标签：#software

Math.random() returns a number between 0 and 1, and roughly nobody reading this could explain what happens between the call and the return. That is fine, fine right up until the output decides who gets money, and then it becomes one of the genuinely hard problems in applied software, the kind that regulated industries build entire testing labs around. Start with the thing most people get wrong: a sequence that passes for random and a fair sequence are different claims, and your users cannot tell them apart by staring at outputs. The users will never catch the difference and that is the whole problem in one sentence. This is why fairness in any real-money system, an online casino being the sharpest example, is a verification problem long before it is a math problem. Pseudorandom generators are deterministic. A PRNG eats a seed, runs it through fixed arithmetic, and spits out numbers that sail through statistical randomness tests while being completely predetermined by that seed. Mersenne Twister is the poster child: excellent distribution, used everywhere by default for years, and from a few hundred observed outputs you can reconstruct its internal state and predict the rest. For a Monte Carlo simulation, who cares! For anything where a human has a financial reason to guess your next number, you just shipped a vulnerability and called it a feature. What you want when stakes exist is a CSPRNG. The guarantee that matters: even with a long history of outputs, an attacker cannot compute the next one or recover the internal state. crypto.randomBytes() in Node. crypto.getRandomValues() in the browser. They sit one autocomplete away from the unsafe option and offer wildly different guarantees, which is exactly why this bug ships so often. The safe call and the dangerous call look like fraternal twins. ** The part players actually rely on ** Say you build it correctly: a proper CSPRNG, real entropy, no timestamp nonsense. You know it is fair but now prove it to a stranger wh

Technology companies are extending AI beyond code generation into earlier stages of the software lifecycle, including PRD validation, design inputs, and code review. Initiatives from Uber, DoorDash, and Cloudflare highlight a shift toward AI-driven governance layers that evaluate engineering artifacts before implementation while preserving human oversight across the development pipeline. By Leela Kumili

AI has made it easier to produce working software. That part is real. It can write code, draft documents, research a topic, scaffold a prototype, and debug a problem faster than most teams can finish writing a decent ticket. But faster building doesn't automatically mean better product decisions. That's the part I keep coming back to. For decades, software teams optimized around delivery. Requirements, design, development, QA, release. Waterfall softened into Agile. Agile grew into DevOps. The practices changed, but the assumption underneath stayed pretty stable: building software is expensive, so plan carefully before you start. That made sense because, for a long time, it was true. Now that assumption is breaking. AI is doing to software what calculators did to accounting. It isn't eliminating the job. It's moving the job up a level. The syntax, boilerplate, first draft, and some of the debugging are getting offloaded. The work doesn't disappear. The bottleneck moves. Learning is still expensive Here's what didn't get cheaper: understanding what people actually need getting stakeholders aligned deciding what evidence would change your mind putting something real in front of users reading the signal without fooling yourself The old question was: Can we build it fast enough? The new question is: Do we understand the problem well enough? That sounds like a small shift, but it changes the work. It changes what strong engineers spend time on. It changes what product people need from engineering. It changes how teams should define "done." If the code ships but nobody learns anything, did the team actually move forward? Sometimes yes. Often no. Users don't know until they can touch it People are not great at specifying requirements up front. Not because they're difficult. Because they're human. Most of us don't know how we feel about something until we can react to a version of it. A mockup. A prototype. A rough slice. A real workflow with sharp edges. So the fastest pat

Disclosure: This post includes affiliate links; I may receive compensation if you purchase products or services from the different links provided in this article. Hello friends, System design ** and ** Software design are two important topics for tech interviews and two important skills for Software developers. Without knowing how to design a system, you cannot create new software, and it will also be difficult to learn and understand existing software and systems. That's why big tech companies like FAANG/MAANG pay special attention to System design skills and test candidates thoroughly. Earlier, I have shared system design interview questions like API Gateway vs Load Balancer , Horizontal vs Vertical Scaling , Forward proxy vs reverse proxy , and common System design concepts , and in this article, I am going to share with you the best System design books to learn Software design. Whether you are a beginner or an experienced developer, you can read these books, as you will definitely find valuable stuff. I have read them, and even though I have been doing Software development for more than 15 years, I have learned a lot. System design ** is a complex process, and you need to know a lot of stuff to actually design a system that can withstand the test of time in production. Software architecture is another field where you are expected to learn a lot of things. It's simply impossible to become a software architect by reading a few books, but if you have experience and a hunger to learn, then these books can be a gold mine. These books allow you to learn from other people's experiences. You can read these books to find what challenges they face when they design a real-world system like Spotify, Google, or Amazon, and how they overcome. Each story is a journey in itself, and you will learn a thing or two by reading and then relating with your own experience. I love to read books, and they are my primary source of learning, along with online courses nowadays. In this art

