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Six months ago, I sat down with a YouTube playlist, a blank notebook, and one goal: learn Python. What I did not expect was how hard it would be, not the Python itself, but figuring out how to actually learn it. I started with a YouTube playlist. Simple enough. Except nobody tells you what to do after you watch a video. Do you rewatch it? Take notes? Jump straight to code? I had no system. I'd watch a concept, feel like I understood it, open VS Code, and stare at a blank file. That's when I realized I had fallen into passive learning. And passive learning in the age of AI is a particularly dangerous trap, because it's so easy to confuse activity with progress. I could watch a video, feel good. I could ask Claude to explain a concept, feel good. I could even ask AI to write code, read it, nod along, and feel like I'd learned something. I hadn't. I'd just consumed. There's a difference. The real moment of honesty came when I was stuck on a coding problem. My instinct, everyone's instinct now is to open ChatGPT or Claude immediately. And I knew, sitting there with the cursor blinking, that if I did that every single time I got stuck, I was building nothing. My brain would never develop the muscle of working through problems. I would be someone who can prompt AI to code, not someone who can think in code. And in a world where AI can already write decent code, the person who can't think independently isn't valuable. They're replaceable. So I had to build a system that forced me to actually learn. After a lot of trial and failure, I landed on a 5-phase checklist that I wrote out by hand and kept next to my laptop. Phase 1: is what I call First Contact — watch one focused video, then write a summary purely from memory, then discuss it with an LLM not to get answers but to pressure-test what I thought I understood. Phase 2: is Deep Understanding — read a written source, write proper notes, map the concept visually, and list every edge case and exception I can find. Phase 3:

In the previous article, we explored how to build our first CRUD API using FastAPI. While our API worked correctly, there was one major problem. We were storing data inside Python lists, which exist only in memory. If you've ever wondered how applications like Instagram, LinkedIn, or ChatGPT remember information even after a server restart, the answer is simple: databases. In this article, we'll solve the problem of in-memory storage by connecting our FastAPI application to SQLite using SQLAlchemy. If you haven't read the previous post, check it out: FastAPI for AI Engineers - Part 2: Building Your First CRUD API Ananya S Ananya S Ananya S Follow Jun 1 FastAPI for AI Engineers - Part 2: Building Your First CRUD API # ai # backend # fastapi # python 7 reactions Comments Add Comment 4 min read By the end of this article, you'll understand: Why in-memory storage is a problem What SQLite is What SQLAlchemy is How ORM works How to create database tables using Python classes How to perform CRUD operations using a real database The Problem with In-Memory Storage Previously, our application stored students inside a Python list. students = [ { " id " : 1 , " name " : " Ananya " , " department " : " CSE " , " cgpa " : 8.9 } ] This worked for learning CRUD operations. However, consider what happens when the server restarts: FastAPI Server Stops ↓ Python Memory Cleared ↓ All Student Data Lost This is unacceptable in real-world applications. We need a place where data can survive application restarts. This is where databases come in. What is SQLite? SQLite is a lightweight relational database. Unlike MySQL or PostgreSQL, SQLite doesn't require a separate database server. Instead, everything is stored inside a single file. students.db Advantages of SQLite: No installation required Lightweight Easy to learn Perfect for local development Great for small projects For this article, we'll use SQLite. What is SQLAlchemy? Before SQLAlchemy, developers often wrote raw SQL queries. Exampl

