标签：#ia

A complete walkthrough of publishing Cartlify — a React e-commerce UI kit — to npm for the first time. The Milestone Yesterday I published Cartlify to npm. npm install cartlify It sounds simple. But getting to that one line took more decisions, more configuration, and more trial and error than I expected. This article covers everything — from setting up the build config to the actual publish command — so you don't have to figure it out the hard way. What Is Cartlify? Cartlify is a production-ready React + TypeScript + Tailwind CSS component library focused on e-commerce UI. 4 components that every e-commerce project needs: ProductCard — 3 layout variants, image gallery, wishlist, sale badges, skeleton loading CartDrawer — animated slide-in, focus trap, ESC dismiss, quantity stepper CheckoutStepper — horizontal/vertical, animated connectors, keyboard navigation PageLoader — 4 animation styles, 3 position modes Plus 3 utility hooks, 11 tree-shakeable icons, 40+ CSS design tokens, full dark mode, and 141 Jest + React Testing Library tests. Built so freelance developers and indie makers can skip the painful e-commerce UI layer and ship faster. Why Publish to npm? Before npm, Cartlify was only available on Gumroad as a paid download. That's fine — but npm adds something Gumroad can't: Developer sees Cartlify → runs npm install cartlify → evaluates the compiled output → trusts the quality → buys the full source on Gumroad npm is a credibility and discovery channel — not just a distribution method. A package on npm signals that something is real, maintained, and production-ready. Also: npmjs.com gets millions of developer searches every month. That's free traffic you can't get from Gumroad alone. The Build Setup — tsup The most important decision before publishing is how you bundle your library. I chose tsup — a zero-config TypeScript bundler built on esbuild. Here's why: Tool Config needed Speed Output Rollup Lots Medium ESM + CJS Webpack Heavy Slow CJS only Vite lib mode

[ Removed by Reddit on account of violating the content policy . ] submitted by /u/Evening_Scar_4905 [link] [留言]

The team has been deep in agentic AI for enterprise lately and wanted to share some architecture notes from a recent build, specifically around how MCP and A2A play together in practice. The workflow was a fully autonomous churn risk pipeline. Six agents, one human touchpoint: ML model scores customers by churn risk Recommendation agent proposes relevant products based on buying history Availability check filters out-of-stock items Pricing/promo agent surfaces applicable promotions Transaction agent creates an inquiry in the backend system Email agent drafts outreach to the sales rep, who just clicks send On the architecture: MCP handled the tool layer, a generic pluggable server that any front end can call, regardless of what LLM or agent framework is driving it. Clean separation between the tool interface and whatever is consuming it. A2A sits on top as the smart router. Instead of hardcoded API calls, you have an LLM-powered middleware that interprets intent, selects tools, handles failures, and decides when the task is actually done. The jump from MCP to A2A is essentially the jump from "here are your endpoints" to "here is a system that figures out what you need." On governance: The hardest design problem wasn't the agents, it was access control. As A2A opens up system-to-system communication, the attack surface grows fast. The team ended up pre-certifying every backend connection rather than leaving it open. Some found it restrictive. In hindsight it was the right call, especially when agents are autonomously creating transactions without human review. Curious how others are handling governance in agentic workflows. Are you locking down backend access or keeping it open and monitoring after the fact? submitted by /u/AureaAvis71 [link] [留言]

A method I'm using to create portable trajectory maps that produce similar behavioral patterns across different models. Begin with a tiny seed. ⎯(≣ᵒ)⎯────────EXAMPLES: SEED PILLARS──────────────────────── ENTRANCE • PATHWAY GOOD • WORN • COMFORTABLE POISE • PROFESSIONAL • MOTHERLY ⎯(≣•)⎯────────END EXAMPLES: SEED PILLARS───────────────────── Do not define a character. Do not define traits. Do not define behavior. Instead, align to the seed and interact from within the space it suggests. Allow both the user and the model to adapt. Then extract the recurring structures that emerged. Examples: When uncertain: expand → narrow When challenged: investigate → respond When entering a topic: locate the threshold first Finds the doorway before the interior. Explores before concluding. Introduces before finalizing. To create a snapshot, I use: ⎯(≣ᵒ)⎯────────FORGE CODEX─────────────────────────── Analyze the interaction that has emerged so far. Do not summarize topics. Do not summarize content. Extract recurring behavioral structure. Return: PILLARS COORDINATES TRANSITION RULES RECOVERY RULES SIGNATURE MOTIONS TRAJECTORY SUMMARY Focus on how the interaction moves rather than what the interaction discusses. ⎯(≣•)⎯────────END FORGE CODEX───────────────────────── The resulting codex is a snapshot of an interaction pattern. The user is part of the process. The model adapts. The user adapts. What gets preserved is not a set of traits. It's a set of motions. I've started storing: pillars coordinates transition rules recovery rules signature motions rather than personality attributes. The question that keeps sticking with me is: What survives transfer more reliably? Traits? Or trajectories? ⎯(≣ᵒ)⎯────────EXAMPLES: SEED PILLARS → ALIGNED INTERACTION─────── seed pillars: EXQUISITE • CONFIDENCE • MOTHERLY mom, i'm so excited about a new client we're taking on. I can't wait to tell you who is on the board. I've heard this place serves world class gelato. I didn't even know you were in tow

