2025-12-19 06:06:35
In the last few years, generative AI has become an increasingly popular avenue for creating software. However, the most mainstream AI development tools are still directed toward professional app developers and engineers who understand the intricate ins and outs of code.
CodeFlying is an end-to-end AI programming tool that comes in as a game-changer in the coding world. Non-developers can describe their requirements in simple, conversational language. CodeFlying then uses this input to build and develop a functioning app–complete with a frontend, backend, and database.
With this platform, the average man can see creative ideas become reality without ever using a single line of code.
KuaFuAI is a software engineering brand focused on multi-agent AI development. Its goal is to “become the go-to AI app builder for non‑developers worldwide — making 'building an app’ as simple as posting a status.” With this idea in mind, CodeFlying came into existence.
CodeFlying integrates LLM and DevOps technologies to create a fully functional text-to-software platform. Beginning software developers with little to no understanding of code can use natural language to build parameters for apps.
So far, CodeFlying’s user-friendly, low-cost program has generated 16 billion lines of code, resulting in 1,000,000 fully developed applications. Users can develop software by literally speaking their ideas into existence. CodeFlying handles all the technical details, with product delivery possible in as little as 10 minutes.
CodeFlying, as an AI-native application generation platform, provides powerful end-to-end capabilities for users across all industries and age groups — enabling anyone to build fully functional apps with ease. Its pricing comes in four affordable tiers, with a free option that allows users to see firsthand how it works. Here are just a few of its main functions:
CodeFlying has strong AI comprehension capable of understanding non-technical conversational prompts with multi-turn context. It generates code quickly and supports real-time edits and immediate usability.
Supports AI inclusions
Besides its role as an AI code generator, CodeFlying also allows users to incorporate other AI features into their programs, such as AI-generated text, AI-generated images, AI image recognition, and AI-generated voice.
One-Click Publishing and mobile-first delivery
Once an app is fully developed, CodeFlying allows users to install the finished product on users’ devices with just one click. This makes it possible to quickly release mobile websites, iOS Apps, Android Apps, and HarmonyOS Apps.
Built-in Admin Panel
Every app comes with a ready-to-use admin panel—no need to integrate Supabase or connect external APIs yourself. All of these backend features are generated automatically through a simple conversation with CodeFlying. View user data, API calls, and content stats instantly.
Every app created through CodeFlying also has a unique management back-end. The AI-native backend automatically sets up a database, authentication, APIs, and context management using automatic code generation.
Codeflying supports payment integrations, such as Stripe and others.
CodeFlying allows users to create custom UIs and custom pages, then preview all their features while still in early creation stages. Users can then adjust and modify apps as needed without ever needing to use code language.
User Cases and Scenarios
CodeFlying has a broad application for creators as an AI software development tool. Here are just a few ways it is currently being used around the globe:
Internal enterprise management tools
Lightweight mini-games
Online ordering systems
Educational tools and teaching platforms
Data visualization dashboards
Intelligent recipe generators
Book discussion spaces
Product launch countdown + hidden Easter eggs
Mall maps + interactive navigation
There are several popular AI app builders on the market. However, many popular brands still expect users to be experts in the language of code. Other AI app generators, such as Cursor, Bolt, and Windsurf, can overcomplicate the process, failing to meet the needs of zero-code and low-code users.
CodeFlying stands out with its natural language interface, allowing anyone to build functional apps without writing code. Unlike many competitors, it offers built-in publishing tools and access to full source code, making it easy for both beginners and experienced developers to bring their ideas to life.
CodeFlying also offers source code and documentation to users who have a solid background in coding. This allows more advanced users to customize software details directly.
CodeFlying is dedicated to seeing the dreams of building apps and software come true for people from all walks of life, including students, independent developers, startup teams, and more.
CodeFlying is quickly becoming a name users can trust, making building an app as simple as posting a status.
For more information, visit codeflying.app
:::tip This story was published as a press release by Btcwire under HackerNoon’s Business Blogging Program. Do Your Own Research before making any financial decision.
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2025-12-19 05:39:07
Hi, I’m Ilyas, a web developer.
\ I want to share a quick story which I hope can help you too 🙂
\ For 18 months, I was trying to land a remote or relocation web dev job. I applied to more than 1,000 positions, went through around 20–30 interviews, and failed most of them.
