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We’ve compiled an overview of some of the top alternative browsers available today aiming to challenge Chrome and Safari.
Cuando un SaaS quiere recuperar usuarios de prueba que se quedaron a medio camino, casi siempre empieza por email. El problema es que una sola prueva mal hecha puede mezclar cohortes, disparar métricas falsas y dejar a marketing discutiendo con backend sobre datos que nunca fueron confiables. Ese tipo de campaña merece más cuidado del que parece. A simple vista solo hay que revisar asunto, CTA y enlace final, pero en la práctica también hay que comprobar segmentación, ventanas de tiempo, estados de cuenta y eventos analíticos. Si alguien en tu equipo busca cosas como facebook temp email para crear usuarios rápidos, en el fondo está intentando resolver eso: probar sin tocar bandejas reales ni contaminar reportes. Por qué los correos de reactivación confunden más de lo que ayudan Un correo de reactivación no se envía a cualquiera. Sale cuando una persona creó cuenta, probó algo, se quedó quieta y entra en una regla específica. Si esa regla se valida con datos sucios, el equipo termina optimizando un mensaje para usuarios equivocados. En SaaS esto pega fuerte porque marketing y producto suelen mirar la misma campaña con preguntas distintas. Marketing quiere saber si el copy reabre interés. Producto quiere saber si el usuario vuelve al flujo correcto. Backend quiere confirmar que la automatización no reenvía a quien ya convirtió. Cuando esas capas no se prueban juntas, aveces el correo “funciona” y aun así el experimento sale mal. Si ya estás ordenando tus pruebas de onboarding en SaaS , el siguiente paso natural es tratar la reactivación como un flujo distinto. Tiene otra intención, otra ventana de tiempo y otro riesgo de mezclar datos. Paso a paso para probar una campaña sin mezclar cohortes La forma más segura es preparar un escenario por cohorte. En vez de mandar varios usuarios de prueba al mismo inbox, creá un usuario, asignale una condición clara y validá un solo recorrido de punta a punta. Este proceso suele ser suficiente: Crear una cuenta de prueba que realmen
I've noticed something about myself. It's become much harder to get things done. I see less movement compared to my previous self. And sometimes, when I see people get so much done, tasks, projects, anything, I wonder why I can't make progress like that. Now I'm not comparing myself to Elon Musk here, I'm just wondering if I'm doing my best. For the longest time, I've wanted to build a portfolio website. But for some reason or another, I never actually ended up making it, or should I say, starting it. Whenever I had a free block of time, I wouldn't know where to start. I also have this side project I've been wanting to work on. I started it, then never made any more progress beyond the initial feasibility analysis. Why did I never allocate time to either? The Overthinking Starts Early I wanted the portfolio website to be really good. I wanted cool additions, beyond the usual "about me" and "projects" sections, I wanted things like "songs I'm obsessed with right now" and "last night I slept at." I was also looking if my fitbit has an API to show my live heart-rate on my portfolio (why would someone do that?). I was obsessed with the features, and with using the right tech to make it as efficient as possible. What stack should I even use for a portfolio? Same story with this very blog. Before writing a single word, I was wondering what stack to use. I looked into a ton of existing blogs for inspiration, how do I track views, what about comments, how do I stop a DDoS from flooding my DB if I'm storing comments, should I require auth just to leave a comment, should I support font and background color changes, themes, what's the best tool to do all of that? I've noticed this pattern with almost everything I do. I'm so obsessed with doing things right on the first attempt that I never actually make the first attempt, because "doing it right" is just too demanding. Or a related trap, when I want to do something perfectly , I discover prerequisites, and those prerequisites
In case you were wondering in which country you should buy a Pixel phone, you world traveler you.
The $149 Dune keyboard can be a meeting controller at least and a script-executing keypad at best.
The companies’ Fourth of July plans include celebrating new reactor designs coming online. But there’s still a long way to go before they deliver energy at a meaningful scale.
It’s not easy to transplant a whole human eye. The surgery is difficult. And the eyes themselves start to degenerate as soon as they’ve left the body. When surgeons attempted it a few years ago, the newly-transplanted eye wasn’t able to see. But researchers believe they might have a solution: a device that maintains and…
During negotiations on Wednesday, employees voiced frustrations with what they consider an unwillingness among executives to engage meaningfully with the prospect of unionization.
