Forwarded from Artificial Intelligence Resources TP . AI Tools . AI Updates
AI Product Management
AI Product Management is evolving rapidly. The growth of generative AI and AI-based developer tools has created numerous opportunities to build AI applications. This is making it possible to build new kinds of things, which in turn is driving shifts in best practices in product management — the discipline of defining what to build to serve users — because what is possible to build has shifted. In this post, I’ll share some best practices I have noticed.
(Note: This is quite a lengthy post, but it is very informative)
Use concrete examples to specify AI products. Starting with a concrete idea helps teams gain speed. If a product manager (PM) proposes to build “a chatbot to answer banking inquiries that relate to user accounts,” this is a vague specification that leaves much to the imagination. For instance, should the chatbot answer questions only about account balances or also about interest rates, processes for initiating a wire transfer, and so on? But if the PM writes out a number (say, between 10 and 50) of concrete examples of conversations they’d like a chatbot to execute, the scope of their proposal becomes much clearer. Just as a machine learning algorithm needs training examples to learn from, an AI product development team needs concrete examples of what we want an AI system to do. In other words, the data is your PRD (product requirements document)!
In a similar vein, if someone requests “a vision system to detect pedestrians outside our store,” it’s hard for a developer to understand the boundary conditions. Is the system expected to work at night? What is the range of permissible camera angles? Is it expected to detect pedestrians who appear in the image even though they’re 100m away? But if the PM collects a handful of pictures and annotates them with the desired output, the meaning of “detect pedestrians” becomes concrete. An engineer can assess if the specification is technically feasible and if so, build toward it. Initially, the data might be obtained via a one-off, scrappy process, such as the PM walking around taking pictures and annotating them. Eventually, the data mix will shift to real-word data collected by a system running in production.
Using examples (such as inputs and desired outputs) to specify a product has been helpful for many years, but the explosion of possible AI applications is creating a need for more product managers to learn this practice.
Assess technical feasibility of LLM-based applications by prompting. When a PM scopes out a potential AI application, whether the application can actually be built — that is, its technical feasibility — is a key criterion in deciding what to do next. For many ideas for LLM-based applications, it’s increasingly possible for a PM, who might not be a software engineer, to try prompting — or write just small amounts of code — to get an initial sense of feasibility.
For example, a PM may envision a new internal tool for routing emails from customers to the right department (such as customer service, sales, etc.). They can prompt an LLM to see if they can get it to select the right department based on an input email, and see if they can achieve high accuracy. If so, this gives engineering a great starting point from which to implement the tool. If not, the PM can falsify the idea themselves and perhaps improve the product idea much faster than if they had to rely on an engineer to build a prototype.
Often, testing feasibility requires a little more than prompting. For example, perhaps the LLM-based email system needs basic RAG capability to help it make decisions. Fortunately, the barrier to writing small amounts of code is now quite low, since AI can help by acting as a coding companion, as I describe in the course, “AI Python for Beginners.” This means that PMs can do much more technical feasibility testing, at least at a basic level, than was possible before.
Continuation Here...
AI Product Management is evolving rapidly. The growth of generative AI and AI-based developer tools has created numerous opportunities to build AI applications. This is making it possible to build new kinds of things, which in turn is driving shifts in best practices in product management — the discipline of defining what to build to serve users — because what is possible to build has shifted. In this post, I’ll share some best practices I have noticed.
(Note: This is quite a lengthy post, but it is very informative)
Use concrete examples to specify AI products. Starting with a concrete idea helps teams gain speed. If a product manager (PM) proposes to build “a chatbot to answer banking inquiries that relate to user accounts,” this is a vague specification that leaves much to the imagination. For instance, should the chatbot answer questions only about account balances or also about interest rates, processes for initiating a wire transfer, and so on? But if the PM writes out a number (say, between 10 and 50) of concrete examples of conversations they’d like a chatbot to execute, the scope of their proposal becomes much clearer. Just as a machine learning algorithm needs training examples to learn from, an AI product development team needs concrete examples of what we want an AI system to do. In other words, the data is your PRD (product requirements document)!
