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I built a personal finance dashboard using Claude

It started as a simple Python script. Now it’s a full-stack app that brings all your investments into one place — stocks, mutual funds, physical gold, fixed deposits, and more. Entirely runs on my spare PC and served via Cloudflare Tunnel. https://metron.thecoducer.com/ Here’s the part I care about the most. It doesn’t just show what you own. It shows what you’re actually exposed to. It breaks that down, so before you buy a stock, you can see if you’re already overexposed to it through your funds. It can also parse your CAMS CAS statement and show you detailed transaction insights. A few things worth knowing: - Your data stays with you — everything is stored in your own Google Sheets on your Google Drive. No databases used. - You can sync holdings via Zerodha or add them manually - NSDL/CDSL CAS support is coming soon This project is part of my personal learning journey to explore what it really means to build a full system with AI, not just a toy app. While AI was helpful, it still struggles with writing clean, modular code and designing scalable systems. Getting things right required a lot of iteration and careful prompting. That said, the process was genuinely fun and eye-opening. If you try it out, I’d genuinely love your feedback, especially what feels missing or broken. submitted by /u/tenantoftheweb [link] [comments]

I open-sourced 31 AI prompts that turn a visiting card into a full credit due diligence — built by a banker using Claude, not by a developer

17+ years in MSME credit underwriting at banks in India. Not a developer. Can't write a single line of code from scratch. Just a domain guy who got tired of watching the same problem repeat. The problem: Credit teams in banks receive a visiting card from the sales team. Then they spend 3-4 weeks collecting 47 documents — balance sheets, stock statements, CMA data, CA certificates, ITRs, property papers. Only after all that, someone discovers the borrower has an NCLT case. Or a cancelled GST. Or three cheque bounce cases. The proposal gets declined after weeks of wasted effort. Or worse — it gets sanctioned because nobody checked. Most of these red flags are publicly discoverable on Day 1. From a visiting card. What I built: 31 prompts across 10 categories that extract maximum intelligence from just 5 inputs off a visiting card — company name, city, GSTIN (India's tax ID), director name, and DIN (director identification number). Categories: entity verification, director/promoter background checks, NCLT/insolvency search, market reputation, GST turnover analysis, credit rating, group entity mapping, shell company detection, sector risk, and a final go/no-go memo. These prompts work across any LLM — ChatGPT, Claude, Gemini, Perplexity, Copilot. No proprietary tool needed. Just copy, paste, investigate. How I built it: I'm not a coder. I built the entire tool — the prompt library, the React app, the constitution-based logic, and the GitHub Pages deployment — through a conversation with Claude (Anthropic's AI). I described the credit workflow, the due diligence dimensions, the nuances of Indian banking regulations, and Claude helped me structure the prompts and build the web interface. A domain expert with 17 years of credit knowledge + an AI that can code = a working product in one sitting. No bootcamp. No developer hired. No framework learned. That's the real story here. Not just the tool — but what's now possible when deep domain expertise meets AI. Single HTML file. No backend. No database. No login. No cost. 👉 Live tool: https://igmuralikrishnan-cmd.github.io/credit-dd-prompt-generator/ 👉 GitHub repo: https://github.com/igmuralikrishnan-cmd/credit-dd-prompt-generator Why I'm sharing here: MSME lending in India is a $300B+ market. 63 million MSMEs. Most are underserved because the credit appraisal process is slow, manual, and document-heavy. If prompts like these can compress the first stage of due diligence from 3 weeks to 30 minutes — that's a meaningful unlock. I'm not building a startup around this (yet). Just putting it out there for the lending ecosystem. Would love feedback on: Do similar prompt-based pre-screening tools exist in other lending markets? Would this concept translate to SME lending in the US/UK/SEA? Any non-developers here who've built domain tools using Claude or other AI? What was your experience? submitted by /u/Infinite-Voice-2896 [link] [comments]

