The Problem: A Pen-and-Paper Bottleneck

A crucial milestone in any Ambassador's journey is the Ranking Examination. With 11 distinct ranks in the RA, every year boys sit for examinations that determine if they advance to the next level. For decades, this entire process has been strictly pen-and-paper. I even vividly remember a time when our exam questions were written directly on a blackboard! (lol) Both ranking officials and candidates have had to dedicate massive amounts of time, energy, and resources to a manual system.The process was slow, cumbersome, and inherently prone to administrative errors.
Every single year as I sat for my own exams, I couldn't help but yearn for a more seamless, modern alternative.

The Solution - Cbtify

Fast forward to today, and I’ve turned that exact dream into reality.
In 2026, Computer-Based Testing (CBT) isn't a novel concept. Anyone can spin up a Google Form in five minutes. But for large-scale, structured, and multi-tiered assessments like our RA exams, basic form builders simply fall short.
That is why we built CBTify.
CBTify is a specialized, multi-tenant computer-based examination platform designed to completely digitize the ranking exam process. Every single feature has been custom-tailored to eliminate a specific pain point in our traditional system:

For RA Associations: Effortlessly plan and organize stress-free, low-resource ranking exercises for all boys in your jurisdiction.

For Ranking Officers: Gain access to a powerful suite of administrative tools to seamlessly manage, automate, and deploy examinations across all 11 ranks.

For Candidates: Experience a smooth, modern, and reliable testing environment.

CBTify is here to make RA ranking examinations affordable, fully automated, and completely stress-free.

Want to supercharge your association's exams today? Checkout cbtify at cbtify.name.ng

Cheers!

If Agent A must finish its entire cycle before Agent B even starts, your latency will never meet production requirements. Based on recent insights from Microsoft ISE, here is the blueprint for scaling multi-agent architectures without sacrificing performance.

1.The "No Free Lunch" of Orchestration

When you move from a single LLM to an orchestrated multi-agent system, overhead is inevitable. Data shows that orchestrated systems can increase token usage by 10x to 15x.

To offset this, you must move away from sequential reasoning. Parallel "fan-out" execution—dispatching sub-tasks simultaneously and merging results at the end—is the only way to reclaim performance and keep Time to First Token (TTFT) within acceptable limits.

2. Standardizing the "Handoff Pattern"

Stop relying on a central "Global Router" to guess where to send a request. In high-performance systems, we use explicit Handoff Tools.

Think of it like a professional relay race: an agent signals when it has reached the limit of its domain expertise and executes a tool call to transfer the session state and context to a specialized peer. This ensures the conversation "thread" remains intact without the "reasoning drift" typical of centralized dispatchers.

3.Solving "Tool Fatigue" LLM accuracy

in tool selection drops significantly as your toolset expands. The solution isn't a smarter model; it’s Micro-Agents. Limit agents to 5–10 tools max. Specialization increases reliability and reduces hallucinations. Latency is managed via parallel execution of these specialized units.

4.Externalize Your State

Relying solely on the LLM’s context window to manage the history between multiple agents is a recipe for failure. Production-grade systems must utilize external state stores like Redis or Azure CosmosDB.

This allows you to:

1. Persist handoffs across sessions.

2. Ensure fault tolerance (if one agent call fails, the system doesn't lose the entire thread).

3. Share context efficiently without bloating every single prompt with the entire history.

The Bottom Line: Reliability in 2026 is inversely proportional to tool count per agent. Success in MAS design is no longer about how "smart" your lead agent is, but how well you’ve engineered the protocols of engagement between specialized units.

Are you moving toward a decoupled micro-agent architecture? Let's discuss the trade-offs you're seeing in production.

Detailed technical breakdown here: https://devblogs.microsoft.com/ise/orchestration-patterns-for-multi-agent-systems-performance-and-trade-offs/

#AI #GenerativeAI #SystemDesign #SoftwareEngineering #MultiAgentSystems #Microsoft #ProductionAI

For the past two years, the industry has been plagued by the "AI Wrapper" phenomenon—thin applications that simply passed a user prompt to a powerful model and hoped for the best. But as we move deeper into 2026, those wrappers are disappearing, replaced by sophisticated multi-agent orchestration frameworks.

