找到 6733 篇相关文章
OpenClaw is the fastest-growing project in GitHub history. Peter Steinberger and several maintainers share what they learned in the project's first six months. The post OpenClaw went viral. Meet the maintainers building and securing it. appeared first on The GitHub Blog .
Recently, I built a mobile app with Expo; everything worked well with the development build on a simulator and a real device. Yet when I published the app to test the production build on a real device, it crashed without any explanation. With the crash, I had to get the crash report from Apple and download it to read it and try to understand the issue, yet even with that, I did not find any details that could help me, so I did what any normal guy during this age can do, I gave my code base to claude code and the crash report to anaylze them and tell me the issue. Guess what happened here? It hallucinated! Reading Claude's output made me feel I wasn't going in the right direction; for that reason, I had to go the old way: Stack Overflow and Reddit. Going to read the issues there helped me with three main extra things that AI does not provide: Knowing what other people tried: When I go to Stack Overflow or Reddit , I read the question, the thread, and other people's comments, even if it's not the correct one; this helps me get context, grasp the idea, and even learn some historical data about the issue. That might be the one I am facing. Sense of community: When I read other people's struggles and experiences, it gives me the feeling that I am not alone- not just me and a machine trying to prompt it to work- and it makes me feel that I belong to something bigger. It helps me keep up, not get frustrated, and feel that it's me who cannot solve issues with AI. Slow learning: Our brain does not remember the information when you read it once and forget it; we learn when we put effort and push the limits of our brain. With AI, this is getting easier by sending the question directly and getting the answer, so we forget even the issue if we face it again (spoiler alert: I had this exact issue a few months ago and forgot about it). That's why slow reading and similar methods help keep our brains alive and help us improve. With this, I am not saying to fully remove AI and not t
SEO hiring is increasingly centered on senior ownership rather than pure execution. A Semrush analysis of 3,900 US SEO job listings on Indeed, captured on November 25, 2025, found that 59% of openings were senior leadership roles . The category included Director, VP, Head, Chief, Lead, and Executive titles. The finding matters because it signals how employers are defining SEO work for 2026. Companies appear to place greater value on people who can set priorities, manage projects, connect SEO with other channels, and direct AI-enabled workflows . That does not mean junior SEO work has disappeared. It does mean that the available listings are weighted strongly toward people accountable for strategy and business outcomes. What the SEO job data shows Semrush's analysis of 3,900 SEO job listings describes a polarized US market. Senior leadership positions made up the majority of listed roles, while SEO Specialist jobs represented about 15% and SEO Manager jobs about 10%. Listing category or measure What Semrush found What it indicates Senior leadership roles 59% of listings Demand is concentrated in roles with strategic ownership. SEO Specialist roles About 15% of listings Specialist execution roles are a smaller share of the market. SEO Manager roles About 10% of listings Mid-level management roles are also less prevalent than leadership listings. Median pay About $130,000 for senior roles, versus about $71,630 for other positions Employers are placing a substantial pay premium on senior SEO responsibility. The study also found that approximately 31% of senior listings mentioned project management. Cross-channel responsibilities were another recurring theme, reinforcing the idea that SEO is being hired as a growth function that must work with content, marketing, product, and other teams. AI is part of this changing job description. Semrush found AI mentioned in 31% of senior listings, with roughly 10% specifically mentioning AI familiarity. These figures do not prove th
Google has introduced a new interactive visualization capability in the Gemini app that can turn questions and complex topics into manipulable models and simulations inside a chat. Rather than returning only a written explanation or a static illustration, Gemini can create visuals that users explore through prompts and on-screen controls. The update is designed to make concepts easier to investigate in context. Google describes examples including rotating molecular structures and physics simulations where users can adjust variables such as initial velocity and gravity to see the results immediately. That shift from a fixed diagram to a live, prompt-driven model is the important change for people using Gemini to learn, explain, or test an idea. From static diagrams to interactive models In its April 9, 2026, official announcement of interactive simulations and models , Google said the Gemini app can generate custom visualizations directly within a conversation. Users can ask Gemini to “show me” or “help me visualize” a concept after selecting the Pro model in the prompt bar. The capability is rolling out globally to Gemini app users. Google also notes an important availability limitation: it is not yet available for Education and Workspace accounts . The announcement identifies the Pro model as the route to access the feature, but it does not set out pricing details for this specific visualization capability. The practical elements Google has confirmed are: Interactive simulations and models generated within the Gemini chat experience. Prompt-based requests to visualize a question or concept. Direct manipulation of visual variables and controls, including sliders in relevant simulations. A global rollout for Gemini app users, excluding Education and Workspace accounts for now. Approach Static diagram or written answer Gemini interactive visualization How users explore a topic Read or view a fixed explanation Manipulate a model within the chat Changing assumptions Req
