China is once again ahead of the US: Tencent released a 389B MoE with only 52B activated parameters which beats the Llama 3.1 405B
huggingface.co
tencent/Tencent-Hunyuan-Large · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Nvidia has had discussions to invest in xAI at around $45 billion
The 18-month-old startup has developed Grok, a chatbot that aims to compete with OpenAI’s ChatGPT. It’s building a massive data center in Memphis using Nvidia’s chips that has caused concern among rivals including OpenAI CEO Sam Altman, The Information has reported.
Nvidia has invested in numerous AI startups that use its chips, though it hasn’t written large checks to startups the way Microsoft, Google and Amazon have.
The 18-month-old startup has developed Grok, a chatbot that aims to compete with OpenAI’s ChatGPT. It’s building a massive data center in Memphis using Nvidia’s chips that has caused concern among rivals including OpenAI CEO Sam Altman, The Information has reported.
Nvidia has invested in numerous AI startups that use its chips, though it hasn’t written large checks to startups the way Microsoft, Google and Amazon have.
The Information
Nvidia Has Been in Talks to Invest in xAI’s Multibillion-Dollar Financing
Nvidia has had discussions to invest in xAI’s latest financing round, which could value the OpenAI rival at around $45 billion, representatives for xAI told a prospective investor.
The talks come just five months after xAI raised $6 billion at a $24 billion…
The talks come just five months after xAI raised $6 billion at a $24 billion…
Researchers built an 'empathy mechanism' into an agent's architecture with a technique that mirrors neurons.
So here (in a simple grid world) the reward comes from the agent's own emotional response through dopamine modulation, rather than from external rules or rewards.
Cool approach to drive altruistic behavior, but less clear how that might apply to language models over SNNs.
So here (in a simple grid world) the reward comes from the agent's own emotional response through dopamine modulation, rather than from external rules or rewards.
Cool approach to drive altruistic behavior, but less clear how that might apply to language models over SNNs.
arXiv.org
Building Altruistic and Moral AI Agent with Brain-inspired...
As AI closely interacts with human society, it is crucial to ensure that its behavior is safe, altruistic, and aligned with human ethical and moral values. However, existing research on embedding...
Parse introduced GigaLab: Revolutionizing Single Cell Research
Parse's GigaLab is breaking barriers with its groundbreaking ability to analyze over 10 million cells in a single run.
Why is this huge?
- Single cell research has been exploding, with studies doubling in size yearly
- Scientists can now explore cellular interactions at an unprecedented scale
- Powered by innovative Evercode technology and advanced automation
Real-world impact:
- Accelerating drug discovery and development
- Enhanced screening capabilities for new treatments
- Powering machine learning algorithms to predict patient therapy responses
- Opening doors to research previously deemed impossible
Think of it as a microscope on steroids – but instead of looking at one cell, you're analyzing millions simultaneously!
This isn't just about bigger numbers; it's about unlocking new possibilities in understanding diseases, developing treatments, and ultimately, saving lives.
This is what the future of biomedical research looks like. Welcome to the era of mega-scale cellular analysis! 🚀
Parse's GigaLab is breaking barriers with its groundbreaking ability to analyze over 10 million cells in a single run.
Why is this huge?
- Single cell research has been exploding, with studies doubling in size yearly
- Scientists can now explore cellular interactions at an unprecedented scale
- Powered by innovative Evercode technology and advanced automation
Real-world impact:
- Accelerating drug discovery and development
- Enhanced screening capabilities for new treatments
- Powering machine learning algorithms to predict patient therapy responses
- Opening doors to research previously deemed impossible
Think of it as a microscope on steroids – but instead of looking at one cell, you're analyzing millions simultaneously!
This isn't just about bigger numbers; it's about unlocking new possibilities in understanding diseases, developing treatments, and ultimately, saving lives.
This is what the future of biomedical research looks like. Welcome to the era of mega-scale cellular analysis! 🚀
Parse Biosciences
Parse GigaLab: 10M+ Cells in a Single Run - Redefining scRNA-seq
Leveraging the Evercode technology and automation, GigaLab enables groundbreaking research at unprecedented speed, scale and quality.