In a similar vein, if someone requests “a vision system to detect pedestrians outside our store,” it’s hard for a developer to understand the boundary conditions. Is the system expected to work at night? What is the range of permissible camera angles? Is it expected to detect pedestrians who appear in the image even though they’re 100m away? But if the PM collects a handful of pictures and annotates them with the desired output, the meaning of “detect pedestrians” becomes concrete. An engineer can assess if the specification is technically feasible and if so, build toward it. Initially, the data might be obtained via a one-off, scrappy process, such as the PM walking around taking pictures and annotating them. Eventually, the data mix will shift to real-word data collected by a system running in production.
Using examples (such as inputs and desired outputs) to specify a product has been helpful for many years, but the explosion of possible AI applications is creating a need for more product managers to learn this practice.
Assess technical feasibility of LLM-based applications by prompting. When a PM scopes out a potential AI application, whether the application can actually be built — that is, its technical feasibility — is a key criterion in deciding what to do next. For many ideas for LLM-based applications, it’s increasingly possible for a PM, who might not be a software engineer, to try prompting — or write just small amounts of code — to get an initial sense of feasibility.
For example, a PM may envision a new internal tool for routing emails from customers to the right department (such as customer service, sales, etc.). They can prompt an LLM to see if they can get it to select the right department based on an input email, and see if they can achieve high accuracy. If so, this gives engineering a great starting point from which to implement the tool. If not, the PM can falsify the idea themselves and perhaps improve the product idea much faster than if they had to rely on an engineer to build a prototype.
Often, testing feasibility requires a little more than prompting. For example, perhaps the LLM-based email system needs basic RAG capability to help it make decisions. Fortunately, the barrier to writing small amounts of code is now quite low, since AI can help by acting as a coding companion, as I describe in the course, “AI Python for Beginners.” This means that PMs can do much more technical feasibility testing, at least at a basic level, than was possible before.
Continuation Here...
Cybersecurity Roadmap
|
|-- Fundamentals
| |-- Introduction to Cybersecurity
| | |-- Importance and Principles of Cybersecurity
| | |-- Types of Cybersecurity (Network, Information, Application, Cloud, etc.)
| | |-- Cybersecurity Threat Landscape (Malware, Phishing, Ransomware, etc.)
| |-- Network Security
| | |-- Firewalls and VPNs
| | |-- Intrusion Detection Systems (IDS)
| | |-- Intrusion Prevention Systems (IPS)
| | |-- Network Access Control
|
|-- Threats and Vulnerabilities
| |-- Types of Cyber Threats
| | |-- Malware (Viruses, Worms, Trojans, etc.)
| | |-- Phishing and Social Engineering
| | |-- Denial of Service (DoS) Attacks
| | |-- Insider Threats
| |-- Vulnerability Assessment
| | |-- Vulnerability Scanning
| | |-- Penetration Testing (Ethical Hacking)
| | |-- Security Audits and Assessments
|
|-- Encryption and Cryptography
| |-- Introduction to Cryptography
| | |-- Symmetric and Asymmetric Encryption
| | |-- Hashing Algorithms (SHA, MD5, etc.)
| | |-- Public Key Infrastructure (PKI)
| |-- Encryption Protocols
| | |-- SSL/TLS
| | |-- IPsec
|
|-- Identity and Access Management (IAM)
| |-- Authentication Mechanisms
| | |-- Password Policies and Multi-Factor Authentication (MFA)
| | |-- Biometric Authentication
| |-- Access Control Models
| | |-- Role-Based Access Control (RBAC)
| | |-- Attribute-Based Access Control (ABAC)
| | |-- Mandatory Access Control (MAC)
|
|-- Incident Response and Forensics
| |-- Incident Response Process
| | |-- Detection, Containment, Eradication, Recovery
| | |-- Incident Response Teams (CSIRT)
| |-- Digital Forensics
| | |-- Evidence Collection and Preservation
| | |-- Data Recovery
| | |-- Forensic Tools (Autopsy, EnCase, etc.)