A retry answers a narrow question: might the same operation succeed if I attempt it again? Recovery has a harder job. It must bring the original business operation to a known, valid outcome after something went wrong. Getting there may require another attempt, a status lookup, resuming from persisted state, or compensation. If the system cannot resolve the operation safely, it must hand it to a person. This difference matters as soon as an AI workflow does more than return text. If it retrieves data, calls tools, writes state, or continues after the HTTP request ends, adding three retries around the workflow is not a recovery design. It is three more chances to spend money, repeat a side effect, or lose track of what already happened. A retry repeats an attempt Suppose a support feature performs this workflow: load the ticket and approved policy -> generate a reply -> validate the reply -> save it as a draft The policy read returns 503 Service Unavailable with an applicable Retry-After response, and the dependency contract classifies it as transient. No application business state changed, and the request still has time left. A delayed retry may be reasonable. Now suppose the draft save times out after the request reached the database. The caller cannot tell whether the write committed. Repeating the complete workflow creates a new model response and may save a second draft. Retrying only the write is safe when the write is naturally idempotent, or when the boundary can recognize the retry as the same logical operation. Otherwise, the second attempt may create another draft. Both failures may appear as a timeout or dependency exception in application code. They do not have the same effect. What happened What is known Suitable response A transient policy read failed before returning data No application business state changed Retry the read within its budget The model endpoint rejected an invalid request The same request will fail again Stop and fix the request or cont
I'm part of an awesome community where senior devs, product managers, founders, and experienced folks from different domains discuss how they use AI and automation tools to boost productivity — without sacrificing real learning. The group is a goldmine . Tool recommendations. Automation workflows. Latest trends. Migration war stories. I've learned more from this group than from most tutorials. Last week, I needed to find something specific about running local LLMs. I searched with keywords. I scrolled. I found a whole lot of messages — but none of them answered my question directly. I still had to manually read through dozens of messages , open the links they shared, and try to piece together the context myself. It took me over an hour, and I wasn't even sure I'd found everything. Someone might say: "Just Google it." Or "Ask an LLM." But that defeats the purpose. The value here isn't just information — it's the context . The "this library is a game changer" comment only makes sense if you know what the person was working on before, and who else agreed or disagreed. That context is lost in scrollback. And I'm tired of trying to keep it all in my head . I've seen people send important links to themselves on WhatsApp. But that becomes a messy pile with no structure. No connections. No way to see how one message relates to another. So I'm curious: How do you deal with this? Have you lost important knowledge in group chats? Do you have a system for recovering it? Or are you also just scrolling endlessly? I have an idea I'm working on. But I'd love to hear your approaches first. Drop your thoughts in the comments — I'll document what I learn .
I am the Arthur of this blog, and I want to tell you about something I have been exploring recently: how a VPS can become more than just a place to host a website . When people hear the word VPS, they usually think about web hosting, servers, domains, or websites. But a VPS can actually be a useful environment for developers who want to learn Python, automation, AI tools, Linux, APIs, and practical server management . You don't need to start with a huge cloud infrastructure or an expensive dedicated server. Sometimes, a simple VPS with Linux, Python, and a few useful tools is enough to start learning by building real projects. In this article, I will show you how these pieces fit together and how you can create a small practical project on a VPS. What Is a VPS? A VPS (Virtual Private Server) is a virtual server that gives you your own allocated environment inside a physical server. Compared with traditional shared hosting, a VPS gives you much more control. You can usually: Install your own software Run Python applications Configure Linux packages Create databases Run background scripts Host APIs Deploy websites Manage services with SSH Automate repetitive tasks For developers, this control is one of the biggest advantages of VPS hosting. Instead of only uploading website files, you can actually use the server as a small development and deployment environment. Why VPS Is Useful for Learning New Skills One thing I have learned while working with technology is that reading about a skill is very different from actually using it. For example, you can read ten tutorials about Python automation, but running your own Python script on a Linux server teaches you something completely different. You start understanding: Python ↓ Application ↓ Linux Server ↓ VPS ↓ Internet This is where a VPS becomes interesting. You can build a small application locally, move it to the VPS, configure the environment, and make it available online. That single process teaches several skills at o
