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For AWS Savings Plans vs Reserved Instances, the default answer is: buy a Savings Plan, not a Reserved Instance. The exception is OpenSearch, Redshift, and (until December 2025) databases, which still need the older Reserved model. That is the whole decision. For most teams a Compute Savings Plan is the right default: same discount as a Convertible Reserved Instance, far less to manage, and it follows your workload across instance families, regions, Fargate, and Lambda. The cases where a Reserved Instance still wins are narrow and specific, and the December 2025 launch of Database Savings Plans shrank them further. This post is the decision — RI vs Savings Plan, when each wins. It is not a deep-dive on how Savings Plans work under the hood; I cover the mechanics — how the $/hour commitment gets applied, the billing-hour math, the queue order against On-Demand — in a separate post. Here I only want to answer the question you actually have when the Cost Explorer recommendation pops up: which one do I buy? (Commitments are step three of a full bill audit — where they sit in the order is in what I'd audit first on a $50K AWS bill .) The short version Both Reserved Instances and Savings Plans are the same trade: you promise AWS a one- or three-year commitment, AWS gives you a discount over On-Demand. The difference is what you commit to . A Reserved Instance commits you to a specific instance configuration — family, and depending on type, size, region, OS, tenancy. A Savings Plan commits you to a dollar amount of usage per hour (e.g. "$10/hour of compute"), and AWS applies that discount to whatever matching usage you actually run. The Savings Plan is the more flexible instrument at the same discount level, which is why AWS itself now recommends Savings Plans over Reserved Instances for compute. ( AWS, Compute Savings Plans and Reserved Instances , accessed June 2026.) The Reserved Instance has not gone away — but for EC2 compute, it is mostly the legacy choice now. The d

If your AWS bill has a NAT Gateway line, you are paying twice for the same packet: once for the gateway to merely exist, and again for every gigabyte it carries. The fix for most teams is dull and free. Add an S3 and a DynamoDB gateway endpoint, route the heavy traffic away from NAT, and only then argue about anything fancier. That single change is free to turn on, takes minutes, and stops the most expensive traffic from ever touching the meter. This is a playbook, not a lecture. The trick with NAT Gateway pricing is that the two charges hide in different places on the bill, so most teams only ever see half of it. Numbers first, then the fixes, in the order I would actually do them. What you are actually being charged for NAT Gateway has two charges, and people forget the second one until they read the bill closely. Hourly charge — you pay for every hour the gateway is provisioned and available, whether or not a single byte moves through it. In us-east-1 (N. Virginia) and us-east-2 (Ohio) this is $0.045 per NAT Gateway-hour . That is roughly $32.85 a month per gateway just to keep the lights on. Partial hours bill as full hours. Data processing charge — you pay $0.045 per GB processed through the gateway, in the same region, on top of the hourly charge. This applies to every gigabyte, inbound or outbound, regardless of source or destination. And then there is the part the pricing page mentions almost in passing: standard AWS data transfer charges still apply on top. NAT processing is an extra meter on traffic you were already paying to move. The hourly charge is fixed and visible. The per-GB charge is the one that catches teams out, because it scales with traffic you mostly cannot see: package installs, container image pulls, S3 reads from private subnets, telemetry shipped out, cross-region calls. The rate varies by region (it runs higher in places like São Paulo, where both the hourly and per-GB rates sit around $0.093), so check your own region rather than trusti

Give me read access to a $50,000/month AWS account and I will tell you within a day where the first 20-30% is hiding, because on a mid-size bill it is almost always hiding in the same four places, in the same order: data transfer you can't see in the console, instances sized for a load test that ran two years ago, on-demand pricing on a baseline that never moves, and storage rotting in the most expensive class AWS sells. None of this needs an architecture rewrite. AWS cost optimization, at least the first and biggest pass of it, is just the bill read in the right order by someone who knows where AWS buries the meter. This is the order I work. It is the same audit I run on every account I'm handed, and it is the offer — if you want me to run it on yours, the post ends with how. But you can run most of it yourself today, and you should, because nobody is going to care about your bill as much as you do. A note before the recipe: I deal in ranges, not promises. The exact saving on your account depends on what you've built. What I can promise is that the mistakes below are common enough that the question is usually how much , not whether . Hour zero: get the real bill, not the dashboard Before touching a single resource, I want the granular data. The AWS console's cost dashboard rounds, groups, and hides the things that matter. Two tools give you the truth. Cost Explorer , with rightsizing recommendations turned on, is the fast view — group by service, then by usage type, and the bill stops being one big number and starts being a list of decisions. Resource-level and hourly granularity costs extra ($0.01 per 1,000 usage records per month), but for one audit pass it's worth pennies. The Cost and Usage Report (CUR) is the ground truth — line-item, hourly, every charge AWS makes, delivered to your own S3 bucket. Generating it is free; you pay only the few cents of S3 storage. If you're going to do this seriously, set up CUR (now delivered via AWS Data Exports) on day one. E

