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Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is free and source-available on Github. Star git-lrc to help devs discover the project. Do give it a try and share your feedback. A single honeybee has exactly one move: find nectar, fly it home. Impressive aviation. Add a few thousand more bees and something strange happens. Now they're making honey, cooling the hive, and defending the colony against threats ten thousand times their size, with no Jira board, no standup, and nobody handing out tickets. That jump from "can fetch nectar" to "runs a self-regulating honey factory" is the best mental model I've found for multi-agent AI systems . So let's steal it xD First, what even is an "agent"? Before we throw thousands of them at a problem, it's worth pinning down what one actually is. An AI agent is an autonomous system that performs tasks on behalf of a user (or another system) by designing its own workflow and using available tools . Three things decide how good an agent actually is: The LLM powering it i.e the brain. Its tools which is the hands. The reasoning framework is how it turns tool outputs into the next decision. A single agent is fine. It's our lone bee, and it can do real work. But ask it to research a topic, run heavy calculations, scrape five websites, and write the summary, and you start to feel the ceiling. Multi-agent systems: bees, but for compute A multi-agent system keeps each agent autonomous but lets them cooperate and coordinate inside a structure . The magic isn't any single agent, it's the choreography between them (claude which is famous for that). And there are a few classic ways to choreograph it. 1. The decentralized network (a.k.a. "everyone's a peer") Every agent can talk to every other agent. They share information and resources, and they all operate with the same authority . No boss. Just message-passing. This is your agent network . It's great for emergent, collaborative problem-solv

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Time zones are one of those topics that look simple until you ship something and a user in another country sees the wrong time. Here are the traps I keep seeing, and how to reason about them in 2026. UTC is not a time zone, and GMT is not UTC UTC (Coordinated Universal Time) is a time standard, not a region. GMT is a time zone that happens to share the same offset as UTC most of the year. For storage and math, always think in UTC. Treat GMT as just another named zone. Rule 1: store timestamps in UTC Store every instant as UTC (or an epoch value). Convert to a local zone only at the edges, when you display to a user. If you store local times, you will eventually lose the offset and never recover the true instant. Rule 2: an offset is not a zone +09:00 tells you the offset right now. It does not tell you the zone, because zones change offset across the year due to daylight saving time. Store the IANA zone name (like America/New_York ), not just the offset. The offset is derived from the zone plus the date. Rule 3: DST is where it hurts The same wall-clock time can happen twice (fall back) or never (spring forward). Scheduling "9am every day" is a zone-aware operation, not an offset-aware one. Libraries like the built-in Intl.DateTimeFormat and Temporal (now widely available) handle this correctly if you give them a zone name. new Intl . DateTimeFormat ( ' en-US ' , { timeZone : ' Asia/Tokyo ' , dateStyle : ' short ' , timeStyle : ' short ' , }). format ( new Date ()); Rule 4: scheduling across teams is an overlap problem For a distributed team, the useful question is not "what time is it there" but "when do our working hours overlap". That is a set-intersection over each person's 9-to-5 expressed in UTC. A tool for the human side When I just need to eyeball overlaps and pick a meeting time without writing code, I use the free tool I built: ZonePlan , a time zone meeting planner and live world clock. If you want the practical playbook for picking meeting times, I wrote

One of the biggest realizations I've had over the last year wasn't about software. It was about focus. When I first started building KiwiEngine, I wanted it to power everything. Business software. CRMs. Inventory systems. Scheduling platforms. Accounting tools. SaaS products. If someone could build it, I wanted KiwiEngine to support it. Technically, I still do. But something changed. I realized there is a difference between building software that can solve every problem and trying to solve every problem yourself. Those aren't the same thing. The Architecture Never Changed KiwiEngine is still designed to power business applications. Nothing about the architecture changed. The modules. The APIs. The philosophy. The engine remains general-purpose. What changed was my focus. Build What You Understand I started asking myself a simple question. Who do I actually understand? Not as a developer. As a creator. The answer wasn't accountants. It wasn't HR departments. It wasn't inventory managers. The answer was musicians. Artists. Game developers. Creators. Builders. Those are the people whose problems I experience every day. Those are the workflows I naturally understand. Open Source Changes The Equation One of the beautiful things about open source is that I don't have to build every application. I can build the engine. I can document it. I can share the philosophy. Someone else can build the CRM. Someone else can build the scheduling platform. Someone else can build the accounting software. Meanwhile, I can focus on building the creative tools I genuinely want to use. The Best Proving Ground Today, KiwiEngine's proving ground is becoming: Artist websites EPKs Music production tools Digital storefronts Creative workflows Game development Media platforms Not because they're the only things KiwiEngine can build. Because they're the things I care deeply enough to refine every day. And I think that creates better software than chasing every possible market ever could.