\ At the end of those 18 months, I finally got what I was aiming for: a web developer job with paid relocation for my family and me.
\ This isn’t a story about getting lucky or being exceptionally smart.
\ It’s about fixing two things I was doing wrong:
\ If you’re a junior, mid-level, or self-taught developer who keeps failing interviews and doesn’t understand why, this might help you.
No tangible results for 16 months ☹️
\ It was exhausting. I felt like I was putting in maximum effort but getting almost no results. I started doubting my skills and questioning if I'm ever gonna find a job I’ll be satisfied with.
\ My average day looked like this:
\ By that time, I knew I needed a new approach, as nothing really clicked.
\
One thing that added ambiguity is that in 2021 I easily found remote job at US company in just three weeks with almost no experience
\
\
After dozens of interviews, I noticed a pattern. I wasn’t failing because I couldn’t solve hard algorithm problems or build projects under pressure. I was failing the basics—the simple questions.
\ Questions like:
\
Questions above cost me two really sweet job opportunities.
\ These weren’t difficult—they were things I knew at some point—but under interview pressure, I blanked.
\ It hit me: I didn’t have a problem with understanding concepts; I had a problem with recall. I needed a way to remember the basics quickly and reliably, so I wouldn’t freeze during an interview.
\ Once I realized this, I started looking for a method to fix it.
Once I understood that forgetting basics was my biggest problem, I needed a method to fix it. I didn’t just want to “study more”—I needed to remember what I already knew.
\ That’s when I stumbled upon flashcards and active recall. Active recall is a simple but powerful idea: instead of passively reading or watching tutorials, you test yourself repeatedly until the information sticks. It’s backed by science—people have been using versions of this method since the late 1800s.
\ The key was that I could practice small, specific pieces of knowledge—like React portals or HTTP methods—over and over until I could recall them instantly. That way, during interviews, my brain didn’t freeze.
\ This discovery completely changed my preparation.
\
\
Once I had the right method, I needed a system. I didn’t want to guess what to study anymore.
Instead of preparing “everything,” I started asking directly.
\ I would email HR or the recruiter and ask something like: \n “What topics should I prepare for the technical interview?”
\ Surprisingly, many of them replied with a clear list.
\ Things like React fundamentals, JavaScript basics, HTTP, and browser behavior.
\ This alone saved me a lot of time. I stopped over-preparing random things and focused only on what actually mattered for that interview.
I would email HR or the recruiter and ask something like: \n “What topics should I prepare for the technical interview?”
Next, I used ChatGPT to generate flashcards for each topic.
\ I asked it to create 20–30 question-and-answer cards and show them to me one by one. I would try to answer before revealing the solution.
\ One problem I noticed was that AI can be wrong sometimes—maybe 1 or 2 cards out of 10. To fix that, I started adding links to official documentation in my prompts, so the answers were grounded in real sources.
\ With this setup, I practiced every day. Short sessions, high focus.
\ Very quickly, I felt the difference.
\
AI can be wrong sometimes—maybe 1 or 2 cards out of 10.
After a few weeks of preparing this way, interviews started to feel different.
\ I was calmer. When interviewers asked basic questions, I didn’t panic anymore. The answers came naturally, without long pauses or guessing.
\ I noticed that I could explain concepts clearly and simply. Not in a “textbook” way, but like someone who actually understands what they’re talking about.
\ In my final job application, I passed four interview rounds in a row. After the technical test, the recruiter told me I scored 95% - 19 out of 20.
\ Soon after that, I received an offer: $5,500 per month and a paid relocation package for my family and me.
\ For the first time in a long while, I felt that my effort finally matched the results.
About six weeks before I got the offer, I changed how I searched for jobs.
\ Until then, I was using the usual platforms: LinkedIn, Arc.dev, and hh.ru. I kept applying, but most applications went into a black hole. No replies, no feedback, just waiting.
\ So, I tried something different. I moved almost entirely to Telegram job groups.
\
\ The first reason was simple: less competition. Many good roles were posted there, but far fewer people applied compared to big platforms.
\
Many small companies with tiny ad budgets post jobs at Telegram. But they still offer a competitive salaries.
