Forem: Swastika Yadav

We gave actual claws to Openclaw agent and it flies a drone now

Swastika Yadav — Mon, 11 May 2026 18:32:32 +0000

A few weeks back, we posted a short demo of a drone following a car in peak SF traffic, controlled entirely by an Openclaw agent through a single natural language prompt. It pulled 617K views and 305 developers showed up in the replies asking for early access. Half the quote tweets were calling it the most exciting thing they'd seen in robotics all year, the other half were genuinely concerned about the surveillance implications. Both reactions told us the same thing, this hit a nerve because people could immediately picture what they'd build with it.
// Detect dark theme var iframe = document.getElementById('tweet-2028645216505549168-887'); if (document.body.className.includes('dark-theme')) { iframe.src = "https://platform.twitter.com/embed/Tweet.html?id=2028645216505549168&theme=dark" }

So we wrote a detailed breakdown of how Openclaw goes from a sentence to a drone tracking a car in real time.

TL;DR: Openclaw agents can now control drones via Mavlink on Dimensional. One natural language query and the agent handles perception, tracking, and drone flight control autonomously. The same agent that ran on a humanoid yesterday flew a drone today. Fully open source.

We gave our Openclaw agent access to a drone through Dimensional, our open-source agentic OS for physical space. 40 lines runfile. No flight controller code. No Mavlink scripting. Just one sentence:

"Follow the next white car that comes through the intersection."

The drone took off, the agent started reading the camera feed, waited for a white car to appear, and followed it autonomously. Every decision about when to launch, what to track, and how to pursue was made by the agent. We just typed a sentence and ran a short runfile we call a blueprint.

How Openclaw actually flies a drone

The key design decision is that the Openclaw agent doesn't know it's flying a drone. It sees typed streams: Out[Image] for camera data, In[Twist] for velocity commands, Out[PoseStamped] for position. It reasons about what it sees and issues commands against whatever interface Dimensional gives it, regardless of whether those streams are coming from rotors or legs or wheels.

So when you type "follow the next white car," the agent starts running YOLO detection on the live camera feed, classifying vehicles frame by frame. Nothing happens yet. It's watching the intersection, waiting. The moment a white car enters the frame, the agent recognizes it and hands off to the drone_visual_servoing_controller, which computes the pixel offset between the car's bounding box and the camera center, converts that into velocity commands, and feeds them to the flight controller. From there the MavlinkConnection takes over and translates everything into protocol commands over UDP port 14550.

Three clean layers doing three different jobs: the agent decides what to follow, the drone_tracking_module figures out how to follow it, and Mavlink handles the actual flying.

The Mavlink layer underneath handles everything about actual flight including rotor speeds, altitude holds, and GPS waypoints. That's 920 lines of guardrailed, low-level control logic sitting between the agent and the hardware, and the agent never touches any of it. What the agent does is call skills like person_follow or gps_nav_skill through the @skill decorator, which exposes regular Python methods as tools the LLM can discover and invoke while it's reasoning. The agent decides what needs to happen, the flight controller figures out how to make it happen, and those two systems run at completely different speeds without stepping on each other.

This is also how we solve the latency question that kept coming up in the replies. Agent reasoning at LLM speed and real-time flight control don't need to run at the same frequency, they just need a clean interface between them. Dimensional is that interface. The agent isn't waiting on the flight controller and the flight controller isn't waiting on the agent.

You also don't need a physical drone to start building with any of this. The repo ships with FakeMavlinkConnection and a full replay system that feeds recorded flight telemetry back to your agent with real timing preserved. Build and test the entire workflow on your laptop, then plug in hardware when you're ready to fly.

You can run the full agentic drone workflow against recorded flight data with one command:
dimos --replay run drone-agentic

Same agent, different robots

The day before we flew the drone, this same Openclaw agent was running on a Unitree G1 humanoid. The day after, a quadruped. Three platforms in three days. Zero rewrites.
This works because the same typed streams the agent was reading from the drone work identically on a humanoid or quadruped. Build the agent workflow once, swap the hardware connection module, and everything carries over. The agent doesn't know or care what body it's in.
We spent months building a custom, pip-installable transport layer to make this possible. That transport layer is how we already cover 80% of robots including Unitree, DeepRobotics, Agibot, Galaxea, AgileX and most drone platforms. It's the reason we could ship on a humanoid one day and a drone the next, and the reason any developer building on Dimensional gets that same iteration speed.

curl -fsSL https://raw.githubusercontent.com/dimensionalOS/dimos/main/scripts/install.sh | bash

One install. Pick your hardware. Write your prompt.

The agent remembers what it sees with Spatial Memory

Following a car is one instruction, one flight. For agents to actually be deployed in physical space they need to remember what they've seen over hours and days, not just react frame by frame.

We're building Spatial Memory to solve this, it gives agents a persistent world model they can query across space and time. The drone runs on a monocular camera but robots on Dimensional with depth sensors or lidar can plug into the same memory system and agents can query this across seven dimensions: object, room, semantic, geometric, time, image, and point cloud. This is what turns a single-instruction drone into a persistent system that understands your space.

Developers are already building for real world

We also set this up at our own office as a proof of concept. If outdoor cameras detect someone loitering, an Openclaw agent deploys a drone to investigate. All cameras, drones, and robots operate in one shared world frame, building spatial memory together as a fleet. We are working on making it production ready soon.