🔥4
How_AI_agents_are_shaping_the_future_of_work_1730893383.pdf
13.3 MB
Deloitte has published report on how AI agents are reshaping the future of work.
Key Highlights
1. AI agents are reshaping industries by expanding the potential applications of GenAI and typical language models.
2. Multiagent AI systems can significantly enhance the quality of outputs and complexity of work performed by single AI agents.
3. Forward-thinking businesses and governments are already implementing AI agents and multiagent AI systems across a range of use cases.
4. Executive leaders should make moves now to prepare for and embrace this next era of intelligent organizational transformation.
Key Highlights
1. AI agents are reshaping industries by expanding the potential applications of GenAI and typical language models.
2. Multiagent AI systems can significantly enhance the quality of outputs and complexity of work performed by single AI agents.
3. Forward-thinking businesses and governments are already implementing AI agents and multiagent AI systems across a range of use cases.
4. Executive leaders should make moves now to prepare for and embrace this next era of intelligent organizational transformation.
40 FREE STARTUP IDEAS from Greg Isenberg ($100 MRR-$1M MRR ideas)
1. build browser extension detecting duplicate GPT-4 queries. startups wasting $5k/month on repeated prompts. one plugin saves companies 30% on OpenAI costs.
2. directory of tech companies still paying for empty offices. brokers need sublease prospects. charge $500/month for verified matches.
3. AI turning support transcripts into API docs. engineers waste weeks writing documentation. charges $2k/month to auto-generate.
4. alert system for startup hiring pages >$10M raised. recruiters checking careers pages daily. makes $15k/month from layoff signals.
5. AI catching Stripe webhook failures and auto-fixing. companies losing $10k/month to failed payments. charge per recovery.
6. marketplace for crypto-friendly accountants. startups getting rejected by traditional firms. matchmaker makes $20k/month.
7. AI analyzing GitHub to predict trending dev tools. VCs paying $5k/month for early adoption signals.
8. Slack scanner finding sensitive data sharing. companies violating compliance. $1k/month per security audit.
9. AI turning raw sales calls into pitch snippets. sales teams recreating same demos. saves 10 hours per rep weekly.
10. database of startups outgrowing HubSpot. some companies overpaying for basic CRM. charge sellers for leads.
11. AI generating industry-specific privacy policies. lawyers charging $2k for templates. $100 per document.
12. marketplace for fractional dev advocates. startups can't afford full-time. platform makes $30k/month matching.
13. AI monitoring competitor pricing changes. product teams missing market shifts. $200/month for alerts.
14. Figma to React component converter. developers spending days on UI. charge per export.
15. AI analyzing VC rejections for feedback. founders missing improvement signals. charge per analysis.
16. tool finding unused SaaS credits. startups waste $50k/year on tools they could get free. charge 20% of savings.
17. AI generating custom investor updates. founders spending 5 hours monthly. one tool makes $10k/month automating.
18. marketplace for cancelled conference booths. last-minute spaces go empty. broker makes $25k/month reselling.
19. AI scanning terms of service for hidden fees. companies missing subscription traps. charge per scan.
20. directory of startups offering AI APIs. developers need GPT alternatives. monetize through affiliate.
21. tool turning spreadsheets into mobile apps. small businesses need custom apps. charge per publish.
22. AI monitoring Twitter for customer complaints. support teams missing mentions. $300/month per brand.
23. marketplace for startup-ready CFOs. companies need part-time finance. $40k/month in placement fees.
24. chrome extension saving LinkedIn to HubSpot. sales teams copying manually. charge per sync.
25. AI generating personalized cold emails. founders spending hours on outreach. $500/month per campaign.
26. tool finding underpriced micro-SaaS. investors manually tracking small companies. charge for deal flow.
27. AI detecting fake reviews on product sites. brands losing sales to competitors. $1k/month per scan.
28. marketplace for verified white-label agencies. founders waste weeks finding contractors. charge listing fees.
29. AI turning blog posts into Twitter threads. content teams duplicating work. $200/month per brand.
30. database of remote dev teams for hire. startups overpaying recruiters. charge teams listing fees.
31. tool monitoring AI hallucination rates. companies blindly trusting outputs. charge per check.
