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Hey everyone 👋 I'm exploring ideas for an AI-powered developer tool, but before building anything, I want to understand the real problems developers face every day. There are already plenty of tools that generate code. What I'm interested in is everything around coding: Debugging Code reviews Technical debt Documentation Dependency upgrades Testing Deployment Architecture decisions Learning large codebases I'd love to hear from you: A few questions: What's the most frustrating part of your workflow? What task takes more time than it should? What's something you wish AI could do for you today? Have current AI tools (ChatGPT, Claude, Cursor, Copilot, Gemini, etc.) failed you in any important way? If you could eliminate one developer headache forever, what would it be? I've also created a short 2-minute survey: 🔗 https://docs.google.com/forms/d/e/1FAIpQLSf1M5d2y-0RXEIhrbDBtS5gC900YuzWl43cJCxGUrU38MyeDQ/viewform?usp=publish-editor I'll happily share the survey results and key findings with the community once I collect enough responses. Thanks in advance for any feedback!

Originally published on rikuq.com . Republished here for Dev.to's readers. The FinOps Foundation Framework is the reference architecture for cloud financial management. It's been maintained by the FinOps Foundation (a Linux Foundation project) since 2018 and has matured into the de facto standard most serious cloud cost work is built on. In 2026 it received a substantial refresh that extended its scope from pure cloud spend to include AI/ML, SaaS, licensing, and broader technology categories. For practitioners thinking about formalising FinOps practice — or evaluating providers who claim to do FinOps — knowing what the Framework actually covers is what separates a real implementation from a marketing label. This post walks through the Framework structure, the 2026 updates, and how it applies specifically to AI/ML spend. I'm Ravi. I run three production AI SaaS solo ( Prism , Citare , BatchWise ) and do advisory work on FinOps via rikuq services . The walkthrough below is what I use when teams ask "what does the FinOps Foundation Framework actually look like in practice?" TL;DR Element What it is Phases Three concurrent operational modes: Inform, Optimize, Operate Principles Six foundational principles guiding all FinOps practice Capabilities The functional areas of activity a FinOps practice covers Personas Engineering, Finance, Procurement, Leadership, Operations, ITAM, Sustainability 2026 additions Executive Strategy Alignment, Technology Categories taxonomy, Converging Disciplines recognition AI/ML extension New Technology Category with specifics on GPU/CPU differential, token pricing, make-vs-buy economics The six foundational principles Before the structural mechanics, the Framework's six principles establish the cultural and operational mindset. They're worth knowing because they're how the Framework's authors test whether something is "really" FinOps or just cloud cost cutting. Teams need to collaborate. Engineering, Finance, Procurement, and Business teams w

Integrations kill product velocity. Every SaaS team knows this. You ship a killer feature, customers love it, then they ask: "Can it sync with Salesforce? What about HubSpot? Zendesk?" Suddenly your roadmap is hostage to building connector after connector. Each one takes 2-3 weeks. Your engineers hate it. Your customers wait. Competitors who solve this faster win deals. The standard response has been iPaaS platforms. They help, but they don't fundamentally change the game. You still need engineers to map fields, handle edge cases, and maintain brittle connections. The real breakthrough isn't just automation , it's making integrations LLM-native from the ground up . What Actually Makes an Integration Layer "AI-Native"? Let's cut through the marketing speak. Every B2B tool now claims to be "AI-powered." Most just added a ChatGPT wrapper to their UI. Real AI-native architecture means three things: 1. LLM-Ready Connectivity via MCP Servers Model Context Protocol (MCP) is Anthropic's standard for connecting LLMs to external data sources. If your integration layer doesn't support MCP servers natively, your AI features will always be bolted on, not built in. MCP servers expose your SaaS data to language models in a structured way. Instead of engineers writing custom API wrappers for every LLM interaction, you get a standardized interface. Claude, GPT-4, and future models can query your integration layer directly. Example: A customer support tool with native MCP integration lets an AI agent pull ticket history from Zendesk, check Stripe subscription status, and update Salesforce records in one conversation flow. No custom code. No brittle middleware. 2. AI-Mapped Data Migration Data migration is where most SaaS deals die. Customer says "we'll switch from ServiceNow to your ITSM if you migrate our 50,000 tickets." Your team estimates 6 weeks. Deal stalls. Traditional migration means: Manual field mapping spreadsheets Custom scripts for data transformation Downtime windows Hi

