AI/ML
AI, machine learning, agents, and building with them.
28 storiesLatest: 25 October 2026
Lead storyI Attended Poland's First AI Safety Day. Here's What I LearnedI spent Saturday at Warsaw AI Safety Day, the first conference in Poland dedicated to AI safety, mostly in the technical track. Here's what I took away, including my two favourite talks of the day, on biosecurity and robotics.More in AI/ML
- 02What Happens When AI Gets Women's Health WrongAt AI Builders Global Conference 2026 I talked about what happens when a health app answers a question it shouldn't. Here is the longer version: how we decide when AI gets to speak at Asele, what we built, and what our evals caught.
- 03What AI Companies Are Quietly Building in the Life SciencesWhile everyone watched the model releases, the AI labs were building verified access programmes, wet labs, domain agents and traces. Here is what they are working on, and what it changes for researchers.
- 04How my content marketing workflow became fully agentic in a yearA year ago I argued startups did not need AI agents yet. That one word was carrying the whole argument, and it has arrived. My content marketing workflow now runs on agents end to end. Here is what changed.
- 05I shipped a whole site with ChatGPT SitesChatGPT Sites can now build and host live web pages. I made one end to end with none of the usual setup in between. The barrier to shipping is gone, so what matters now is what you do with it.
- 06AI Marketing Agents Need Audit Trails TooMarketing is handing real decisions to autonomous agents. Regulated industries already know what happens next, and the fix is an audit trail that records why.
- 07Reflecting on AI's Progress (and Mine)From learning data science on a phone to running a host of AI agents, a personal reflection on how far AI has come, the TESCREAL bundle, and where things go next.
- 08Why Startups Don’t Need AI Agents (Yet)AI agents are powerful, but for early-stage startups they can be expensive and unnecessary. Learn why custom GPTs, prompt libraries, and scheduled scrapes in ChatGPT are often enough and when agents actually make sense.
- 09A Simplified Guide to AI Governance in AfricaAn accessible overview of how African countries govern AI through policies and regulations, with key players, core issues, and a Rwanda case study.
- 10Responsible AI Practices for Product ManagersAs AI has become an integral part of our lives, shaping almost everything from our online experiences, critical decision-making processes and handling day-to-day interactions.As product managers, it is our responsibility to ensure that the AI systems we develop adhere to ethical guidelines and promote responsible practices.By implementing these principles, we can create products that utilize AI to have a positive impact on users and society at large.In this article, we will explore some essentia
- 11Inclusivity, Values to Shape Future Scenarios When AI Outwits HumansOnly if humanity can devise AI systems that are inclusive and align with its shared values may it avert the AI apocalypse.
- 12Sourcing Data for AI Model Building: Exploring Methods and ConsiderationsIn the field of AI product management, the availability and quality of data play a crucial role in building successful models.The process of sourcing data involves considering various factors such as open and closed sources, outsourcing data collection and annotation, in-house efforts, and alternative methods.In this article, we will explore these different approaches, their pros and cons, and determine which methods work best based on specific situations. Let’s begin:1. Open SourcesOpen sources
- 13Challenges and Considerations for AI Product ManagersWith great power comes great responsibility. As product managers embark on the journey of implementing machine learning systems, they must navigate various ethical and privacy concerns, ensure data quality and mitigate bias, integrate with existing systems, and prioritize continuous learning and improvement. Furthermore, effective collaboration with the data and machine learning team is crucial for success.In this article, we will delve into these key considerations and explore how product manag
- 14Key Steps in Implementing Machine Learning ProjectsSuccessful implementation of machine learning projects requires careful planning and execution.This article will outline the key steps in implementing machine learning projects and provide questions to help product managers navigate the processes effectively.Problem Definition and Goal SettingThe first step in implementing a machine learning project is to clearly define the business problem that needs to be addressed and establish specific goals for the ML project.Product managers must have a de
- 15The Basics of Machine Learning for Product ManagersInthis article, we will delve into the fundamental concepts of artificial intelligence, machine learning and explore their significance for product managers.Let’s begin.What is Artificial Intelligence (AI)?Artificial Intelligence (AI) refers to developing computer systems that can perform tasks that typically require human intelligence.It encompasses a wide range of technologies, algorithms, and approaches to enable machines to mimic cognitive functions like learning, problem-solving, perception
- 16Exploring Prototyping Methods for AI Products: From Wireframes to Functional ModelsPrototyping is crucial in developing AI products, allowing for iterative testing, refining, and gathering user feedback.When it comes to AI, prototyping involves not only the user interface but also the underlying intelligence.In this article, we will delve into various prototyping methods specifically tailored for AI products. From low-fidelity wireframes to functional models, these techniques enable rapid experimentation, validation, and refinement of AI-driven ideas.Let’s take a look:Wirefram
- 17Policy, Advocacy Seek to Safeguard Africa's Internet AccessOver the past three years, Africa has seen several internet blackouts across the continent. In the age of AI, these shutdowns hinder growth, and policy and advocacy have a role to play in protecting access.
