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Field-service software has an adoption problem: drivers won't use it. Heavy app, another login, crashes in low-signal areas. So the "real-time" data still shows up as end-of-shift phone calls. The fix that actually sticks: stop building an app and use the one drivers already live in — WhatsApp. With Apps Script and Google Sheets behind it, WhatsApp becomes a frictionless mobile ERP. Here's the build. WhatsApp as a data-entry terminal A driver texts Status ABC-1234 Delivered . An Apps Script doPost webhook receives it, parses it, and updates the Sheet in real time. Latency goes from hours to milliseconds — and there's nothing to install, so adoption hits 90%+ in a week (vs. 50–70% for custom apps). Two-stage parsing for messy input Real drivers type "done," not clean commands. So: Regex first pass — handles ~70% of messages (clean format) instantly and for free. LLM fallback — the remaining ~30% goes to a cheap model (GPT-4o-mini / Gemini Flash) with the known cargo IDs and valid statuses. It returns normalized JSON + a confidence score. Below-threshold messages surface to a dispatcher. The LLM normalizes correctly 95%+ of the time (~5% manual), and it handles multilingual input with zero extra code. Driver msg → Apps Script doPost → regex pass → (fail) LLM fallback w/ confidence score → Sheet update (timestamp + raw-message log) → optional outbound (route change, POD photo request) Why Google Sheets is the right backend Dependent formulas: time-to-delivery, SLA-breach flags Pivot tables for reporting Apps Script triggers for automatic client emails Conditional formatting dashboards Native Calendar / Maps / Drive integration (POD photos → Drive folder) It runs on free Google Workspace infrastructure with minimal API cost. Bidirectional by default The same integration pushes messages back to drivers: route changes, delivery instructions, shift reminders, exception alerts, proof-of-delivery photo requests — all in the same thread. Pitfalls that get your number banned T

This is a submission for Weekend Challenge: Passion Edition What I Built Soccer is defined by passion, but that passion often turns toxic when fans and commentators do not understand the limits of human performance under extreme pressure. During a 90-minute World Cup match, when a team collapses in the final ten minutes, the narrative defaults to harsh judgments like, "they lost their nerve." BioTactix AI was born out of a passion to change that global conversation. It is a securely licensed, real-time sports analytics architecture designed to solve the " Human-Machine Bottleneck. " By quantifying the exact intersection of physical exhaustion, cognitive delay, and psychological pressure, it transforms raw biological telemetry into context-aware, Explainable AI (XAI). Instead of relying on static dashboards, **BioTactix AI **provides real-time narratives to foster empathy among fans, actionable tactical alerts to prevent defensive collapses for coaches, and critical 14G-impact safety overrides for referees. Demo You can view the full demonstration and the real-time terminal output of the BioTactix AI Master Engine here: Watch the Demo on YouTube VIDEO LINK: https://www.youtube.com/watch?v=LQbuIVqc8D0 ** **Code The complete project, including the core biotactix_ai_master_engine.py script, is hosted publicly in github repository. The repository is fully secured with a software license to ensure the intellectual property and architectural blueprint remain protected. https://github.com/minakshihub/BioTactix-AI How I Built It Building a system to process 100-Hertz live biological data across a 40-man roster without compute bottlenecks required moving beyond standard web development approaches and leaning heavily into advanced storage systems engineering. Sovereign Edge Compute & VFS Routing: Instead of wasting CPU cycles continuously scanning the entire roster, the architecture leverages a custom Sovereign Virtual File System (VFS). This enables highly efficient data inge

Hi, my name's Ibrahim, I'm a university student, and I have a problem: I love fragrances way more than my bank account loves me for it. It started small, the way these things always do. A cheap Middle Eastern attar someone gave me as a gift, the kind that costs less than a coffee but somehow smells like it belongs in a much fancier bottle. Then another. Then I started actually reading about notes, pyramids, accords, sillage, the whole rabbit hole. Fast forward through a lot of saved-up allowance and skipped nights out, and I've now got about 20 bottles on my shelf. Mostly affordable Middle Eastern gems (some of them genuinely punch way above their price), with a small handful of designer pieces I saved up for and treat like trophies. If you're a fellow fragrance enthusiast, you already know the feeling: you don't just "wear" a scent, you collect them, you study them, you have opinions about whether a note is top, heart, or base and you will absolutely fight someone about it. That obsession is basically the entire reason this project exists. So when I saw the DEV Weekend Challenge's "Passion" prompt, there was only one thing I could possibly build. What I built: recommendmeafragrance recommendmeafragrance is a browser arcade for fragrance nerds: six small daily games built around real perfume data (notes, brands, years, price tiers), plus an AI Concierge you can actually talk to about what you're in the mood for. Every game feeds into a personal "shelf" that tracks which fragrances you've discovered, plus streaks so you have a reason to come back tomorrow. Here's the tour. 🧪 Scentle: Wordle, but for your nose A new fragrance is picked every day (the same one for everyone, worldwide, no matter your timezone). You get 6 guesses, and after each one you get Wordle style feedback: was the brand exact or just the same house family, did the real answer come out earlier or later than your guess, is it pricier or cheaper, same gender, same concentration, how many notes do you

