Vortex Next Gen Trends
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Vortex Next Gen Trends (@vortexnextgen) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 50 006 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 2 680-o'rinni va Xalqaro mintaqasida 589-o'rinni egallagan.
π Auditoriya koβrsatkichlari va dinamika
Π½Π΅Π²ΡΠ΄ΠΎΠΌΠΎ sanasidan buyon loyiha tez oβsib, 50 006 obunachiga ega boβldi.
30 Oktabr, 2026 dagi oxirgi maβlumotlarga koβra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -2 993 ga, soβnggi 24 soatda esa -164 ga oβzgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya oβrtacha 46.74% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 42.89% ini tashkil etuvchi reaksiyalarni toβplaydi.
- Post qamrovi: Har bir post oβrtacha 23 434 marta koβriladi; birinchi sutkada odatda 21 506 ta koβrish yigβiladi.
- Reaksiyalar va oβzaro taβsir: Auditoriya faol: har bir postga oβrtacha 842 ta reaksiya keladi.
- Tematik yoβnalishlar: Kontent generator, scene, nanabanana, reference, workflow kabi asosiy mavzularga jamlangan.
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Order promo π PR@blockchainworld.aiβ
Yuqori yangilanish chastotasi (oxirgi maβlumot 31 Oktabr, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli boβlib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim taβsir nuqtasiga aylantirishini koβrsatadi.
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Obunachilar
-16424 soatlar
-8287 kun
-2 99330 kun
Postlar arxiv
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A new neural network for image generation has appeared on the network β Loras[.]dev. AI creates anime picchis, logos, icons, sketches, and even tarot cards. It's all free.
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We do voiceovers for videos β a model for creating audio from text, Kokoro v1.0, has been released.
Simple but high-quality audio tracks are created. The neural network works locally in the browser, so user data does not leak anywhere.
Test it for free here
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Figure AI to produce over 100,000 humanoid robots by 2029 π
Figure AI has signed a second contract for the supply of robots of its own production, the client being an unnamed large company from the USA.
Thus, by 2029, Figure AI should produce 100,000 humanoid robots.
The robots are expected to be used to replace physical labor in manufacturing.
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Prime intellect introduced SYNTHETIC-1: Collaboratively generating the largest synthetic dataset of verified reasoning traces for math, coding and science using DeepSeek-R1.
SYNTHETIC-1:
- 1.4 million high-quality tasks & verifiers
- Public synthetic data run - allowing anyone to contribute compute
- GENESYS: open, extendable synthetic data generation framework + call for crowdsourcing tasks & verifiers.
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Google showed a very cool commercial for its Gemini AI chatbot
According to the plot of the video, a man with a Google Pixel 9 in his hands asks Gemini to help him prepare for an interview. Talking about himself, cute family shots from his past appear in the video.
At the end of the video, Gemini says that the hero is ready and the man joins the call with future employers.
By the way, this video will be shown at the Super Bowl, which will be held on the night of February 9th to 10th.
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State of AI: China - Key Insights from Q1 2025
While the US maintains an overall lead in the intelligence frontier, China is no longer far behind.
1. Closing the AI Gap
- Chinese AI models have rapidly caught up to US capabilities
- DeepSeek R1 achieved 89 on Intelligence Index, approaching OpenAI's o3 (94)
- Multiple Chinese models now match frontier US model performance
2. Leading Players & Performance
Top Chinese Models:
- DeepSeek R1: 89
- Qwen 2.5 Max (Alibaba): 79
- DeepSeek V3: 80
Market Leaders:
- Big Tech: Alibaba, Baidu, ByteDance, Tencent
- Rising Stars: DeepSeek, MoonShot AI, Zhipu
3. Export Control Impact
- US restrictions on high-end AI chips continue
- NVIDIA H100 (989 TFLOPs) banned for export
- China-approved NVIDIA H20 limited to 148 TFLOPs
4. Investment & Scale
Major Funding:
- MoonShot AI: $1.67B
- Zhipu: $1.12B
- Baichuan: $1.04B
5. Emerging Trends
- Focus on reasoning capabilities
- Open-source model development
- Multiple companies releasing frontier-level models
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ARK Invest has released their highly anticipated "Big Ideas 2025" report, outlining a future where technological convergence drives unprecedented economic growth and innovation.