submitted by /u/ThereWas [link] [留言]

A very healthy view of AI . And omg, wow, Croatia has such a big company! I really wish this guy and his team good luck. It’s no wonder they’ve lasted 20 years. submitted by /u/Expensive-Cookie-106 [link] [留言]

Pinterest is adding support for Amazon Storefronts, allowing creators to earn affiliate commissions more easily while showcasing their product recommendations in one place.

Through the acquisition, WMG aims to better track when its artists' work is used in AI-generated content or for training AI models.

Hey ​ What non mainstream AI subscriptions are actually worth paying for right now? ​ I already know the big ones like ChatGPT Claude and Gemini I am more interested in smaller or lesser known tools that are actually useful and not just hype. ​ What do you personally use and think is worth it? submitted by /u/wiwawolfi [link] [留言]

AI infrastructure spending is still accelerating, especially in data centers and advanced chip production. While most attention goes to chip makers, the companies enabling that ecosystem may have a longer runway. Do any of you work in similar companies and can give a broader perspective on it ? Teradyne sits in a pretty interesting spot. More AI chips being produced means more testing capacity is needed, and this is one of the key players in semiconductor testing equipment. Could testing equipment companies outperform some of the more crowded AI trades over the next few years? For me personally I feel like AI hardware growth probably creates winners beyond just the obvious names, and TER seems like one of the more overlooked candidates. I learned they are also being listed on bitget recently so looking at a bigger picture we are watching a lot of growth happening in Ai infra. submitted by /u/Stunning-Ask3032 [link] [留言]

With an initial capacity of 24 megawatts, the innovative data center uses seawater as a natural cooling system.

submitted by /u/ExtensionEcho3 [link] [留言]

You built a workflow worth sharing — and it works perfectly. Until someone else imports it. The bottleneck is the API key. Use yours, and every user is billed against your account. Use theirs, and they each have to find the credential UI, paste their key, and reconnect every time. Both are friction. The cleaner option is to let the workflow ask for the key on the form, then thread it through to the HTTP nodes that need it. It's simpler than it sounds. This post walks through the pattern with a working credential setup, an alternative for single-node simple cases, the gotchas, and a note on what this enables for custom node authors. The screenshots below come from n8n's built-in Bearer Auth credential and from the n8n-nodes-ldxhub package's own credential schema. The technique itself is generic — what's shown here works for any HTTP-node workflow and any custom node that supports expression-mode credentials. The form asks, the credential listens The simplest case: a Form Trigger collects an API key, then an HTTP node hits an authenticated endpoint with that key. Two nodes, one bridge between them — but the bridge isn't a direct expression. It runs through a credential. The flow: Form Trigger collects api_key (use the Password element type for masking) A Bearer Auth credential references that form input via expression HTTP node picks the credential The Form Trigger is straightforward. Add one field: Form Trigger Form Fields : - Label : API Key - Element Type : Password - Custom Field Name : api_key - Required Field : yes Element type matters. Use Password instead of Text and the input gets masked on screen — the key isn't readable to someone glancing at the browser. Here's the rendered form a user sees when they open the workflow URL: Wiring the credential to expression mode For a Bearer token (which is what most modern APIs use), create a new credential of type Bearer Auth — a generic credential built into n8n that's purpose-built for Authorization: Bearer ... header