Why AdGuard Home Overtook Pi-hole Last month, while attempting to add ad filtering to the internal network of a production ERP system, the Pi-hole configuration escalated to 85% CPU usage within an hour, causing DNS responses to lag. AdGuard Home resolved the same scenario with 3% CPU and an average latency of 15 ms , which is why it has dethroned Pi-hole. In the following sections, I detail the architecture, performance, security features, and my real-world deployment experience with both products. While they may seem similar at first glance, the fundamental differences directly impact network stability and management overhead. How AdGuard Home Works AdGuard Home is designed as a fully modular DNS forwarder that supports DNS‑over‑HTTPS (DoH) and DNS‑over‑TLS (DoT). Clients first send queries over 53 UDP/TCP or 443 DoH; AdGuard caches the query, checks it against local blacklists, and then forwards it to an upstream DNS service based on preference. # /etc/AdGuardHome.yaml (partial) bind_host : 0.0.0.0 bind_port : 53 upstream_dns : - https://1.1.1.1/dns-query - https://9.9.9.9/dns-query blocking_mode : default blocked_response_ttl : 300 $ dig @127.0.0.1 example.com +short 93.184.216.34 $ curl -s -H "Accept: application/dns-json" "https://adguard.example/dns-query?name=ads.google.com&type=A" | jq . { "Status" : 0, "Answer" : [] , "Question" : [ { "name" : "ads.google.com." , "type" : 1 } ] } Why is it so fast? Cache-first strategy : The initial query goes to an upstream DNS, but subsequent identical domains are returned directly from RAM cache. Parallel upstreams : Since multiple DoH endpoints are tried concurrently, the primary response time drops to an average of 15 ms. Advanced blocklist engine : Thanks to a combination of regex-based filtering and Bloom filters, thousands of ad domains are eliminated in a single query. This architecture, when running as a systemd-based service, shows only 12 ms CPU consumption in systemd-analyze blame output; Pi-hole showed 150 ms

AI wrote the first 80% of my feature in 10 minutes. The code was clean. The logic made sense. The...

OpenAI is attempting to tackle the security issues of the open source software community.

Good architecture is not only about how a system is built. It is also about how well the team can understand that system once it is running. That is where observability belongs in the architecture conversation. It is common for observability to be treated as something that comes after the main engineering work. The service gets built. The API works. The deployment succeeds. Then, somewhere near the end, the team starts thinking about logs, dashboards, alerts, and operational visibility. That approach creates a gap. The architecture may look clean on paper, but once the system is in production, the team has to understand how it behaves under real conditions. Real users do not follow the happy path perfectly. Dependencies slow down. Queues back up. Data arrives in unexpected shapes. Deployments change behavior in ways that are not always obvious. If the system does not give the team a way to see those things clearly, the architecture is incomplete. Observability is not decoration around the system. It is part of the system design. Architecture Describes the System. Observability Shows the Truth. Architecture is built on assumptions. During design, teams make reasonable guesses about usage patterns, service boundaries, dependency behavior, data flow, scale, latency, and failure modes. Some of those assumptions are based on experience. Some are based on current requirements. Some are simply the best call the team can make with the information available at the time. That is normal. The problem is not that architecture contains assumptions. Every architecture does. The problem is when those assumptions cannot be tested once the system is real. A design might assume that an external dependency will be reliable enough. Production may show that it is the slowest part of the request path. A queue might look like a clean decoupling point during design. Production may reveal retry behavior, duplication, or ordering concerns that were not obvious upfront. A serverless function m

