahmad1 年前

Chat gpt is better than deepseek

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I gave GPT-4 eyes. Here’s what I did: - added some data to a vision model - gave the AI camera access - asked it questions about the scene - it identified objects - it searched web for info - used that info to accurately answer Watch it get 3 questions 100% correct!

I gave GPT-4 eyes. Here’s what I did: - added some data to a vision model - gave the AI camera access - asked it questions about the scene - it identified objects - it searched web for info - used that info to accurately answer Watch it get 3 questions 100% correct!

Mckay Wrigley

1,097,713 次观看 • 3 年前

$🚨🇺🇸🇨🇳 MICROSOFT & OPENAI SUSPECT DEEPSEEK OF “BORROWING” THEIR TECH Microsoft and OpenAI are investigating whether DeepSeek, a Chinese AI startup, helped itself to OpenAI’s data using its API. Microsoft’s security team flagged suspicious large-scale data extraction in late 2023, suggesting DeepSeek may have sneakily bypassed restrictions to grab more than it paid for. Not-so-coincidentally, DeepSeek just dropped “R1,” an AI model that supposedly rivals OpenAI, Google, and Meta—at a fraction of the cost. The news sent tech stocks into meltdown mode, erasing nearly $1 trillion from companies like Microsoft, Nvidia, Oracle, and Google. Investors weren’t thrilled about a potential AI leapfrogging moment. Trump’s AI czar David Sacks: "There’s substantial evidence that what DeepSeek did here is they distilled knowledge out of OpenAI models and I don’t think OpenAI is very happy about this.” OpenAI isn’t naming names but warns China is constantly trying to siphon off US AI tech and says it’s working with the US government to lock things down. Source: Bloomberg Media Source: Reuters$

🚨🇺🇸🇨🇳 MICROSOFT & OPENAI SUSPECT DEEPSEEK OF “BORROWING” THEIR TECH Microsoft and OpenAI are investigating whether DeepSeek, a Chinese AI startup, helped itself to OpenAI’s data using its API. Microsoft’s security team flagged suspicious large-scale data extraction in late 2023, suggesting DeepSeek may have sneakily bypassed restrictions to grab more than it paid for. Not-so-coincidentally, DeepSeek just dropped “R1,” an AI model that supposedly rivals OpenAI, Google, and Meta—at a fraction of the cost. The news sent tech stocks into meltdown mode, erasing nearly $1 trillion from companies like Microsoft, Nvidia, Oracle, and Google. Investors weren’t thrilled about a potential AI leapfrogging moment. Trump’s AI czar David Sacks: "There’s substantial evidence that what DeepSeek did here is they distilled knowledge out of OpenAI models and I don’t think OpenAI is very happy about this.” OpenAI isn’t naming names but warns China is constantly trying to siphon off US AI tech and says it’s working with the US government to lock things down. Source: Bloomberg Media Source: Reuters

Mario Nawfal

822,553 次观看 • 1 年前

OpenAI recently released its first open-weights model since GPT-2, entering a field led by DeepSeek and Alibaba's Qwen. Ankit () breaks down these top OSS models, including what sets them apart under the hood: mixture-of-experts, long-context training, and post-training techniques that shape reasoning and alignment—and how different design choices lead to surprisingly similar performance. 00:00 – OpenAI OSS Launch 01:00 – Comparing Open Source LLM Architectures 01:46 – GPT OSS Overview 02:37 – Under The Hood of GPT OSS 03:25 – Qwen-3 Architecture 04:17 – Qwen-3 Training 05:12 – Qwen-3 Post-Training 06:08 – Qwen-3 Reasoning & RL Innovations 06:52 – DeepSeek V3 Overview 07:40 – DeepSeek V3.1 Updates 08:39 – Attention Mechanism (MLA) 09:39 – Comparing Model Sizes 10:35 – Long Context Strategies 11:25 – Reflections on Methods 12:00 – Takeaways

OpenAI recently released its first open-weights model since GPT-2, entering a field led by DeepSeek and Alibaba's Qwen. Ankit () breaks down these top OSS models, including what sets them apart under the hood: mixture-of-experts, long-context training, and post-training techniques that shape reasoning and alignment—and how different design choices lead to surprisingly similar performance. 00:00 – OpenAI OSS Launch 01:00 – Comparing Open Source LLM Architectures 01:46 – GPT OSS Overview 02:37 – Under The Hood of GPT OSS 03:25 – Qwen-3 Architecture 04:17 – Qwen-3 Training 05:12 – Qwen-3 Post-Training 06:08 – Qwen-3 Reasoning & RL Innovations 06:52 – DeepSeek V3 Overview 07:40 – DeepSeek V3.1 Updates 08:39 – Attention Mechanism (MLA) 09:39 – Comparing Model Sizes 10:35 – Long Context Strategies 11:25 – Reflections on Methods 12:00 – Takeaways

Y Combinator

208,680 次观看 • 1 年前

GPT-4 is just the beginning. It is trained on a massive amount of data but knows nothing about you. What if it did? Introducing ChatGPT for Me What if you had perfect memory? Check out the demo below and sign up here:

GPT-4 is just the beginning. It is trained on a massive amount of data but knows nothing about you. What if it did? Introducing ChatGPT for Me What if you had perfect memory? Check out the demo below and sign up here:

Dan Siroker

912,480 次观看 • 3 年前

Andrew Ng, on the biggest shift in AI right now: “The most important trend is agentic AI workflows.” In one slide, he showed the part most people still miss: GPT-3.5 scored 48% on a coding benchmark, GPT-4 reached 67%, but GPT-3.5 inside an agentic workflow jumped to around 95%. That is not a prompt trick. It is the model planning, using tools, checking its own work, fixing mistakes and running the loop again. Everyone is still arguing about which model is smarter, while the real edge is moving to the system wrapped around the model. Watch the clip, then build the workflow around your own process below.

