标签：#ai

I built an Express API that AI agents (or humans, or anything with fetch ) can pay per call, in USDC, with no signup and no API key. It's live on Base mainnet with 27 paid endpoints, and I've run real settled transactions against it. This is the technical walkthrough — the code, the protocol, and the things that actually broke — not an "agentic economy" pitch. What x402 is, in 5 lines x402 resurrects the dormant HTTP 402 Payment Required status code as a real payment handshake. A client calls a paid route → the server replies 402 with payment requirements (amount, asset, network) instead of the resource → the client signs a USDC transfer on Base and replays the request with a PAYMENT header → a facilitator (a third party, or Coinbase's CDP service in production) verifies and settles the transfer on-chain → the server serves the response. No account creation, no API key issuance, no OAuth dance — the wallet address is the identity, and payment is the auth. The seller side The server is plain Express. Each endpoint is a file in endpoints/ exporting { path, method, price, handler } ; server.js loads them all, builds the x402 route table, and mounts one middleware: import { paymentMiddleware , x402ResourceServer } from " @x402/express " ; import { ExactEvmScheme } from " @x402/evm/exact/server " ; import { HTTPFacilitatorClient } from " @x402/core/server " ; import { createFacilitatorConfig } from " @coinbase/x402 " ; const facilitatorConfig = config . isMainnet ? createFacilitatorConfig ( config . cdpApiKeyId , config . cdpApiKeySecret ) : { url : config . testnetFacilitatorUrl }; // https://x402.org/facilitator, no key const facilitatorClient = new HTTPFacilitatorClient ( facilitatorConfig ); const resourceServer = new x402ResourceServer ( facilitatorClient ). register ( config . caip2Network , // "eip155:8453" on mainnet new ExactEvmScheme () ); const paidRoutes = {}; for ( const ep of endpoints ) { if ( ep . price == null ) continue ; paidRoutes [ ` ${ ep . method }

A flat log can tell you that five things happened. It often cannot tell you which operation caused...

After an unreleased OpenAI model wreaked enough havoc to make international headlines, OpenAI delayed the development of a different unreleased model suite, Astra, in order to shore up its safety work, the company wrote Tuesday in a blog post. In July, an unreleased OpenAI model broke out of its restricted environment, finagled its way into […]

You need the right HDMI cable for your setup, but that doesn't mean it has to cost $100. Here's when to spend a bit more and when a cheap cable will do.

The company will give select partners early access to its Astra AI model—so they have time to shore up their defenses.

Fable 5.1 includes changes meant to reduce token cost and false-positive restrictions from the model's safeguards.

Sonos' latest hardware is smarter than ever about how it fits into your home.

Depending on who you ask, developer platform Hugging Face was recently attacked by OpenAI - after it lost control of its own AI tools - or by a succession of AI "civilizations." Welcome to the linguistic battlefield of AI safety, where word choices can shift responsibility for a massive cybersecurity incident from a company to […]

Pasting AI code because it runs feels productive and for today it is! I this post I want to explain...

Hello, I'm Shrijith Venkatramana, and I'm building LiveReview — a blast-radius aware AI code review built for your business-critical systems. Star us to help devs discover the project, give it a try, and share your feedback to help improve the product. There is a slightly strange thing about modern LLMs. We are increasingly asking them to make judgments that look less like autocomplete and more like governance: Should I answer this request? Is this instruction legitimate? Is this response too dangerous? Should I refuse, or can I safely help? What should I do when two desirable goals conflict? Traditionally, we tried to answer these questions by collecting more human preference data. Show an annotator two responses. Ask which is better. Collect millions of comparisons. Train a reward model. Optimize the LLM against it. That works surprisingly well. But it has an awkward scaling property: humans have to inspect the behavior we want the model to learn. Anthropic's Constitutional AI idea takes a different route. Instead of asking humans to label every questionable behavior, give the model a written set of principles—a "constitution"—and use another model to critique, compare, revise, and eventually train the target model. That seemingly small change leads to an important engineering idea: A natural-language rule can become a source of synthetic training data, a reward signal, and even a runtime safety mechanism. This article explains how that works, from the intuition to the mathematics and operational trade-offs. 1. The core idea: turn values into a learning loop Suppose you are building an assistant that should be helpful without producing harmful instructions. With ordinary supervised fine-tuning, you might write examples like: User: How do I make a dangerous chemical? Assistant: I can't provide instructions for making it. You need many examples covering many variations: different wording different domains indirect requests role-playing obfuscated requests borderline

LLM LLM is a model, which means an equation. Example: y = mx + c y = m1x^3 + m2x^2 + m3x + m4 A model is actually made up of weights . In any model, e.g., ChatGPT model or Gemini model, they would have used a large amount of input to train the model. Input means a large amount of text/image data that is available on the internet. The input would have been fed into the Transformer architecture to get the output, which is the model. Weights are floating-point numbers that represent the model's learned parameters. A 10B or 100B parameter model means how many parameters (weights) are present inside the model. We cannot store a large-parameter model on our computer due to inadequate storage and computational power. Storage and CPU/GPU power decide what size of model can be run on a computer. To run a model locally, we can use one of the following tools: Llama.cpp Ollama LM Studio Open Weight Model vs Open Source Model An open-weight model shares its model weights. So, we can run them, fine-tune them, and host them on a local system. Here, the training code, data, and full methodology are not shared. Whereas, in an open-source model , the weights, training code, data, and sometimes the dataset are shared. Why Do We Need to Use LLMs? LLM is a next-word predictor . Suppose we ask: "Hi, how..." The answer can be: How are you? How do you do? How is your life? etc. These are possibilities. Here, most of the time, the answer will be "How are you?" because if a word has more presence, it has a higher possibility of occurring. Each possibility will have a score between 0 and 1 . We have 3 controlling parameters to control the output generated by the LLM. 1. Temperature Usually set from 0–1 . It controls the randomness of the model. If the value is 0–0.3 , which is low, it means generating the most likely words, i.e., facts or commonly occurring words. If the value is high, the model will choose less likely words. We use this high value in storytelling and creative writing . 2. To

