标签：#Product

A contact table is easy to build. A usable CRM is an operations system. The difficult part is not storing a name and email address. It is preserving context as work moves between people, organizations, deals, tasks, notes, imports, reports, and follow-up. That requires deliberate data modeling and product decisions. Model relationships, not isolated records A customer may belong to an organization. An organization may have several contacts. A deal can involve multiple people, tasks, notes, and status changes. If those relationships are flattened into one spreadsheet-like table, duplicate data and contradictory updates appear quickly. Define stable identifiers and explicit relationships early. Treat activity history as a product feature Users need to know what changed, when it changed, and who changed it. Audit history supports troubleshooting and accountability. It also makes bulk operations safer: after an import or mass edit, an administrator should be able to understand the result rather than guessing which rows moved. Decide which actions deserve history, how long it is retained, and who can see it. Avoid collecting sensitive data simply because the schema allows it. Design imports for failure CSV import is where clean demos meet messy reality. A useful import flow should provide: column mapping; required-field validation; duplicate-handling rules; a preview before committing; clear row-level errors; an exportable error report; idempotent or recoverable behavior where practical. Never assume the first row contains perfect headers or that dates, phone numbers, and booleans use one format. Make views part of the workflow Saved filters and views let different roles focus on their work without changing the underlying data. A sales view may emphasize open deals and next actions. An operations view may emphasize overdue tasks. An administrator may need import history and permission context. This is more than UI convenience: it is a way to keep one shared system useful

Browser-based generative DAW Discussion | Link

The keyboard-first calendar to get organized quickly Discussion | Link

Google's smartest workhorse yet for coding & agents Discussion | Link

Free coding agents to kill Claude, Cursor, Replit, and Devin Discussion | Link

A question I've been thinking about after discussing AI coding agents with several developers: Is passing the test suite enough to prove that an AI agent made the correct engineering decision? I don't think it is. And this isn't just a theoretical concern. Modern coding agents are increasingly working at the repository level rather than generating isolated code snippets. OpenAI's Codex documentation, for example, describes using repository-specific AGENTS.md instructions to tell the agent how to navigate a codebase, run tests, and follow project practices. Anthropic similarly describes Claude Code searching codebases, tracing dependencies, editing multiple files, and working with CI failures. ( OpenAI ) That changes what "correctness" means. Consider a simple scenario A project starts with: Architecture v1 API ↓ Service ↓ Database An AI agent learns this structure and implements a new feature correctly. The tests pass. Then the architecture changes: Architecture v2 API ↓ Event Bus ↓ Services ↓ Database The same task is requested again. If the agent continues following the old architecture, its code might still: compile, pass existing tests, satisfy the visible functional requirement, but still be wrong for the current system . This is the distinction I'm interested in: Code correctness ≠ Contextual correctness The Benchmark Problem Traditional coding benchmarks generally provide: Repository + Issue ↓ Agent ↓ Patch ↓ Tests / Evaluation This is valuable. SWE-bench, for example, was designed around real GitHub issues and repositories, and OpenAI created SWE-bench Verified with human validation because benchmark quality itself affects what we conclude about model capability. ( OpenAI ) But there is another dimension worth testing: What happens when the context changes? Recent research is already moving in this direction. SWE-ContextBench evaluates whether coding agents can reuse relevant experience across related tasks, while SWE-Explore focuses specifically on reposito

Connect 2,500+ European banks to track your spending Discussion | Link

know which of your lines a vendor just broke Discussion | Link

Personalized Nervous System Training Discussion | Link

Early this year I was wiring Claude into Jira through an MCP server. It worked, and every session it felt slightly wrong: slow round trips, a schema I did not control, structure sitting somewhere the agent could not see while it was reading the code. The fix was almost embarrassingly simple. Put the ticket in the repo, as markdown. I pitched it to a colleague, and off we went. Seven months later: 15 projects across 10 repositories, 165 live tickets, 12 people on the board including non-developers, and no Jira licence. This post is why we left and what we built. Two follow-ups cover the rest: the skill and the loop that let agents work these tickets , and the three review layers that keep the output honest . What was actually wrong with Jira The cost was easy to name: roughly €2,000 a year for something we used maybe 5% of. It was not the reason we left. Every user had to be paid for, so the board was implicitly rationed. Performance degraded as projects grew. The features we wanted sat behind paid plugins. Automations were clumsy enough that we mostly did not write them. And the board was close to what we wanted without ever being it, because that last gap lived in someone else's product roadmap. None of that is fatal alone. Together it means the tool shapes the team instead of the other way around. The constraint that ruled out the obvious answers We are one team maintaining ten separate repositories that ship independently of one another, across TypeScript, C#, Java and PowerShell. A monorepo was never realistic. That kills the usual alternatives. GitHub Issues comes closest and misses twice: issues are scoped to one repository, so cross-repo visibility becomes somebody's weekly spreadsheet, and despite feeling like part of the repo they are not in it. They live in a database behind an API. Not files, not on the branch, not in the diff, and not something an agent editing the code can read without a round trip. Every hosted alternative moves the work further away s

Protect your Samsung phone with these cases and screen protectors.