|
|-- Security Operations
| |-- Security Monitoring
| | |-- Security Information and Event Management (SIEM)
| | |-- Log Management and Analysis
| | |-- Threat Intelligence
| |-- Security Operations Center (SOC)
| | |-- SOC Roles and Responsibilities
| | |-- Incident Management
|
|-- Cloud Security
| |-- Cloud Security Principles
| | |-- Shared Responsibility Model
| | |-- Data Protection in Cloud Environments
| |-- Cloud Security Tools
| | |-- Cloud Access Security Brokers (CASB)
| | |-- Security in Cloud Platforms (AWS, Azure, Google Cloud)
|
|-- Application Security
| |-- Secure Software Development
| | |-- Secure Coding Practices
| | |-- Software Development Life Cycle (SDLC)
| | |-- Secure Code Reviews
| |-- Web Application Security
| | |-- OWASP Top 10
| | |-- SQL Injection, Cross-Site Scripting (XSS), CSRF
|
|-- Compliance and Regulations
| |-- Cybersecurity Standards
| | |-- ISO/IEC 27001, NIST Cybersecurity Framework
| | |-- CIS Controls, SOC 2
| |-- Data Privacy Regulations
| | |-- GDPR
| | |-- HIPAA, CCPA, PCI DSS
|
|-- Advanced Topics
| |-- Advanced Persistent Threats (APT)
| | |-- Detection and Mitigation
| | |-- Threat Hunting
| |-- Blockchain Security
| | |-- Cryptographic Principles
| | |-- Smart Contracts and Security
| |-- IoT Security
| | |-- Securing IoT Devices
| | |-- Network Segmentation for IoT
|
|-- Emerging Trends
| |-- AI and Machine Learning in Cybersecurity
| | |-- AI-Based Threat Detection
| | |-- Automating Incident Response
| |-- Zero Trust Architecture
| | |-- Principles of Zero Trust
| | |-- Implementing Zero Trust in an Organization
|
|-- Soft Skills
| |-- Communication and Collaboration
| | |-- Reporting Security Incidents
| | |-- Collaboration with Other Departments
| |-- Ethical Hacking
| | |-- Red Teaming and Blue Teaming
| | |-- Bug Bounty Programs
Free CyberSecurity Courses For FREE
Link 1: intercomuniversity.com/free-courses/artificial-intelligence/microsofts-secret-stash-of-100-free-courses-you-need-to-know-about/
Link 2: Hacking manuals: t.me/zerotrusthackers/236
|
|-- Fundamentals
| |-- Introduction to Cybersecurity
| | |-- Importance and Principles of Cybersecurity
| | |-- Types of Cybersecurity (Network, Information, Application, Cloud, etc.)
| | |-- Cybersecurity Threat Landscape (Malware, Phishing, Ransomware, etc.)
| |-- Network Security
| | |-- Firewalls and VPNs
| | |-- Intrusion Detection Systems (IDS)
| | |-- Intrusion Prevention Systems (IPS)
| | |-- Network Access Control
|
|-- Threats and Vulnerabilities
| |-- Types of Cyber Threats
| | |-- Malware (Viruses, Worms, Trojans, etc.)
| | |-- Phishing and Social Engineering
| | |-- Denial of Service (DoS) Attacks
| | |-- Insider Threats
| |-- Vulnerability Assessment
| | |-- Vulnerability Scanning
| | |-- Penetration Testing (Ethical Hacking)
| | |-- Security Audits and Assessments
|
|-- Encryption and Cryptography
| |-- Introduction to Cryptography
| | |-- Symmetric and Asymmetric Encryption
| | |-- Hashing Algorithms (SHA, MD5, etc.)