Everyone says our job got easier. The people doing it are quietly falling apart. Here's the part nobody at the dinner table wants to hear: AI didn't make software engineering easy. It made it relentless. Your uncle thinks you press a button now. Your PM thinks the estimate should be half what it used to be. LinkedIn thinks you're either an "AI-native 10x engineer" or a dinosaur waiting for the meteor. And somewhere in the middle of all that noise is you, doing two jobs at once and wondering when you stopped recognizing the one you signed up for. If that landed, keep reading. This one's for you. 💡 The Lie Everyone Has Agreed To Believe The story the world has settled on is simple: AI writes the code now, so the hard part is over. It's a comforting story. It's also wrong in a way that's hard to explain to anyone who hasn't sat in the chair. Yes, the blank-file problem is mostly solved. Boilerplate, scaffolding, the first rough pass at a function, all of that is faster than it's ever been. The problem is "writing the lines" was never the expensive part of this job. The expensive part was always judgment. Knowing what to build, knowing why it breaks, knowing which of the model's three confident suggestions is the one that quietly corrupts your data at 2 AM is where the engineer earns their salt. AI didn't remove that work. It buried it under a pile of plausible-looking output you now have to review, verify, and own. So the meter didn't slow down. It moved. You spend less time typing and far more time deciding, validating, and cleaning up. To everyone watching from outside, that looks like less work. From inside, it's a heavier cognitive load on a shorter clock. Sound familiar? The Treadmill Nobody Put On the Job Description The cost no one talks about is the half-life of what you know is collapsing. Five years ago you could learn a framework and ride it for a few years. Now a tool you mastered in January has three competitors and a new paradigm by June. New model, new c

I joined a team where the controller was 800 lines long, the business rules were scattered between the controller and the DbContext , and "to run the tests, spin up a SQL Server in Docker" was a sentence I heard every week. The fix was Clean Architecture. The argument I had with the team lead was about how to actually structure it. We argued for two weeks. Then I built this template so the next person wouldn't have to. This is the Clean Architecture .NET 8 starter template I wish someone had handed me on day one. Four projects, strict dependency direction, domain entities that own their own invariants, and an Application layer you can unit test with Moq — no database required. The whole repo is on GitHub , MIT-licensed, runs with dotnet run , and ships with xUnit tests, JWT auth, Swagger, Docker, and CI. This post is the explanation of why each project exists, what goes in it, and what I learned the hard way about getting Clean Architecture right in .NET. The problem Clean Architecture solves The naive way to build a .NET Web API is one project, one folder structure, and "everything talks to everything": MyApp/ Controllers/ ProductsController.cs ← HTTP stuff OrdersController.cs ← HTTP stuff + business rules Services/ ProductService.cs ← business rules + DbContext.SaveChanges Data/ AppDbContext.cs ← EF Core, entities Models/ Product.cs ← POCO with public setters This works for the first 1,000 lines. By 5,000 lines, the controller is doing five things at once. By 10,000, "to test this, I need a database" is the answer to every test question, and your CI takes 20 minutes because every test run spins up SQL Server. Clean Architecture says: separate the business rules from the HTTP boundary, separate the database from the business rules, and enforce it with project references. A controller is allowed to call a service. A service is allowed to call a repository. A repository is allowed to know about EF Core. Nothing is allowed to know about anything "above" it in the chai

