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Maximize your portfolio, analyze markets, and make data-driven investment decisions using Python and generative AI. Investing for Programmers shows you how you can turn your existing skills as a programmer into a knack for making sharper investment choices. You’ll learn how to use the Python ecosystem, modern analytic methods, and cutting-edge AI tools to make better decisions and improve the odds of long-term financial success. In Investing for Programmers you’ll learn how to: Build stock analysis tools and predictive models Identify market-beating investment opportunities Design and evaluate algorithmic trading strategies Use AI to automate investment research Analyze market sentiments with media data mining In Investing for Programmers you'll learn the basics of financial investment as you conduct real market analysis, connect with trading APIs to automate buy-sell, and develop a systematic approach to risk management. Don’t worry—there’s no dodgy financial advice or flimsy get-rich-quick schemes. Real-life examples help you build your own intuition about financial markets, and make better decisions for retirement, financial independence, and getting more from your hard-earned money. About the Technology A programmer has a unique edge when it comes to investing. Using open-source Python libraries and AI tools, you can perform sophisticated analysis normally reserved for expensive financial professionals. This book guides you step-by-step through building your own stock analysis tools, forecasting models, and more so you can make smart, data-driven investment decisions. About the Book Investing for Programmers shows you how to analyze investment opportunities using Python and machine learning. In this easy-to-read handbook, experienced algorithmic investor Stefan Papp shows you how to use Pandas, NumPy, and Matplotlib to dissect stock market data, uncover patterns, and build your own trading models. You’ll also discover how to use AI agents and LLMs to enhance your financial research and decision-making process. What's Inside Build stock analysis tools and predictive models Design algorithmic trading strategies Use AI to automate investment research Analyze market sentiment with media data mining About the Reader For professional and hobbyist Python programmers with basic personal finance experience. About the Author Stefan Papp combines 20 years of investment experience in stocks, cryptocurrency, and bonds with decades of work as a data engineer, architect, and software consultant. Quotes Especially valuable for anyone looking to improve their investing. - Armen Kherlopian, Covenant Venture Capital A great breadth of topics—from basic finance concepts to cutting-edge technology. - Ilya Kipnis, Quantstrat Trader A top tip for people who want to leverage development skills to improve their investment possibilities. - Michael Zambiasi, Raiffeisen Digital Bank Brilliantly bridges the worlds of coding and finance. - Thomas Wiecki, PyMC Labs

Pre-registration is REQUIRED. Add to your calendar - https://hubs.li/Q03kk6YJ0

AI agents are only as good as what they remember. Their ability to recall, reason, and act on relevant information is becoming a make-or-break factor. But building that memory—from raw data to structured, useful context—is still a major challenge.

In this webinar, we kick things off with a perspective on why this matters now. Drawing on experiences from data science and intelligent systems, we’ll highlight the growing importance of agent memory in real-world applications—and the role of knowledge graphs and contextual retrievers in making agents smarter, faster, and more trustworthy.

Then, we’ll dive into the how. Using Hypermode’s open-source tools, let’s walk through the process of building agent-ready context: crafting retrievers and feature stores, and designing tools that expose your graph in ways agents can actually use. We share the key design decisions behind these tools, including trade-offs, live demos, and lessons learned from real implementations.

What you’ll learn: - Why memory is a core capability for powerful agents - What it takes to expose graph data meaningfully to agents in live environments - How to design and curate tools that align with your agents’ worldviews and goals

This session is great for those building their first AI agent and those looking to improve how agents reason and act.

Speaker#1: Kevin Van Gundy, CEO of Hypermode He has spent his career building for developers, data scientists, and engineers. Previously he was the Chief Operating Officer and Chief Revenue Officer at Vercel and has held various leadership roles at Domino Data, Tray.io, and Neo4j. Kevin is involved in investing and advising startups through his involvement at Van Gundy Farms. His experience ranges from working in private equity to leading global go-to-market strategies for various companies. He has a Bachelor of Arts in English and Economics from the University of California, Davis.