\ The second reason was even more important: direct communication.
\ Before applying, I would DM the recruiter and say something like: \n “I saw this position. Here’s my CV and LinkedIn. Do you think I’m a good fit?”
\ If the recruiter said “yes,” I applied and stayed in touch for feedback.
\ If the answer was “no,” I moved on immediately.
\ This approach saved me hours every week. I stopped applying blindly and focused only on roles where I actually had a chance.
\ Looking back, this change alone made my job search much more efficient.
\
I saw this position. Here’s my CV and LinkedIn. Do you think I’m a good fit?
\ I would strongly suggest moving to Telegram for job search - it was a game-changer for me.
While preparing for interviews, I ended up creating a large collection of flashcards for myself. Over time, it became hard to manage everything in notes and files.
\ That’s when I decided to turn this system into a small tool called 99cards.dev.
\ It’s simply a collection of web development flashcards, grouped by topics, built for interview prep and knowledge refresh. Nothing fancy—just the same approach that helped me stop failing basic questions.
\ It currently has over 4900 flashcards in 24 categories. All core web dev technologies.
\ I originally built it for myself, but later shared it with a few other developers who were also preparing for interviews.
This whole experience taught me a few important lessons.
\ First, failing interviews doesn’t always mean you lack skills. Sometimes it just means you can’t recall things fast enough under pressure. That’s a fixable problem.
\ Second, studying more isn’t the same as studying better. Passive learning—reading, watching videos, redoing tutorials—didn’t help me much. Active recall did.
\ Third, job searching is also a skill. Sending hundreds of applications without feedback is exhausting and inefficient. Fewer applications, better targeting, and direct communication worked much better for me.
\ And finally, consistency matters more than intensity. Short, focused daily practice beats long, stressful cramming sessions every time.
If you’re struggling with interviews right now, especially as a junior, mid-level, or self-taught developer, I want you to know this: getting rejected doesn’t mean you’re bad at what you do.
\ In my case, the problem wasn’t talent or effort. It was preparation and approach. Once I fixed how I studied and how I applied, things started to move fast.
\ If you want something practical to help you prepare, I put together a free interview checklist based on my own experience. \n
It includes 8 checklists covering:
\
\ I hope this helps you prepare better and saves you some of the time and stress I went through.
\ Remember, you are just one interview away… \n Ilyas
\
2025-12-19 05:00:09
2 Original Study: Research Questions and Methodology
3 Original Study: Validity Threats
5 Replicated Study: Research Questions and Methodology
6 Replicated Study: Validity Threats
\
In recent years, several experiments on defect detection technique effectiveness (static techniques and/or test-case design techniques) have been run with and without humans. Experiments without human compare the efficiency and effectiveness of specification-based, code-based, and fault-based techniques, as for example the ones conducted by Bieman & Schultz [8], Hutchins et al. [27], Offut et al. [43], Offut & Lee [44], Weyuker [51] and Wong & Mathur [53].
\ Most of the experiments with humans evaluate static techniques, as for example the ones run by Basili et al. [5], Biffl [9], Dunsmore et al. [18], Maldonado et al.[37], Porter et al. [45] and Thelin et al. [48]. Experiments evaluating test-case design techniques studied the efficiency and effectiveness of specification-based and control-flow-code-based techniques applied by humans, as the ones run by Basili & Selby [4], Briand et al. [10], Kamsties & Lott [29], Myers [40] and Roper et al. [46]. These experiments focus on strictly quantitative issues, leaving aside human factors like developers’ perceptions and opinions.
\
There are surveys that study developers’ perceptions and opinions with respect to different testing issues, like the ones performed by Deak [13], DiasNeto et al.[15], Garousi et al. [23], Goncalves et al. [24], Guaiani & Muccini [25], Khan et al. [31] and Hern´andez & Marsden [38]. However, the results are not linked to quantitative issues. In this regard, some studies link personality traits to preferences according to the role of software testers, as for example Capretz et al. [11], Kanij et al. [30] and Kosti et al. [33]. However, there are no studies looking for a relationship between personality traits and quantitative issues like testing effectiveness.