The depth perception comes from monocular depth estimation, so you don’t need expensive sensor arrays to get started. A standard camera feed is enough to build a working spatial model. OpenClaw can now understand physical space and temporality, and it integrates with any lidar, stereo, or RGB camera you throw at it.

When we open-sourced the full stack, Dimensional hit 3 trending on GitHub within 72 hours. A developer built a Telegram bot controlling a Unitree Go2 in 180 lines of Python. Hackathon teams built ROSClaw, bridging every ROS robot to Openclaw agents. Companies are already shipping inspection drones and warehouse automation on the same stack.

Join our Discord, share what you build and hang out with fellow builders!

Early Raises $5M to Transform Software Development

Swastika Yadav — Tue, 22 Oct 2024 07:24:43 +0000

Read the Tech Crunch article about Early's funding and investors.

We are excited to announce that Early has raised $5 million in seed funding led by Zeev Ventures with the participation of Dynamic Loop Capital.

We are dedicated to letting developers ship high-quality and bug-free code, faster than ever while we take care of writing unit tests on their behalf.EarlyAI lets you generate tests with a single click, review the generated tests, fix bugs, and see gaps in test coverage.

Everyone is aware of the pain of writing unit tests and still failing to catch bugs, we are not the first ones addressing it. Although, most of them focus on improving processes or creating better tracking tools that does not stop developers from introducing bugs in the first place. At Early, we realized that to make a significant impact in improving software quality, we needed to take a different path. Instead of adding more tasks to developers' already full plates, we decided to remove one of their most time-consuming responsibilities: writing unit tests.

The Vision

Early's vision is to redefine software development, quality and possibilities by liberating developers from bugs.

The software industry has long grappled with the challenge of maintaining code quality. In the US alone, poor software quality in 2022 costs $2.41 trillion. We created EarlyAI because we believe that with new AI technology, we can make a tool that changes how we approach code quality. We plan to take on the whole job of creating tests, acting as the AI test engineer working alongside every developer.

This will not only help developers avoid bugs but also free up their time and mental energy to focus on the creative aspects of coding that truly drive business value. It is the first step in our vision to reshape the software development landscape.

Future Plans

With this funding, we plan to create even better product and experiences for developers, speed up our work on supporting more languages, IDEs and types of testing. And this is just the start, with the best-in-class team, we aim to become an AI agent for test generation and code quality, relieving developers from bug fixing altogether, and our starting point is far off from any solution that exists today.

We have already made substantial progress since the soft launch in August, having created over 35,000 unit tests and more than 3,000 developers who installed our VSCode extension.

The best part about EarlyAI is that it is built for anyone and everyone who has touched code. Whether you are a developer, a tech leader, or an indie hacker, Try EarlyAI today and let us know your feedback. We are working hard to improve your experience, and your feedback and support mean the world to us.

We are excited about what is ahead and thankful for the trust our investors, users, and supporters have placed in us.

To know more about why we started Early and our vision, read here.

Join us on this journey, for a simple installation flow click here.

You code, leave the Test to us!

Hadoop/Spark is too heavy, esProc SPL is light

Swastika Yadav — Wed, 22 Feb 2023 13:38:53 +0000

With the advent of the era of big data, the amount of data continues to grow. In this case, it is difficult and costly to expand the capacity of the database running on a traditional small computer, making it hard to support business development. In order to cope with this problem, many users begin to turn to the distributed computing route, that is, use multiple inexpensive PC servers to form a cluster to perform big data computing tasks. Hadoop/Spark is one of the important software technologies in this route, which is popular because it is open source and free. After years of application and development, Hadoop has been widely accepted. Not only can it be applied to data computing directly, but many new databases are developed based on it, such as Hive and Impala.

The heaviness of Hadoop/Spark

The goal of Hadoop is to design a cluster consisting of hundreds of nodes. To this end, developers implement many complex and heavy functional modules. However, except for some Internet giants, national communication operators and large banks, the amount of data in most scenarios are not that huge. As a result, it is common to see a Hadoop cluster of only a few or a dozen nodes. Due to the misalignment between goal and reality, Hadoop becomes a heavy product for many users whether in technology, use, or cost. Now we will explain the reason why Hadoop is heavy in the said three aspects.

The heaviness of technology

If a cluster consisting of thousands of computers do exist, it is impossible to rely on the manual operation to perform personalized management. We can imagine that if these computers are all listed, the number of computers will be more than the eyes of the operation and maintenance personnel can take in, let alone manage and assign tasks. Besides, it is inevitable that various failures will occur now and then when so many machines are running. In this case, how to ensure the smooth execution of computing tasks? To solve these problems, Hadoop/Spark developers write a lot of code to implement the functions including automated node management, task distribution, and strong fault tolerance.

However, these functions themselves will take up a lot of computing resources (CPU, memory, hard disk, etc.). If such functions are used on a cluster of several to a dozen nodes, it will be too heavy. The cluster of a few nodes is not large originally, yet Hadoop takes up a considerable part of resources, which is very uneconomical.

Beyond that, the product line of Hadoop is very long. To put these functional modules on one platform to run, it needs to sort out the interdependence between various products, and therefore, it has to implement an all-encompassing complex architecture. Although most scenarios only use one or two products of the product line, they have to accept this complex and heavy platform.

