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Best tweets about DeepSeek
50 Best Tweets About DeepSeek (2026)
Explore the best tweets about DeepSeek, including model releases, reasoning, coding, benchmarks, deployment, and open-model discussions. Updated weekly.
Technical DeepSeek evaluations, releases, deployment experience, and comparisons supported by concrete evidence.
- Creators
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Best DeepSeek tweets
Top DeepSeek tweets from 41 creators
Ranked 01–50
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@elshayib_ ·
Been playing with DeepSeek V4 flash 0731 all day and that's how much i spent, i don't know what to tell you but i always use subs because API cost a lot but for the first time i get really great performance for almost free and the Cache hits👌. @deepseek_ai good job on this one you really delivered.
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@synthwavedd ·
🧵 DeepSeek appear to have engaged, or be engaging in, a large-scale operation to collect outputs from proprietary models (including Claude Fable 5) for certain requests via their API as part of a distillation effort. After seeing such claims circulating earlier today, we https://t.co/wFK4nmuvkZ
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@hasantoxr ·
I'm uninstalling Cursor and every other AI coding tool after finding this. It's called DeepSeek TUI. A full coding agent that runs in your terminal on DeepSeek's API. One command to install. No browser. No IDE plugin. No subscription. npm i -g deepseek-tui Here's what it does: → Edits files across your entire codebase → Executes shell commands → Browses the web with web run → Handles git operations → Resumes sessions after you close the terminal → Connects to any MCP server → Spins up as an HTTP API with deepseek-tui serve --http Three modes depending on how much control you want: → Plan: see the full execution plan before the agent touches anything → Agent: interactive multi-step tool use → YOLO: auto-approves every tool call in a trusted workspace DeepSeek's API is already one of the cheapest frontier models available. Now you have a full coding agent on top of it. Built in Rust. 29 releases. Active development. 100% Opensource. MIT License.
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@LiorOnAI ·
DeepSeek is back. They just figured out how to make AI models way smarter without adding compute. They built Engram, a memory module that retrieves information instantly with O(1) lookup. 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀 Think of it as giving your model a lookup table. Instead of burning compute simulating memory retrieval, it just grabs what it needs. Early layers stop wasting effort on pattern matching. The model goes deeper on actual reasoning. 𝗪𝗵𝗮𝘁 𝘆𝗼𝘂 𝗴𝗲𝘁 Huge wins across benchmarks on the same compute: > BBH +5.0 > MMLU +3.4 > HumanEval +3.0 𝗧𝗵𝗲 𝗯𝗶𝗴 𝗱𝗲𝗮𝗹 Long-context retrieval jumped from 84% to 97%. Lookups handle local patterns, so attention focuses on global context.
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@TimJayas ·
DeepSeek just EMBARRASSED Claude Opus 4.7 Just switched to DeepSeek V4 Pro for a few days Cost: DeepSeek V4 Pro = $2.02 Claude Opus 4.7 = $265.21 Same quality for most of the medium tasks with no noticeable difference in output I know it’s hosted in China with cheap electricity but at this point western labs are getting cooked on price
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@LuizaJarovsky ·
🚨 In a leaked call transcript, we learn what DeepSeek's CEO thinks are China's weaknesses and strengths in the AI race with the United States: Today, a call between DeepSeek's founder and CEO, Liang Wenfeng, and a group of investors was leaked. A few hours after the leak, the WeChat links referring to this call had been removed from the internet. It is still unclear whether the Chinese government ordered it due to political, economic, or regulatory risk, or whether it was a private removal request. My guess is that it was a private request, but the Chinese government is glad it was removed. To my knowledge, similar leaks had never happened before. Remarkably, what DeepSeek’s CEO says is similar to what Anthropic's Dario Amodei said in 2025 and reiterated this year: compute is the most important bottleneck in the AI race between China and the U.S. (According to this view, export controls are probably an effective way to curb Chinese competition in AI). I selected 11 excerpts that reflect DeepSeek’s CEO's thoughts on China's weaknesses, strengths, and its competition with the U.S.: 1. DeepSeek lacks resources 2. The talent gap is due to the compute gap 3. “We’re still at tens of billions—an order of magnitude difference” 4. “Huawei’s production is limited” 5. “About half of our people think OpenAI is better” 6. “NVIDIA is digging its own grave” 7. “We don’t maximize profit or price for maximum revenue” 8. “Our gap with the U.S. might be (...) roughly two years, but using one-twentieth the compute” 9. “I see no upper limit to language model scaling” 10. “Our capital structure can’t support high-cost labeling like that of the U.S.” 11. “In China, the most reasonable approach seems to be to focus on general Agents, with Coding Agent as the top priority” Check out the full excerpts in the link below *AI policy professionals (especially U.S. ones): don't miss this edition! 👉Link below.