PostgreSQL LISTEN/NOTIFY for Real-Time Multi-Tenant Events: Ditching Polling and WebSocket Complexity I've shipped real-time features in CitizenApp using three different approaches: naive polling (embarrassing), Redis pub/sub (overkill), and now PostgreSQL's native LISTEN/NOTIFY. The third option is what I should have started with. Most teams reach for Redis or RabbitMQ the moment they need real-time updates. It's the conventional wisdom. But here's the truth: if you're already running PostgreSQL, you have a battle-tested pub/sub system sitting right there. It handles multi-tenancy correctly, scales to thousands of concurrent connections, and eliminates an entire infrastructure dependency—which matters when you're deploying to Render or Vercel where every added service is friction. Why LISTEN/NOTIFY beats the alternatives Polling is dead. HTTP requests every 2-5 seconds for "new notifications"? That's technical debt masquerading as simplicity. It wastes bandwidth, kills your database with unnecessary queries, and users see stale data. Redis is powerful but expensive. Not just in dollars—in operational overhead. You need to manage connection pools, handle failover, monitor memory usage, and keep another service running in production. At CitizenApp's scale (thousands of concurrent tenants), we were paying $50/month for Redis on top of Render just to broadcast notifications that PostgreSQL could handle natively. WebSockets without a broker are a nightmare. If you're running multiple FastAPI workers (and you should be), a WebSocket connection to Worker A doesn't know about events published by Worker B. You need a message broker to fan-out events across processes. Unless you use PostgreSQL LISTEN/NOTIFY, which handles that automatically. PostgreSQL's pub/sub is: Transactional. Notifications only fire after a transaction commits. Tenant-aware. Use channel names like tenant_123_notifications and broadcast only to the right subscribers. Zero extra infrastructure. It's part

Thanks to the newly detailed FROST technique, telltale SSD activity can be measured in the browser using simple JavaScript.

The use of AI Agents creates a distinctive smell... One can tell the GH Repo owner was high on...

One day last October, sitting in the courtyard of his house in China’s Henan province, Dong Hui decided to see if he could hold a pen to write. Dong, 39, had sustained spinal cord injuries in a car accident six years earlier that left him paralyzed from the neck down. Slowly but determinedly, he wrote…

A question for guys who into unusual programming stuff. i been programming for +12 years now .. first few years it was fun trying all sort of things from assembly x86 to neural networks and computer vision but after finishing college few years ago and getting into corporates and the job market .. i been coding only for work as a backend engineer and programming stoped being a hobby anymore .. especially with ai propaganda that is going on right now. now i want to try a completely different part of programming as a hobby again away from web/app/game development or CV and ML something that is useful and somehow low level. i thought about learning CUDA and exploring other aspects now but its hard to find super geeks now to ask after the AI boom. what other fields you guys are into that are fun, complex and unusual? submitted by /u/zeXas_99 [link] [留言]

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I’ve been working on Keylight, a licensing layer for macOS apps, and one of the more interesting technical problems has been designing license checks that work both online and offline. The basic problem sounds simple: “Is this app allowed to run?” But in practice, there are a lot of edge cases: What happens if the user bought the app, but is currently offline? How long should an offline lease stay valid? How do you prevent a license from being copied forever? How do you handle device limits without making the app annoying? What happens when a subscription expires while the app is offline? How do you rotate SDK/API keys without breaking old app versions? How much should be checked locally vs on the server? The approach I’m using is roughly: The app validates against the server when online The server returns a signed local license lease The app can continue working offline for a limited period Device activations are tracked server-side Renewals, upgrades, and revocations update the next lease Old SDK keys can be retired gradually instead of breaking existing builds It’s a small part of the product, but it has been one of the most interesting engineering decisions so far. Curious how others would design this. Would you keep most licensing logic server-side, or allow more local verification with signed license files? And for desktop apps, what do you think is a fair offline grace period? submitted by /u/nicolas1410 [link] [留言]