Spark, which appeared later, makes up for Hadoop's lack of memory utilization. Herecomesaquestion: can it make technology lighter? It’s a pity that Spark goes to another extreme. From a theoretical model perspective, Spark only considers in-memory computing. In particular, the RDD in Spark adopts the immutable mechanism, and a new RDD will be copied after each calculation step, resulting in a large occupation and waste of memory space and CPU. It cannot even run without large memory, so it is still technically heavy.

The heaviness of use

Hadoop is technically too complex, which means that installation, operation and maintenance will be very troublesome. Even when a cluster has only a few computers, it also has to use the node management, task distribution, and fault tolerance functions designed for cluster with thousands of nodes, and thus you can imagine how difficult the installation, configuration and debugging, as well as the maintenance and management of daily operation, will be.

Even if these difficulties are solved, and Hadoop runs normally, you will also get in bigger trouble when writing the calculation code for big data. The core framework of programming in Hadoop is MapReduce, and programmers only need to write Map and Reduce actions when they write parallel programs. Indeed, it is effective to solve simple problems such as summation and counting, however, when encountering complex business logic, programming in MapReduce will be very difficult. For example, the JOIN calculation, which is very common in business computing, is difficult to be implemented in MapReduce. In addition, it is also difficult to implement many order-related operations.

Spark's Scala has a certain ability to calculate structured data, will it be simpler to code in Scala? Unfortunately, it is very difficult to use and learn Scala, and it is more difficult to master. It is also difficult for Scala to write complex operation logic.

Since MapReduce and Scala are all difficult, the computing syntax of Hadoop/Spark begins to return to SQL. Hive is very popular as it can convert SQL to MapReduce, and Spark SQL is more widely used than Scala. But, while it is relatively simple for SQL to do some regular queries, it is still very cumbersome to handle multi-step procedural calculation or order-related operations, for it needs to write very complex UDFs. Moreover, although many computing scenarios can be barely implemented in SQL, the computing speed is not ideal, and it is difficult to optimize the performance.

The heaviness of cost

Although Hadoop software itself is open source and free, it is technically complex, difficult to use, resulting in a high comprehensive cost.

As mentioned earlier, Hadoop itself will consume too much resources of CPU, memory and hard disk, and Spark needs large memory to support normal operation. To solve this problem, you have to purchase server with higher configuration for Hadoop/Spark, this will increase the hardware expense.

Due to the difficulty in using Hadoop/Spark, more personnel are required for the installation, operation and maintenance to ensure its normal operation, and more developers are required to program various complex business calculations. As a result, the cost of human resources is increased.

Because it is too difficult to use, many users have to purchase the non-free version of Hadoop/Spark from commercial companies. Since the price is quite high, it will greatly increase the cost of software procurement.

Since Hadoop is so heavy, why do many users still choose it? The reason is simple: users can't find an alternative for the time being, and only Hadoop can be barely used, at least its reputation is higher.

Thus, users can only install and configure heavy applications of Hadoop, and endure the huge consumption on computing resources from Hadoop itself. The number of servers in a small-scale cluster is not large originally, and Hadoop takes up many of them, which will lead to a phenomenon that a cluster with less servers run a calculation task that is beyond its capacity, and thus you can imagine how slow the running effect will be. In short, Hadoop is expensive and laborious, but the actual computing performance is not ideal.

Isn't there other choice?

Lightweight choice

The open-source esProc SPL is a lightweight big data computing engine, which adopts a new implementation technology, and boasts the advantages of light in technology, simple in use, and low in cost.

Light in technology

As mentioned at the beginning of this article, the growing data makes traditional databases unable to hold such a big amount of data, so users have to turn to distributed computing technology. The reason is that it is difficult to implement high-speed algorithms in SQL, and the computing performance for big data can only rely on the optimization engine of database, but for complex calculations, these optimization engines can often do nothing.

Therefore, we should find ways to design more efficient algorithms, rather than blindly pursuing distributed computing. According to this idea, SPL provides many high-performance algorithms (many of which are pioneered in the industry) and efficient storage schemes, so that we can obtain a computing performance that far exceeds that of database under the same hardware environment. SPL installed on a single machine can achieve many big data computing tasks, and its architecture is much simpler than that of cluster, so it is naturally much lighter in technology.

SPL’s high-performance algorithms include:

For larger amount of data, SPL achieves the computing function of lightweight cluster. This function is designed for cluster of a few to a dozen nodes, which adopts a completely different implementation method from Hadoop.

SPL cluster does not provide complex and heavy automated management function. Instead, it allows you to personalize the configuration of each node. Programmers can decide what kind of data each node stores, and what kind of calculation each node performs based on data’s characteristics and calculation objective. In this way, not only is the architecture complexity decreased greatly, but it is also an important means to improve performance.

Let's take order analysis as an example. When the order table is large, and we want to associate the product number field with the primary key of the smaller product table, and then group and aggregate the order amount by product supplier. SPL cluster can easily store the order table in segments on the hard disk of each node, and then read the smaller product table into the memory of each node. When calculating, each node only needs to associate the local order segments with product data, and then group and aggregate, which can shorten the total calculation time, after that, the aggregated result is transmitted to next node for secondary aggregation. Since what is transmitted is the first aggregated result, the amount of data is small and the network transmission time is short. Overall, this scheme achieves the best performance. Although the programmer needs to do some more detailed work, the increased workload is not large for small-scale cluster.