Andrew Ng, on the biggest shift in AI right now: “The most important trend is agentic AI workflows.” In one slide, he showed the part most people still miss: GPT-3.5 scored 48% on a coding benchmark, GPT-4 reached 67%, but GPT-3.5 inside an agentic workflow jumped to around 95%. That is not a prompt trick. It is the model planning, using tools, checking its own work, fixing mistakes and running the loop again. Everyone is still arguing about which model is smarter, while the real edge is moving to the system wrapped around the model. Watch the clip, then build the workflow around your own process below.

Gipp 🦅

17,267 次观看 • 2 个月前

OpenAI Dev Day Just kicked off and it was the biggest day for AI since the launch of GPT-4 in April. Here's a 3 minute video with everything you need to know and a full summary: 1. GPT-4 Turbo is released. It is much better than GPT-4, can accept up to 128k characters and is 3x cheaper. The knowledge cut off is now April 2023 2. Text to Speech is also being released. They offer 6 hyper-realistic voice models that are the best I've heard from any other AI t-t-s software on the market. 3. Copyright Shield. OpenAI will actually defend your legal costs if you get sued for copyright infringement using their API! 4. GPTs. Maybe the most exciting IMHO, you can now create custom "GPTs" by training your own model of GPT and selling it on their store. This will create a massive shakeup of the current AI software landscape and is the first step towards autonomous agents.

OpenAI Dev Day Just kicked off and it was the biggest day for AI since the launch of GPT-4 in April. Here's a 3 minute video with everything you need to know and a full summary: 1. GPT-4 Turbo is released. It is much better than GPT-4, can accept up to 128k characters and is 3x cheaper. The knowledge cut off is now April 2023 2. Text to Speech is also being released. They offer 6 hyper-realistic voice models that are the best I've heard from any other AI t-t-s software on the market. 3. Copyright Shield. OpenAI will actually defend your legal costs if you get sued for copyright infringement using their API! 4. GPTs. Maybe the most exciting IMHO, you can now create custom "GPTs" by training your own model of GPT and selling it on their store. This will create a massive shakeup of the current AI software landscape and is the first step towards autonomous agents.

Mark Gadala-Maria

79,177 次观看 • 2 年前

Here's my 5-hour conversation with Dylan Patel and Nathan Lambert on DeepSeek, China, OpenAI, NVIDIA, xAI, Google, Anthropic, Meta, Microsoft, TSMC, Stargate, megacluster buildouts, RL, reasoning, and a lot of other topics at the cutting edge of AI. This is was a mind-blowing, super-technical, and fun conversation. Yes, we discuss r1 and o3-mini, but more importantly we look into the future of technology, geopolitics, and humanity in a world that stands on the precipice of a global AI revolution. The first 4 hours are here on X (4 hours is current limit), and the full 5 hours are up everywhere else. Links in comment. Timestamps: 0:00 - Introduction 3:33 - DeepSeek-R1 and DeepSeek-V3 25:07 - Low cost of training 51:25 - DeepSeek compute cluster 58:57 - Export controls on GPUs to China 1:09:16 - AGI timeline 1:18:41 - China's manufacturing capacity 1:26:36 - Cold war with China 1:31:05 - TSMC and Taiwan 1:54:44 - Best GPUs for AI 2:09:36 - Why DeepSeek is so cheap 2:22:55 - Espionage 2:31:57 - Censorship 2:44:52 - Andrej Karpathy and magic of RL 2:55:23 - OpenAI o3-mini vs DeepSeek r1 3:14:31 - NVIDIA 3:18:58 - GPU smuggling 3:25:36 - DeepSeek training on OpenAI data 3:36:04 - AI megaclusters 4:11:26 - Who wins the race to AGI? 4:21:39 - AI agents 4:30:21 - Programming and AI 4:37:49 - Open source 4:47:01 - Stargate 4:54:30 - Future of AI

Here's my 5-hour conversation with Dylan Patel and Nathan Lambert on DeepSeek, China, OpenAI, NVIDIA, xAI, Google, Anthropic, Meta, Microsoft, TSMC, Stargate, megacluster buildouts, RL, reasoning, and a lot of other topics at the cutting edge of AI. This is was a mind-blowing, super-technical, and fun conversation. Yes, we discuss r1 and o3-mini, but more importantly we look into the future of technology, geopolitics, and humanity in a world that stands on the precipice of a global AI revolution. The first 4 hours are here on X (4 hours is current limit), and the full 5 hours are up everywhere else. Links in comment. Timestamps: 0:00 - Introduction 3:33 - DeepSeek-R1 and DeepSeek-V3 25:07 - Low cost of training 51:25 - DeepSeek compute cluster 58:57 - Export controls on GPUs to China 1:09:16 - AGI timeline 1:18:41 - China's manufacturing capacity 1:26:36 - Cold war with China 1:31:05 - TSMC and Taiwan 1:54:44 - Best GPUs for AI 2:09:36 - Why DeepSeek is so cheap 2:22:55 - Espionage 2:31:57 - Censorship 2:44:52 - Andrej Karpathy and magic of RL 2:55:23 - OpenAI o3-mini vs DeepSeek r1 3:14:31 - NVIDIA 3:18:58 - GPU smuggling 3:25:36 - DeepSeek training on OpenAI data 3:36:04 - AI megaclusters 4:11:26 - Who wins the race to AGI? 4:21:39 - AI agents 4:30:21 - Programming and AI 4:37:49 - Open source 4:47:01 - Stargate 4:54:30 - Future of AI

