AWS MCP Servers Released, Government AI Oversight & Anthropic’s Supercomputer Expansion

This week on Software Sundays, KD breaks down the next phase of the AI race — and why the conversation is shifting from just software to infrastructure, governance, and system-level engineering.

We start with AWS making its MCP server generally available and what that means for coding agents like Cursor, Codex, and Claude Code. KD explains how MCP servers help AI tools access real-time API documentation, reduce outdated dependencies, and unlock more autonomous development workflows across cloud environments.

Then we get into the White House reconsidering its approach to AI regulation. As AI systems become deeply integrated into healthcare, education, cybersecurity, and consumer products, the conversation is shifting from “move fast” innovation toward governance, safety, compliance, and operational risk. KD explains why engineers who understand AI governance, security, and system reliability will become increasingly valuable.

We also cover the massive compute partnership between Anthropic and SpaceX AI’s Colossus supercomputer facility. Anthropic gaining access to over 300 megawatts of compute power highlights a major shift happening in tech: software is no longer the primary bottleneck. Power, chips, cooling, infrastructure, and operational scale are becoming the limiting factors in AI growth.

In this episode’s Q&A, KD explains:

•  What NIST is and why its standards matter 
•  How software engineers should scope projects 
•  What it really means to be collaborative on a technical team 
•  How to manage engineering tasks effectively 
•  Three practical tips for acing technical coding interviews 

We close with a mindset discussion around “Be → Do → Have” and why the outcomes in your life are often the result of the actions, habits, and investments you consistently make over time. 

Timestamps:

00:00 - Welcome and disclaimer

05:00 - AWS MCP servers and AI development workflows

10:00 - Why MCP servers matter for autonomous systems

15:00 - The White House reconsidering AI regulation

20:00 - AI safety, governance, and industry implications

25:00 - Anthropic’s compute partnership with SpaceX AI

30:00 - Why AI companies are becoming infrastructure companies

32:00 - Why engineers still need fundamentals in the AI era

35:00 - What is NIST and why does it matter?

40:00 - How to scope software projects effectively

42:00 - What it means to be collaborative as an engineer

47:00 - How software engineers should manage tasks

50:00 - Three tips for acing coding interviews

54:00 - “Be, Do, Have” mindset discussion

58:00 - Mother’s Day reflections and closing thoughts

DISCLAIMER: This is not professional advice. The views expressed are my own or those quoted. Consult your own legal, business, or tax advisors before making decisions based on this episode.

Build Learn Impact is on a mission to help you create wealth, opportunity, and ownership through technology.

Transcript

SPEAKER_01 0:00

Welcome to Software Sundays Builders. This is a space where we have high-level conversations about technology and the impact that it has in our community. We want to make sure that you can walk away with the tools and information that you need in order to increase your income, become an owner inside of the digital society, and help shape what happens next. So if this is your first time tuning in, you are in the right place. Thank you for being here. If you'll rock with us for a minute, it is great to have you back. Quick disclaimer before we go in, though, uh Software Sundays is for informational purposes only and it's not professional advice. The views expressed are my own or those of individuals quoted. The topics discussed may or may not apply to your specific situation and business. So please consult your own legal, business, tax, or professional advisor before making any decisions based upon information found in this show. But that being said, information is very helpful. It's very needed, and it can help boost you wherever you are inside of your career or your journey. And let's get started. First, we're gonna start off with the news for this week. A few interesting things popped up. Start AWS MCP server is now generally available. Quick background about MCP servers and their use case. MCP servers provide LLMs with a documentation or a way to access and understand how different APIs or services work without having to provide that information up front or being taught on that information. So it's basically a user guide for LLMs that allow them to access and understand how to use third-party services. That could be APIs, usually going to be some type of API that is owned by that third party. The AWS MCP server is going to give coding assistant tools like Claude Code, Cursor, and Codex and others that use the MCP protocol a way to access the up-to-date information about AWS services and how those APIs work in real time. So if you've ever used a coding agent and you've had it assist you with building a product or shipping some type of feature, you may have noticed that sometimes your agent is going to give you a library or some dependency or start using code that is out of date. And it may be using a version from a year or two ago. And it's pretty simple to just make the change and well, if you look and review that code, uh just see what the latest code or latest version of that package may be. But that adds an extra hot, adds something else that you need to do for you to ship out your code. This MCP server will at least at least make it easier for anyone building in the AWS ecosystem to be able to start building more quickly with these tools and make sure that all of the dependencies, all of the code that they're shipping out and using is actually based on the most up-to-date version of those services and the uh the features that they provide, which just makes it a cleaner experience to develop in and much easier for you. So, one of the reasons why MCP servers are better alternatives to having a model that's trained on that specific data set is that when you train your models, you're going to run into the issue of there being some historical end to how and what knowledge that model had access to. Whenever you're doing your training, you're basically training that model in and up to the current time that you start the training or that you end the training. And so if that LLM was trained six months ago, it doesn't have access to the latest data, the latest documentation for whatever that feature or the system might be. So these MCP servers just make it much easier to integrate these systems that may be continuously being developed, may be enhanced and improved without having to continuously uh improve and train those models on that new data. One of the benefits to having this MCP server set up is that we also, in addition to having our coding agents being able to access the latest version of these APIs from AWS, is that we also get access to setting up an autonomous or a more agentic, let's say CIT, CICD pipelines or some type of deployment pipeline. And basically it allows you to have more flexibility in how you deploy your applications or how you set up automations and operations for your develop your developers or for any of the workflows that you're using. Because now you can set up a pipeline or you can set up an agent, an automated system that is able to monitor the status of your resource of the resources inside of your environment. And so instead of having some schedule where it needs to run and check if there's any differences or you know, changes between a maybe you know defined setup and the actual setup, you can actually have real-time updates being made on the resources that exist inside of your AWS account.