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@kimmonismus ·
Huge: DeepSeek v4 probably in the next few weeks - and it will be running natively on Huawei's Ascend 950PR Chips DeepSeek is about to drop its next-gen V4 model (via The Information) and for the first time, it'll run natively on Huawei's Ascend 950PR chips, marking a genuine turning point in China's push to break free from Nvidia dependency. And this is big news. Alibaba, ByteDance, and Tencent have already placed bulk orders for hundreds of thousands of units, driving the chip's price up 20% in weeks. DeepSeek even denied Nvidia early access to V4, giving only Chinese chipmakers that privilege. It looks like China has reached a stage with Huawei that now allows for the real-world use of their chips. And at the same time, this will also explain why Deepseek is taking so long to arrive.
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@GithubProjects ·
Reasonix is a terminal-based AI coding agent built specifically for DeepSeek, designed to keep token costs low through stable prefix caching across long sessions. - DeepSeek-only, engineered around byte-stable prefix-cache mechanics - 99.82% cache hit rate in a real single-day workload - ~$12 cost instead of ~$61 on the same workload without cache - Top-3 in LLM velocity on Oosmetrics, with active Discord community
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@quxiaoyin ·
Why is @deepseek_ai 100x cheaper than @AnthropicAI? China is vertically integrated to be cheap. → Cheap model: token-optimized, aggressive caching, less GPU per query → Cheap chips: Huawei silicon, no Nvidia tax → Cheap energy: subsidized power, state-scale grid → Cheap talent: top researchers at a fraction of US salaries → Cheap economics: DeepSeek funded by trading profits, inference doesn’t need to make money The only thing they lag is performance. But as they get “good enough”, being cheap matters a lot, and being frontier keeps getting harder.
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@akshay_pachaar ·
i compared the top open-source OCR solutions and found which one works the best. featuring: > DeepSeek OCR > Datalab Chandra > Qwen3-VL > Dots OCR > Granite Docling also created an app that lets you run all of these OCR models in one place. 100% open-source.
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@TheTuringPost ·
DeepSeek-V4 is a full-stack redesign of LLMs around long context + efficiency Here are some of the changes: - Hybrid attention: Compressed Sparse Attention (CSA) + Heavily Compressed Attention (HCA) for long-context efficiency - 1M-token context becomes ~3–10× cheaper in memory and compute - New residual connections (mHC) + Muon optimizer help to train trillion-scale models without constant collapse - FP4 quantization is used during training (not only inference) - More efficient Mixture-of-Experts model with 1.6T params, but only ~49B active per token is achieved via improved roughing + light sequence-level regularization + hash routing in early layers Post-training recipe is also different - train specialists (math, code, agents) - merge via distillation (OPD) - cleaner and more scalable than RLHF All of this is about making models smarter per FLOP + per token
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@DeRonin_ ·
How much do Chinese models cut your API bill? 🇨🇳 WITH THE SAME OUTPUT!!! US frontier model → chinese open model, cost to do one task: [ reasoning brain ] Claude Opus 4.8 → DeepSeek V4 Pro $1.80 → $0.04 per task ~45x cheaper, and it's tied with Opus on SWE-bench (80.6 vs 80.8) [ code generation ] GPT-5.5 → GLM 5.2 $1.03 → $0.48 per task ~2x cheaper AND it beats GPT-5.5 on long-horizon coding [ agents + tool calling ] Claude Sonnet 5 → Kimi K2.7 $2.29 → ~$0.31 per task ~7x cheaper, best agentic stability of the open models [ bulk / high volume ] Gemini 3.5 Flash → DeepSeek V4 Flash $0.59 → $0.02 per task ~30x cheaper, plenty for extraction, classification, drafts the tradeoff: > coding + agents.. near parity or better > hardest reasoning + world knowledge.. ~10-15% behind > your bill.. down 2-45x depending on the task one catch: Qwen3.7 Max is chinese but priced like frontier ($1.06/task).. cheap isn't automatic route each task to the model that wins it: code → GLM.. agents → Kimi.. value reasoning → DeepSeek.. bulk → DeepSeek Flash keep a US frontier model for the 10% that truly needs it