A blind test across 240 images and 10 professional designers just dropped. Ideogram 4.0 against Gemini 3.1, Grok Imagine, and FLUX.2 Max. The results are clean. Ideogram won typography in nearly half of every blind matchup. 47.9 percent. Next closest was Gemini at 30 percent. FLUX.2 and Grok sat around 15 percent each. On the question that actually matters to designers -- would I ship this -- Ideogram scored 3.55 out of 5. Gemini got 2.84. Nobody else cleared 3. That is a real lead in text rendering. The model was trained exclusively on structured JSON caption datasets, which means it understands composition and layout differently than models trained on alt-text scraped from the web. The JSON prompting is genuinely useful for automated pipelines. You can specify bounding boxes, color palettes, object positions. It is not just better at text. It is more controllable. I tested it. It works. The text in images is readable. That has been the white whale of AI image generation for two years and Ideogram 4.0 mostly solves it. But as an overall image model, it is just good. Competitive, not dominant. On busy, highly detailed scenes with specific counts and attributes, Ideogram scored 3.42. Gemini scored 3.37. That is a statistical tie. FLUX.2 scored 3.01 and Grok 2.82, which are worse, but the gap between the top two is noise. For general image quality, you are splitting hairs between Ideogram and Gemini. For photorealism, FLUX and Reve still lead. For artistic generation, Midjourney is Midjourney. The prompting behavior is interesting. Lean prompts won across the board. Long, over-specified prompts lost. The model was trained on structured data, so it wants structure, not paragraphs. "A poster for a coffee shop. The text says Morning Blend in serif. Warm tones, natural light." That works. Adding stylistic directives and adjectives and "make it pop" language degrades the output. Where to actually use this thing: fal.ai has it at three cents per megapixel in Turbo mode. Tha

Ideogram 4.0 launched this week and within 48 hours it was available on seven platforms. That is unusual. Most model launches trickle onto one or two platforms over weeks. Ideogram went wide immediately, which suggests the open weights strategy is working as intended. Here is what you will pay depending on where you use it. fal.ai The cheapest API access. Turbo mode at three cents per megapixel. That is roughly three cents per 1K image. Balanced at six cents. Quality at ten cents. Pay-per-use, no minimums. If you are generating through an API, this is your starting point. Krea Included in all paid plans. Basic is $5.25 per month billed annually with 5,000 compute units. Pro is $21 per month with 20,000 CUs. The CU cost for Ideogram 4.0 specifically is not published yet, but Krea includes 150 plus models in their CU pool, so you are not paying extra for access. If you already use Krea for other models, Ideogram 4.0 is effectively free to try. ComfyUI Free if you have the GPU. The model is open weights at 9.3 billion parameters. Native ComfyUI support means you can download the weights and run it locally. No per-generation cost. No API calls. Just your electricity bill and GPU time. For volume generation or iteration, this is the cheapest path by far. Leonardo Announced as a day zero launch partner but the pricing page still lists Ideogram 3.0. Plans range from $12 to $60 per month with token allowances from 8,500 to 60,000. Third party models on Leonardo always consume tokens, no relaxed generation. Until they publish the 4.0 token cost, you are guessing. Assume it will be similar to their other premium models. Replicate The Ideogram 3.0 listing is live but 4.0 is not there yet. Replicate prices by hardware time rather than per-image, which can be cheaper or more expensive depending on your batch size and the GPU allocated. Worth checking when it lands. FLORA Available in FLORA. Pricing unclear. FLORA is primarily a creative platform, not an API provider, so you are

[While preparing for interviews, I realized I had a strange habit. I would solve a problem, get stuck, open the solution, understand it, and move on feeling productive. A few days later, I couldn’t solve a similar problem on my own. The issue wasn’t lack of practice. The issue was that I was consuming solutions faster than I was developing problem-solving skills. So I changed my approach. Instead of looking for answers, I started forcing myself to think longer, write down my ideas, identify where I was stuck, and only then seek guidance. That worked much better. But I couldn’t find a tool that supported this style of learning. Most platforms either: Give you the answer. Give you the editorial. Give you AI that writes the code for you. So I started building my own. The goal was simple: An AI coach that guides the thought process instead of generating the solution. Over time I added: DSA practice System Design preparation Low-Level Design preparation Company-wise interview questions Topic-wise strength and weakness analysis Personalized revision lists The interesting part wasn’t building it. The interesting part was realizing that interview preparation is less about collecting solutions and more about training how you think. What has helped you improve more during interview prep? Reading solutions? Or struggling with the problem first? Sde vault - https://sdevaultweb.onrender.com/