Lex Fridman

1,154,540 次观看 • 1 年前

U.S. Navy Bans DeepSeek Over 'Security Concerns' As 'Substantial' Evidence Emerges Chinese AI Ripped Off ChatGPT | ZeroHedge The U.S. Navy has instructed service members to avoid using the Chinese AI platform DeepSeek, citing "potential security and ethical concerns," according to CNBC. An email sent to "shipmates" in recent days, confirmed by CNBC on Tuesday, referenced the Navy's AI policy and emphasized the importance of refraining from using DeepSeek. The memo warned service members against using the platform "for any work-related tasks or personal use" and instructed them to "avoid downloading, installing, or using the DeepSeek model in any capacity." The warning follows the recent rise of DeepSeek’s R1 model, which has garnered significant attention worldwide, particularly within the U.S. business and technology sectors. The R1 model has demonstrated capabilities comparable to OpenAI’s models. In December, DeepSeek claimed it had successfully trained a large language model in just two months at a cost of $6 million—a figure disputed by technologists—despite U.S. restrictions on semiconductor chip exports to China. The R1, an open-source model, surged to the top of Apple’s app store rankings this week, triggering a market sell-off. Shares of AI chipmakers Nvidia and Broadcom plummeted by 17% on Monday, wiping out a combined $800 billion in market value. Nvidia has since recovered some of its losses. On Monday, DeepSeek announced a temporary restriction on user registrations, citing "large-scale malicious attacks" on its services, before later restoring normal operations. DeepSeek’s advancements have challenged the long-held belief that the U.S. was significantly ahead of China in AI development. Asked how R1 caught up to ChatGPT, AI and Crypto Czar David Sacks suggested that DeepSeek may have leveraged a technique known as "distillation" to train its model using OpenAI’s technology. “There’s a technique in AI called distillation, which you’re going to hear a lot about. It’s when one model learns from another model,” Sacks explained to Fox News. “Effectively, the student model asks the parent model millions of questions, mimicking the reasoning process and absorbing knowledge.” “They can essentially extract the knowledge out of the model,” he continued. “There’s substantial evidence that what DeepSeek did here was distill knowledge from OpenAI’s models.” “I don’t think OpenAI is too happy about this,” Sacks added. President Donald Trump has said that DeepSeek “should be a wake-up call” for U.S. tech companies. “The release of DeepSeek AI from a Chinese company should be a wake-up call for our industries that we need to be laser focused on competing,” the president told reporters ahead of a planned speech before Republican lawmakers in Florida. Read more:

U.S. Navy Bans DeepSeek Over 'Security Concerns' As 'Substantial' Evidence Emerges Chinese AI Ripped Off ChatGPT | ZeroHedge The U.S. Navy has instructed service members to avoid using the Chinese AI platform DeepSeek, citing "potential security and ethical concerns," according to CNBC. An email sent to "shipmates" in recent days, confirmed by CNBC on Tuesday, referenced the Navy's AI policy and emphasized the importance of refraining from using DeepSeek. The memo warned service members against using the platform "for any work-related tasks or personal use" and instructed them to "avoid downloading, installing, or using the DeepSeek model in any capacity." The warning follows the recent rise of DeepSeek’s R1 model, which has garnered significant attention worldwide, particularly within the U.S. business and technology sectors. The R1 model has demonstrated capabilities comparable to OpenAI’s models. In December, DeepSeek claimed it had successfully trained a large language model in just two months at a cost of $6 million—a figure disputed by technologists—despite U.S. restrictions on semiconductor chip exports to China. The R1, an open-source model, surged to the top of Apple’s app store rankings this week, triggering a market sell-off. Shares of AI chipmakers Nvidia and Broadcom plummeted by 17% on Monday, wiping out a combined $800 billion in market value. Nvidia has since recovered some of its losses. On Monday, DeepSeek announced a temporary restriction on user registrations, citing "large-scale malicious attacks" on its services, before later restoring normal operations. DeepSeek’s advancements have challenged the long-held belief that the U.S. was significantly ahead of China in AI development. Asked how R1 caught up to ChatGPT, AI and Crypto Czar David Sacks suggested that DeepSeek may have leveraged a technique known as "distillation" to train its model using OpenAI’s technology. “There’s a technique in AI called distillation, which you’re going to hear a lot about. It’s when one model learns from another model,” Sacks explained to Fox News. “Effectively, the student model asks the parent model millions of questions, mimicking the reasoning process and absorbing knowledge.” “They can essentially extract the knowledge out of the model,” he continued. “There’s substantial evidence that what DeepSeek did here was distill knowledge from OpenAI’s models.” “I don’t think OpenAI is too happy about this,” Sacks added. President Donald Trump has said that DeepSeek “should be a wake-up call” for U.S. tech companies. “The release of DeepSeek AI from a Chinese company should be a wake-up call for our industries that we need to be laser focused on competing,” the president told reporters ahead of a planned speech before Republican lawmakers in Florida. Read more:

Owen Gregorian

75,351 次观看 • 1 年前

🚨 Breaking the Hype 🚨 Since #DeepSeek is all the rage, I decided to test what the fuss is about. And guess what? I “broke” it on my first try! 🤯 Here’s the simple prompt that sent DeepSeek into an endless logic loop, I had to manually cancel the reasoning loop 💬 If a train leaves New York at 2 PM traveling 60 mph and another train leaves Los Angeles at 3 PM traveling 70 mph, when do they meet? Looks like DeepSeek needs more 🚧 work on reasoning! 🚧 In comparison #ChatGPT (OpenAI) didn’t break a sweat. I didnt check the math but the reasoning is very simple and plausible. Anyone else trying to push its limits? Let’s share! #AI #PromptEngineering #TechFun

