Architecting AI-Native Platforms in the Real World: A Conversation with Amar Akshat
Agentic architecture, cell-based boundaries, and prompts-as-code for reliable, auditable AI in payments and wallets
AI is already in the loop for writing code, reviewing changes, and even drafting architecture diagrams—but turning those capabilities into resilient, auditable, production-grade systems in regulated domains is still hard. In payments and financial services especially, architects have to reconcile non-deterministic models with deterministic guarantees around correctness, security, and compliance.
In this conversation, we speak with Amar Akshat—SVP of Architecture at Paysafe Group and author of the forthcoming book Decode the Compiler (Packt, 2026). At Paysafe, Amar has led large-scale modernization and AI-native transformation across payments, wallets, and compliance platforms. Earlier, at Apple, he helped shape the architectural foundations of Apple Pay and contributed to wallet and tokenization frameworks. His work focuses on making architecture itself intelligent—blending principles like CAP, Twelve-Factor, and Zero Trust with AI-driven reasoning and automation.
Over the course of the interview, Amar explains how his teams are bringing AI into the architecture loop through MCPX, ArchX, and “cell” architectures that keep analysis and decision paths safely bounded. We dig into when to keep workflows purely deterministic versus putting an AI in the path, how to structure data, guardrails, and system prompts as first-class design elements, and how to choose between modular monoliths and microservices for AI-heavy workloads. Amar also shares concrete practices around confidence-based routing and trust deltas, prompts-as-code and AI Behavior Reviews, prompt manifests as “Dockerfiles for AI,” cost control with “cache, batch, distill,” and vendor-neutral orchestration via protocols like chat completions and MCP.
Looking ahead, Amar reflects on the skills architects now need and how compiler-level thinking informs the design of AI-driven systems. We close with a preview of Decode the Compiler and why understanding what compilers actually do to our code can change how we reason about performance, optimization, and large-scale architecture.
You can watch the full conversation below or read on for the complete Q&A transcript.
Introduction
1: Can you give us a quick overview of your current focus areas at the AI–architecture intersection? Which lens do you think we should use today—compiler-centric system design or product architecture, and why?
Amar Akshat: Right now my focus is what I call agentic architecture—designing systems that can reason about themselves. At Paysafe, that’s embodied in MCPX, which you talked about.
And we have something else called Archx, which is basically an AI-native workflow that powers things like onboarding, fraud analysis, and observability, but also reasoning with systems and their own capabilities—such as Zero Trust and the CAP theorem. A lot of what we do today is about codifying architectural experience.
For example, we have trained internal AI agents to analyze architectural decision records, or ADRs, and suggest reusable design patterns, effectively learning from the scars of every project before it.
And when you talk about lens, it’s an interesting analogy you use with compilers and system design. I would want to use the system design lens. You see, architecture isn’t abstract for me—it’s very progressive, it’s very pragmatic, and it has building blocks like servers, data flows, queues, failure domains.
My work sits between things like compiler intelligence and distributed systems and their logic. So if you think about it, the compiler is just an early architect. It takes intent, it optimizes it under constraints, and it produces an executable structure—and that’s the same mental loop I want our AI agents to have when designing complex systems through architecture.
AI’s Impact on the Architect’s Role
2: I think you are probably one of the best people to ask my next question to, because you sit exactly at this intersection, which I think a lot of people are still trying to make sense of. So where does AI practically help architects now, and where is human judgment non-negotiable?
Amar Akshat: I get this asked a lot in my team as well. We have tools like Cursor, for example, or Replit Agent 3, or GitHub Copilot Workspace. They basically act as junior architects today.
They help me generate documentation. They suggest failure patterns from known premises and known previous experiences, and they help me validate deployment diagrams. For example, every good generative AI can create brilliant Mermaid diagrams. So it can start off as your starting whiteboard—where you throw in your constraints and components—and it’ll start with a basic Mermaid diagram that would take an architect a few hours to actually come up with.
At Paysafe, we are using AI during architectural reviews. It will ingest our ADRs from before, diagrams, and codebases, and then it will flag inconsistencies between what we said we would build and what we actually deployed, because it has the whole lineage of design from scratch all the way to deployment. So it can reason and tell you, with evidence, that this was the original plan to be deployed, this was the scale pattern, and what we ended up deploying.
Human judgment, on the other hand, still owns context, risk appetite, regulatory nuance, and product trade-offs—the politics between product and regulatory. That is all still owned by humans. The AI can propose, but humans prioritize.