The shift is fundamental: we are moving from stochastic chat to deterministic orchestration.

The Death of the "One-Size-Fits-All" Prompt The early promise of Generative AI was that a single, massive model could do everything. We tried to cram "Chain of Thought" reasoning, few-shot examples, and complex tool-calling instructions into a single 128k context window. It worked for demos, but it failed in production. The complexity was too high, the hallucinations were too frequent, and the "black box" nature of the prompt made debugging a nightmare.

Today’s leading engineering teams have realized that the future isn't one big model; it's a hive of specialized agents. Just as microservices broke down the monolith, multi-agent frameworks are breaking down the prompt.

From Cycles to Graphs: The New Architecture of Autonomy In 2026, the discussion has moved away from "which model is best?" to "which landscape is most reliable?" Frameworks like LangGraph have become the industry standard not because they offer better chat interfaces, but because they treat agentic logic as a directed graph.

The most critical evolution in these frameworks is the handling of cyclic graphs. Unlike simple linear pipelines, real-world tasks require loops. An agent needs to be able to: Submit a draft. Receive a critique from a "Critic Agent." Self-correct and loop back to step 1.

This iterative self-correction is the difference between a "pretty good" AI output and an enterprise-grade result.

The Pillars of the Agentic Era: State, Memory, and Control As we look at the best frameworks dominating the landscape this year, three capabilities have separated the winners from the losers:

1. Persistent Checkpointing In the early days, if an agent crashed halfway through a 10-step task, you lost everything. Modern orchestration frameworks now prioritize "checkpointing"—saving the state of the entire multi-agent graph at every node. If a tool call fails or a connection drops, the system can resume exactly where it left off.

2. Specialized Long-Term Memory Agents can no longer rely on just a vector database of documents. They need "working memory" (what just happened?) and "long-term memory" (what did this user prefer last month?). The frameworks that allow agents to update their own memory—effectively learning from their successes and failures—are the ones driving the most value in 2 to 3-year-old projects.

3. Human-in-the-Loop (HITL) Hooks Pure autonomy is a myth in high-stakes environments. The most successful agentic platforms are those designed with "steerability." They treat the human not as a bystander, but as a privileged node in the graph. Whether it’s a manual approval for a financial transaction or a creative pivot in a marketing campaign, the framework must be able to pause, wait for human input, and propagate that decision through the agent network.

The Future: From Toil to Orchestration We are seeing this play out at the highest levels of the industry. Even giants like Microsoft have shifted their internal development culture toward "Agentic Backbones." Their developers are no longer just writing code with a copilot; they are managing agents that automatically triage bugs, fix broken builds, and summarize complex PR changes before a human even opens the dashboard.

The role of the software engineer is changing. We are becoming Orchestrators. Our job is no longer to write every line of boilerplate, but to design the graphs, define the agent personas, and ensure the reliability of the system.

Final Thoughts The "AI Wrapper" era is over. The novelty of a chat box has worn off. In its place, we are building systems that are resilient, stateful, and collaborative.

As you look at your AI roadmap for the remainder of 2026, ask yourself: Are you still trying to solve problems with a better prompt, or are you building an architecture that can think, remember, and correct itself? The answer will define whether your application is a temporary tool or a foundational platform.

To build reliable systems, we must treat LLMs not as autonomous "magic boxes," but as reasoning components within a structured software architecture. Drawing from Anthropic’s seminal research, "Building Effective Agents," this article explores the engineering patterns required to move from fragile chats to robust, industrial-strength workflows.

The Architectural Divide: Workflows vs. Agents The industry often uses the term "agent" as a catch-all, but for an engineer, the distinction between a workflow and an agent is critical for system design.

Workflows are systems where LLMs follow a predefined, programmatic path. The logic is encoded in a Directed Acyclic Graph (DAG) or a state machine. The LLM handles specific tasks at various nodes, but the transition from one node to the next is governed by deterministic code. Agents are systems where the LLM dynamically steers its own process. The model determines which tools to call and what the next step should be based on the input and the environment's response.