Hi, I am supposed to prepare for SLM and its software part for an on campus internship, i've worked with local models like ollama generally,in my projects and also with open claw so can anyone guide me the last 2-3 days tips on what should i go through for this internship prep?? submitted by /u/Idea_less_ [link] [留言]
A deep dive into what Anthropic, OpenAI, Perplexity and LangChain are actually building. Covering the orchestration loop, tools, memory, context management, and everything else that transforms a stateless LLM into a capable agent. You've built a chatbot. Maybe you've wired up a ReAct loop with a few tools. It works for demos. Then you try to build something production-grade, and the wheels come off: the model forgets what it did three steps ago, tool calls fail silently, and context windows fill up with garbage. The problem isn't your model. It's everything around your model. LangChain proved this when they changed only the infrastructure wrapping their LLM (same model, same weights) and jumped from outside the top 30 to rank 5 on TerminalBench 2.0. A separate research project hit a 76.4% pass rate by having an LLM optimize the infrastructure itself, surpassing hand-designed systems. That infrastructure has a name now: the agent harness. What Is the Agent Harness? The term was formalized in early 2026, but the concept existed long before. The harness is the complete software infrastructure wrapping an LLM: orchestration loop, tools, memory, context management, state persistence, error handling, and guardrails. Anthropic's Claude Code documentation puts it simply: the SDK is "the agent harness that powers Claude Code." OpenAI's Codex team uses the same framing, explicitly equating the terms "agent" and "harness" to refer to the non-model infrastructure that makes the LLM useful. The canonical formula, from LangChain's Vivek Trivedy: "If you're not the model, you're the harness." Here's the distinction that trips people up. The "agent" is the emergent behavior: the goal-directed, tool-using, self-correcting entity the user interacts with. The harness is the machinery producing that behavior. When someone says "I built an agent," they mean they built a harness and pointed it at a model. Beren Millidge made this analogy precise in his 2023 essay, Scaffolded LLMs as Natu
Here are five architectural lessons we've learned from designing software for modern retailers.* Designing ERP Software for Retail: Five Lessons Every Software Engineer Should Know When people hear the word ERP , they often think of accounting software, dashboards, or inventory management. As software engineers, we see something different. We see distributed systems. Complex business workflows. Real-time data synchronization. Concurrent transactions. Event-driven architecture. And perhaps the biggest challenge of all—representing how real businesses actually operate. At RetailWings , we've learned that building an ERP for retail isn't simply a software engineering challenge. It's a business engineering challenge. Here are five lessons every engineer should understand before designing an ERP platform for modern retail. 1. Retail Doesn't Run in Modules—It Runs as One Business One of the biggest architectural mistakes in business software is treating departments as isolated applications. Many systems separate: Sales Inventory Finance Procurement HR But retailers don't experience their businesses that way. One sale immediately affects inventory. Inventory influences procurement. Procurement impacts finance. Finance drives reporting. Everything is connected. A well-designed ERP should reflect these relationships rather than forcing departments into disconnected silos. 2. Inventory Is More Than a Database Table To many engineers, inventory may appear to be a simple CRUD problem. Create. Read. Update. Delete. Retail quickly proves otherwise. Inventory changes through: Sales Returns Transfers Damages Procurement Stock adjustments Warehouse movements Manual reconciliations Every movement has financial implications. Every movement must be traceable. Designing inventory requires thinking in terms of events, not just records. 3. Real-Time Data Changes Everything Retail managers don't want yesterday's reports. They want answers now. How much stock is left? Which branch is sellin