\
There are some approaches for helping developers to select the best testing techniques to apply under particular circumstances, like the ones made by Cotroneo et al.[12], Dias-Neto & Travassos [16] or Vegas et al. [50]. Our study suggests that this type of research needs to be more widely disseminated to improve knowledge about techniques.
\ Finally, there are several ways in which developers can make decisions in the software deveelopment industry. The most basic approach is the classical perceptions and/or opinions, as reported in Dyb˚a et al. [19] and Zelkowitz et al. [55]. Other approaches suggest using classical decision-making models [2]. Experiments can also be used for industry decision-making, as described by Jedlitschka et al. [28]. Devanbu et al. [14] have observed the use of past experience (beliefs). More recent approaches advocate automatic decision-making from mining repositories[7].
\
The goal of this paper was to discover whether developers’ perceptions of the effectiveness of different code evaluation techniques are right in absence of prior experience. To do this, we conducted an empirical study with students plus a replication. The original study revealed that participants’ perceptions are wrong. As a result, we conducted a replication aimed at discovering what was behind participants’ misperceptions. We opted to study participants’ opinions on techniques. The results of the replicated study corroborate the findings of the original study.
\ They also reveal that participants’ perceptions of technique effectiveness are based on how well they applied the techniques. We also found that participants’ perceptions are not influenced by their opinions about technique complexity and preferences for techniques. Based on these results, we derived some recommendations for developers: they should not trust their perceptions and be aware that correct technique application does not assure that they will find all the program defects.
\ Additionally, we identified a number of lines of action that could help to mitigate the problem of misperception, such as developing tools to inform developers about how effective their testing is, conducting more empirical studies to discover technique applicability conditions, developing instruments to allow easy access to experimental results, investigating other possible drivers of misperceptions or investigating what is behind opinions. Future work includes running new replications of these studies to better understand their results.
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:::info Authors:
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:::info This paper is available on arxiv under CC BY-NC-ND 4.0 license.
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2025-12-19 04:00:04
2 Original Study: Research Questions and Methodology
3 Original Study: Validity Threats
5 Replicated Study: Research Questions and Methodology
6 Replicated Study: Validity Threats
Next, we summarize the findings of this study and analyse their implications. Note that the results of the study are restricted to junior programmers with little testing experience, and defect detection techniques.
– RQ1.1: What are participants’ perceptions of their testing effectiveness? The number of participants perceiving a particular technique/program as being more effective cannot be considered different for all three techniques/programs.
– RQ1.2: Do participants’ perceptions predict their testing effectiveness? Our data do not support that participants correctly perceive the most effective technique for them. Additionally, no bias has been found towards a given technique. However, they tend to correctly perceive the program in which they detected most defects.
– RQ1.3: Do participants find a similar amount of defects for all techniques? Participants do not obtain similar effectiveness values when applying the different techniques.
– RQ1.4: What is the cost of any mismatch? Mismatch cost is not negligible (mean 31pp), and it is not related to the technique perceived as most effective.
– RQ1.5: What is expected project loss? Expected project loss is 15pp, and it is not related to the technique perceived as most effective.
– RQ1.6: Are participants perceptions related to the number of defects reported by participants? Results are not clear about this. Although our data do not support that participants correctly perceive the most effective technique for them, it should not be ruled out. Further research is needed.
\ Therefore, the answer to RQ1: Should participants’ perceptions be used as predictors of testing effectiveness? is that participants should not base their decisions on their own perceptions, as they are not reliable and have an associated cost.
– RQ2.1: What are participants’ opinions about techniques and programs? Most people like EP best, followed by both BT and CR (which merit the same opinion). There is no difference in opinion as regards programs
– RQ2.2: Do participants’ opinions predict their effectiveness? They are not good predictors of technique effectiveness. A bias has been found towards EP.
\ Therefore, the answer to RQ2: Can participants’ opinions be used as predictors for testing effectiveness? is that participants should not use their opinions, as they are not reliable and are biased.
– RQ3.1: Is there a relationship between participants’ perceptions and opinions? Participants’ perceptions of technique effectiveness are related to how well they think they applied the techniques. We have not been able to find a relationship between the technique they like best and find easiest to apply, and perceived effectiveness. Participants do not associate the simplest program with the program in which they detected most defect.
