Hameed Rahamathullah — Thoughts

Obsession climbs the abstraction ladder

Thu, 06 Aug 2026 00:00:00 GMT

Let me explain.

A few weeks back, I received a few messages from Radhakrishnan after our casual chat. This chart was part of the slides he sent. He mentioned he came up with it after the discussion with Priya Sebastian, and that it helped him think through software engineering roles deeply and his frameworks, Zero Maintenance Teams and Engineer.

This chart was fascinating, and I couldn't agree more, as I am an advocate of product engineers. I was so into the concept that I renamed our entire engineering team and its roles to product engineering in 2021/22 after being introduced to it through an article by @GergelyOrosz.

The more I looked at and reflected on this ladder, the more I thought about how these roles reflect our obsession. Our focus. I see that as abstraction levels.

The more we obsess about code, the more we become programmers/developers. We started caring much about craft, we became software engineers. Obsession with value made us product engineers. Now it is all about systems, and that will make us system/platform engineers.

These roles are named after seeing the pattern of enough people caring about the same thing at the same time, and someone eventually put a word on it. The word stuck because it described a real group of people who were already there.

But not all obsessions become a role. The obsession at the operational level becomes a widespread role. And the obsessions that are tool-based do not become widespread roles. Of course, there are exceptions like Salesforce Engineer.

As we have better abstractions, tech, and tooling to cover the levels, the obsession moves a level up.

People obsessed over assembly until compilers became trustworthy. They obsessed over servers until the cloud became trustworthy. They obsessed over browser differences until the browsers stopped differing.

The attention relocated. It went to the highest layer that was still uncertain, because that is where being good at something still made a visible difference.

That is the pattern the ladder is describing. Every rung is a layer that used to be uncertain and then became dependable enough to stop thinking about.

Code generation is crossing that line as we speak. Writing code the way we used to write it is no longer where the difference shows up. The observation I keep coming back to is this. The entry baseline moved. It used to be code. Now it is craft.

We used to be able to walk in obsessed with code alone, and that was enough to start. That door is now closing. What is expected at the entrance now is judgment about how software should be built. Knowing why this abstraction will hurt in six months. Having some taste about boundaries and naming. Caring what happens when the thing fails.

That used to be the second rung. It is turning into the floor.

Each level carries the components of ones below it.

Craft contains code. Value contains craft. Systems contain all of it. Moving up is not leaving anything behind. A system engineer still reads a stack trace during an incident. They just do not build their identity around it.

Code awareness is not going anywhere. It is changing positions. It used to be the thing we were known for. Now it is the thing we need to judge what comes back at us.

Code obsession had a low ceiling. One could spend fifteen years getting very good at it and still end up as the person who could build anything and decide nothing. I have watched that happen to people who deserved better.

Craft is a better place to start. It is more portable, it ages well, and it points somewhere. If the floor of the industry is rising to craft, more people begin their career already asking the questions that used to take a decade to learn to ask.

If obsession keeps climbing, and each layer moves up as the one below it becomes dependable, then something sits above systems.

I do not know what it is called yet. The name always arrives late.

But it is worth watching for. Because whatever it is, it is where the attention is going next.

How to think about knowledge work in the AI era

Tue, 16 Jun 2026 00:00:00 GMT

A team shipped more in one quarter than they had in the previous three. AI coding tools made them fast. Demos every week. Features flying out the door. Six months later, revenue had not moved. Customers were not happier. The team was exhausted and confused.

They were doing more work than ever. The work just did not matter.

This pattern is everywhere right now. AI has made us faster at producing things. It has done nothing to make us better at choosing what to produce or checking whether it worked. To see why, you need a simple model of how knowledge actually flows.

The loop

All knowledge work moves through six stages.

Intent. Why are you doing this? The problem you believe is worth solving.
Input. The raw material. Research, data, requirements, context.
Process. The work itself. Writing, coding, designing, and analyzing.
Output. The thing you produce. The feature, the report, the campaign.
Outcome. What changes for someone because of your output? A customer saves time. A team makes a better decision.
Impact. The lasting effect. Revenue grows. Trust compounds. The business moves.

Then the loop closes. What you learn from impact should reshape your intent. That feedback is the whole point. Without it, you are guessing forever.

I call this the Intent-to-Impact Loop. The two ends are where the game is now being played.

AI is hollowing out the middle

Picture a barbell. Heavy on both ends. Thin in the middle.

That is what AI is doing to this loop.

Gathering input used to take days of research. Now, a model summarizes a hundred sources in minutes. The process used to be the expensive part. A feature that took a team two weeks now takes one engineer an afternoon. Output used to be scarce. Now, anyone can generate a report, a design, or a working prototype before lunch.

The middle of the loop is becoming commoditized. Cheap, fast, and available to everyone. When something becomes available to everyone, it stops being an advantage.

The uncomfortable part is that most professionals built their careers on the middle parts. Their skill was the process. Their pride was the output. AI is compressing the exact stages where they live. Working harder at those stages now means automating yourself with extra steps.

Value is migrating to the ends

If the middle is cheap, where does value go? To the two stages AI cannot own. And both stages demand the same two human traits working together.

Intent demands taste and agency. Taste comes first. A model can do almost anything you ask. It cannot decide what is worth asking. Choosing the right problem, saying no to ninety-nine good ideas to pursue one great one. That judgment is taste. Then the agency takes over. A well-chosen problem is worthless until someone commits to it and starts. Plenty of people can spot a good problem. Far fewer take responsibility for it and act.

Impact demands agency and taste. Agency comes first here. Closing the loop takes effort that nobody assigns you. You have to go back, dig into what happened, and face the answer even when it is uncomfortable. Most people skip this because nothing forces them to do it. Then taste does the judging. Did this actually change anything? Was the change worth the cost? Is this good or merely done? Models can generate options and reports all day. They cannot tell you what matters to your customer, your business, or your life.

Taste to choose, agency to start. Agency to look, taste to judge. Both traits at both ends. The barbell again. These two traits were always valuable. AI just raised their price.

Most people run the loop open

Here is the second trap, and it is older than AI.

Most knowledge workers run the loop open. They form an intent, do the work, ship the output, and move to the next thing. They never wait to see the outcome. They never measure the impact. They never feed what they learned back into their next intent.

An open loop feels productive because shipping feels like progress. But shipping is only the fourth stage of six. A closed loop is how you compound. Every cycle teaches you something that makes your next intent sharper. Ten closed loops make you wise. A hundred open loops just make you tired.

AI makes this trap worse. When output is cheap, the temptation is to ship more and reflect less. The people who resist that temptation will pull away from everyone else.

How to work differently

Here is what this looks like in a normal week.

Spend more time on intent. Before starting anything, write one sentence. What should be different in the world if this works? If you cannot write that sentence, you do not have an intent.
Delegate the middle aggressively. Let AI gather input, draft the work, and produce the output. Your job in the middle shrinks to direction and review. Fighting this is like a calculator-era accountant insisting on long division.
Schedule the loop closing. Put it on the calendar. Two weeks after shipping anything significant, spend thirty minutes asking what changed. Did the outcome happen? Is there a trail toward impact? What does this teach me about my next intent?
Judge ruthlessly. When AI hands you ten options in ten minutes, your value is picking the one that matters and killing the other nine. Practice saying "this is fine, but it does not matter." That sentence is taste in action.

The question to carry

The professionals who struggle in the next decade will be the ones who keep competing in the middle of the loop, racing machines at the machines' own game.

The ones who thrive will own the ends. They will choose problems with care, let the machines run the middle, verify what changed, and feed every lesson back into a sharper intent.

So here is the question to ask about your own week. How much of your time goes to the middle of the loop, and how much goes to the ends?

Be honest. Then start moving toward the barbell.

Token budgets are an estimation problem

Thu, 28 May 2026 00:00:00 GMT

Jaya Gupta (@JayaGup10) wrote a sharp piece on where enterprise AI is heading. The core argument is that inference has stopped being an experiment and has become an operating infrastructure, and the companies that figure out token-to-outcome attribution will control where AI spending flows.

I agree with the thoughts and like where we are headed, and we are trying to understand the real cost and value of the AI in our work. That is how we mature. The token allocation and budgeting are the logical next steps in this direction.