32. AI analyzing Zoom calls for sales insights. calls have gold but no one rewatches. $100/seat monthly.
33. marketplace for startup acquisition targets under $5M ARR. charge for deal access.
34. AI turning product screenshots into landing pages. marketers spending days designing. charge per page.
35. tool finding open source alternatives to SaaS. companies overpaying for basic features. affiliate revenue.
36. AI generating custom app store screenshots. developers using generic images. $50 per app update.
37.
1. build browser extension detecting duplicate GPT-4 queries. startups wasting $5k/month on repeated prompts. one plugin saves companies 30% on OpenAI costs.
2. directory of tech companies still paying for empty offices. brokers need sublease prospects. charge $500/month for verified matches.
3. AI turning support transcripts into API docs. engineers waste weeks writing documentation. charges $2k/month to auto-generate.
4. alert system for startup hiring pages >$10M raised. recruiters checking careers pages daily. makes $15k/month from layoff signals.
5. AI catching Stripe webhook failures and auto-fixing. companies losing $10k/month to failed payments. charge per recovery.
6. marketplace for crypto-friendly accountants. startups getting rejected by traditional firms. matchmaker makes $20k/month.
7. AI analyzing GitHub to predict trending dev tools. VCs paying $5k/month for early adoption signals.
8. Slack scanner finding sensitive data sharing. companies violating compliance. $1k/month per security audit.
9. AI turning raw sales calls into pitch snippets. sales teams recreating same demos. saves 10 hours per rep weekly.
10. database of startups outgrowing HubSpot. some companies overpaying for basic CRM. charge sellers for leads.
11. AI generating industry-specific privacy policies. lawyers charging $2k for templates. $100 per document.
12. marketplace for fractional dev advocates. startups can't afford full-time. platform makes $30k/month matching.
13. AI monitoring competitor pricing changes. product teams missing market shifts. $200/month for alerts.
14. Figma to React component converter. developers spending days on UI. charge per export.
15. AI analyzing VC rejections for feedback. founders missing improvement signals. charge per analysis.
16. tool finding unused SaaS credits. startups waste $50k/year on tools they could get free. charge 20% of savings.
17. AI generating custom investor updates. founders spending 5 hours monthly. one tool makes $10k/month automating.
18. marketplace for cancelled conference booths. last-minute spaces go empty. broker makes $25k/month reselling.
19. AI scanning terms of service for hidden fees. companies missing subscription traps. charge per scan.
20. directory of startups offering AI APIs. developers need GPT alternatives. monetize through affiliate.
21. tool turning spreadsheets into mobile apps. small businesses need custom apps. charge per publish.
22. AI monitoring Twitter for customer complaints. support teams missing mentions. $300/month per brand.
23. marketplace for startup-ready CFOs. companies need part-time finance. $40k/month in placement fees.
24. chrome extension saving LinkedIn to HubSpot. sales teams copying manually. charge per sync.
25. AI generating personalized cold emails. founders spending hours on outreach. $500/month per campaign.
26. tool finding underpriced micro-SaaS. investors manually tracking small companies. charge for deal flow.
27. AI detecting fake reviews on product sites. brands losing sales to competitors. $1k/month per scan.
28. marketplace for verified white-label agencies. founders waste weeks finding contractors. charge listing fees.
29. AI turning blog posts into Twitter threads. content teams duplicating work. $200/month per brand.
30. database of remote dev teams for hire. startups overpaying recruiters. charge teams listing fees.
31. tool monitoring AI hallucination rates. companies blindly trusting outputs. charge per check.
32. AI analyzing Zoom calls for sales insights. calls have gold but no one rewatches. $100/seat monthly.
33. marketplace for startup acquisition targets under $5M ARR. charge for deal access.
34. AI turning product screenshots into landing pages. marketers spending days designing. charge per page.
35. tool finding open source alternatives to SaaS. companies overpaying for basic features. affiliate revenue.
36. AI generating custom app store screenshots. developers using generic images. $50 per app update.
37.
❤6🔥2👍1🦄1
Microsoft Research’s AI2BMD, an AI-based system that efficiently simulates a wide range of proteins in all-atom resolution, can advance drug discovery and biomolecular research.