Also, SPL does not provide strong fault tolerance. Unlike Hadoop, there is no need for SPL to ensure that any task is executed successfully in the case of node failure. In fact, the execution time of most computing tasks is within a few hours, and cluster with a few or a dozen machines can operate normally for a long time in general without failing frequently. Even if the task execution fails due to occasional node failure, it is acceptable to recalculate. After all, this does not happen frequently. Therefore, the fault tolerance capability of SPL only ensures that the entire cluster can continue to work and accept new tasks (including recalculation) when a few nodes fail, which greatly reduces the complexity of SPL cluster.

In terms of in-memory computing, SPL does not use the immutable mechanism of Spark RDD, and instead use the pointer-style reuse mechanism. This mechanism uses the address (pointer) to access memory, and directly uses the addresses of original data to form a result set under the condition that the data structure is not changed. Since there is no need to copy the data in each calculation step, and the only thing that needs to do is to save one more address (pointer), it can reduce the consumption of CPU and memory space at the same time, and hence it is much lighter than Spark in operation. Moreover, SPL improves current algorithm system of external storage computing, reduces the complexity and expands the scope of adaptation. Furthermore, SPL can combine in-memory and external storage calculations to fully improve the computing performance without relying on large memory as Spark does.

Simple in use

SPL adopts lightweight technology, it is naturally easier to install, configure, run and maintain. Not only can SPL be used as an independent server, but it can also be easily integrated into applications that need high-performance computing. For example, you only need to import a few jars for the instant query system. In contrast, it is difficult to integrate Hadoop into such applications, and hence Hadoop has to be run outside applications as a data source. Some temporary data need to be handled at any time, you can use SPL’s desktop IDE to calculate visually to get the result quickly. However, if you want to handle such temporary data tasks through installing and deploying Hadoop, they may be outdated after Hadoop environment is built.

SPL’s high-performance algorithms shown in the figure above also make the programming of big data computing become simple. Programmers can master the functions of such algorithms in a shorter time, and thus the learning cost is relatively low. Moreover, it is very easy to achieve various complicated computing requirements by using these ready-made functions. Therefore, SPL is simpler than MapReduce/Scala, and also simpler than SQL.

Let’s take common funnel analysis of e-commerce platform as an example. SQL code for implementing three-step funnel is roughly as follows:

with e1 as (
     select gid,1 as step1,min(etime) as t1
     from T
     where etime>= to_date('2021-01-10', 'yyyy-MM-dd') and etime<to_date('2021-01-25', 'yyyy-MM-dd')
          and eventtype='eventtype1' and …
     group by 1
),
with e2 as (
     select gid,1 as step2,min(e1.t1) as t1,min(e2.etime) as t2
     from T as e2
     inner join e1 on e2.gid = e1.gid
     where e2.etime>= to_date('2021-01-10', 'yyyy-MM-dd') and e2.etime<to_date('2021-01-25', 'yyyy-MM-dd') and e2.etime > t1
          and e2.etime < t1 + 7
          and eventtype='eventtype2' and …
     group by 1
),
with e3 as (
     select gid,1 as step3,min(e2.t1) as t1,min(e3.etime) as t3
     from T as e3
     inner join e2 on e3.gid = e2.gid
     where e3.etime>= to_date('2021-01-10', 'yyyy-MM-dd') and e3.etime<to_date('2021-01-25', 'yyyy-MM-dd') and e3.etime > t2
          and e3.etime < t1 + 7
          and eventtype='eventtype3' and …
     group by 1
)
Select
  sum(step1) as step1,
  sum(step2) as step2,
  sum(step3) as step3
from
  e1
  left join e2 on e1.gid = e2.gid
  left join e3 on e2.gid = e3.gid

We can see that it needs to code more than 30 lines in SQL, and it is quite difficult to understand this code. If the task is performed in MapReduce/Scala, it will be more difficult. Even in SQL, the code is related to the number of steps of funnel, one more sub-query is needed for every extra step. In contrast, SPL is much simple.

When executing this code, the computing resource required for task management (in-memory loading, task splitting, merging, etc.) is far less than that consumed on associating, grouping and aggregating. The task management function is so light that it can be executed on any node or even IDE.

Low in cost

Like Hadoop, SPL is also open source and free. However, unlike Hadoop, the comprehensive cost of SPL is very low, for the following two reasons:

One is that the cost of human resources is reduced. For one thing, since the installation, configuration, operation and maintenance of SPL are very easy, relevant cost of human resources is greatly reduced; For another, since SPL reduces the programming difficulty of big data computing, programmers can implement various complicated calculations very easily, and the development efficiency is significantly improved, the cost of programmers is thus saved.

Another is that the hardware cost is reduced. Since SPL technology system is very light, and the system itself occupies very little CPU, memory and hard disk resources, it enables more resources to be used for business computing, and hence the hardware utilization is greatly improved. In addition, SPL does not rely on large memory as Spark does. Overall, the cost of hardware procurement is greatly reduced.

Light and fast SPL

SPL Because SPL is light in technology consumes less by itself, and provides many high-performance algorithms, SPL performs better than Hadoop/Spark for a cluster of a few, or dozens of, or even only one machine.

Case 1: Funnel analysis and calculation of an e-commerce business.

Spark: 6 nodes with four CPU cores each, average computing time: 25 seconds.