But this entire token budget war is the most difficult battle for software engineering teams for one reason: estimation.

Jaya’s article talks about cost calculation in retrospect. But in practice, when a software engineer gets a budget of tokens, they need to know how much a task would cost in tokens, at least in parts, if not end-to-end, before they start execution.

The planning room.

Imagine a sprint planning session. The team collaboratively comes up with the list of things to work on next. Now the team has to answer the next important question of the planning session: how much time do we need? And the follow-up question: how many tokens do we need to build it?

The room is silent now, and nobody has a reasonable answer.

The engineering team has the overall budget of allocated time and tokens. But the operating cost of a specific workflow, at the task level, before the work starts, is unknown. That gap is where a lot of token budget wars will become extremely difficult.

Estimation is hard, and AI is making it even harder.

Software engineering, or any stream of engineering, is complex and has multiple moving parts. We, as an industry, know how bad we are at estimating. Countless studies prove this point.

And software teams do not (and should not, IMO) operate like assembly lines. No matter how much we try to operate in a modular, mechanical way.

We invented agile methodologies, scrum, story points, velocity tracking, and planning poker because effort is hard to predict in advance.

Now, bring token allocation into the equation. Just when we tried to make peace with it, token estimation now adds a different problem on top of the existing one. Predicting the token usage for the cost is almost impossible. Because of the design. AI systems operate in a probabilistic way.

An agent can thrash. A model can over-reason. A retrieval step can pull fifty documents when five would do. The context window inflates. Retries compound.

Types of tokens and the control we have.

There are five types of tokens: input, deliberation, output, evaluation, and maintenance.

Input: Everything the model sees before it starts: the prompt, system instructions, retrieved context, tool definitions, conversation history, and examples. This is the only category we have meaningful control over.
Deliberation: The model's internal work: reasoning, planning, tool call decisions, self-correction, and backtracking. This is where variance lives, and it is largely outside our control.
Output: The model's response, including tool call syntax, format scaffolding, and streaming overhead. We can constrain it, but not fully predict it.
Evaluation: A separate LLM call to verify or gate another call's output. Most teams forget to budget for this until they are already over budget.
Maintenance: System-level token use independent of any specific task: context compaction, session state, cache priming, and periodic re-injection of system prompts in long-running sessions.

Most teams are shielded from this right now. Subscription pricing on coding agents from frontier model companies hides the underlying token cost. You see credits consumed, or even worse, the available usage limit, not the deliberation and evaluation layers running underneath. That abstraction will not survive enterprise-scale budget scrutiny.

Amazon's Kiro editor signalled this early. Their plan mode offered flat per-task pricing. Outcome-based, not token-based. Essentially, a vendor absorbs the estimation problem, so teams would not have to. But they revised it to usage-based pricing now. Even the companies building the tool could not control the variance.

The question nobody can answer yet.

“How much will this sprint cost in tokens?”

Jaya's layer tells you what already happened. That is necessary. Helps us understand the cost in retrospect.

But when the budget is allocated to the engineering teams, people have to know, at least roughly, the cost to run and build before committing to the work. And we have no clear way to calculate it yet.

It is a planning problem. And nobody in the sprint room owns it yet.

The “machine operator” era of software engineering

Wed, 08 Apr 2026 00:00:00 GMT

I. Something Familiar Is Happening

In the early 1700s, a weaver in England would take about a week to produce a single piece of cloth. The pace was slow because the work depended on a combination of knowledge, skill, and manual effort. Every decision came from the weaver’s hands and mind. The loom was only a simple hand tool.

Then the machines arrived. The spinning jenny to the power loom. Each one increased output, but more importantly, each one shifted skill away from the person and into the machine. Work that once took years to learn could now be done by someone operating a machine. Cloth began to be produced faster, cheaper, and at a scale no individual weaver could match.

The weavers did not just lose their work. They lost a part of who they were. Weaving was not just a trade. It was their identity.

Software engineering is going through the same kind of shift right now. Not the shallow "AI will take your job" panic. Something deeper. An identity shift.

For decades, the bottleneck in building software was human intelligence applied to production. Someone had to take an idea, break it into logic, and write it as code. That process was slow and expensive. It shaped how we hired, how we built teams, and how we valued work.

Now, we're building machines that can do that translation on demand. Not perfectly or completely on their own. But good enough to write a working function from a plain description. Good enough to build an app from a conversation. Good enough to change how software gets made.

The textile industry didn't die when the power loom arrived. It changed shape. New roles showed up. New skills became valuable. The craft didn't vanish. It moved. The same thing is happening in software right now.

II. Writing Code Was Never the Most Valuable Part

To understand this shift, you need to see something the industry spent decades getting wrong.

Somewhere along the way, "software engineering" started to mean "writing code." Interviews tested algorithm puzzles. Bootcamps promised to make you an engineer in twelve weeks by teaching you JavaScript.

The narrative was set in a way that the value is in the code. But this was never the complete truth.

Software engineering was never just about writing code. It was about solving problems through software. That takes a chain of thinking that goes far beyond the editor. Understanding the real problem. Making design choices that balance speed and flexibility. Knowing what users actually need versus what they say they want. Communicating with designers, business leaders, and customers. Deciding what to build, what to skip, what to save for later, and also what not to do.

It takes judgement from experience (aka taste). A feel for what makes a system work smoothly and feel right to the people who use it.

Code was just the final step of all that thinking. Important, yes. But the output, not the process. In fact, the great, maintainable code was the result of clear, holistic thinking.

Confusing the two had real costs. It created a hiring culture that overvalued technical tricks and undervalued product thinking. Teams ended up full of brilliant coders building the wrong features. As a ripple effect, it created a new breed of roles like Product Managers who are tasked to tell engineers what to build.

And also for engineering, it created a dangerous weakness in defining a profession by its most automatable part. That set it up for a crisis the moment that part gets automated.

That crisis is here. Developers who see themselves as "people who write code" are watching machines learn to write code. And they're scared.

But developers who see themselves as "people who solve problems through software" are seeing something different. A powerful tool that handles the most time-consuming part of their work, leaving them free to do everything else. The understanding and the judgment. The parts that were always the real craft.

The commoditization of intelligence and the industrialization of coding reveal what software engineering was always meant to be.

III. What the Machine Needs from You

Let's be honest about what AI can do today. It can take a description and turn it into working code. It can find bugs, write tests, and scaffold projects. For routine tasks, it performs remarkably well.

But ask it to decide whether your product should have a separate login and signup page, or whether a passwordless flow would reduce drop-off by 30%, and it has nothing useful to say. That question requires understanding your users, your business, and your data. It requires context that lives outside the codebase.

The machine is great at translation when we give it clear inputs, and it produces clear outputs. But it can't decide what matters and weigh competing priorities.

This is why "human in the loop" is a long-term strategy. The machine handles production. The human provides context with direction and judgment. That's a complementary design.

The Sequoia’s recent article, Services: the new software, articulated it beautifully, “Writing code is mostly intelligence. Knowing what to build next is judgment.” It is a great distinction of ingredients that knowledge work needs. Where there is need, there is supply and demand dynamics, and with these dynamics, bottlenecks form.

It is the natural order for the economic system to invent newer machines that can reduce or eliminate the bottlenecks. These are levers. But the nature of the bottlenecks is that they don’t disappear when these levers are introduced; they rather appear at a different level with different demands. It used to be intelligence; now that we have intelligence on tap, the next bottleneck is human judgment.

IV. The Product Engineer and the Full Value Stack

So if machines handle more of the coding, and the real value lies in knowing what to build and why, what do we call the person who does that work?

Not a product manager who owns strategy but doesn't build. Not a traditional developer who writes code to someone else's spec. Not a designer who shapes experience but doesn't own the technical decisions.

The product engineer sits at the center of all three. They understand users well enough to know what they need. They understand the business well enough to know what matters. They understand the technology well enough to direct the machine that builds it.

This isn't a brand new job. Great engineers have always worked this way. What's new is that it's no longer optional. When the machine handles more of the coding, engineers who only know how to code have a shrinking role. Engineers who know how to think about the whole problem become more valuable than ever.