I know that my business can reasonably make money in EMEA and Europe for now, so I will prioritize regulatory nuances of EMEA and Europe, and then put through my roadmap what will come in the Americas. So that is the beautiful mix between how humans and AI interact in architectural designs.
3: What’s a good mental model for deciding when to put AI in the loop versus keeping a purely deterministic path?
Amar Akshat: If a task mainly benefits from pattern recognition, that’s where putting AI in the loop makes sense. If it instead requires legal, financial, or compliance certainty, we keep it deterministic. I think of AI as a kind of auto-complete for patterns: it can look at data and say, “this is PII,” “this is PCI,” “these are the compliance guardrails you’ll need.” That’s where this kind of AI can work. The parts that demand strict, predictable behavior should stay more deterministic, and that’s where we sometimes choose not to use AI.
Architectural Patterns for AI‑Infused Systems
4: What baseline architecture patterns do you recommend for shipping AI features reliably?
Amar Akshat: The first one is the data postulate, the second one is the guardrail postulate and the third one is the system prompt package itself.
So when I say data, I mean what is the current state of the data that is made available by MCP servers. Such as transactions, such as user records, addresses, etc.
Guardrail is about making sure what is allowed to be done and what is not allowed to be done. Do you want to completely ground the system? Do you want to have fairly only deterministic responses or do you want to use the existing LLMs?
And then the system prompt is about saying, what is my input format and what am I expecting the results to be in? And what are the other nuances I want my system to take care of automatically? So, for example, do you want more deterministic performance or do you want more accuracy? Do you want more transparency or do you want more speed? These are the kinds of trade-offs that we encode into that system prompt package. This also includes things like Langchain and Open AI or Azure’s AI Foundry for RAG.
And we have our own prompt manifests for governance. So, each inference has a manifest attached to it, and that is published to a data plane. So you can imagine things like Kafka plus Fast API. And each inference is observable, so you can actually observe the latency, the accuracy, etcetera. That is the current model which works for us. Where it breaks is things where execution is critical, user experience is critical. If things need to be made quickly using judgment, then you cannot rely on LLMs. Then we deploy lightweight sidecar models. Things like Open AI Mini or Llama 3 local for shared and even fraud scoring, which has to be real time in a transaction. We try to do these things in a centralized fashion.
5: How do you decide boundaries for AI components such as separate services, sidecars or embedded libraries?
Amar Akshat: So it is all what I call an architecture based on cells. A cell, you know, is a human component which is the tiniest unit of life. So, all of our AI deployments are tiniest units of life with their own regulatory nuances within themselves.
So, for example, if I’m talking about a wallet cell, everything which can support the wallet—its guardrails, its prompt package, its MCP servers, RAG, plus its fine-tuned models—will also participate in that cell and it won’t leak any data. The idea is the critical path of analysis never leaves the cell boundary, and it only leaves the cell boundary for audit and storage purposes. That keeps us safe first of all, fast, and then deterministic. No other data is going to change the way my wallet cell behaves, for example. The same thing applies to payment execution, the same thing applies to transaction ledgers, and so on and so forth.
6: When would you favor modular monolith over microservices for AI and vice versa?
Amar Akshat: If shared memory and stateful context really matter—say in a conversational commerce system—a modular monolith with well-defined internal modules works best. Imagine two people asking what to buy for Diwali (an Indian festival) in different parts of India, but their shared history and the same product catalog matter for recommendations—that’s a great case for a modular monolith with clear internal boundaries.
If scalability and isolation are paramount—for example, in fraud detection—microservices tend to win, because AI workloads often oscillate between those two needs. Many of us think of this as the context–isolation trade-off: which is more important for your use case, rich shared context or strong isolation?
Reliability, Safety, and Testing
7: How do you design for correctness and failure isolation when models are non-deterministic?
Amar Akshat: We route by confidence, really. If the model’s output confidence is less than a threshold, it escalates to a deterministic rule-based system, or we bring a human in the loop. We use things like LangSmith and internal logging to track trust deltas per request. We have effective guardrails and fallbacks—prompt validation and schema enforcement; we use things like Pydantic. We are a big Python shop for some AI-based workflows, and we use Pydantic plus semantics and sanity checks.