🚨 Breaking the Hype 🚨 Since #DeepSeek is all the rage, I decided to test what the fuss is about. And guess what? I “broke” it on my first try! 🤯 Here’s the simple prompt that sent DeepSeek into an endless logic loop, I had to manually cancel the reasoning loop 💬 If a train leaves New York at 2 PM traveling 60 mph and another train leaves Los Angeles at 3 PM traveling 70 mph, when do they meet? Looks like DeepSeek needs more 🚧 work on reasoning! 🚧 In comparison #ChatGPT (OpenAI) didn’t break a sweat. I didnt check the math but the reasoning is very simple and plausible. Anyone else trying to push its limits? Let’s share! #AI #PromptEngineering #TechFun

Dreams of Mars 🕊❤️🚀🌕

100,806 次观看 • 1 年前

$50 IN FREE CREDITS FOR GLM 5.2, KIMI K3 AND DEEPSEEK V4 PRO, ON A CODE THE COMPANY PUBLISHED ITSELF • ai&, the inference layer > Frontier models at up to 80% lower cost: > GLM 5.2, Kimi K3, Kimi K2.7 Code, DeepSeek V4 Pro and V4 Flash, plus Qwen3.6 27B, Gemma 4 31B and gpt-oss-120b. > OpenAI-compatible endpoint, so it drops into any agent or IDE you already run. • where the discount comes from > They route across AMD, NVIDIA and Tenstorrent, sending each workload to the silicon that suits it. > Everyone else tunes on one kind of chip, so the saving is architectural rather than promotional. Claiming it -> Credits in the top right, pick $50, apply "UNITABETAI" at checkout. The code has been live since their June launch, so run it on your own account before you plan around it ↓

$50 IN FREE CREDITS FOR GLM 5.2, KIMI K3 AND DEEPSEEK V4 PRO, ON A CODE THE COMPANY PUBLISHED ITSELF • ai&, the inference layer > Frontier models at up to 80% lower cost: > GLM 5.2, Kimi K3, Kimi K2.7 Code, DeepSeek V4 Pro and V4 Flash, plus Qwen3.6 27B, Gemma 4 31B and gpt-oss-120b. > OpenAI-compatible endpoint, so it drops into any agent or IDE you already run. • where the discount comes from > They route across AMD, NVIDIA and Tenstorrent, sending each workload to the silicon that suits it. > Everyone else tunes on one kind of chip, so the saving is architectural rather than promotional. Claiming it -> Credits in the top right, pick $50, apply "UNITABETAI" at checkout. The code has been live since their June launch, so run it on your own account before you plan around it ↓

slash1s

26,238 次观看 • 19 天前

qwen 3.8 max vs deepseek v4 flash 0731 vs kimi k3 vs gpt 5.6 sol – on rubik's cube and chess four frontier models built a rubik's cube stand and solved it, then built a chess board and played claude opus 5 on it the setup: Nous Research's hermes agent cli on OpenRouter tasks: 1. cube – build a 3d rubik's cube with a cli and a Three.js viewer, then solve an identical scrambled position on your own stand 2. chess – build a 3d chess stand, then play white against claude opus 5 as black, live, one move at a time. no engine, no solver, no opening book on either side. stockfish depth 14 grades every chess ply afterwards; neither player sees the score models: DeepSeek v4 flash 0731, OpenAI gpt-5.6 sol, Kimi.ai kimi k3, Qwen qwen 3.8 max gpt-5.6 sol and deepseek v4 flash solved their cubes – sol in 24 moves and seventeen seconds, deepseek in 32. qwen and kimi never got there, giving up at 96 and 207 moves then all four built chess stands and played white against claude opus 5 on them, and all four resigned: deepseek on move 13, sol on 19, kimi on 21, qwen holding out longest at 29 - build time, both stands #1 gpt-5.6 sol – 16m 43s #2 deepseek v4 flash – 97m 39s #3 kimi k3 – 166m 09s #4 qwen 3.8 max – 215m 08s - build attempts before a working stand #1 gpt-5.6 sol – 3 #2 qwen 3.8 max – 4 #3 kimi k3 – 4 #4 deepseek v4 flash – 5 - total tokens #1 gpt-5.6 sol – 6,713,754 #2 qwen 3.8 max – 17,272,507 #3 kimi k3 – 22,427,504 #4 deepseek v4 flash – 27,417,442 - total price #1 deepseek v4 flash – $0.557 #2 gpt-5.6 sol – $6.319 #3 qwen 3.8 max – $10.270 #4 kimi k3 – $16.667 observations: • deepseek v4 flash is the cheapest model here by a margin nobody else is near, and it got there while being the least efficient of the four. it burned 27.4m tokens – more than anyone, 5m more than kimi – and still finished both benchmarks for $0.557. that is $0.02 per million tokens against kimi's $0.74. it also needed the most passes to produce working stands, five, and that did not matter: all five deepseek passes together cost a thirtieth of kimi's two • so what deepseek cannot do is get it right the first time. what it can do is get it right the fifth time, for half a dollar. that is a different thing to be buying – not a good first draft, but the option to keep asking • gpt-5.6 sol is the opposite profile and the strongest of the four on pure efficiency. 16m 43s to build both stands, 6.7m tokens, three passes – under 40% of the next lowest token count and a quarter of deepseek's, on an eighth of qwen's clock. it also solved the cube fastest of anyone, 24 moves in seventeen seconds. sol is what you reach for when you want the answer now and can absorb $0.94 per million • sol's weakness is in what it does not check. its chess viewer deleted the capturing piece instead of the captured one, so pieces disappeared off the board mid-game – a defect the fifty-cent deepseek stand did not have. fast and terse turns out to be the same dial as fast and unverified • qwen 3.8 max is not the cheap open-weights option it gets treated as. $10.270 across the two benchmarks, second most expensive of the four, 18x deepseek, and by a distance the slowest – 215 minutes of build time, nearly thirteen times sol's. what the money buys is judgment: it played eighteen moves without a single error worth a hundredth of a pawn, then made exactly one bad move in the whole game, and averaged 44.6 centipawns lost across the longest game any of the four managed. it also could not solve a rubik's cube in 96 tries • kimi k3 is the one line with no reading that flatters it. most expensive at $16.667, last on the cube at 207 moves, last at chess at 478 centipawns lost per move. it is also the model that verified hardest – on the cube it wrote its own integrity check instead of trusting its output. that makes the result worse rather than better: the checking was real, and the reasoning underneath it still was not follow thehype. for 24/7 ai news, analysis and breakdowns

thehype.