How to make LLMs deterministic, in plain English. The version I share with founders and product teams before they make decisions worth real money. You use AI tools every day. But can you explain what happens when you hit send? Most people cannot. And that gap is costing them. Bad prompts. Broken products. Decisions made on the wrong assumptions. The Hard Truth Every LLM explainer out there is written for researchers or so basic it tells you nothing useful. Neither helps you build better products or work with AI more effectively. This is the version I share with senior leaders, founders, and product teams before they make decisions worth real money. 1. It Is Not a Search Engine. It Is Not a Database. It Is a Prediction Machine. When you type a prompt and hit send, the LLM is not finding an answer from somewhere. It is predicting the most likely words to follow your input. Based on patterns it learned from billions of documents. That is the whole process. Wrong: "The AI knows the answer." Right: "The AI predicts the most likely answer based on what it has seen." This changes everything about how you use it. When an AI gives you a wrong answer confidently, it is not broken. It is doing exactly what it was built to do. Predict. Not verify. 2. The Autocomplete Comparison (And Why It Only Gets You Halfway) You have probably heard the phrase "autocomplete on steroids." It is not wrong. But it misses something important. Your phone autocomplete learned from your messages. An LLM learned from most of the written internet. Books. Research papers. Code. Billions of examples. At that scale, the patterns start to look a lot like real thinking. Not because the model understands in the way you do. Because it has seen so much that it can predict what a good answer looks like. When I was building AstroNayak I fed Vedic astrology principles into the system prompt. The LLM produced interpretations that genuinely surprised me. It did not know Vedic astrology. It had seen enough of it t

One of the biggest myths in the software industry is that great engineering teams are built by hiring great engineers. They aren't. I've worked with incredibly talented developers who eventually became disengaged, indifferent, and unwilling to contribute beyond the bare minimum. I've also worked with average developers who grew into exceptional engineers because they were surrounded by a culture that rewarded curiosity, ownership, and continuous improvement. The difference was never talent. The difference was culture. The Toxicity Nobody Talks About When people hear the term toxic workplace , they usually imagine shouting managers, impossible deadlines, public humiliation, and constant pressure. Those environments certainly exist. But some of the most damaging engineering cultures are far more subtle. On the surface, everything appears professional. Meetings are calm. Nobody raises their voice. Everyone speaks politely. The company presents itself as collaborative and mature. Yet beneath that polished exterior exists a culture that quietly destroys accountability and discourages anyone from caring too much. A Simple Pull Request That Revealed a Bigger Problem Recently, while reviewing a pull request, I asked a few straightforward questions: Why are we passing an empty string to a component that doesn't function without an ID? Why is a skeleton component living in a file where it doesn't logically belong? Could this conditional statement be simplified for readability? These weren't major architectural concerns. They weren't requests to redesign the application. They were ordinary engineering discussions—the kind that happen every day inside healthy teams. When Ownership Disappears What happened next was far more interesting than the code itself. Instead of discussing whether the observations were valid, the conversation immediately shifted toward ownership. Who originally wrote the code? Who moved the code? Who was responsible for introducing it? The discussion was n

Previously, we learned how to send and receive data in Go. Now, we will combine those concepts and build a simple CRUD API. CRUD stands for: C reate R ead U pdate D elete These four operations form the foundation of most backend applications. In this tutorial, we will build a simple task API in Go using only the standar library. By the end, you will understand: how CRUD APIs work how to handle multiple HTTP methods how to store data in memory how to send and receive JSON data how backend APIs manage resources Prerequisites To follow along, you should have: Go installed basic familiarity with Go syntax understanding of the net/http package basic understanding of JSON handling You can confirm if Go is installed by running: go version Step 1 — Create the Project Create a new folder for the project: mkdir go-crud-api cd go-crud-api Now initialize a Go module: go mod init go-crud-api This creates a go.mod file for managing project dependencies. Step 2 — Create the Server File Create a file called main.go . Your project structure should now look like this: go-crud-api/ ├─ go.mod └─ main.go Step 3 — Write the CRUD API Open main.go and add the following code: package main import ( "encoding/json" "net/http" ) type Task struct { ID int `json:"id"` Title string `json:"title"` } var tasks [] Task func tasksHandler ( w http . ResponseWriter , r * http . Request ) { w . Header () . Set ( "Content-Type" , "application/json" ) switch r . Method { case http . MethodGet : json . NewEncoder ( w ) . Encode ( tasks ) case http . MethodPost : var task Task err := json . NewDecoder ( r . Body ) . Decode ( & task ) if err != nil { http . Error ( w , "Invalid JSON" , http . StatusBadRequest ) return } tasks = append ( tasks , task ) json . NewEncoder ( w ) . Encode ( task ) default : http . Error ( w , "Method not allowed" , http . StatusMethodNotAllowed ) } } func main () { http . HandleFunc ( "/tasks" , tasksHandler ) http . ListenAndServe ( ":8080" , nil ) } Now let's unpack what is hap