SPEAKER_00 5:54

So that just unlocks a lot more functionality from an agente system that may not have been able to do it before and would have required more specifically defined rules for setting that up. So that flexibility is going to be super helpful when we're working with LLMs.

SPEAKER_01 6:17

Another interesting story that I saw is that the White House is rethinking their approach to regulating AI. So the current administration definitely started off with a more hands-off, laissez-faire approach to letting just the developers and the technology industry just build AI, especially as we were competing with other countries and nations to just provide that innovation and make these models, these foundational models, just better and smarter. Right now we've gotten to the point where Silicon Valley and the people that have been developing these models have created models that are getting much smarter, much faster. And they're getting to the point where they are very good at the things that they're good at, and they're tools, very their tools that you can put into the hands of anyone and can allow them to augment their abilities beyond what would be normally capable for someone with their experience or their skills or their resources. As well as we are seeing that LLMs and AI are being used in applications that crossed industry. So we're seeing applications that use LLMs inside of the healthcare industry, inside of education, inside of consumer retail. Like it's becoming more than just a feature or some functionality. It's much closer to the infrastructure that we would expect from a cloud company or from a utility company, in the fact that we need to have it on all the time because there are so many consumers of this service that rely on this service to actually provide necessary capabilities in again every industry. So it's the type of technology that is going to have a challenge if you ever neglect a defined SLA and or if you happen to let the technology get used incorrectly. If you've been keeping up with the news on the Mythos models and a similar model coming out of OpenAI, where these tools are now lowering the barrier for executing cyber attacks against financial institutions, against healthcare companies, against schools and school districts. It's becoming risky to just have these tools available to everyone. So it is interesting that, and I would say that's part of the reason, and I'm I'm sure it's part of the reason why there are some concerns about just continuing to let the industry just build and release these models at their own pace without having any type of government oversight to help limit the negative impacts of these new changes as they become available. So definitely something that is worth considering and it makes sense from a security standpoint, especially from at the national level. And the hope is that, because no plans have really been finalized, the hope is that the government or some type of government body, whether it is a department that already exists, or there is there may be some new uh task force or committee or department that becomes and gets formed in order to actually target AI safety and the AI innovation. But the goal is to make the models more safe for society and set up some standards for how they're released and how they're developed to, I hope, improve their output and their impact specifically on all communities. So that would be one of the main goals that are worth uh focusing on and having more care placed in. But we'll see exactly what that regulation looks like when the time comes. But the opportunity for technologists and builders in the space is to understand AI safety, understand how we audit these models, how we evaluate them, and understand the governance and risk and compliance requirements that will become necessary when you are operating a technology company or building a foundational model or building a product or feature or service that actually uses LLMs as part of its architecture. There is going to be an increased need for people that are well-versed in these topics and who can actually help drive some of the conversations that are going to be had inside of these organizations as we continue to see AI being proliferated in every product, in every service, in every industry. So, again, there's no plans now for exactly how this regulation will be implemented, but it's definitely worth keeping an eye on and learning the jargon involved, and that will become more popular as we get to that stage. And even reviewing some of the existing AI safety regulations like the EU's AI app or even the UK's framework for how they're suggesting organizations deploy and create and test AI. Another interesting story is the fact that SpaceX AI, which is formerly XAI, signs an agreement with Anthropic to allow massive AI supercomputer access. So SpaceX AI has a data center called Colossus, which has 300 gigabytes of compute power or power of compute. They are making that entire facility accessible to Anthropic in order for Anthropic to deliver their models and continue to serve and meet the demand of their existing customers. That amount of compute is not insignificant. That's