– RQ3.2: Is there a relationship between participants’ opinions? Yes. Opinions are consistent with each other.
Therefore, the answer to RQ3: Is there a relationship between participants’ perceptions and opinions? is positive for some of them.
Participants’ perceptions about the effectiveness of techniques are incorrect (50% get it wrong). However, this is not due to some sort of bias in favour of any of the three techniques under review. These misperceptions should not be overlooked, as they affect software quality. We cannot accurately estimate the cost, as it depends on what faults there are in the software. However, our data suggest a loss of from 25pp to 31 pp. Perceptions about programs appear to be correct, although this does not offset the mismatch cost.
Our findings confirm that:
– Testing technique effectiveness depends on the software faults.
Additionally, they warn developers that:
– They should not rely on their perceptions when rating a defect detection technique or how well they have tested a program. Finally, they suggest the need for the following actions:
– Develop tools to inform developers about how effective the techniques that they applied are and the testing they performed is.
– Develop instruments to give developers access to experimental results.
– Conduct further empirical studies to learn what technique or combination of techniques should be applied under which circumstances to maximize its effectiveness.
Participants prefer EP to BT and CR (they like it better, think they applied it better and find it easier to apply). Opinions do not predict real effectiveness. This failure to predict reality is partly related to the fact that a lot of people prefer EP but are really more effective using BT or CR. Opinions do not predict real effectiveness with respect to programs either.
These findings warn developers that:
– They should not be led by their opinions on techniques when rating their effectiveness.
Finally, they suggest the need for the action:
– Further research should be conducted into what is behind developers’ opinions.
The technique that participants believe to be the most effective is the one that they applied best. However, they are capable of separating their opinions about technique complexity and preferences from their perceptions, as the technique that they think is most effective is not the one that they find easiest to apply or like best.
Our findings challenge that:
– Perceptions of technique effectiveness are based on participants’ preferences.
They also warn developers that:
– Maximum effectiveness is not necessarily achieved when a technique is properly applied.
Finally, they suggest the need for the following actions:
– Determine the best combination of techniques to apply that is at the same time easily applicable and effective.
– Continue to look for possible drivers to determine what could be causing developers’ misperceptions.
:::info Authors:
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:::info This paper is available on arxiv under CC BY-NC-ND 4.0 license.
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2025-12-19 01:00:15
6 COMPARING THE STATE-OF-THE-ART AND THE PRACTITIONERS’ PERCEPTIONS
8 CONCLUSIONS AND ACKNOWLEDGMENTS
\
In this paper, we conducted a systematic mapping study and a survey to provide an overview of the different research themes on Modern Code Reviews (MCR) and analyze the practitioners’ opinions on the importance of those themes. Based on the juxtaposition of these two perspectives on MCR research, we outline an agenda for future research on MCR that is based on the identified research gaps and the perceived importance by practitioners.
\ We have extracted the research contributions from 244 primary studies and summarized 15 years of MCR research in evidence briefings that can contribute to the knowledge transfer from academic research to practitioners. The five main themes of MCR research are:
(1) support systems for code reviews (SS),
(2) impact of code reviews on product quality and human aspects (IOF),
(3) modern code review process properties (CRP),
(4) impact of software development processes, patch characteristics, and tools on modern code reviews (ION), and
(5) human and organizational factors (HOF). We conducted a survey to collect practitioners’ opinions about 46 statements representing the research in the identified themes.
\ As a result, we learned that practitioners are most positive about the CRP and IOF theme, with special focus on the impact of code reviews on product quality. However, these themes represent a minority of the reviewed MCR research (66 primary studies). At the same time, the respondents are most negative about human factor- (HOF) and support systems-related (SS) research, which constitute together a majority of the reviewed research (108 primary studies). These results indicate that there is a misalignment between the state-of-the-art and the themes deemed important by most respondents of our survey.
\ In addition, we found that the research that has been perceived positively by practitioners is generally also more frequently cited, i.e., has a larger research impact. Finally, as there has been an increased interest in reviewing MCR research in recent years, we analyzed other systematic literature reviews and mapping studies. Due to the different research questions of the respective studies, there is a varying overlap of the reviewed primary studies. Still, we find our observations on the potential gaps in MCR research corroborated. Analyzing the data extracted from the reviewed primary studies and guided by the answers from the survey, we propose nineteen new research questions we deem worth investigating in future MCR research.