SPL: one machine within 8 threads, average computing time: 10 seconds. The amount of code is only half that of Spark Scala.

Case 2: Analyze the user profile of a large bank.

An OLAP server on Hadoop: virtual machine with 100 CPU cores, computing time: 120 seconds.

SPL: virtual machine with 12 CPU cores, computing time: 4 seconds only. The performance is improved by 250 times.

Case 3: Query the details of current accounts via the mobile banking APP of a commercial bank. The amount of data is large and the concurrency number is high.

A commercial data warehouse based on Hadoop: since a response speed in seconds cannot be achieved when the concurrency is high, it has to replace the warehouse with a cluster of 6 ESs.

One machine in SPL: Achieves the same response speed as that of a cluster of 6 ESs under the same concurrency number.

In summary, Hadoop/Spark sources from the heavy solution of the top Internet enterprises, and is suitable for very large enterprises that need to deploy the very large cluster. Although the amount of data in many scenarios is not small, a small cluster or even one machine is fully capable of handling them, because the scale of data amount is far less than that of large enterprises, and there is not so much hardware equipment and maintenance personnel. In this case, SPL, the lightweight big data computing engine, is the first choice, for the reason that it can achieve light technology, simple use, higher development efficiency and higher performance at a very low cost.

5 Mobile Apps useful for every Developer 2022

Swastika Yadav — Mon, 18 Jul 2022 06:13:00 +0000

Here's the thing, the 22nd century isn't only about hard work in isolation anymore. If you are a developer, this fact applies to you even more. Productivity and community-based mobile apps, especially for developers and programmers, play a major role in defining the success and reach of one's development journey.

The essential attributes of a developer not only include logic building and skills. The art of building community, engaging with people, and staying up-to-date with the latest trends is also a key part of being a developer. Those programmers are in high demand and can write high-quality lines of code faster.

Here are the top 5 mobile apps that'll help you achieve that:

DevBytesApp

Being a developer, it is very important to stay updated with all the new findings and advancements in the technological world. DevBytesApp links the developers with the latest technology and coding news within 64 words.

DevBytesApp comes up with updated technology news, awesome job updates, technical topic explainers, and programming tips daily. From operating systems like Android and iOS to programming languages and web technologies, you get the latest coverage of everything at your fingertips.

It hosts two sections:
All news: It covers all the short articles (DevBytes shorts) hosted on the app.
My feed: It is a personalized feed that covers the topics of your choice. DevBytes App gives you the flexibility to choose the content you consume.

Not just the news, the cool topic explainers and tips within 64-crisp-words, along with dedicated code snippets, help you understand and perform any development task. With the two sections, news, explainers, etc. DevBytes also comes up with a Daily Digest section that hosts important hand-picked shorts for you.

Scrolling through this app 20-30 minutes a day can help you enhance your knowledge and understanding of technology.

Download the app here

Discord

Discord is a free voice chat and texting app that was originally designed for gamers and other groups. It can be used for team communication, video conferencing, and one-on-one chats. Discord has become the first choice of developers to build communities.

Here are some reasons why it is useful for developers like you:
You can run your own server to communicate with like-minded developers.
You can join channels and create your own channels to learn or teach programming technologies.
Discord offers special features for developers, such as a live chat feature that allows you to get instant feedback from your customers through your website or app.

It is also great for social programming, as it comes with a built-in Voice Chat feature that lets you code or develop with peers synchronously. Discord can help you find collaborators, create documentation, and track bugs more efficiently.

Pocket Casts

Pocket Casts is a podcast player that allows you to listen to podcasts on your phone or tablet. You can use it as a replacement for your existing podcast app or as a standalone alternative. If you're a developer or designer who needs podcasts in their day-to-day work and wants to streamline your workflow, Pocket Casts can help.

Podcast listening is extremely popular among developers. There are many reasons for this:
The ability to listen while working on the computer.
The ability to listen while traveling.
The ability to access their favorite shows when they are not at their desk.

One advantage of Pocket Casts over other podcast players is that it integrates with your other apps. For example, if you have downloaded a podcast onto your phone that you want to listen to later, then you can use Pocket Casts as an audio player for that show.

DEV Community

DEV Community is the largest online community of professional developers and engineers. With over 10 million users, DEV Community has a broad reach and provides a great platform for developers to network, share knowledge and grow their careers.

DEV Community was created by people who love technology, software development, and making things. We are not just looking to make money from our site but also to help developers become better professionals through education, experience sharing, and certification programs.

As a developer, you can use the DEV Community app to:
Find new customers for your apps
Share your ideas with others who are building similar products
Get feedback on your code from people who know it inside out

Meetup

Meetup is a platform that helps developers find like-minded people while hosting meetups and events. You can use the Meetup platform to organize, manage, and promote your own meetups and events.

The Meetup platform helps developers by:
Organizing events (meetups) - Organize your local developer community in one place. Build a community of developers who are interested in your specific topic of interest.
Managing groups - Manage all your groups in one place. Create new groups, invite members, and manage their profiles effortlessly.
Promoting events - Promote your meetups on social media platforms like Facebook, Twitter, Linkedin, etc., with ease!

Developers can use Meetup to find fellow developers in their area and schedule meetings or get advice from experts on topics like web development, software design, and marketing.

