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.

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.

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.

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.

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.

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

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

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.

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.

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.

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.

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.

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

And just like that, after 3 years and 3 days, total crypto market cap is back over $3 trillion and hits a fresh all-time high 🚀

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.

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.

Sanofi, OpenAI, Formation debut patient recruiting tool, will use in Phase 3 multiple sclerosis studies

Muse is AI tool for patient recruitment strategy & content creation.

AI systems like Muse will enable to drastically reduce cost + time of bringing new medicines to patients.

New paper on scaling laws in primate vision modeling

Researchers trained and analyzed 600+ neural networks to understand how bigger models & more data affect brain predictivity.

Precision-Aware Scaling Laws: A New Perspective on Language Model Training and Inference

A groundbreaking paper from researchers at Harvard, Stanford, MIT, and CMU reveals crucial insights into the relationship between model precision, training data, and performance in language models.

Key Findings:

1. Post-Training Quantization Challenge
The researchers discovered a counterintuitive phenomenon: models trained on more data become increasingly sensitive to post-training quantization. This means that after a certain point, additional training data can actually harm performance if the model will be quantized for inference.

2. Optimal Training Precision
The study suggests that the current standard practice of training in 16-bit precision may be suboptimal. Their analysis indicates that 7-8 bits might be the sweet spot for training, challenging both current high-precision (16-bit) and ultra-low precision (4-bit) approaches.

3. Unified Scaling Law
The team developed a comprehensive scaling law that accounts for:
- Training precision effects
- Post-training quantization impacts
- Interactions between model size, data, and precision

4. Practical Implications
- Larger models can be trained effectively in lower precision
- The race to extremely low-precision training (sub-4-bit) may face fundamental limitations
- There's an optimal precision point that balances performance and computational efficiency

5. Methodology
The research is backed by extensive experimentation, including:
- 465+ pretraining runs
- Models up to 1.7B parameters
- Training datasets up to 26B tokens

This work provides valuable insights for ML engineers and researchers working on large language models, suggesting that precision choices should be carefully considered based on model size and training data volume rather than following a one-size-fits-all approach.

The findings have significant implications for future hardware design and training strategies, potentially influencing how we approach model scaling and efficiency optimization in the AI field.

Donald Trump named Elon Musk to a role aimed at creating a more efficient government

Musk and former Republican presidential candidate Vivek Ramaswamy will co-lead a newly created Department of Government Efficiency, an entity Trump indicated will operate outside the confines of government.