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The main website of the language https://mojolang.org/ displays an announcement bar that says "Mojo will be open source soon! Join us at ModCon '26 for an update." submitted by /u/baldierot [link] [留言]

A few years back I was a junior dev on a car financing product, and I got handed the deal jacket. A deal jacket is the full picture of a deal. How much the buyer puts down, what the car is worth, the terms, all of it packaged up and sent to a bank so the bank can come back with a yes or a no. The flow I had to build would send that package to one bank, wait about a minute for an answer, check whether the offer that came back was any good, and if it wasn't, send the whole thing to the next bank. A pipeline. Under the hood it was a recursive call with state managed in between, talking to Route One on the other side. It kept breaking. I wrote it, tested it, read the logs, fixed one thing, watched it break somewhere else. Day three, day four, still broken. Then on the fourth day I hit send in Postman one more time, watched the logs roll past, and it just worked. The approval came back clean. I jumped out of my chair. I was loud enough that the whole room looked over, and the two guys who knew what I'd been stuck on for four days were already grinning, because they knew exactly what had just happened. That feeling is the whole reason I'm writing this. Not the code. The feeling. The joy had two parts, and I only saw the second one once it was gone The first part is obvious. It's the problem solving. The thing fought back for four days and then it didn't, and I had beaten it. You chase a bug through the logs, you argue with it, and at some point it gives. That is a real high and every engineer knows it. The second part is quieter. I built that. Me. Back then if I shipped something, even a plain HTML page, it was mine end to end. I had to learn HTML before I could build the page, so the page was proof that I had learned. You could point at the thing and say that came out of my head and my hands, and nobody could take that from you. So the joy was solving the problem, and it was owning what you solved. That second part is the one that broke. Same problem, four years apart Ta

Package managers aren't as interchangeable as they seem. This post digs into the real differences in how npm, Yarn, pnpm, Bun, and Deno handle dependencies under the hood and why that matters when things break in practice. submitted by /u/OtherwisePush6424 [link] [留言]

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Cutting OpenAI Costs From Scratch: What Nobody Tells You Three months ago I sat down with my finance lead and watched her scroll through our OpenAI invoice. The number was $14,200 for the month. That was the moment I knew we had a problem. Not a "maybe we should optimize" problem — a real, existential, "this kills our margins before we hit Series B" problem. I run a B2B SaaS platform that does a lot of LLM-powered document processing. Summarization, extraction, classification, the boring stuff that makes real money but burns tokens like crazy. We were routing everything through GPT-4o because, honestly, it was the path of least resistance when we started. Then the bills started arriving. This is the story of how I cut our LLM spend by 97%, the architecture decisions that made it possible, and the things I wish someone had told me before I started. The Math That Made Me Sweat Let me put actual numbers on the table. Here's what I was paying versus what I pay now: Model Provider Input $/M Output $/M vs GPT-4o GPT-4o OpenAI $2.50 $10.00 — GPT-4o-mini OpenAI $0.15 $0.60 16.7× cheaper DeepSeek V4 Flash Global API $0.18 $0.25 40× cheaper Qwen3-32B Global API $0.18 $0.28 35.7× cheaper DeepSeek V4 Pro Global API $0.57 $0.78 12.8× cheaper GLM-5 Global API $0.73 $1.92 5.2× cheaper Kimi K2.5 Global API $0.59 $3.00 3.3× cheaper Look at that DeepSeek V4 Flash row. 40× cheaper than GPT-4o. For comparable quality on the workloads I was running. I had been leaving 97.5% of my budget on the table. Doing the mental math: a $500/month OpenAI bill becomes $12.50. My $14,200 bill? Theoretically $355. That's not optimization, that's a different business. Why I Almost Didn't Do It Here's the thing nobody tells you about cost optimization at a startup: it's not a technical problem, it's a willpower problem. The reason I was paying OpenAI 40× too much wasn't because their API is hard to use. It was because switching felt risky. I had deadlines. I had a roadmap. I had investors asking about g