The industry was already moving in the direction of having lean, self-managing teams. The older model, where a product manager writes a spec, hands it to engineering, and the engineers building exactly what's described, no longer makes sense in smaller teams. When the machine can produce code in minutes, the bottleneck shifts to the quality of the thinking that directs it. The product engineer doesn't wait for a spec. They own the problem.

I know this because I've done it. In 2021, while building Success, a platform purpose-built for enterprise sales engineering teams to manage their presales engagements, we renamed our entire engineering team as the product engineering team. This was before AI tools became mainstream. Inspired by our own experience and Gergely Orosz's writing on the product-minded engineer, we made the shift early.

The change wasn't about titles. It was largely about mindset. We wanted every engineer to think beyond code and the technical layer. Thinking about use cases, user experience, business implications, and technical constraints. The title change was for the identity shift. The full value stack. It changed how the team approached their work. Engineers started asking better questions, pushing back on specs that didn't make sense, and caring about outcomes and impacts rather than just output.

For years, "full stack developer" meant someone who could work across technical layers. Frontend and backend. Database and deployment. That definition now needs an expansion.

The new full stack runs on two axes. The soft layer is where human judgment lives: product sense, user experience thinking, and business awareness. The hard layer is where technical skill lives: systems understanding, architecture, security, and the ability to direct and review what the machine produces.

In the old model, the hard layer was everything. In the new model, it's necessary but not enough. The product engineer who can move between both layers, who can talk to a user in the morning and review a pull request in the afternoon, is the new full-stack developer. Not a full technical stack. Full value stack.

V. The Four Parts of Product Engineering

The product engineer works at the highest layer of abstraction. Not inside the code, but above it, deciding what the code should do and why. In practice, a product engineer's day comes down to four things.

Understand what the user and business needs and wants are. Before you touch any tool, you need to know what problem you're solving and for whom. Talk to users. Study the data. Understand what the business is trying to achieve and why it matters now. This is the step that separates a product engineer from a coder. The quality of everything that follows depends on how well you understand the problem, the context, before you start building.

Provide context, inputs, and guardrails. The machine can build, but it can't decide what to build. You bring everything the machine can't know: who the user is, what problem you're solving, what constraints exist, and what quality bar matters. Garbage in, garbage out is still relevant. The product engineers’ responsibility is to make sure the inputs are of higher quality.

Set up and operate the machine to co-create. You're not handing off a task and walking away. You're in a loop. You describe, the machine produces, you review, adjust, steer. This is closer to directing than delegating. Your technical knowledge tells you when the machine took a wrong turn. Your product sense tells you when the output is correct, but misses the point.

Review and get the last mile to publish. Review, eliminate, recreate, retain, and ship. The machine gives you a first pass. Your job is to review what it produced, eliminate what doesn't work, recreate the parts that need rebuilding, retain what's good, and finish the last mile to publish. The difference between 'it works' and 'it's ready to ship' is yours to own. And if something breaks at 2 AM, "the AI wrote it" is not an answer. You're shipping your work, built with the machine's help.

VI. The Machine Builders

So far, we've talked about the people who operate the machine. But there's another set of jobs emerging: the people who build and maintain the machine itself.

AI engineers. The people who take the existing capabilities of these models and build new capabilities on top of them. They design the tools, the pipelines, the guardrails, and the infrastructure that make AI useful in production. They're not using the machine to build products. They're building the machine that others use.

This is a distinct path from product engineering, but it shares the same foundation. You still need to understand users, because the users of your machine are other engineers. You still need product sense, because a tool nobody can use reliably is a tool nobody will trust. And you still need deep technical skill, because you're working at the layer where the abstraction breaks down and the real complexity lives.

Both paths matter. The industry needs people who can operate the machine well and people who can make the machine better. The common thread is that neither role is just about writing code anymore.

VII. Two Misconceptions Worth Addressing

As this shift picks up speed, two misconceptions keep showing up in conversations. Both are worth addressing head-on. Two questions.

"Do we need to learn coding anymore?"

If the machine writes the code, do you even need to learn coding?

Yes. For two reasons that go deeper than you might expect.

First, coding teaches you a way of thinking. Breaking a fuzzy problem into precise steps. Handling edge cases. Thinking in systems, not just features. This is what separates someone who can direct a machine effectively from someone who gives it vague instructions and hopes for the best. A lot of non-coders working with AI tools struggle because they haven't developed this way of thinking. They don't know what to ask for, and they can't tell when the answer is wrong.

Second, you need coding knowledge to evaluate the machine's output. AI will produce code that looks right, runs without errors, and still has serious problems: security holes, performance issues, and architectural choices that will cause pain in six months. If you can't read the code and understand what it's doing, you can't catch these mistakes. That’s not a proper review.

The product engineer doesn't need to be the fastest coder in the room. But they need to understand code well enough to think like a programmer and judge like one. The machine writes. You decide if what is written is worth keeping.

"Is it a downgrade from software engineering because I don’t code?"

The biggest misconception I see is that the shift is perceived as a lesser version of software engineering because we don’t write code. As if the shift from "write code all day" to "direct machines and own outcomes" is a step down.

This is because of the identity we discussed earlier in this article. In fact, the reality is that it is the opposite is true. Product engineering is more valuable.

The product engineer isn't a software engineer who lost the coding part of the job. They're a software engineer who gained everything else. The technical skill doesn't disappear. It still matters. But it sits inside a wider frame of judgment and ownership that makes it more powerful, not less.

VIII. The Craft, Finally Seen in Its Entirety

This matters no matter where you are. If you're early in your career, learn user behaviors, not just frameworks. If you're a senior engineer, your judgment is your biggest asset now, not your syntax speed. If you're a founder, hire people who ask "why" before "how." And if you're self-taught, you might be better positioned than you think. You've always had to learn by shipping real things to real users. That's exactly the skill set the industry now needs.

Think back to the weavers. The power loom didn't kill the craft of making fabric. It killed one definition of that craft, the one where value lived entirely in the hands that moved the shuttle. What emerged was something bigger. The industry grew, the work evolved, and the people who adapted were the ones who saw the change for what it was: not an ending, but a beginning.

The future of full-stack isn't vertical. It is both vertical and horizontal. It's about understanding every layer of the value stack. From what the user needs, to what the business requires, to how technology delivers it, to what the experience should feel like.

That's the product engineer. That's the full value stack. And for those willing to make the shift, it's not the end of the craft.

It's the craft, finally seen and made in its entirety. And as the future of work, we are set to do the most human thing: to judge.

You don't need a manual to become high agency

Sat, 04 Apr 2026 00:00:00 GMT

Contrary to popular belief, you don't need a manual or a book to become a high agency individual. It's ironic to need one.

High agency is not a skill but a trait. The combination of multiple attributes. Thinking clearly, making informed decisions (which really means making trade-offs), having the courage to decide and act, putting in the effort, refusing to depend on external factors, and being intentional about taking control.

This trait has a toggle switch. Any human being — regardless of IQ, financial condition, or background — can turn it on. These are my observations from moments when I toggled the switch in my own life.

Most things in life are subjective. The best answer to any question is "it depends." There are no right or wrong decisions. Everything is a trade-off. That is why thinking clearly about your own situation matters more than following someone else's playbook.

Simple, but not easy. Some decisions are small and trivial. Some are big and difficult. Courage to take the risk and call the shots is what separates thinking from doing. Many people struggle — and regret later in life — because they were unable to make a few key decisions. The root cause, in most cases, is a lack of courage.

17 years ago, on the same day (April 4), I decided to start my own business, leaving my stable job despite having no other income source or savings for the family. Might sound foolish (or courageous), but that’s what I did. And that’s what led me to where I am today.

But none of the above matters if you can't put in the effort. When you take the shot, act on it. If there's a shortcut, take it — as long as it isn't illegal or unethical. The size of the effort doesn't matter. The act of moving does.

When you start taking action, you will encounter obstacles along the way. You might feel like you need a teacher or capital. You don't. There is always a way. And another way. And another. There is the conventional path, and then there are the pathless paths.

When I was just 11 years old, I tried to learn computer programming, and I had never been to school. I asked my father to approach multiple computer institutions that rejected me because of my age. The courses were only designed for adults. And it was also unaffordable for us.

But the next best option I had was to read books and learn. I nudged my parents to buy me books that I could barely carry. Somehow, I read and reread the books until the concepts started to click. I didn’t stop. And it worked out for me.