| | |-- Public Key Infrastructure (PKI)
| |-- Encryption Protocols
| | |-- SSL/TLS
| | |-- IPsec
|
|-- Identity and Access Management (IAM)
| |-- Authentication Mechanisms
| | |-- Password Policies and Multi-Factor Authentication (MFA)
| | |-- Biometric Authentication
| |-- Access Control Models
| | |-- Role-Based Access Control (RBAC)
| | |-- Attribute-Based Access Control (ABAC)
| | |-- Mandatory Access Control (MAC)
|
|-- Incident Response and Forensics
| |-- Incident Response Process
| | |-- Detection, Containment, Eradication, Recovery
| | |-- Incident Response Teams (CSIRT)
| |-- Digital Forensics
| | |-- Evidence Collection and Preservation
| | |-- Data Recovery
| | |-- Forensic Tools (Autopsy, EnCase, etc.)
|
|-- Security Operations
| |-- Security Monitoring
| | |-- Security Information and Event Management (SIEM)
| | |-- Log Management and Analysis
| | |-- Threat Intelligence
| |-- Security Operations Center (SOC)
| | |-- SOC Roles and Responsibilities
| | |-- Incident Management
|
|-- Cloud Security
| |-- Cloud Security Principles
| | |-- Shared Responsibility Model
| | |-- Data Protection in Cloud Environments
| |-- Cloud Security Tools
| | |-- Cloud Access Security Brokers (CASB)
| | |-- Security in Cloud Platforms (AWS, Azure, Google Cloud)
|
|-- Application Security
| |-- Secure Software Development
| | |-- Secure Coding Practices
| | |-- Software Development Life Cycle (SDLC)
| | |-- Secure Code Reviews
| |-- Web Application Security
| | |-- OWASP Top 10
| | |-- SQL Injection, Cross-Site Scripting (XSS), CSRF
|
|-- Compliance and Regulations
| |-- Cybersecurity Standards
| | |-- ISO/IEC 27001, NIST Cybersecurity Framework
| | |-- CIS Controls, SOC 2
| |-- Data Privacy Regulations
| | |-- GDPR
| | |-- HIPAA, CCPA, PCI DSS
|
|-- Advanced Topics
| |-- Advanced Persistent Threats (APT)
| | |-- Detection and Mitigation
| | |-- Threat Hunting
| |-- Blockchain Security
| | |-- Cryptographic Principles
| | |-- Smart Contracts and Security
| |-- IoT Security
| | |-- Securing IoT Devices
| | |-- Network Segmentation for IoT
|
|-- Emerging Trends
| |-- AI and Machine Learning in Cybersecurity
| | |-- AI-Based Threat Detection
| | |-- Automating Incident Response
| |-- Zero Trust Architecture
| | |-- Principles of Zero Trust
| | |-- Implementing Zero Trust in an Organization
|
|-- Soft Skills
| |-- Communication and Collaboration
| | |-- Reporting Security Incidents
| | |-- Collaboration with Other Departments
| |-- Ethical Hacking
| | |-- Red Teaming and Blue Teaming
| | |-- Bug Bounty Programs
Free CyberSecurity Courses For FREE
Link 1: intercomuniversity.com/free-courses/artificial-intelligence/microsofts-secret-stash-of-100-free-courses-you-need-to-know-about/
Link 2: Hacking manuals: t.me/zerotrusthackers/236
OpenAI will start watermarking ChatGPT’s text in the EU
OpenAI announced it will begin applying an invisible text watermark, named textGrain, to ChatGPT and Codex outputs in the European Union to comply with the EU AI Act's transparency mandates. Developed alongside UPenn and Yale researchers, the method subtly shapes word choices using a secret key, leaving a detectable pattern that travels across copy-pasted text without altering user anonymity or model performance.
The watermark will roll out to all EU subscription plans in the coming weeks, while API developers worldwide can opt in. OpenAI is limiting initial access to its detector tool to approved researchers, noting that text modifications, short passages, math, and translations can reduce detection accuracy. The move follows Anthropic’s global rollout of text watermarking for Claude, though OpenAI previously delayed the feature over concerns about user retention.
🔆 t.me/techpsyche
OpenAI announced it will begin applying an invisible text watermark, named textGrain, to ChatGPT and Codex outputs in the European Union to comply with the EU AI Act's transparency mandates. Developed alongside UPenn and Yale researchers, the method subtly shapes word choices using a secret key, leaving a detectable pattern that travels across copy-pasted text without altering user anonymity or model performance.