Originally published at Perl Weekly 775 Hi there! I try to keep track of the Perl-related events. You can find them listed at the bottom of each edition of the newsletter and on the events page on the Perl Weekly web site. There you can also find a link to embed the calendar in your calendar program. There are a number of events scheduled for this month. Most of them online, so if your time-zone permits, you can join those events. The big in-person event is at the end of the month The Perl and Raku Conference in Greenville, South Carolina, USA. In the last couple of weeks I have been using various AI tools extensively. It still needs some hand-holding, but it already writes code that seems to be way better than the average code I've seen. So I wonder, would it be possible to ask one of the AI tools to convert Python libraries to Perl? You know, we have been complaining for many years that companies provide implementation for their SDK/API/client in several language, but not in Perl. We also saw that CPAN could not keep up with the growth of PyPI, npm and the other 3rd party library registries. So maybe some of you would like to explore the idea of converting some of these libraries to Perl using AI. Finally a personal note, I am planning a trip to Korea and Japan in September-October. If you live there and would have any travel recommendations, I'd love to get that. Enjoy your week! -- Your editor: Gabor Szabo. Articles Introducing ZuzuScript Toby Inkster created a programming language which blends a fairly JavaScript-like syntax with fairly Perl-like semantics, and a few other features that he hasn't really seen in many programming languages. ANNOUNCE: Perl.Wiki V 1.47, JSTree copy V 1.21 Teaching AI About the British Monarchy with MCP The site already exposes information through a traditional web interface and a JSON API. But those interfaces were designed for humans and developers respectively. MCP gives AI systems a much cleaner integration point. ANNOUNCE: Perl

This article was originally published on aicoderscope.com In early 2023, Andrej Karpathy coined the term "vibe coding" to describe a new mode of software development: you describe what you want, the AI writes the code, and you ship without reading every line. He meant it as a genuine observation about where the craft was headed. By 2026, vibe coding is the default mode for most solo developers, with AI tools handling roughly 70% of keystroke work on a typical feature. The other 30%—direction, review, judgment—still belongs to the human. The pitch is real. Solo developers can now build features that would have taken a week in a day. The bottleneck isn't code volume anymore; it's knowing what to build. That's a genuine productivity unlock. The problem is also real. Codebases vibe-coded without guardrails develop a specific pathology: inconsistent patterns across files (because each AI session starts with no memory of the last), logic errors masked by plausible-looking code, and no rollback culture because no one committed before letting the AI loose. The developer who vibed their way through six weeks of feature work often can't explain what the codebase does anymore, because they never had to think through it. This guide is not about slowing down. It's about the ten rules that let you keep the speed without the debt. The Promise vs. the Reality What vibe coding looks like in 2026: you open Cursor, describe the feature in natural language, and Agent mode writes the file. You review the diff, accept what looks right, reject what looks wrong, and move on. For standard CRUD features, state management, boilerplate API clients, and UI components, this works well. The AI has seen enough patterns that its output is often correct on the first try. Why it works especially well for solo devs: there's no code review bottleneck. A team has to slow down to onboard the AI's changes into shared mental models. A solo developer owns the whole context and can iterate without waiting fo

This article was originally published on aicoderscope.com On paper, Windsurf and Cursor are the same product. Both are standalone IDEs forked from VS Code. Both charge $20/month for their entry paid tier. Both ship a tab-completion model and a multi-file agent. Both wire in the same frontier models — GPT-5, Claude Opus, Gemini. Reviews that score them feature-by-feature end up in 47-43 ties because the feature lists genuinely match. That kind of comparison misses the point. The two editors feel different to use, and the difference matters more than the feature checklist. This piece tests both side-by-side across two weeks of normal client work — Python, TypeScript, Go — and lands on a clear verdict at the end about which one fits which kind of developer. Pricing and feature claims here were verified against Windsurf's pricing page and Cursor's pricing page on May 5, 2026. Both vendors change pricing more than most editors — re-verify before subscribing. Pricing: nearly identical The pricing tables converged in 2025 and have stayed mirrored since: Tier Cursor Windsurf Free Hobby (limited Agent + Tab) Free Entry paid Pro $20/mo Pro $20/mo Heavy individual Pro+ $60/mo (3× usage) / Ultra $200/mo (20× usage) Max $200/mo (heavy users, unlimited extra at API pricing) Team Teams $40/user/mo Teams $40/user/mo Enterprise Custom Custom Cursor offers a middle tier (Pro+ at $60) that Windsurf doesn't match exactly. Windsurf has a "Light" plan with unlimited usage on cheaper models that Cursor doesn't have. These are minor — for the typical individual developer choice, both are $20/month for Pro and $200/month for the power-user tier . The entry decision is therefore not a price decision. It's a workflow-fit decision. Both ship a standalone editor A common misconception: "Windsurf is a VS Code extension, Cursor is its own editor." Both are standalone applications. Both fork VS Code. Both can install most VS Code extensions from the Open VSX Registry (with occasional compatibility