Final words:)

Gone are the days when a developer's only job was to learn one skill and do the same thing for years. Developers have become more versatile than ever.

The above mentioned were my top 5 mobile apps that contribute to this versatility. I'll be coming up with such articles more often.

Stay tuned and give a thumbs up.

I keep on sharing such stuff on Twitter, lets's connect:)
Follow me in swastika0015
Thank you:)

Some Popular CSS Units

Swastika Yadav — Wed, 29 Sep 2021 12:47:58 +0000

CSS has several different units for expressing a length. Many properties take 'length' values, such as height, border, margin, padding, font-size, etc etc. But we all know how confusing these CSS Units are! In this article, I'll try to briefly explain the most common CSS Units.

So basically, there are two types of CSS length units: Absolute and Relative.

Absolute
The absolute length units are fixed and length expressed in these units will appear exactly the same size for all screens. Units like cm, px, in, pc are absolute.

Relative
Relative length units in CSS specify a length relative to another length property. %, em, vh, ch are a few Relative length units. Better responsive sites can be created using Relative length units.

Below is the list of some commonly used CSS Length units with description.

Name	Unit Type	Decsription
px	Absolute	1px = 1/96th of 1in, Pixels (px) are relative to the viewing device
in	Absolute	1in = 2.54cm = 96px
pt	Absolute	1px = 1/96th of 1in
rem	Relative	Relative to font-size of the root element
em	Relative	Relative to font size of the element
vh	Relative	Relative to 1% of the height of the viewport*
%	Relative	Relative to the parent element

Generally, for output purposes, relative units are used and for print, absolute. For example, we don't typically use cm on screen because screen size can vary from device to device.
There are several other units like mm, pc, ch, vmax, vmin, ex, try exploring them and see how it all works. Moreover, As of now, almost all browsers latest version support these units.

That's it for now! Thanks and happy learning :)

Roadmap to FrontEnd Web Development

Swastika Yadav — Tue, 28 Sep 2021 16:09:05 +0000

Nowadays, the word "Roadmap" has been heard every now and then. But, do people know what a roadmap actually is? The roadmap is planned to reach a goal that includes all major steps and answers to how, what, when has to be done to achieve the desired outcome. To be honest, there's no particular roadmap to learn coding or Web Dev, everyone has their own different path. In this article, I will try to break down every step I followed to learn (or say, still learning) FrontEnd Web Development.

I will be dividing the steps into three major parts; First, we will see some essential tools you should know, which will make your life easier once you're into FrontEnd. Then, the programming languages you need to learn and then I will walk you through some libraries and Frameworks (you will have some choices to make here) you can work on. Let's get started!

Tools you need to know

There are a few basic tools you must know before getting into development, making your life a lot easier.

VS Code

Visual studio code is one of the best code editors for web development. Very easy to use and yet a powerful tool. There are different extensions you can install, which will come in pretty handy. In addition, it also has built-in Git commands (don't worry, we will discuss it later in the article). I'd suggest you look at how VS code works and learn a few basic shortcuts.

Command Line

The Command-line is a text interface for your computer. It's a program that takes in commands, which it passes on to the computer's operating system to run. Through CL, you can navigate from one directory to another and create new files as you would in File Explorer or Finder on Windows and IOS. It would be best to learn how to create new files or directories and navigate through them.

Git and GitHub

The most useful and important tool in a developer's life is Git! It is a version control system used to manage and track the source code history of your projects. Github is a platform that lets you manage Git repositories, track, manage your code and also collaborate with other developers. Remember, Git and GitHub are not the same.

Git and Github might look overwhelming at first, but don't go too deep into it. Just learn how to create repositories, make a new branch, make pull requests, merge pull requests. That would be enough at the start.

Programming Languages

The three things that are the heart and soul of Web Development are HTML, CSS and Javascript. I'm sure, if you've decided to learn Web Dev, you must already know something about them. So let's see how to get started with the most important step of your Frontend Developer journey.

HTML and CSS

HTML stands for Hyper Text Markup Language, it is the skeleton of a website. It is the main file which is loaded on a Web browser. First, get into the syntax and basics of it like tags, elements, attributes, forms. Generally, people find HTML pretty easy.

CSS stands for Cascading Styling Sheet, it is the layout and structure of a website. CSS provides styling to the content like colours, fonts, the layout of elements and more. But before getting more into it, I want to tell you one thing, you can never master CSS there will always be something new, something extra which you weren't aware of before. But again, you dont need to master it; You can learn a few things like:

Box model- You will understand how margins, padding, border works.
Flexbox, CSS Grids- It is used for Responsive layouts, which means how the layout of a website will change if the size of the screen changes.

Website deployment: Once you get a little hold on HTML and CSS, immediately start building mini projects like a portfolio website or Wikipedia page. This is the best way to learn Web Development. Build! Build! Build!

Now, you've built some sort of website with HTML and CSS, it's time to deploy it and show it to the world. As you already know about Github, you can deploy your website using it, or there are many other platforms like Netlify, Heroku you can use.

JavaScript

Here comes the part where most people back off because things can get a bit overwhelming from here, but you just need a few constant efforts to get a hold of Javascript.
Javascript is a programming language that runs a website on a browser like any other language. If you give some input, it will provide you with output.
For example, if you click on a "like" button on YouTube, it automatically increases the no. of likes, and you might also notice some changes in button style. Here, the user gave input by clicking on the button and Javascript provided output by changing the button and increasing the no. of likes.

