标签：#Product

Put your startup on the map. Literally. Discussion | Link

The Asus PG27UCWM brings a new sub-pixel layout to the world of OLED gaming monitors, and in my testing, I appreciated the improvements it brings to the table.

Most automation purchases in ops organizations go wrong at the category level, before a single vendor demo happens. A team buys RPA because "automation" was the ask, then discovers six months in that the actual problem needed process orchestration across systems, not a bot clicking through a legacy UI. Or they buy an intelligent automation platform for a workflow that was always going to be simple, straightforward API orchestration, and pay for a cognitive layer they never needed. The three categories solve genuinely different problems, and the confusion between them is expensive in a specific, avoidable way. RPA: automating the UI, not the process Robotic process automation does one thing, and it's narrower than most pitches suggest: it mimics a human interacting with a screen — clicking buttons, reading fields, typing values — usually through UI selectors or an application's accessibility tree, not through an API. That's the entire point of it: RPA exists for systems that don't expose an API at all, or where getting API access would take longer than building a bot that just uses the interface the way a person would. That mechanism is also its defining weakness. A bot built against specific UI coordinates or element selectors breaks the moment the underlying application changes — a button moves, a field gets relabeled, a vendor ships a UI update — and the failure is usually silent until someone notices the process stopped completing. This is the single most common complaint about production RPA deployments, and it's not a implementation mistake, it's structural: robotic process automation is fundamentally built on mimicking a visual interface, and visual interfaces change for reasons that have nothing to do with the automation depending on them. RPA is the right tool specifically for swivel-chair tasks — moving data between systems that don't talk to each other, with no API available on at least one side, at a volume that makes manual entry genuinely costly. It's t

A personal blog sounds like a simple Laravel project. Create posts, show them on the homepage, and you are done. But once you start adding search, categories, tags, comments, SEO, authentication, analytics, and an admin panel, things become much more interesting. I built this Laravel Personal Blog as a real world project to explore those problems instead of building another basic CRUD application. The complete source code is available on GitHub: https://github.com/arafat-web/laravel-personal-blog Table of Contents What Is This Project? Technology Stack Main Features Project Structure How Visitor Analytics Works SEO and Content Management How to Run the Project What I Learned Final Thoughts What Is This Project? This is a complete single-author blogging platform built with Laravel. It includes both a public blog and a custom admin panel. The project was built without additional application packages, so most of the important functionality is visible in the codebase itself. The public side contains: Homepage Blog posts Categories Tags Search Comments RSS feed Sitemap SEO metadata Post view tracking The admin panel contains: Dashboard Post management Category and tag management Comment moderation User management General settings SEO settings Visitor analytics Technology Stack The project uses: PHP 8.3+ Laravel 13.17 MySQL or SQLite Blade Eloquent ORM JavaScript CSS PHPUnit The current project configuration requires PHP 8.3 and Laravel 13.17. Main Features The project goes beyond basic CRUD. For example, posts can have categories, tags, comments, authors, featured images, publishing status, and view counts. The Post model defines these relationships using Eloquent: public function user (): BelongsTo { return $this -> belongsTo ( User :: class ); } public function categories (): BelongsToMany { return $this -> belongsToMany ( Category :: class ); } public function tags (): BelongsToMany { return $this -> belongsToMany ( Tag :: class ); } public function comments (): HasMa

AI agents can look reliable after one impressive demo and still fail the moment real users, messy repositories, and conflicting instructions enter the room. The dangerous part is not that an agent makes mistakes. The dangerous part is that teams often change agent rules based on vibes, not evidence. If you are building an AI feature, internal coding agent, support assistant, research workflow, or automation layer, your standards need tests. Not just model evals. Not just unit tests. You need a way to answer a practical question: Did this new rule, skill, prompt, or tool instruction actually make the agent better? This guide shows a lightweight experiment system for AI agent standards. You can use it before rolling out new agent instructions across a product, engineering team, customer workflow, or multi-tenant AI application. No vendor pitch. No magic framework. Just a repeatable way to stop guessing. Why Agent Standards Need Experiments Most teams already have standards for human developers: code review rules security policies testing expectations deployment checklists naming conventions observability requirements AI agents need the same kind of guidance, but they behave differently from humans and traditional software. A human may read a coding standard once and remember the intent. An agent may load the wrong instruction file, ignore a rule buried deep in context, over-follow a stale example, or select no skill at all. That means the main risk is not only bad instructions. It is unreliable instruction delivery. Recent practitioner discussion around agentic development points to the same pattern: teams are moving from simple prompts toward skills, rules files, context packs, tool registries, desktop agents, and workflow harnesses. At the same time, developers are asking harder questions about governance, cost, reliability, and whether agents can be trusted with production work. What Counts as an AI Agent Standard? An AI agent standard is any reusable instruction t