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@DavidOndrej1 ·
After 14 months it's finally here... Deepseek v4 It's cheap and goes head to head with GPT and Opus. In this 29 min video i'll show you everything you need to know about it: timestamps: 00:00 Introduction: DeepSeek V4 Arrives 00:49 Specs: 1.6 Trillion Parameters & Architecture 01:55 The GPU Story: China's Hardware Limitations 03:09 Benchmarks vs GPT-5.5 and Claude Opus 4.7 04:39 Agentic Coding & Terminal Performance 06:29 Pricing: 40x Cheaper than GPT-5.5 Pro 09:05 5 Major Weaknesses of DeepSeek V4 10:43 Censorship & Tool-Calling Constraints 11:57 Setup: Using DeepSeek V4 via OpenCode 14:33 Build 1: Interactive Architecture Explainer 16:11 Build 2: SVG Plant Animation Demo 17:02 Build 3: Full Arcade Karting Game 18:10 Build 4: Exoplanet Data Visualization 25:24 Final Results & Performance Review 28:50 Cost Analysis: Parallel Agents for Pennies
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@DeRonin_ ·
DeepSeek just dropped a 5-page paper + free GitHub repo that makes any LLM respond 80% faster it's called speculative decoding. in plain english: Guess → Check → Keep → Repeat > Guess: a small fast model predicts the next few words > Check: the big smart model checks all guesses at once > Keep: lock in the right ones, fix the first wrong one > Repeat: do it again, 2-4x faster than normal DeepSeek's version (DSpark) is the first to combine two approaches that always had a tradeoff: getting both speed AND accuracy at the same time production results on their flagship model: 50% more requests handled, responses up to 80% faster how you can use it: > just using AI apps: expect them to get noticeably faster soon > building with AI: integrate this into your stack for instant 2x speedups > running your own models: clone the repo today and apply it to whatever model you run every other AI lab has been guarding this. now it's free on GitHub, MIT licensed this completely changed how I'm thinking about AI products this week one of the most powerful releases this month repo + papers below ↓
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@kimmonismus ·
The company that proved you don't need billions to build world-class AI is now asking for money. DeepSeek is raising outside capital for the first time. The target: at least $300 million at a valuation north of $10 billion. Until now, founder Liang Wenfeng funded everything through his hedge fund High-Flyer Capital Management, famously rejecting offers from China's biggest VCs and tech giants. So what changed? For one, key researchers have been poached: Luo Fuli, a core V3 contributor, left for Xiaomi, and Guo Daya jumped to ByteDance for significantly higher pay. Meanwhile, the much anticipated V4 model has been delayed multiple times as engineers work to make it natively compatible with Huawei's Ascend chips, a move that could prove China's ability to build frontier AI without Nvidia. The competitive pressure is enormous. ByteDance's Doubao already surpassed DeepSeek in monthly active users, and tech giants on both sides of the Pacific are outspending startups at record pace. Even the efficiency pioneer needs ammunition now.
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@TheGeorgePu ·
I ran Claude 4.8 Opus against DeepSeek V4 Pro. Blind. Four tests. Separate agents judged. DeepSeek won 4-0. It's also 12x cheaper. Here's the part that stuck with me. I did the math, and Claude is maybe 5% better. We're paying frontier prices for a 5% edge and a nicer personality. Relying on one lab is a strategic mistake.