I'm 12 years old. I build websites and full-stack apps. And recently, I decided to test something I've never seriously tried before: Can I actually make money from coding? Not from ads. Not from selling a course. Not from some complicated SaaS business model. Just by making a simple website for a local business. So I started looking for businesses that could use a better online presence. And then I sent my first message. The idea I noticed that a lot of local businesses have good services and good customer reviews, but their online presence isn't always great. Some don't have a website. Some have an old website. Some mostly depend on WhatsApp and Google Maps. So I thought: «What if I make a simple website demo and show them what their business could look like online?» I already had a generic demo website that I could use to show the idea. It wasn't supposed to be a huge SaaS product. It was just a simple website that looked professional enough to make a business owner say: "Okay, I can see how this could help my business." Then I started messaging businesses I searched for local businesses and looked at what they offered. Electrical shops. Car washes. CCTV companies. Painting contractors. Home service businesses. I didn't send the exact same message to everyone. I tried to mention their actual business and services. Then I waited. And waited. Most of the messages weren't even seen. That's when I learned something important: Building the product is only half the problem. You also have to get someone to care about it. Then one business replied I contacted a local business called DHARSHINI CCTV SURVEILLANCE. I told them I was making simple, modern websites for local businesses and showed them my demo. Then I asked: «"Would you like me to show you?"» A while later, they replied with: "💐" I thanked them and offered to make a free sample specifically for their business. And then they said: "Send" That one word made me ridiculously happy. 😂 Because this wasn't just someone

I rent six dedicated servers from a company in Germany. Together they have more cores, more memory, and more SSD than most production clusters I worked on a decade ago. I run my own Kubernetes on them. Not managed. Not EKS. Not GKE. The whole stack, from the immutable OS up to the workloads. People who hear this ask me why, and the question usually arrives in one of two tones. The dangerous tone is "that's amazing, how do I do it" . The responsible tone is "why on earth would you do that to yourself" . This post is for the second group. Why the cloud is the right answer for almost everyone Let me get this out of the way honestly: by every conventional metric, I should be using the cloud. Managed Kubernetes has become genuinely good. EKS has dramatically improved over the last three years. GKE has always been better than people gave it credit for. The serverless options are mature. The serverless databases are mature. The observability is mature. The bill is predictable in the way a Tuesday is predictable. Self-hosting violates almost every assumption that makes a startup productive. Time is the most expensive resource you have. The cloud sells you abstractions that turn that time into product. Running your own substrate means the time goes into the substrate. If you are trying to ship a product to customers — go use the cloud. Stop reading this post. It will only confuse you. What the cloud does not sell you Here is what the cloud will not sell you, even if you are willing to pay extra: control over your own roadmap. The cloud's roadmap is the cloud's. They decide which APIs deprecate. They decide which regions get the new feature. They decide what your egress bill looks like. They decide whether your monitoring vendor — sitting on top of their infrastructure — is allowed to charge you eight times what it would cost you to host the same software yourself. They decide whether the small ML company hosting your fine-tuned model gets acquired by someone with very differ

Humanistic Architecture Learning to See Problems Differently I had the opportunity to attend a class called “Humanistic Architecture” by Mr. Chakrit Riddhagni. The class was about applying humanistic principles to software development . Before talking about what I learned, I would like to share a little about my own perspective on software development. Personally, I have a quote and a belief about software development: it is both a science and an art. I see software development as something that has an artistic side, while being grounded in logic, with almost endless possibilities. “Crafting software requires artistry, guided by imagination, grounded in logic, endlessly enduring.” This quote has been one of my inspirations since I started working as a developer. What I mean by this is that I have always liked thinking about software development as a kind of literature . We are not simply writing code. We are solving problems. We are developing and creating something to solve a problem that either we or our customers are facing. Because of this, as a developer, I naturally work with problems every day. But… I never really thought much deeper about what a “problem” actually is. Usually, when we solve a problem for a customer or develop software for them, we receive an issue or a scope of work and then start working on it. We know that something is a problem, so we focus on solving it. But we do not always stop and ask What is the actual structure of this problem ? Is this really the problem? And does the solution we are building actually address the problem we are trying to solve? That changed when I attended the “Humanistic Architecture” class. One of the biggest things I gained from this class was a new perspective and a set of tools for defining what a “problem” really is. Anatomy of a Problem We can look at a problem through the Anatomy of a Problem , which consists of three parts: Current State — where we are now Gap — the difference between where we are and wher