AI Does Not Cancel Reality I watched the conversation between Mo Gawdat and Marina Mogilko about the future of AI. The conversation is strong. It contains important ideas, but it also contains many claims that sound large in scale, although on closer inspection they rely on very broad generalizations. AI is indeed changing the labor market, education, startups, content, hiring, and ways of thinking. But it does not cancel money, connections, trust, the human vector, creativity, necessity, morality, or people’s ability to adapt. Video on YouTube AI in hiring: automation amplifies chaos Many people have entered the job market. Companies receive huge volumes of resumes. HR departments cannot handle the volume. It is natural that part of the selection process is moving to AI. But there is a serious problem here. Candidates are also starting to play against AI. Resumes are adjusted to vacancies. Cover letters are assembled around keywords. Profiles become optimized for the filter, not for real work. In such a system, the best specialist does not necessarily pass. Often, the person who understood the selection mechanism better passes. The result: the picture becomes cleaner, while the quality of the decision becomes lower. The company gets not the strongest candidate, but the candidate who matched the algorithm best. This leads to lower hiring quality, lower productivity, and slower development. “I built a startup in six weeks”: a product is not a startup The conversation includes the idea that an AI startup would once have taken years and hundreds of engineers, and now it can be built in weeks. Technically, this is true. Prototypes are now built faster. Small teams have powerful tools. One person can now do more than a group could do before. But two different things are mixed here. Building a product faster has become real. Building a startup faster has become real only when resources are present. A startup is not only code. A startup is money, connections, trust, reputa

Unpopular opinion: most AI tools don’t actually save time. They just move the work around. You still have to prompt it, check it, edit it, and sometimes redo it. That’s not automation — that’s just a different kind of work. The only ones I’ve seen genuinely cut time are search tools like Perplexity and coding tools like Cursor. Everything else feels like it’s optimized for the demo, not real use. Change my mind submitted by /u/aiprotivity_ [link] [留言]

Hi! I’ve been working in IT for over seven years now, and my office is next to some healthcare professionals. During a lunch break sitting on a bench in the sun, one of them asked me: If I enter my patients’ personal information into ChatGPT, is that a problem? I wasn’t sure how to answer him, in my opinion, yes, but what do you think? I’d be curious to hear your thoughts, and if there are any studies on the subject, I’d love to see them too! Thanks in advance for your responses! Have a great day, everyone ☀️ Alex submitted by /u/No_Computer_1247 [link] [留言]

I don’t know if this is the right sub-reddit to ask this type of question. I am quite ignorant about hardcore technical stuff. I want to say that I love the idea of an agnostic approach to AI and being able to understand and decide which model is best suited for a specific task. As well as the ability to have citations, being able to have it look through health research and stuff for queries regarding health, etc. Now I do not know if this is just in a general sense people just complaining or something else entirely, but I am seeing a lot of negative stuff on the Perplexity sub-reddit. In terms of like how the quality has gone down, asking how such a company is still even in business. I was just wondering if any of this holds any water or is overly exaggerated submitted by /u/No-Main6695 [link] [留言]

Created a Google Chrome extension that instantly turns any highlighted text on a webpage into a Google Calendar event

Cloudflare recently described how a slowdown in its billing pipeline was traced to contention inside the query planning stage of ClickHouse. The team profiled the bottleneck and patched ClickHouse to replace an exclusive lock with a shared lock, drop the per-query copy of the parts list, and improve part filtering. By Renato Losio

submitted by /u/chunmunsingh [link] [留言]