Here's what you need to know:
The report identifies five key innovation platforms that are evolving and converging:
- AI
- Robotics
- Energy Storage
- Public Blockchains
- Multiomics Sequencing
Economic Impact
ARK projects global GDP growth could reach 7.3% by 2030, significantly higher than the IMF's forecast of 3.1%. Disruptive innovation companies could command more than two-thirds of global equity markets, with market value expanding beyond the current tech giants.
Key Technology Trends
1. AI Agents
- OpenAI projected to surpass $10B in revenue by 2025
- AI-mediated advertising could capture 54% of the $1.1T digital ad market by 2030
- AI agents revolutionizing consumer interaction and business workflows
2. Bitcoin & Digital Assets
- Bitcoin reached new all-time highs in 2024
- Spot Bitcoin ETFs marked the most successful ETF launch in history
- 2030 price targets: $300,000 (bear case) to $1.5M (bull case)
- Stablecoins overtook Mastercard and Visa in transaction value
3. Autonomous Vehicles
- Robotaxis could reduce transport costs to $0.25 per mile
- Market potential of $34T by 2030
- Tesla and Waymo leading commercial deployment
4. Energy & Infrastructure
- AI computing driving unprecedented energy demand
- Nuclear energy gaining renewed attention
- Battery costs continuing to decline
- Renewable energy integration accelerating
5. Robotics & Manufacturing
- Humanoid robots entering commercial use
- $26T+ global revenue opportunity
- 3D printing market growing 40% annually
- Manufacturing becoming increasingly automated
6. Healthcare Innovation
- AI dramatically reducing drug development costs
- DNA sequencing costs falling faster than Moore's Law
- Precision medicine becoming mainstream
- Multi-cancer screening revolutionizing diagnostics
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How will institutions evolve as AI agents enter the workforce? From legacy systems to multi-agent startups, from human quirks to principal-agent efficiency.
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OpenAI announced Deep Research, a new ChatGPT model that can conduct autonomous research by searching the web and synthesizing knowledge into a research paper as output, in what is the next step towards AI discovering new knowledge for itself
OpenAI's Deep Research scores a new state-of-the-art score on Humanity's Last Exam, surpassing the accuracy of o3-mini.
Sam Altman says new Deep Research AI agent powered by their o3 model can do "a single-digit percentage of all economically valuable tasks in the world" and this step into synthesizing knowledge will soon be followed by agents that can invent new knowledge.
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Top 3 AI Tokens That May Rise in February π¬
βΊTAO. Over the past month, TAO has fallen by 18% and formed a bottom at $362. Then it began to recover, which highlights the interest in AI tokens. If the upward momentum accelerates, TAO may test the resistance levels at $459 and $495.
βΊGRIFFAIN. The token grew steadily in December, and by January 22, the coin's market cap reached $600 million. But then GRIFFAIN faced a sharp correction. If interest in AI agents returns, the coin may recover above the resistance levels of $0.218 and $0.31.
βΊARC. The token showed strong growth, reaching a peak capitalization of $622 million. The subsequent correction reduced the total value of all ARC tokens in circulation to $221 million. In case of a reversal, ARC may rise to the resistance levels of $0.279 and $0.348.
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+1
Google rolled out Gemini 2 Flash for free to its products.
β one of the most high quality non-reasoning LLMs
β super fast (150tok/s+)
β 1M tok context window.
API price isnt out but was previously $0.075/$0.30 per M input/output tokens. Big move from Google.
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Mistral AI released a new small model
Mistral Small 3, a latency-optimized 24B-parameter model released under the Apache 2.0 license.
Mistral Small 3 is competitive with larger models such as Llama 3.3 70B or Qwen 32B, and is an excellent open replacement for opaque proprietary models like GPT4o-mini.