The bill that started this I was paying $40/month for a PDF generation API to power a tiny internal invoicing tool for a client project. Forty bucks a month to convert some JSON into a PDF. That's it. That's the whole service. I finally sat down on a Saturday to see if I could kill that subscription. Three weekends later, not only did I kill it — the replacement is faster than the API ever was, because there's no network round-trip at all. This post is the log of how it went, in the order I actually hit the problems, not the order that makes me look competent. Attempt #1: jsPDF on the main thread (it worked, until it didn't) First pass was the obvious one — jsPDF running directly in the click handler: function generateInvoice ( data ) { const doc = new jsPDF (); doc . text ( data . clientName , 20 , 20 ); data . lineItems . forEach (( item , i ) => { doc . text ( ` ${ item . description } — $ ${ item . amount } ` , 20 , 40 + i * 10 ); }); doc . save ( ' invoice.pdf ' ); } Fine for a 3-line invoice. Once I tested with a 40-line-item invoice (a real client sent me one to test against), the tab froze for almost two full seconds. Not crashed — frozen. Scroll didn't work, buttons didn't respond, and on a mid-range Android phone it was closer to five seconds. The main thread doing synchronous PDF math while also being responsible for painting the UI is exactly the kind of thing that looks fine in a demo and falls apart the moment a real user pastes in real data. Attempt #2: move it to a Web Worker Web Workers get talked about like they're this exotic tool for WASM and video processing. They're also just... a really good fit for "expensive synchronous work that a user is waiting on." I'd never reached for one before this project, mostly out of habit. The tricky part isn't the worker itself, it's that jsPDF assumes it has access to document and window in a couple of code paths (font metrics, mostly), which don't exist inside a worker. I ended up switching to pdfkit compiled

A random game is easier to debug when the same inputs produce the same result. In HoopTrait, a browser basketball project, the Lab mode combines eight selected traits and generates a fictional career. The interesting engineering problem is keeping replay, sharing and validation consistent. This is a technical development note, not a claim that a game score predicts an athlete's real performance. Store the decisions, not just the result The Lab state records a seed, a dataset version and an ordered list of actions. An action is a pick or a reroll. Replaying those actions reconstructs the build. A seed alone is not a complete replay contract: changing the player pool or its order can change a seeded draw. A dataset version therefore matters alongside the random seed. For a future release, the same principle should apply to changes in the rules themselves. Test invariants across many runs The Lab test suite iterates through 1,000 seeds. For each seed it shuffles the order of the eight skills, uses the two allowed rerolls, and completes a build. It checks that: Eight distinct players were selected. All eight traits are present, and no player remains to be drawn after completion. The overall game score stays between 0 and 99 and matches the shared rating function. Packing and unpacking the share state returns the original state. Recomputing the fictional career returns the same output. The ten simulated seasons sum to the displayed career earnings. Those assertions catch different problems. A stable score does not prove that a shared link reproduces the same selections. A complete build does not prove that its season totals add up. Reject impossible histories A share payload is untrusted input, even in a client-side game. Negative or fractional seeds, duplicate skill picks, a third reroll, unknown action types, a mismatched dataset version and actions after completion are rejected. The tests also cover malformed encoded payloads and unexpected fields. Local state is usef

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To get started with competitive Pokémon battles, all you need is your phone. But to compete at the highest level, like at the Pokémon World Championships, you're going to need a Switch. And there doesn't seem to be a good reason why. Battle simulator Pokémon Champions originally launched on the Switch and Switch 2 in […]

The Fairphone 6 Plus feels like an extremely average midrange Android phone and I couldn't be more thrilled. The mission has always been admirable. Fairphone seeks out ethically sourced materials and offers a high degree of repairability for its devices. But the phones themselves required a lot of sacrifices, like putting up with underpowered processors […]

Overview In this article, I'll introduce the concept of Multi-Version Concurrency Control (MVCC) and explain how Postgres implements this protocol across different isolation levels. I'm assuming you already have a basic understanding of isolation levels, database locks, and concurrency in general. I won't cover those concepts here, so if you're not familiar with them, I highly recommend checking out A Straightforward Guide for Isolation Levels first before continuing. The goal of this article is to help you understand: What Multi-Version Concurrency Control is How Postgres implements MVCC across different isolation levels Multi-Version Concurrency Control High-Level Concept The idea behind MVCC is simple: it's a protocol designed to accomplish one goal — when two or more transactions run concurrently on the same data, the end result should look as if those transactions ran one after another, in sequence. Take a look at the diagram above. Two transactions are running concurrently, and we want the end result to look as if either the first transaction ran and committed before the second one started, or vice versa. MVCC guarantees there are only two possible outcomes — never a third. But in reality, these transactions are running at the same time, so this is exactly the core idea of MVCC: it's a protocol that gives us this guarantee even though the transactions genuinely overlap in time. Note that other protocols aim for the same goal, like Two-Phase Locking and Optimistic Concurrency Control. They all take different approaches, but they're all working toward the same thing. The core idea of MVCC is that whenever a transaction updates a row, it doesn't mutate the value in place. Instead, it creates a new record as the latest version and links it back to the old version. After the update, the row has a new version, and the old version is never changed. This version chain exists per record — every update to a row creates a new version, and since each version is linked to