7 Infra Improvement Strategies to Prevent Next.js Deployment Build Failures in 2026 Recently, our team's deployment pipeline started showing serious instability. Specifically, we encountered recurring build failures related to the chat build. As a result, the entire development team was preoccupied with battling these build failures. Attempts and Pitfalls Initially, I thought the --preload detection logic was the problem. I modified it to detect only specific lines, but this ended up causing issues in other areas. The recurring chat build failures were actually caused by the next.config file not properly recognizing file extensions. I modified it to allow extensions like .mjs , .js , .ts , and .cjs , but even that didn't work correctly at first, leading to some wasted effort. # .github/workflows/deploy.yml (Excerpt from initial version) - name : Run Preload Detection run : | # ... existing logic ... if [[ "$LINE" == *"some_pattern"* ]]; then echo "Preload detected" # ... fi I modified it to detect only specific lines like the above, which led to unintended behavior. // next.config.js (Initial configuration) module . exports = { // ... experimental : { // ... }, // ... }; Regarding extensions, I initially allowed only a few types, and only after experiencing chat build failures did I modify it to support more extensions. Root Causes In the end, it was a combination of several complex issues. There were flaws in the --preload detection logic, and the range of supported extensions in the next.config file was too narrow, which was the direct cause of the chat build failures. Additionally, there was confusion arising from the chat server builds being inconsistent between P1/P2 and P0 stages. Problems also occurred because the .next directory was not preserved, and the smoke gate was too lenient, failing to catch build failures. Finally, there was an unexpected side effect where the next/font/google library caused GCE outbound connection errors. Solutions To address these

Modern applications depend on secrets. Every application requires: Database Passwords API Keys SSH Keys TLS Certificates Cloud Credentials OAuth Tokens Service Account Keys The biggest question is: Where should we store them securely? Unfortunately many organizations still store secrets in: Git Repository Docker Image Application Config Files Environment Variables Shared Documents Excel Sheets This creates a massive security risk. This is why Secret Management platforms like HashiCorp Vault became critical in modern cloud-native environments. 🔗 Resources ** Support the Journey on GitHub: If you're following along, consider starring and forking the repo:** https://github.com/17J/30-Days-Cloud-DevSecOps-Journey What is a Secret? A secret is any sensitive piece of information used to authenticate or authorize access. Examples: Database Password AWS Access Key JWT Signing Key API Token TLS Certificate Private Key OAuth Secret If a secret gets exposed: Attacker ↓ Application Access ↓ Database Access ↓ Infrastructure Compromise What is Secrets Management? Secrets Management is the process of: Store Protect Rotate Control Audit sensitive credentials securely. A modern secrets management platform provides: Centralized storage Encryption Access control Secret rotation Audit logs Dynamic credentials Why Secrets Management Matters Imagine this scenario: database : username : admin password : Password123 committed into GitHub. Result: Developer Pushes Code ↓ GitHub Repository ↓ Credential Leak ↓ Database Breach This happens more often than people realize. The Problem with Traditional Secret Storage Many teams use: .env Files Kubernetes Secrets Configuration Files Hardcoded Passwords Problems: Difficult rotation No audit trail Poor access control Risk of accidental exposure Compliance failures What is HashiCorp Vault? HashiCorp Vault is a centralized secrets management platform designed to securely store, access, and manage secrets. Think of Vault as: Central Secret Bank for you

I've been building a content production tool for my company, which uses AI for things like structure and automatically inserting links with defined anchor text. 2 days ago, I started testing the results in AI text detection scanners and kept getting inconsistent results, even when I knew my articles looked more natural than a previous test. Revision after revision of code, 10 hours spent trying to get it right. And then I decided to pop in a few articles I had personally written, where I knew AI was not involved. Not a single one of the major scanners got it correct. Most of them flagged my original content as having more AI text than the articles my tool was producing. Now that I've gone down this rabbit hole and understand how AI writes and how the detectors work, I'm not sure that any tool is ever going to be able to do this correctly. For obviously written AI articles, sure, it will catch those. But for original content, I just don't see how it's ever going to work. What is everyone's thoughts on this? Has anyone done the same experiment? submitted by /u/Sypheix [link] [留言]

