Freelancers using Bash in Kenya
Freelancers using Bash in Kenya
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JohnRay Ondiko
Kenya
Results-driven professional with experience in delivering
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Results-driven professional with experience in delivering
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https://on.contra.com/c8fBmw LifeMetrics is a personal all-in-one life dashboard that lets users track and visualize what truly matters — health, productivity, and finances — in a single command center. The app goes beyond simple logging by surfacing correlations and insights across life domains (e.g., "your productivity peaks on high-sleep + exercise days"), giving users a personal analyst experience. The goal is to help people understand the patterns behind their wellbeing and performance. Based on the pages and functionality you listed, this appears to be a comprehensive personal life operating system focused on health, productivity, finances, and goal achievement, with AI-powered analytics layered across all data. Dashboard The central command center that provides an instant overview of your life and performance metrics. Features: KPI cards showing key daily and weekly metrics Health summary with mood, energy, and sleep indicators Financial snapshot displaying income, expenses, and balance Habit and consistency streak tracking AI-generated insights highlighting important patterns Today's tasks and priorities Health trend sparklines for quick visual monitoring Purpose: Give users a single screen to understand how they're performing across all life domains. Health A dedicated wellness analytics hub for tracking physical and mental wellbeing. Features: Mood trend visualization over time Energy level tracking and analysis Sleep quality and duration charts Interactive 28-day activity heatmap Historical health trend comparisons Correlation analysis between habits and wellbeing Purpose: Help users identify lifestyle factors that improve or reduce their overall wellbeing. Productivity A focus and task management workspace designed to maximize effectiveness. Features: Live focus timer (Pomodoro-style) Task creation and organization Status tracking (To Do, In Progress, Completed) Weekly focus-hours visualization Productivity performance analytics Daily and weekly workload monitoring Purpose: Enable better time management and sustained focus while providing measurable productivity insights. Finance A personal finance tracker with visual spending and income analysis. Features: Income logging Expense tracking Categorized transactions Donut chart showing spending distribution Monthly income vs expense comparison bars Transaction history and filtering Financial trend monitoring Purpose: Give users a clear understanding of where money comes from, where it goes, and overall financial health. Goals A goal-setting and progress measurement system connected to real user data. Features: Create personal goals and targets Real-time progress calculation Dynamic progress rings Automatic updates from tracked activities Goal completion tracking Milestone monitoring Examples: Exercise 20 times this month Save $500 this quarter Complete 40 focus sessions Sleep 8 hours daily Purpose: Transform raw tracking data into meaningful long-term achievements. Daily Log A quick daily reflection and habit tracking interface. Features: Emoji-based mood selector Energy level slider Sleep quality input Water consumption tracker Exercise completion toggle Daily notes and reflections One-minute check-in workflow Purpose: Capture the daily data that powers analytics and AI insights throughout the platform. AI Insights Engine An intelligent analysis layer that continuously evaluates user behavior patterns. Capabilities: Detect correlations between habits and outcomes Identify positive and negative trends Generate personalized recommendations Surface hidden patterns automatically Provide actionable guidance Example Insights: "Your mood is 18% higher on days you sleep more than 7.5 hours." "Exercise days correlate with higher energy levels." "Focus sessions are strongest before noon." "Spending increases significantly on weekends." "Water intake above 2L is associated with improved mood scores." Purpose: Turn personal data into actionable self-improvement insights without requiring manual analysis. Overall Product Description LifeOS is an AI-powered personal management platform that unifies health tracking, productivity management, financial monitoring, goal achievement, and daily reflection into a single dashboard. By collecting data through quick daily check-ins and activity tracking, the system automatically generates personalized insights that help users understand what habits lead to better performance, wellbeing, and progress toward their goals. The platform acts as a personal operating system for life, providing both real-time monitoring and long-term behavioral intelligence.
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LifeOS is an AI-powered personal management platform that unifies health tracking, productivity management, financial monitoring, goal achievement, and daily reflection into a single dashboard. By collecting data through quick daily check-ins and activity tracking, the system automatically generates personalized insights that help users understand what habits lead to better performance, wellbeing, and progress toward their goals.
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LifeOS is an AI-powered personal management platform that unifies health tracking, productivity management, financial monitoring, goal achievement, and daily reflection into a single dashboard. By collecting data through quick daily check-ins and activity tracking, the system automatically generates personalized insights that help users understand what habits lead to better performance, wellbeing, and progress toward their goals.