So a human only steps in for logic failures, not syntax, really. And we have a comprehensive testing strategy for AI features. For example, one of my cohorts runs drift pipelines. They will evaluate daily by comparing outputs to gold datasets—datasets that are deterministic and known to be correct—and any semantic drift triggers a review. Basically, you have to look at AI prompts as code. That’s it. Our CI/CD basically treats prompts as code. Every change goes through the peer-review process, with automated regression and some kind of sandbox deployment to test these against the gold dataset.
8: You mentioned guardrails and fallbacks earlier. If you had to distill it down to a couple, which guardrails, fallbacks, or human-in-the-loop steps have been most effective in practice?
Amar Akshat: In terms of guardrails and fallbacks, first of all, as I was describing before, guardrails are learned. We learn these guardrails from execution. Every prompt package has a version, and with every new execution and failure we put in more guardrails.
For example, if the AI system ended up putting someone’s email address from the RAG into a response that was meant to be PII-sanitized, we will again augment the guardrail to include that sanity check. Those guardrails are implemented by tools like prompt security, which ensure that none of these guardrail filters let you pass the data back to the customer.
If you apply a middleware kind of concept like prompt security—or any of those use cases where you can apply these guardrail policies before the prompt goes into the LLM and before the response comes back to the user—you will have effectively masked your failure pattern.
Human-in-the-loop is usually very, very important when it comes to response quality. Every response has a confidence score, and if the confidence score goes below a certain threshold, a human will come in and try to analyze what was wrong. Was the data too noisy? Was there too much guardrail or too little guardrail? Or was it a format problem, right—did we come back with bad formats, like something breaking the CI/CD somewhere or changes, etc.? So the combination of middleware components like prompt security and the usage of guardrails with a human in the loop is very important.
9: Can you describe a sensible testing strategy for AI features covering eval data drift and regressions?
Amar Akshat: I think the testing strategy for AI systems is fundamentally about learning from mistakes. Similarly, we have to make sure the AI learns from its own mistakes. The idea is that we have to monitor things like semantic drift, hallucination rate, and related metrics, and you have to monitor them with real-world data in sandboxes.
And then you have to, first of all, come up with a reasonable notion of success for yourself. So let’s say you are dealing with a lot of complaints. You have an AI system which analyzes your complaints and makes sure they’re being handled correctly. You run it in sandboxes with masked PII so that you have a reasonable testing ground around them, and then per execution you look at things like their semantic drift, hallucination rate, and the trust delta, right?
Every pipeline will come back with these metrics, and those evals plugging into your CI/CD are very important because your prompt is changing—changing just like code on a daily basis—and your prompt changes can sometimes be exponentially impacting your determinism.
Observability and Operability
10: What production signals matter most in production for AI features?
Amar Akshat: Yeah. So these signals—basically everything we tested for—now start to matter in production as well.
The first is cost. We are a financial company, we have millions of transactions going through, and a small change in cost per transaction can exponentially impact our revenue or margins.
The second thing is hallucination rate. Each hallucination in something as deterministic as fraud analysis costs us money, because it can lead to incorrect decisions on transactions.
And then the third part is obviously things around the sanity of the whole system itself. You should be making sure that, as you introduce or tune AI, you’re not unintentionally impacting real transactions or degrading the user experience—you might otherwise be causing attrition in your user base. These things matter for us, and we monitor them very closely in our production systems.
11: How do you set up automated and human feedback loops to improve models or prompts without breaking user-reliable behavior?
Amar Akshat: Yeah, so feedback is pretty automated. The agent will log all low-confidence events, and a human reviews them, and it will relabel itself. And as I was telling you, prompts are versioned with Git tags so we can replay failures exactly. Because it is an agent, you can always augment asynchronous activities by itself. So what we have today is that every failure in them is then analyzed by a different model so that we don’t have model bias itself, right?
For example, a failure derived in an OpenAI model will now be reviewed by a Sonnet Claude model. And the feedback we obtain from there will be asynchronously applied to the OpenAI package, which went into OpenAI—the whole thing. And we then, over time, figure out what is working better for us. Which model is able to review the feedbacks and failures of a different model? And then we have these model couplings formed by that, and all of it is tracked via Git tags. So every release has a JSON in it which says, here was the analysis and scoring, and here were the recommended prompt changes or guardrail changes, applied it, and got this score as the final one. So auditability is incredibly important in our ecosystem because this is real data you’re dealing with, this is real developers’ time you’re dealing with, and then also sometimes you’re dealing with real transactional data. So we need to understand which particular change and which particular recommendation caused us transactional benefit across the feedback loops.