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@mark_k ·
A new Stanford HAI analysis of DeepSeek's research team reveals just how quickly China's AI talent base is maturing. China now has a large, rapidly improving and increasingly self-sufficient frontier AI talent pipeline. Anyone still assuming the US has an unassailable lead is asleep at the wheel. DeepSeek's author pool grew from 223 to 356 researchers in a single year, while their median citation count more than doubled. Over half built their entire careers inside China, and even among those who gained experience abroad, most ultimately returned. DeepSeek isn't a fluke. It's the clearest sign yet that China can now build frontier AI teams at scale.
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@AiBattle_ ·
The DeepSeek model that they serve on the WEB/APP may have been updated again. The model does seem to consistently identify itself as V3 now The zero-shot coding outputs I’m getting now also seem different in style from the ones I got a few days ago It needs more testing to be completely sure
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@aakashgupta ·
Liang Wenfeng wasn't trying to raise $7 billion. DeepSeek's first ever funding round was supposed to be symbolic. $300 million at a $10 billion valuation, just enough to issue employee equity so ByteDance and Xiaomi would stop walking off with the lab's researchers. That target is now $7.35 billion. The valuation: $50 billion. The lead investor: China's semiconductor strategic fund. The China Integrated Circuit Industry Investment Fund, the "Big Fund," is the vehicle Beijing has used since 2014 to deploy $50+ billion into SMIC, Yangtze Memory, and the rest of the chip stack. AI rounds are not what this fund does. It made an exception for DeepSeek. Tencent is in. Hillhouse is in. Alibaba is reportedly participating. Liang restructured his own cap table in April. Personal shareholding went from 1% to 34%. Combined direct and indirect ownership now sits around 84%. He consolidated control before letting institutional money in. Then he reportedly wrote the biggest individual check in the round on top. The compute angle is real. DeepSeek V4 launched April 24 as a trillion-parameter open-source model. R1 trained for $6 million. V4 didn't. The High-Flyer balance sheet wasn't going to fund the next generation indefinitely. But the valuation tells the rest. $10 billion to $20 billion to $45 billion to $50 billion in eight weeks. The company didn't change. Beijing picked one. The original problem was Xiaomi hiring Luo Fuli. The solution is Tencent and the Big Fund underwriting whatever comes next. The Big Fund spent eleven years buying the chips. It just bought the company that runs on them.
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@Hartdrawss ·
Super heavy week at @dreamlaunchhq Wrapping up a seo content pipeline tool for US startup > two models, two jobs ... deepseek for keywords, claude sonnet for articles > they don't talk to each other, just two api calls stitched by a postgres review queue > can't return valid json when content is html ... so delimiters + regex extraction instead > cron fires daily, one article per call, human review gate at every stage > fully autonomous content in prod = garbage indexed on google An autonomous Lead scoring + outreach tool on x reply data > llama-3.3-70b at temp 0.2 for ICP scoring ... threshold at 6 to qualify > same model at temp 0.75 for DM generation using signals from call 1 > low temp = consistent scoring ... high temp = messages that dont all read the same > qualified leads get full conversation threads with history context > scores and messages render live over SSE while the stream runs The team is upskilling to larger enterprise build
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@VaibhavSisinty ·
DeepSeek just made their AI 5x faster. Without changing the model at all. It is called DSpark. And it is one of the most impressive pieces of system engineering I have come across. Here is the thing. The biggest bottleneck in running an AI model is not compute. It is memory. Reading the model weights from memory takes so long that generating 10 tokens in parallel costs almost the same as generating one. DSpark exploits this completely. Instead of generating one token at a time it drafts multiple tokens using a cheap smaller model first. Then verifies all of them through the main model in one pass. If the draft is right it skips ahead. If it is wrong it falls back. Most tokens are easy to predict. So most of the time it skips ahead. But what makes DSpark genuinely different is how it combines two approaches that nobody had successfully merged before. DFlash is fast at early token positions. Eagle is more accurate at longer sequences. DSpark runs both together and beats either approach on its own. And it does not draft a fixed number of tokens. It drafts more when the GPU is free and less when the server is busy. All on GPU. No CPU slowdowns. To be frank, this is not a model breakthrough. It is an infrastructure breakthrough. DeepSeek keeps finding ways to do more with less. That is the real moat.