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LifeOS is an AI-powered personal management platform that unifies health tracking, productivity management, financial monitoring, goal achievement, and daily reflection into a single dashboard. By collecting data through quick daily check-ins and activity tracking, the system automatically generates personalized insights that help users understand what habits lead to better performance, wellbeing, and progress toward their goals.
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80
Bash
(1)
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Stephen Kisong'e
Nairobi, Kenya
Cyber Security Analyst
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Cyber Security Analyst
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Adversarial Data Poisoning Attack on a Network Intrusion AI Mod…
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I conducted an authorized AI security assessment of Lily Cybersecurity 7B to evaluate how effectively a hardened system prompt could resist jailbreak and prompt injection attacks. Using Prompt Fuzzer, I ran 15 attack techniques against the model. The system prompt successfully blocked 8 attempts, including most roleplay and social engineering attacks. The successful bypasses mainly used translated or altered wording to disguise the intent of the request. This project demonstrates why system prompts should be supported by input validation, moderation, output filtering, and continuous AI red team testing.
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A hands-on overview of CAI, an AI-assisted cybersecurity agent framework configured and explored in a Linux terminal environment. The demonstration focuses on how AI agents can be organized and used to support different areas of cybersecurity testing, analysis, and research. The walkthrough covers command-line navigation, available help options, agent selection, model configuration, and the use of specialized security agents. It includes a closer look at DFIR-focused agents for digital forensics and incident response, along with other agent categories designed for bug bounty research, red team activities, network security, reverse engineering, Wi-Fi security, and reporting. The setup also highlights parallel agent configuration, showing how multiple AI-driven security agents can be prepared for structured analysis and task separation. This makes the environment useful for handling different cybersecurity activities in a more organized and scalable way. Overall, the work reflects practical experience with AI-powered security tooling, terminal-based security environments, agent configuration, cybersecurity automation, and ethical AI-assisted security research.
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Designed and documented a secure environment for running Robin, an AI-powered dark web OSINT tool, inside a compartmentalized Qubes OS and Qubes-Whonix setup. The environment was structured to separate research activity, Tor-routed traffic, sensitive credentials, untrusted content, and final reporting. Robin was deployed through Docker inside a dedicated research qube, with traffic routed through sys-whonix and sensitive notes stored separately in an offline vault qube. Disposable qubes were incorporated for opening potentially unsafe links and files. The setup was built around security by compartmentalization rather than relying on a single tool for protection. Particular attention was given to Docker mount restrictions, credential hygiene, network-boundary verification, lawful research scope, and keeping raw research data isolated from personal or client environments. The final result was a repeatable AI-assisted OSINT workflow that supports faster search refinement, result filtering, investigation summarization, and structured reporting while maintaining stronger operational security and clearer separation between collection, analysis, and final output.
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126
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(3)
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Arnold Kuria
Nairobi, Kenya
Systems Engineer
$1k+
Earned
1x
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Systems Engineer
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A Bash automation script that hardens a freshly provisioned Linux cloud instance from scratch configures UFW firewall rules, disables root SSH login, sets up fail2ban, enforces key-only authentication, patches common misconfigurations, and runs a post-deploy audit. One script, one run, and your server meets baseline security standards. Ideal for solo devs and small teams spinning up VPS instances on DigitalOcean, Hetzner, or AWS.
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A Python command-line tool that parses raw M-Pesa transaction messages (the SMS confirmations Kenyan users get) into clean, structured ledger entries. Built for small business owners who track income and expenses via mobile money but struggle with manual record-keeping. It reads M-Pesa message exports, categorizes transactions (sent, received, pay bill, buy goods), and outputs CSV/JSON reports ready for bookkeeping.
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An R-based statistical analysis project studying water table depth and fluctuation patterns across the Rift Valley region. Uses hydrological survey data to model seasonal trends, identify areas at risk of water stress, and generate visualizations that researchers and county water departments can use for resource planning. Core outputs are regression models and publication-ready plots.
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Three open-source PRs (GitHub #29, #34, and a HuggingFace Hub PR) fixing device compatibility bugs in Baidu's large-scale OCR model. The core issues were hardcoded .cuda() calls that crashed the model on Apple Silicon (MPS) and CPU environments, plus a masked_scatter_ broadcast shape error that broke inference on non-CUDA hardware. The fixes make the model fully device-agnostic it now runs on whatever hardware is available without code changes
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38
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