A few days ago, Vaibhav Sisinty posted something on X that stopped my scroll: someone had wired up an actual, physical stick shift to switch between Claude models. Not a settings menu. Not a dropdown. A gear shifter, like the one in a car, sitting on a desk. Fable 5 in one gear. Sonnet in another for daily driving. Opus when the problem needs real depth. Slam the stick into position, and the model underneath your workflow changes. The detail that makes this more than a novelty: he built the shifter with Claude, specifically to make his own use of Claude faster. That's a nice little loop — using the model to remove friction from using the model. Why this is a smarter idea than it sounds On the surface it's a gimmick. Under the surface, it's solving a real problem that every heavy AI user runs into: model selection is a decision tax . Every time you open a chat and have to think "is this a Sonnet task or an Opus task?", you're spending attention on meta-work instead of the actual problem. It's a tiny cost, but it's a cost you pay dozens of times a day, and it never shows up on any productivity dashboard. A physical control collapses that decision into a single motor action — the same way a car driver doesn't consciously reason about gear ratios, they just feel the road and shift. That's the actual insight here: the best interface for a decision you make constantly is the one that requires the least conscious thought. A menu makes you look, read, decide, click. A physical lever makes you feel and move. For something you do fifty times a session, that difference compounds fast. A plausible look at how something like this comes together Nobody's published exact wiring diagrams here, but the architecture almost writes itself if you've worked with hobbyist hardware and API-based model switching. Here's roughly what a build like this involves: 1. The physical input layer A repurposed automotive or sim-racing shifter has a set of positions, each one closing a different switc

Introduction I used to think "connecting to a database" was one skill. Turns out it's two: connecting to a database chilling quietly on your own laptop, and connecting to one living in the cloud, behind a login, in this case, an SSL certificate that will not let you in until you treat it with respect. This week I did both. Same tool (Power BI), same dataset, two very different vibes. Grab a coffee, here's the full walkthrough local PostgreSQL first, then Aiven's cloud version, side by side, screenshots and all. Part 1: Local PostgreSQL → Power BI Step 1 : Create a schema Nothing fancy, just giving my table a home: CREATE SCHEMA powerbi ; Step 2 : Import the dataset Right-click the new schema → Import Data in DBeaver, point it at your CSV, and let the wizard do its thing. Step 3 : Check the table landed properly A quick peek at the columns to make sure nothing got mangled on the way in. Step 4 : Connect Power BI In Power BI Desktop: Get Data → Database → PostgreSQL database. In the Server field, type localhost (or 127.0.0.1 ) and your database name. localhost Choose Import , hit OK, and log in with your local username and password. Click Load . That's it. That's the whole local experience. Part 2: Aiven PostgreSQL (Cloud) → Power BI Now for the part that actually taught me something. Step 1 : Grab your connection details Everything you need lives on Aiven's Overview page: Host, Port, Database name, User, SSL mode. Your service URI will look something like this (don't worry, this isn't a real password, Aiven masks it in the console): postgres : // avnadmin : •••••••• @ pg - xxxxxxxx - yourproject . c . aivencloud . com : 22016 / defaultdb ? sslmode = require Step 2 : Import the dataset into Aiven Same DBeaver wizard as before, just pointed at the Aiven connection instead of local. CREATE SCHEMA powerbi ; Step 3 : Aiven's certificate. Download the CA cert from the Overview page: Now here's the part that actually tripped me up: Power BI's PostgreSQL connector doesn't ha

Every application we use today—from banking apps to social media platforms—has something working behind the scenes. That hidden engine is called the backend. The backend is responsible for processing requests, storing data, handling authentication, enforcing business rules, and ensuring everything works as expected when users interact with an application. One of the first decisions backend developers make is choosing a framework. A framework provides the tools, structure, and best practices needed to build applications faster and more securely. Today, let's look at three popular backend frameworks: Django, Gin, and Ruby on Rails. Django (Python) Django is one of the most mature and feature-rich backend frameworks available. Built using Python, it follows the philosophy of "batteries included." This means many features developers need are already built into the framework, including: User authentication Admin dashboard Database ORM Security protections URL routing Form validation Because so much comes ready to use, developers can spend more time solving business problems instead of rebuilding common features. Best for: Content management systems E-learning platforms Business applications APIs Startups building products quickly Advantages: Fast development Excellent security features Large community Extensive documentation Scales well for many applications Trade-offs: The framework includes many components, so it can feel heavier than minimalist frameworks. Gin (Go) Gin is a lightweight web framework built for the Go programming language. Unlike Django, Gin keeps things minimal. It gives developers speed and flexibility while letting them choose many of the additional tools they want to use. One reason many developers enjoy Gin is its impressive performance. Since Go is a compiled language designed for concurrency, Gin can efficiently handle many requests simultaneously while using relatively few system resources. Best for: REST APIs Microservices High-performance syst

