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A new android has been created in Norway β NEO Gamma. The mechanical servant from 1X Technologies will do housework and bring coffee to its owners.
Robots do the hard work, not humans
Robots do the hard work, not humans
π₯104π96π95β€82
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Anthropic to release Claude Sonnet 3.7 on Feb 26
Itβs expected to have step-by-step thinking, never before seen coding capabilities and web search.
The best coding model which powers Cursor and Windsurf is about to get a whole lot better.
Claude 3.7 Sonnet is Anthropic's most intelligent model to date and the first Claude model to offer extended thinking - the ability to solve complex problems with careful, step-by-step reasoning.
Anthropic is the first AI lab to introduce a single model where users can balance speed and quality by choosing between standard thinking for near-instant responses or extended thinking or advanced reasoning.
Claude 3.7 Sonnet is state-of-the-art for coding, and delivers advancements in computer use, agentic capabilities, complex reasoning, and content generation. With frontier performance and more control over speed, Claude 3.7 Sonnet is the ideal choice for powering AI agents, especially customer-facing agents, and complex AI workflows.
Supported use cases: RAG or search & retrieval over vast amounts of knowledge, product recommendations, forecasting, targeted marketing, code generation, quality control, parse text from images, agentic computer use, content generation
Model attributes: Reasoning, Text generation, Code generation, Rich text formatting, Agentic computer use
Itβs expected to have step-by-step thinking, never before seen coding capabilities and web search.
The best coding model which powers Cursor and Windsurf is about to get a whole lot better.
Claude 3.7 Sonnet is Anthropic's most intelligent model to date and the first Claude model to offer extended thinking - the ability to solve complex problems with careful, step-by-step reasoning.
Anthropic is the first AI lab to introduce a single model where users can balance speed and quality by choosing between standard thinking for near-instant responses or extended thinking or advanced reasoning.
Claude 3.7 Sonnet is state-of-the-art for coding, and delivers advancements in computer use, agentic capabilities, complex reasoning, and content generation. With frontier performance and more control over speed, Claude 3.7 Sonnet is the ideal choice for powering AI agents, especially customer-facing agents, and complex AI workflows.
Supported use cases: RAG or search & retrieval over vast amounts of knowledge, product recommendations, forecasting, targeted marketing, code generation, quality control, parse text from images, agentic computer use, content generation
Model attributes: Reasoning, Text generation, Code generation, Rich text formatting, Agentic computer use
π174π168π₯159β€149
AI models now handle voice/speech yet building with them in Python is very frustrating
FastRTC is here to solve
- Automatic Voice Detection
- Handling WebRTC & the backend for real-time apps
- Calling Phones
Github
FastRTC is here to solve
- Automatic Voice Detection
- Handling WebRTC & the backend for real-time apps
- Calling Phones
Github
huggingface.co
FastRTC: The Real-Time Communication Library for Python
Weβre on a journey to advance and democratize artificial intelligence through open source and open science.
β€72π₯72π68π55
DeepSeek makes 2 major announcements
1. Starting today, DeepSeek is offering significant discounts on their API Platform during off-peak hours (16:30-00:30 UTC daily):
β’ DeepSeek-V3: 50% OFF
β’ DeepSeek-R1: Massive 75% OFF
This means you can access powerful AI models at a fraction of the cost during these hours. For example, DeepSeek-R1 output cost drops from $2.19 to just $0.550 per 1M tokens!
2. DeepSeek has also released DeepGEMM - an impressive FP8 GEMM library that supports both dense and MoE GEMMs, powering their V3/R1 models.
Key features:
- Up to 1350+ FP8 TFLOPS on Hopper GPUs
- Lightweight with no heavy dependencies
- Fully Just-In-Time compiled
- Core logic at just ~300 lines of code
- Outperforms expert-tuned kernels on most matrix sizes
- Supports dense layout and two MoE layouts
1. Starting today, DeepSeek is offering significant discounts on their API Platform during off-peak hours (16:30-00:30 UTC daily):
β’ DeepSeek-V3: 50% OFF
β’ DeepSeek-R1: Massive 75% OFF
This means you can access powerful AI models at a fraction of the cost during these hours. For example, DeepSeek-R1 output cost drops from $2.19 to just $0.550 per 1M tokens!