Data, Privacy, and Governance
12: How do you protect sensitive data while keeping AI useful?
Amar Akshat: That’s a great topic, and it’s at the top of every executive’s mind in the industry right now. We basically redact personally identifiable data before inference and use hybrid RAG, where private embeddings always stay in-house.
For example, we can use something like Pinecone Local, where it runs as a local instance and private embeddings never leave our environment. Public context is then fetched externally in a secure and deterministic way—for example, a regulatory change, the impact of that change, or human sentiment around a new law. Those external signals are handled in a more deterministic, controlled way.
At the heart of all this is our middleware. That’s where we apply these policies: even if you wanted things like sentiment or PII, it will not flow into the inference layer if we don’t want it to. All AI access is integrated with SAML-based authentication, so we know who is accessing it and can augment their prompt with their role, etc. On top of that, there’s a guardrail middleware where we always apply a particular set of rules based on their role and permissions.
So even if you accidentally put my email address into the prompt, it will be filtered out before it leaves the system. That’s where our middleware stack plays a huge role, along with lightweight governance around prompting. Our prompt manifest defines who owns the prompt, what its data scope is, and its safety rating. You can think of a prompt manifest as a Dockerfile for AI—basically, it’s auditable but still fast to work with.
And finally, for governance, auditability and traceability are paramount for us. We log every inference as an “architectural replay,” which includes things like model ID, prompt version, and data snapshot. That way, our compliance teams can reproduce any decision path deterministically.
13: What is your take on auditability and traceability for AI decisions when regulation applies.
Amar Akshat: Regulation is paramount. I’m dealing with EU regulation and the AI Regulation Act on a daily basis, and basically it goes back to how deterministic you can make your decision paths.
Our whole goal is that anytime anything breaks our determinism, or that score, we either chuck it out of production immediately or we treat it as a P1—like a priority-one incident, right? So any production workflow losing determinism at a given threshold will be treated as a production incident. It is no longer a developer playground or anything.
And because we are able to log the model ID—basically the architectural snapshot, the replay of it—we are able to log the model ID, prompt version, and the data. We can go back to the decision path and change any of these variables to make sure determinism can be achieved immediately. Our Ops teams are actually trained to do this on a daily basis.
Cost, Performance, and Vendor Strategy
14: How do you avoid provider lock-in without slowing delivery?
Amar Akshat: We try to stick to the protocols the market is standardizing on—for example, the chat completions APIs and MCP. These may start with a single company, but over time they become common practice across the industry. So we abstract orchestration through these well-known protocol APIs.
When I talked about MCPX, that’s essentially our multi-provider orchestrator across OpenAI, Azure, Anthropic, and our on-prem models. The reason this works is that all of them support chat completions–style APIs and MCP-compatible patterns. So as long as any external or internal AI provider follows those APIs and protocols, we’re fine.
On top of that, we put an AI gateway in front. Based on things like request headers or your SAML identity information, we can route you to an Azure model versus an OpenAI model, or to an internal model. That is how we avoid lock-in in practice while still moving fast.
15: If we talk about capacity planning and cost control, what has worked for you in terms of caching, batching, smaller models, etc.?
Amar Akshat: I think the mantra is very simple: cache, batch, distill. We use a tiny Llama for high-volume routing tasks and GPT-4 Turbo for design-time reasoning. So if it is dynamic data like customer support or architecture, design, etc., we stay with prompt engineering because, in that case, flexibility beats precision.
16: When do you feel training or fine-tuning is worth it versus prompt engineering when it comes to a foundation model?
Amar Akshat: I think if your domain is stable—maybe KYC or risk scoring—the signals are very well known, the domain is stable, and then we use fine-tuning, because it’s a very well-known, stable, signal-based domain.
And as I was saying before, if it is dynamic—if it is changing a lot—Spanish customers complain in a different way, English customers complain in a very sarcastic tone, and Indian customers complain in a very direct tone, sometimes in Hindi or regional languages. Then we stay with prompt engineering, because we have specialized customer teams who know how their customers complain and can create prompts more easily to manage those customer complaints. So yeah, that’s my short answer.
Team, Skills, and Process
17: What new skills should architects or senior engineers acquire in 2025 and beyond to stay effective with AI in the stack?
Amar Akshat: Architects and senior engineers must learn prompt literacy, model evaluation, and probabilistic reasoning. That’s paramount. You don’t need to train models; you need to design systems that can survive their uncertainty.