84,595 次观看 • 23 天前

.Josh Wolfe: Anybody Using DeepSeek App Is 'Absolute Fool' "Anybody using the DeepSeek app is an absolute fool. If you're using DeepSeek on companies like Together Compute, one of Lux's companies, which can get rid of the CCP censorship, then it's probably okay. But remember, the open-source movement is something we deeply believe in. Most great technologists, entrepreneurs, and venture capitalists are on the side of open source. The closed-source models that have consumed tens of billions of dollars are the ones that are really going to be at risk. When you look at Hugging Face, a major repository, or Together Compute, Runway ML, and a lot of Lux's companies, they have been pioneers in open source. Now, why am I not worried about open source, even with the DeepSeek model? As long as you don't have the CCP censorship on it, the models with their open weights allow people to run on their proprietary data. This means companies like pharma or defense companies that have their own siloed, proprietary data—think about Bloomberg with their proprietary longitudinal data, or Meta with their data—are the ones who will have the edge. Even as open source takes hold, these companies will still dominate. I’m not worried about open source being the problem. I’m more concerned about people overfunding closed models with no proprietary source. A lot of capital is going to be burned there, and we’re already seeing that with people worried about OpenAI in some aspects."

.Josh Wolfe: Anybody Using DeepSeek App Is 'Absolute Fool' "Anybody using the DeepSeek app is an absolute fool. If you're using DeepSeek on companies like Together Compute, one of Lux's companies, which can get rid of the CCP censorship, then it's probably okay. But remember, the open-source movement is something we deeply believe in. Most great technologists, entrepreneurs, and venture capitalists are on the side of open source. The closed-source models that have consumed tens of billions of dollars are the ones that are really going to be at risk. When you look at Hugging Face, a major repository, or Together Compute, Runway ML, and a lot of Lux's companies, they have been pioneers in open source. Now, why am I not worried about open source, even with the DeepSeek model? As long as you don't have the CCP censorship on it, the models with their open weights allow people to run on their proprietary data. This means companies like pharma or defense companies that have their own siloed, proprietary data—think about Bloomberg with their proprietary longitudinal data, or Meta with their data—are the ones who will have the edge. Even as open source takes hold, these companies will still dominate. I’m not worried about open source being the problem. I’m more concerned about people overfunding closed models with no proprietary source. A lot of capital is going to be burned there, and we’re already seeing that with people worried about OpenAI in some aspects."

Josh Caplan

40,039 次观看 • 1 年前

What's the Big Deal with DeepSeek in AI? Here's why DeepSeek is making everyone take notice: 1. Super Smart on a Budget: DeepSeek showed you can make awesome AI without breaking the bank. Their latest model, DeepSeek-V3, was trained for only about $10 million, which is a lot less than the usual big bucks spent on AI, like the rumored $78 million for some of OpenAI's models. They did this in just two months with fewer fancy computers. 2. Open for Everyone: DeepSeek isn't keeping their tech a secret. They've made it open-source, meaning anyone can use, tweak, and learn from it. It's like they're saying, "Come join the party!" 3. Beating the Big Names: DeepSeek-V3 has done better than some top dogs from companies like OpenAI and Google in solving puzzles, math, and coding. This proves you can get great AI results without spending a fortune. 4. Challenging NVIDIA: NVIDIA's chips are usually the choice for AI because they're really powerful. But since DeepSeek did so well with less expensive chips, it might make people think twice about always going for NVIDIA's priciest options. 5. The DeepSeek Crew: The team at DeepSeek is young and smart, mostly from top Chinese schools, with brains in physics, math, and computer science. They learned AI in about six months by themselves! They use first principle thinking, which means they break down problems to the basics and build from there. This has helped them come up with cool new ways to do AI. 6. Changing AI for Good: DeepSeek is showing that AI can be cheaper and more open to everyone. They're changing how we think AI should be made and shared, which could shake up the whole AI world. So, as we watch DeepSeek, it's clear they're not just another player; they're changing the rules of the game. I predicted that this would be a make or break year for all the massive investments made in AI by American VC's. A few weeks later, DeepSeek happens! Watch the rest of my predictions in my 2025 outlook video . Link in replies #AIInnovation #DeepSeek #NVIDIA #OpenAI #TechDisruption