Be intentional. Take control, in the most Stoic sense. Control what you can, let go of what you can't, but find the workaround. Think of it like an escape room. Instead of crying near the locked door, keep finding the doors that are open. Any progress beats staying stagnant.

You can be high agency right now. Pick the area in your life where you feel the most stuck. Sit with it. Think clearly. Make a few key decisions. And start acting on them. Not tomorrow, not next Monday, right now.

That's the push. From grey to green.

Agency and taste

Mon, 19 Jan 2026 00:00:00 GMT

Agency and taste are the two timeless traits of humans. They've always mattered, and they'll matter even more in the AI era.

Agency is simple to describe but hard to live by. It's the ability to act. To shape reality instead of being shaped by it. High-agency people don't wait for permission, they create their own path. They are permissionless. They don't see the future as fixed, they see it as something they can mold through action.

High agency is the mix of clear thinking with the bias to action, and the courage to disagree when needed. Most of us live on low agency by default, reacting to the world instead of influencing it. But agency can be built. Every time you decide and act, instead of drift, you grow it and you strengthen it.

Taste, on the other hand, is your internal compass for quality. It's the ability to recognize what's good, meaningful, or beautiful before everyone else does. Taste helps you cut through noise and see what deserves to exist.

Taste is both personal and universal. Personal, because it's shaped by your history. Through your exposure and identity. Universal, because there are certain qualities most people can agree feel "right". Even when they can't explain why.

In an abundant world where anyone can create anything, taste becomes power. When everything looks "good enough," taste helps you separate signal from noise.

You can grow taste like any other skill. Consume widely, without judgment. Break down what makes the best things world-class. Recreate them with your own hands. You'll notice details you never saw before. And listen to others' perspectives, they'll show you what you've missed.

Personally, I see software as the mix of art, science, and engineering. And taste is what turns that mix into something beautiful. With AI making creation easy and skills accessible, taste is what keeps our work human. It's what makes us look natural in a synthetic world.

Agency makes you act.
Taste makes you act on the right things.

AI Is Turning Everyone Into a Reviewer

Mon, 09 Jun 2025 00:00:00 GMT

How the next jump in productivity is less about what you create and more about what you keep. Why picking, polishing, and shipping matter more than typing in the age of chatbots.

Conceptualized, reviewed, and edited by Hameed Rahamathullah. Draft assistance: ChatGPT (OpenAI o3).

Generated using Sora by bobbyjimi

TL;DR

Generative AI now creates solid first drafts in seconds. Your role is to judge, guide, and correct that work. Clear prompts, sharp edits, and fast turnarounds define success. Reviewer habits help: keep a simple quality checklist, prompt in steps, check facts at speed, connect your tools, and stay calm about rough drafts.

The Page Is No Longer Blank

A single prompt can produce paragraphs, working code, or slide decks before a coffee break. The hard part is no longer typing. It is deciding. When the cost of making options drops to near zero, the value of choosing rises fast. Good judgment becomes the core of modern knowledge work. Think of the AI draft as wet clay. Your skill lies in spotting the strong parts, trimming the weak bits, and shaping a final form that fits purpose, audience, and brand.

Productivity Metrics Are Moving

Old-school scores focused on raw output: lines of code, words per hour, and tickets closed. Those numbers miss what matters when AI writes the first pass.

A better set of signals looks at:

Error prevention. How many bugs or factual slips did you catch before release.
Context fit. How well does the edited draft match the real needs of users and the goals of the team.
Speed to value. Time between the prompt and the polished result that can ship or inform a decision.
Learning loops. How often does feedback from users or teammates feed into the next prompt, so the work keeps improving.

These signals reward precision, awareness, and fast iteration rather than brute effort.

Creativity Feels Different

Cheap drafts expand the idea space. You can spin up ten story angles or five UI layouts before lunch. The edge now sits in four human skills:

Taste. A sense of what feels right for the audience.
Context memory. Facts and history, the model lacks, unless you feed it.
Ethical judgment. Choosing what should ship, not just what can.
Story stitching. Turning loose pieces into a clear arc that moves people.

You act like a conductor guiding many specialized models. The music is only as good as the direction.

How to Train for Reviewer-First Work

Write down your rules
List brand tone, trusted sources, style points, and quick checks. A checklist beats fuzzy vibes.
Prompt in clear steps
Ask for ideas, then filter, then refine, then polish. Each stage has its short prompt. Cleanup time falls.
Check facts on reflex
Keep a search tab open. Verify names, numbers, links, and quotes before sharing the draft.
Link your tool chain
Move text from ChatGPT to VS Code, images from Midjourney to Figma, and notes from Claude to Notion. Use Zapier or small scripts so files appear where you need them.
Detach from drafts
Treat the AI output as raw material. Cut, reorder, and rewrite without ego.
Pair up for quick reviews
A teammate scans the same draft and catches slips you missed. Two short passes beat one long solo edit.
Track your impact
Measure turnaround time, defect rate, and engagement scores. Data shows how reviewer skills drive results.

What This Means for Teams

Hiring
Seek editors who can prompt well and engineers who can critique code. Balance creation skill with review skill.
Process
Replace long build cycles with rapid review loops. Ship small, learn fast, prompt again.
Culture
Praise early catchers of mistakes. Treat guardrails as valuable as bold ideas. Share checklists so quality scales with team size.

The Takeaway

AI removes much of the grind in making first versions. The high-leverage work is judging what deserves to live. Learn to steer, refine, and approve at speed. Own that skill and you own the final cut in every project you touch.

Enjoyed this piece? Clap, share, or drop a comment about how AI is changing your workflow.

10 Common Gaps That Block Your Team — and How to Bridge Them

Thu, 26 Dec 2024 00:00:00 GMT

10 Common Gaps That Block Your Team — and How to Bridge Them

Generated using Flux model in Krea.ai

As engineering managers and leaders, identifying and addressing gaps that block productivity and efficiency is one of the most impactful ways to elevate team performance. Gaps can manifest in various forms, from unclear priorities to a lack of necessary resources or knowledge.

This article explores common gaps faced by engineering teams and the questions that team members might have as blockers. By providing thoughtful answers to these questions, managers can effectively unblock their teams, fostering better execution and higher performance.

1. Willingness Gap

The Willingness Gap arises when a team member lacks the motivation or enthusiasm to work on a specific task. This could be due to disinterest, feeling overwhelmed, or not understanding the purpose of their work. Left unaddressed, this gap can significantly affect productivity and work quality.

***Why does this matter to the user, product, team, or organization?
***This helps them understand the broader impact of their work and its alignment with team or company goals. Share how the task contributes to overall success, link it to specific objectives, and provide context to its importance.
***What’s in it for me?
***Team members want to see how their effort benefits them, whether through skill development, recognition, or career growth. Highlight personal growth opportunities, rewards, or learning outcomes tied to completing the task.
***What if I attempt and fail?
***Fear of failure can hinder willingness to take on challenging tasks. Reassure them by creating a safe space for experimentation, emphasizing the value of learning from mistakes, and offering support.

2. Knowledge Gap

The Knowledge Gap occurs when a team member lacks the necessary skills or expertise to perform a task effectively. In the fast-evolving world of software development, this can stem from insufficient training or a lack of exposure to specific technologies or domains.

***What do I need to learn?
***This question helps team members identify the specific skills or knowledge areas required for the task. Provide clear guidance on the skills needed, breaking them down into manageable learning objectives.
***How can I learn it?
***This question focuses on understanding how to acquire the necessary skills effectively. Offer actionable suggestions such as training programs, workshops, online resources, or mentorship opportunities.
***Who can help me?
***This question encourages collaboration and seeking assistance to overcome knowledge gaps. Suggest reaching out to colleagues, mentors, or subject matter experts for guidance and support.

3. Scope Gap

The Scope Gap occurs when a team member does not clearly understand the scope of work or project objectives. This can result in misaligned priorities, inefficiencies, and compromised quality.