Originally published on the Keylight blog . A short founder note on why Keylight exists. Every product starts as somebody's unsolved problem; this is mine, and if you are shipping a paid app you have probably run into the same one. The problem I kept hitting I wanted to sell a desktop app directly. Not through the App Store — directly, to customers I could actually talk to. The payment side was easy: Stripe is excellent and the decision took an afternoon. Then I got to licensing, and everything slowed down. Stripe takes the money. It does not give you a license key. It does not sign anything your app can verify. It does not know what a device activation is. The moment a customer has paid, you are on your own: you need to mint a key, sign it so it cannot be forged, deliver it, let the app check it, track devices, and revoke it on a refund. None of that is payment processing, so none of it is in Stripe. So I looked at the platforms that do bundle licensing. Why the merchant-of-record platforms did not fit Paddle, Gumroad, and Lemon Squeezy all advertise license keys. I looked hard at each, and the same three problems came up. The fee. As merchants of record they charge around 5%, against Stripe's ~2.9%. On every sale, forever. Reasonable if it solved my problem well — but it did not. Offline validation. This was the dealbreaker. Their licensing is built around an online validation API: to check a key, the app calls the platform's server. My app is a desktop app, and desktop apps run on planes, behind firewalls, and offline. An online-only check leaves no good option. Fail closed — refuse to run without a server response — and a paying customer who is simply offline cannot use what they bought. Fail open — keep running when the server is unreachable — and the check is trivially bypassed: block the app's network access and it can never re-check the license or learn it was revoked. The app never actually verifies anything itself; it only knows what the server last told i

Originally published on the Keylight blog . The thing that stops developers from moving their licensing isn't the work. It's the fear of one specific moment: a paying customer opens the app after you've switched, and it tells them they're unlicensed. That's the nightmare — you reach for lower fees and customer ownership, and the bill comes due as a wave of "I already paid for this" support tickets. It's a reasonable fear, and it's also avoidable. Migrating onto Keylight doesn't require invalidating anything, re-issuing anything, or asking customers to do anything. This post is about the one rule that keeps everyone working, the two situations you might be in, and why a scary-sounding "major version" jump changes none of it. When you're ready for the click-by-click mechanics, the companion piece covers them: How to Import an Existing Customer Base into Keylight . Why migrating licensing feels risky A license check is binary in the moment a customer experiences it: the app either lets them in or it doesn't. So any change to the system behind that check feels like it's playing with a live wire. Switch the layer that answers "is this person allowed in," the thinking goes, and you risk every existing customer getting the wrong answer at once. That instinct is right about the stakes and wrong about the mechanism. The wave of lockouts people picture comes from one specific mistake: treating migration as a cutover , where the old keys stop being recognized the instant the new system goes live. If your migration invalidates the old keys, yes — everyone breaks. The entire trick is to not do that. The one rule: old keys stay valid Here's the rule the whole migration hangs on: you bring your customers' keys in as they are, and nothing gets invalidated. When you import an existing customer, their license is a live, active record from the first second. If you include the key string they already have, that key is what Keylight stores — not a replacement. So when your new build ask

Originally published on the Keylight blog . "Should I charge once or charge monthly?" is one of the first real decisions an indie app faces, and it is usually answered by copying whoever the founder admires rather than by what fits the product. Both models are legitimate. This post lays out when each one actually makes sense, the honest tradeoffs, and how Keylight models perpetual keys and renewing subscriptions so the licensing follows your pricing instead of constraining it. The two models, defined A one-time (perpetual) license is a single payment for a license that does not expire. The customer owns that version — and usually some agreed window of updates — forever. Think of the classic "buy version 3, use it as long as you like" desktop app. A subscription license is a recurring payment for continued access. The license is valid while the customer keeps paying; stop paying and access ends or degrades. The recurring revenue funds ongoing development and any server-side costs the app carries. The distinction is not about the dollar amount — it is about what the customer is buying: ownership of a thing, or ongoing access to a service. Get that framing right and the model usually picks itself. When a one-time license is the right call A perpetual license fits when your app is a tool the customer owns and runs locally , with low ongoing cost to you per user. A focused Mac utility, an audio plugin, a developer tool that does its job on the user's machine — these have little marginal server cost, so charging rent for access is hard to justify and customers feel it. One-time pricing also builds trust. There is no metering, no "what happens if I stop paying," no fear of being locked out of work they already did. For tools people depend on, that ownership feeling is a genuine selling point, and it is exactly the kind of no-value-extraction stance that earns goodwill with developers and power users. The tradeoff is honest: revenue is lumpy and front-loaded. You get paid o

A Go interview is rarely about syntax alone. Yes, you should know how slices work, how goroutines...