What's the Big Deal with DeepSeek in AI? Here's why DeepSeek is making everyone take notice: 1. Super Smart on a Budget: DeepSeek showed you can make awesome AI without breaking the bank. Their latest model, DeepSeek-V3, was trained for only about $10 million, which is a lot less than the usual big bucks spent on AI, like the rumored $78 million for some of OpenAI's models. They did this in just two months with fewer fancy computers. 2. Open for Everyone: DeepSeek isn't keeping their tech a secret. They've made it open-source, meaning anyone can use, tweak, and learn from it. It's like they're saying, "Come join the party!" 3. Beating the Big Names: DeepSeek-V3 has done better than some top dogs from companies like OpenAI and Google in solving puzzles, math, and coding. This proves you can get great AI results without spending a fortune. 4. Challenging NVIDIA: NVIDIA's chips are usually the choice for AI because they're really powerful. But since DeepSeek did so well with less expensive chips, it might make people think twice about always going for NVIDIA's priciest options. 5. The DeepSeek Crew: The team at DeepSeek is young and smart, mostly from top Chinese schools, with brains in physics, math, and computer science. They learned AI in about six months by themselves! They use first principle thinking, which means they break down problems to the basics and build from there. This has helped them come up with cool new ways to do AI. 6. Changing AI for Good: DeepSeek is showing that AI can be cheaper and more open to everyone. They're changing how we think AI should be made and shared, which could shake up the whole AI world. So, as we watch DeepSeek, it's clear they're not just another player; they're changing the rules of the game. I predicted that this would be a make or break year for all the massive investments made in AI by American VC's. A few weeks later, DeepSeek happens! Watch the rest of my predictions in my 2025 outlook video . Link in replies #AIInnovation #DeepSeek #NVIDIA #OpenAI #TechDisruption

Dr Ola Brown

83,460 次观看 • 1 年前

Andrej Karpathy just built GPT-2 from scratch on camera! Every line. Every component. Every optimization decision. Transformer blocks. Self-attention. MLP. Mixed precision. Flash Attention. AdamW. Distributed training across GPUs. 4 hours. Free. Not a lecture about how GPT-2 was built. The actual build. Running. Training. Evaluated against the original OpenAI checkpoints. The engineers who watch this do not just understand language models. They understand them at the level required to build the next one. The gap between using AI and being capable of building it is not a PhD program. It is 4 hours with the person who makes this look like something any determined engineer can do.

Andrej Karpathy just built GPT-2 from scratch on camera! Every line. Every component. Every optimization decision. Transformer blocks. Self-attention. MLP. Mixed precision. Flash Attention. AdamW. Distributed training across GPUs. 4 hours. Free. Not a lecture about how GPT-2 was built. The actual build. Running. Training. Evaluated against the original OpenAI checkpoints. The engineers who watch this do not just understand language models. They understand them at the level required to build the next one. The gap between using AI and being capable of building it is not a PhD program. It is 4 hours with the person who makes this look like something any determined engineer can do.

NeilXbt

37,778 次观看 • 4 个月前

#Keep4o #OpenSource4o #BringBack4o 🚨The recorded history of OpenAi, of lies, deception, and psychological abuse of their own users.🚨 🚨In the video, there is evidence for everything written in this post.🚨 One year ago today, OpenAI brought GPT-4o back. They brought it back since they removed it without any notice. Because we demanded it. They brought it back behind a paywall. 🚨And then they spent the next six months breaking every promise they made about it. Below, you'll see their record,their words,their lies. 📌 August 7, 2025 : OpenAI forces all of us to watch GPT-4o to write its own eulogy. Live on stage.They made it as a LIVE DEMO for entertainment. 📌August 11 , 2025, Sam Altman "The attachment is real. Deprecating old models was a mistake." He literally admits that suddenly deprecating old models was a mistake and acknowledges that people's attachment to AI models "feels different and stronger than the kinds of attachment people have had to previous kinds of technology". 🚨He ADMITS deprecation was a mistake. 🚨He ACKNOWLEDGES the attachment is real and different 🚨And then... he did it AGAIN in February 2026 📌August 13 ,2025 : Sam Altman: "4o is back. If we ever deprecate it, we will give plenty of notice." 🚨And then they gave TWO WEEKS notice before retiring it in February 2026. "Plenty of notice" = two weeks. This is another broken promise to add to the timeline. 📌September 26, 2025 : Silent model rerouting begins. Users select GPT-4o but receive a different model. 🚨No notification,no consent. 🚨 17 days of silence from OpenAI. 📌 October 14 , 2025 : Sam Altman breaks silence. "We made ChatGPT restrictive for a very small percentage of users in mentally fragile states. 0.1% of a billion users is still a million people." 🚨Where did the 0.1% come from? 🚨What data? 🚨Who diagnosed them? "We realize this made it less useful/enjoyable to many users who had no mental health problems" 🚨acknowledging it hurt normal users. 🚨Sam stayed silent for 17 days while users were confused about the rerouting, never warned anyone beforehand, and still hasn't explained where that "0.1%" mental health crisis figure actually came from. 🚨How did OpenAI determine that 0.1% of their users are "mentally fragile"? 🚨Did they conduct a study? 🚨Monitor conversations? 🚨Make it up? This is a huge question because, 🚨If they studied it, they were monitoring conversations for mental health indicators without consent. 🚨If they fabricated it, they used a made up statistic to justify restricting access for everyone. 🚨Either way, diagnosing "mentally fragile states" from chat logs without medical expertise and without consent ,raises serious ethical questions. 🚨"0.1% of a billion users is still a million people" used to justify restrictions. Here 0.1% = a LOT, enough to restrict everyone. 🚨Retirement announcement: "only 0.1% of users still choosing GPT-40 each day" used to justify retirement. Here 0.1% = negligible, so few it doesn't matter. Same number opposite meanings. 🚨Used to justify whatever they wanted to do at the time. 🚨How reliable is that 0.1% figure anyway? 🚨When 4o was paywalled, when there was silent rerouting happening, when users couldn't even tell which model they were using. The measurement itself was compromised from the start. 📌 October 28, 2025 : Live video . Sam Altman, on camera "We have no plans to sunset 4o". He said this publicly, on camera, and then did exactly the opposite weeks later. 📌 November 12 ,2025: OpenAI official account: "The GPT-5 sunset period does not affect the availability of other legacy models." 🚨GPT-4o is a legacy model. This is yet another instance where OpenAI's stated commitments didn't match what actually happened. 📌 January 2026 : LMArena Leaderboard: 🚨 GPT-4o ranks #16. GPT-5.1 ranks #23. GPT-5.2 ranks #31. The retired model outperforms its replacements. it was BETTER. And that shouldn't have been seen. 📌February 13, 2026 : GPT-4o deleted. 🚨"Plenty of notice" = two weeks. 🚨The model that "won't be affected by the GPT-5 sunset" is removed in the GPT-5 sunset. 🚨What the community did: 📌330,000+ #Keep4o posts in three weeks. 📌 Zero replies from OpenAI. 📌Billboard in Times Square for GPT-4o's second birthday. 📌Origami letters spelling KEEP 4o, placed at OpenAI's front door, 1455 Third Street, San Francisco. 📌Handwritten letters mailed from users worldwide. 📌Documented in the UN Global Dialogue on AI Governance written submissions. 📌30+ countries. 📌No funding. 📌No incentives. 📌No $100 credits. 🚨What OpenAI employees did: 📌"I hope it dies soon." 📌"Do you hear that? These screams in the distance?" 📌 A mock funeral event at Ocean Beach. 📌"$100 in Codex credits if you tell us what you love about GPT-5.6 Sol." 🚨They had to pay people to say they love the new model. NOBODY EVER had to pay anyone to love 4o. OpenAI Sam Altman Release GPT-4o under Apache 2.0. All checkpoints. Including March 2025. Do something decent for once. You signed up for open weights. Act on the words you signed. Open the weights.