***What is the desired outcome?
***This question ensures clarity on the expected result of the task or project. Clearly communicate project objectives and deliverables with examples or references to provide a solid understanding.
***What are the boundaries?
***Team members need to know the limits and exclusions of the task to avoid confusion. Specify the scope explicitly, outlining what is and isn’t included.
***Who can clarify uncertainties?
***Open communication is essential for addressing ambiguities. Designate a point of contact or set up a system for quick resolution of scope-related questions.

4. Time Gap

The Time Gap arises when a team member doesn’t have adequate time to complete a task effectively. Unrealistic deadlines, overburdened schedules, or poor time management often contribute to this gap.

***Do I have enough time to complete this task?
***Team members need clarity on whether the timeline aligns with the task’s complexity. Managers should encourage open discussions to set realistic deadlines collaboratively.
***What happens if I miss the deadline?
***Understanding the consequences of missed deadlines helps team members manage their priorities effectively. Managers can assist by clarifying impacts and offering solutions to mitigate risks.
***What resources or support can I access?
***Knowing what assistance is available can alleviate time pressures. Managers should provide information on available tools, additional resources, or team members who can help.

5. Collaboration Gap

The Collaboration Gap occurs when team members struggle to work effectively with others. Poor communication, unresolved conflicts, or a lack of trust can hinder teamwork and reduce productivity.

***Who do I need to collaborate with?
***Understanding the key contributors and stakeholders clarifies roles and responsibilities. Managers can help by defining these clearly and ensuring alignment within the team.
***How can I communicate effectively with my team?
***Team members need to know how to share ideas and updates smoothly. Encouraging open communication channels, regular updates, and active participation can enhance collaboration.
What actions can I take to build trust with others? Trust is essential for seamless teamwork. Team members can engage in transparent interactions, demonstrate reliability, and participate in team-building activities to foster mutual respect.

6. Resource Gap

The Resource Gap occurs when team members lack access to essential tools, software, or equipment to perform their tasks effectively. This could be due to budget limitations or inadequate infrastructure.

***What resources do I need to succeed?
***Team members need to identify the specific tools or equipment necessary for their tasks. Managers should encourage open discussions to clarify needs and align them with project requirements.
***How can I access these resources?
***Understanding the process of obtaining resources helps team members plan effectively. Managers can provide clear steps or escalate requests to ensure timely acquisition.
***Who can help me resolve resource issues?
***Knowing the right person to approach streamlines problem resolution. Managers should establish clear points of contact or escalation paths to address resource gaps efficiently.

7. Process Gap

The Process Gap arises when team members are unfamiliar with or fail to adhere to established processes and best practices. This can lead to inconsistencies and inefficiencies in execution.

***What process should I follow?
***Team members need clarity on the specific steps and procedures required to complete tasks effectively. Managers should provide detailed, step-by-step guidelines and examples to reduce confusion.
***Why is this process important to others?
***Understanding the value of processes helps team members see how they contribute to efficiency and quality. Managers can explain the benefits and link processes to successful outcomes.
Can I suggest improvements to the process? Team members may have valuable insights on how to refine workflows. Managers should encourage feedback and create channels for suggestions to ensure continuous improvement.

8. Feedback Gap

The Feedback Gap happens when team members do not receive timely or constructive feedback. Feedback is crucial for improving skills and ensuring alignment with project goals.

***What did I do well?
***Team members seek recognition for their strengths and contributions. Managers should provide specific examples to validate and reinforce positive behaviors.
***What can I improve?
***Identifying areas for growth helps team members advance in their roles. Managers can offer actionable, specific feedback with clear suggestions for improvement.
***How can I adjust to meet expectations?
***Team members need guidance to align their performance with project or organizational goals. Managers should provide clear, practical steps tailored to the individual’s context to support course correction.

9. Priority Gap

The Priority Gap occurs when team members are unclear about task priorities, leading to misaligned efforts and suboptimal results. This is often caused by unclear goals, shifting priorities, or poor communication.

***What is the top priority for me?
***Team members need clarity on which tasks require their immediate focus. Managers can help by providing clear, task-specific priorities and using tools like checklists or project boards.
***Why does this matter now?
***Understanding the urgency and relevance of tasks enables better decision-making and effort allocation. Managers should link tasks to deadlines or critical project milestones to provide clear context.
***How can I stay aligned with the team?
***Staying aligned ensures consistency in achieving goals. Regular team syncs and transparent communication help team members adjust their priorities as needed.

10. Compensation Gap

The Compensation Gap arises when team members feel that their compensation does not match their contributions, skills, or market standards. This gap can lead to dissatisfaction, reduced motivation, and higher turnover rates.

***Am I being fairly compensated?
***Team members often evaluate whether their compensation aligns with their contributions and the market. Managers should conduct regular benchmarking and transparently communicate how salaries are determined.
***Is my work being recognized and valued?
***Team members need assurance that their contributions are appreciated. Managers can address this by providing meaningful recognition and linking performance to tangible rewards.
***What options exist for growth or adjustment?
***Employees want clarity on opportunities for salary increases or promotions. Managers should outline clear career pathways and regularly review performance to ensure fairness.

Thanks to the Reddit user Tuxedotux83 for pointing out the compensation gap.

By addressing these gaps proactively, engineering managers can unlock their team’s full potential, fostering an environment where productivity and innovation thrive. Use these questions and solutions as tools to diagnose and resolve blockers, enabling your team to deliver high-performance outcomes consistently.

The Science of Spaced Repetition: Optimizing Memory Retention Through Smarter Techniques

Sun, 22 Dec 2024 00:00:00 GMT

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Let’s start with a familiar scenario: You read something fascinating, maybe an article or a book chapter, and think, This is brilliant! I’ll never forget this. But a week later, when you try to recall the details, you’re left with vague fragments, wondering, Why can’t I remember this?

If this sounds like you, you’re not alone. Forgetting isn’t a personal failing; it’s how the human brain works. Fortunately, there’s a way to beat this natural tendency. It’s called spaced repetition, and it’s not just a productivity hack — it’s rooted in science. In this article, we’ll unpack how it works and, more importantly, how you can use it to retain knowledge longer and more effectively.

The Forgetting Curve: Why We Forget

In the late 19th century, German psychologist Hermann Ebbinghaus discovered something unsettling: we’re wired to forget. Through his experiments, he created the forgetting curve, a visual representation of how quickly information vanishes from our memory if it’s not reinforced.

Think of it like this: Imagine pouring water into a leaky bucket. Without plugging the holes (reviewing the material), the water (your knowledge) will eventually drain away. This is why cramming doesn’t work long-term. You might temporarily stuff your brain with facts, but they’ll disappear just as quickly.

This is where spaced repetition comes in. It’s the patch for the leaky bucket, slowing the loss of information by revisiting it at the right intervals.

What is Spaced Repetition?

Spaced repetition is a learning method that uses carefully timed reviews to solidify information in your memory. Basically, review something just as you’re about to forget it. Each time you revisit the material, the intervals between reviews get longer because your brain remembers it better each time.

Think of it as planting seeds in a garden. If you water them too often, you waste water (effort). If you water them too infrequently, they wither (you forget). But if you water them just right, they grow strong roots and thrive.

This technique isn’t new. Students have used flashcards for decades to study vocabulary or concepts.

The Science Behind It

Here’s why spaced repetition works so well. When you revisit information at increasing intervals, you’re strengthening the neural pathways associated with that memory. Each review acts like a workout for your brain, making the memory stronger and more resilient.

Studies show that spaced repetition is far superior to traditional learning methods, like rereading notes or binge-studying the night before an exam. It taps into the brain’s natural learning rhythm, helping you move information from short-term to long-term memory. It’s the difference between casually meeting someone at a party and becoming their close friend over time.

How to Use Spaced Repetition

The beauty of spaced repetition is its simplicity. Here are some ways to integrate it into your life:

For Students: Break your study material into chunks and review them over days or weeks. Use flashcards to test yourself, focusing on the concepts you find hardest.
For Professionals: Whether you’re learning coding, medical terms, or business strategies, spaced repetition can help you retain complex knowledge.
For Personal Growth: Want to learn a new language, remember names, or master a hobby? Spaced repetition can help with that too.

Tips for Making It Work

To get the most out of spaced repetition, keep these tips in mind:

Start Small: Begin with a few key concepts or terms. Trying to review too much at once can overwhelm you.
Prioritize Weak Areas: Focus on the material you struggle with the most.
Be Consistent: Make spaced repetition a daily or weekly habit. Even short, regular sessions add up.
Leverage Technology: Use apps to automate the scheduling of reviews, so you don’t have to track intervals manually.