Docker management, monitoring that goes deeper than a green dot, backups I have actually restored, and everything else that showed up once deploying stopped being the hard part. Moving off Vercel solved exactly one problem: deploying. Everything else I used to get for free, quietly, as part of the platform, I now had to go find and wire up myself. A month into running my own server, I had a list of ten tools taped to the inside of my head, each one solving a problem I did not know I had until it happened to me at a bad time. This is that list, in the order I actually needed them, with the mistake or the moment that made me install each one. I lean JS and Rust wherever I can, partly out of preference and partly because those are the tools that keep pace with how fast the rest of my stack moves. A couple of these are not JS or Rust at all, and I kept them anyway because they were simply the best tool for the job. 1. Dokploy, for everything I wrote about yesterday This is the one I already spent an entire post on, so I will keep it short here. Push to main, Dokploy builds the container, Traefik points a domain at it, done. Four apps running on one $24 droplet, and adding a fifth would not move the bill. If you deploy anything with Docker and are still doing it over SSH, start here. Everything else on this list assumes you already have a platform under you, not just a server. 2. Neon, for the database half of preview environments The first crack after Dokploy was previews. Dokploy gives every pull request its own preview URL, which is one of the nicest things about the whole setup, right up until every preview hits the same production database. I corrupted a batch of test data twice before I noticed what was happening. Neon branches Postgres the way git branches code, copy-on-write, so a preview PR gets its own preview database that costs almost nothing until it actually diverges from main. The storage engine underneath is written in Rust, and it quietly closed the othe

An AI agent that has no job description will invent one. That is the whole reason agents drift, and it is the reason most of the agents I have seen deployed inside Indian businesses are quietly switched off within a few months of going live. Nobody would hire a person, point them at the office, and say "handle things". Yet that is exactly how most owners deploy an agent. They connect it to WhatsApp or email or the accounts folder, give it a paragraph of instructions, and let it run. Then they are surprised when it starts answering questions it was never meant to answer, promising delivery dates it cannot know, or filing something that a human should have looked at first. The fix is not a better model. It is the same discipline you already use for people: defined duties, an escalation path, a probation period with a review date, and one named person who is accountable for it. What drift actually looks like Drift is not a dramatic failure. It is a slow widening of scope that nobody approved. A distributor in the FMCG trade sets up an agent to acknowledge incoming orders on WhatsApp and log them into a sheet. Week one, it does that. Week three, a retailer asks "when will my stock reach?" and the agent, being helpful, answers with a guess. Week five, a retailer asks for a discount, and the agent, having seen discounts mentioned in earlier messages, offers one. None of this was in the brief. All of it followed naturally from "be helpful to customers", which is what the owner wrote because they did not know what else to write. By the time the owner notices, the agent has made commitments in writing to twenty retailers, and the sales team is cleaning up after it. The agent did not malfunction. It did what an unsupervised new employee does: it filled the vacuum with its own judgement. The mistake was upstream, at the moment of deployment. The job description A job description for an agent is not a prompt. It is a one-page document the owner can read and sign off, written in

In a previous post , I covered why TypeScript types alone can't protect you from a backend that returns something you didn't expect, and how to build a small apiRequest boundary that validates both the outgoing request and the incoming response against Zod-style schemas before your application ever touches the data. That post answered one question: Is this data actually shaped the way I think it is? It left another question open: When the answer is no, or when the request fails for a completely different reason (like a timeout or a dropped connection), what does the rest of the app do with that failure? In practice, "the rest of the app" usually does something different depending on who's writing it: One component checks error.response?.status directly. Another checks error.code === "ECONNABORTED" . A form manually digs through the error to find field-level messages. A toast just displays whatever string happens to be on error.message . The app works, but every layer speaks a different error dialect. This post is Part 2: it takes the validation boundary from Part 1 and builds the missing piece on top of it, a single, normalized ApiError shape that every layer of the app can speak, plus the logging, messaging, and form-mapping that make it actually usable. Quick Recap: The Validation Boundary From Part 1, the apiRequest wrapper validates request payloads and response bodies against schemas, and throws one of two typed errors when something doesn't match the contract: export class ApiRequestValidationError extends Error { constructor ( public readonly url : string , public override readonly cause : unknown ) { super ( `API request input does not match the contract for ${ url } .` ); this . name = " ApiRequestValidationError " ; } } export class ApiResponseValidationError extends Error { constructor ( public readonly url : string , public override readonly cause : unknown ) { super ( `API response does not match the contract for ${ url } .` ); this . name = " ApiRespon