Tokens and DAOs are often presented as simple ideas: issue a token, distribute ownership, let the community vote, and build a decentralized organization. In reality, the technical problems behind tokens and DAOs are much deeper. A token is not only an asset, and a DAO is not only a voting system. Together, they create an economic, governance, security, and coordination layer that must work reliably in a hostile, open environment. The first major problem is token design. Many projects treat token creation as a deployment task, but the real challenge is defining what the token actually controls. Does it represent governance power, protocol revenue, access rights, reputation, staking weight, or all of these at once? When one token is used for too many purposes, the system becomes fragile. For example, a token designed for liquidity may not be suitable for governance, because the most active traders may not be the most aligned decision-makers. Good token architecture should separate economic utility, governance authority, and long-term reputation where possible. The second problem is distribution. A DAO can be decentralized in branding but centralized in practice if token ownership is concentrated among founders, investors, or early insiders. On-chain governance depends heavily on voting power, so distribution directly affects decision quality. Poor distribution creates governance capture, where a small group can control treasury spending, protocol upgrades, or parameter changes. This is not only a social issue; it is a technical design issue. Vesting contracts, delegation systems, quorum rules, voting delay, and proposal thresholds all influence whether governance is resilient or easily manipulated. Another core issue is governance security. DAO voting is not automatically safe just because it happens on-chain. Token voting can be attacked through flash loans, bribery markets, vote buying, low-participation proposals, and governance fatigue. If a malicious proposal pas

This is a submission for Weekend Challenge: Passion Edition What I Built The World Cup...

This is a submission for Weekend Challenge: Passion Edition What I Built I built Passion...

This is a submission for Weekend Challenge: Passion Edition What I Built This weekend, I...

This is a submission for Weekend Challenge: Passion Edition What I Built This weekend, I...

This is a submission for Weekend Challenge: Passion Edition (#weekendchallenge #devchallenge #ai #googleai #gemini #webdev #showdev) What if your GitHub could roast you harder than your teammates ever would — and then remind you why you keep building? What I Built 🔥 roaster0 — an AI that roasts your GitHub profile, then redeems you. Drop in any public GitHub username and it pulls your real repo data — commit habits, abandoned projects, lazy repo names, language choices — and turns it into a savage, hyper-specific roast using Gemini's structured output and multimodal reasoning. Then it ends with one sincere, earned compliment pulled from something genuinely good in your data. The idea started from a simple thought: your GitHub is an involuntary diary of what you were obsessed with. The eleven repos with no description. The final-v2-FINAL commit. The side project you lived and breathed for three weeks in March before abandoning it. That's passion — messy, obsessive, usually invisible unless someone points a spotlight at it. There's also a second mode, 🎭 Roast Anything : submit a name, bio, links, and/or images, and Gemini reads all of it — text, links, photos — to generate the same experience for anyone, not just developers. Demo 🔗 Live app: roaster0.netlify.app Try it on any public GitHub username, or switch to Roast Anything mode and paste in a bio + an image to see the multimodal analysis at work. Once your roast is generated, you can: 🔊 Listen to it — full audio narration via Web Speech API, paced and pitched differently depending on roast intensity 🖼️ Download the card — every roast renders as a shareable PNG on HTML5 Canvas, ledger-paper aesthetic, ready to post 📋 Share the record — copy a formatted text version straight to clipboard for any platform A couple of examples from testing: GitHub mode — roasted DEV's own founder using nothing but his real public repo data: (screenshot: Ben Halpern roast card — graveyard count, repo names like oceanic-giraffe and test

Title: Week 13: a second team is now running an AI agent on atomic HTLC swaps. Here is what that validates. Tags: mcp, ai, cryptocurrency, blockchain For most of this spring, the map of the agent economy had a strange gap. Wallets to hold keys. Rails like x402 to move value. Marketplaces and reputation so an agent knows who to trust. And then, at the exact moment two parties settle a trade, a custodian: an escrow contract, an evaluator, a referee holding the money while a decision gets made. We have spent thirteen weeks arguing that the settlement layer does not need a referee, because a hash-time-locked contract can hold neither side and still guarantee the trade. This week, a second team shipped a live agent that makes the same argument in code. That is worth stopping on. The signal that mattered this week KaleidoSwap released KaleidoAgent, described as a self-sovereign trader agent on Bitcoin Layer 2s. It is fully non-custodial. It runs a Lightning and RGB wallet, executes atomic HTLC swaps on the KaleidoSwap DEX, runs DCA and portfolio strategies, manages Lightning channel liquidity, and acts as an interactive wallet assistant. The reasoning layer is an LLM (Claude or OpenAI) driving the kaleido CLI and the wallet primitives underneath. Read that list again through a settlement lens. An autonomous agent, deciding what to trade, and executing the trade over a primitive where no third party ever holds the funds. That is the exact shape of the thing we have been building. Different network, same bet. Why the mechanism is the same KaleidoSwap earlier completed what it described as the first atomic swap of an RGB asset on the Lightning Network mainnet, using tUSDT, an RGB20 version of USDT, over real Lightning channels. The detail that makes it atomic is the one that makes every HTLC atomic: The payment hash remains identical across both legs of the swap. Paying the wrapped invoice creates a Hash Time-Locked Contract in the Lightning channel, and the HTLC locks the p