标签：#Product

These WIRED-tested ebook readers let you take your library anywhere.

Your voice-supported AI financial planning partner Discussion | Link

If you're someone who needs to (or likes to) take their work on the go, a portable monitor will make a huge difference. These are my favorite that I tested.

Post to 11 social platforms in seconds, on one flat plan Discussion | Link

The first native Plex client for Nintendo Switch Discussion | Link

This impressively light carbon commuter makes switching to an ebike easier than ever.

The musician created his own line of loopers that record and layer riffs in a loop. The pricey Looper X does what it claims, but it isn’t without quirks.

Every tool sprawl thread I read starts with license math, and license math is a decoy. Last quarter I added up what our seven-tool delivery stack actually cost us, and the subscriptions came to about 15% of the total. The other 85% never appears on an invoice, which is exactly why nobody budgets for it and nobody fixes it. Some background so you can judge whether my numbers transfer to your team. I spent years building automation in banking before running my own product team, so I am professionally allergic to process waste. Despite that, our stack had drifted into the usual shape: Jira for tickets, Confluence for docs, Lucidchart for architecture, TestRail for test cases, two spreadsheets doing unpaid overtime in the gaps, and an AI chatbot bolted on the side that had never seen any of it. The licenses for all of that, for six people, ran about $700 a month. Annoying. Not a crisis. And that is precisely why the "consolidate your tools" pitch dies in so many budget conversations. Saving a few hundred dollars a month does not justify a migration, and everyone in the room knows it. If licenses were the real cost, I would side with the skeptics. The audit: two weeks of logging every re-key So we measured the part nobody measures. For two weeks, everyone on the team logged every re-key: any moment a human moved or restated information that already existed in another tool. Copying acceptance criteria from Confluence into a Jira ticket. Updating TestRail because a story changed shape. Redrawing a Lucidchart flow that had drifted from the code. Reassembling a status update by hand from three tabs. Pasting project context into the chatbot, again, because it forgot everything since yesterday. The rules were strict so the number would survive scrutiny. Log transfer time only, not thinking time. Round down when unsure. If the same fact got re-keyed twice, log it twice, because it cost twice. Each entry went into a shared CSV with four columns, and this script turned it into th

USD account & credit cards for freelancers & creators Discussion | Link

A gimbal camera that sees with you Discussion | Link

Block social media until you've actually slept Discussion | Link

proof-of-execution for AI agents Discussion | Link

AI coding tools are changing how software gets built. Claude Code, Cursor, GitHub Copilot, Windsurf and other tools can generate code incredibly fast. For small tasks, they are already useful: write a component, explain a function, scaffold an endpoint, create a test, refactor a file. But after using AI in real projects, one thing becomes obvious: The problem is no longer code generation. The problem is engineering control. Most AI coding workflows still look like this: text idea -> prompt -> code -> fix -> prompt again -> more code -> lost context -> start over It feels fast at the beginning. Then the project grows. Requirements change. Architecture decisions disappear inside chat history. The AI forgets previous context. You start acting as product manager, architect, reviewer, QA, DevOps, and prompt engineer at the same time. That is not software engineering. That is vibe coding. ## Vibe coding works until it doesn't Direct AI coding is great when the task is isolated. Ask for a React component. Ask for a SQL query. Ask for a utility function. Ask for a unit test. No problem. But real software is not a collection of isolated snippets. Real software has: - business rules - architectural constraints - existing patterns - security concerns - database impact - deployment requirements - edge cases - regression risk - long-term maintenance When AI jumps directly from prompt to code, it often skips the thinking that should happen before implementation. The result may compile. But does it fit the architecture? Does it respect the domain? Does it create hidden technical debt? Does it solve the right problem? That is the gap we are trying to close with N45.AI. ## What is N45.AI? N45.AI is a framework that turns AI coding tools into a structured engineering workflow. It works with the tools developers already use, including Claude Code, Cursor, GitHub Copilot, and Windsurf. The idea is simple: Instead of treating AI as one generic assistant, N45.AI organizes the work like a