The watermark will roll out to all EU subscription plans in the coming weeks, while API developers worldwide can opt in. OpenAI is limiting initial access to its detector tool to approved researchers, noting that text modifications, short passages, math, and translations can reduce detection accuracy. The move follows Anthropic’s global rollout of text watermarking for Claude, though OpenAI previously delayed the feature over concerns about user retention.
🔆 t.me/techpsyche
Forwarded from Crypto Stract | Web 3, Finance, Business, Blockchain, Crypto, Growth, Stock Market, Money
You can learn for free inside Binance (Binance Academy)
There are plenty of free resources on YouTube, you should learn what is Technical and Fundamental Analysis, Futures and Spot trading, Leverage and Risk Management before you start trading.
After you learn these concepts you should trade at least for 2 months using tools like TradingView. After 2 months and if you have positive results you can then start trading but keep in mind that real trading involves other variables like psycological behaviour.
Types of Technical Analysis: t.me/cryptostract/156
Exploring the Benefits of Non-Recourse Loans in Crypto: t.me/cryptostract/160
What are the signs of a bearish trend in the crypto market?: t.me/cryptostract/163
More Resources Here:
whatsapp.com/channel/0029VajB00n0LKZ7cSloGR1M
There are plenty of free resources on YouTube, you should learn what is Technical and Fundamental Analysis, Futures and Spot trading, Leverage and Risk Management before you start trading.
After you learn these concepts you should trade at least for 2 months using tools like TradingView. After 2 months and if you have positive results you can then start trading but keep in mind that real trading involves other variables like psycological behaviour.
Types of Technical Analysis: t.me/cryptostract/156
Exploring the Benefits of Non-Recourse Loans in Crypto: t.me/cryptostract/160
What are the signs of a bearish trend in the crypto market?: t.me/cryptostract/163
More Resources Here:
whatsapp.com/channel/0029VajB00n0LKZ7cSloGR1M
Forwarded from Intercom University
Intercom University
Apply for the Fully Funded St. Gallen Symposium Global Leadership Challenge 2026 - Intercom University
Are you a passionate young changemaker eager to drive meaningful global impact? Applications are officially open for the prestigious St. Gallen Symposium
🚨
Apply for the Fully Funded St. Gallen Symposium Global Leadership Challenge 2026
Application Details: https://intercomuniversity.com/contests/apply-for-the-fully-funded-st-gallen-symposium-global-leadership-challenge-2026/
Scholarship AlertApply for the Fully Funded St. Gallen Symposium Global Leadership Challenge 2026
Application Details: https://intercomuniversity.com/contests/apply-for-the-fully-funded-st-gallen-symposium-global-leadership-challenge-2026/
Forwarded from The World Point
Human Losses of World War II - Ranked by Country(Military & Civilian)
Build-your-own-X
Master programming by recreating your favorite technologies from scratch.
Creator: Codecrafters-io
Stars ⭐️: 327k
Forked by: 30.3k
Github Repo:
https://github.com/codecrafters-io/build-your-own-x
Essential Git:: https://t.me/techpsyche/1918
Master programming by recreating your favorite technologies from scratch.
Creator: Codecrafters-io
Stars ⭐️: 327k
Forked by: 30.3k
Github Repo:
https://github.com/codecrafters-io/build-your-own-x
Essential Git:: https://t.me/techpsyche/1918
Forwarded from KT Remote Jobs
`
📢Role: Developer Advocate
🏪 Company: SerpApi
📍Location: Remote (Global)
👔 Experience Years: Not stated
💸 Salary: 100K – 125K a year + profit share
⏰ Employment Type: Full-time
📌How to Apply:
Send us your resume at careers@serpapi.com
Telegram Channel👇
t.me/remotejobskt
WhatsApp Channel👇
whatsapp.com/channel/0029VageofA3GJP3bu7Wyd37
All the Best🥳
REMOTE JOB`📢Role: Developer Advocate
🏪 Company: SerpApi
📍Location: Remote (Global)
👔 Experience Years: Not stated
💸 Salary: 100K – 125K a year + profit share
⏰ Employment Type: Full-time
📌How to Apply:
Send us your resume at careers@serpapi.com
Telegram Channel👇
t.me/remotejobskt
WhatsApp Channel👇
whatsapp.com/channel/0029VageofA3GJP3bu7Wyd37
All the Best🥳
Forwarded from Mobile Dev Resources . Android . iOS . Flutter . Kotlin . Swift . Java . React Native
As an app developer, there are important things you need to add before uploading your app to the store, whether the customer requests it or not.