Update your dependencies and undo anything Discussion | Link

Every local project gets a real address. Discussion | Link

The memory layer that decides what's worth remembering Discussion | Link

Docking stations expand what your laptop can do, and I’ve been testing the best of the best to see which you should buy.

From halting my doomscrolls to automating air-quality checks, Apple’s streamlined Shortcuts app is my favorite iOS 27 feature.

Claude Code Is Burning Your Token Budget. Here's the Receipt. I found $2,500/year of hidden token waste in my Claude Code setup. It was the MCP servers. The Discovery Last week I noticed my Claude Code conversations were dying at around message 15. Context window full. The model starts forgetting earlier instructions. Tool calls fail. The conversation degrades into hallucination. I assumed it was my fault — too many messages, too much context. So I started measuring. Here's what I found: Session start: Claude system prompt: ~8,000 tokens MCP schema injection: ~111,000 tokens User's first message: 50 tokens ────────────────────────────────────────── Total before any work: ~119,000 tokens Remaining context: ~81,000 tokens I was starting every conversation with 60% of my context already consumed. The culprit wasn't my prompts. It was the 10 MCP servers I had proudly configured in my claude_desktop_config.json . The Receipts I measured each server's schema injection using tiktoken: Server Why I Installed It Token Cost Times Used/Week GitHub PR reviews, issues 12,440 3 Slack Message reading 14,672 0 Google Drive Doc access 47,293 1 Notion Knowledge base 13,780 2 Postgres Query DB 8,231 4 Puppeteer Screenshots 5,890 0 Filesystem File access 3,847 15 Brave Search Web search 2,103 5 Memory Context persistence 2,567 0 Sequential Thinking Reasoning 890 2 Total 111,713 Look at the "Times Used/Week" column. Three servers were used zero times. Two more were used once or twice. But every single one of them was injecting 100% of its schema into every conversation. I was paying $0.33 per conversation — $2,500/year — to load schemas for tools I barely used. The Moment I Realized Everyone Has This Problem I posted my findings on Bluesky. Within hours: "I had the same issue. Removed 6 MCP servers and my conversations went from dying at message 15 to lasting 40+ messages." — @developer1 "GitHub MCP is 12K tokens but Claude Code already has gh CLI built in. Why did I install it?" — @dev

Hit a perfect 7-day streak this week, shipping 177 commits across systems architecture and personal...

Search and act across all your work apps Discussion | Link

A feature takes three days to code and three weeks to deliver. The difference is not always engineering capacity. A developer starts implementation and discovers that an eligibility rule is undefined. Product needs an answer from operations. A missing UX state appears next. Then engineering finds that the requested behavior conflicts with the current data model, which forces a scope decision. The code may still take three days. The delivery system takes three weeks. This is where product engineering alignment becomes an engineering leadership problem. The visible work happens in code, but much of the elapsed time happens between decisions: waiting for clarification, resolving constraints, revisiting scope, and discovering assumptions that should have surfaced earlier. The common response is to improve requirements, add meetings, or demand better estimates. Those actions may help, but they do not address the core issue. Product-engineering alignment is primarily a decision-flow problem . The useful question is not: Are product and engineering communicating enough? It is: Where does work stop because the person holding it cannot make the next decision? That question is more useful because it exposes where delivery actually slows down. Why Product and Engineering Become a Delivery Bottleneck Product and engineering approach the same feature with different knowledge. Product typically understands the customer problem, business priorities, stakeholder expectations, commercial constraints, and desired outcome. Engineering typically understands architecture, dependencies, operational risk, implementation alternatives, and the cost of changing the system. Neither side has the full picture, that is normal. The problem begins when the process assumes one side can finish its thinking before the other begins. Consider a requirement that appears simple: Allow customers to cancel an order. Engineering cannot implement that correctly without answering several questions: Until what