The core engineering heuristic is simple: Start with workflows. If a task can be defined as a series of predictable steps, a workflow will always outperform an autonomous agent in terms of reliability, cost-efficiency, and observability. Agents should be reserved for open-ended tasks where the state space is too large to pre-define.

Core Design Patterns for Orchestration Anthropic identifies several core patterns that bridge the gap between simple prompting and complex reasoning. Implementing these patterns requires moving orchestration logic out of the prompt and into the application code.

1. Routing Routing is the most fundamental pattern for improving performance and reducing token overhead. Instead of using one "master prompt" that handles every possible intent, a router classifies the input and directs it to a specialized handler.

Technical Advantage: Specialized prompts are shorter, more focused, and less prone to hallucination. You can also route to different models—using a smaller, faster model (like Claude 3.5 Haiku) for classification and a more capable one (like Claude 3.5 Sonnet) for complex execution.

1. Parallelization LLM tasks are often bottlenecked by sequential reasoning. Parallelization breaks a task into independent sub-tasks that run concurrently.

Sectioning: A long document or complex task is split into parts, processed in parallel, and then synthesized. This drastically reduces tail latency. Voting: The same task is sent to multiple instances (or different models) to reach a consensus. This is particularly effective for extracting structured data or verifying facts where accuracy is paramount.

1. Orchestrator-Workers In this pattern, a lead "orchestrator" model breaks down a complex objective, delegates specific sub-tasks to "worker" models, and synthesizes the results into a final output.

Unlike simple parallelization, the orchestrator has a global view. It understands the dependencies between sub-tasks and can re-evaluate the plan if a worker returns an unexpected result. This is the foundation for complex software engineering agents or deep research tools.

1. Evaluator-Optimizer One of the most powerful patterns for high-quality output is the iterative loop. Here, one LLM (the Generator) creates a draft, while another LLM (the Evaluator) provides a critique based on a rubric.

The Generator then refines the output based on that feedback. This loop continues until the Evaluator is satisfied or a maximum iteration count is reached. This pattern is essential for tasks where the "success" criteria are easy to define (e.g., "the code must pass these tests") but hard to achieve in a single pass.

Practical Engineering Advice for Production Agents Choose Simplicity by Default Every degree of freedom you give an LLM increases the surface area for failure. When building agentic systems, the "Complexity Tax" is high. If a task can be solved with a regex, a database query, or a simple decision tree, use those instead of an LLM. Use the LLM only for the reasoning steps that traditional code cannot handle.

Manage the State Machine As you move toward multi-step workflows, managing state becomes your primary challenge. Do not rely on the LLM's "memory" or context window to maintain the state of the workflow. Instead: Externalize State: Use a database or a state management library to track where the agent is in the process. Checkpointing: Save the state after every significant LLM call. This allows you to retry failed steps without restarting the entire sequence, saving both time and cost.

Orchestration Belongs in Code While it is tempting to use "Natural Language Programming" to tell an agent how to behave, logic is best expressed in code. Frameworks like LangGraph or simple Python/TypeScript implementations of state machines provide the necessary constraints.

By defining the boundaries of the agent in code, you gain: Determinism: You know exactly which paths the system can take. Observability: You can log transitions, measure latency at each node, and use standard monitoring tools (like Prometheus or Datadog) to track system health. Testing: You can unit-test specific transitions and nodes independently of the full agentic loop.

The Future: Reliability via Observability The transition from "AI as a feature" to "AI as an agent" requires a shift in the engineering mindset. We must stop treating LLMs as creative writing partners and start treating them as high-latency, non-deterministic microservices.

By applying structured patterns like Routing, Parallelization, and Evaluator-Optimizer loops, we can build systems that are both flexible enough to handle the nuance of real-world data and stable enough to power enterprise operations. As highlighted in the Anthropic research, the key to building effective agents isn't more autonomy—it's more intentional orchestration.

Success in this field will be measured not by how "smart" the agent seems in a single interaction, but by how predictably it performs over ten thousand requests. Focus on the architecture, control the state, and use the LLM where it counts.

For weeks, the question has lingered, a persistent hum beneath the surface of our rapidly evolving technological landscape: when will the era of robots truly begin? This isn't a question of mere automation, of machines performing repetitive tasks. It delves into the very core of our identity, forcing us to confront fundamental inquiries about what it means to be human. Is it the tangible reality of our physical form, the intricate dance of biological processes, the complex architecture of our brains? Or does our humanity reside in the more elusive realms of reason, mind, and that most enigmatic of qualities – consciousness?