It is a privacy-first, local-first photo organizer powered by deep learning face recognition. It detects, embeds, and groups faces to organize your photos automatically—all 100% offline. 🔥 Highlight Features: ✅ 100% Local: No cloud APIs, no telemetry, no leaks. ✅ Deep Learning: Driven by OpenCV DNN (YuNet + SFace ONNX models). ✅ Smart Automation: Copies matches, partial matches, and individual profiles into organized folders, complete with ZIP archives and JSON reports. ✅ Standalone EXE: Run it on Windows instantly with zero dependencies. ✅ Dynamic UI: Fully responsive Tailwind dashboard with Dark/Light modes. Check out the repository, download the EXE, or contribute: 👉 https://github.com/Shaan-alpha/face-sort-studio Let me know what you think! ⭐ machinelearning #computervision #python #localfirst #privacy #developers #opensource #ai Internet access on first launch only (to fetch the AI models ~40-50mb)

If you've ever tried to use an AI resume builder, you've probably hit the same wall I did. You sign up, poke around, find the one feature you actually need — and then boom: "Upgrade to Pro for $29/month." It's frustrating. Resume help shouldn't be locked behind a paywall. So I built my own. Meet Persona Persona is an AI-powered resume builder that you run completely on your own machine . No deployment required. No subscription. No account on some third-party service. You clone the repo, set it up, and it's yours. It's a fork of the excellent open-source project ResumeLM , but I've added a bunch of features I couldn't find anywhere else — especially around local AI and template variety. 👉 GitHub: github.com/nithiin7/persona (Drop a ⭐ if you find it useful!) The Big Deal: Run AI Completely Offline with Ollama This is the feature I'm most proud of. Most AI resume tools call out to OpenAI or Anthropic and charge you for every request. Persona supports Ollama — which means you can run the AI model locally on your own hardware, with zero API costs and zero data leaving your machine. Here's how simple it is: Install Ollama on your computer Pull any model ( ollama pull llama3 , for example) Open Persona's settings, point it to your local Ollama URL Done — the AI now runs entirely on your machine No OpenAI key. No Anthropic key. No usage limits. Your resume data never touches an external server. If you do want to use cloud models, Persona supports those too — GPT-5, Claude Opus 4.7, Claude Sonnet 4.6, and a handful of open-source models via OpenRouter. But the Ollama path is what makes this genuinely different from everything else out there. It's 100% Free — Everything Unlocked The original ResumeLM had Stripe payments baked in. I ripped all of that out. Every single feature in Persona is available to every user, always. There's no "Pro plan." There's no feature gating. You self-host it, you own it, you use all of it. 10 Resume Templates Persona ships with ten distinct templ

After 8 months of daily Claude Code use, I've distilled my workflow into a 5-layer system. Each layer builds on the previous one. Skip one, and the whole thing falls apart. The Problem with Most Claude Code Users Most people use Claude Code like ChatGPT — open terminal, ask a question, close, repeat. The next day, they explain their project from scratch. Again. The symptom: 20% of every session is wasted on context re-establishment. The root cause: No project memory, no workflow discipline. Here's the system that fixed it for me. Layer 1: CLAUDE.md — Your Project's Memory Anchor This is the foundation. Without it, nothing else works. CLAUDE.md is a file at your project root. Claude reads it automatically at the start of every session. It tells Claude: What this project is (one sentence) The tech stack (specific technologies, not "Python web framework") The architecture (the big picture you'd need 3 files to understand) Unique conventions (not generic advice like "write tests") Quality priorities Bad CLAUDE.md (you've probably written this): # My Project A web application built with Python and FastAPI. ## Development - Write clean code - Add unit tests - Use Git This tells Claude nothing it doesn't already know. Good CLAUDE.md: # CLAUDE.md ## Project Overview Internal RAG knowledge base serving 500+ employees. ## Tech Stack FastAPI + LangChain + Milvus + PostgreSQL + Redis ## Commands - Start: `uvicorn app.main:app --reload --port 8080` - Test: `pytest -x --cov=app --cov-report=term-missing` ## Architecture Request flow: router → service → retriever → Milvus → generator → LLM API Key directories: - app/router/ - API layer - app/service/ - Business logic orchestration - app/retriever/ - Retrieval strategies (vector/BM25/hybrid) - app/generator/ - LLM calls and prompt management ## Key Conventions - All APIs return `{"data": ..., "error": null}` - Retrieval results MUST include source field - Milvus collection naming: `{env}_{doc_type}` The rule: Only write what's uniq