2. DeepSeek has also released DeepGEMM - an impressive FP8 GEMM library that supports both dense and MoE GEMMs, powering their V3/R1 models.
Key features:
- Up to 1350+ FP8 TFLOPS on Hopper GPUs
- Lightweight with no heavy dependencies
- Fully Just-In-Time compiled
- Core logic at just ~300 lines of code
- Outperforms expert-tuned kernels on most matrix sizes
- Supports dense layout and two MoE layouts
π₯213π180π177β€168
New announcements from DeepSeek Optimized Parallelism Strategies
1. DualPipe - a bidirectional pipeline parallelism algorithm for computation-communication overlap in V3/R1 training.
2. EPLB - an expert-parallel load balancer for V3/R1.
3. Analyze computation-communication overlap in V3/R1.
1. DualPipe - a bidirectional pipeline parallelism algorithm for computation-communication overlap in V3/R1 training.
2. EPLB - an expert-parallel load balancer for V3/R1.
3. Analyze computation-communication overlap in V3/R1.
GitHub
GitHub - deepseek-ai/DualPipe: A bidirectional pipeline parallelism algorithm for computation-communication overlap in DeepSeekβ¦
A bidirectional pipeline parallelism algorithm for computation-communication overlap in DeepSeek V3/R1 training. - deepseek-ai/DualPipe
π81π75π₯69β€64
GPT-4.5 is out! Knowledge Still Stuck in October 2023, itβs not going to blow your mind, but it might befriend you.
It's more like a personality, communication, and creativity upgrade than a huge intelligence leap. It's like OpenAI is pivoting its base model from "bland assistant" to "AI bestie."
What it does do well:
- OpenAI says it scores 64% on SimpleQA (double GPT-4's score)
- Much better writing with cleaner, better structured, more human-like prose
- Genuinely warmer and more emotionally intelligent (gave me some good advice!)
- Less robotic, more opinionated responses
4.5 is more extroverted, agreeable, and less neurotic than 4o.
It's sometimes worse at following instructions and because it's less sycophantic and more creative.
The model received approximately 10x more computational resources during pre-training compared to GPT-4. Training occurred simultaneously across multiple data centers.
Pricing $75 per million input tokens and $150 per million output tokens β 15-30x more expensive than GPT-4o! This pricing reflects the model's scale and resource requirements.
Performance and Context Generation is noticeably slower than its predecessors, context length remains at 128K tokens. Knowledge cutoff stays at October 2023, which is disappointing for many users.
Functionality Supports Canvas, search, and file uploads. Currently lacks multimodal features like voice mode or video.
Availability:
Already available to Pro users and developers of all API tiers
Coming to Plus subscribers ($20) next week
OpenAI plans to add "tens of thousands of GPUs" next week to expand access
Independent Benchmark Results:
Aider Polyglot Coding Benchmark: Recent tests show that GPT-4.5 Preview significantly outperforms its predecessor but lags behind specialized models:
Claude 3.7 Sonnet with thinking mode (32k tokens) β 65%
Claude 3.7 Sonnet without thinking mode β 60%
DeepSeek V3 β 48%
GPT-4.5 Preview β 45%
ChatGPT-4o β 27%
GPT-4o β 23%
It's more like a personality, communication, and creativity upgrade than a huge intelligence leap. It's like OpenAI is pivoting its base model from "bland assistant" to "AI bestie."
What it does do well:
- OpenAI says it scores 64% on SimpleQA (double GPT-4's score)
- Much better writing with cleaner, better structured, more human-like prose
- Genuinely warmer and more emotionally intelligent (gave me some good advice!)
- Less robotic, more opinionated responses
4.5 is more extroverted, agreeable, and less neurotic than 4o.
It's sometimes worse at following instructions and because it's less sycophantic and more creative.
The model received approximately 10x more computational resources during pre-training compared to GPT-4. Training occurred simultaneously across multiple data centers.