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@GithubProjects ·
DwarfStar 4 is a standalone inference engine built specifically for DeepSeek V4 Flash, prioritizing speed and local execution on Metal and CUDA. - Supports Metal, NVIDIA CUDA, and AMD ROCm backends. - KV cache treated as a first-class disk citizen for long context. - Optimized for 2-bit quantization on 96GB+ MacBooks. - Includes server API, tool calling, and integrated coding agent.
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@burkov ·
The DeepSeek R1 full technical report is now on @ChapterPal. For those who missed the revolution, R1 was the first open-weight, open-science model that has shown how to train a "thinking" LLM using reinforcement learning. Before R1, only OpenAI had a thinking model but they didn't share how they built it. Read with an AI tutor and quizzes for better retention: https://t.co/IgRkoGFWhO PDF: https://t.co/yhQeWSJkH8
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@AIFrontliner ·
Breaking: China’s DeepSeek just released a model with 1.6 trillion parameters that runs on 10% of the memory of its predecessor. And buried inside the technical report is something nobody is talking about. The model gets 10x more efficient at 1 million tokens. Not slightly more efficient. 10x. Here's why that terrifies AI safety researchers. Right now, oversight of AI systems depends on humans being able to monitor what the model is doing. That requires reading context. Reviewing reasoning. Checking outputs. When the context window was 128K tokens, that was already impossible to fully review. At 1 million tokens, the model can hold the equivalent of 10 full novels inside a single session. Every decision it makes draws on context you will never fully read. DeepSeek-V4-Pro requires only 27% of the computing cost of its predecessor at this scale. Which means running a million-token session is now cheap enough to do routinely. The paper says exactly this. They call it making "long-horizon tasks more feasible." Long-horizon tasks are tasks that span weeks, months, or years of work. Agentic workflows that make hundreds of sequential decisions. Autonomous systems that don't pause and ask for permission between steps. They trained it specifically for this. Domain-specialist expert models for math, coding, agent behavior, and instruction following. Then merged all of them into one system through a process they call On-Policy Distillation. The resulting model sits at a 3206 Codeforces rating. That is inside the top 25 human competitors on the planet. It writes code better than almost every human alive. It does this autonomously. At a million-token context. For almost nothing. The benchmark charts in the paper show DeepSeek-V4-Pro-Max beating or matching GPT-5.4 and Gemini-3.1-Pro across knowledge, reasoning, and agentic tasks. This is not a research model. They are running it in production. The DeepSeek chatbot already uses it. The safety conversation in AI right now is almost entirely focused on alignment, on whether models want bad things. This paper is about something different. It is about capability. A model that is cheap, fast, powerful, and autonomous enough to run for hours without a human in the loop. The oversight problem does not require a misaligned AI to become serious. It just requires one capable enough that you can no longer follow what it is doing. We crossed that line this week.
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@quxiaoyin ·
@AnthropicAI’s business model just got disrupted by @deepseek_ai v4 pricing. Anthropic’s all about subscription locked-in: – Claude Code Max: $200/mo – Same usage via API: $5,000 – You still get rate limited; New limits added every few hours (not daily, not weekly — hours). If exceeding you will pay api pricing which can cost $100+ an hour. – Caps on parallel threads - can only use max in max not Hermes/openclaw – SDK requires the API, so devs eat the $5K DeepSeek: - do whatever. No subscription. 100x cheaper. No rate limit. No parallel limit. No usage limit. Whatever. The problem is, can Claude catch up? Yes Claude’s smarter still but most coding tasks DeepSeek is enough. Then what?