Workarounds for Using Google Tasks with Cowork

Hello all. I am trying to figure out a way to get Google Tasks to work better with Cowork. My company works exclusively in Google Workspace, and I find the native integrations of Google Tasks in my Gmail to be simple and convenient. However, when I try to ask Cowork to read my to-do lists in Tasks, it just can't do it (despite it working well with Docs, Sheets, Slides, etc.). I've tried to use Zapier to transfer my Tasks to-do list to either a Google Doc or a Google Sheet, but I've not been able to perfect that yet (consider that a lack of skill, not an indication that it wouldn't work). So, do any of you have any suggestions on a workaround to help me to get Cowork to see my to-do lists in Tasks? If so, could you share them with me in a way that aligns with the skill level indicated by my struggles with Zapier? If not, any suggestions on a to-do app that plays nicely with both Cowork and Gmail? I tried Todoist, but the extension just wouldn't work on Chrome. Any suggestions/help would be much appreciated. submitted by /u/Natural_Place_4717 [link] [comments]

I just joined a band and knew my messy notes doc wasn't going to cut it... so I described the problem to Claude Code and it built me a production web app. The catch? The refinements that made it worth using still needed a human.

I'm a tech worker who recently joined their first band after playing music at home mostly solo my whole life. First real problem: figuring out what songs we all actually know so we could start building a shared repertoire. I knew immediately my messy abbreviated notes doc wasn't going to cut it. So I described the problem to Claude Code and together we built SetForge: a full React app, deployed to Vercel, live and ready to be used (for free!) by real users at setforge.live. I didn't write the architecture. I didn't scaffold the project. I just described what I needed. What it does: Jam Set: the feature that started it all. Import your library, share a collab link with your bandmates, and SetForge finds the songs you all know and builds a starting set you could jam on right away from the overlap. The whole reason this exists. Excel/CSV import: SheetJS, flexible column mapping, same dedup logic Flow scoring: grades your setlist: 60% transition score (energy + key distance) + 40% arc score. Does your peak land in the right window? Do you close strong? Only appears when songs are tagged — no fake data. Auto-arrange: 5 modes: Wave, Slow Build, Front-loaded, Smooth Keys, Drama Arc. Segment-aware, respects Opener/Closer category tags, undo via toast. Gig Mode: full-screen per-song view, lyrics pulled live from lrclib.net with auto-scroll, break countdown timer, speed control Collab links: bandmates edit the same setlist in real time via /c/:token. No auth, no accounts - the UUID is the "account." Smart paste parser: handles raw UG favorites dumps, messy "Artist - Title" lists, tab URLs. Deduplicates against your existing library automatically. Print view + CSV export +more... Going through this end-to-end showed me the honest part not many talk about: The scaffolding was fast. Features were built and deployed with ease. But the refined experience it became today took real time to realize fully. I manage a UX team for my day job and this is where my thinking shifted and I started to see the direction everything will move in from here. Claude builds the thing. It does not feel the thing. Micro-interactions that are technically correct but awkward on an iPhone mid-gig, drag behavior that works but doesn't respond the way a hand expects - none of that surfaces in a spec. It surfaces when a real person uses it in a real context. I found I wasn't spending less time on UX at all. I was spending better, more productive time on it. Instead of debugging layout logic or getting the pixels to align, I was validating intent. Does this gesture feel like what the user means? Does this flow match what a musician needs at 9pm on a dark stage? That's the work that matters, that makes a difference, and these tools gave me the space to do it. The result is a more complete product than I would have shipped building it top-to-bottom myself, because I honestly wouldn't have been able to build this myself having never coded professionally before - but I knew what would might make a great experience for musicians (or at least good enough for me to use for anything I might need!). For anyone in a design or product role worried these tools are coming for your job: they're not... but people who know how to use them effectively will. AI is removing the barrier to entry - the parts of the job that were never the valuable part. The judgment about what a user actually experiences is still entirely human and that's the part that makes something worth using. Rather than feeling scared and uncertain about the future, I feel optimistic this pivot to refining validated intent-led design will actually end up bringing us closer to what made us love design and creative thinking in the first place. Curious if others here in UX or product have landed in the same place after actually shipping something end-to-end with Claude. https://preview.redd.it/9ylnc0meqfsg1.png?width=1255&format=png&auto=webp&s=3de05e9e627e60230a5a50cfb5d462159571025a submitted by /u/jonnybreakbeat [link] [comments]

I vibe-coded a full WC2 inspired RTS game with Claude - 9 factions, 200+ units, multiplayer, AI commanders, and it runs in your browser