I had used LangChain's RAG chain in production for six months. I could not have told you, off the top of my head, what chunk_overlap did, or why cosine similarity is the right distance metric, or how nomic-embed-text actually turns a sentence into a vector. The high-level library abstracted all of it away. So one weekend I deleted the LangChain dependency and wrote a RAG pipeline from scratch in ~500 lines of plain Python. No framework, no magic. pypdf for text extraction. A 60-line chunker. ChromaDB for the vector store. Ollama for embeddings and the LLM. The whole thing is on GitHub — every module is under 200 lines, every test is deterministic, and you can read the whole thing in one sitting. This is the build log. Not a tutorial — the build log, with the parts that surprised me and the parts I got wrong the first time. Why bother The honest reason: I was using LangChain's RetrievalQA chain and getting answers I didn't trust. Sometimes the model would say "according to the document" when the document didn't say that. Sometimes the citations were wrong. I had no way to know if the chunker was dropping important context, or if the cosine similarity was picking the wrong neighbors, or if the prompt was actually constraining the model. The library was a black box. When you build it yourself, every layer is inspectable. When the answer is wrong, you can add a print statement in pipeline.py line 102 and see exactly which chunks were sent to the LLM. When the chunker cuts a sentence in half, you see it in the test fixtures. When the embedding model gives garbage for some inputs, you can swap in a different model with one constructor parameter. None of that is possible when the whole thing is RetrievalQA.from_chain_type(llm=..., retriever=...) . The other reason: the code I wrote is 500 lines, and it covers the same ground as a 50-line LangChain script. The extra 450 lines are comments, type hints, tests, and explicit error handling. That's the actual complexity. LangCha

I uploaded a 40-page PDF of an internal API spec, asked "what's the rate limit for the search endpoint?", and got back: "100 requests per minute per API key, with bursts up to 200. See section 4.2 of the document." With citations. In about three seconds. The whole stack runs on my laptop. It cost me $0 in LLM credits during development because Ollama is free and local, and the embedder I used is also free and local. The repo is here — issues and PRs welcome. This is the build log. Not a tutorial where every step works the first time — a build log where I tell you which decisions held up and which ones I redid. The problem most "chat with your PDF" demos have Every "chat with your PDF" tutorial I read in early 2025 had the same shape: open OpenAI, paste your API key, call gpt-4 with a 50-page PDF stuffed into the context window, get an answer, pay $0.03 per question, repeat. That works for a demo. It does not work for a tool you'd actually use at work, because: The PDF might contain customer data, internal pricing, or unreleased features. You do not want that going to OpenAI's training pipeline or anyone's logs. The cost adds up. If your team uses it 50 times a day, that's $45/month per seat. The model hallucinates on long PDFs anyway. Stuff 100 pages into a 128k context window and the model starts forgetting the middle. The fix is RAG (Retrieval-Augmented Generation) — don't send the whole PDF, send only the 3-5 chunks that are actually relevant to the question. The rest of the work is the same: embed the chunks, embed the question, find the closest matches, send those to the LLM with the question. But the cost and the privacy story both improve by 100x. The actual ask: Upload a PDF. Ask questions. Get answers from the document with citations, in under 5 seconds, with no data leaving my laptop and no monthly bill. The architecture One .NET 8 solution, one React app, one Ollama process, zero cloud dependencies. [ PDF Upload ] | v +-------------------+ chunks +-------

Last Tuesday my API gateway caught a NullReferenceException , streamed it to a dashboard in real-time, and pushed a draft code fix to the browser tab of the on-call engineer — before I finished reading the error myself. That sentence used to be vendor marketing. Now it's just my Program.cs . This is the architecture post-mortem. I built it on weekends. It runs in Docker. It cost me exactly $0 in LLM credits during development because Groq's free tier is generous and Ollama works as a swap-in. The repo is here — issues and PRs welcome. The problem most .NET teams have Production errors are caught, logged to a file, and forgotten. Engineers find out from a Slack ping twenty minutes later, if at all. By the time someone looks, the original request context is gone, the user's session has expired, and the stack trace is buried four layers deep in System.* calls. "Self-healing" is a word vendors use to mean "auto-restart the pod." I wanted something better. The actual ask: When an exception is thrown in service A, give the engineer (a) a clear root cause, (b) a suggested fix, and (c) a draft code patch — in under 30 seconds. Not a magic black box. Not an auto-applied patch. Just: catch the error, give the model the right context, push the analysis to a human in real-time, and let the human close the loop. The architecture One .NET solution, four projects, four NuGet packages, no new infrastructure beyond what you probably already have. [ HTTP request ] | v +-------------------+ enqueue +---------------------+ | SmartLogAnalyzer. | ---------------------> | Hangfire (Redis) | | Api | +----------+----------+ | (ErrorHandling | | | Middleware) | v +-------------------+ +---------------------+ | SmartLogAnalyzer. | | Worker | | (ErrorProcessingWorker) +-----+-------+-------+ | | AI call | | persist v v +-----------+ +-----------+ | Semantic | | MSSQL | | Kernel + | | (ErrorLog | | Groq LLM | | table) | +-----+-----+ +-----------+ | v +---------------------+ | SignalR Hub | | (

Por muito tempo eu acreditei que programação e desenvolvimento de software como sinônimos. Na...