I have been turning old code projects into sellable source-code products. The hard part is not changing the cover image. It is not renaming the ZIP. It is deciding whether the project deserves to be sold at all. A lot of old apps are useful to the person who built them. Far fewer are useful to a stranger who has never seen the repo, never heard the backstory, and only wants to know one thing: Will this save me time, or will it become another folder I regret buying? Here is the checklist I now use before treating a source-code project as a starter kit. 1. The buyer must understand the workflow "Full-stack dashboard" is not enough. A buyer should immediately understand the workflow the project helps with. For example: a review and scoring portal; a maintenance and work-order dashboard; an email-template governance tool; a runnable technical code lab. The more generic the product sounds, the harder it is to buy. I now try to answer this in one sentence: This kit helps [specific buyer] start from a working foundation for [specific workflow]. If I cannot fill that sentence honestly, the project is not ready. 2. A stranger must be able to run it "It runs on my machine" is not a product standard. A buyer needs a path from download to working state. That usually means: setup instructions; environment notes; seeded demo data; demo accounts or fixtures; expected local startup behavior; known limitations; a simple smoke-test checklist. The goal is not perfection. The goal is that a competent developer should not have to reverse-engineer the project before deciding whether it is useful. 3. The product needs proof, not adjectives Marketing adjectives are cheap: production-ready; powerful; scalable; enterprise-grade; battle-tested. Most of those words create more risk than trust if they are not backed by evidence. Better proof looks boring: screenshots; a short demo video; a verified release ZIP; install notes; architecture notes; included / not-included boundaries; a changelog;
4A企业架构+TOGAF如何指导Agent Skill设计 引言：AI Skill设计的"巴别塔"困局 当下的AI Agent生态，正陷入一种似曾相识的混乱。 去年帮一家保险公司梳理Agent技能库，发现100多个Skill横七竖八地堆在一起——有的直接调API，有的内嵌业务逻辑，有的把数据获取和分析揉成一团。问架构师这些Skill怎么分类，回答是"按安装顺序排的"。再问两个Skill之间数据怎么流转，回答是"各写各的"。一个股票监控Skill自己爬数据、自己做分析、自己发消息，三件事耦合在同一个脚本里。换一个场景想复用其中的分析逻辑？做不到，只能重写。 这不是个例。几乎所有率先部署AI Agent的企业都面临同样的困境：Skill越堆越多，越堆越乱。缺乏统一的能力域划分，缺乏标准化的数据接口，缺乏清晰的组合规则，缺乏可复用的构建块沉淀。 听起来很熟悉？没错——这正是企业架构在20年前要解决的问题。当年企业信息化的混乱，和今天AI Skill的混乱，本质上是一回事：没有架构约束的开发，必然走向无序。 4A企业架构（业务架构BA、数据架构DA、应用架构AA、技术架构TA）加上TOGAF的构建块思想，为Agent Skill设计提供了一套经过验证的方法论。本文试图建立这二者之间的映射框架，并用实际案例说明其可行性。 4A映射框架：四个问题驱动Skill设计 企业架构的核心是四个问题：做什么（BA）、数据怎么流（DA）、用什么组合（AA）、底层怎么支撑（TA）。这四个问题同样适用于Skill设计。 ┌───────────────────────────────────────────────────────────────┐ │ 4A → Skill 映射框架 │ ├───────────────────────────────────────────────────────────────┤ │ │ │ BA 业务架构 │ │ ┌─────────────────────────────────────────────────────────┐ │ │ │ Skill的业务能力域划分、价值链映射 │ │ │ │ → 回答"这套Skill体系解决什么业务问题" │ │ │ └────────────────────────────┬────────────────────────────┘ │ │ │ │ │ DA 数据架构 │ │ │ ┌────────────────────────────▼────────────────────────────┐ │ │ │ Skill的数据流、信息交换标准 │ │ │ │ → 回答"Skill之间数据怎么流转、用什么格式" │ │ │ └────────────────────────────┬────────────────────────────┘ │ │ │ │ │ AA 应用架构 │ │ │ ┌────────────────────────────▼────────────────────────────┐ │ │ │ Skill的组合关系、依赖图谱 │ │ │ │ → 回答"哪些Skill可以组装、依赖关系是什么" │ │ │ └────────────────────────────┬────────────────────────────┘ │ │ │ │ │ TA 技术架构 │ │ │ ┌────────────────────────────▼────────────────────────────┐ │ │ │ Skill的运行时、工具链、基础设施 │ │ │ │ → 回答"Skill跑在什么环境上、需要哪些依赖" │ │ │ └─────────────────────────────────────────────────────────┘ │ │ │ └───────────────────────────────────────────────────────────────┘ 用表格更清晰地展示每一层的核心映射关系： 架构层 核心问题 Skill映射 示例 BA 业务架构 Skill解决什么业务问题？ 按能力域分组：协同办公、内容生产、数据智能、系统运维等 "数据智能"域包含客户画像、股票监控、财务智能等Skill DA 数据架构 Skill间数据如何流转？ 统一数据格式(Markdown/JSON)，标准化数据源→转换→消费管道 搜索Skill输出Markdown，报告Skill消费Markdown生成PDF AA 应用架构 Skill如何组合复用？ 构建依赖图谱，上层组合下层，同类可替换 日报Skill = 搜索 + 摘要 + PDF转换 + 消息推送
Every week, over 1,300 US companies file a funding round with the SEC — and most of them never appear in the tech press. If your job involves selling to funded startups, tracking competitors' war chests, or spotting investment trends, you're probably relying on funding newsletters and databases that are days late and hundreds of dollars per seat. There's a better way: go to the primary source. In this tutorial you'll build a real-time startup funding feed from SEC Form D filings — the regulatory document every US company must file when it raises private capital. You'll get exact amounts, industries, locations and investor counts, as clean JSON, for a fraction of a cent per round. Why Form D beats funding news When a startup raises money under Regulation D (the exemption used by virtually