- 18AI Hierarchy of Needs: Exploring the Building Blocks of AI ProductsSimilar to Maslow’s Hierarchy of Needs, which outlines the basic needs required for human survival, the AI hierarchy of needs outlines the foundational elements that every AI system needs to be successful.AI Hierarchy of NeedsLet’s take a look at each of these levels.Level 1: DataAI systems require large and diverse amounts of training data to learn from. Asides quantity, the quality of this data matters as the data has to be clean, accurate and relevant. Data quality and quantity are critical f
- 19Not all problems require AI solutions: Understanding when to invest in AIThe use of artificial intelligence (AI) is on the rise, particularly conversational and generative AI. You may be contemplating whether AI is a necessity for your business or product. However, it’s crucial to understand that not every problem requires an AI solution, and not every AI solution is a good fit for your business.Table of Contents· Here are some factors to consider before implementing AI:∘ Your company’s needs and objectives∘ Feasibility of implementing an AI solution∘ The potential r
- 20Differences between Traditional Product Management and AI & Data Product ManagementIt’s important to understand the differences between traditional product management and product management for AI and data products.They may have similarities but have very important differences that we will be sharing in this piece. Before we go into their differences, let’s take a look at the similarities between these roles.Table of Contents· Similarities between Traditional Product Management and AI Product Management· Differences between Traditional Product Management and AI Product Managem
- 21Mastering AI Product Management: 5 Essential Courses for Career GrowthAI has come to stay, and it’s important that product managers and business professionals understand how AI can be useful for enhancing product efficiency.AI product managers ensure the development of AI products meets user needs, aligns with business goals and complies with privacy, ethics and legal standards.If you wish you explore AI product management, here are five courses that will help you get started and cover various aspects of the field from ideation to deployment and ensure you are equ
- 22Introduction to AI Product Management, what is it? why is it important?Of course, with the rapid growth of AI in these recent times, there are several companies now seeking ways to integrate AI into their products.This will lead to the need for more AI Product Managers or Product Managers who are knowledgeable about artificial intelligence and its benefits and additional enhancements to a product.So what is AI Product Management?AI Product Management is the practice of overseeing the development, design and marketing of an AI product or a range of AI products throu
- 23Nigerians in the Age of AIAs AI becomes ever more integral to daily life, it is now no novelty even in the most remote countries. Yet many Nigerians remain unaware of its potential impact on them and on society in Africa's most populous country.
- 24AI's Role in Improving Femtech for African WomenFemtech refers to technology that supports women's health, livelihoods, and overall wellbeing. Africa is one place where it has been most impactful, opening up opportunities to women across the continent.
- 25Opening Africa's Medical Data Presents Both Pros and ConsOpen medical data hold both advantages and disadvantages, but they will encourage dialogue and partnerships. Africa's data are underrepresented, hard to access, or unavailable, and open access would mark a major step toward solving that.
- 26The Future of Precision Medicine in Africa, the WorldWhat will the future of healthcare look like, and how can Africa overcome inequality and other obstacles to benefit from health data and technologies tailored to the unique genetics of its diverse population?
- 27AI Offers Therapy to Mental Healthcare Sufferers in NigeriaGovernment inertia, negative stereotypes, and stigma still surround mental health in Nigeria. Here is how technology reinforces the illness, and how artificial intelligence could help treat it.
- 28Evolution of AI in AfricaArtificial intelligence is developing rapidly in Africa, enhancing a wide variety of sectors, such as healthcare, energy, agriculture, and finance. Despite the challenges of poor infrastructure, AI has been adaptable to solving many of the continent's problems on a local level.
A note from the editor
Hi, I'm Gigi.
AI content marketer, product storyteller, and founder of Asele. I turn technical work into content that moves people to act.
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