Hello Devs, for months, I felt confident about system design interviews. I'd watched endless YouTube videos. I'd studied architecture diagrams. I could explain how Netflix builds recommendation systems. I understood Kafka, Redis, load balancers, and microservices. I'd memorized the designs of Twitter, Uber, YouTube, and TinyURL. Then I sat down for my first real system design interview and froze. The interviewer asked: "How would you design a notification system?" I had memorized notification systems. I knew about push notifications, email queues, delivery workers, and retry logic. I could recite architectural patterns. But suddenly, none of that helped. I didn't know which questions to ask first. I started designing before understanding the actual requirements. I built architecture for problems that didn't exist. I missed obvious bottlenecks. I couldn't articulate why I made specific trade-offs. When the interviewer pushed back, I had no framework to adjust. I failed that interview. But that failure taught me something crucial: System design interviews aren't about knowing technologies. They're about knowing how to think. After that, I went back and systematically practiced 20 system design problems. Not passively watching solutions. Actually designing. Making mistakes and refining my approach. And somewhere around problem 12, a pattern emerged. The best candidates didn't know more technologies than anyone else. They had a framework . They asked the same questions in the same order. They structured their thinking consistently. They could handle curveballs because their framework was flexible. They reasoned through trade-offs explicitly. Here's the framework that finally made it click for me. The Problem with Memorization Before I share the framework, let me explain why memorizing designs fails. When you memorize " How to Design Twitter," you learn: Use relational databases for users and tweets Use NoSQL for timelines Cache with Redis Use message queues for fanout S

You built a desktop app — macOS, Windows, Linux, native or Tauri/Electron — and you want to sell it directly instead of handing 15–30% to Apple or Microsoft. Selling outside the stores means you keep the margin and own the customer relationship. It also means the plumbing the stores quietly handled is now yours: distribution, payments, licensing, updates, support. Here's the whole path, in roughly the order you'll hit it — with the licensing part (the one most people underestimate) covered properly. Why sell outside the app stores Margin. You keep 85–100% instead of giving up the store's cut. Control. Your own pricing, trials, upgrades, and refund policy — no review gatekeeping, no waiting on approval to ship a fix. The relationship. You get the customer's email and can actually support and re-sell to them. The tradeoff is that the things the store did invisibly — vouching for your binary, taking payment, enforcing the purchase — are now your job. This isn't a Mac thing. Windows devs sell direct constantly, Linux too, and a Tauri or Electron app ships to all three from one codebase. The work below applies across the board. 1. Distribution and updates Before anyone pays, they have to trust and install the thing. macOS: sign with a Developer ID certificate and notarize with Apple, or Gatekeeper will scare users off. Windows: an Authenticode code-signing certificate, ideally EV to build SmartScreen reputation faster. Linux: package as AppImage, .deb / .rpm , or Flatpak depending on your audience. Then updates, because the store won't push them for you: Sparkle (macOS), Squirrel/electron-updater (Electron), the Tauri updater , or your own endpoint. Decide this early — retrofitting auto-update onto a shipped app is miserable. 2. Getting paid Two real models: Stripe (you're the merchant). Lower fees, full control, your brand on the receipt. The catch: sales tax and EU VAT are your responsibility (handle it yourself or bolt on a tax service). Merchant of Record (Lemon Sque

In my journey as a backend developer, I had already spent time working with APIs, databases, authentication flows, and background processing. I understood the basic idea that not everything should occur within a request-response cycle, especially when dealing with expensive operations such as sending emails, processing files, or generating reports. Offloading work to the background felt like a solved problem to me. That confidence was exactly what led me into confusion. When I first encountered message brokers and task queues, they looked like different names for the same idea. Both involved queues, both involved workers, and both involved asynchronous processing. In my head, the distinction didn’t seem important, so I treated them interchangeably and assumed that choosing one over the other was just a matter of preference or framework availability. The real issue was that I had not yet understood the difference in intent between communication and execution. What I thought was a simple design choice actually turned into an architectural mistake that affected how I structured an entire system. How I Misunderstood the Problem At the time, I was building systems where the backend had to handle multiple heavy operations. A user could upload files, request reports, or trigger processes that should not block the main API response. Naturally, I reached for a queue-based solution because it is the standard answer for background work. However, instead of asking what role the system needed to play, I focused on what tool could make things asynchronous. That small shift in thinking created the confusion. I assumed that anything that gets delayed or processed later should automatically go into a queue, without distinguishing whether I was dealing with a job that must be executed or an event that other services should react to. This is where I started building the wrong abstraction. Where Task Queues Actually Fit A task queue exists primarily to assign work that must be complete

estou tentando resolver algo q me incomoda no Clion com C++ que é o terminal dele, estou tentando adiconar um terminal proprio com SFML 3, minha ideia é escrever o codigo e ele ser compilado e assim aparecer na janela do SFML, alguem tem alguma ideia dew como posso fazer isso? submitted by /u/wzinho_667 [link] [留言]

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