Microsoft Research
Advancement in protein dynamics: AI2BMD unveiled
Microsoft Research’s AI2BMD, an AI-based system that efficiently simulates a wide range of proteins in all-atom resolution, can advance drug discovery and biomolecular research.
Quantum Computing. The Banque de France and the Monetary Authority of Singapore have announced the successful completion of a groundbreaking joint experiment in post-quantum cryptography (#PQC) conducted across continents over conventional Internet technologies.
The PQC experiment aims to strengthen communication & data security in the face of quantum computing advancements, and the successful experimentation marks a crucial milestone in the evolution of the protection of international electronic communications against the cybersecurity threats posed by quantum computing.
Using Microsoft Outlook as the email client coupled with a PQC email plugin, BdF and MAS successfully exchanged digitally-signed and encrypted emails using PQC algorithms, namely CRYSTALS-Dilithium and CRYSTALS-Kyber.
The PQC experiment aims to strengthen communication & data security in the face of quantum computing advancements, and the successful experimentation marks a crucial milestone in the evolution of the protection of international electronic communications against the cybersecurity threats posed by quantum computing.
Using Microsoft Outlook as the email client coupled with a PQC email plugin, BdF and MAS successfully exchanged digitally-signed and encrypted emails using PQC algorithms, namely CRYSTALS-Dilithium and CRYSTALS-Kyber.
Salesforce introduced CRMArena - a work-oriented benchmark for LLM agents to prove their mettle in real-world business scenarios
CRMArena features nine distinct tasks within a complex business environment filled with rich and realistic data, all validated by domain experts.
Code.
Leaderboard.
CRMArena features nine distinct tasks within a complex business environment filled with rich and realistic data, all validated by domain experts.
Code.
Leaderboard.
arXiv.org
CRMArena: Understanding the Capacity of LLM Agents to Perform...
Customer Relationship Management (CRM) systems are vital for modern enterprises, providing a foundation for managing customer interactions and data. Integrating AI agents into CRM systems can...
MIT introduced DART: breaking the barriers for robotic data collection by enabling anyone, anywhere in the world to control robots without even having a robot.
No need for resets or setting environments
Support for multiple robots
Robot bootstraps and autonomously collects data in simulation while you sleep!
This is the first step towards a fully crowdsourced and open-source foundation model for robotics.
No need for resets or setting environments
Support for multiple robots
Robot bootstraps and autonomously collects data in simulation while you sleep!
This is the first step towards a fully crowdsourced and open-source foundation model for robotics.
🆒2
A Comprehensive Survey of Small Language Models
Nice survey on small language models (SLMs) and discussion on issues related to definitions, applications, enhancements, reliability, and more.
What are SLMs?
Think of them as compact versions of large language models (like GPT-4), typically with fewer than 7 billion parameters. They're designed to be efficient while maintaining impressive capabilities.
Key Advantages:
• Run directly on mobile devices
• Better privacy (no cloud required)
• Lower computational costs
• Faster response times
• More energy-efficient
Use Cases:
• Question answering
• Code generation
• Recommendation systems
• Web search
• Mobile applications
• Domain-specific tasks
Why They're Revolutionary:
1. Privacy First: Process data locally without sending it to the cloud
2. Accessibility: Work on standard hardware without expensive GPUs
3. Cost-Effective: Lower operational costs for businesses
4. Eco-Friendly: Reduced energy consumption
Future Potential:
• Enhanced efficiency through specialized architectures
• Broader adoption in mobile apps
• Improved performance in specific domains
• Better integration with larger models
Nice survey on small language models (SLMs) and discussion on issues related to definitions, applications, enhancements, reliability, and more.
What are SLMs?
Think of them as compact versions of large language models (like GPT-4), typically with fewer than 7 billion parameters. They're designed to be efficient while maintaining impressive capabilities.
Key Advantages:
• Run directly on mobile devices
• Better privacy (no cloud required)
• Lower computational costs
• Faster response times
• More energy-efficient
Use Cases:
• Question answering
• Code generation
• Recommendation systems
• Web search
• Mobile applications
• Domain-specific tasks
Why They're Revolutionary:
1. Privacy First: Process data locally without sending it to the cloud
2. Accessibility: Work on standard hardware without expensive GPUs
3. Cost-Effective: Lower operational costs for businesses
4. Eco-Friendly: Reduced energy consumption
Future Potential:
• Enhanced efficiency through specialized architectures
• Broader adoption in mobile apps
• Improved performance in specific domains
• Better integration with larger models
arXiv.org
A Comprehensive Survey of Small Language Models in the Era of...