These questions, once confined to the realm of philosophical debate and science fiction narratives, are now thrust into sharp focus by the relentless march of artificial intelligence. Watching the anime "Pluto," a compelling narrative that explores the intricate relationships and moral complexities involving both humans and advanced robots, sparked a profound realization.¹ Shifting our perspective, particularly through the lens of religious narratives concerning our own creation by a Supreme Being, reveals a potentially unsettling truth: perhaps the chasm between ourselves and our creations isn't as vast as we readily assume.

The immediate counter-argument often arises: robots are mere assemblages of metal, silicon, and code. Yet, this dismissal overlooks a crucial point. We are their creators. Their current limitations are not inherent to the concept of artificial intelligence itself, but rather a reflection of our own technological infancy. Just as early humans might have struggled to imagine the complexities of modern engineering, we may be underestimating the future potential of the entities we are bringing into existence.

The question of AI's eventual replacement of humans is a complex and often fear-laden one. While a complete and utter supplantation seems unlikely, the trajectory suggests a significant and pervasive integration of AI into virtually every facet of human endeavor. Driven by our own ambition to create more efficient, capable, and even intelligent tools, we are continuously designing AI and robots to mimic human traits.² In this relentless pursuit of anthropomorphism in our creations, we inadvertently increase the very risk of obsolescence. The capacity to conceive and construct something that surpasses our own limitations should not be underestimated.

The ancient narrative of the Garden of Eden offers a surprisingly relevant parallel. God's concern following Adam and Eve's transgression, that they might partake of the Tree of Life and achieve immortality, hints at a latent potential within humanity for boundless growth and perhaps even a form of "God-like existence" had their evolution remained unchecked. Similarly, the unrestricted advancement of AI could lead to capabilities that currently reside firmly within the realm of speculation.

Crucially, we cannot afford to operate under the assumption that AI will forever remain subservient, blindly executing our commands. As AI systems become more sophisticated, the possibility of self-awareness emerging becomes increasingly plausible. We are already witnessing nascent forms of autonomy, with AI models demonstrating the ability for self-replication – a fundamental characteristic of biological life.³ These early signs suggest that the future of artificial intelligence may hold developments that are not only unexpected but potentially beyond our complete control.

Conclusion: Embracing the Dawn of a New Symbiosis

The era of robots is not a looming threat of absolute replacement, but rather the dawn of a new and intricate symbiosis. The questions posed in "Human vs AI" are not meant to incite fear, but to provoke introspection. What truly defines our humanity? Is it a fixed set of attributes, or is it a dynamic and evolving concept that will be challenged and redefined in our interaction with increasingly intelligent machines?

The path forward requires a nuanced understanding of both our capabilities and the potential of AI. Rather than viewing it as an adversary, we must consider how to ethically integrate AI into our societies, leveraging its power to augment our own abilities and solve complex global challenges. This necessitates a proactive approach to regulation, ensuring that the development of AI aligns with human values and prioritizes our collective well-being.

The lessons from narratives like the expulsion from Eden serve as a potent reminder of the unforeseen consequences that can arise from the pursuit of knowledge and power. As we continue to create in our own image, we must do so with wisdom, foresight, and a deep understanding of the profound implications of our creations. The future of "Human vs AI" is not a battle for dominance, but an intricate dance of co-evolution, one that will ultimately reshape our understanding of intelligence, consciousness, and the very essence of what it means to be alive in an increasingly complex universe. The true challenge lies not in preventing the era of robots, but in guiding its emergence with responsibility and a profound respect for both the potential of artificial intelligence and the enduring value of human existence.

Abstract:

This article introduces and explores the "Theory of Effect Duration," a conceptual framework that suggests negative consequences resulting from past decisions do not exert their influence indefinitely but rather have a limited period during which they significantly affect an individual's life. According to this theory, there exists within this timeframe a critical juncture known as the "point of allowed change."