Control your company's internal AI agents and tools Discussion | Link

Every coding agent says it is efficient. Almost none of them publish the bill. So we ran the boring experiment: the same bugfix, the same model, the same API, the same prices, and a byte identical prompt, once through opencode and once through the coding agent inside Locally Uncensored. Headline: opencode averaged 2157 credits over three runs. Our 2.6.6 agent finished the identical task for 1298 . That is about 40 percent less, and even the cheapest opencode run came in 29 percent above our number. The interesting part is not the headline. It is why the gap exists, and it is not the reason most people guess. Setup A cost comparison is only worth reading if everything that drives cost is nailed down. What was held constant: Held constant Value Task Fix a failing test in a small npm repo, then commit Repository Three files, a one line bug in add.js , tests red at the start Prompt Byte identical, sha256 29cec6c3...cf62687 Model deepseek-ai/DeepSeek-V3.2 Endpoint The same OpenAI compatible API for both agents Prices Same account, same tier, same per token rate Counting One wire proxy in front of the API, credits read before and after every run opencode 1.18.21 from npm, wired as an OpenAI compatible provider, opencode run --auto , otherwise defaults Success was defined before the runs, not after: npm test passes exactly one commit, with the required message only add.js changed clean working tree at the end All four runs cleared that bar. Nothing failed, so cost is the only variable that moved. The numbers Run Credits Requests Prompt tokens Success opencode, run 1 1679 8 98,789 yes opencode, run 2 2433 11 146,058 yes opencode, run 3 2358 11 146,387 yes Locally Uncensored 2.6.6 1298 16 74,629 yes Locally Uncensored 2.6.5 4395 30 257,270 yes Read the last row first. Our own shipped agent from one release earlier is the most expensive thing in that table, by a lot. This is not a chart built so that we win by construction. It is a chart that shows what one efficiency pass is

Turning desktop chaos into curated spaces. Discussion | Link

Talk plainly and create macOS native automations Discussion | Link

One mouse, keyboard and second screen for all your computers Discussion | Link

I was staring at a broken Next.js and Express backend integration late at night, convinced my AI agent had lost its mind. It was supposed to be a straightforward n8n automation pipeline. Yet, every time it ran, it hallucinated non-existent packages and dumped its context halfway through. My System 1 intuitive reaction flared up immediately: The LLM just isn't smart enough. I sat there, exhausted, ready to rewrite the prompt for the twentieth time. Engaging System 2 Taking a step back, I forced myself to engage my analytical System 2 brain. I wasn't dealing with a lack of model intelligence; I was dealing with a lack of infrastructure. I was running a massive, powerful AI model with zero guardrails. No persistent memory. No verification. Just dumping a giant Mongoose schema into a prompt and hoping for the best. I was essentially dropping a Formula 1 engine onto a wooden skateboard and wondering why it crashed at the first turn. What is Harness Engineering? I stopped obsessing over prompt engineering and started focusing on Harness Engineering. The model is just the engine; the harness provides the chassis, the steering, and the brakes. Here is how I completely restructured my agentic workflow: Context Management: Instead of flooding the context window with raw codebase dumps, I implemented targeted retrieval. The agent now only sees the specific files required for the immediate task. Standardized Tools: I integrated Model Context Protocol (MCP) servers, giving the model bounded, secure ways to execute actions rather than just generating text. Durable State: If a long-running workflow pauses or fails, the system now checkpoints its progress. It resumes exactly where it left off instead of starting from scratch. Strict Verification: "Looks good to me" is no longer an acceptable output. The agent is forced to run tests and verify the CLI output before concluding a task. Learn to Break the System The results were immediate. The hallucinations stopped, and the agent shif