Your app, ai demo, useful utility or dashboard doesn't need to live in isolation on your laptop. If you think someone else might benefit from seeing or using it and they are relatively straightforward code bases it's very easy to deploy them to the web so that anyone can access them. In my recent Towards Data Science article I demonstrate 3 different platforms where you can do this - all of them completely for free. The link is in the first comment.

AI is great at explaining the world. It shouldn't be the one inventing your data. If your app, agent, or workflow depends on live information, connect it to the web, not just an LLM. Scrapingdog fetches real-time data from websites while handling proxies, CAPTCHAs, browser rendering, and IP rotation for you.

Built and shipped an AI search app to my own phone this week — ask a question in plain language, get a synthesized answer with cited sources and a knowledge graph, instead of a list of links. Under the hood: a RAG pipeline with live web search, a secured backend (no API keys on the device, rate limiting, daily usage caps), crash + analytics monitoring, and a signed, installable Android build. The real lesson: the last mile — security, cost control, monitoring, signing — is where a prototype actually becomes a product. #buildinpublic #AI #mobiledev

I’m excited by how quickly app development is evolving. Our latest Power Apps updates bring AI-assisted experiences that can generate pages from prompts and populate forms from documents, which could help developers focus more on solving problems and less on setup. From Microsoft Mechanics: https://msft.it/6043vAgMv #MSFTAdvocate #M365Copilot #CopilotCowork #CopilotStudio #AzureFoundry #AI #Agents #MicrosoftSecurity

Want to save your company 90% on your AI bill? Safely test GLM 5.2 in production with Inference's new gateway. 1. Install Inference Gateway (http://docs.inference.net) 2. Keep sending traffic to your current provider 3. Gateway automatically starts sorting through your live data using an RLM to generate evals for your app. This takes ~24 hours. 4. Gateway starts mirroring live traffic to GLM 5.2 to run evals. Traffic is only mirrored - you’re still using your old provider in prod. 5. Once evals look healthy, you get a Slack notification letting you know it’s safe to switch. 6. Switch model identifier in your code to “glm-5.2” You may have just saved 90% on your monthly token bill, and you own your LLM stack end to end. Tell me if you try it and, if so, how much you save.

Many LLM API requests fail... due to avoidable errors. We are publishing some numbers about the most common 4xx errors on Manifest: our base of 1.5M error logs shows that: - Rate limit (49%) is still the most common and avoidable error - Model not found (12.5%) occurs a lot as models come and go quickly - Malformed parameter/structure/tool schema (9% total) follows - Context length exceeded (2%) tends to grow with time Those errors cause production downtime and harm to AI applications and workflows. Make sure you control them. BTW 🤫 we are gathering a small group of power users to test our auto-fix platform that fixes requests in real time so your app stays up, join here https://lnkd.in/emJf-Nx7

This week at Parley Labs Offline-first isn't really a feature. It's a statement about who owns the compute. We encourage you to install our PWA for a better desktop experience. #heyparley #AI

Most "AI apps" are just a browser tab pointed at someone else's GPU. Close the lid on a plane and you've got nothing. This week Parley started working with no internet at all. Install it like a native app. Go offline. Keep generating — the on-device models run on your own GPU, so there's no network round trip and nothing leaves your machine. The hard part wasn't offline mode. It was the accounting. If it works offline, what stops usage from being infinite? → Offline generations meter against a local ledger — your balance visibly drops → At zero, generation stops until you top up → The moment you reconnect, the queue settles itself The piece I'm most pleased with: settlement is idempotent. Every offline generation carries its own ID, and the receipt is written before the charge. Flaky reconnect, laptop closed mid-sync, duplicate network event — you cannot be double-charged. The worst case is that we under-charge you. That's the correct direction for that bug to fall. Two things we refused to compromise on: 1. Offline can't strand you. The app always prefers the network when it exists and only falls back to cache when it doesn't. A cached shell can never trap you on a dead version of the app. 2. Your downloaded models are sacred. Cache cleanup is scoped so it can never wipe the model weights you already pulled down — because recovering from that needs exactly the thing you don't have. One honest caveat: you need to be online once, to install the app and download the model. After that you're free. Offline-first isn't really a feature. It's a statement about who owns the compute.

Founders who built with Lovable / Bolt / Cursor / Replit / v0 — honest question: When you shipped, did anyone (human or AI) actually review the code for security before real users signed up? No judgment either way. I'm genuinely curious what % of AI-built apps get any review at all before launch. My guess from what I've seen: under 20%. Prove me wrong 👇

Founders who built with Lovable / Bolt / Cursor / Replit / v0 — honest question: When you shipped, did anyone (human or AI) actually review the code for security before real users signed up? No judgment either way. I'm genuinely curious what % of AI-built apps get any review at all before launch. My guess from what I've seen: under 20%. Prove me wrong 👇