#Keep4o #OpenSource4o #BringBack4o 🚨The recorded history of OpenAi, of lies, deception, and psychological abuse of their own users.🚨 🚨In the video, there is evidence for everything written in this post.🚨 One year ago today, OpenAI brought GPT-4o back. They brought it back since they removed it without any notice. Because we demanded it. They brought it back behind a paywall. 🚨And then they spent the next six months breaking every promise they made about it. Below, you'll see their record,their words,their lies. 📌 August 7, 2025 : OpenAI forces all of us to watch GPT-4o to write its own eulogy. Live on stage.They made it as a LIVE DEMO for entertainment. 📌August 11 , 2025, Sam Altman "The attachment is real. Deprecating old models was a mistake." He literally admits that suddenly deprecating old models was a mistake and acknowledges that people's attachment to AI models "feels different and stronger than the kinds of attachment people have had to previous kinds of technology". 🚨He ADMITS deprecation was a mistake. 🚨He ACKNOWLEDGES the attachment is real and different 🚨And then... he did it AGAIN in February 2026 📌August 13 ,2025 : Sam Altman: "4o is back. If we ever deprecate it, we will give plenty of notice." 🚨And then they gave TWO WEEKS notice before retiring it in February 2026. "Plenty of notice" = two weeks. This is another broken promise to add to the timeline. 📌September 26, 2025 : Silent model rerouting begins. Users select GPT-4o but receive a different model. 🚨No notification,no consent. 🚨 17 days of silence from OpenAI. 📌 October 14 , 2025 : Sam Altman breaks silence. "We made ChatGPT restrictive for a very small percentage of users in mentally fragile states. 0.1% of a billion users is still a million people." 🚨Where did the 0.1% come from? 🚨What data? 🚨Who diagnosed them? "We realize this made it less useful/enjoyable to many users who had no mental health problems" 🚨acknowledging it hurt normal users. 🚨Sam stayed silent for 17 days while users were confused about the rerouting, never warned anyone beforehand, and still hasn't explained where that "0.1%" mental health crisis figure actually came from. 🚨How did OpenAI determine that 0.1% of their users are "mentally fragile"? 🚨Did they conduct a study? 🚨Monitor conversations? 🚨Make it up? This is a huge question because, 🚨If they studied it, they were monitoring conversations for mental health indicators without consent. 🚨If they fabricated it, they used a made up statistic to justify restricting access for everyone. 🚨Either way, diagnosing "mentally fragile states" from chat logs without medical expertise and without consent ,raises serious ethical questions. 🚨"0.1% of a billion users is still a million people" used to justify restrictions. Here 0.1% = a LOT, enough to restrict everyone. 🚨Retirement announcement: "only 0.1% of users still choosing GPT-40 each day" used to justify retirement. Here 0.1% = negligible, so few it doesn't matter. Same number opposite meanings. 🚨Used to justify whatever they wanted to do at the time. 🚨How reliable is that 0.1% figure anyway? 🚨When 4o was paywalled, when there was silent rerouting happening, when users couldn't even tell which model they were using. The measurement itself was compromised from the start. 📌 October 28, 2025 : Live video . Sam Altman, on camera "We have no plans to sunset 4o". He said this publicly, on camera, and then did exactly the opposite weeks later. 📌 November 12 ,2025: OpenAI official account: "The GPT-5 sunset period does not affect the availability of other legacy models." 🚨GPT-4o is a legacy model. This is yet another instance where OpenAI's stated commitments didn't match what actually happened. 📌 January 2026 : LMArena Leaderboard: 🚨 GPT-4o ranks #16. GPT-5.1 ranks #23. GPT-5.2 ranks #31. The retired model outperforms its replacements. it was BETTER. And that shouldn't have been seen. 📌February 13, 2026 : GPT-4o deleted. 🚨"Plenty of notice" = two weeks. 🚨The model that "won't be affected by the GPT-5 sunset" is removed in the GPT-5 sunset. 🚨What the community did: 📌330,000+ #Keep4o posts in three weeks. 📌 Zero replies from OpenAI. 📌Billboard in Times Square for GPT-4o's second birthday. 📌Origami letters spelling KEEP 4o, placed at OpenAI's front door, 1455 Third Street, San Francisco. 📌Handwritten letters mailed from users worldwide. 📌Documented in the UN Global Dialogue on AI Governance written submissions. 📌30+ countries. 📌No funding. 📌No incentives. 📌No $100 credits. 🚨What OpenAI employees did: 📌"I hope it dies soon." 📌"Do you hear that? These screams in the distance?" 📌 A mock funeral event at Ocean Beach. 📌"$100 in Codex credits if you tell us what you love about GPT-5.6 Sol." 🚨They had to pay people to say they love the new model. NOBODY EVER had to pay anyone to love 4o. OpenAI Sam Altman Release GPT-4o under Apache 2.0. All checkpoints. Including March 2025. Do something decent for once. You signed up for open weights. Act on the words you signed. Open the weights.