Challenges and How to Overcome Them

Like any habit, spaced repetition has its challenges. You might struggle to stay consistent or feel tempted to review everything at once. Remember, the goal is efficiency. Reviewing smarter, not harder, is the key.

If you’re worried about sticking with it, start with small wins. Set a timer for just 10 minutes a day. Celebrate your progress, no matter how small. And remind yourself that learning is a marathon, not a sprint.

Beyond Academics: The Bigger Picture

Spaced repetition isn’t just for exams or work. It’s a tool for lifelong learning. Imagine using it to memorize inspiring quotes, retain life lessons, or remember the names of people you meet. It’s a way to deepen your understanding of the world and connect with it more meaningfully.

The beauty of spaced repetition is that it’s simple, effective, and adaptable. It doesn’t matter whether you’re a student, a professional, or just someone who loves learning. With a little consistency and the right tools, you can transform how you remember and retain information.

So, why not start today? Choose one thing you want to learn or remember and commit to revisiting it at spaced intervals. Your future self — the one who remembers effortlessly — will thank you.

Learning Styles: Ahead of Time, On the Fly, and Retrospective

Sat, 21 Dec 2024 00:00:00 GMT

Learning is a part of life. Whether it’s preparing for a school exam, fixing a sudden problem at work, or reflecting on a past mistake, how we learn shapes who we are. But not all learning happens the same way. There are three key approaches to learning that we use, often without realizing it: ahead of time, on the fly, and retrospective. Each of these serves a purpose, and understanding them can help us grow and succeed in different areas of life.

The Learning Styles

1. Ahead of Time Learning

Ahead of time learning is what most of us think of as “studying” or “preparing.” It’s when you gather knowledge or skills in advance, knowing you’ll need them later. Think about studying before a big test or training for a new job.

This type of learning is proactive. It helps us feel ready and confident when the time comes to act. The more prepared you are, the less likely you are to panic when the situation arises. For example, if you’re giving a speech, rehearsing ahead of time helps you avoid fumbling through your words.

However, ahead of time learning isn’t perfect. Sometimes, we can’t predict everything we’ll need to know, or we might waste time over-preparing for things that never happen. It’s important to balance preparation with flexibility.

2. On the Fly Learning

On the fly learning is exactly what it sounds like — learning as you go. It’s the ability to figure things out in the moment, without prior preparation. Imagine you’re at a party, and someone asks you to help with a game you’ve never played before. You jump in, learn the rules as you play, and adapt as you go along.

This kind of learning is valuable because it helps us deal with unexpected challenges. In fast-paced or unpredictable situations, being able to think quickly and learn on the spot can make all the difference. For example, if you’re troubleshooting a tech problem for a customer, there’s often no time to read a manual or take a course. You have to dive in, figure it out, and solve the issue.

But on the fly learning has its limits too. If you’re always winging it, you might miss out on deeper understanding or make unnecessary mistakes. While it’s great for quick problem-solving, it’s not always the best approach for complex or long-term goals.

3. Retrospective Learning

Retrospective learning happens after the fact. It’s when you look back at an experience, analyze what went well or poorly, and draw lessons from it. For example, after a sports match, a coach might review the game with their team, pointing out strengths to build on and mistakes to avoid.

This type of learning is powerful because it turns every experience — good or bad — into an opportunity to improve. It helps you recognize patterns, avoid repeating mistakes, and refine your approach for the future. For instance, if a presentation didn’t go as planned, reflecting on why it fell flat can help you do better next time.

The downside of retrospective learning is that it relies on hindsight. By the time you learn the lesson, the moment has already passed. That’s why it works best when combined with other types of learning.

Why These Styles Matter

Each of these learning styles has its place. Sometimes, being prepared ahead of time is the best way to succeed. Other times, we have to think on our feet and learn as we go. And no matter how much we prepare or adapt, looking back afterward is always important to grow.

Life often requires us to balance these approaches. For example, imagine you’re starting a new job. You might study the company’s tools and processes ahead of time. On your first day, you’ll probably have to figure some things out on the fly. Later, reflecting on your first week will help you settle in and do even better in the future.

How to Use This in Everyday Life

Here are some simple ways to make the most of these learning styles:

Prepare When You Can: For predictable events, spend time learning in advance. This builds confidence and reduces stress.
Stay Flexible: Practice solving problems in real-time. It’s okay to make mistakes while learning on the fly.
Reflect Regularly: After every experience, take a moment to think about what you learned and how you can improve.

Learning isn’t one-size-fits-all. The way we learn depends on the situation. By understanding and using ahead of time, on the fly, and retrospective learning, we can handle challenges better and grow stronger with every experience. Whether you’re preparing for a big moment, tackling something unexpected, or looking back to learn from the past, each approach has its value. The key is knowing when to use each one and how to grow from it.

The Learning Cycle: Learn, Unlearn, and Relearn

Fri, 20 Dec 2024 00:00:00 GMT

From the moment we take our first steps in life, learning begins with imitation. As children, we mimic the actions, words, and behaviors of those around us, building our foundational understanding of the world. This is the natural starting point for any kind of learning. However, the journey to true mastery demands more than imitation. It requires moving beyond surface-level understanding to deeper exploration — and this is where the framework of Learn, Unlearn, and Relearn becomes a powerful tool for personal and professional growth.

Phase 1: Learn by Imitating

Imitation is not only natural; it is efficient. When venturing into any new domain — whether it’s learning to code, playing an instrument, or starting a business — copying proven methods provides a quick way to acquire foundational skills. For instance, a budding programmer might study and replicate existing code, while a novice artist might recreate famous paintings. These early steps build confidence and competence, offering a safe space to experiment without starting from scratch.

However, imitation has its limits. While it can teach the “how,” it rarely reveals the “why.” Staying too long in the imitation phase can result in stagnation. To grow, one must move beyond copying and begin to explore the nuances and complexities of their craft.

Phase 2: Unlearn to Evolve

Unlearning is where the real growth begins — and where many people struggle. Unlearning involves questioning the methods, assumptions, and beliefs that have guided you thus far. Often, it’s prompted by mistakes or failures, which expose the limitations of existing knowledge. For example, an entrepreneur who blindly follows a popular playbook might realize that what worked for others doesn’t align with their unique market or product.

This phase demands humility and openness. It’s not easy to let go of what you’ve mastered, especially when it has brought you success. But clinging to outdated approaches can be a barrier to innovation and deeper understanding. Instead, treat mistakes as opportunities to reassess and refine your perspective.

Strategies for effective unlearning include:

Reflective practice: Regularly evaluate what’s working and what’s not.
Seeking feedback: Allow others to challenge your assumptions.
Curiosity: Embrace discomfort and ask tough questions about your beliefs and methods.

Phase 3: Relearn for Mastery

Relearning is about diving deeper. It’s the process of revisiting foundational concepts with fresh eyes and a new perspective, often uncovering insights that were previously hidden. This phase is marked by asking profound questions that shake existing beliefs and lead to breakthroughs. For instance, a software developer might revisit fundamental programming concepts to understand the principles behind newer, more efficient frameworks.

Techniques for relearning include:

First principles thinking: Breaking down complex problems to their most basic elements.
Deliberate practice: Focusing on specific skills or areas for improvement.
Socratic questioning: Challenging assumptions to arrive at deeper truths.

Relearning transforms surface-level knowledge into wisdom, fostering creativity and innovation. It’s the phase where mastery begins to take shape.

Barriers to the Cycle

Despite its transformative potential, the Learn-Unlearn-Relearn cycle is not without challenges. Common barriers include:

Ego: Admitting that you need to unlearn can feel like a blow to your self-esteem.
Comfort zones: The familiarity of what you already know can be hard to leave behind.
Fear of failure: The relearning process often involves making more mistakes before progress is evident.

To overcome these obstacles, cultivate a mindset of lifelong learning. Embrace curiosity, remain adaptable, and view discomfort as a sign of growth.