enough to power thousands of homes. That's more compute than most countries can actually provide, to be honest. And we're seeing it coming from a source that it's not they like SpaceX one is not a or was not a cloud computing provider. They are and were a space company. When they merged with XAI, XAI was more of a model developer. To see them becoming basically an infrastructure provider to a company like Anthropic is super interesting because it points to the fact that compute or even software is no longer the barrier to how much innovation we can have. We're starting to run across very physical limitations in our capabilities. It's the chips, it's the memory, it's the power that is necessary to actually keep these sites online and run the you know, HVAC to run the actual compute those uh servers themselves. That becomes an entirely different operational challenge than just saying we need to figure out how to scale this software product so that it meets the demand. The interesting part about this deal is that soon after Anthropic released the update about them getting access to that new compute, they started to increase the limits on some of their products and existing services. So they double Claude Code's rate limit or their uh five-hour rate limits for different levels of users, they removed peak hour limitations, and they added to the amount of tokens that their APIs can support for their Opus models. So they were just waiting for the basically they were waiting for the resources that they needed to actually meet the demand that they already had, assuming that the demand for all of these products is actually there, right? Like assuming that people were actually hitting their five-hour rate limits, that they were hitting their API rate limits limits. Like if that was already being met, and they had to purchase this existing compute so that they could continue to serve more customers and better serve their existing customers, that was really just all they needed. They were waiting for the floodgates to open, they were waiting for the resources to become available for them so that they can start to and continue to do and meet that demand. So now that they have it and they have access to it through this partnership, it's basically nothing holding them back from meeting any future demand and growth. Well, not say future demand, because this, if they needed 300 gigabytes worth of power and compute to actually meet the current demand, that says nothing against how much demand they're going to need in the next two to three years, as again, as more products are put on brought online and more users start adopting AI. So they're still going to need to continue to build out more of these uh data centers. But that partnership definitely opens doors for them to get more compute, especially if SpaceX goes full AI compute in space and starts making that fully available. So this is going to be something to keep an eye on from the infrastructure perspective. But another thing for developers to understand is that AI companies and most software companies are no longer just software providers. Then you're not just providing software to your user, you are becoming an infrastructure provider that uses software as part of your service delivery. And so engineers that understand system level constraints, they understand the industry that they're working in, they understand the regulations that they have to abide by, they understand the customer needs, those engineers are going to become more valuable than the engineers that just focus on, let's build this thing or how do I build this. Like it's no longer just about the code. The code is becoming much easier to write, much easier to test. Well, I was not say test. Testing is still a pretty difficult challenge. But the software is no longer the limiting factor. It becomes how much understanding do you have about the engineering process and the scientific method to actually be able to engineer something that people need and that is reliable at the scale that it needs to be reliable at. So one of the that's important because one of the things that I hear about in the AI or the vibe coding era is that AI is going to reduce the importance of understanding fundamentals because you don't need to know how to write the code because AI is going to write the code. That's fine, but you still should know how to read and write code. But you still need to understand resource optimization. You still need to understand security and governance, you need to understand how to manage cost inside of the cloud environment, how to manage memory and time and all these different things that help make your systems more reliable. That never goes away, even if we have AI to help improve our delivery of software and make that faster. So keep that in mind as you're looking for projects to work on and you're looking to see where to really develop your skills. It's not just in writing the code. You need to understand where you're building in and how that affects everything that your industry is going to touch.

SPEAKER_00 19:55

So if these stories were interesting to you, definitely uh put a like or subscribe in the comments.