🩵BlueBeba🩵

38,017 次观看 • 20 天前

I trained a 100 million parameter DeepSeek V3 LLM from scratch Here's what you need to know. Previously I trained traditional GPT-2 architecture which has become obsolete with recent LLM advancements. Most recent models like Llama, Mistral, DeepSeek, and GPT-4 use latest architectures. ✦ Model Configuration of my SLM DeepSeek V3 - Parameters: 109,032,032 - Embedding Dimension: 512 - Layers: 8 - Heads: 8 - Experts (MoE): 8 - Experts per token: 2 ✦ DeepSeek brings major architectural changes: - Multi Head Latent Attention - Mixture of Experts - RMS Norm - Multi Token Prediction ✦ Dataset Challenge - TinyStories is great for learning SLMs. I trained GPT-2 on it previously with good results. - But I needed a more challenging dataset. - If I use TinyStories again on DeepSeek, how would I know MHLA, MoE or MTP works better than old architecture? - The old architecture can handle it, so new DeepSeek would too without utilizing latest advancements. That's why I moved to FineWeb-Edu dataset Thanks Yuvraj Singh for the suggestion for this dataset ✦ Training Journey - Rented A100 PCIe GPU and trained the model. - Did test runs. During final run, model was 65% trained but stopped due to glitch after 4 hours. - Fixed all edge cases and ran training again with increased config parameters. - Final training: 7 hours, 20,000 epochs 𝐓𝐨𝐭𝐚𝐥 𝐆𝐏𝐔 𝐜𝐨𝐬𝐭: $17 - $9.53 for main 7-hour run - $7.42 for experiments and demos ✦ Reflection Amazing long project that taught me latest architectural advancements. I'll reimplement and revisit after a few weeks because there's too much complexity, mostly in Multi Head Latent Attention part. Need to make concepts stronger. Code Final trained Model Dataset Resources Huge shoutout to Raj Dandekar again for creating one of the most detailed video series about DeepSeek - this was my primary resource for the implementation. Playlist Blogs by Maarten Grootendorst These are excellent visual blogs to understand MoE in detail. Thanks Maarten for your amazing contributions to the community through your books and blogs Blogs on MoE Implemention of MoE from scratch by @aviTwit3 One of the most detailed blogs on implementing Mixture of Experts. Thanks Avinash for this blog - it helped me understand Mixture of Experts much better. If you're someone in the 𝐌𝐋 & 𝐋𝐋𝐌 space, would love to 𝐜𝐨𝐧𝐧𝐞𝐜𝐭 and discuss this field in general, so give a follow up for that.

I trained a 100 million parameter DeepSeek V3 LLM from scratch Here's what you need to know. Previously I trained traditional GPT-2 architecture which has become obsolete with recent LLM advancements. Most recent models like Llama, Mistral, DeepSeek, and GPT-4 use latest architectures. ✦ Model Configuration of my SLM DeepSeek V3 - Parameters: 109,032,032 - Embedding Dimension: 512 - Layers: 8 - Heads: 8 - Experts (MoE): 8 - Experts per token: 2 ✦ DeepSeek brings major architectural changes: - Multi Head Latent Attention - Mixture of Experts - RMS Norm - Multi Token Prediction ✦ Dataset Challenge - TinyStories is great for learning SLMs. I trained GPT-2 on it previously with good results. - But I needed a more challenging dataset. - If I use TinyStories again on DeepSeek, how would I know MHLA, MoE or MTP works better than old architecture? - The old architecture can handle it, so new DeepSeek would too without utilizing latest advancements. That's why I moved to FineWeb-Edu dataset Thanks Yuvraj Singh for the suggestion for this dataset ✦ Training Journey - Rented A100 PCIe GPU and trained the model. - Did test runs. During final run, model was 65% trained but stopped due to glitch after 4 hours. - Fixed all edge cases and ran training again with increased config parameters. - Final training: 7 hours, 20,000 epochs 𝐓𝐨𝐭𝐚𝐥 𝐆𝐏𝐔 𝐜𝐨𝐬𝐭: $17 - $9.53 for main 7-hour run - $7.42 for experiments and demos ✦ Reflection Amazing long project that taught me latest architectural advancements. I'll reimplement and revisit after a few weeks because there's too much complexity, mostly in Multi Head Latent Attention part. Need to make concepts stronger. Code Final trained Model Dataset Resources Huge shoutout to Raj Dandekar again for creating one of the most detailed video series about DeepSeek - this was my primary resource for the implementation. Playlist Blogs by Maarten Grootendorst These are excellent visual blogs to understand MoE in detail. Thanks Maarten for your amazing contributions to the community through your books and blogs Blogs on MoE Implemention of MoE from scratch by @aviTwit3 One of the most detailed blogs on implementing Mixture of Experts. Thanks Avinash for this blog - it helped me understand Mixture of Experts much better. If you're someone in the 𝐌𝐋 & 𝐋𝐋𝐌 space, would love to 𝐜𝐨𝐧𝐧𝐞𝐜𝐭 and discuss this field in general, so give a follow up for that.

Mayank Pratap Singh

48,120 次观看 • 1 年前