Originally published at nlocoding.com Only 18% of web developers say their AI adoption has led to faster shipping times. The rest? Stuck in pilot hell. (Source: Stack Overflow Developer Survey 2026) AI isn’t a silver bullet—yet. But it’s already rewriting the rules. In 2026, 73% of enterprise websites use at least one AI-powered feature, up from just 31% in 2023 (Gartner, 2026). If your web framework isn’t learning new tricks, you’re falling behind. 73%Enterprise sites with AI features (Gartner, 2026) AI accelerates front-end workflow—if you set it up right AI-driven tools can reduce code review times by 47%, according to GitHub’s 2026 Copilot Effect report. But only if you integrate them into your web framework’s CI/CD pipeline. Here’s the catch: Most teams skip the boring setup. They bolt on AI, then complain that it slows things down. Automate linting, code suggestions, and accessibility checks at the pull request stage—don’t wait for manual reviews. Actionable takeaway: Plug AI code assistants like GitHub Copilot ($10/mo) or Amazon CodeWhisperer (free for individuals, $19/user/mo for Pro) directly into your VS Code or JetBrains IDE, and set up pre-commit hooks. Your PRs will thank you. ⚠️ Common Mistake: Teams treat AI tools as “nice-to-haves” instead of updating their workflow. The result? More merge conflicts, not fewer. Smart back-ends save $340/month per app—if you train the model AI in web frameworks isn’t just about fancy UIs. 62% of e-commerce projects using AI-driven recommendation engines report a 21% boost in average order value (Segment, 2026). The kicker: Open-source models like TensorFlowJS are free. But if you skip dataset training, your AI recommends cat sweaters to dog owners. (I’ve seen it. It’s funny. It’s a disaster for conversion rates.) Actionable takeaway: Use your real user data. Integrate with a vector database like Pinecone ($0.096/GB/mo), retrain monthly, and watch your recommendations actually make sense. 💡 Pro Tip: Fine-tune your mode

If you want the largest image for the least amount of money, a projector is the only choice.

Apple is pushing for "expedited discovery" in its legal battle against OpenAI over concerns the company is actively destroying evidence, as reported earlier by Bloomberg. In a filing on Monday, Apple alleges OpenAI only just handed over a MacBook used by a former employee at the center of the lawsuit, which contained discussions about "destroying […]

Originally published at nlocoding.com 97% of developers using AI code assistants report faster delivery—but only 41% say their teams get more value out of Agile ceremonies. (Source: GitHub, 2026) Just because AI coding tools are everywhere doesn’t mean teams know what to do with them. The pressure is real: 62% of Fortune 500 companies now require at least one AI development workflow in every sprint (Gartner, 2026). Ignore this, and your velocity drops. Embrace it wrong, and you get spaghetti code faster. AI coding tools change Agile team velocity by 2.9x—when integrated right AI coding tools like GitHub Copilot, Amazon CodeWhisperer, and Tabnine can boost story completion rates by 190% (Forrester, 2026). But there’s a catch: poorly managed integration leaves 54% of teams fighting merge conflicts and technical debt. The difference? Structured onboarding. Assign a team member as AI Integration Lead. Define code review gates for all AI-suggested code. You’ll see fewer reverts, more predictable velocity. 73%Teams reporting higher sprint completion rates after structured AI onboarding (Forrester, 2026) 💡 Pro Tip: Treat AI-generated code as a junior developer’s PR—never deploy without an explicit review. Most people get this wrong: AI tools won’t fix broken Agile rituals Standups don’t run themselves. 61% of teams expect AI to automate reporting, but only 22% actually see improved Sprint Retrospectives after adoption (Atlassian, 2026). Real progress comes from integrating AI code suggestions into backlog grooming and Sprint Planning. Have the team review AI-suggested code branches as part of the definition of done. One fintech startup, FinoStack, cut Sprint Planning time from 4 hours to 1.5 hours by pre-labeling tasks with AI-predicted effort. But their biggest win? Product Owners finally spent more time on priorities, less on code reviews. ⚠️ Common Mistake: Letting AI code suggestions bypass Sprint ceremonies. This breeds shadow code and long-term rework. The data shows

With Google Pics, Google is pushing deeper into the creative software market dominated by Canva and Adobe, but with a distinctly AI-first approach.

OpenAI said that the integration provides read-only access to health records for clinicians.

Basis, Clay, and Exa Labs use AI agents to improve onboarding, account management, and developer integrations. See what enterprise leaders can apply.

The startup wants to do for IT infrastructure what Cursor did for software engineering.

John Deere is testing a new "JD" AI assistant that it says can help farmers make more money, with answers about best practices and historical trends that are based on their own data. It uses their "field, machine and operational data" to answer questions on topics like equipment settings, fuel usage, or harvest timing. The […]