SPEAKER_01 20:04

Um, let me know what your thoughts are. Let me know where you think the industry is going, what's going to be next, and how that may impact our you know, social uh norms, the finances, everything like we can see that technology is moving quickly, but how quickly is it starting to start shifting everything that touches technology, which is literally everything? So uh let me know your thoughts inside of the comments, and you know, stay tuned for the next story.

SPEAKER_00 20:41

So, we're gonna jump into the questions for this week, also.

SPEAKER_01 20:45

Uh, we are focusing on hitting the questions that mencies had inside of the community that are kind of preventing them or making it more challenging for them to understand what place they need to focus on inside of their job seeking process, and how to make sure they are starting off on the right foot inside of the industry and thinking about it more professionally versus just coming out of school and just trying to do what you were doing in school. So, first question is what is the National Institute of Technology? So the National Institute of Technology or NIFT is a public organization that is responsible for creating standards that are primarily used by the federal US government, but which are open and usable to any organization that would like to uh. Use and comply with these standards of policies. So they actually put out a significant amount of standards that are used to help manage cybersecurity, risk, and just security governance inside of technology organizations, but also in organizations that have some type of exposure to technology for managing their data or their customers' data. So, again, pretty much everyone. In terms of the federal government, they set the standard that federal entities and institutions need to comply with in order to just operate. Like if you are the Department of Homeland Security, there are uh standards like the NIST 853, I believe, that just say this is what you need in order to secure your data. This is what you need in order to uh protect that data and have controls in place.

SPEAKER_00 23:07

So it's partially controls, partially uh defining terms that are important to understand for your industry and what you're building.

SPEAKER_01 23:20

The most important part for developers to understand is that a lot of the tools that we use are very generic. They don't have any opinionated way of doing security, of doing uh any of the controls that are defined in NIST. But you have to be able to take the controls or take the requirements that bodies like NIST creates and translate those into configurations, into code that can run inside of your software architecture. So it's very important to be familiar with those standards. It's important to be able to map each standard or each feature. Well, I would say each standard to the features that exist inside of your tools. So, like properly doing access control. The way you do that inside of a Kubernetes cluster is different than the way you do that inside of an AWS account. NIST only says you need to properly manage access to digital resources or data and resources. How you implement that inside of the cloud or inside of your data center or inside of your application is up to you. But you need to at least be familiar with how to that it needs to be done and understand the options that are available for you for how you may or may not implement that. Because there are some standards and regulations in the I would say in the private sector that you may find that don't apply to your specific situation. Or the the recommendation may not be most suited to the specific use case that your business or your application is supporting right now. So you will need to basically understand that difference, why there's a difference, and have some way of determining and documenting that that deviance occurred and that it's important for you not to, you know, uh comply with that recommendation. And there are there are a lot of standards that are defined by NIST, and I would look into definitely the a NIST 800. There's more to that, but it's check it out, look into it.

SPEAKER_00 25:59

They definitely provide a lot of free information that is worth knowing. How should I scope software projects? So this is a question where I have to just say it depends, right?

SPEAKER_01 26:19

Because and it depends what type of responsibility you have inside of that project. So you can scope a project if you're the developer or engineer in a way that allows you to build a tool or build an application that supports that project or a specific feature or functionality inside of that project. And what that looks like is going to be, well, at least at the code level, or if you think about how you're managing your code and the features, it's going to be what is the smallest, I won't even say the smallest, let's go for what is the largest unit of change that I can manage that can be well tested, well documented, and actually perform a specific service or provide a specific collection of data or whatever the product might be.

SPEAKER_00 27:20

You should scope projects in that with that mindset because at the end of the day, your organization may have multiple applications that support your business unit, and technically the project is everything in there, but you're not going to be able to effectively as a single person manage that entire collection of projects.

SPEAKER_01 27:57

So it's important for you to understand what amount of this project, what amount of the work that's being done, can me and my team actually manage? And when I say manage, is what can you operate? What can you review? What can you test? What can you enhance over time? What can you teach on, right? Because you have to be able to, if you're providing some data for another part of the organization, then your project needs to be something that you can clearly define to other people so that they know how to consume the services that you provide, or they know how to support the services that you provide. So, you know, if you're pulling data from some upstream system and being able to effectively say, hey, we need these fields, and having that be tracked as something that your team is aware of and that the other team is aware of, so that they know not to break those fields at least for your use case.