Large language models (LLMs) have demonstrated emergent abilities in text generation, question answering, and reasoning, facilitating various tasks and domains. Despite their proficiency in...
eBook-How-to-Build-a-Career-in-AI.pdf
3.5 MB
Key Insights from Andrew Ng's "How to Build Your Career in AI"
Andrew Ng, the founder of DeepLearning.AI, shares his comprehensive guide on building a successful career in AI.
Here are the essential takeaways:
🎯 Three Core Steps to Career Growth:
Learning foundational skills
Working on projects
Finding the right job
🧠 Must-Have Technical Skills:
Machine Learning fundamentals
Deep Learning
Software Development
Mathematics (Linear Algebra, Statistics, Probability)
Data structures and algorithms
📚 Project Development Strategy:
Start small with learning projects
Graduate to personal projects
Build value-creating solutions
Show progression in complexity
Create a strong portfolio
💼 Job Search Tips:
Use informational interviews
Build a supportive network
Focus on one transition at a time (either role or industry)
Choose great teammates over exciting projects
Pay attention to company culture
🌟 Keys to Success:
Embrace teamwork
Build genuine connections
Maintain personal discipline
Practice continuous learning
Help others grow
💭 Overcoming Imposter Syndrome:
Remember: 70% of people experience it
Focus on your strengths
Find supportive mentors
Celebrate small wins
Keep learning and growing
Andrew Ng, the founder of DeepLearning.AI, shares his comprehensive guide on building a successful career in AI.
Here are the essential takeaways:
🎯 Three Core Steps to Career Growth:
Learning foundational skills
Working on projects
Finding the right job
🧠 Must-Have Technical Skills:
Machine Learning fundamentals
Deep Learning
Software Development
Mathematics (Linear Algebra, Statistics, Probability)
Data structures and algorithms
📚 Project Development Strategy:
Start small with learning projects
Graduate to personal projects
Build value-creating solutions
Show progression in complexity
Create a strong portfolio
💼 Job Search Tips:
Use informational interviews
Build a supportive network
Focus on one transition at a time (either role or industry)
Choose great teammates over exciting projects
Pay attention to company culture
🌟 Keys to Success:
Embrace teamwork
Build genuine connections
Maintain personal discipline
Practice continuous learning
Help others grow
💭 Overcoming Imposter Syndrome:
Remember: 70% of people experience it
Focus on your strengths
Find supportive mentors
Celebrate small wins
Keep learning and growing
BlackRock’s Bitcoin ETF Achieves Record $1.1 Billion in Single-Day Inflows
BlackRock’s iShares Bitcoin Trust (IBIT) has set a new benchmark with over $1.1 billion in net inflows recorded on Thursday, marking the highest single-day inflows for the fund.
This surge comes on the heels of a broader trend, as the 12 U.S. spot Bitcoin ETFs collectively reported total daily net inflows of $1.38 billion—also a record since their inception in January.
BlackRock’s iShares Bitcoin Trust (IBIT) has set a new benchmark with over $1.1 billion in net inflows recorded on Thursday, marking the highest single-day inflows for the fund.
This surge comes on the heels of a broader trend, as the 12 U.S. spot Bitcoin ETFs collectively reported total daily net inflows of $1.38 billion—also a record since their inception in January.
CoinMarketCap Academy
BlackRock’s Bitcoin ETF Achieves Record $1.1 Billion in Single-Day Inflows
BlackRock’s iShares Bitcoin Trust (IBIT) has set a new benchmark with over $1.1 billion in net inflows recorded on Thursday, marking the highest single-day inflows for the fund.
Nvidia unveiled new updates to Project GR00T, it's comprehensive humanoid robot development suite
It includes environment generation with 2,500+ 3D assets, motion learning, and advanced dexterity training
All trained in sim, deployable to real robots.
It includes environment generation with 2,500+ 3D assets, motion learning, and advanced dexterity training
All trained in sim, deployable to real robots.