Pricing $75 per million input tokens and $150 per million output tokens β 15-30x more expensive than GPT-4o! This pricing reflects the model's scale and resource requirements.
Performance and Context Generation is noticeably slower than its predecessors, context length remains at 128K tokens. Knowledge cutoff stays at October 2023, which is disappointing for many users.
Functionality Supports Canvas, search, and file uploads. Currently lacks multimodal features like voice mode or video.
Availability:
Already available to Pro users and developers of all API tiers
Coming to Plus subscribers ($20) next week
OpenAI plans to add "tens of thousands of GPUs" next week to expand access
Independent Benchmark Results:
Aider Polyglot Coding Benchmark: Recent tests show that GPT-4.5 Preview significantly outperforms its predecessor but lags behind specialized models:
Claude 3.7 Sonnet with thinking mode (32k tokens) β 65%
Claude 3.7 Sonnet without thinking mode β 60%
DeepSeek V3 β 48%
GPT-4.5 Preview β 45%
ChatGPT-4o β 27%
GPT-4o β 23%
Openai
Introducing GPT-4.5
Weβre releasing a research preview of GPTβ4.5βour largest and best model for chat yet. GPTβ4.5 is a step forward in scaling up pre-training and post-training.
π₯73β€70π70π63
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The Magnific neural network can change the style of pictures in seconds, while accurately preserving their essence. AI can turn even a simple sketch on paper into a high-quality render or photorealistic picture.
π122π₯111π109β€97
Want next-level access control that is fast, secure, and scalable? π Mercury Access by JM Digital is a cutting-edge solution designed to provide seamless authentication, advanced security, and unparalleled access management for businesses and institutions.
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β Seamless integration with modern digital infrastructure
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π Discover Mercury Access today: https://www.jmdigital.tech/mercury-access
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π₯93π77β€72π68
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AI has learned to play Pokemon. The newest model Claude 3.7 Sonnet was ordered to play Pokemon Red, and it has already beaten the leader of the first stadium.
β€176π164π₯156π154
DeepSeek introduced DeepSeek-V3/R1 Inference System Overview
Optimized throughput and latency via:
1. Cross-node EP-powered batch scaling
2. Computation-communication overlap
3. Load balancing
Statistics of DeepSeek's Online Service:
- 73.7k/14.8k input/output tokens per second per H800 node
- Cost profit margin 545%
Optimized throughput and latency via:
1. Cross-node EP-powered batch scaling
2. Computation-communication overlap
3. Load balancing
Statistics of DeepSeek's Online Service:
- 73.7k/14.8k input/output tokens per second per H800 node
- Cost profit margin 545%
GitHub
open-infra-index/202502OpenSourceWeek/day_6_one_more_thing_deepseekV3R1_inference_system_overview.md at main Β· deepseek-ai/openβ¦
Production-tested AI infrastructure tools for efficient AGI development and community-driven innovation - deepseek-ai/open-infra-index
π130β€113π₯100π96
Huge VLM release from Cohere for AI is just in
Aya-Vision is a new VLM family based on SigLIP and Aya, and it outperforms many larger models.
> 8B and 32B models covering 23 languages and two new benchmark dataset
> supported by HF transformers from get-go
Aya-Vision is a new VLM family based on SigLIP and Aya, and it outperforms many larger models.
> 8B and 32B models covering 23 languages and two new benchmark dataset
> supported by HF transformers from get-go
huggingface.co
Cohere Labs Aya Vision - a CohereLabs Collection
Aya Vision is a state-of-the-art family of vision models that brings multimodal capabilities to 23 languages.
