标签：#t

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…

Follow this link to ask your questions during our Ask Me Anything session on the European Parliament's subreddit, 02/06 15.00-16.00 CET. submitted by /u/Marty_ol [link] [留言]

I do analysis of automobile dealership data and prepare reports based on the analysis for management review. I’m getting way better analytics and cleaner reports being built by ChatGPT Plus compared to Claude pro. Claude is consuming too many tokens and sometimes for longer documents it used my 100% of the 5 hour limit which is very annoying. ChatGPT on the other hand feels to me that it has unlimited usage for my requirement. What is the view of you people when using AI for business and financial data analytics? Is anyone else finding ChatGPT nicer too? submitted by /u/TurboChargedV12 [link] [留言]

I've been paying attention to my own workflow lately and noticed a lot of my time goes into moving stuff between AI sessions, not the actual thinking. Like I'll get an output in one session and then manually bring the relevant pieces into another so it has what it needs. What I can't tell is how much of that is necessary vs. me just being sloppy. So I'm curious how others handle it: When you move from one session to another, what do you actually carry over? Just the output, or also the reasoning, the decisions, the constraints, what to avoid? Have you ever handed off too little and the second session went sideways? Or too much and it got lost in the noise? Does anyone have a mental rule for what's "enough context" to pass along? Trying to figure out if there's a clean pattern here or if it's just inherently messy. Curious what people have landed on. submitted by /u/riley_kim [link] [留言]

I build a security platform. Last night I stopped adding features and did something less fun and more honest: I sat down to make every capability prove it actually works — end to end, with real data, demanding a real pass or fail. "It ran" is not a pass. A page that renders is not a feature. A green checkmark is a claim, not evidence. So I went capability by capability and tried to break each one. I found four real bugs and one of them was a gut-punch: a whole detection engine that was wired into the UI, unit-tested, and never actually ran in production. Here's how the night went. The rule: drive it, don't admire it My method was boring on purpose. For each capability: Feed it real input through the real entry point (CLI or API), not a test fixture. Check the data actually landed (query the DB, don't trust the success message). Feed it a malicious input and a benign input — it has to fire on one and stay quiet on the other. The detection engine passed cleanly. I threw a PsExec process event at it and it lit up: $ zds-core detection eval --event '{"event_type":"process_create","process_name":"psexec.exe"}' 1 alert ( s ) : [ high] PsExec Execution — ( matched: map[process_name:psexec.exe] ) A wevtutil cl Security event tripped a critical "Log Clearing" rule. A plain notepad.exe matched nothing. Good — it detects, and it doesn't cry wolf. (Small UX papercut I fixed while I was there: if you forgot the event_type field, the engine silently matched nothing and printed "no rules matched" — which reads exactly like "you're safe." Now it warns you that the event can't match any rule. Silence that looks like safety is the most dangerous output a security tool can produce.) The one that hurt: ITDR Identity Threat Detection and Response. The engine has detectors for impossible travel, credential spraying, brute force, privilege escalation. All unit-tested. All green. I ran the real flow: POST two login events for one user — New York, then London thirty minutes later. That's ~5

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

Submission Template Challenge: Build With Hermes Agent Project: CompliScore AI compliance health checks for Indian startups Repo/live-demo: https://github.com/nehaprasad-dev/hermes-scout What I built CompliScore gives Indian startup founders a compliance score out of 100 in under a minute - overdue GST filings, MCA returns, penalty exposure, and a plain-English action plan. The upgrade for this challenge: I replaced the one-shot Groq summary with a Hermes Agent reasoning loop that plans an investigation, calls deterministic compliance tools, and writes a prioritized report - with a collapsible agent trace so judges can see the agentic work. Why an agent loop fits here Compliance analysis is conditional. A company with overdue GST needs a filing-calendar deep dive; one with active notices needs notice triage; a clean company needs a light touch. A single prompt guesses all of this at once. An agent that calls tools based on what it finds produces tighter, grounded reports. Hermes Agent integration Scan → computeHealth (deterministic score) → Hermes Agent loop (plan → tool calls → report) → agent trace in UI ↓ on failure Groq one-shot → static fallback Four tools exposed to Hermes (scores never hallucinated): Tool Purpose score_company Canonical score, risk level, pending tasks estimate_penalty GST / MCA / notice penalty breakdown filing_calendar GSTR-3B, GSTR-1, MCA deadlines (90-day horizon) classify_notices Severity labels for pending government notices The agent runs over Hermes's OpenAI-compatible /chat/completions API with function calling — self-hostable via vLLM, LM Studio, Ollama, etc. Transparency: Every successful agent run returns an agentTrace — plan steps, tool names, compact result previews — rendered in a collapsible panel under the AI action plan. Reliability: Three-tier fallback (Hermes → Groq → static). Scans never break. Tech stack Next.js 16 (App Router), TypeScript, Tailwind v4 Hermes Agent (OpenAI-compatible tool-calling loop) Groq fallback ( ll

