Ten from the weekend 02/15: A few interesting reads that I came across:

Focus areas: Blockchain| ML-AI| Data science/Analytics applications |eSports| CRISPR| Design thinking

1. Competitive Programming with Large Reasoning Models by open AI: https://arxiv.org/abs/2502.06807

2. An AI controlled cloud computer: https://github.com/e2b-dev/open-computer-use

3. An Interview with Uber CEO Dara Khosrowshahi About Aggregation and Autonomy: https://stratechery.com/2025/an-interview-with-uber-ceo-dara-khosrowshahi-about-aggregation-and-autonomy/ || Not a big fan of uber but worth a read

4. https://github.com/dzhng/deep-research || Open deep search not as good as o1 pro but a good engine || another one: https://huggingface.co/blog/open-deep-research

5. Unbundling the BPO: AI agents will eat BPO: https://a16z.com/unbundling-the-bpo-how-ai-will-disrupt-outsourced-work/ || this is going to be same for KPO likely — needs articulation. Deep search is a first domino in KPO.

6. https://fortune.com/2025/02/12/exclusive-fal-generative-media-platform-for-developers-raises-49-million-series-b/ || FAL is an amazing product.

7. https://openai.com/index/sharing-the-latest-model-spec/ || its agentic ai models going forward (all of them) || https://blog.samaltman.com/three-observations

8. Deep Research and Knowledge Value: https://stratechery.com/2025/deep-research-and-knowledge-value/

9. Ingesting Millions of PDFs and why Gemini 2.0 Changes Everything: https://www.sergey.fyi/articles/gemini-flash-2

10. Methods and Strategies for Building and Refining Reasoning Models: https://magazine.sebastianraschka.com/p/understanding-reasoning-llms

Some very nice videos from industry leaders for free:

CS153 Infra at Scale

TL DR: — Summary of the links if you need to do a quick read (summary by AI agent ofcourse):

This week’s roundup of key developments in artificial intelligence and AI-driven agents.

• Competitive Programming with Large Reasoning Models by OpenAI
• Link: https://arxiv.org/abs/2502.06807
• Summary: OpenAI explores how large language models can be fine-tuned to tackle high-level competitive programming tasks. The paper illustrates novel approaches for translating human-like reasoning into code, moving beyond traditional coding assistants. This has practical implications for teams looking to automate parts of the software development cycle, particularly complex algorithmic challenges.
• An AI-Controlled Cloud Computer
• Link: https://github.com/e2b-dev/open-computer-use
• Summary: This GitHub project showcases how AI agents can be granted system-level access to a cloud environment for rapid experimentation and execution of tasks. The system automates environment setup, deployment, and resource allocation under AI control. It signifies a step toward fully autonomous cloud-based development and operation workflows.
• An Interview with Uber CEO Dara Khosrowshahi About Aggregation and Autonomy
• Link: https://stratechery.com/2025/an-interview-with-uber-ceo-dara-khosrowshahi-about-aggregation-and-autonomy/
• Summary: Despite any reservations about Uber, this interview offers insight into how the company envisions AI-driven mobility services and data aggregation. Dara Khosrowshahi discusses self-driving technology, customer engagement, and how Uber plans to integrate AI for operational efficiency. The conversation provides a glimpse of how large-scale platforms might leverage AI for end-to-end service automation.
• Open Deep Research
• Links:
• https://github.com/dzhng/deep-research
• https://huggingface.co/blog/open-deep-research
• Summary: These projects focus on providing a search engine and toolkit for deep-learning–based queries, aiming to democratize advanced research workflows. While not as sophisticated as some commercial solutions, they offer accessible options for researchers and developers working on smaller-scale projects. This open-source approach allows for faster innovation and collaboration across the AI community.
• Unbundling the BPO: How AI Will Disrupt Outsourced Work
• Link: https://a16z.com/unbundling-the-bpo-how-ai-will-disrupt-outsourced-work/
• Summary: Andreessen Horowitz delves into how AI agents could revolutionize the Business Process Outsourcing (BPO) industry — and by extension, Knowledge Process Outsourcing (KPO). By automating routine tasks, AI threatens to replace or drastically alter current outsourcing models. Deep search capabilities, highlighted here as a “first domino,” underscore the trajectory toward more specialized, AI-driven knowledge work.
• FAL’s Generative Media Platform Raises $49 Million in Series B
• Link: https://fortune.com/2025/02/12/exclusive-fal-generative-media-platform-for-developers-raises-49-million-series-b/
• Summary: FAL’s significant fundraising signals strong investor confidence in AI-powered media generation. The platform enables developers to seamlessly integrate generative capabilities into their projects, from video creation to interactive applications. Such investment underscores the growing demand for creative and automated content solutions across industries.
• OpenAI’s Latest Agentic AI Models
• Links:
• https://openai.com/index/sharing-the-latest-model-spec/
• https://blog.samaltman.com/three-observations
• Summary: OpenAI provides insights into a new line of “agentic” models geared toward more autonomous decision-making. Sam Altman’s blog post highlights three observations about scaling, data efficiency, and the paradigm shift toward AI systems capable of continuous learning. Together, these resources point to a future where AI can increasingly operate independently and iteratively.
• Deep Research and Knowledge Value
• Link: https://stratechery.com/2025/deep-research-and-knowledge-value/
• Summary: This piece argues that true value in AI goes beyond surface-level data analysis, emphasizing the importance of rigorous, deep research. The article also highlights how strategic, knowledge-oriented AI investments can lead to sustainable competitive advantage. It underscores the economic and operational benefits of investing in robust research infrastructures.
• Ingesting Millions of PDFs and Why Gemini 2.0 Changes Everything
• Link: https://www.sergey.fyi/articles/gemini-flash-2
• Summary: This technical deep dive discusses Gemini 2.0’s capacity to parse and process massive document repositories, notably millions of PDFs. The underlying architecture suggests major benefits for industries reliant on large-scale document analysis, like legal, finance, and medical research. Gemini 2.0’s approach highlights how next-gen models handle vast, unstructured data more efficiently and intelligently.
• Methods and Strategies for Building and Refining Reasoning Models
• Link: https://magazine.sebastianraschka.com/p/understanding-reasoning-llms
• Summary: Sebastian Raschka outlines best practices for designing and refining large language models with strong reasoning capabilities. Topics include prompt engineering, iterative fine-tuning, and interpretability — key aspects for developers and researchers seeking to create more transparent and reliable AI systems. This guidance helps unify the theoretical underpinnings of AI models with practical deployment strategies.