Table of Contents The Integration Problem That Broke Industry 4.0 MCP: The Vertical Connection Layer How MCP Connects to Servers, Tools, and Databases MCP in Real World Industrial Automation ACP: The Horizontal Communication Layer How ACP Works Under the Hood ACP in Real World Industrial Coordination The Six Precise Differences How They Work Together: The Complete Stack Decision Framework for Industrial AI Architects 1. The Integration Problem That Broke Industry 4.0 Industry 4.0 promised connected factories, intelligent automation, and seamless data flow between machines, systems, and humans. The technology arrived. The connectivity did not. The reason is a number called N times M. An enterprise manufacturing facility might have 12 AI agents across quality, maintenance, and planning — and 28 data sources including ERP, MES, SCADA, IoT sensors, databases, CAD repositories, and supplier APIs. Without a standard protocol: 12 agents multiplied by 28 data sources equals 336 custom integrations. Each integration is bespoke code. Each breaks when either side updates. Each requires maintenance. Each represents a point of failure and a security surface that must be independently managed. IBM VP Armand Ruiz stated this precisely: "Without a common standard, every integration is costly duct tape." MCP and ACP together replace 336 pieces of duct tape with two standard protocols — one governing how agents connect to systems, one governing how agents connect to each other. The smart manufacturing market is projected to reach 374 billion dollars by 2025 at 11.8 percent CAGR. Over 50 percent of companies in industrial automation are expected to adopt MCP-based connectivity. The integration problem is not theoretical. The solution is being deployed at scale right now. 2. MCP: The Vertical Connection Layer MCP connects agents to tools and data — the vertical integration layer. It handles the connection between an AI agent and everything it needs to interact with in the external worl

This is the third and final part of my first React project for the Frontend Mentor's Digital Bank Landing Page Challenge . I'm excited to say that I finally finished it. Live Demo: https://bank-landing-page-react-gmtz.vercel.app/ Github Repo: https://github.com/ayra-baet/bank-landing-page-react Learning Component Reusability Beyond Small Elements At first, I thought this final part would mostly involve finishing the Articles and Footer. But while building, I realized something more important: React's reusability isn't limited to small UI elements like buttons or cards; entire sections can be reusable too. Earlier in this project, I reused a single Button component across the header, hero, and footer. This time, I noticed that the Features and Articles sections shared almost the same structure: both had an h2 heading both used a grid layout both wrapped child components The only real difference was that the Features section included a description paragraph. That immediately felt like a perfect use case for a reusable component with conditional rendering. So I created a reusable Section component: function Section ({ backgroundColor , title , description , children }) { return ( < section className = { backgroundColor } aria-labelledby = { ` ${ title } -heading` } > < div className = "container section__container" > < div className = "section__header" > < h2 id = { ` ${ title } -heading` } > { title } </ h2 > { description && < p > { description } </ p > } </ div > < div className = "section__grid" > { children } </ div > </ div > </ section > ); } Then I reused it inside my LandingPage component: function LandingPage () { return ( <> { /* other LandingPage JSX */ } < section id = "features" > < Section backgroundColor = "section--gray-100" title = "Why choose Digitalbank?" description = "We leverage Open Banking to turn your bank account into your financial hub. Control your finances like never before." > < Features /> </ Section > </ section > < section id = "articl

TL;DR — The DIFP protocol was designed to be data-compact and geo-aware from day one. We recently discovered it maps almost perfectly onto the Nostr event format. Here's how, and why it matters for decentralized food infrastructure. Background: What Is DIFP? DIFP (Djowda Interconnected Food Protocol) is an open protocol designed to sync food product data across distributed nodes — compactly, efficiently, and with geo-location awareness built in by default. One of its core design decisions is the PAD system (Preloaded Asset Distribution): Apps ship with a preloaded asset pack — item metadata, compressed images, category structure — all bundled at install time. Only price and availability need to travel over the wire during sync. This means the data footprint per product is tiny. Very tiny. Enter Nostr Nostr is a simple, open protocol for decentralized communication. One of its key specs: events support up to 64 KB of content . When we started exploring Nostr as a potential transport layer, we ran the numbers — and the fit was surprisingly clean. The Math: Products Per Event Baseline encoding A product represented with three fields: { "id" : 500 , "available" : true , "price" : 30000 } At this level of verbosity, a single 64 KB Nostr event can hold approximately: ~1,500 – 2,000 products Already useful. But we can do better. Optimized encoding Two key optimizations: 1. Drop the availability key — If a product entry exists in the JSON, it's available. If it's absent, it's not. No boolean needed. 2. Drop the field names — Instead of {"id": 500, "price": 30000} , just store: 500,30000 Field mapping is handled at the app level, not the protocol level. The device knows position 0 is the product ID, position 1 is the price (in smallest currency unit, e.g. cents). Result ~6,000 – 7,000 products per single Nostr event Possibly more, depending on the price distribution and ID ranges in a given catalog. Geo-Discovery: MinMax99 Cells DIFP uses a geo-cell system called MinMax99 to