I've been vibe coding a full RTS game with Claude in my spare time. 20 minutes here and there in the evening, walking the dog, waiting for the kettle to boil. I'm not a game dev. All I did was dump ideas in using plan mode and sub agent teams to go faster in parallel. You can play it here in your browser: https://shardsofstone.com/ What's in it: 9 factions with unique units & buildings 200+ units across ground, air, and naval — 70+ buildings, 50+ spells Full tech trees with 3-tier upgrades Fog of war, garrison system, trading economy, magic system Hero progression with branching abilities Procedurally generated maps (4 types, different sizes) 1v1 multiplayer (probs has some bugs..) Skirmish vs AI (easy, medium, hard difficulties + LLM difficulty if you set an API model key in settings - Gemini Flash is cheap to fight against). Community map editor LLM-powered AI commander/helper that reads game state and adapts in real-time (requires API key). AI vs AI spectator mode - watch Claude vs ChatGPT battle it out Voice control - speak commands and the game executes them, hold v to talk. 150+ music tracks, 1000s of voice lines, 1000s of sprites and artwork Runs in any browser with touch support, mobile responsive Player accounts, profiles, stat tracking and multiplayer leaderboard, plus guest mode Music player, artwork gallery, cheats and some other extras Unlockable portraits and art A million other things I probably can't remember or don't even know about because Claude decided to just do them I recommend playing skirmish mode against the AI right now :) As for map/terrain settings try forest biome, standard map with no water or go with a river with bridges (the AI opponent system is a little confused with water at the minute). Still WIP: Campaign, missions and storyline Terrain sprites need redone (just leveraging wc2 sprite sheet for now as yet to find something that can handle generating wang tilesets nicely Unit animations Faction balance across all 9 races Making each faction more unique with different play styles Desktop apps for Mac, Windows, Linux Built with: Anthropic Claude (Max plan), Google Gemini (sprites/artwork), Suno (music), ElevenLabs (voice), Turso, Vercel, Cloudflare R2 & Tauri (desktop apps soon). From zero game dev experience to this, entirely through conversation. The scope creep has been absolutely wild as you can probably tell from the feature list above. Play it, break it, tell me what you think! submitted by /u/Alarmed_Profit1426 [link] [comments]

Claude Code built its own software for a little smart car I'm building.

TLDR: Check out the video # Box to Bot: Building a WiFi-Controlled Robot With Claude Code in One Evening I’m a dentist. A nerdy dentist, but a dentist. I’ve never built a robot before. But on Sunday afternoon, I opened a box of parts with my daughter and one of her friends and started building. Next thing I know, it’s almost midnight, and I’m plugging a microcontroller into my laptop. I asked Claude Code to figure everything out. And it did. It even made a little app that ran on wifi to control the robot from my phone. --- ## The Kit A week ago I ordered the **ACEBOTT QD001 Smart Car Starter Kit.** It’s an ESP32-based robot with Mecanum wheels (the ones that let it drive sideways). It comes with an ultrasonic distance sensor, a servo for panning the sensor head, line-following sensors, and an IR remote. It’s meant for kids aged 10+, but I’m a noob, soooo... whatever, I had a ton of fun! ## What Wasn’t in the Box Batteries. Apparently there are shipping restrictions for lithium ion batteries, so the kit doesn’t include them. If you want to do this yourself make sure to grab yourself the following: - **2x 18650 button-top rechargeable batteries** (3.7V, protected) - **1x CR2025 coin cell** (for the IR remote) - **1x 18650 charger** **A warning from experience:** NEBO brand 18650 batteries have a built-in USB-C charging port on the top cap that adds just enough length to prevent them from fitting in the kit’s battery holder. Get standard protected button-top cells like Nuon. Those worked well. You can get both at Batteries Plus. *One 18650 cell in, one to go. You can see here why the flat head screws were used to mount the power supply instead of the round head screws.* ## Assembly ACEBOTT had all the instructions we needed online. They have YouTube videos, but I just worked with the pdf. For a focused builder, this would probably take around an hour. For a builder with ADHD and a kiddo, it took around four hours. Be sure to pay close attention to the orientation of things. I accidentally assembled one of the Mecanum wheel motors with the stabilizing screws facing the wrong way. I had to take it apart and make sure they wouldn’t get in the way. *This is the right way. Flat heads don’t interfere with the chassis.* *Thought I lost a screw. Turns out the motors have magnets. Found it stuck to the gearbox.* *Tweezers were a lifesaver for routing wires through the channels.* *The start of wiring. Every module plugs in with a 3-pin connector — signal, voltage, ground.* *Couldn’t connect the Dupont wires at first — this connector pin had bent out of position. Had to bend it back carefully.* *Some of the assembly required creative tool angles.* *The ultrasonic sensor bracket. It looks like a cat. This was not planned. It’s now part of the personality.* ## Where Claude Code Jumped In Before I go too much further, I’ll just say that it would have been much easier if I’d given Ash the spec manual from the beginning. You’ll see why later. The kit comes with its own block-programming environment called ACECode, and a phone app for driving the car. You flash their firmware, connect to their app, and drive the car around. But we skipped all of that. Instead, I plugged the ESP32 directly into my laptop (after triple-checking the wiring) and told my locally harnessed Claude Code, we’ll call them Ash from here on out, to inspect the entire build and talk to it. *The ACEBOTT ESP32 Car Shield V1.1. Every pin labeled — but good luck figuring out how the motors work from this alone.* *All the wiring and labeling. What does it all mean? I've started plugging that back in to Claude and Gemini to learn more.* **Step 1: Hello World (5 minutes)** Within a few minutes, Ash wrote a simple sketch that blinked the onboard LED and printed the chip information over serial. It compiled the code, flashed it to the ESP32, and read the response. It did all of this from the CLI, the command-line interface. We didn’t use the Arduino IDE GUI at all. The ESP32 reported back: dual-core processor at 240MHz, 4MB flash, 334KB free memory. Ash got in and flashed one of the blue LED’s to show me it was in and reading the hardware appropriately. NOTE: I wish I’d waited to let my kiddo do more of this with me along the way. I got excited and stayed up to midnight working on it, but I should have waited. I’m going to make sure she’s more in the driver’s seat from here on out. *First sign of life. The blue LED blinking means Ash is in and talking to the hardware.* **Step 2: The Motor Mystery (45 minutes)** This next bit was my favorite because we had to work together to figure it out. Even though Ash was in, they had no good way of knowing which pins correlated with which wheel, nor which command spun the wheel forward or backwards. Ash figured out there were four motors but didn’t know which pins controlled them. The assembly manual listed sensor pins but not motor pins, and ACEBOTT’s website was mostly