NVIDIA Technical Blog
Advancing Humanoid Robot Sight and Skill Development with NVIDIA Project GR00T
Humanoid robots present a multifaceted challenge at the intersection of mechatronics, control theory, and AI. The dynamics and control of humanoid robots are complex, requiring advanced tools…
Coinbase and Langchain AI introduced Agentkit: a production-ready, model-agnostic framework for building AI agents with infinite onchain and web2 functionality
Based Agents on base were just the beginning. it’s time to change the way we interact onchain.
Langchain is a powerful framework with pre-existing integrations for all sorts of web2 APIs, including Gmail, browsing the internet, X, and more.
Imagine the potential of combining these APIs with onchain, autonomous actions, like this demo integrating Aave labs using AI tools.
What’s next?
Over the following weeks, team’ll be releasing Agentkit templates to motivate and inspire some of the use cases.
GitHub
Replit.
Based Agents on base were just the beginning. it’s time to change the way we interact onchain.
Langchain is a powerful framework with pre-existing integrations for all sorts of web2 APIs, including Gmail, browsing the internet, X, and more.
Imagine the potential of combining these APIs with onchain, autonomous actions, like this demo integrating Aave labs using AI tools.
What’s next?
Over the following weeks, team’ll be releasing Agentkit templates to motivate and inspire some of the use cases.
GitHub
Replit.
Coinbase Developer Documentation
Welcome to AgentKit - Coinbase Developer Documentation
Epoch AI launched FrontierMath, a benchmark for evaluating advanced mathematical reasoning in AI
Existing math benchmarks like GSM8K and MATH are approaching saturation, with AI models scoring over 90%—partly due to data contamination.
FrontierMath significantly raises the bar.
Existing math benchmarks like GSM8K and MATH are approaching saturation, with AI models scoring over 90%—partly due to data contamination.
FrontierMath significantly raises the bar.
Epoch AI
FrontierMath
FrontierMath is a benchmark of hundreds of unpublished and extremely challenging math problems to help us to understand the limits of artificial intelligence.
DeFi. Governor Christopher J. Waller of the Federal Reserve Board recently gave a speech on ‘Centralized and Decentralized Finance: Substitutes or Complements?’ at the Vienna Macroeconomics Workshop, Institute of Advanced Studies, Vienna, Austria.
Key Takeaways of the speech:
1. DeFi allows asset trading without intermediaries, distinguishing it from centralized finance, yet it also has applications that complement traditional finance.
2. DLT offers faster and more efficient recordkeeping, useful for 24/7 markets, and is being explored by traditional financial institutions.
3. Tokenizing assets and using DLT can speed up transactions and enable automated, secure trading through #smartcontracts, reducing #settlement and counterparty risks.
4. Smart contracts streamline transactions by automating multiple steps, enhancing #security and #efficiency in both #DeFi and #centralizedfinance.
5. Stablecoins, typically pegged to $, facilitate decentralized trading and have potential in reducing global #payment costs, though they require regulatory safeguards to address #risks.
6. DeFi poses unique risks, including the potential for funds to reach bad actors, raising questions about the need for #regulations similar to those in traditional finance.
7. DeFi technologies can enhance centralized finance by improving efficiency, benefiting households and businesses through a more effective financial system.
Key Takeaways of the speech:
1. DeFi allows asset trading without intermediaries, distinguishing it from centralized finance, yet it also has applications that complement traditional finance.
2. DLT offers faster and more efficient recordkeeping, useful for 24/7 markets, and is being explored by traditional financial institutions.
3. Tokenizing assets and using DLT can speed up transactions and enable automated, secure trading through #smartcontracts, reducing #settlement and counterparty risks.
4. Smart contracts streamline transactions by automating multiple steps, enhancing #security and #efficiency in both #DeFi and #centralizedfinance.
5. Stablecoins, typically pegged to $, facilitate decentralized trading and have potential in reducing global #payment costs, though they require regulatory safeguards to address #risks.
6. DeFi poses unique risks, including the potential for funds to reach bad actors, raising questions about the need for #regulations similar to those in traditional finance.
7. DeFi technologies can enhance centralized finance by improving efficiency, benefiting households and businesses through a more effective financial system.