Here are a few topics which must be clear to you before moving ahead:

1. Fundamentals
Basic syntax, data types, loops are the most important and inevitable parts. You can't ignore it. Then you can learn about objects, arrays, array methods, functions, and the Oops concept, which includes classes.

2. JS Async
Without Asynchronous programming, it would take quite a long time to load and run websites. For example, once you fill up your details in a form and right after clicking on the Submit button, you start doing something else on the website or move to another site while the data is still loading in the database.
In this step, you need to learn about things like Promises, callback, Async/Await.

3. DOM
Document Object Model is a programming interface for web documents. It represents the page so that programs can change the document structure, style, and content. Here you will learn about DOM methods, events, DOM CSS, etc.

Now you've quite a good grasp of everything to build projects, it doesn't matter how big or small they are. So build projects, I'll say it again and again in this blog. It's everything that can make you better at development.

4. Fetch APIs
The Fetch API is a modern interface that allows you to make HTTP requests to servers from web browsers. There are other APIs also like form API, storage APIs. You can look into it according to your need.

Javascript FrameWorks

A framework is a collection of JavaScript code libraries that provide developers with pre-written code for routine programming tasks. Of course, you can absolutely make any web app without using frameworks, but frameworks make the process a lot easier. Moreover, most companies and startups use Frameworks, so you have to know the framework to work with them.
Below I will list down some popular frameworks from which you can choose one for you.

React

React.js is technically a library used for building user interfaces created by Facebook. React has been designed from the start for gradual adoption, and you can use as little or as much React as you need, whether you want to add some interactivity to a simple HTML page or start a complex React-powered app. Everything is possible with it. React is the most popular and my personal favourite framework. If you're a beginner, I'd suggest you go with React.js. Facebook, Atlassian, Netflix, Codecademy use React.

You can also go ahead and learn Next.js also as it is a React framework that enables several extra features, including server-side rendering and generating static websites.

Vue

Vue.js is an open-source progressive JavaScript framework for building user interfaces (UIs) and single-page applications. The core library is focused on the view layer only and is easy to pick up and integrate with other libraries or existing projects. Some of the most important companies using Vue are Xiaomi, Alibaba, Grammarly and Gitlab. But still, the job market for Vue is comparatively low.

Angular

Angular is one of the oldest and the largest JavaScript frameworks. It is an application design framework and development platform for creating efficient and sophisticated single-page apps. Angular relies heavily on real DOM for its performance, while Vue.js and React function using virtual DOM. Having said that, Angular is still a preferred choice of many professional developers to create large-scale applications. It is used in Gmail, Microsoft Office, Upwork, Paypal and many more. As a beginner, you might find Angular a bit more overwhelming than React and Vue, but it is worth all your efforts and time.

CSS Frameworks

CSS Frameworks are a bonus when you're building a Web app. CSS framework is a library allowing for easier, more standards-compliant web design using CSS. So, knowing CSS frameworks will save a lot more time for you. Below I've mentioned a few CSS frameworks, you can choose any of them depending on the need.

Tailwind

A utility-first CSS framework packed with classes and tools that can be composed to build any design directly in your markup. Tailwind CSS is not opinionated and lets you create your own unique design.

Material UI

Material-UI is an open-source, lightweight CSS framework specially designed for newcomers. You can start with the essential building blocks because it has split into modules. It aims to provide a strong foundation for building dynamic UIs .

Bootstrap

Bootstrap is world’s most popular front-end open-source toolkit, featuring Sass variables and mixins, responsive grid system, extensive prebuilt components, and powerful JavaScript plugins.

Others

There are a few more Frameworks you can look into like Bulma, Materialize CSS, Pure CSS.

That's it for the blog! Remember, you will never get the feeling of knowing it all; keep learning while building projects and watch yourself grow. As I've said before, building projects is the only way to get better in this field. Just google 'Javascript project', and you will find a plethora of ideas. The best thing you can do is, try making a clone of existing platforms like Facebook, Instagram, Microsoft Teams. If you can do something like that, congratulations, you're way ahead of many people.

Till then, Happy learning! Happy coding!

Python Projects for beginners

Swastika Yadav — Mon, 14 Jun 2021 14:53:22 +0000

List of 10 Python projects to build as beginners:

Most basic:

Python story Generator
Project tutorial
Hangman Game
Project tutorial
Junk File Organizer
Project tutorial
Random Password Generator
Project tutorial
Currency Converter
Project tutorial

Using GUI:

Calculator
Project tutorial
Tic tac toe
Project tutorial
To-do list
Project tutorial
Countdown Timer
Project tutorial
Calendar
Project tutorial

Popular Icon Sets for VueJs Apps

Swastika Yadav — Thu, 17 Dec 2020 14:05:38 +0000

To help you build your next project in Vue, you must need some nice icons that can help you make your design much better.
To save your time and energy, here are some of the popular icon sets available for Vue.js applications.

To continue reading click here

Connect with me on socials😊:
Twitter
Instagram
LinkedIn

Best React Icons Library 2020

Swastika Yadav — Wed, 18 Nov 2020 07:42:01 +0000

Many elements such as text, images, illustrations, colors, etc., make up the UI of a website more impressive and connecting to the user. One of these elements is icons, which are used in almost all websites with different styles.