I've been frustrated with SQL learning resources for a while. Most are either: Dry reference docs Toy exercises with no context ("SELECT * FROM employees") Paid platforms with paywalls after level 3 So I built SQLwak — a free, browser-based SQL game where you're hired as a Graduate Analyst at Lion City Bank , a fictional Singapore bank. How it works Instead of abstract exercises, every challenge is a real business request from a colleague: "The Operations team needs all Central region branches for an upcoming audit." "Risk wants customers with credit scores below 600 who have active loans." "Finance needs vessels ranked by cargo revenue — use window functions." You write actual SQL against a realistic 9-table banking database and get immediate feedback. 57 levels across 4 tiers Tier Skills 🟢 Foundational SELECT, WHERE, ORDER BY, LIMIT 🟡 Intermediate JOINs, GROUP BY, HAVING, subqueries 🔴 Advanced CTEs, multi-table aggregations ⚫ Expert Window functions (RANK/DENSE_RANK OVER PARTITION BY), UNION ALL, compound CTEs The database schema Lion City Bank has two divisions: Retail Banking: customers, accounts, transactions, loans, branches, products Maritime Trade Finance (Advanced/Expert levels): vessels, cargo_shipments, trade_finance_facilities — covering voyages between Singapore, Port Klang, Bangkok, Jakarta, and Ho Chi Minh City. The maritime division exists because Singapore is a major trade hub. It makes the Expert levels genuinely interesting — you're ranking vessels by cargo revenue and analyzing trade finance utilisation rates, not just counting rows. Technical details Next.js 15 + TypeScript + Tailwind CSS SQLite via WebAssembly — all query execution is client-side, no backend needed Deployed on Vercel Fully open source: github.com/martinl5/sqlwak No signup. No download. Just SQL. Open the link and start writing queries: sqlwak.vercel.app Would love feedback on difficulty progression, new level ideas, or schema additions. What SQL concepts do you wish you'd pract

What Ollama actually is Ollama is an open-source runtime for large language models that runs on your own computer — Mac, Windows, or Linux. Think of it as the “Docker for LLMs”: instead of wrestling with Python environments, model weights, and GPU drivers, you type one command and a model is running. The pitch is simple: keep your data on your machine, pay nothing per token, and work offline. When you run ollama run gemma4, Ollama downloads the model, loads it into your GPU’s memory (or system RAM if you don’t have a GPU), and drops you into a chat prompt. That’s it. Behind that simplicity, Ollama is doing a lot of work for you: Model management — pulling, versioning, and storing models from its registry, the way a package manager handles software. Quantization — automatically using compressed (GGUF) versions of models so a 27-billion-parameter model fits in consumer memory. GPU layer allocation — deciding how much of the model lives on your GPU versus CPU, based on the VRAM you have. Context and KV-cache management — handling the memory that grows as a conversation gets longer. A REST API — exposing everything on http://localhost:11434 so your own apps can talk to it. How it works under the hood Ollama is not itself an inference engine. It’s an experience layer wrapped around one. Under the hood it uses llama.cpp, the C++ engine that does the actual math of running a quantized model efficiently on CPUs and GPUs. As of v0.19 (March 2026), Ollama also uses Apple’s MLX backend on Apple Silicon — a change that delivered enormous speedups (on an M5 Max running Qwen 3.5, decode throughput nearly doubled). The workflow looks like this: You run a command — ollama run qwen3 from the terminal, or a request to the API. Ollama resolves the model — if it isn’t already downloaded, it pulls the GGUF weights from the registry. It loads the model into memory — splitting layers between GPU and CPU based on available VRAM. It serves responses — either interactively in your terminal o