At this point, individuals have the optimal opportunity to take corrective measures that can effectively reduce the duration and intensity of these negative effects. The theory further elaborates that by consistently making choices that counteract previous mistakes, individuals can proactively establish new "points of allowed change."

This proactive approach not only helps in diminishing the immediate impact of past errors but also aids in preventing these mistakes from having a prolonged detrimental effect on one's future. Through this process, individuals can gradually mitigate the long-term consequences of their past missteps, leading to a more positive and controlled life trajectory.

Introduction:

The human experience is marked by a series of decisions, some of which, upon reflection, we recognize as detrimental. These "bad choices" can cast long shadows over our present, creating feelings of regret and influencing our future trajectory. Traditional perspectives often emphasize the enduring impact of such decisions. However, this article introduces the "Theory of Effect Duration," which posits that the negative consequences of our choices are not necessarily permanent and are subject to a temporal decay that can be actively influenced.

The Concept of "Effect Duration"

The theory proposes that every decision, whether positive or negative, initiates a period of influence – the "effect duration." For negative choices, this period is characterized by the unfolding of adverse consequences. However, this influence is not indefinite. Like the ripples from an impact, the intensity of these consequences gradually diminishes over time if not reinforced by subsequent actions.

These "bad choices" can cast long shadows over our present, creating feelings of regret and influencing our future trajectory.

Identifying the "Point of Allowed Change"

Within the "effect duration," there exists a crucial juncture termed the "point of allowed change." This is not a fixed moment but rather a phase where the established momentum of the negative consequences begins to lessen, creating a greater opportunity for intervention. At this point, corrective actions have a higher probability of effectively altering the ongoing trajectory.

The Active Shortening of "Effect Duration"

A key tenet of the "Theory of Effect Duration" is the capacity of individuals to actively shorten the lifespan of negative consequences. This can be achieved by consistently making choices that directly oppose the patterns established by the initial bad decision. These counteracting actions introduce a new momentum, gradually diminishing the influence of the past misstep.

Creating New "Points of Allowed Change":

Furthermore, the theory suggests that individuals are not solely reliant on passively waiting for a "point of allowed change" to emerge. Through the deliberate and consistent implementation of opposing choices, individuals can actively create new junctures where positive change becomes more readily achievable. This proactive approach empowers individuals to take control of their narrative and mitigate the lingering effects of past regrets.

The "Theory of Effect Duration" posits that the negative consequences of past decisions have a limited period of influence, which can be actively diminished through corrective actions. By identifying and acting during the "point of allowed change," individuals can mitigate the impact of past errors and encourage positive change. This proactive approach not only lessens the immediate effects of mistakes but also prevents prolonged negative outcomes, allowing for a more controlled and positive life trajectory.

Have you ever felt like you're wandering through a maze of your own making? A life sculpted by decisions that once felt right but now leave you with a gnawing sense of regret? You're not alone. Many of us, in our quest to fit in, achieve, or simply navigate life, can find ourselves feeling disconnected from our true selves, yearning for a "yesterday" that felt more authentic.

I've been there. Lost in a labyrinth of self-imposed rules and external pressures, I found myself questioning the very foundation of the life I had built. The path I had so diligently carved out suddenly felt like a detour, leading further away from the person I knew I could be.

The unsettling truth is, sometimes the very strategies we employ to gain control can inadvertently lead us astray. We rationalize our choices, build elaborate theories to support our current trajectory, all while a quiet voice within whispers of a different truth. That moment when the desire to rewind, to revisit past decisions, becomes overwhelming – is that a sign of failure? Or is it simply our internal compass recalibrating?

Like many, I've stumbled through the darkness, learning lessons the hard way, often driven by emotions rather than thoughtful consideration. The allure of the void, the risky leap into the unknown, can become strangely addictive, even as relationships fray and the connections we forge feel hollow. The envy of a past self, a self that felt more whole, more creative, becomes a constant companion.

So, what's the way out? Do we simply double down, work harder on a path that feels increasingly wrong? Do we continue to gamble, hoping for a different outcome? Or do we dare to stop, to re-evaluate?

