Vortex Next Gen Trends

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Ai video generator progress in just a year is incredible!

❗️A new way to teach robots has been created, which can be compared to how we teach small children. Former Google employees, creators of the Physical Intelligence company, have developed a new method FAST (Fast Action Signal Tokenization) for more efficient control of robots. Previously, they presented GPT for robots. The problem that FAST solves Existing AI models for controlling robots require a simple breakdown of actions into detailed parts, which is not efficient enough for complex and precise tasks. Solution - FAST is a new way to tokenize (transform) robot actions that: - Uses compression methods similar to those used in JPEG. - organize work on performing a complex task with a high level of complexity - Learns 5 times faster than previous methods Robots with FAST can perform complex tasks, such as folding laundry, cleaning tables, and packing groceries. For the first time, it was possible to create a universal model that can solve various problems in new conditions, simply by receiving commands in natural language. The system was successfully tested at 3 American universities. The company released a public version of the FAST tokenizer, training 1 million sequential robot actions that researchers can use for their projects. https://www.pi.website/research/fast

The Berkeley Sky computing lab just trained Sky-T1-32B-Preview, a GPT-o1 level reasoning model, spending only $450 to create the instruction dataset. The data is 17K math and coding problems solved step by step. They created this dataset by prompting QwQ at $450 cost. Can it be done without another reasoning model to distill? Teach a 1000 student class and assign 17 homework problems. Side benefit: make $10M by charging $10K tuition. Model data and full code here. Very interesting work that shows simple SFT is all you need (if you have good data).

Another top notch open source model at OpenAI/Meta/Google levels from &MiniMax AI (Chinese lab, ex Sensetime, $850m raised). Massive MoE similar to Deep-seek. Excels on long context (4m tokens!) which is really interesting, need to dig into their lighting attention variant. Paper.

Another top notch open source model at OpenAI/Meta/Google levels from &MiniMax AI (Chinese lab, ex Sensetime, $850m raised). Massive MoE similar to Deep-seek. Excels on long context (4m tokens!) which is really interesting, need to dig into their lighting attention variant. Paper.

Reddit launched their own LLM-based search engine It aggregates and summarizes relevant responses across threads + links you directly to read more. Even if you assume GPT already includes all Reddit data, this is still helpful given the clear citations. This is similar to the value of Perplexity over ChatGPT in clearer answers and fewer hallucinations.

Stanford launched a free Google Deep Research clone called STORM. It uses GPT 4-o + Bing Search under the hood to generate long cited reports from many websites in ~3mins. It's also completely open-source and free to use. GitHub.

The first-ever official jetpack races took place in Austria! All athletes soared through the air using identical jetpacks designed by Gravity Industries. These incredible machines boast 1,060 horsepower engines and can reach speeds of up to 128 km/h. What other thrilling sports might emerge thanks to technology in the next 10 years?

Neural networks that will do all the work for you. ▪️ Whisper is a tool for transcribing (translates speech into text) audio and video. The service is free. When you don’t want to listen to audio or watch video, but you need to write an article on the material. ▪️ Qwen2-Math is an indispensable assistant in solving complex problems in mathematics. You can use it for free. Suitable for both schoolchildren and students, and at work it can be useful for a number of specialties. ▪️ Storm - helps to create texts or prepare essays based on verified data. Everything we love: a free neural network. Similar to GPT chat, but logically should give a better result due to specialization. ▪️ Explain Me Like I’m Five - explains complex topics in simple language that even a five-year-old can understand. Free service. Now everyone can understand what a "modular blockchain" or "singularity" is.

Find a person by one (!) photo: the Eagle Eye neural network will find any person from all Internet platforms in 3-5 seconds.

— Everything is anonymous: even if hacked, no one will know your search history. — Almost no data required. AI will find an object or person by minimal information. — The neural network is still being actively developed. In 2025, the developers promise an AI revolution.

AI will impact the crypto industry, the crypto market capitalization is predicted to grow to $10 trillion by the end of 2025. This will be facilitated by 3 factors: 1. Improved regulatory climate after the US elections 2. Influx of institutional capital through the expansion of the ETF line 3. Development of practical applications in finance and AI. According to Hashdex research, the market is on the verge of fundamental changes. Merging AI and blockchain A new direction is being formed: the use of decentralized networks to verify data when training AI models and creating an infrastructure for AI agents that can independently conduct transactions via blockchain. Technological changes The Dencun update in the Ethereum network has radically changed the transaction economy. Commissions in L2 networks have fallen by 99%, and throughput has increased 50 times in 4 years. Ethereum maintains its lead thanks to its mature ecosystem of L2 solutions, but Solana is gaining ground as a major competitor.