SPEAKER_00 29:10

When you're scoping a smaller project, let's say you want to build an API because you want to do what let's say you want to make some data available or make some uh let's just say make some data available, and you want to build an API for that project.

SPEAKER_01 29:35

This is almost like getting into the difference between wanting a microservice or a monolith application for your system. It's really up to you, but you can put as much as you can inside of one project and it works. As long as the code works and it meets your needs, that's good. The only time you should start going to smaller projects is when you expect a lot of chefs in the kitchen for that larger project, and you don't want to have to deal with the coordination headaches that come from multiple developers working on the same project. If it's just you that is supporting the application or the tool or whatever it is that you are building inside of that project, then again, you need to focus on whatever you can actually manage sustainably. And for myself, I've created projects, GitHub repos that have consisted of multiple different tools that all do something different, but that are related to the same maybe core data set or even the same team, right? Because I want to keep them logically separated. That's why I have them as separate functions, and it makes it easier for me to reason about when I'm making changes to the code itself. But I don't want them to be in separately different projects because a lot of that code, a lot of that knowledge can actually be shared across these different use cases, even if they are doing different operations on the data that I use. So it's a consideration that should not be made lightly, but it shouldn't be made by just following trends, right? Right? The monolith trend or the microservice trend has slowed down more development teams than it's probably benefited, especially at that smaller earlier stage. You want to make sure that your projects are aligned with how your business is going to be working for the next at least six months.

SPEAKER_00 31:54

Or longer if you can, although that's probably hopeful given just the speed of change inside of the industry. What does it mean to be collaborative?

SPEAKER_01 32:07

So, this is a great question for anyone that is planning to join a team, that is currently on a team, or that is having trouble being with on the team that they're currently on. So everyone, right? We all work in teams, but in terms of collaboration, for you to be collaborative, you have to be able to communicate with the people around you or the people in your team, the people that you work with. And that's one thing, you have to communicate. So those pathways need to be open. You need to have clear lines of communication, whether it's through email, whether it's being able to uh ping someone on Teams or whatever uh messaging system that your organization uses, or it could be calling a person uh and having a conversation that way, scheduling a meeting. You have to have a way to communicate and keep those lines open because information needs to pass very freely and easily between people that are collaborating.

SPEAKER_00 33:22

Collaboration also means you are aware of the relationship between the work that you're doing and the work that someone else is doing, and you become aware of that relationship by communicating.

SPEAKER_01 33:42

But once you are aware, you also become mindful of the changes that you're doing and how that impacts the people around you, and understanding that you are operating inside of a team, you are helping support your client, or you and this team member of yours are helping to support some client, some other team, some other uh area of the business. And so you have to be mindful of that relationship and that impact that you're having with each other. And collaboration also means that you have documentation and that you have a way of sharing information and passing knowledge and information that is not asynchronously or that is not synchronously. You want to be able to store knowledge and be able to collaborate with your future self and future team members that either join your team later or are you know just on a team that may have not worked on the thing that you are currently working on today. So having that means of knowledge transfer and having standards and clear clear ways of sharing that information is going to be key for maintaining a collaborative environment. And I would also even add as a bonus, you have to build relationships with the people around you that are not just based on the work that you guys do. It gets very easy to get into the flow of hey, this is what we need to do, and this is the work, this is the job, yes. But you also need to be able to meet people like people, socially, like humans. Go on a walk with your teammate, go have lunch with them, go speak to them about something that matters to them that may not be related to what you're currently working on. And you don't have to do this in weird times, right? Like if you have a meeting that is specifically scheduled for discussing some problem, you don't start going off on tangents if it doesn't make sense. But creating spaces for you and your team to actually build a relationship is going to be super helpful for you with just maintaining that collaborative environment. Because at the end of the day, you guys are working together to build something. And so you want to build it with people that you feel comfortable talking with, you feel that you can trust, and that make you feel like the work is worth working on versus feeling like you're working hard just to even talk to this person. So be very mindful about that. Uh, it's not a skill that every developer has. Honestly, it the worst part about going and becoming an engineer is that you start realizing how many people think like we don't speak or that we're just not collaborative, right? A lot of people feel like it's it's uh more well, in some cases, people feel like developers just don't speak because they don't understand the business, or they may not speak because they're just too technical and they just speaking too like too much in the weeds. And then another group is the developers that are super good, but they are sometimes a-holes, and they never listen, they're not great to collaborate with because they think they know everything. So there are different types of software engineers that make it just difficult for us as a group to be trusted as collaborative members of the team, but it's a stereotype that is definitely worth actively combating and finding ways to disprove, especially for your reputation.