18: How do you adapt design reviews, ADRs, and incident response for AI-specific risks and ongoing learning?
Amar Akshat: Our design reviews have introduced a first-class concept called AI Behavior Reviews. We explicitly acknowledge that AI behavior is non-deterministic, and we treat that as a first-class part of the review process.
ADRs now capture prompt decisions and fallback strategies as part of the architecture record. And on the operations side, our SREs include an AI SRE role—someone who understands when it’s model drift, not code, that broke the system.
As I mentioned earlier, we’ve trained Ops people to understand the determinism profile of every AI pipeline. So now they can recognize that it wasn’t the code that failed; it was drift in the AI behavior—and they know when to switch off that pipeline or replace it with something else.
Case Study
19: Can you walk us through a recent AI‑related technical decision you’ve made: the options, the trade‑offs, and how you validated the outcome.
Amar Akshat: That’s actually a very good question. I recently had a very interesting case. We create wallet workflows almost daily, and one of my teams was tasked with designing the checkout experience I was telling you about earlier—for our digital wallet.
This is the same problem I’ve solved multiple times before, in products like Paysafe and Paysafe Checkout. So it’s a problem I know well, and I had a clear sense of where I wanted to end up. What we did this time was use an AI assistant to generate candidate designs and then critique its own designs whenever they broke our zero-trust rules.
Eventually it produced essentially the same Mermaid diagram I would have drawn myself. It compressed many years of my experience into about 35 minutes of assessment, and it did a beautiful job of reproducing that design while honoring the constraints: partition tolerance was paramount, zero trust was paramount, and it respected those.
Then we validated it against failure scenarios—almost like a chaos check. For example: what if the system crashed at point A, B, or C? Does the system remain deterministic? Is the integrity of transaction persistence still correct? As I mentioned, the AI kept iterating until all the constraints were met. Its initial few iterations achieved consistency but not zero trust.
Next time, I plan to pair it with a chaos agent of some sort to automatically explore failure domains, and we’ll see how that goes.
20: Are there any emerging patterns or standards you’re watching that could reshape how AI components integrate?
Amar Akshat: I think all of this starts from orchestration. You can look at things like OpenAI’s protocols, Google’s APIs, or Visa’s agentic commerce protocol—everything starts from orchestration. And when orchestration is involved, zero trust is involved. And when zero trust is involved, deterministic fallback is also involved.
You’re an orchestrator: you’re orchestrating tasks, and you cannot blindly trust anyone in the world. So you apply zero trust, and then you ask, “When something fails, how do I fall back?” That’s where workflow engines come in, and I’m watching patterns that bring those engines together with AI.
I’m especially interested in cases where ambiguity is not known until the ambiguity actually shows up. I don’t think the existence of ambiguity has been mathematically described yet—when will ambiguity occur? When ambiguity occurs, it’s obviously not a clean mathematical situation, but predicting when it will occur is still unknown to systems.
That’s why I want to see chaos agents enter the market—agents whose job is to disrupt AI workflows. Right now we live too much in “happy path syndrome,” where we assume the happy path is the only path that really happens in execution. That is not true; anything can happen, anything can fail.
Every design must still be explainable by a junior engineer, basically. And simplicity is still the ultimate scaling factor. That’s all.
Hot takes
21: According to you, which production metric most correlates with perceived quality?
Amar Akshat: The trust delta.
22: OK, and what’s the smallest useful model card or change log for shipped prompts or agents?
Amar Akshat: We use Microsoft Guidance, and Microsoft Guidance lets you treat your prompt as code. So even the commit messages become the smallest kind of change log that tells you what changed between two versions of a prompt. I would say commit messages, now.
Looking Ahead
23: What constraints or first principles do you feel keep AI projects grounded, and what will look obvious in five years from now about architecting AI-heavy systems?
Amar Akshat: So first principles still apply, as in, any AI project will still not break the CAP theorem. The CAP theorem, when it applies to the determinism of applications—distributed applications—will still apply. So you will have trade-offs when you want consistency and partition tolerance. Availability will suffer irrespective of whether a human or an AI is writing the code or designing the system, right?
So those first principles remain, and an app will still be judged by its Twelve-Factor App principles,. AI apps are no exception. They may be self-healing, but their app constraints are still Twelve-Factor. Zero Trust is a model defined to safely execute critical workloads in the world, and that will still continue to apply.