If you build apps for jewelers, fintech dashboards, or e-commerce price automation, you eventually need one thing: reliable, low-latency gold and currency prices . Scraping fragile sources breaks constantly. A dedicated price API solves this. In this post I'll show how to consume real-time gold (gram, quarter, coin) and FX rates over both REST and WebSocket (Socket.IO) using the Hasfiyat Gold & Currency API . Why a price API instead of scraping? Stability — a documented contract instead of HTML that changes without notice. Low latency — prices are pushed as the market moves, not on a slow cron. Multiple sources with failover — if one provider drops, the feed keeps flowing. 1. Polling with REST The simplest integration: request the prices you need with your API key. curl -X GET \ 'https://api.hasfiyat.com/api/prices?symbols=HAS,GRAM,CEYREK' \ -H 'Authorization: Bearer YOUR_API_KEY' \ -H 'Accept: application/json' // Node.js const res = await fetch ( " https://api.hasfiyat.com/api/prices?symbols=HAS,GRAM,CEYREK " , { headers : { Authorization : " Bearer YOUR_API_KEY " } } ); const data = await res . json (); console . log ( data ); REST is ideal for periodic reporting, server-side jobs, and updating e-commerce product prices. 2. Live updates with Socket.IO For price screens, signage, and mobile apps where every tick matters, keep a connection open and let the server push changes: import { io } from " socket.io-client " ; const socket = io ( " https://api.hasfiyat.com " , { auth : { token : " YOUR_API_KEY " } }); socket . on ( " gold_prices " , ( data ) => { // { symbol: "HAS", type: "Has Altın", buy: 2450.85, sell: 2455.10, timestamp: "14:32:01.045" } console . log ( data ); }); No polling, no hammering the server — each market move arrives instantly. 3. A minimal live ticker in the browser <div id= "gold" ></div> <script src= "https://cdn.socket.io/4.7.5/socket.io.min.js" ></script> <script> const socket = io ( " https://api.hasfiyat.com " , { auth : { token : " YOUR

If you have ever wired up a cellular modem, a WiFi module, or a Bluetooth radio and typed something like AT+CGMR into a serial terminal, you have used a command language that is older than most of the engineers using it. The humble AT command set that still configures a huge share of today's connected hardware was born in 1981 , with a device called the Hayes Smartmodem. Four decades and billions of devices later, it refuses to die, and that longevity has a lesson in it for anyone building embedded systems. What AT actually stands for When Dennis Hayes and his company released the Hayes Smartmodem 300 in 1981, they faced a small but real design problem: how does a computer tell a modem the difference between a command to the modem and data to be sent down the phone line ? Their answer was an attention sequence. Every command line began with the two letters AT , short for attention , which told the modem to wake up and listen to what followed. ATD dialled a number, ATH hung up, and so on. It was readable, it was easy to implement on the microcontrollers of the day, and crucially you could type it by hand to debug a link. That simplicity is exactly why it spread. Competing modem makers cloned the Hayes command set to stay compatible, it became a de facto industry standard, and later it was formally captured in telecom standards. A convention that started as one company's pragmatic shortcut turned into the lingua franca of getting a device onto a network. From phone lines to the Internet of Things Here is the part that surprises people. The AT command set never retired when dial-up modems did. It quietly migrated into the components that make modern IoT possible. Cellular modules that put a device on a 4G or LTE network, from vendors like Quectel, SIMCom, and u-blox, are almost universally driven by AT commands. Classic Bluetooth and many WiFi modules expose an AT interface too. Even the ESP8266 and ESP32, the microcontrollers behind an enormous number of hobby and com

Two hundred and fifty years after the signing of the Declaration of Independence, a new commercial asks: What if the Founding Fathers had access to Google Workspace?

I am joining a small ~10 person startup in SF next week as a full time W2 employee. I am a software dev. The work is in a very regulated space (ITAR/ SOC2/etc) type stuff. My manager (who is the CTO) texted me to bring my personal laptop in on the first day. This already seems like a red-flag and if needed I will not install any software or visit any sites beyond payroll. How should I handle this situation if they refuse to provide me equipment. This is a in-person role.

I’ve started working on my own Selfhosting Cloud Service, 2 Weeks work to get the basics done. Programming in Golang, understanding Docker, Caddy and how to use a terminal to do commands 😅 I’ve included AES-256 decryption for datas, a license key to check if a license is valid. Its own right-wing administration. All in one to install all dependencies at install. Any idea what could add more safety, or features I didn’t planned myself actually? submitted by /u/CaptainPM-Ger [link] [留言]