β€225π216π₯202π197
Today Anthropic submitted their recommendations to the OSTP for the U.S. AI Action Plan
Anthropic predicts powerful AI systems will appear by late 2026 or early 2027, with intellectual abilities matching Nobel Prize winners, able to autonomously handle digital tasks (text, audio, video, internet browsing), reason independently over hours or weeks, and control physical equipment digitally
They recommend stronger national security actions, including government testing of AI models for security risks, stricter export controls on key chips like the H20, and secure communication channels between AI labs and intelligence agencies
They suggest the government build 50 gigawatts of additional power capacity dedicated to AI by 2027, speed up AI adoption across federal agencies, and improve economic data collection to prepare for AIβs impact on jobs and society
Anthropic predicts powerful AI systems will appear by late 2026 or early 2027, with intellectual abilities matching Nobel Prize winners, able to autonomously handle digital tasks (text, audio, video, internet browsing), reason independently over hours or weeks, and control physical equipment digitally
They recommend stronger national security actions, including government testing of AI models for security risks, stricter export controls on key chips like the H20, and secure communication channels between AI labs and intelligence agencies
They suggest the government build 50 gigawatts of additional power capacity dedicated to AI by 2027, speed up AI adoption across federal agencies, and improve economic data collection to prepare for AIβs impact on jobs and society
Anthropic
Anthropicβs recommendations to OSTP for the U.S. AI action plan
Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.
π₯200β€187π165π157
a16z introduced the Top 100 Gen AI Consumer Apps
In just 6 months, the consumer AI landscape has shiftedβsome AI apps surged, others stalled, and a few unexpected players vaulted over the competition.
A few key insights:
β’ DeepSeek is outpacing competing general assistant LLMs β in growth and engagement
β’ The AI apps people are willing to pay for diverge from the most popular
β’ After a year-long plateau, ChatGPT's growth has come roaring back.
β’ AI video has finally broken through, with some high-quality new players
β’ So-called βvibecodingβ tools are reshaping who can create with AI, not just who can use it
In just 6 months, the consumer AI landscape has shiftedβsome AI apps surged, others stalled, and a few unexpected players vaulted over the competition.
A few key insights:
β’ DeepSeek is outpacing competing general assistant LLMs β in growth and engagement
β’ The AI apps people are willing to pay for diverge from the most popular
β’ After a year-long plateau, ChatGPT's growth has come roaring back.
β’ AI video has finally broken through, with some high-quality new players
β’ So-called βvibecodingβ tools are reshaping who can create with AI, not just who can use it
π344β€323π318π₯308
MALT: Improving Reasoning with Multi-Agent LLM Training
A new multi-agent post-training method that uses credit assigned synthetic data to improve the reasoning capabilities and self-correction rates of a generator, critic, and refinement model working together.
Paper.
A new multi-agent post-training method that uses credit assigned synthetic data to improve the reasoning capabilities and self-correction rates of a generator, critic, and refinement model working together.
Paper.
π73β€67π₯65π61
Cortical Labs announced the world's first biocomputer
CL1 merges real living neurons with a chip to solve complex problems and redefine research.
CL1 merges real living neurons with a chip to solve complex problems and redefine research.
Tom's Hardware
World's first 'body in a box' biological computer uses human brain cells with silicon-based computing
Cortical Labs said the CL1 will be available from June, priced at around $35,000.
π₯204π202β€200π194
OpenAI has evolved their thinking about AGI development.
Rather than viewing AGI as a sudden leap, they now see it as a continuous series of increasingly capable systems.
Key Risks They're Addressing
1. Human Misuse: People using AI in ways that violate laws and democratic values.
2. Misaligned AI: AI systems acting in ways that don't align with human values and intentions.
3. Societal Disruption: Rapid AI-driven changes causing unpredictable effects on society and inequality.
Their Core Safety Principles
Embracing Uncertainty
They treat safety as a science, learning through real-world deployment rather than just theory. This includes rigorous measurement of risks and proactive mitigation strategies.
Defense in Depth
OpenAI applies multiple layers of safeguards, similar to other safety-critical fields. They teach models to understand safety values, follow instructions, and remain reliable even under uncertainty.
Methods that Scale
They develop safety approaches that become more effective as AI capabilities increase, even using current AI systems to help align more advanced ones.
Human Control
OpenAI places humans at the center of their alignment approach, creating transparent systems that people can meaningfully supervise. They incorporate public feedback into policy formation and work on interfaces that help humans guide AI effectively.