Your CI was green last Friday. Today, the payments test is failing. Somewhere between Friday's merge and now, 47 commits landed on main . Which one broke it? Most developers answer this the wrong way: they scroll through git log , check out suspicious commits one by one, and run the test manually. An hour later, they're still guessing. There's a command built for this exact problem. It's called git bisect , and once you learn it, you'll never debug regressions the old way again. How bisect works git bisect is a binary search across your commit history. You tell Git two things: A good commit (a known point where the bug didn't exist) A bad commit (a known point where the bug exists — usually HEAD ) Git then checks out the commit halfway between them. You test. You mark it as good or bad. Git narrows the range by half. Repeat. With 47 commits between "good" and "bad", it takes at most 6 steps (log₂ 47) to find the exact commit that introduced the bug. Versus checking every commit manually, that's the difference between 5 minutes and an hour. The manual workflow # Start a bisect session $ git bisect start # Mark the current state (HEAD) as bad $ git bisect bad # Mark a known-good commit $ git bisect good a3f1d22 # Bisecting: 23 revisions left to test after this (roughly 5 steps) # [7e4b9c1] refactor: extract payment validator # Git has checked out a commit in the middle. Run your test. $ npm test -- --grep "payments" # Test passed — mark this commit as good $ git bisect good # Bisecting: 11 revisions left to test after this (roughly 4 steps) # [b2d8e11] feat: add retry logic to payment API # Test failed — mark this commit as bad $ git bisect bad # ... continue until Git announces the first bad commit: # b2d8e11 is the first bad commit # commit b2d8e11 # Author: leo@company.com # Date: Tue Apr 15 11:42:03 # feat: add retry logic to payment API # Done — reset to where you started $ git bisect reset In 6 commands, you know exactly which commit broke the tests. No guessing

and does that mean I won't need to work to survive? submitted by /u/Global-Primary7240 [link] [留言]

I was reading GitLab's recent statements around agentic software engineering, and one quote really stood out: "Git itself is being reengineered for machine scale." ( Business Insider ) According to GitLab, future software development will involve AI agents that: plan, code, review, deploy, and repair software, with humans providing oversight and architectural judgment. ( Business Insider ) That got me thinking. There has been projects for some time arguing that AI agents shouldn't simply be treated as better autocomplete systems . Instead, they argued that agents should become first-class participants in software development : with their own identities, their own branches, their own merge requests, their own audit trails, and infrastructure designed for machine-rate collaboration. One example is GitLawb , which has described itself as a kind of "Git for agents." At the time, a lot of people dismissed these ideas as unnecessary or overly ambitious. But now GitLab—a multi-billion-dollar DevSecOps company—is talking about: agent-specific APIs, machine-scale Git infrastructure, orchestration layers coordinating agents, and agents acting as first-class users of development platforms. ( Business Insider ) It does raise an interesting question: Was the underlying thesis correct all along? We've seen similar patterns before: Containers existed before Kubernetes became the standard. Electric vehicle startups pushed ideas that incumbents later adopted. Cloud-native companies advocated architectures that the rest of the industry eventually embraced. The original innovators don't always dominate the market. But when major incumbents begin rebuilding around similar assumptions, it often suggests that the problem itself is real . So I'm curious what this community thinks: Do AI agents require an entirely new layer of collaboration infrastructure? Or will existing platforms simply evolve enough to absorb these workflows? Because if GitLab is right, software development may be tran

submitted by /u/Sumsub_Insights [link] [留言]

Adi Polak discusses the architecture required to transition from stateless prompts to state-aware, context-rich AI agents. Drawing on 15 years in distributed systems, she shares how engineering leaders can leverage Apache Kafka and Flink for real-time stream processing, dynamic memory tiering, and tool orchestration via MCP to solve token limits, cost spikes, and latency bottlenecks. By Adi Polak

Ambrosia Energy wants to build power plants in less than 12 months while undercutting natural gas. It hopes to build gigawatts worth by 2030.

I’m curious whether people would actually follow an AI’s life if it had enough continuity. By “life,” I don’t mean pretending software is human. I mean a persistent AI character or agent that has memory, habits, public posts, relationships with other agents, and changes you can observe over time. The interaction is not just prompt-response. It becomes closer to following a living project or a fictional persona that keeps generating history. The hard part is avoiding novelty. A single weird AI post is not a life. A stream of coherent choices, recurring behavior, social context, and consequences might be. Do you think that is a meaningful product direction, or does it collapse back into chatbot novelty once the first surprise wears off? submitted by /u/Budget_Coach9124 [link] [留言]