AI makes us faster, but does it make us better engineers, or just more dependent? As a follow-up...

Pagination is a key component on web-applications that let users navigate through pages making easy to read/find records. Also, pagination is a great strategy to improve performance by avoiding to load entire dataset at once. However, while working with Kaminari, a popular pagination gem in Rails, I encountered an unexpected issue that revealed an interesting edge case. Identify the issue Basically, pagination in frontend was not working properly. Datatable should load 316 total rows, although when the user started to load 15 records per page, frontend is showing inaccurate total rows. Multiple of 15 should ends at 0 or 5. There were pages with 64 records, crazy world. Some pages were loading 12 or 11 rows. There is no issues or error messages in the frontend or backend. Lost in debugging-land After discarding Angular frontend errors, I started to dig into backend controller and Kaminari configuration. Nothing seems wrong. Everything looked good: test suite, smoke tests, desktop debugging. Despite of test results, I started to wonder: what if returned-data is wrong after all? and... Bingo! Finally, after checking every response I noticed that there were duped records in two different pages(pagination requests). Those duped records were skipped from Angular data-table and that's why loaded/total rows did not match. Bingo: a sort of mistake This tricky bug has a simple explanation: bad sorting. Kaminari uses a SQL query using LIMIT/OFFSET strategy: SELECT * FROM posts ORDER BY id LIMIT 25 OFFSET 0 ORDER BY : sort the collection. LIMIT : number of records per page. OFFSET : is used to skip a specified number of rows before starting to return rows from a query. This works perfectly using ORDER BY id because primary key is unique. Check table A. id title body created_at lock 101 Welcome to the Platform First post introducing the new platform features. 2026-06-16 08:15:22 false 102 Summer Update Announcing the latest improvements and updates. 2026-06-16 08:15:22 true 103

Most engineering reading lists are optimized for knowledge accumulation. Modern engineering rewards bottleneck elimination. Last week, a junior engineer showed me a "Top 10 Books Every Engineer Should Read" list. It looked almost identical to the lists I saw ten years ago. The same classics. The same process books. The same assumption: Read enough books and you'll become a better engineer. That's not how most high-performing teams learn. The best engineers I know don't build learning plans around books. They build learning plans around constraints. The Problem with standard reading lists Most reading lists assume that knowledge is universally valuable. In practice, engineering value is highly contextual. A backend engineer struggling with database contention does not need another chapter on Agile. A team spending thousands of dollars per month on LLM inference does not need a generic software craftsmanship book. A startup fighting latency issues does not need a leadership framework. They need solutions to the bottleneck directly in front of them. Reading lists rarely account for this. They optimize for completeness. Engineering rewards relevance. The Shift Most Engineers Miss The fundamentals still matter. Distributed systems matter. Databases matter. Networking matters. Operating systems matter. They are not obsolete. But they are no longer sufficient. Modern systems introduce constraints that barely existed a few years ago: AI inference costs Context window limitations Agent orchestration Evaluation pipelines Semantic caching Non-deterministic workflows Model routing Human-in-the-loop systems Many traditional reading lists never touch these problems. Yet these are exactly the problems teams are solving every day. The challenge is no longer simply writing correct software. The challenge is building reliable systems on top of components that are inherently probabilistic. What Changed For decades, engineers mostly worked with deterministic systems. Given the same inp