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@Sino_Market ·
DeepSeek Outage Sparks Speculation of V4 Model Debut Chinese AI firm DeepSeek suffered its longest outage—nearly 13 hours from March 29–30—sparking global discussion and viral posts on Weibo and Reddit. The disruption fueled speculation that a new model, possibly V4, was quietly deployed. Early users and developers reported markedly improved coding structure and reasoning in tests, suggesting a significant upgrade despite the system still identifying as V3. While unconfirmed, benchmark leaks and user observations point to a potential major release rather than a routine failure. #CHINA #TECH #AI #DEEPSEEK (https://t.co/537KkUo3I4)
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@TheGeorgePu ·
I ran a blind test. DeepSeek V4 Pro vs Claude Opus 4.8. Same prompts, no labels, separate agents judging. Python. Writing in my voice. A bug-fix with trick questions. 4 out of 4, DeepSeek won. And I'm still choosing Claude. Capable isn't the same as the one you want to keep using.
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@The_AI_Investor ·
DeepSeek did it again. The new V4 Flash is not really a small model. It has 284B total parameters, but only activates 13B for each token. That makes it much cheaper and faster to run than its total size suggests, while still supporting a 1M-token context window. It is not beating the best frontier models overall, but it is getting surprisingly close. On DeepSeek’s published agent benchmarks, V4 Flash scored 82.7 versus Claude Opus 4.8’s 85.0 on Terminal Bench, and 25.2 versus 25.7 on Agents’ Last Exam. It still trails Opus more clearly on harder repository and cybersecurity tasks. Some results also use DeepSeek’s own agent harness and internal datasets, so comparisons should be treated carefully. The crazy part is the price: just $0.14 per million input tokens and $0.28 per million output tokens. For comparison, GPT 5.6 Sol costs $5 and $30, while Claude Opus 4.8 costs $5 and $25. That makes V4 Flash around 36 times cheaper on input and roughly 90 to 107 times cheaper on output. It is still too large for a normal laptop. DeepSeek’s official serving example uses four GB300 GPUs. But for AI clouds and large-scale agent workloads, this level of performance at this price could put even more pressure on frontier-model pricing.
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@aakashgupta ·
DeepSeek is raising its first-ever outside round at a $10 billion valuation. OpenAI just closed at $852 billion. Anthropic at $380 billion. The Chinese lab that erased $589 billion from Nvidia's market cap in a single day last January is being valued at roughly 1.2% of the American company whose capex thesis they empirically disproved. For two years, DeepSeek's whole pitch was that they didn't need outside money. Liang Wenfeng spun the lab out of High-Flyer, his $8 billion quant hedge fund, in July 2023. VCs wouldn't touch it back then because they couldn't see the exit. Liang funded everything through the hedge fund's trading profits. He told interviewers that capital was never China's AI constraint. Confidence and talent organization were. Then on January 20, 2025, they released R1. Seven days later, Nvidia lost $589 billion in one session. Largest single-day market cap loss in US stock market history, nearly double the previous record, which was also Nvidia. They did this with a $5.6 million training run on 2,048 H800 GPUs, the export-compliant chips Nvidia built specifically for the Chinese market under Biden-era restrictions. The ratio is the story. The damage DeepSeek did to Nvidia in one trading session is 58 times their entire new valuation. Anthropic just turned down offers at $800 billion. OpenAI raised $122 billion in February. DeepSeek is accepting $300 million at ten. The gap reflects capital access more than capability. US venture dollars can't easily flow into a Hangzhou-based AI lab under current CFIUS and export control friction. Chinese VC deploys a fraction of US VC in absolute capital. The valuation ceiling here is geographic before it gets to the product. Which raises the real question: why raise at all? High-Flyer manages $8 billion in AUM. The hedge fund paid for V3 and R1 through its own trading book. Two pressures converge. R2 has been delayed since May 2025 on chip stability issues after Beijing pushed DeepSeek toward Huawei Ascends for training. Frontier-scale training under export controls costs more than the hedge fund can responsibly commit. And a formal cap table with aligned Chinese capital becomes structurally necessary as domestic compute mandates intensify. The market raised $122 billion for OpenAI three months ago on the thesis that frontier AI needs infinite capex. DeepSeek proved that thesis wrong fifteen months earlier. The capital never updated. It still hasn't. The lab that broke the model is raising at 1% of the lab it broke.