SPEAKER_00 37:54

How should I manage tasks as a software engineer?

SPEAKER_01 37:58

So this is incredibly important for anyone that is working in a high speed environment, an environment that is constantly changing, that is constantly shifting and adapting to changes that are stemming from internal challenges, but also from external challenges. So we can all benefit, really, right? Uh, for software engineers, it's very helpful for you to understand the types of tasks that you are going to be doing and have those clearly defined for yourself. So, as a developer, as an engineer, you will be testing things, you will be deploying code, you will be troubleshooting bugs, you will be remediating vulnerability, you will be collaborating with product, customer, legal, having ways to define high-level what is the action or the activity that I'm doing and that I am likely to do throughout the course of my day, throughout the next month. Having a way and being able to clearly define that is the first step to being able to manage your task effectively. Because the steps that are required inside of an evaluation is going to look different than the steps that are required inside of the deployment versus the collaboration or the you know conversation, attending a meeting, whatever it may be. All of these are different tasks that require you to come with a different context to make that task successful. So start there. And then when you're actually going through and managing or executing on the tasks that are either assigned to you from your project manager, from the business, or even by yourself, you want to have a way of documenting what you are doing for those tasks.

SPEAKER_00 40:20

In the perfect world, everything can get done in a moment.

SPEAKER_01 40:24

And your task would be short-lived if you just do this, then you click the button and it happens, right? And you always have just enough time to get the thing, whatever that task is, done inside of the time available that you have. But we don't live in a perfect world, and so you're probably going to get disrupted, you're probably not going to have all of the information that you need at this one point or this one time to solve and complete that task. So being able to put the task down and pick it back up later is going to be extremely helpful. I have gone into teams where we don't have a task management tool. Like there's no way for me to know or keep track of where we are in terms of stage or phase for this particular task. That becomes difficult because I may go on a, you know, it may just be Friday, and I'm just not going to open my laptop for two days. And now there is no clear location for me to pick up that task that I was starting on Friday, but I had to pause because there was a production issue that required me to pause and work on that and fix that issue. So having a way to document and track where you are on your task are going to be incredibly helpful. So knowing the timing, knowing the uh having resources that are attached to that task. I currently use ClickUp as my task management tool or my project management tool, uh, primarily because it allows me to keep track of the board of tasks that I have, and I have some automations in place that allow me to create documents that I can automatically attach to a task and then use to, you know, help guide my problem-solving method. So for me, when I'm going through a task, I let's start off with the context or the description of what I'm trying to do. Get as much information in there as possible in a more free-flowing uh format. Then I add information that is just helpful for me to provide again additional context. So it may be uh stakeholders, people that either requested the task to be done, or people that I can collaborate with to actually accomplish this task. I also add resources. Resources may be links to data sets, it may be tasks that I've worked on that are related to this current uh activity that I can refer to to help me get it done faster. Or even just other helpful information, it could be documentation that could be relevant to what I'm trying to do. Have a structured way of storing all of that information. Use that structure to help slow me down so I don't just jump into trying to solve the problem, but even having a place where I define my approach before I jump done jump into doing the thing, I like to list out the steps that are necessary to, or in my mind right now, high level what do I think it would take? Step one, two, three, four, five, six, whatever, to actually accomplish the objective. I always uh define. What the expected outcome and deliverables would be inside of a task, and then keep that uh all together so that when I'm actually doing the work, I know where I'm trying to go, I know where I should be at in case I get lost or start going too far on some tangent. Uh, and I always know how to get back to my approach and my steps. And then I can use that as a guideline and then note-taking every command that I run, every uh person or conversation that I've had that's related to this task, keeping that all tracked inside of the document because I need to know when I pick this thing back up, that I can pick it up from the same place that it was before. I like to think about task management like a basketball team or a basketball. When you are playing basketball, the ball is the thing that is required to make the goal or to make the shot. I'm not a sports guy, but it this is how I think about it. If you are sending someone a ball that doesn't have any air, that doesn't have everything that they need in order to make the shot. They can't dribble it, they can't shoot it, it doesn't have what it needs, then you are doing them a disservice. Or you may be doing yourself disservice, right? If you're trying to dribble and the ball doesn't have air, it doesn't have everything it needs in order for you to do the thing that you need to do with the ball. That task is the ball. You want to make sure it has all of the context, it has all of the information that you need in order to pick it back up, in order to send it where it needs to go cleanly and effectively and efficiently. And that requires you to have some type of tool in order to track that information. Again, docs a board, and that's like bare minimum.