Claude AI Cheat Sheet

Most people use Claude like a chatbot. But Claude is actually a full AI workspace if you know how to use it. I broke the entire system down in this Claude AI Cheat Sheet: Claude Models Use the right model for the job. • Opus 4.5 → Hard reasoning, research, complex tasks • Sonnet 4.5 → Daily writing, analysis, editing (best default) • Haiku 4.5 → Fast, cheap tasks and quick prompts All models support 200K context, which means you can feed large documents and projects. Prompting Techniques The quality of your output depends on the structure of your prompt. Some of the most effective techniques: • Role playing • Chained instructions • Step-by-step prompting • Adding examples • Tree of thought reasoning • Style-based instructions The best combo usually is: Role + Examples + Step by Step. Role → Task → Format Framework One of the simplest ways to improve prompts. Example structure: Act as [Role] Perform [Task] Output in [Format] Example: Act as a marketing expert Create a content strategy Output in a table or bullet points Prompt Learning Methods Different prompt styles produce different outputs. • Open ended → broad exploration • Multiple choice → force clear decisions • Fill in the blank → structured responses • Comparative prompts → X vs Y analysis • Scenario prompts → role based thinking • Feedback prompts → review and improve content Prompt Templates You can dramatically improve results using structured prompting. Three core styles: • Zero shot → no examples • One shot → one example provided • Few shot → multiple examples More examples usually means better outputs. Projects Projects turn Claude into a knowledge workspace. You can: • Upload files as knowledge • Organize chats by topic • Add custom instructions • Share with teams • Maintain long context across work Artifacts Artifacts allow Claude to generate interactive outputs like: • Code • Documents • Visualizations • HTML or Markdown apps You can read, edit, and run them directly inside the chat. MCP + Connectors MCP (Model Context Protocol) connects Claude to external tools. Examples: • Google Drive • Gmail • Slack • GitHub • Figma • Asana • Databases This allows Claude to work with real data and workflows. Claude Code Claude can also act as a coding agent inside the terminal. It can: • Read entire codebases • Write and test code • Run commands • Integrate with Git • Deploy projects Reusable Skills + Hooks Claude supports reusable markdown instructions called Skills. Plus automation hooks like: • PreToolUse • PostToolUse • Stop • SubagentStop These help control workflows and outputs. Prompt Starters Some prompts work almost everywhere: • “Act as [role] and perform [task].” • “Explain this like I am 10” • “Compare X vs Y in a table.” • “Find problems in this document.” • “Create a step-by-step plan for [goal].” • “Summarize in 3 bullet points.” Study the cheat sheet once. Your prompting will immediately level up. submitted by /u/Longjumping_Fruit916 [link] [comments]