One thing AI will add is the ability to self-heal with ample data and context at hand, which is a great principle we should actually capitalize on and try to create systems which, over time, go towards determinism rather than away from it. And “fail fast” is still very important, right? If something is not working for you—if determinism is not there—we should fail fast rather than have our transactional integrity or our customers suffer.
Looking ahead, I think if all the architects are on the same page, we should start versioning and feeding our contexts back into the AI. All the ADRs should go into the AI. The codebase should be scanned and understood by the AI on a regular basis. And then we should keep ourselves honest whenever the AI tells us that our ADRs and our codebases have diverged, which means we haven’t been true to our architectural design, right?
That will allow our AIs to have even more context in the world, and then they can apply these contextual patterns to create any advanced AI system, right? Any advanced AI system will still have deterministic models and dimensions—it is still working under those same constraints of the CAP theorem, etc. These are solved problems in every nook and cranny of the world. We just have to bring them together in an architectural model—not just a conversation, but an actual architectural model out there—and then let it weigh in with you on your high-scale design as a senior architect.
About the book: Decode the Compiler
24: You’re working on Decode the Compiler. According to you, how does deeper knowledge of compilation or codegen inform how we design AI-driven systems today?
Amar Akshat: Actually, that’s a great question. I’ll start with an anecdote. When I was growing up, I read this book by Yashwant Kannadkar, called Let Us C++ , and I was taught that when you initialize a pointer—or when you allocate a pointer in C using malloc—you must always typecast it to the right variable type you’re using. I kept that in my head; it was my first education, and it stayed with me until I went into the depths of the GRU compiler at Apple, the Clang.
And I realized that I should not be doing this extra typecasting, because I am now telling the compiler what to do. The compiler knows what to do. It has seen your system, it has seen your code—however beautiful or ugly it may be. It has known your system constraints. It knows what to do, right? Let it do what it does best. The problem is we don’t understand what it does.
How a compiler makes your for loop efficient, for example, or makes your incrementing variable within the loop efficient, for example—we don’t know. Many of us don’t know that compilers will automatically make some variables register variables in C and C++, and it is very important for us to know that so that, when we are writing more advanced code, those design patterns can stick with us. And those same patterns we can apply in larger-scale habits.
In one way, compilers are trying to spoil us—trying to make us lazy—because they let us not take care of those finer performance details by ourselves and do it on our behalf, which is great, but then we are also losing that sharp curve of learning there. So my book is about understanding, from the compiler’s own output or the compiler’s own dump of what it has performed, what it has done on your code, right?
You shouldn’t be surprised. I think it’s a very, very interesting thing to learn—even for a simple for or while loop—how many performance improvements the compiler is making on your behalf. And that is what my book is all about: trying to decode the compiler’s kindness towards us.
25: Is there a personal motivation or vision that led you to, you know, make the decision to write this book at this point in your life?
Amar Akshat: Oh yes, of course. So when I was at Morgan Stanley and when I came into Apple, I was deeply involved in build and integration systems. I was deeply involved in deep compiler workflows—understanding common build failure patterns—and I was at the heart of a team which was basically accepting code changes from the entire Apple operating system developer base inside Apple.
So I was seeing these common failure patterns across, and I was like, I wish I could run a podcast and almost every week tell people that this is a very common failure pattern all of you have. It’s just that it is not well documented. And, you know, sometimes the compiler steps in and does it for you and things like that. So syntax failures—sure, the compiler will reject you. But the subtle efficiency improvements which the compiler does, or sometimes we as humans do to make a couple of integrations work correctly, were almost too beautiful for me to just keep to myself.
So I wanted people to understand—when students go into engineering college today, or write their first few C programs—that they should be surprised to see what is happening beneath the compiler, right? Even if a “hello world” just comes in front of you, what it took the compiler to do it for you is a beautiful experience I went through, and I want the world to go through that as well.
26: Who is the ideal reader for this book? According to you, who will find it to be the most useful?
Amar Akshat: I think the architects and the senior developers would be the most ideal readers, because they understand that when they look at how the compiler optimizes their code, they will be surprised and inspired. Those optimizations apply to us in real-world architecture as well,. You would realize that the compiler does so many things to scale your tokens, your token chunking, or to make your lookup of a particular data structure faster.
And those are the same patterns which we apply in our day-to-day architecture as well, like when we do caching or when we do streaming of tokens, etc. So senior developers and architects will be inspired. Junior developers and people who are upcoming in the market will be surprised. So it will also apply to them—to get surprised, beautifully surprised, flabbergasted, I would say.
 |
|