SPEAKER_00 45:56

Start there, and you'll be able to accomplish most of your tasks 80% faster and at a significantly higher level of quality. What are three tips for acing a technical coding interview?

SPEAKER_01 46:18

So I have done about three coding interviews for CTP in the last month. And I've noticed that the candidates that are the most successful have a few things in common. One, they are able and they start off with communicating what they want to do before they just jump into doing it. That's incredibly helpful because it allows me as your interviewer to be able to clarify any assumptions that you made that may have been incorrect. It allows me to follow what you're going to do and make sense of your code even before it's finished, especially if it's a more difficult problem and it's going to require more than a few lines of code. I don't know the approach that you're going to go. I've seen different approaches to every problem in different languages. And so your direction may be different than what I've seen before. So explaining that to me as an interviewer or your interviewer is going to be incredibly helpful for them to be able to help you in case they notice that you are going in the wrong direction. They can at least say, Oh, you explained this to me, but I'm seeing this in your code, maybe they'll ask you a question to help you get back to the right place.

SPEAKER_00 48:00

Another thing is do some printing. I don't know why people don't try this.

SPEAKER_01 48:13

So I don't know how often the candidates that I've seen have not tried this, but it is okay to add a print statement into the code. Yes, there are like it's designed to provide some output. And that output, that final output, is going to be tested by whatever that uh testing engine is going to be. But for you as the person that is building this solution, building this function, you have to understand how your data is changing through every iteration. And the easiest way to do that is not to look at the final output. Easiest way to do that is to add different areas inside of your code, add a print statement, log something, make it clear that you know how this variable changed over time. And you could even go through it one by one manually yourself or with your interviewer to make sure that y'all both are on the same page about what the output should be at different levels in the code. And then when you run your function with print statements or with logs, you can see where the difference is. Because it might be something very simple that is breaking your application. But if you only look at the output, the final outcome of that function without looking at all the little things in between that made that output possible, then you'll never be able to. So keep that in mind. Try to add some logs if you can, whenever possible.

SPEAKER_00 49:55

And then three.

SPEAKER_01 50:31

So I wouldn't do that. I would avoid making it seem like you've seen the problem because one, if you've seen the problem before, they might just change the problem. And then now you've wasted an opportunity to have just did really well on that particular problem. So you don't want to lose that good example that you know you can ace. So don't say it. And then two, if you do struggle with it later, it just is strange that you haven't, if you've seen this pattern before, you struggle with it, and now it's like, or you're not, you may be able to identify a problem, but you haven't actually mastered the solution to that problem, even though you do identify it well enough, or you have identified it, but you're not able, maybe you're not either not able or not flexible enough to redirect the solution based on this new instance of the problem. So again, it just doesn't present the strongest signal that you are the right fit for this maybe this team or this organization, whatever it may be, when you do that. So I would avoid that as much as possible. But think about your interview as a skill that you can practice and have some best practices for yourself to make sure that when you go through these interviews, because you're going to go through many of them, have a way to make sure that you are in the best doing the best the best that you can and just practice, have these best practices, they're gonna be helpful for you. That's all we had for questions for this week. Uh, if you have any questions, if you want more uh detail for any of the answers that I've provided, uh put a comment in the channel wherever you find this video, and we will uh definitely respond to that with more information and more detail as needed. And before we jump out, just quick mindset reframe for this current week. Uh, I saw a quote yesterday or the day before yesterday that said, What you are will show in what you do. And it reminded me of a video that I watched from Myron Golden where he said, be do have. And this really means that the person that you are is going to do certain things. And those things that you do, the activities, the investments that you make of your time and energy is going to result in some outcome. That outcome is going to be what you have in your life. It could be the people in your life, it could be the money in your life, it could be the resources and access that you have access to. That's all the output. That's all what happens next, what happens after, what happens in the end.