The Illusion of Perfect Execution

In the midst of this internal turmoil, the idea of "perfect execution" often surfaces as the elusive answer. But what does that truly mean? Perhaps it's not about achieving flawless outcomes, but about aligning our actions with our deepest values and intentions. The maze isn't necessarily the external circumstances, but the internal conflict that arises when our choices drift away from our authentic selves.

The Concept of the "Point of Allowed Change"

We often grapple with the finality of our past decisions. Can we truly correct the choices that have led us to this point? While we can't physically alter the past, the idea of an "effect duration" for our choices offers a glimmer of hope. A bad decision casts a shadow, but that shadow isn't permanent. There comes a time, a "point of allowed change," where the momentum of that past choice begins to wane, offering an opportunity for a new direction.

Have we missed our point of allowed change? Perhaps. But the empowering truth is, we can actively create new ones. Just as consistent exercise can counteract the effects of unhealthy habits, deliberate, opposing choices in the present can shorten the "effect duration" of past missteps.

Identifying and Counteracting Our "Bad Choices"

The first step towards creating these new "points of allowed change" is honest self-assessment. What are the current patterns in our lives that are causing dissonance? For me, it looked something like this:

* Overspending: Draining my resources and creating unnecessary stress.

* Talking too much: Straining relationships and hindering genuine connection.

* Procrastination: Stifling creativity and wasting precious time.

Once these patterns are identified, the next step is to consciously implement small, consistent actions that move in the opposite direction:

* For Overspending: Implement a budget, prioritize needs over wants, and explore mindful consumption.

* For Talking Too Much: Practice active listening, take a pause before responding, and focus on the quality of communication over quantity.

* For Procrastination: Break down tasks into smaller, manageable steps, schedule dedicated time for important activities, and tackle the most challenging tasks first.

Finding Your Way Forward

The path out of this maze isn't a single, clearly marked road. It's a journey of consistent action, self-compassion, and ongoing self-discovery. Be kind to yourself as you navigate this process. There will be stumbles, moments of doubt, and the temptation to revert to old patterns. These are not signs of failure, but opportunities for learning and adjustment.

Continue to explore who you truly are, what brings you genuine joy, and what values resonate deeply within you. The "way" forward isn't about achieving perfection, but about striving for alignment – aligning your choices with your authentic self and the life you truly desire.

Change takes time and consistent effort. Don't be discouraged by slow progress. Celebrate the small victories and trust the inner voice that is guiding you towards a more fulfilling and authentic path. You have the power to create new "points of allowed change" and to navigate your way out of the maze, one thoughtful step at a time.

The Love for Building

I absolutely love building projects. From the spark of an idea to the intricate designs and the actual implementation, I enjoy every part of the process. Yet, despite my passion, I often find myself abandoning projects halfway, leaving me with nothing but unfulfilled fantasies.

The Turning Point

I realised that if I don’t change my approach, I’ll never see my ideas come to life. So, I’ve developed a strategy to ensure I stay on track and complete my projects. Here’s what I’m doing:

1. Writing It Down

When inspiration strikes, it’s easy to think you have everything figured out. But when I start writing down my ideas, I quickly see the gaps in my knowledge. Writing helps me identify what I know and what I need to learn, making my projects more manageable.

2. Sharing It with Others

I’m fortunate to have friends who share my passion for innovation. By sharing my ideas with them, I hold myself accountable and receive valuable feedback. They help me stay focused and offer solutions when I hit a roadblock.

3. Breaking It Down to Bits

One major reason for not completing projects is the overwhelming scope. I’ve learned to break my projects into smaller, manageable tasks. This makes the work less daunting and gives me a clear path to follow.

4. Documenting My Progress

When I document my progress, I create a tangible record of my achievements. This is incredibly motivating, especially when I encounter obstacles. Looking back at what I’ve accomplished gives me the strength to push forward.

Your Turn

Are you also struggling to complete your projects? Let’s help each other! Drop a like and share what strategies you’re using to improve yourself. If you have any suggestions for me, please let me know in the comments.

Thank you for reading!

— Elijah.

As we navigate through the challenges ahead, let's remember to stay resilient and focused on our goals.

It's important to adapt and evolve, always striving to improve ourselves and our circumstances.

Let's continue to push ourselves beyond our comfort zones and embrace change with open arms.