Community Effort
They recognize that ensuring safe AGI requires collaboration across industry, academia, government, and the public. OpenAI shares research, provides resources to the field, funds external research, and engages with policymakers.
While OpenAI has a clear vision for safety, they remain open to being wrong about how AI progress will unfold and welcome diverse perspectives on AI risk management.
Rather than viewing AGI as a sudden leap, they now see it as a continuous series of increasingly capable systems.
Key Risks They're Addressing
1. Human Misuse: People using AI in ways that violate laws and democratic values.
2. Misaligned AI: AI systems acting in ways that don't align with human values and intentions.
3. Societal Disruption: Rapid AI-driven changes causing unpredictable effects on society and inequality.
Their Core Safety Principles
Embracing Uncertainty
They treat safety as a science, learning through real-world deployment rather than just theory. This includes rigorous measurement of risks and proactive mitigation strategies.
Defense in Depth
OpenAI applies multiple layers of safeguards, similar to other safety-critical fields. They teach models to understand safety values, follow instructions, and remain reliable even under uncertainty.
Methods that Scale
They develop safety approaches that become more effective as AI capabilities increase, even using current AI systems to help align more advanced ones.
Human Control
OpenAI places humans at the center of their alignment approach, creating transparent systems that people can meaningfully supervise. They incorporate public feedback into policy formation and work on interfaces that help humans guide AI effectively.
Community Effort
They recognize that ensuring safe AGI requires collaboration across industry, academia, government, and the public. OpenAI shares research, provides resources to the field, funds external research, and engages with policymakers.
While OpenAI has a clear vision for safety, they remain open to being wrong about how AI progress will unfold and welcome diverse perspectives on AI risk management.
Openai
How we think about safety and alignment
The mission of OpenAI is to ensure artificial general intelligence (AGI) benefits all of humanity. Safetyβthe practice of enabling AIβs positive impacts by mitigating the negative onesβis thus core to our mission.
π81π74π₯73β€69
Google introduced Gemini Robotics is the most advanced VLA in the world
Gemini Robotics featured in the post, builds on Gemini 2.0, introducing advanced vision-language-action capabilities to control robots physically.
The technology enables robots to understand and react to the physical world, performing tasks like desk cleanup through voice commands, as part of a broader push toward embodied AI.
Gemini Robotics-ER, a related model, enhances spatial understanding, allowing robots to adapt to dynamic environments and interact seamlessly with humans.
Tech report.
Gemini Robotics featured in the post, builds on Gemini 2.0, introducing advanced vision-language-action capabilities to control robots physically.
The technology enables robots to understand and react to the physical world, performing tasks like desk cleanup through voice commands, as part of a broader push toward embodied AI.
Gemini Robotics-ER, a related model, enhances spatial understanding, allowing robots to adapt to dynamic environments and interact seamlessly with humans.
Tech report.
π168π₯155β€147π140
Cohere introduced Command A: a new AI model that can match or outperform GPT-4o and DeepSeek-V3 on business tasks, with significantly greater efficiency.
Command A is an open-weights 111B parameter model with a 256k context window focused on delivering great performance across agentic, multilingual, and coding usecases.
Runs on only 2 GPUs (vs. typically 32), offers 256k context length, supports 23 languages and delivers up to 156 tokens/sec.
API.
Command A is an open-weights 111B parameter model with a 256k context window focused on delivering great performance across agentic, multilingual, and coding usecases.
Runs on only 2 GPUs (vs. typically 32), offers 256k context length, supports 23 languages and delivers up to 156 tokens/sec.
API.
Cohere
Introducing Command A: Max performance, minimal compute | Cohere Blog
Cohere Command A is on par or better than GPT-4o and DeepSeek-V3 across agentic enterprise tasks, with significantly greater efficiency.
β€229π222π215π₯194
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Gemini Robotics is an artificial intelligence model based on Gemini 2.0, designed to introduce artificial intelligence capabilities into the physical world.
The new system allows robots to understand the environment, interact naturally with humans, and perform complex tasks with agility.
The new system allows robots to understand the environment, interact naturally with humans, and perform complex tasks with agility.
π70β€60π60π₯58