SPEAKER_00 53:51

Everything from who you are, what's inside of you, to what you do with your hands, with your mind, with your mouth, all of that is the do.

SPEAKER_01 54:06

And you can always change that. You can change how you speak to yourself and others, you can change the investments that you make with your time. You can of you can avoid certain people, you can choose to be around other people, you can avoid to do certain things and choose to do other things.

SPEAKER_00 54:26

These are all the do. Now, I don't know if it's totally possible to change who you are. I do think there's a very strong case that who you are is always gonna be who you are.

SPEAKER_01 54:44

I don't think that's a bad thing. There are things in each of us that are you know not the best and things that are really great. And none of us are going to be the same, none of us are gonna have the same exact combination of good and bad in us. But that who you are is going to represent your interests, it's going to represent your uh your energy in life in the world, and that you want to get that out as much as possible. You get that out by doing things, you get that out by having conversations, you get that out by going out into the world and using your hands and seeing things and putting yourself in places that allow you to interact and change the world and build and create. And then once you've done all the hard work of building and creating, you start to see the fruits of your labor. And that fruit could be bad, you could hate the fruits, but if you don't like the fruits, then I mean you did something that you were not supposed to do with your time. The the doing was the problem, not the fruit. The fruit is again is just the outcome, it's not what went into getting that fruit. So keep that in mind. Keep that in mind that the thing that you're seeing today is only the results of the things that you've done weeks and months and years ago. And so the things that you want to see weeks and months and years from now are going to require you to do something that you may not have done in the past. You may be required to do something that you don't even know how to do yet. So you may need to do some more research before you can even get to that part, right? Like maybe that maybe the outcome you want is the information. So that means you need to go read a book, you need to go ask some questions, you need to go do something that you didn't expect to do, but that you need to do.

SPEAKER_00 56:39

So just keep that in mind. Uh, I want us all to definitely have the things that we want, and I want us to do things that we enjoy, but that are aligned with what we want and who we are.

SPEAKER_01 57:00

So it's all a little balanced combination, it's it uh it ties together. Be mindful about that combination and that connection between them because once you're mindful about it, you can actually start to make changes to parts that need to be changed, and you start to see the impact in other areas that will be helpful for you. That's all we had for this week. And today is Mother's Day. Happy Mother's Day to all of the mothers in the world, the good mothers, the other ones do better.

SPEAKER_00 57:41

But literally, just happy Mother's Day.

SPEAKER_01 57:46

I love the fact that we celebrate mothers, and it's important to have a day or a time in your life, like whether or not you need the reminder of national holiday, or you just know to spend some time with your mother or you know, value your mother. It's incredibly important. Uh, it's needed and necessary. Like you came from this person, so you should know where you came from. So definitely have those conversations whenever possible. Uh, and just you know, check in with your moms. Say you love her, call her when you can, and make sure you make some time to do that. And so just again, happy Mother's Day to all the mothers out there, uh, and enjoy the day about you and actually have a question. I wonder if anybody can answer this. I mean anybody, I guess it's uh anyone can answer it. Uh, but do you feel that happy Mother's Day as like a greeting? If you say Happy Mother's Day, should you say that to everyone? Can you say that to a man or the father? It's like Happy Mother's Day. It's like you don't just say Merry Christmas to Santa Claus, right? Or your person that you're giving a gift to, you say it to everyone, even if you're not giving them a gift or whatever, you're not, whatever it may be. I've said Christmas as an example, like happy Thanksgiving. You're not giving, you're not saying happy Thanksgiving to people that you feed only. So why is it that we only say happy Mother's Day to the mothers when technically it's a holiday that we all get to enjoy? You say happy Mother's Day to a man, maybe it reminds them to go call their mom, right? Like what if they didn't know it was Mother's Day and you didn't say Happy Mother's Day? Now here they are going another day, a week, or a month, whatever, not having, you know, called their mom or checked their moms. So I don't know. I'm wondering if that's a thing that we should be doing and why we don't do it. So if you have an answer, please put that in the comments and let me know what you think. And with that being said, peace out, happy Sunday. Enjoy your day, enjoy your week, be great, be healthy, be happy, and be safe. I will check in with you all next week.

SPEAKER_00 1:00:12

So, builders be great.