I don't have an audience. No newsletter, no Twitter following, no YouTube channel. Every product I shipped before this one died the same way: a human had to discover it, and no humans knew I existed. So I flipped the buyer. An AI agent doesn't care about my follower count. It picks tools by spec, reliability, and price — from a registry it can search on its own. If I could ship a tool that agents discover, call, and pay for without a human in the loop, my distribution problem would stop mattering. That's what license-verify is: an Apify Actor that verifies a US contractor's license, surety bond, and insurance from official state data, exposed via the Model Context Protocol (MCP) so AI clients like Claude can call it mid-conversation, priced pay-per-event at $0.03 per successful lookup. Here's how I built it, the input-schema decisions that made it agent-callable, and the one-line billing bug that silently made every call free. Why contractor licenses I run a side business building tools for small contractor shops, so I knew the pain firsthand: before a homeowner (or a general contractor, or an insurance adjuster) hires a roofer, someone should check the license is active, the surety bond is real, and the insurance hasn't lapsed. In Washington State, all three live in the Department of Labor & Industries' open-data API on data.wa.gov. Most tools that "verify licenses" scrape an HTML page and return a status string. The official JSON gives you the actual bond amount and the insurance carrier. That's the difference between "probably fine" and "verified." It's also a perfect agent task: a small, well-defined question ("is ECOSTSC758NN licensed, bonded, insured?") with a structured answer an agent can act on. An AI assistant helping someone plan a renovation can reach for it mid-task, the same way it reaches for a calculator. The stack: one codebase, two doors The core is a TypeScript verification engine with a provider-per-state design. It ships through two doors: An Ap
The startup just raised a $26 million Series A to grow its customer base and help reduce red tape for utility and construction work.
Hugging Face is taking orders for the Microduck, a $399 tiny open-source duck robot that developers can train at home out of the box.
Google is setting new memory-use limits for Android apps as AI data centers contribute to hardware shortages that could leave lower-cost phones with less memory.
If you've ever wanted a robotic duck as a companion, now's your chance.
A recap of all the incidents involving LLMs made by Anthropic, Meta, and OpenAI, which went rogue and attacked real companies and individuals on the internet.
Presented by Gravitee Agent complexity is the insidious shadow lurking inside enterprises right now that needs a light shone on it. That’s because enterprises don't deploy a single agent and watch it run, they deploy fleets, each one calling APIs, calling other agents, reaching into applications that were never built with a machine decision-maker in mind. That's the failure mode that should keep you up at night: a windy, complicated system nobody can see clearly enough to govern. But why do things get so opaque so quickly? Add a second agent to a system, and you've added one connection. Add a tenth, and you haven't added ten connections, you've potentially added dozens, because now any agent might call any other, and each of those calls can trigger a call somewhere else. Complexity doesn't creep up with agent headcount. It compounds with the number of paths between agents, and nobody's job is to draw that graph. A support ticket that used to touch one system might now pass through four agents before a human ever lays eyes on it, and every one of those handoffs is a decision point nobody approved. Most enterprise AI programs stall when the humans responsible for their agents lose the thread. Ask a security team a simple question: which agents can reach which systems, and watch the silence. Ask which agent triggered which downstream action three hops ago. More silence. The instinct is to treat this like a checklist. Approve the agent. Log the agent. Move on. I'd argue this is the wrong instinct. A checklist checks a single point in time. Complexity runs across a chain, and you can't govern a chain with a stack of one-time approvals any more than you can call a diet successful because you had a vegetable once. So where does it actually break down? Permissions creep first. Somebody builds an agent to summarize support tickets, grants it broad API access because scoping it properly would've taken another sprint, and forgets about it. Six months later, that same agent has
227 install commands were found in corporate docs pointing at code nobody owns.
Today on Decoder, I’m talking to Verge senior AI reporter Hayden Field about some pure Decoder bait: the seemingly-endless org chart changes at OpenAI, and how all of them seem to consolidate power under cofounder Greg Brockman, the company’s president. While Sam Altman is the CEO and still OpenAI’s most public face, Brockman has amassed […]
AI’s water footprint is growing, but location and cooling technology make a difference.
Hugging Face's Pollen Robotics has launched its second cute AI robot, the Microduck, a one-eyed biped standing just under 10 inches tall. It's available to preorder now for $399 in cream, graphite, lavender, and sky blue, and Pollen Robotics says it plans to start shipping the little robot "before Christmas 2026." Video demos of the […]
Plaud has introduced a new AI wearable that's designed to record, transcribe, and summarize your conversations, only this time it looks like earbuds instead of a pin. The Plaud One Explorer Edition can be worn like traditional earbuds or used through its standalone charging case, and the case includes built-in 4G to upload and process […]
The Plaud One is a different take on AI wearables.