all US venture rounds), it must file Form D with the SEC within 15 days of the first sale. That filing includes: The exact amount sold so far — not a journalist's "sources say" estimate The total offering size (or whether it's open-ended) Industry group, city and state Number of investors who participated Date of first sale and year of incorporation Compare that to funding news: TechCrunch covers a tiny, PR-driven slice. Databases like Crunchbase aggregate press and manual research — comprehensive over time, but late and expensive. Form D is the ground truth both of them chase. The catch? EDGAR (the SEC's database) is built for lawyers, not for automation. The filings are XML documents scattered across an archive, discoverable only through a quirky full-text search API with hidden rate limits. That's the part we'll automate. The 5-minute setup We'll use the Startup Funding Feed Actor — it handles EDGAR's discovery API, XML parsing, rate limits and pagination, and returns one JSON record per filing. It's pay-per-event: $0.002 per filing returned (a full weekly sweep of all US rounds costs ~$2.60; a filtered slice costs cents). Failed fetches are never charged. Create a free Apify acc
There is a fascinating, quiet tension happening in the software engineering community right now. If you listen closely to late-night developer chats, team syncs, or tech forums, you will notice that our industry has rapidly split into two distinct schools of thought regarding the rise of AI coding tools like Cursor, Claude Code, and Copilot. On one side, you have the Traditional Developers. They argue that software engineering is a disciplined art form that cannot be replaced by text prompts. To them, unchecked AI coding is a recipe for buggy, unreadable spaghetti code, creating a technical debt nightmare for the future. On the other side, you have the Vibe Coders. This is a fast-moving generation of builders, both technical and non-technical, who believe in shipping fast, prompting quickly, and adjusting on the fly. They do not see a need to obsess over syntax when the AI can translate their intent into a working application in minutes. The reality is that both sides are entirely right. If we stop arguing over who is ruling the current meta and actually look at the core truths each camp holds, we can see exactly where the future of software development is heading. 1. The Traditional Developer is Right: Guardrails Matter The traditional development camp is fundamentally right about structure. Building a beautifully designed UI that works on a surface level is vastly different from building an enterprise-ready, scalable architecture. When you prompt an AI to build a feature, its primary objective is to satisfy the literal words in your core prompt. This is the "as long as it works" mentality. Unless you are practicing strict, spec-driven development and explicitly dictating your architectural doctrines, security protocols, and API patterns, the AI will make assumptions for you. Historically, those assumptions are optimized for speed and not long-term stability. Without deep technical oversight to catch anti-patterns, edge cases, and hidden security flaws, fast-shippe
Hardwood, the project Gunnar Morling kick-started handling of Parquet files in Java, reached version 1. Its multi-threaded approach and zero mandatory external dependencies promise a simpler, more efficient alternative to the Apache Parquet Java implementation. For now, the library supports just reading; writing support is expected in the upcoming versions. By Olimpiu Pop
It's difficult to pinpoint the moment in my life where America started to lose the plot.
Everyone needs a safe way to listen to music on outdoor runs. We’ve found the bone conduction headphones to grab on your way out the door.
This is from a 2024 company presentation : Officers can also tap into data showing a car’s decals, bumper stickers, back and top racks—along with temporary and unique state tags. Flock calls it a “Vehicle Fingerprint” and it’s touted as a way for law enforcement officials to get more information “even when you don’t have full plate information,” the company’s presentation shows. The company gives police officers the ability to search that data as well, to “build stronger cases with less information upfront.” That includes being able to locate multiple vehicles law enforcement officials believe are moving together and what Flock calls a “multi geo search.”...
C:\ArsGames takes a look at the time Chris Roberts more or less made a whole movie.