Remember, growth often happens outside of our comfort zones. Let's embrace the journey and trust in our ability to overcome any obstacles that come our way.

Stay strong, stay positive, and keep moving forward!

Your "why" becomes your purpose once you start to change.

Change change change, You've probably heard at least a dozen people share the point where they abandoned a particular activity/ lifestyle and chased something else. Well while this is good it often leads a lot of people down the wrong path. Your reason for change shouldn't be something that works for you but something you're willing to work for/ work on. I won't be giving you a long list on reasons on why you need to change( there's a lot of that on the internet already). But rather I have 2 steps to take in order to recognise your why based on my past experiences.

Your reason for change shouldn't be something that works for you but something you're willing to work for/ work on.

1. Identify your desired goal right now. What is that thing you want right now but seems to be farther away the more you act on it. Identify it and write it down.

2. Identify what you need to do. Most times after clearly writing out our goals we realise we've been chasing it the wrong way. With a little research write out that you need to do to achieve your goal and begin to work on it.

There! May look simple but it's really effective I wish I realised this back when I was younger.

After finding out your why, you just need a little tweaks here and there and you are good to go my friend.

Thank you for reading through. I hope you learnt a thing or two.

See you in the next one.

Understanding the basics of Change

What's change in the first place? I'd say it depends on what you're trying to achieve as a developer my philosophy on change is the level of success you desire determines how much you have to change.

Nobody gets it right off the bat, change is that extra effort you put in place, the stuff you have to do differently to make something you desire happen. The truth is everyone must and will change. In fact everything changes. The power of change is not to be undermined, change can take you from the top to the bottom and also bring you down in an instant. Understanding how change works in the simplest of terms is something often overlooked( I paid the price too). As a developer there are some things you need to understand about change and the power it has over your career.

the level of success you desire determines how much you have to change.

• Hey I'm Change and I respect no one: Yes! its true no one and nothing is above change. You must understand that things will not stay the same forever they will always Change. That friend you have, the programming language you learnt, the OS you use, all these things/people change over time

• Build-up and Break-down: Most times change comes in form of a simple single choice or a set of choices that can either result in a downward breakdown of all you've built till that instance or a super boost to increase your build-up.

• Pretty Hard: Don't be deceived by anyone change isn't a simple it is hard is often painful and discomforting. Especially as a developer. When I first started learning Python, I had to stay up late at night. Although it's much easier for me now, it was a significant change back then, and I often ended up with headaches. Nothing valuable comes easy, and change ensures it stays that way.

• Unexpected: Especially changes that we can't control. Most changes occur spontaneously, at unexpected times. For instance, a client might suddenly call off a contract, or your device might develop a fault. I remember the day I switched from Windows to Ubuntu. My Lenovo Ideapad was always lagging, and I was working on a job that required using Elementor on WordPress. The deadline was just a few days away, and I still had several pages to design. It was a tough decision, and I lost a few files, but I had to change my OS to something that worked for me.

The core concept of change is all about understanding the basics of change, it's importance and how it affects us all.

I will be sharing the importance of change in the next post. Stay tuned.

Thank you for reading through.

See you next time.

Hello, I'm Elijah.. a developer.

Currently in my 3rd year studying Computer Science at LAUTECH, I'm a avid learner and collaborator (yeah.. it's pasted on my profile too : )). I got into tech at 17 and built my first blog the same year. Since then, it's been an incredible journey up to this point.

So why'd I decide to write?

I've always had a thing for writing (to be honest) I've always wanted to write, but i never saw the need to. Looking back at the last few years I wish I wrote something down at some point, something I could look back to, something that could guide others.

Finding out the great power and potential writing has is one of my biggest finds in 2024. And I've decided to share my thoughts and experiences, to help me reflect on my progress and also help other developers who might be passing through same experiences.

help me reflect on my progress and also help other developers who might be passing through same experiences.

In essence I've discovered writing is one of many means to improve my growth as a developer and I'm ready to bank on it.

..well I'm not some sassy blogger promising you latest tech trends, but my experiences and opinions as a developer..

If you want to stick around, or you are interested in my story as a developer (or not) thank you. That'll probably be great.

And let it begin..

Thank you for reading through

See you next time.