Speaker2: William Lyon, AI Engineer at Hypermode He works to improve the developer experience of building agentic flows. Previously he worked as a software developer at Neo4j and other startups. He is also the author of the book "Fullstack GraphQL Applications" and earned a masters degree in Computer Science from the University of Montana. You can find him online at lyonwj.com

ODSC Links: • Get free access to more talks/trainings like this at Ai+ Training platform: https://hubs.li/H0Zycsf0 • ODSC blog: https://opendatascience.com/ • Facebook: https://www.facebook.com/OPENDATASCI • Twitter: https://twitter.com/_ODSC & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science • Slack Channel: https://hubs.li/Q038cQBy0 • Code of conduct: https://odsc.com/code-of-conduct/

Pre-registration is REQUIRED. Add to your calendar - https://hubs.li/Q03kk6YJ0

AI agents are only as good as what they remember. Their ability to recall, reason, and act on relevant information is becoming a make-or-break factor. But building that memory—from raw data to structured, useful context—is still a major challenge.

In this webinar, we kick things off with a perspective on why this matters now. Drawing on experiences from data science and intelligent systems, we’ll highlight the growing importance of agent memory in real-world applications—and the role of knowledge graphs and contextual retrievers in making agents smarter, faster, and more trustworthy.

Then, we’ll dive into the how. Using Hypermode’s open-source tools, let’s walk through the process of building agent-ready context: crafting retrievers and feature stores, and designing tools that expose your graph in ways agents can actually use. We share the key design decisions behind these tools, including trade-offs, live demos, and lessons learned from real implementations.

What you’ll learn: - Why memory is a core capability for powerful agents - What it takes to expose graph data meaningfully to agents in live environments - How to design and curate tools that align with your agents’ worldviews and goals

This session is great for those building their first AI agent and those looking to improve how agents reason and act.

Speaker#1: Kevin Van Gundy, CEO of Hypermode He has spent his career building for developers, data scientists, and engineers. Previously he was the Chief Operating Officer and Chief Revenue Officer at Vercel and has held various leadership roles at Domino Data, Tray.io, and Neo4j. Kevin is involved in investing and advising startups through his involvement at Van Gundy Farms. His experience ranges from working in private equity to leading global go-to-market strategies for various companies. He has a Bachelor of Arts in English and Economics from the University of California, Davis.

Speaker2: William Lyon, AI Engineer at Hypermode He works to improve the developer experience of building agentic flows. Previously he worked as a software developer at Neo4j and other startups. He is also the author of the book "Fullstack GraphQL Applications" and earned a masters degree in Computer Science from the University of Montana. You can find him online at lyonwj.com

ODSC Links: • Get free access to more talks/trainings like this at Ai+ Training platform: https://hubs.li/H0Zycsf0 • ODSC blog: https://opendatascience.com/ • Facebook: https://www.facebook.com/OPENDATASCI • Twitter: https://twitter.com/_ODSC & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science • Slack Channel: https://hubs.li/Q038cQBy0 • Code of conduct: https://odsc.com/code-of-conduct/

Pre-registration is REQUIRED. Add to your calendar - https://hubs.li/Q03kk6YJ0

AI agents are only as good as what they remember. Their ability to recall, reason, and act on relevant information is becoming a make-or-break factor. But building that memory—from raw data to structured, useful context—is still a major challenge.

In this webinar, we kick things off with a perspective on why this matters now. Drawing on experiences from data science and intelligent systems, we’ll highlight the growing importance of agent memory in real-world applications—and the role of knowledge graphs and contextual retrievers in making agents smarter, faster, and more trustworthy.

Then, we’ll dive into the how. Using Hypermode’s open-source tools, let’s walk through the process of building agent-ready context: crafting retrievers and feature stores, and designing tools that expose your graph in ways agents can actually use. We share the key design decisions behind these tools, including trade-offs, live demos, and lessons learned from real implementations.

What you’ll learn: - Why memory is a core capability for powerful agents - What it takes to expose graph data meaningfully to agents in live environments - How to design and curate tools that align with your agents’ worldviews and goals

This session is great for those building their first AI agent and those looking to improve how agents reason and act.

Speaker#1: Kevin Van Gundy, CEO of Hypermode He has spent his career building for developers, data scientists, and engineers. Previously he was the Chief Operating Officer and Chief Revenue Officer at Vercel and has held various leadership roles at Domino Data, Tray.io, and Neo4j. Kevin is involved in investing and advising startups through his involvement at Van Gundy Farms. His experience ranges from working in private equity to leading global go-to-market strategies for various companies. He has a Bachelor of Arts in English and Economics from the University of California, Davis.