AI support that learns your business and hands off to humans Discussion | Link

Save anything with a screenshot. Discussion | Link

The End of Vibe Coding: Why I Switched to Structured AI Workflows I spent 3 months "vibe coding" my SaaS. Then I realized I was spending more time fixing AI's mistakes than if I'd written it myself. Here's the system that changed everything. In early June 2026, two HN threads with a combined ~1,300 comments told me something had shifted. Thread 1 (~1,100 comments): "What was your 'oh shit' moment with GenAI?" Thread 2 (~230 comments): "What tools have you made for yourself since AI?" Both threads had the same pattern: people started with unfiltered excitement ("I built a whole app in one weekend!"), then hit a wall ("I'm spending more time fixing its bugs than writing code from scratch"). I know the feeling. I lived it. The Vibe Coding Trap When I started building MultiPost — an AI-powered cross-platform content tool — I was deep in "vibe coding" mode: Me: "Make it look better" AI: *adds Tailwind, restyles everything* Me: "Add a filter by date" AI: *adds a date picker, breaks the layout* Me: "Fix that bug where posts don't show" AI: *fixes the filter, introduces a null pointer* Me: "Okay now add a dark mode toggle" AI: *regenerates half the component from scratch* Three weeks later I had a working feature and zero understanding of how any of it actually held together. This was my daily rhythm for weeks. Fast output, slow cleanup. The ratio kept getting worse as the codebase grew. I was optimizing for speed of generation instead of speed of delivery . The "Oh Shit" Moment It came when I reviewed a feature I'd built entirely through unstructured AI sessions. The feature worked. But: The code had 3 different patterns for the same thing (Auth0 token handling in one place, hardcoded keys in another) Error handling was inconsistent — some functions returned null, others threw, others returned Result types Database queries were scattered across the codebase instead of in a repository layer A security reviewer would have cried The AI didn't do this maliciously. It did this

I Had 6 Side Projects Open in One Browser Window. Here's What That Was Costing Me. I counted once, on a normal Tuesday. 41 tabs, one window, six different side projects. A repo here, a localhost there, a Stripe dashboard, two Notion pages, a half-read Stack Overflow thread I was scared to close. I was using a tab manager to hold it all together. Save the session, restore it later, feel organized. It worked, in the sense that nothing got lost. But something was off, and it took me a while to name it. The tab manager was keeping my tabs. It was not keeping my projects. And the gap between those two things was quietly costing me. The number that bothered me I did a rough audit of one week. Every time I sat down to work on a project, I had to reconstruct where I was. Which task was next? When was that thing due? Where did I save that reference last month? The tabs were there, but the answers were not in the tabs. I timed it loosely. Five to ten minutes of "wait, where was I" at the start of every session, multiplied across six projects, multiplied across a week. Call it an hour, maybe more, spent just getting back to the surface before any real work started. An hour a week is not a catastrophe. But it was an hour spent doing something a tool should do for me, and the friction was enough that I started avoiding the projects with the most tabs. The cost was not really the time. It was that the heaviest projects felt the worst to open, so I opened them least. Why the tab manager could not fix this Here is the thing I had to admit. A tab manager is excellent at one job: saving and restoring tabs. It is not built to know anything about the project those tabs belong to. A tab is a URL. A project is a URL plus: A task that is due Friday A reference I saved three weeks ago and need again now A subscription renewing on the 14th A sense of what I actually shipped last time I worked on it When all of that lives outside the tab manager, in a to-do app, a notes file, my memory, rest