Embedded signup v4

Embedded signup v4

Video by Meta Developers via YouTube
Embedded signup v4

Embedded signup v2/v3 deprecating Oct 15, 2026.
✅ One flow for WhatsApp, Instagram & Messenger APIs
✅ Fewer onboarding screens for your customers
✅ Upgrade takes less than one week
Full migration tutorial → https://bit.ly/4dvLuuM
Developer docs → https://bit.ly/4957smT
#MetaForDevelopers #BusinessMessaging #WhatsAppAPI #Shorts #DeveloperTools

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“We’re not teaching marketers to use AI. We’re teaching AI to speak marketing.”

Video by Rasa via YouTube

In this clip from The Dialogue Architects, Eugenia Zeibig, a marketing AI leader in pharma, shares why enterprise AI only becomes useful when it understands the nuance of the business it serves — and why trustworthy outputs matter more than abundant ones.

Watch the full episode: From Reports to Responses: AI in Pharma Marketing

Views shared are Eugenia Zeibig’s own.

#EnterpriseAI #ConversationalAI #PharmaMarketing #AIInnovation #MarketingAI #HealthcareAI #Rasa

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Open Source & Tech Digest: Kernel Bugs, Wikipedia Editors & More

Open Source & Tech Digest: Kernel Bugs, Wikipedia Editors & More

Software & Security Updates A logic bug in the Linux kernel’s __ptrace_may_access() function has been reported, potentially allowing privilege escalation. Maintainers are reviewing the patch. A discussion thread asks: “What’s an open-source project you genuinely can’t believe is free?” – highlighting popular free tools like Blender, VLC, and FFmpeg. CRuby threads don’t offer true parallelism … Read more

Open-Source AI: Security, Strategy & New Models

Analysis This week’s top stories revolve around the dual nature of open-source AI: its rapid innovation versus emerging security concerns. The standout insight is that open-source AI is entering a strategic phase—both as a corporate strategy (Forrester’s OCX 2026) and as a trust-building tool (Red Hat). However, security flaws exposed by Okta and the OpenAI … Read more

AI Reshapes Coding, Content, and Careers

The latest wave of open source and AI news signals a clear shift: AI is no longer just a tool for coders—it’s becoming a platform for everyone. From automating entry-level programming jobs to optimizing prompts without hand-tuning, the landscape is evolving fast. For open source enthusiasts, this means both risk and opportunity. The key is … Read more

Computer Science Graduates in 2026 💀

Computer Science Graduates in 2026 💀

Video by OpenSource via YouTube
Computer Science Graduates in 2026 💀

POV: You spent 4 years studying computer science… then AI replaces entry-level jobs.

This graduate’s answer shocked everyone 😳

Do you think AI will make degrees useless or create new opportunities?

#computerscience #ai #programming #softwareengineer #kenya #graduates #artificialintelligence #coding #tech #shorts

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Codex for Everyday Work: AI Agents Beyond Coding

Codex for Everyday Work: AI Agents Beyond Coding

Video by OpenAI via YouTube
Codex for Everyday Work: AI Agents Beyond Coding

Codex began as a tool for developers. Today, people are using it for much more: research, planning, file organization, automation, data analysis, presentations, and other everyday knowledge work.

In this OpenAI Forum conversation, Chris Nicholson of OpenAI Global Affairs speaks with Thibault Sottiaux, Head of Codex, about how Codex is evolving beyond software engineering and what that shift means for workers, teams, and organizations.

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MLflow Prompt Optimization with GEPA: Training Data, Scorers & Registry Versioning (Notebook 1.8)

MLflow Prompt Optimization with GEPA: Training Data, Scorers & Registry Versioning (Notebook 1.8)

Video by MLflow via YouTube
MLflow Prompt Optimization with GEPA: Training Data, Scorers & Registry Versioning (Notebook 1.8)

In the eighth installment of Mastering MLflow for GenAI, Jules Damji shows how to go beyond manual prompt iteration (covered in Notebook 1.5 / Episode 5) and use GEPA (Genetic‑Pareto) prompt optimization in MLflow to automatically evolve a baseline prompt into a stronger variant—while keeping everything versioned in the Prompt Registry and measurable with a clear before vs after comparison.

This episode uses a deliberately simple benchmark style inspired by short‑answer QA (similar in spirit to HotpotQA‑style “single token / one‑to‑two word” expectations): the model must stop being verbose and return only the expected short answer so an exact‑match scorer can fire cheaply (pure Python, no LLM calls in the scorer for this demo).

What you’ll learn
🔹 Automated prompt optimization with GEPA using MLflow’s integrated API: mlflow.genai.optimize_prompts
🔹 How to wire the three required pieces: training examples (input + expected output), a predict function (load prompt from registry → fill template → call LLM), and scorers (here: a @scorer exact‑match judge for fast iteration)
🔹 How GEPA’s loop works in practice: Evaluate → Reflect → Improve → Select → Repeat until convergence/budget
🔹 What “budget” means in this context (metric calls / iterations, not “dollars”), plus early stopping when improvements stall ( max_iterations_without_improvement in the walkthrough)
🔹 How optimization produces a new Prompt Registry version (baseline vs optimized), and how to read the run comparison from a weak baseline score to a strong post‑optimization score on the toy setup

Key takeaways
🔹 Scorer design is the product decision: exact match is great for crisp targets; LLM judges are for semantic nuance—but they change cost/latency inside optimization loops.
🔹 Prompt Registry + optimization is the scalable combo: treat optimized prompts as versioned artifacts, not one‑off string edits.
🔹 GEPA is meant to reduce the human “try prompt v17” grind by making improvement systematic—while MLflow keeps the evidence in traces/runs/metrics you can audit.

Resources
🔗 Notebook 1.8: https://github.com/dmatrix/mlflow-genai-tutorials/blob/main/08_prompt_optimization.ipynb
🎥 Full series playlist: https://youtube.com/playlist?list=PLaoPu6xpLk9EI99TuOjSgy-UuDWowJ_mR
📚 MLflow prompt optimization docs: https://mlflow.org/docs/latest/genai/prompt-registry/optimize-prompts/

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MetriCal and AutoCal: Calibration Forever

MetriCal and AutoCal: Calibration Forever

Video by OpenCV via YouTube
MetriCal and AutoCal: Calibration Forever

OpenCV Live! returns with a new episode on one of the toughest problems in computer vision: camera and sensor calibration. Luckily for us, we’ve got Brandon Minor, CEO of Tangram Vision, to tell us how it’s done. Join our live stream to see the latest calibration technology and stick around for our giveaway of a free OpenCV University course to one lucky viewer.

OpenCV is a 501(c)(3) registered non-profit in the United States. See how you can support open source CV & AI: http://opencv.org/support/

Watch along for your chance to win during our live trivia segment, and participate in the live Q&A session with questions from you in the audience.

Become a paid member of the channel to help us make more episodes https://www.youtube.com/channel/UCkrcW82Y2kbgU-U9RaYfgxw/join

Got a cool project of your own? Send it to us and you may be featured https://www.jotform.com/form/233105358823151

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