Speaker2: William Lyon, AI Engineer at Hypermode He works to improve the developer experience of building agentic flows. Previously he worked as a software developer at Neo4j and other startups. He is also the author of the book "Fullstack GraphQL Applications" and earned a masters degree in Computer Science from the University of Montana. You can find him online at lyonwj.com

ODSC Links: • Get free access to more talks/trainings like this at Ai+ Training platform: https://hubs.li/H0Zycsf0 • ODSC blog: https://opendatascience.com/ • Facebook: https://www.facebook.com/OPENDATASCI • Twitter: https://twitter.com/_ODSC & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science • Slack Channel: https://hubs.li/Q038cQBy0 • Code of conduct: https://odsc.com/code-of-conduct/

Pre-registration is REQUIRED. Add to your calendar - https://hubs.li/Q03kk6YJ0

AI agents are only as good as what they remember. Their ability to recall, reason, and act on relevant information is becoming a make-or-break factor. But building that memory—from raw data to structured, useful context—is still a major challenge.

In this webinar, we kick things off with a perspective on why this matters now. Drawing on experiences from data science and intelligent systems, we’ll highlight the growing importance of agent memory in real-world applications—and the role of knowledge graphs and contextual retrievers in making agents smarter, faster, and more trustworthy.

Then, we’ll dive into the how. Using Hypermode’s open-source tools, let’s walk through the process of building agent-ready context: crafting retrievers and feature stores, and designing tools that expose your graph in ways agents can actually use. We share the key design decisions behind these tools, including trade-offs, live demos, and lessons learned from real implementations.

What you’ll learn: - Why memory is a core capability for powerful agents - What it takes to expose graph data meaningfully to agents in live environments - How to design and curate tools that align with your agents’ worldviews and goals

This session is great for those building their first AI agent and those looking to improve how agents reason and act.

Speaker#1: Kevin Van Gundy, CEO of Hypermode He has spent his career building for developers, data scientists, and engineers. Previously he was the Chief Operating Officer and Chief Revenue Officer at Vercel and has held various leadership roles at Domino Data, Tray.io, and Neo4j. Kevin is involved in investing and advising startups through his involvement at Van Gundy Farms. His experience ranges from working in private equity to leading global go-to-market strategies for various companies. He has a Bachelor of Arts in English and Economics from the University of California, Davis.

Speaker2: William Lyon, AI Engineer at Hypermode He works to improve the developer experience of building agentic flows. Previously he worked as a software developer at Neo4j and other startups. He is also the author of the book "Fullstack GraphQL Applications" and earned a masters degree in Computer Science from the University of Montana. You can find him online at lyonwj.com

ODSC Links: • Get free access to more talks/trainings like this at Ai+ Training platform: https://hubs.li/H0Zycsf0 • ODSC blog: https://opendatascience.com/ • Facebook: https://www.facebook.com/OPENDATASCI • Twitter: https://twitter.com/_ODSC & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science • Slack Channel: https://hubs.li/Q038cQBy0 • Code of conduct: https://odsc.com/code-of-conduct/

We talked about:

Bela's background Why startups even need investors Why open source is a viable go-to-market strategy Building a bottom-up community The investment thesis for the TKM Family Office and the blurriness of the funding round naming convention Angel investors vs VC Funds vs family offices Bela's investment criteria and GitHub stars as a metric Inbound sourcing, outbound sourcing, and investor networking Making a good impression on an investor Balancing open and closed source parts of a product The future of open source Recent successes of open source companies Bela's resource recommendations

Links:

Understand who is engaging with your open source project article: https://www.crowd.dev/ Top 6 Books on Developer Community Building: https://www.crowd.dev/post/top-6-books-on-developer-community-building Which open source software metrics matter: https://www.bvp.com/atlas/measuring-the-engagement-of-an-open-source-software-community#Which-open-source-software-metrics-matter

Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

How investors evaluate startups for success - Bela Wiertz

About this event

Outline:

• Investors and why are they needed
• Investing in Open-Source startups
• Evaluating and analyzing open-source startups

About the speaker:

Bela Wiertz is working for a German family office investing in VC Funds and early-stage startups with a focus on open-source Data, AI & Developer Tooling. In his work he is sourcing, evaluating and working with many different open-source companies in the early stages of finding their product-community-fit.

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