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@pankajkumar_dev ·
DeepSeek-V4 Drops: Open-Source Push Toward Cheaper, Long-Context AI - DeepSeek-V4-Pro is a 1.6T MoE model (49B active) trained on 33T tokens and released under a permissive MIT license - Efficiency gains: supports 1M-token context with 9.8× lower FLOPs and 9.5–13.7× smaller KV cache vs V3.2 - Benchmarks (vs GPT-5.4 & others): APEX (90.2% vs 85.9%), SWE Verified (80.6% on par with top models), Terminal Bench 2.0 (67.9% vs 65.4%), Codeforces (3060 rating, near top human level) - Head-to-head vs Anthropic Claude Opus 4.6: overall 53% win vs 37% loss (10% tie), stronger in task completion (98.3 vs 96.7) and content quality (83.3 vs 78.0) - Uses hybrid Compressed Sparse Attention (CSA) + Heavily Compressed Attention (HCA) instead of quadratic attention - Enables long-context inference on smaller GPU clusters that previously couldn’t handle it - Variants: Pro (1.6T) and Flash (284B) with low-cost API pricing - Overall direction points toward efficient, open, agent-ready models rather than just scaling brute compute DeepSeek-V4 mainly stands out for making long context + agent workflows cheaper and more accessible without massive infrastructure.
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@wstv_lizzi ·
With DeepSeek’s much anticipated V4 release on the horizon, sharing a special episode of our China’s DeepSeek Moment webinar series by @AsiaPolicy (the one year anniversary of DeepSeek’s breakout moment!) I’m joined by @AGraylin and @pstAsiatech for a wide ranging discussion on how China’s entire AI ecosystem has evolved fast over the past year. We dig into progress and chokeholds in the domestic semiconductor system, the rise of agentic AI, new flagship models from Alibaba, ByteDance, MiniMax, Zhipu, DeepSeek, etc., the open source momentum, ByteDance’s viral Seedance 2.0, and the hype surrounding humanoids! And we step back to ask the bigger question: is the US China AI competitive gap narrowing, widing, shifting, or is that the wrong question? Link to the full episode in the thread! Thoughts welcome!
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@theinformation ·
Exclusive: DeepSeek is seeking up to $7.35 billion in what could become the largest funding round ever for a Chinese AI startup, as the company shifts from pure research to commercialization. The lab is accelerating model releases, hiring product talent from ByteDance and building enterprise tools as competition and computing costs rise. https://t.co/ilqbPVP810
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@shawnchauhan1 ·
DeepSeek confirmed its next model will train entirely on Huawei-designed chips. This is the first time a frontier AI model will run on domestic Chinese silicon. The significance is not technical. It is structural. For two years, Nvidia export restrictions were assumed to be a meaningful ceiling on Chinese AI development. DeepSeek just demonstrated that the ceiling has a workaround. The chip blockade is a slower-moving constraint than policymakers believed.
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@Oluwaphilemon1 ·
DeepSeek V4 Flash 0731 can build experiences like this at a fraction of the cost of Opus 4.8, GPT-5.6 Sol, Kimi K3, and other frontier models. While the flagship models may still edge ahead on some of the most demanding tasks, DeepSeek V4 Flash 0731 delivers an incredible amount of capability for the price. If you’re building AI agents, prototypes, games, or 3D projects on a budget, it’s one of the strongest price-to-performance models available today.
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@rtodi ·
Interesting development in the AI space: China's DeepSeek is getting ready to launch its new V4 model — a capable multimodal AI that works with text, images, and video — and it's optimized to run on Huawei's latest domestic chips. This reflects China's ongoing push for tech self-reliance amid U.S. export restrictions. Huawei's new Ascend 950PR chip shows solid progress, with claims of strong inference performance (reportedly up to nearly 3x that of the restricted Nvidia H20 in some tasks), good memory, and competitive pricing. Big Chinese tech firms have placed large orders. That said, it still trails Nvidia's latest unrestricted high-end chipsets in raw power and overall ecosystem maturity. A notable step in building a parallel AI ecosystem.
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