The most important things you should add are:
1. A notification feature. This is a free service from Firebase. With this service, you can notify users of any changes or new developments in the app.
2. A forced update service: This forces the user to update the app in the event of, for example, problems or vulnerabilities, and a new update is released. The user is prompted to update the app, otherwise they cannot use it. There are several ways to implement this feature.
3. Adding a crash and bug tracking feature to the app (crashlytics). This is also a free service from Firebase. If an error or malfunction occurs in the app, an email notification is sent to you, explaining the error and its location.
This makes it easier for you to discover and pinpoint the exact location of the problem and address it easily.
Mobile UI UX: t.me/mobiledevresourcestp/151
Kotlin Multiplatform: t.me/mobiledevresourcestp/145
Native vs. Cross-Platform Mobile Dev: t.me/mobiledevresourcestp/153
Join Our WhatsApp Channel:
whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
The most important things you should add are:
1. A notification feature. This is a free service from Firebase. With this service, you can notify users of any changes or new developments in the app.
2. A forced update service: This forces the user to update the app in the event of, for example, problems or vulnerabilities, and a new update is released. The user is prompted to update the app, otherwise they cannot use it. There are several ways to implement this feature.
3. Adding a crash and bug tracking feature to the app (crashlytics). This is also a free service from Firebase. If an error or malfunction occurs in the app, an email notification is sent to you, explaining the error and its location.
This makes it easier for you to discover and pinpoint the exact location of the problem and address it easily.
Mobile UI UX: t.me/mobiledevresourcestp/151
Kotlin Multiplatform: t.me/mobiledevresourcestp/145
Native vs. Cross-Platform Mobile Dev: t.me/mobiledevresourcestp/153
Join Our WhatsApp Channel:
whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
PAKISTAN🇵🇰 foodpanda is HiringData Analyst (Quick Commerce & Partnerships)
Organization: FOODPANDA
📍Location: Karachi, Pakistan
How To Apply:
jobs.smartrecruiters.com/DeliveryHero/744000151531609-data-analyst-quick-commerce-partnerships-
Telegram Channel👇
t.me/jobsourceglobal
WhatsApp Channel👇
whatsapp.com/channel/0029VbAqbkw6hENvEJZLl22d
If you’re not using AI, you’re training yourself for a world that no longer exists.
❤1
Forwarded from Kenya Talent Help | Jobs & Opportunities | Jobs in Kenya | Kenya Jobs | Careers | Job Vacancies Kenya
KenyaTrends.co.ke
Apply for the Fully Funded 2027 UGHE Mastercard Foundation Scholars Program - Trends
The 2027 University of Global Health Equity (UGHE) Mastercard Foundation Scholars Program is officially open for applications.
🚨FULLY FUNDED SCHOLARSHIP ALERT
🎓Apply for the Fully Funded 2027 UGHE Mastercard Foundation Scholars Program
Key Deadlines & Next Steps
• Application Deadline:October 25, 2026
• Program Start:2027 Academic Year
Application Details Here 👉 https://kenyatrends.co.ke/opportunities/apply-for-the-fully-funded-2027-ughe-mastercard-foundation-scholars-program/
🎓Apply for the Fully Funded 2027 UGHE Mastercard Foundation Scholars Program
Key Deadlines & Next Steps
• Application Deadline:October 25, 2026
• Program Start:2027 Academic Year
Application Details Here 👉 https://kenyatrends.co.ke/opportunities/apply-for-the-fully-funded-2027-ughe-mastercard-foundation-scholars-program/