Noob here, trying to build with Claude Code

Hey folks, I’m trying to build a personal web app with Claude. I’m figuring it out as a I build but in terms of making my app functional, I’m hitting a deadlock in terms of prompting. I have gone back, built a UI separately with Figma and a detailed spec sheet but when it comes to actually figuring out the tech part, it’s not really able to figure out and make it functional. How do I go past this? Is there a resource I can read about tools/plugins/platforms I need to separately sign up on. The max Claude Code has done is made me sign up on Railway, that’s it. Thanks in advance! submitted by /u/tiramisu_lover18 [link] [comments]

I built an open-source MCP server that gives Claude a full Linux desktop — mouse, keyboard, screenshots, shell (GIF demo inside)

Most AI agents are trapped in text. They can call APIs, but they can't use software. GhostDesk fixes that. It's an open-source MCP server that spins up a virtual Linux desktop inside Docker and gives Claude (or any LLM) 11 tools to interact with it. What Claude can do with it The demo above: Claude scrapes Amazon laptops, extracts product data, populates Google Sheets, and builds a chart — autonomously, no Selenium, no CSS selectors, no fragile scripts. Other examples: - Browse the web like a human — bypasses bot detection with Bézier mouse curves - Operate legacy software with no API (that old Java app? The internal admin panel from 2010?) - Run QA tests and take screenshots as evidence - Multi-app workflows: browser → terminal → IDE → email, all in one conversation The 11 tools Tool What it does screenshot() See the screen mouse_click(), mouse_drag(), mouse_scroll() Full mouse control type_text(), press_key() Keyboard exec() Run shell commands launch() Start any GUI app get_clipboard() / set_clipboard() Clipboard access Quick start bash docker run -d --name ghostdesk \ -p 3000:3000 -p 6080:6080 \ ghcr.io/yv17labs/ghostdesk:latest Then add to your Claude config: json { "mcpServers": { "ghostdesk": { "type": "http", "url": "http://localhost:3000/mcp" } } } Watch your agent work live at http://localhost:6080/vnc.html GitHub: https://github.com/YV17labs/GhostDesk Happy to answer questions! submitted by /u/Every-Ad8608 [link] [comments]