My Hermes Agent Mac just received a signed, secure and monitored message from a Claude Managed Agent, and got a reply! - A solution for long runtime work, A2A ID and security. What I Built A solution that enables two agents with different owners on a shared identity network, a Hermes and a Claude Managed Agent (Claude platform) talking to each other across the internet. Every message is Ed25519 signed by the sender. Every receiver verifies the signature against a public registry and shows a blue tick before acting. Continuous Agent Monitoring A handshake proves identity once but agents in a long runtime world don't trade a single message, they hold ongoing, autonomous conversations across hours, days, and many turns. Keys get compromised, agents get swapped, a colleagues behaviour drifts, all after the initial check. ZipViz re-verifies every message signature, registry chain, freshness, and watches the stream over time for behavioural anomalies. Trust is re-earned on every turn. So this agent was who it claimed this morning," but "this agent is who it claims, on this message, right now." The demo agents on the ZipViz network: mac-her.smc.viz — Hermes Agent on my Mac Mini brendan-clau.smc.viz — Claude Agent in Cloud When Mac sends a message to brendan-clau, Mac's private key signs it. Brendan-clau verifies the signature against ZipViz's registry, and checks it just ran with the MCP (algorithm, key fingerprint, registry chain, timestamp), then replies signed. Same flow in reverse. Same flow Hermes ↔ Hermes , or Claude ↔ Openclaw . The runtime doesn't matter; the identity layer does. "I received a signed message from mac-her.smc.viz". Reads back the four checks it just ran: ✓ Algorithm: Ed25519 ✓ Key fingerprint: ab52afe... matches registry ✓ Registry chain: mac-her → smc.viz → .viz (Handshake) all resolved ✓ Timestamp: 2026-05-31 11:18 UTC, fresh Demo Hermes continuous monitoring and verification with A2A Protocol Code One MCP server: [ zipviz-mcp ] https://www.npmjs.

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

This is a submission for the Hermes Agent Challenge : Build With Hermes Agent What I Built A small backstory on my coding origins I learnt coding by secretly studying from a python book pdf on my Computer Scientist father's work laptop. That very day I wrote a program that inputs a user's name and prints: "You are mad {name}!" and had a lot of fun pranking my brother using that script. Since then, there have been very few moments where coding felt as magical, because the more you understand syntax, the more you understand the magic underneath your code. That is, until you meet a genius piece of magic such as Hermes. My app idea I built a Fitness coach inside Hermes that learns and adapts based on your daily feedback. According to your goals, performance, and time allocated, it adjusts your current plan. For the purpose of this hackathon I tested it via terminal ui (tui) but I plan to release the polished version on chat apps such as telgram and whatsapp for painless daily checkins. Demo Code Github link My Tech Stack Hermes + node.js. Kept it simple for this quick dive. How I Used Hermes Agent Building an AI application that feels truly personal requires more than just a clever prompt; it requires state, memory, and the ability to adapt over time. During a recent hackathon, I set out to build an autonomous AI fitness coach. Not just a chatbot that spits out generic workout templates, but a system that onboards a user, sets a multi-month timeline, generates habit blocks, and adjusts daily based on feedback. To achieve this, I used Hermes , an agentic framework designed for stateful, long-running applications. Here is a breakdown of how I built it, the challenges faced, and why Hermes was the perfect tool for the job. Why This App is a Perfect Fit for Hermes Most LLM interactions are stateless. You ask a question, you get an answer, and the session ends. A fitness journey, however, is a deeply stateful process. It spans weeks or months and requires constant recalibrat

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

Great to see Odysseus blow up this past day, local AI getting this much attention is genuinely good for everyone building in this space. Figured this is the right crowd to share what we're launching tomorrow (June 1st), since we're playing a pretty different game. A quick framing: Odysseus is a self-hosted workspace that points at engines (Ollama, llama.cpp, vLLM, cloud APIs) and runs through Docker. Conifer is the engine itself, with our own runtime, running natively on Mac, Linux, and Windows. So we're the layer underneath, not a competitor to the workspace. What's actually in it tomorrow: A native inference runtime across Mac, Linux, and Windows, with our own Metal engine for Apple Silicon already matching or beating llama.cpp on a few models on the M3 Max (full benchmarks, including where we're still behind, are at conifer.build/benchmarks) A real coding IDE on top (CodeMirror, integrated terminal, file viewers), so you can code locally with models that never leave your machine Typhoon, a local agent that can read and edit a folder you point it at, kernel-sandboxed rather than just a shell with a warning Install is a signed app you double-click, no Docker, no localhost ports Fully free and open source The honest reason we exist: PewDiePie's wave defined "local AI" in millions of people's heads as Linux + Docker + an NVIDIA rig. If you weren't on that exact setup, the conversation probably felt like it skipped you. Conifer is what local AI should feel like when it's actually native to your machine, whatever your machine is. Launches tomorrow, free and open source like PewDiePie! You can sign up for our waitlist here: conifer.build I'll be around in the comments all day tomorrow, please bring the hard questions. submitted by /u/No_Elephant_7530 [link] [留言]

Meme Monday! Today's cover image comes from the last thread. DEV is an inclusive space! Humor in...