HM Godoy

Video by OpenProject | Open Source Project Management via YouTube

The release brings various features and improvements for you, e.g.

00:00 – Introduction
0:15 – A major update: Evolving backlogs and sprints
1:34 – Action boards released to community
2:06 – Improved project home page
2:24 – Sharing of meeting templates (Basic plan and higher)
2:48 – Option to safely change project identifiers
3:03 – Improved workflow configuration

Find out more about all features and improvements in our release notes: https://www.openproject.org/docs/release-notes/17-3-0/

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Video by Open Source Connect via YouTube

🗓 Date: 3rd March
⏰ Time: 8:30 PM IST
🎤 Speaker: Sebastiano Fuccio

Don’t forget to like, share, and subscribe for more talks on AI and emerging technologies!

#OpenSource #ai #cloud #TechTalk #Developers #SebastianoFuccio

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Video by OpenSource via YouTube

Are Kenyan developers actually making money?
We went to JKUAT to find out the truth, from street opinions in Nairobi to real developers at a tech event. Some said devs are struggling… others are making serious money
We also ran a 30-minute challenge where teams had to come up with real solutions for small businesses, and the results were crazy
Plus:
A senior developer breaks down AI in tech
Insights from the Angular community in Kenya
Raw, unfiltered opinions from devs on the ground
This isn’t just a vlog… it’s the reality of tech in Kenya 🇰🇪
Be honest… do you think developers in Kenya are actually making money?

CHAPTERS
0:00 Highlights
0:50 Nairobi Street Interviews
2:36 Breakfast
2:52 JKUAT
3:20 Tech Event
3:39 Senior Dev Interview
6:00 Event Ends
6:32 30 Mins Challenge
6:47 Brainstorming
8:21 Teams Presenting Ideas
23:35 Winner
23:40 Interview with Angular Lead

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Video by OpenSourceLowTech via YouTube

I’m testing out the Flashforge AD5X 3D printer for prop work, printing mask components for the upcoming feature film Life®.

Purchase link (US): https://flashforge.sjv.io/vPrVqj

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Video by OpenAI via YouTube

Gabriel Goh, Kenji Hata, Kiwhan Song, Alex Yu, Boyuan Chen, and Nithanth Kudige join host Sam Altman to introduce and demo ChatGPT Images 2.

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Video by H2O.ai via YouTube

How Enterprise h2oGPTe manages prompt templates, version control, and multilingual AI agent deployment at scale.

Bridging predictive models and end users requires well-engineered, maintainable prompts. h2oGPTe provides a centralized prompt library where teams can create, clone, version, and share templates across the organization. The H2O Super Agent connects natural language prompts directly to predictive scoring APIs—enabling real-world actions like addressing customer churn. Multilingual template support and UI localization allow consistent AI behavior to be deployed across global markets.

Technical Capabilities & Resources

➤ Prompt Templates & Libraries: Create, clone, and share prompt templates from a managed organizational catalog.
🔗 https://docs.h2o.ai/enterprise-h2ogpte/guide/prompts

➤ Prompt Version Control & Iteration: Define system behaviors, iterate on prompt designs, and manage template settings.
🔗 https://docs.h2o.ai/enterprise-h2ogpte/guide/prompts#create-a-prompt-template

➤ Template Sharing Across Teams: Distribute prompt templates for consistent AI behavior organization-wide.
🔗 https://docs.h2o.ai/enterprise-h2ogpte/guide/prompts#share-a-prompt-template

➤ Custom Multilingual Prompts: Configure language-specific templates for consistent, localized global AI deployment.
🔗 https://docs.h2o.ai/enterprise-h2ogpte/guide/prompts#create-a-prompt-template-for-a-specific-language

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Video by MLflow via YouTube

In this sixth episode of this series, Jules Damji dives deep into MLflow’s extensive framework integrations. MLflow supports over 30 different open-source agent-building frameworks, allowing you to automatically trace and evaluate complex AI workflows regardless of your chosen architecture.

This tutorial provides a hands-on comparison of three open source agent building frameworks and demonstrates how MLflow provides full visibility into their execution:
🔹 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻: Learn how to use high-level primitives like ChatPromptTemplate and StringOutputParser to build sequential workflows. We demonstrate both simple chains and complex multi-step sequences connected via the pipeline operator.
🔹 𝗟𝗹𝗮𝗺𝗮𝗜𝗻𝗱𝗲𝘅: See how to build a Retrieval-Augmented Generation (RAG) system. We walk through creating an in-memory vector index, generating embeddings with OpenAI, and using a query engine to retrieve document-based answers, all while capturing the entire operation trace in MLflow.
🔹 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵: For more advanced use cases, we explore building stateful, hierarchical agent workflows. We demonstrate a customer service triage system that uses a supervisor node to classify queries and route them to specialized handlers for billing, tech support, or general inquiries.

Key Takeaways:
🔹 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰 𝗧𝗿𝗮𝗰𝗶𝗻𝗴: All frameworks integrated with MLflow are automatically traced, capturing inputs, outputs, and intermediate steps without manual instrumentation.8
🔹 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗦𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻: Choose LangChain for sequential chains, LlamaIndex for heavy document indexing, and LangGraph for complex, stateful branching or looping workflows.
🔹 𝗩𝗶𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆: Use the MLflow UI to inspect timelines, verify embeddings, and debug the internal logic of your AI agents.

Resources:
🔗 Notebook 1.5: https://github.com/dmatrix/mlflow-genai-tutorials/blob/main/06_framework_integrations.ipynb
🎥 Full Series Playlist: https://youtube.com/playlist?list=PLaoPu6xpLk9EI99TuOjSgy-UuDWowJ_mR&si=jdbAbxTCRuxFxfnG

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Video by Hugging Face via YouTube

The Hugging Face research team discusses Apple’s Embarrassingly Simple Self-Distillation Improves Code Generation paper.

Paper: https://huggingface.co/papers/2604.01193

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