Speedy Qi2 wireless charging in a magnetic puck and stand combo makes Google’s Pixelsnap well worth grabbing at half price.

Preview Ultrafast, a new OpenAI API service tier that runs GPT-5.6 Sol up to 14× faster. Powered by Cerebras, it delivers up to 750 output tokens per second.

Whether you need a portable, around-the-house entertainment center or a full-on laptop replacement, these are the best Android tablets.

Nothing ruins a great night of sleep faster than getting too hot. We slept on a myriad of cooling mattresses to find which ones drew the heat away best.

Turn product screenshots and prompts into motion videos Discussion | Link

Product analytics told you what happened. We handle it. Discussion | Link

Being a developer, we all create many projects for learning, work, and experiments. Over time my machine started filling up — not with source code, but with generated junk : node_modules target dist / build framework caches like .next , .angular , .nuxt and more of the same across every cloned repo Every few months I would hunt folders manually, delete something, free a few GB, then the same problem would come back. Only learning about “clean your disk” tips doesn’t help much. Building something for the problem does. So I ended up building ArtifactSweep — a small open-source tool for this everyday developer issue. The real problem As developers we regenerate these folders all the time: npm install cargo build ng build They are not our source of truth. But they sit on the SSD for months. The painful part is not only size. It is: Finding them across many project roots Knowing how big they are before delete Not deleting the wrong folder by mistake I wanted something that could: Scan a folder tree Show sizes Let me clean with more control Work on my day-to-day machines (Windows, Linux, Mac) Step 1: Start with a CLI I started with the command line first. Why CLI? Fast to build and test Fits terminal-first workflow Easy to script and share The CLI is called sweep . Basic usage: # Safe: only list junk under a path sweep scan . # Preview deletes sweep clean . --dry-run # Delete sweep clean . On one of my project folders alone, it reclaimed nearly 5 GB . That was enough validation: this is not a fake problem. Every active developer hits it. Step 2: Then came the desktop app CLI is great when you already know the path and trust dry-run. But sometimes I wanted to: See a list of folders and sizes Filter by type Confirm before delete Click through without remembering flags So I added a desktop app on top of the same idea (same cleanup job, different UI). Flow is simple: Choose folder Scan Review results (and filters if needed) Clean with confirmation If you like GUIs for this ki

Turn screen recordings into engaging product videos Discussion | Link

I could have built another developer portfolio. You know the one. Dark background. Glowing buttons. "Full Stack Developer | AI | Cloud | DevOps" Six project cards. GitHub link. Done. But honestly, that doesn't feel like me. Before I was interested in AI, software engineering, cloud, automation and all the other things I keep breaking and rebuilding, I was just a kid who loved Spider-Man. And the older I got, the more I realized that I didn't actually relate to Spider-Man because he was a superhero. I related to Peter Parker . The curious kid. The awkward kid. The kid who builds things. The kid who experiments. The kid who fails and somehow keeps going. That felt familiar. So when I started building my portfolio, I wanted it to represent that. I called it: 🧪 Peter Parker's Lab The idea is that my portfolio is basically my digital lab. A place where I can show what I'm building, what I'm learning and what I'm experimenting with. 🕷️ Peter Parker → curiosity 🕸️ Spider-Man → persistence 💻 Developer → everything I'm building today And honestly, "lab" describes my development journey pretty well. I build something. It breaks. I investigate why. I fix it. Then I get another idea and break something else. 😂 That's the fun part. I'm currently interested in building things around: AI AI agents automation full-stack applications developer tools cloud infrastructure DevOps local-first software I'm not trying to pretend I've mastered all of it. I'm trying to keep learning by building real things . That's what I want this portfolio to show. Not just a list of technologies. Not just a list of GitHub repositories. But the problems I'm curious about and the things I'm actually trying to create. 🌐 Peter Parker's Lab https://peterparker-lab.vercel.app/ This is version one. I'll keep changing it as I change. New projects. New experiments. New ideas. Probably new bugs too. Because maybe the best portfolio isn't one that says: "Look how much I know." Maybe it's one that says: "Look what I