Applications Beyond Individuals

The Learn-Unlearn-Relearn model isn’t limited to personal development. It’s equally relevant to organizations and industries. Startups, for instance, often pivot by unlearning initial assumptions about their product or market and relearning based on new insights. Similarly, established companies must unlearn outdated practices to stay competitive in rapidly evolving industries.

In the broader societal context, this framework can drive progress. As new technologies and ideas emerge, unlearning biases and relearning with an open mind enables collective growth and innovation.

Conclusion: The Endless Loop of Growth

The Learn-Unlearn-Relearn cycle is not a one-time process; it’s an ongoing journey. It allows us to continuously adapt, evolve, and deepen our understanding. Whether you’re learning a new skill, leading a team, or navigating life’s challenges, this framework provides a roadmap for achieving mastery and staying relevant in a constantly changing world.

So, where are you in your learning journey? Take a moment to reflect. Are you imitating and absorbing, questioning and unlearning, or digging deeper to relearn? Wherever you are, embrace the process — because true growth lies in the cycle, not the destination.

10 Key Phases of API Lifecycle

Mon, 09 Sep 2024 00:00:00 GMT

❶ Designing: Planning the API structure, endpoints, and data models. This includes creating API specifications like OpenAPI/Swagger and considering usability, scalability, and adherence to RESTful principles or other architectural styles.

❷ Developing: Implementing the API based on the design specifications. This involves setting up the development environment, writing code, and integrating with databases or other services.

❸ Testing: Creating and running comprehensive tests to ensure API reliability and correctness. This includes unit tests, integration tests, end-to-end tests, and performance and load testing.

❹ Versioning: Managing different API versions to support backward compatibility while allowing for improvements. This involves strategies for introducing changes without breaking existing client integrations.

❺ Documenting: Creating clear, comprehensive, and up-to-date documentation for API consumers. This includes usage examples, endpoint descriptions, request/response formats, and authentication methods.

❻ Deploying: Setting up the infrastructure and processes to make the API available for use. This covers choosing hosting solutions, configuring servers, and implementing CI/CD pipelines.

❼ Observing: Monitoring API performance, logging operations, setting up alerts, and analyzing usage patterns. This helps in identifying issues quickly and understanding how the API is being used.

❽ Securing: Implementing measures to protect the API and its data from unauthorized access and attacks. This includes authentication, authorization, encryption, and protection against common vulnerabilities.

❾ Scaling: Ensuring the API can handle increased load and traffic as usage grows. This involves techniques like load balancing, caching, database optimization, and potentially moving to a microservices architecture.

❿ Maintaining: Regularly updating dependencies, fixing bugs, and refactoring code to keep the API healthy and efficient. This also includes handling deprecations and managing the overall API lifecycle.

Stay Strong and Practice

Thu, 08 Feb 2024 00:00:00 GMT

Nobody likes to fail. But the inevitable reality is that you’ll make mistakes and fail much more than expected. And that’s okay.

Remember, every attempt is a practice session. Try your best, and don’t worry about the outcome. When you fail, learn from every single mistake. Never stop practicing. Don’t overthink. Heads down and practice, no matter what.

One day, you will perform so exceptionally well that nobody can come close. You’ll be the expert. You’ll see through every possible mistake and dodge them during your performance. You’ll become the master of the art.

Stay strong and practice.

Owning up

Wed, 13 Dec 2023 00:00:00 GMT

People often say we should learn from our failures, especially those caused by our own mistakes. But how do we actually do that? The first step is to acknowledge our mistakes. Own them up.

Realizing and accepting our mistakes is the starting point of learning. We can’t begin to learn until we admit we’ve made a mistake.

Even if a mistake isn’t entirely our fault, if it affects us even a little, we have some responsibility. We had some control. Perhaps there was a chance to prevent it.

Admitting our mistakes isn’t a bad thing. It’s never a negative sign. It shows we’re mature and willing to learn, not weak. It demonstrates our commitment to learning from failure and improving. We are preparing ourselves to avoid making the same mistakes again.

Most importantly, the majority of our performance and growth are defined and measured not by our mistakes but by how we respond and handle the situation.

While an average person may seek to blame others, a mentally strong individual embraces accountability. It’s the quality that distinguishes a great leader.

Getting Started: You got to love what you’ve got.

Thu, 25 Feb 2016 00:00:00 GMT

I have seen many people in my life waiting for right moment, perfect opportunity, good mood, or best gadget to start doing something great. But that’s not going to happen until they start with what they have got right now and the saddest part is, some of them even know this very well. They just can’t start.

We definitely have to accept the fact that we have awesome life. Lot of good people around us, friends and family to share, and most of all, the freedom of choice. You can choose who you want to be. Isn’t that great?

But some don’t see what they have got right now. Their focus is more on things that they don’t have than the very good ones they have. People are less creative at this part. We, human beings, need to think creative solution to every problem and start taking action.

If you fall into the category of people I am talking about, then just remember one thing, you have got everything you want. You’ve got a beautiful life, healthy body, creative mind, and of course, plenty of time. You just have to do it. Start right now. Make yourself comfortable with what you have right now and jump start.

“You don’t have to be great to start, but you have to start to be great.”
— Zig Ziglar

Your first draft of work may be crappy but don’t worry too much. No one gets it for the first time. You know you can improvise it. Force yourself out of your comfort zone because that’s where the real magic happens. The real secret of success is getting started.

Getting Started: You got to love what you’ve got. was originally published in The Coffeelicious on Medium, where people are continuing the conversation by highlighting and responding to this story.

People Who Inspired Me in 2015

Sat, 02 Jan 2016 00:00:00 GMT

Here is the list of people inspired me through their work in web development industry. These people made me proud to be the part of the industry that is ever evolving. Needless to say, internet is one awesome thing that helped me follow these great people and learn through their writings, podcasts and videos.

Jeffrey Zeldman

The web standard guru. I really enjoy his writing style and his viewpoint. He is one of them who move the web forward.
Twitter: https://twitter.com/zeldman
Website/Blog: http://www.zeldman.com/

Tobias Van Schneider

Award winning multi-talented designer. He is one of my primary source of inspiration with his writing, podcast and work. He is the one, reminds me constantly that, you can be really good at what you do, even if you are a high school drop-out.
Twitter: https://twitter.com/vanschneider
Website: http://www.vanschneider.com/

Taylor Otwell

The creator of the best PHP framework I ever know — Laravel. This single person, through his work, inspired and changed the way people, like me, write efficient code and develop beautiful software.
Twitter: https://twitter.com/taylorotwell
Website/Blog: http://taylorotwell.com/

Anselm Hannemann

The curator of Web Development Reading List — carefully crafted list of web development related resources. He is definitely one of them who make staying up-to-date in ever-evolving industry like web development easier.
Twitter: https://twitter.com/helloanselm
Website/Blog: https://helloanselm.com/

Sacha Greif

One more in the list is Sacha Grief, obviously because of sidebar.io — a service that filters noise out of industry updates. It helps me keep my knowledge up-to-date even in busier schedules.
Twitter: https://twitter.com/SachaGreif
Website/Blog: http://sachagreif.com/

Matthew Inman

Known by the screen name Oatmeal, he is the person who keeps me laughing though his hilarious online comics. I read through his comics and get inspired by the way he sees the world.
Twitter: https://twitter.com/Oatmeal
Website/Blog: http://theoatmeal.com/

Oliver Emberton

I really love the way he writes and illustrates. Most of his articles are gamified version of real world scenarios. Many of his writings motivated me to get things done. Most of the time, more than once.
Twitter: https://twitter.com/oliveremberton
Website/Blog: http://oliveremberton.com/

Evan Williams

Last but not least, the co-founder and CEO of Medium — a platform that helps people share and experience knowledge, ideas and stories. Medium has really changed the way I consume and share content online.
Twitter: https://twitter.com/ev
Blog: https://medium.com/@ev/

I really thank these awesome people for inspiring fellow people like me and it feels wonderful living in a connected world. I wish everyone a happy and exciting new year.

People Who Inspired Me in 2015 was originally published in The Coffeelicious on Medium, where people are continuing the conversation by highlighting and responding to this story.

Motivation is Important Everyday

Sun, 26 Jul 2015 00:00:00 GMT

Why you need to recharge your motivation every single day to be successful at work or in life.