Salesforce stops hiring programmers, but hires sellers of AI products Salesforce CEO Marc Benioff announced that the company will not hire new programmers in 2025 due to increased productivity due to AI. The productivity of the engineering teams increased by 30% thanks to their Agentforce product and other AI technologies. At the same time, the company plans to reduce the number of support engineers and hire 1,000-2,000 salespeople who will explain the advantages of AI solutions to customers. In January 2024, 700 employees (about 1% of the staff) were laid off. In January 2023, 7,000 people (about 10% of the company) were cut At the same time, Benioff believes that in 5 years the company will probably become bigger, despite the current changes.

Nvidia introduced Cosmos, an open-source, open-weight Video World Model It's trained on 20M hours of videos and weighs from 4B to 14B. Cosmos offers two flavors: diffusion (continuous tokens) and autoregressive (discrete tokens); and two generation modes: text->video and text+video->video. Physical AI has a big data problem. Synthetic data to the rescue. Nvidia apply Cosmos to large-scale synthetic data generation for robotics and autonomous driving, and now you can too.

Top AI agents in 30 days DropsTab presented the top AI agents for Twitter activity in 30 days. We're taking over the tracking

Wow! A real big humanoid robotics dataset just got open sourced: AgiBot World is the first large-scale robotic learning dataset designed to advance multi-purpose humanoid policies With 1M+ trajectories from 100 robots, AgiBot World spans 100+ real-world scenarios across five target domains, tackling fine-grained manipulation, tool usage, and multi-robot collaboration. Cutting-edge multimodal hardware features visual tactile sensors, durable 6-DoF dexterous hands, and mobile dual-arm robots with whole-body control, supporting research in imitation learning, multi-agent collaboration, and more. Github. HuggingFace Dataset Highlights: - Cutting-edge sensor and hardware design - Wide-spectrum of scenario coverage - Quality assurance with human-in-the-loop

Google released white paper on AI agents It covers the basics of llm agents and a quick Langchain implementation.

Enthusiasts from ZooDev have assembled an AI innovation, namely a CAD system that can handle any (!) task.

• Every little detail, hole, or optimization will be taken over by the built-in AI. All you need is a text promt. • The internal simulator allows you to immediately (!) test the model. • Everything you do in the editor turns into code — you have changed a number and the model is updated with new parameters.

According to the developers, AI is already speeding up design by 10 times, and soon — by 50 (!) times.

Anthropic's Bold Vision: Building the HTTP of AI with Model Context Protocol Anthropic published a near-term development roadmap for the model context protocol (MCP). In a strategic move that could reshape the AI landscape, Anthropic has revealed its ambitious plans for the MCP - potentially laying the groundwork for how we'll interact with AI in the years to come. Just as HTTP revolutionized the web by standardizing how we access and share information, MCP aims to become the universal language for AI interactions. Anthropic's H1 2025 roadmap reveals a vision that extends far beyond developing individual AI models like Claude. Instead, they're architecting the fundamental infrastructure that could power the next generation of AI interactions. Here's what makes this approach revolutionary: 1. Building an Open Ecosystem - Development of an open protocol for standardized AI model interactions - Inviting other AI providers to shape MCP as an industry standard - Focus on community-led development and shared governance 2. Enabling Decentralization - Support for remote MCP connections - Secure cross-system AI interactions - Infrastructure for distributed AI systems 3. Scaling for the Future - Advanced support for hierarchical agent systems - Preparation for multimodal interactions (text, audio, video) - Standardized packaging and distribution mechanisms 4. Democratizing Access - Simplified installation and usage processes - Creation of a universal server registry - Open community participation in protocol development The HTTP Parallel The comparison to HTTP is particularly apt. Just as HTTP provided the foundational protocol that enabled the modern web to flourish, MCP could serve as the standard protocol for AI interactions. This standardization could: - Enable seamless communication between different AI systems - Create a more accessible and interoperable AI ecosystem - Foster innovation through standardized interfaces Strategic Implications This move positions Anthropic not just as an AI company, but as a potential architect of the fundamental infrastructure that could power the future of AI interactions. By focusing on building this foundation, they're taking a long-term view that could significantly influence how AI systems are developed, deployed, and integrated in the years to come. The success of this initiative could establish MCP as the de facto standard for AI interactions, similar to how HTTP became the backbone of web communications. This would not only benefit the broader AI community but could also cement Anthropic's position as a key player in shaping the future of AI.

AI tokens became the most profitable sector in 2024 – 2,940% This is followed by memes (2,185%) and RWA (820%).