Board of Governors of the Federal Reserve System
Centralized and Decentralized Finance: Substitutes or Complements?
Thank you for inviting me to speak today.1 I have participated in this conference for nearly 20 years and have often presented my research on monetary theory, banking, and payments. So, I believe this is the right audience to speak to regarding the role of…
Alibaba introduced Qwen2.5-Coder-32B-Instruct: A New Era in AI Coding
Meet the groundbreaking family of coding models that's revolutionizing AI-assisted programming!
The results are nothing short of incredible.
The flagship Qwen2.5-Coder-32B-Instruct achieves remarkable benchmark scores:
✨ HumanEval: 92.7
✨ MBPP: 86.8
✨ CodeArena: 68.9
✨ LiveCodeBench: 31.4
Key highlights that make it special:
- Outperforms GPT-4 in several benchmarks!
- Available in multiple sizes: 0.5B, 1.5B, 3B, 7B, 14B, and 32B
- Supports popular quantization formats: GPTQ, AWQ, GGUF
- Seamless integration with Ollama for local deployment
- Fully open source
Get your hands on it now:
📍 Hugging Face
📍 ModelScope
📍 Kaggle
📍 GitHub
Meet the groundbreaking family of coding models that's revolutionizing AI-assisted programming!
The results are nothing short of incredible.
The flagship Qwen2.5-Coder-32B-Instruct achieves remarkable benchmark scores:
✨ HumanEval: 92.7
✨ MBPP: 86.8
✨ CodeArena: 68.9
✨ LiveCodeBench: 31.4
Key highlights that make it special:
- Outperforms GPT-4 in several benchmarks!
- Available in multiple sizes: 0.5B, 1.5B, 3B, 7B, 14B, and 32B
- Supports popular quantization formats: GPTQ, AWQ, GGUF
- Seamless integration with Ollama for local deployment
- Fully open source
Get your hands on it now:
📍 Hugging Face
📍 ModelScope
📍 Kaggle
📍 GitHub
Qwen
Qwen2.5-Coder Series: Powerful, Diverse, Practical.
GITHUB HUGGING FACE MODELSCOPE KAGGLE DEMO DISCORD
Introduction Today, we are excited to open source the “Powerful”, “Diverse”, and “Practical” Qwen2.5-Coder series, dedicated to continuously promoting the development of Open CodeLLMs.
Powerful: Qwen2.5-Coder…
Introduction Today, we are excited to open source the “Powerful”, “Diverse”, and “Practical” Qwen2.5-Coder series, dedicated to continuously promoting the development of Open CodeLLMs.
Powerful: Qwen2.5-Coder…
AlphaFold 3 is now open source!
AlphaFold 3 is a revolutionary AI model developed by Google DeepMind and Isomorphic Labs that can predict the 3D structures and interactions of virtually all biological molecules (including proteins, DNA, RNA, and small molecules) with crazy accuracy, achieving at least 50% improvement over previous existing methods.
AlphaFold 3 is a revolutionary AI model developed by Google DeepMind and Isomorphic Labs that can predict the 3D structures and interactions of virtually all biological molecules (including proteins, DNA, RNA, and small molecules) with crazy accuracy, achieving at least 50% improvement over previous existing methods.
GitHub
GitHub - google-deepmind/alphafold3: AlphaFold 3 inference pipeline.
AlphaFold 3 inference pipeline. Contribute to google-deepmind/alphafold3 development by creating an account on GitHub.
Justin Drake proposed a new consensus layer upgrade proposal "Beam Chain" at the Devcon conference, which is called "Ethereum 3.0" by the community.
The proposal aims to achieve faster block times, lower validator staking requirements, "chain snarkifaction" and quantum security improvements.
It is expected to formulate specifications in 2025 and enter the full testing phase in 2027.
The proposal aims to achieve faster block times, lower validator staking requirements, "chain snarkifaction" and quantum security improvements.
It is expected to formulate specifications in 2025 and enter the full testing phase in 2027.
The Block
Justin Drake proposes 'Beam Chain,' an Ethereum consensus layer redesign
Ethereum Foundation researcher Justin Drake announced Beam Chain, a new consensus layer upgrade proposal for Ethereum at Devcon on Tuesday.