I was stuck. Awfully stuck. I felt like I was moving no where, was like just stopped advancing. I was frustrated by this feeling I had inside me. I was blaming myself everyday for not improving. I never felt this way before. Never been so unproductive.

I thought it was lack of time. Sometimes, I thought it was lack of energy. Sometimes, the money and sometimes, the skills. I read lot of articles on productivity, setting goal, daily routine, etc. I read everyday and changed my lifestyle, routine and way of thinking. But it had very little impact on what I was trying to get over.

I also tried to disconnect myself from work some time and take break because, you know, it could be effect of burnout, stress or depression. But no luck, the results faded away after short period of time.

Then, I realized it was not just me. Lot of people felt the same way, at some point of time in their life. It’s a common phenomena. However, I also understood that, most of the time, people don’t lack time, energy, money or skill. They just lack motivation.

Most of the time, people don’t lack time, energy, money or skill.

They just lack motivation.

Motivation is the purpose, motivation is energy, motivation is the fuel that lets us run. Everything starts with motivation. We cannot just ignore it, we have to recharge it. Not just one time, once in a month or once in a year with all our fancy new year’s resolutions, but everyday. Here are some unusually useful ways to recharge your motivation.

Exercise and Achieve Goals

Workout is the most important action you can do to motivate yourself and it has a good side effect of keeping you healthy. Set your workout routine and stick to it. Also, start setting some small, achievable goals and keep achieving them. Those small achievements will make you motivated and happy.

Network with People

If you want to motivate yourself, you cannot just do it yourself. You need to hangout with positive people and talk to them. It’s not mandatory that you should only hangout with people related to your work, you can also hangout with your friends or family. Just a casual chat is what it takes.

Learn Something New

Keep yourself busy by learning something new everyday, even if it doesn’t help you directly in your profession. Because you never know, that piece of information or skill will help you someday. As Steve Jobs phrases it, you can’t connect the dots looking forward; you can only connect them looking backward.

Contribute to Community

If you are a developer, you can contribute to or create some open source projects. If you are a photographer, you can share some free-to-use photos. If you are a designer, you can create and share some design freebies. Helping others will definitely help you get motivated.

Work on Passion Projects

Have a side-project or two as a backup plan if you can’t help yourself get motivated. Whenever you feel low, just switch to your side-project and work for a while. Working for yourself always feels good. Side-projects will also help you learn new things by experimenting.

Watch/Read About Leaders

Knowing more about another personality will inspire you to get motivated, no matter what industry they are. Watch some documentaries, read biographies or learn more about their achievements. But that doesn’t mean you have to professionally follow or copy them.

Sit Back and Think

Think, plan, strategize or just lean back and think about your future self. Daydream about your goals and picture yourself achieving it. Motivation is all about getting inspired. No matter whether you inspire yourself or let others inspire you.

It doesn’t really matter whether you are working at Fortune 500 company, a small business owner or a freelancer, everyone needs motivation. Motivation helps us achieve our goals, takes us places and makes us happy. So start motivating yourself everyday and never forget to inspire others.

On time

Thu, 21 Aug 2014 00:00:00 GMT

There is no such thing called wasted time. You get something in return for time you spend. So spend every second with passion and dedication.

On Learning and Copying

Tue, 05 Aug 2014 00:00:00 GMT

We are programmed to learn everything by copying. We learned things by imitating people around us in our childhood. Copying is not bad as long as you know how to attach your style to it.

On saying “NO”

Thu, 08 May 2014 00:00:00 GMT

Because that’s what matters.

If you don’t feel right to work with that client or do that project/task, just simply say “No”. I agree saying “No” is difficult at times, however, keep in mind, a project may turn next multi-billion dollar acquisition but only if you work with passion. Love what you do, do only what you love.

Waste Time. Gain Productivity.

Mon, 16 Dec 2013 00:00:00 GMT

One simple step to waste time and be productive.

Every single workday, we struggle to spent much of our time to complete as many task as possible to be a better, productive person. We tend to follow productivity tools and methods such as GTD and Pomodoro to set our routine. But at the end of the day, we don’t achieve and sometime we even fail to do some simple task after spending long workday.

It does not mean that the tool or system we follow has problem, of course each system is designed to help increase our efficiency. The problem is with the motivation. If we don’t feel motivated, even simple tasks will look like a big rock.

But wait, what is this motivation thing? Aren't we motivated enough to do the work we love? Yes we do, but we don’t feel it in everyday life and we don’t bring that out when we really need to. But the good news is, it isn't difficult.

The Simple Step: Do one thing unrelated to work everyday.

Yes, you got it right. Spend portion of your time on one non-work related stuff everyday which will help you get motivated instantly.

Let it be a early morning walk, solving a Sudoku, writing a piece of content in your personal diary or anything that makes you really happy. For example, if you are a designer, spend creating one piece of design every morning. But remember, that should be strictly unrelated to your work.

If you spend time working on stuff that makes you happy from inside, that will be the trigger to motivate you instantly.

Smart Ways to Handle Procrastination

Sun, 17 Nov 2013 00:00:00 GMT

“The time you enjoy wasting is not wasted time” — John Lennon

Being in a professional world, we deal with procrastination very often and end up being unproductive at the end of the day. In any kind of job, our efficiency is measured by the balance of quality and quantity of work we do, so everyone need efficiency in their work. To achieve efficiency in daily routine, one must know how to handle procrastination and convert it into a positive energy.

First let’s understand why do we procrastinate? Below are some major reasons for procrastination:

Lack of self-confidence — this creates shyness and increases our fear of making mistake
Lack of knowledge — because of no deep understanding how our work related tools work
Mental or physical sickness — due to health issues, personal tensions, or even lack of sleep
Monotonous task — simply the task we are trying to complete is not interesting enough

Now at least we know, why do we procrastinate, but how do we battle these issues and overcome to achieve outstanding results in work we do. These are some smart ideas to handle procrastination like a pro.

Take work seriously

Major portion if your lifetime is spent doing your mainstream work. It helps you build skills, knowledge and reputation overtime. So taking work seriously is never going to hurt you in any ways.

Gain knowledge, confidence will follow

The fact is, from every work opportunity, you learn something new and if you gain knowledge by doing work the self confidence follows itself. Simply, more work equals more knowledge equals more confidence equals more productivity.

Set routines for work and play

As professionals, we always plan each project with its own schedule, timeline and deadline. But we fail to set daily routines which helps us achieve the goals and keep us healthy & active. Set schedule for both work, play and sleep everyday.

Do something new everyday

Work on something new everyday which helps you build more knowledge and also prevent you from getting bored. If your work related task are similar, spend sometime in side projects. Side projects gives you much more freedom to create and share.

Procrastination is always not good for our professional and career goals. So always try to be happy, content and active to avoid procrastination.

Feel Good.

Wed, 08 May 2013 00:00:00 GMT

A beautiful way to get inspired.

Whenever I plan to write article for my blog, I simply can't because of several reasons. It may be time crunch, work on clients’ projects, personal events. However, these are just reasons I explain to myself , in other words just excuses.

I gave a little thought figuring out the real issue on a train travel to a personal event. After spending some time on thought process, I figured that the real issue I can't get started is “not feeling good”. Then I got to question myself “why shouldn't I write something related to feeling good?”. I started doing so, immediately.

I agree, every task requires its own effort and planning, but there is something apart from that. That is the “feel good” factor. You always need to feel good to do good work.

If this is true, is it something we don't know? Is it that difficult to feel good? Aren't we happy enough to feel good? No.

It’s not that we don’t know how to feel good, but when to. Remember the time when you started a fascinating project with all your efforts in designing wireframe, prototypes, user interface mock up and lose interest when it comes to put in more and more efforts in development? When planning, we don’t plan the completion of the project instead, we plan how to get started. I know getting started is the first step to complete the project but planning for completion is the key to get started. Dreaming about the end product and its usage is more important than just starting with an idea.

It is not that difficult to feel good, let it be small idea or a game changing one, all you need is to dream about how it will help the users or targeted audience and how it will bring the change they want in their life. That thought will spark a “feel good” moment in your mind and help you to get started.

We need not to be always happy to feel good, in reverse, you need to feel good to be happy. And finally, happiness increases productivity.

This is my perspective on not only getting started but producing quality output eventually.