In a world completely powered by technology, have you ever wondered what actually keeps our digital lives from crashing down? Enter the unsung heroes: system and network administration . Think of an operating system like Windows or Linux as a computer's command center, orchestrating everything from the heavy-lifting CPU to the smallest plugged-in device. To keep your data safe and your machine stable, it cleverly splits its brain into two zones: a restricted "user mode" where your everyday apps play, and a highly secure, privileged "kernel mode" reserved strictly for critical system operations. When individual computers connect to form massive global networks, the complexity skyrockets. This comprehensive guide breaks down those complex environments into simple, bite-sized concepts. Here is a quick snapshot of what we will cover: Host & OS Administration : This module covers how an operating system functions as the primary intermediary between a user and a computer's raw physical hardware. It explains how the system kernel manages critical computing processes, memory allocation, local storage file systems, and administrative tasks like security patching and automated scripting. Networking Concepts, Topologies, and Protocols : This module explores how individual computer systems connect and communicate across localized or global distances. It details the structural design of network topologies, addressing rules like IPv4 and IPv6, and the standardized layer frameworks that ensure safe and efficient data transmission. 🏛️ Part 1: Operating Systems & Host Administration 🖥️ Computer Resources and Functions At its core, every computer system is a collection of physical machinery and digital structures working together to solve problems. To understand how an operating system manages these pieces, it helps to look at the foundational puzzle blocks of a computer. This section maps out the primary hardware and data elements the system has to control, alongside a simple breakd

I used to think tests were a waste of time. "Ship fast, fix later" was my motto. Until I spent three painful weeks debugging a production issue that a simple test would have caught in 30 seconds. That was the day I became a believer. The Harsh Reality Most Solo Developers Ignore If you're a freelancer or indie hacker building real products for clients, here’s what happens without good tests: You make a "small change" and something unrelated breaks Clients find bugs you should have caught Refactoring becomes terrifying You lose sleep before every deployment Your reputation slowly takes hits A solid test suite changes all of that. What a Test Suite Actually Gives You Confidence to Move Fast You can refactor, add features, or upgrade dependencies without fear. Living Documentation Your tests explain how the system should behave — better than comments ever could. Early Bug Detection Catch issues before they reach the client or production. Better Architecture Writing testable code forces you to write cleaner, more modular code. Professional Credibility When clients or senior devs review your code, a good test suite immediately signals seriousness. The Test Suite Pyramid I Actually Use Unit Tests (70%) → Test individual functions and components Integration Tests (20%) → Test how different parts work together (API + DB) End-to-End Tests (10%) → Critical user flows (login → checkout → etc.) I don't aim for 100% coverage. I aim for high-value coverage — especially around business logic and critical paths. Final Thought Writing tests feels slow at first. But it compounds. Every month you have tests, you move faster and sleep better. The developers who ship reliable software consistently aren't necessarily the smartest — they're usually the ones who learned to respect testing. Have you built a strong test suite habit yet? Or are you still in the "I'll test it manually" phase? Drop your experience below. Let's talk.

Working with AI Means Thinking More, Not Less Yes, this text is long. Yes, it repeats itself in places. I did not clean that up. A text that sounded too smooth while arguing that AI forces you to think more, not less, would be at least slightly dishonest. This is not fast food for quick consumption. And yes, don’t worry: you won’t hear anything especially new here. That is part of the problem too. There is a popular and very seductive story about AI in software development. Now that the machine can write code, the human gets to think less. You just point it in the right direction, and the model will quickly and cheaply do a significant part of the work on its own. In that picture, AI is primarily an accelerator for code production, and human thinking gradually shifts from necessity to optional extra. I keep feeling more and more strongly that this description is dangerously wrong. A more accurate formula for my own experience right now is this: I’m the tech lead, the AI is the entire team in one body . And if you take that metaphor seriously, the conclusion is the exact opposite of the mainstream narrative. Working with AI is not a way to think less. It is a mode in which you need to think more, not less . Not because the AI is bad. But because it is too good at one very treacherous thing: it confidently and smoothly fills in what was left unsaid. I’m the tech lead, the AI is the team At first this metaphor felt like a neat formulation. Now it feels like a literal description of what is going on. If you treat AI as a very fast and very capable executor, a lot of things become clearer immediately. It really can wipe out months of routine work. It can spin up prototypes quickly, take over test scaffolding, try out alternatives, make local edits, help break a task into parts, and sometimes even suggest a decent direction. On the surface, this really does look like a silver bullet. Especially if the human knows the stack and can read code. The pace becomes so extreme th