This article was originally published on aicoderscope.com On April 28, 2026, Warp open-sourced its terminal client under AGPL-3.0, picked up 60,000 GitHub stars, and declared itself an "agentic development environment." OpenAI signed on as founding sponsor. The announcement looked like a triumph of developer-first idealism. Read the fine print and a different picture emerges: the terminal is free; the product that matters — Oz, Warp's cloud agent orchestration platform — remains fully proprietary. Warp is not becoming an open-source project. It is becoming an enterprise SaaS company with an open-source frontend. None of that is inherently bad. But it is what this review is actually about: does the $20/month Build plan deliver enough AI value to justify adding Warp to a stack that probably already includes Cursor or Claude Code? What Warp is in May 2026 Warp's product now has three layers: Warp Terminal — the terminal client, open-source AGPL-3.0. Rust-based, GPU-accelerated, available on Mac, Linux, and Windows. The core terminal features (blocks, Warp Drive, session sharing, settings file) are free and remain free. Warp Agent — an AI coding agent embedded in the terminal. Runs locally for interactive work. Handles natural language command generation, code review, debugging assistance, codebase Q&A, and voice input. Consumes credits from your plan. Oz — Warp's proprietary cloud orchestration platform. Runs agents in the background, coordinates multi-agent workflows, triggers on events from Slack, Linear, or GitHub Actions, and orchestrates third-party CLI agents including Claude Code and Codex. Oz is where the enterprise pitch lives. Around 1 million developers use Warp as their primary terminal. The pivot to agentic tooling is a bet that those developers will pay to automate their workflows beyond what a local agent session can handle. Pricing breakdown Warp simplified its pricing in December 2025, replacing the old Pro/Turbo/Lightspeed tiers with two paid plans. P

Tuples, Sets, and Dictionaries in Python In the previous lesson, we learned about Lists and how they are used to store multiple items in a single variable. Today, we will learn about three important Python data structures: Tuples Sets Dictionaries These data structures help programmers organize and manage data efficiently in different situations. 1. Tuples in Python A Tuple is a collection of items stored in a single variable. Tuples are: Ordered Unchangeable (Immutable) Allow duplicate values Tuples are created using parentheses "()". Example languages = ( " Python " , " Java " , " C++ " ) print ( languages ) Output ( ' Python ' , ' Java ' , ' C++ ' ) Accessing Tuple Items Tuple items are accessed using indexes. Example languages = ( " Python " , " Java " , " C++ " ) print ( languages [ 0 ]) print ( languages [ 1 ]) Output Python Java Negative Indexing in Tuples Example languages = ( " Python " , " Java " , " C++ " ) print ( languages [ - 1 ]) Output C ++ Tuple Length The "len()" function returns the number of items in a tuple. Example numbers = ( 10 , 20 , 30 ) print ( len ( numbers )) Output 3 Why Tuples are Important Tuples are useful when data should not be modified accidentally. They are commonly used for: Fixed data Coordinates Database records Returning multiple values from functions 2. Sets in Python A Set is a collection of unique items. Sets are: Unordered Unchangeable items Do not allow duplicates Sets are created using curly brackets "{}". Example numbers = { 1 , 2 , 3 , 4 } print ( numbers ) Output {1, 2, 3, 4} Duplicate Values in Sets Sets automatically remove duplicate values. Example numbers = { 1 , 2 , 2 , 3 , 4 } print ( numbers ) Output {1, 2, 3, 4} Adding Items to a Set The "add()" method inserts a new item into a set. Example numbers = { 1 , 2 , 3 } numbers . add ( 4 ) print ( numbers ) Output {1, 2, 3, 4} Removing Items from a Set The "remove()" method removes an item from a set. Example numbers = { 1 , 2 , 3 , 4 } numbers . remove ( 2 ) print

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