We would like to acknowledge that this work was supported by the Knowledge Foundation through the projects SERT – Software Engineering ReThought and OSIR Open-source inspired reuse (reference number 20190081) at Blekinge Institute of Technology, Sweden. We would also like to acknowledge all practitioners who contributed to our investigation.
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:::info This paper is available on arxiv under CC BY-NC-SA 4.0 license.
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2025-12-19 01:00:11
Hi there! I'm Temmarie, a freelance Software developer and technical writer.
When I'm not coding or writing tech stuff, you can find me curled up with a Stephen King book, trying out new recipes in the kitchen, or experimenting with natural hair care.
I’m currently working on no-code platforms like Framer, Webflow, and similar tools while also delving into AI and its endless possibilities. I’ve also been exploring the cybersecurity space, curious about user protection and safe digital experiences.
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My writing journey began with an assignment at Microverse, a remote software development bootcamp. In my bid to complete the task, I fell in love with writing and have since gone on to write more technical articles and documentations and even dabbled in a bit of ghostwriting. \n A special moment in my journey was when I got my first subscriber, and it wasn’t a friend or family member. Shortly after, someone sent me a message saying how much they enjoyed my tutorials and how easy they were to understand — even though they didn’t have a technical background. That event is etched in my mind and was a big moment of clarity and happiness for me. It reminded me that my words could reach and help people far beyond my circle, and I’ve been writing ever since.
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Most of my writing is software development–centred, often focusing on authentication and user account protection — those are the topics I’ve published on HackerNoon. My other writing projects range from simple user manuals to technical documentation for apps, and even academic writing for college students.
I fell in love with how simple Devisemade the user authentication in web applications, and that curiosity led me to explore other authentication methods across different languages and frameworks. \n As for the future, there’s no guarantee I’ll keep exploring the user security niche. Still, I know my work will remain in the software development space — unless I eventually start a personal lifestyle blog for my hobbies.
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The HackerNoon Fellowship was an amazing experience for me. It had its ups and downs, but it connected me with many like-minded individuals who were also trying to improve their craft. Seeing everyone’s ideas come to life made me realise there’s still so much in the software development space that I’ve yet to explore, and that was exciting.
Limarc was an incredible mentor. He responded to my 3 AM submissions, provided thoughtful reviews, and taught me that technical writing doesn’t have to sound stiff — it can have warmth and storytelling that pulls the reader in and inspires learning. I’ll never forget how he even answered my queries right after his wife had a baby. He was an amazing mentor and editor.
The course was intense, and writing an article every week was a huge challenge, which I unfortunately was unable to meet due to other commitments at the time.
But the process reshaped my writing style; now, my work has more softness, warmth, and narrative depth, although many of those pieces haven’t been published here on HackerNoon yet.
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To me, content writing bridges the gap between digital evolution and everyday use. In today’s world, writers play a huge role in making technology accessible and meaningful. This evolution would be nearly impossible for non-technical users to grasp without help from writers breaking it down.
And with the rapid pace of innovation and technological advancements, I believe we’ll need even more writers who can simplify these complex advancements into knowledge people can actually use.
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I wanted to give back to the HackerNoon community and support new writers the same way I was supported. There are so many resources and guidance I wish I had at the start of my career — and I want to help others find them. \n Limarc was also a major factor in that decision. He made such a lasting impact on me in such a short time, and I want to be that kind of mentor for someone else — firm, patient, and genuinely invested in helping people grow.
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Just write. Consistency is key. You don’t have to publish everything you write, but write something every day. Read books, do your research, and write about what you understand. If you don’t know what you’re writing, the reader will notice.
Write because you have a story or information you truly want to share — not as a chore. The moment writing becomes a chore, it’ll show in your tone. \n Also, start small. You don’t have to write huge publications or use sophisticated words to sound smart. Simplicity is elegant. For me, the beauty of writing lies in the ability to express complex ideas in simple words that anyone can understand.
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You can explore my portfolio and current projects here, where I share my work in development and writing. Look forward to my updates, and connect with me on LinkedIn for a collaboration or even a chat.
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