Mistral Small 3 is on par with Llama 3.3 70B instruct, while being more than 3x faster on the same hardware.
Mistral Small 3 is a pre-trained and instructed model catered to the β80%β of generative AI tasksβthose that require robust language and instruction following performance, with very low latency.
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Why DeepSeek's Success Doesn't Change the AI Race: Dario Amodei's View
Anthropic's CEO explains why the apparent breakthrough fits into the expected trajectory of AI development.*
3 Laws of AI Development
1. Scaling Law:
- More resources = better results
- $1M = 20% tasks, $10M = 40%, $100M = 60%
- Progress is smooth and predictable
2. Curve Shifting:
- Innovations improve efficiency
- Typical improvements:
* Small (1.2x)
* Medium (2x)
* Large (10x)
- Overall pace: ~4x per year
3. Paradigm Shifts:
- 2020-2023: text training
- 2024: adding Reinforcement Learning
- Now: unique "crossover point"
What DeepSeek actually achieved?
- Performance similar to 7-10 month old US models
- Lower costs, but within normal trend
- Significant resources (~50,000 chips, ~$1B)
Not a revolution because:
- Cost reduction follows expected 4x/year trend
- V3 more innovative than R1
- Total company spending comparable to US labs
The Future (2026-2027)
According to Amodei, truly advanced AI will require:
- Millions of chips
- Tens of billions of dollars
- 2-3 years of work
Key Takeaway
DeepSeek demonstrates an expected point on the progress curve, not a revolutionary breakthrough. The real race for superhuman AI is just beginning, and it will require unprecedented resources.
"Making AI that is smarter than almost all humans at almost all things will require millions of chips, tens of billions of dollars (at least), and is most likely to happen in 2026-2027"- Dario Amodei
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HyperCard: Shop Anywhere, Earn Big: The Secret Behind HyperPayβs Crypto Card!
https://youtu.be/IrDC9TayyRg?si=tXgPx_juAD2jKxyU
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DeepSeek, a Chinese AI app, has overtaken ChatGPT to become the No. 1 app in the US App Store.
Open source is catching up with closed models.
By the way, who doesn't know DeepSeek works for free and without a VPN.
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Neural networks make schoolchildren smarter. In Nigeria, children were given access to a chatbot for 6 weeks, and then tested their knowledge with a test. The results of the experiment killed:
π€ Students with AI score above average, without AI β below average.
π€6 weeks of AI studies turned out to be equal to 2 years of regular
π€AI has surpassed other learning methods by 80%.
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DeepSeek showed us in just 4 days:
1. Open-source AI is only <6 months behind closed AI
2. China is leading the open-source AI race
3. we are entering the LLM RL golden era
4. distilled models are powerful, we'll have highly intelligent AI running locally on our phones.
Reactions:
βοΈOpenAI o3-mini available for free-tier
π½hopefully we will see less AGI/ASI vagueposting.
Itβs hard to predict who will ultimately win, but donβt forget the power of the last mover advantage: Google invented the Transformer, but OpenAI unlocked its true potential.
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NVIDIA Senior Researcher: DeepSeek Proves AI Infrastructure and Base AI Models Will Become a Commodity
Jim Fan, NVIDIA, says:
βWhether you like it or not, the future of AI is its democratization, where every Internet user will be able to run advanced models even on weak devices. This is a historical trend that is pointless to fight.β1. DeepSeek has shown the best results in several independent tests. 2. Most importantly, they have achieved this with much less computing resources. DeepSeek proves that it is possible to get the same level of intelligence for 10 times less. This means that with current computing power, it is possible to create 10 times more powerful AI. The timeline of AI development is compressing.
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OpenAI released a computer-using agent Operator as a research preview.
Ensuring safety for agentic models is far more complex than for chatbots.
Errors can lead to serious consequencesβfor instance, the agent might make costly real-world decisions, like accidentally spending money from a credit card.
Achieving full agentic safety will be as challenging as ensuring the safety of self-driving cars, but with an added layer of complexity.