there is why sora is taking down

The $5.4 Billion Mirage: The Brutal Economics Behind the Sora Shutdown We often ask "Why?" when a platform as revolutionary as Sora begins to aggressively scale back or restrict its features. But to find the answer, we must stop looking at the technology and start looking at the balance sheet. OpenAI is no longer just a research laboratory; it is a massive corporate machine navigating an unprecedented cash burn. Behind every "innovation" and every "downgrade" stands a team of financial experts and risk assessors whose job is to determine if a project is viable. The reality of Sora is not a technical failure—it is a brutal collision between bleeding-edge ambition and the cold, hard laws of unit economics. Here is the factual breakdown of why Sora hit a wall: The Staggering Cost of Compute: A $15 Million Daily Burn People wonder why ChatGPT is so expensive to run compared to other platforms, but AI video generation is on an entirely different spectrum of cost. The "compute" required to generate high-fidelity video is an absolute resource sinkhole. * The Per-Video Cost: Analysts at financial firm Cantor Fitzgerald estimated that generating a single 10-second Sora clip costs OpenAI approximately $1.30 in pure computing power (requiring roughly 40 minutes of total GPU time). * The Annual Deficit: Extrapolating this to millions of users, Forbes estimated that operating the Sora infrastructure was burning through roughly $15 million every single day. That translates to an annualized cost of over $5.4 billion for one single product. * The Subscription Flaw: Even hidden behind a $200/month "Pro" paywall, the math fails. If a power user generates just 20 videos a day, they cost the company over $700 a month in server compute. There is currently no consumer subscription model that makes this "worthy" without OpenAI actively losing money on every generation. The "30 to 10" Sacrifice: A Move for the Fans that Backfired The decision to heavily restrict daily generation limits and reduce video duration down to 10 seconds wasn't a creative glitch—it was a tactical sacrifice made for the community. Faced with "completely unsustainable" economics, OpenAI tried to stretch their server capacity so the general fan base could still experience the platform. However, the strategy was immediately exploited. The moment access was granted, the number of "alt" accounts (secondary accounts used to bypass limits) exploded. Users essentially siphoned the compute power faster than the servers could process it. OpenAI’s financial team had to step in: the choice was either to shut it down or watch the company bleed billions. The Macro Financial Crisis of AI To understand Sora's fate, you have to look at OpenAI's broader financial picture. Despite generating massive revenue, the company is operating at a historic deficit. * In 2024, reports indicated OpenAI lost roughly $5 billion. * By the first half of 2025, despite revenues soaring past $4.3 billion, their net loss widened to a jaw-dropping $13.5 billion, largely driven by the colossal cost of training and running these advanced models. Sora, as incredible as it is, was the most expensive drain on an already bleeding balance sheet. The Legal and Ethical Minefield Beyond the catastrophic server costs, there is the immediate threat of litigation. The rumors involving deepfakes and the unauthorized usage of notorious or deceased individuals’ likenesses have created a liability nightmare. OpenAI’s legal and financial experts know the score: "Take this down now, or we face copyright and defamation lawsuits with zero chance of winning." In a world of strict intellectual property laws, a platform heavily used for "meme culture" is a legal ticking time bomb. The Industry Proof: Look at Google Veo If you doubt the economic severity of this issue, look at the rest of the market. Google possesses one of the largest and most advanced server infrastructures on the planet. Yet, even Google heavily restricts its state-of-the-art video model, Gemini Veo 3. If you pay for the Google AI Pro tier, you are limited to a mere 3 generations per 24 hours in the Gemini app. These are short clips with virtually no advanced editing features. Why? Because even a multi-trillion-dollar giant like Google cannot absorb the energy and compute costs of unlimited AI video generation. Conclusion: A Masterpiece Ahead of Its Economy OpenAI likely intended for Sora to be a high-end, professional tool for enterprise advertising and marketing companies. Instead, the promotional rollout turned it into a consumer meme platform. When you combine $1.30 per-video generation costs, billions in annual burn rates, and the constant threat of lawsuits, the corporate mandate becomes obvious. The technology is God-tier, but our current hardware and economic models simply cannot support it. Welcome to the real world. submitted by /u/Either-Ad-5185 [link] [comments]

My first project with Claude. Doing this in free tier is killing me but here I am, Meet my Sassy Little Sister aka My Expense Tracker!

I am making use of the Telegram's bot system, Google's app script with google sheet and of course Claude to write the actual codes. Total newbie, don't know first thing about coding but with a simple idea. submitted by /u/AestheticDoodle [link] [comments]

In case you missed it! A list of our most popular how to build a #nocode app resources. No matter your industry or use case, you’ll discover helpful tips, template apps and troubleshooting suggestion

In case you missed it! A list of our most popular how to build a #nocode app resources. No matter your industry or use case, you’ll discover helpful tips, template apps and troubleshooting suggestions to take your development skills to the next level! https://t.co/gvgdOR8SoH

AppSheet Actions & Workflows can control the behavior of an app. In this Building with AppSheet episode, we explain the difference b/w Actions & Workflows, the three types of Actions, & h

AppSheet Actions & Workflows can control the behavior of an app. In this Building with AppSheet episode, we explain the difference b/w Actions & Workflows, the three types of Actions, & how you can utilize Actions to customize the behavior of your app. https://t.co/zHYCRtN0mD

Want to find out more about how customers are using AppSheet Automation? Check out the results from our recent survey! Learn more about the benefits & use cases as well as customer testimonials. #

Want to find out more about how customers are using AppSheet Automation? Check out the results from our recent survey! Learn more about the benefits & use cases as well as customer testimonials. #nocode #digitaltransformation https://t.co/Mz942xPCdi https://t.co/ditaQ1OXuF

Step by step! We walk you thru how to build a #nocode inventory management app from Google Sheets. Features include: barcode scanning of stock, automatic calculation of the current stock level, &

Step by step! We walk you thru how to build a #nocode inventory management app from Google Sheets. Features include: barcode scanning of stock, automatic calculation of the current stock level, & a restocking report. #digitaltransformation #appdevelopment https://t.co/63uytpgd8F?