So, to make the task easy, there are many libraries available that let us use the icons directly on the website.
Let’s have a look at some best React Icon libraries which can be used on your websites in 2020.
To continue reading, click here

Connect with me on:
Twitter
Instagram
LinkedIn

Create your own card with Google

Swastika Yadav — Thu, 13 Aug 2020 07:56:28 +0000

Google India introduced the people's card on Google Search.

So, now instead of showing your business card just ask them to search you on Google. How cool is that, no?

To get your own people card you just need an android or IOS device. Remember, the feature is not available on the desktop.

STEPS to create your own people card:

Log in to your Google account on your device and search for "add me to search" OR just type in your name in the search.
'Get Started' will appear for you, here it's 'edit' as I've already created the card. Tap on 'Get Started'!
Fill all your details and socials and tap 'Preview' to review the card last time.
Be careful will uploading phone number and Email, it'll be shown publicly.
Tap on 'Save' and that's it.
Congratulation!, Now you have your own card on Google.

Thanks for reading :)
Connect with me through: Linkedin Twitter

All you need to know about GitHub as a beginner

Swastika Yadav — Thu, 25 Jun 2020 05:09:57 +0000

To know what is Git and what does it do. Kindly check out the last article What is Git, Why it is so important
Before getting into commands, there are a few terms to which you must be familiar.

Important Git Terminologies

Repository
Repository or repo is nothing but the collection of source code or files or projects
Working / Current Directory
The directory or folder in which you are currently working or where you have your latest code.
Branch
The branch is essentially a unique set of code changes with a unique name. It has its own copy of the project history and develops on its own code.
Fork
Creating a copy of a repository in your Git account to use the code or to make changes in it.
Clone
The clone is an action of downloading an existing Git repository to your local computer. You will then have a local version of that Git repo in your local machine to work.
Pull Request (PR)

How to use GitHub

Git Account
If you don't have an account then first make an account on any platform of your choice (I'll using GitHub here).
GitHub Download
Make sure you download the Dekstop Github, if not click from here
Creating Repositories
In the Repositories section, click on "New" and fill the details about your Repository.
Adding a project to new repo
You can use the command prompt or Visual Studio Code(VS Code)
to add a project from your local machine to GitHub.
- If using command prompt, change into the directory where your source code is located. And for VS code, open Terminal there.
- Create a new Repository and copy its URL.
- The commands to push the source code in the repo are written below the Quick setup, you can have a look at the picture below.
Pull Request
- Fork the repository to which you want to commit changes.
- Click on Clone and open the file in Dekstop GitHub (make sure you have downloaded Dekstop GitHub)
- Now, you can open the complete file in your local editor and make a new branch to commit changes.
- Push it back to your repo by using $ git push -u origin new branch command.
- After pushing the commit, click on Compareand you will be taken to the original repo where you wanted to commit changes
- Write the description of your changes (optional) and click on Create Pull Request
- The maintainers/owners of the original repo can see your contribution and accept your request to merge with the source code.

I hope all the basics are covered in the last two articles. If it helped, do tell me here.

Connect with me on: Linkedin Twitter

Okay bye! Have a good day :)

What is Git? Why it is so important?

Swastika Yadav — Mon, 15 Jun 2020 19:50:54 +0000

What is Git?

Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency.

Now, this is a hell lot of heavy words. Don't worry! it's not that hard to understand.
Let me put it this way-Git is a place where you can:

Store your source code
Track changes in your source code
Anybody can access the source code if needed.

Distributed version control system is a type of version control system where the complete code (including its full version history) is mirrored on everyone's computer.
Version control system is a type of software tool that manages the changes to source code over time and keep the track of every change.

I hope now you have a rough idea of what Git is.
The Git tool runs locally on a computer, is completely self-sufficient, and it does not require the use of an external function.

You may have heard about GitHub, GitLab, or Bitbucket. Well, these are cloud-based hosting service that lets you manage Git.

As of now, I'll be using GitHub to explain further.
GitHub offers plans free of charge, and professional and enterprise accounts. Free GitHub accounts are commonly used to host open source projects or for personal projects.

Why is it so important?

If you are a coder/ programmer/ developer, you have to know Git.

Most importantly, your code is saved for life. You can't lose it.
Suppose, you and your friend are working a project/software and want to make some modifications, how would you do so?
You can track each and every change in the source code and check how it looked 1 day, 1 month, 1 year, or 10 years before.
You can look for people working on similar projects and work with them.
Contributing to an open source is such great exposure to one. It makes you future ready for the Tech industry.

This is important👇
If you are a beginner and don't have projects or anything, don't get into the hype of it. Git is made to make our life easy, it's completely on us how do we need to use Git.

Whatever you code, even if it's a simple pattern question just put it on GitHub. Maybe someone is looking for your patterns for reference. Remember, you don't have to perfect before starting anything.

In my case whenever I take any challenge for example take 100DaysofCode, I put up every problem solved by me every day. And Trust me! people do check for those code snippets, I made many connections.

Once you start putting up your stuff there. I'm pretty sure, you'll crave to do more, I have been coding for 2 years and started using Git just 1-2 months before. Nothing makes me as happy as the green squares in the activity graph.

In the next article, we'll see how to use GitHub and a few basic Git commands.

If the article helps you in any way, do tell me. Nowadays I'm pretty active here ;)

Okay bye, See ya❤!