标签：#AI

If you're building AI agents that work with browser screenshots, you already know the pain. You take a full 1920×1080 screenshot, pass it to GPT-4o or Claude, and watch your token bill climb — while the model downscales the image anyway and blurs the exact text you needed it to read. There's a better way. The problem Vision LLMs are expensive for two reasons when you feed them full screenshots: Token cost — a full screenshot can cost 10–20x more tokens than a small crop Accuracy loss — models internally downscale large images, blurring fine text, labels, and UI elements But your agent already knows where to look. Browser automation tools like Playwright and Puppeteer give you getBoundingClientRect() — the exact pixel coordinates of any element on screen. So why are you sending the whole screenshot? The solution I built a stateless pay-per-use API that takes a screenshot and pixel coordinates, and returns just the cropped element as a lossless PNG — ready to pass directly to your vision LLM. POST /crop { "image" : "<base64 screenshot>" , "x" : 120 , "y" : 45 , "width" : 640 , "height" : 80 } Returns: { "success" : true , "data" : { "base64" : "iVBORw0KGgo..." , "mime" : "image/png" , "width" : 640 , "height" : 80 , "bytes" : 4821 } } A 4KB crop instead of a 2MB screenshot. Same information. 95% fewer tokens. How payment works Here's where it gets interesting. The API uses the x402 payment protocol — HTTP's long-dormant 402 Payment Required status code, finally put to use. There are no API keys. No accounts. No subscriptions. The agent pays $0.0005 USDC per crop on Base L2 automatically. The flow: 1. Agent POSTs to /crop (no payment header) ← 402 with payment instructions in headers 2. Agent transfers 0.0005 USDC to recipient wallet on Base (near-zero gas, ~2 second settlement) 3. Agent POSTs again with x-payment-tx-hash header ← 200 with cropped PNG The entire exchange happens inside the HTTP request cycle. No human intervention. No billing dashboard. The money lands

Your product might rank on page one of Google and still be invisible to AI. When someone asks ChatGPT "what's the best project management tool for small teams," does your product show up? For most SaaS companies under 50 employees, the answer is no. At Inithouse, we built Be Recommended to answer that question with a number: a single AI visibility score from 0 to 100 that tells you exactly where you stand across four major AI engines. Here is how the scoring works under the hood. What the score measures The Be Recommended score captures how often, how prominently, and how positively AI engines mention your product when users ask category-relevant questions. A score of 0 means no AI engine mentions you at all. A score of 100 means every tested prompt across all four engines names your product as a top recommendation. The four engines we test against: ChatGPT (OpenAI), Perplexity , Claude (Anthropic), and Gemini (Google). Step 1: Prompt generation We start by building a bank of 50+ real prompts that a potential customer would actually type into an AI assistant. These are not keyword-stuffed test queries. They mirror how real people ask for recommendations. For a CRM product, that looks like: "What CRM should a 10-person startup use?" "Best alternatives to Salesforce for small businesses" "Compare CRM tools with good API integration" "Which CRM has the best free tier in 2026?" We group prompts into three categories: direct (user names the product category), comparative (user asks for alternatives or comparisons), and situational (user describes a problem without naming a category). Each category tests a different signal: brand recognition, competitive positioning, and contextual relevance. Step 2: Multi-engine querying Each prompt gets sent to all four AI engines through their APIs. We capture the full response text, not just a yes/no for whether your product appeared. The raw responses go into a structured analysis pipeline. We run queries from neutral accounts with n

TL;DR - A finished app that only runs on one laptop is a private demo. Getting it online means connecting three things: a place to store the code (version control), a place to run it (a host), and an address people can type (a domain). The same AI tool that helped build the app can walk a beginner through all three, often without ever opening a terminal. An important step you don’t want to skip is the security check before going live, because the fastest way to ruin a launch is to ship with the database wide open. So you’ve done it. You built your first tool. And it works. The button does the thing. Now’s the moment. It’s time to get your tool online, but how? A project running on a laptop is real, but it lives in exactly one place, the machine it was built on. Nobody else can open it. Getting that project online is its own small skill, separate from building, and it trips up more beginners than the building did. A new coder can finish a working photo booth app in an afternoon and still have no idea how to hand it to a friend short of pulling up the GitHub link while sitting together over coffee. The good news is that the part that used to eat a whole weekend now takes a conversation. Three Things Every App Needs to Go Live Almost every deployment, whatever the tool, comes down to three things working together. Version control: This is a place to store the code and track every change made to it. For most people that means GitHub, which we’ve talked about before. The same way Google Docs keeps a version history, GitHub keeps one for a project. This piece does not re-explain it; the GitHub walkthrough covers the whole thing. A host: A host is really just a computer that stays powered on and connected to the internet with a public address of its own. When a visitor types in the app's address, their browser sends a request across the internet to that machine, the machine runs the code, and it sends the finished page back. A laptop was quietly doing both jobs during the

It's a spicy start for the AI company's collaboration with the chip maker.

The tokenmaxxing era was brief. We now appear to be entering the era of token rationing.

Privacy advocates and domestic violence groups say ID mandate is a big mistake.

Rep. Anna Paulina Luna (R-FL) says her staff used AI for "spellcheck" in an amendment summary for a major defense bill, but denies it was used for the bill text itself and says "NO Legislation is ever drafted with AI." Luna issued the response after accounts on X began sharing screenshots of an amendment summary […]

The AI arms race between China and the US has researchers on both sides worried about a “Chernobyl moment.”

Rockstar Games has finally given Grand Theft Auto VI a price ahead of the game's November 19th launch. But while announcing that the game would cost $79.99, Rockstar also confirmed that the physical versions of GTA VI won't include a disc, and instead will only contain a download code inside the box. Not only is […]

Table of Contents The Moment That Changed Everything What It Actually Is The Setup Nobody Believes Is This Simple Focused Sessions — One Agent, One Mission Sub-Agents With Specific Skill Sets Building MCPs for Your Own Stack What We've Actually Achieved How It Became a Force Multiplier for Incident Response From OpenCode to FRIDAY — The Agent That Investigates Incidents Autonomously From FRIDAY to JARVIS — Thinking About Write-Path Autonomy Deleting 130,000 Accounts Without Writing Code Learning the Entire Product in Conversations Why This Is Different From ChatGPT The Uncomfortable Truth What's Next The Moment That Changed Everything It was 11pm on a Tuesday. A cache migration in our production environment had just caused thousands of authentication failures for two of our largest enterprise customers. Our VP of Product wanted answers. Our support team was fielding escalations. And our engineers were alt-tabbing between AWS console, Datadog, GitHub, Azure DevOps, and PagerDuty trying to piece together what happened. Three weeks later, when we needed to attempt the same change again, an engineer typed this into a terminal: "Review the ADO change ticket, compare the MOP against the actual ElastiCache configuration in prod region, check the K8s config repo for how Redis env vars are wired on the Green cluster, and tell me if this approach avoids the token validation failure that caused the previous customer impact." Fourteen seconds later , the system had pulled the work item, queried AWS ElastiCache across four regions, read the Kubernetes configuration from GitHub, cross-referenced the deployment patches, and delivered a precise technical assessment including a risk it identified that the team hadn't documented: in-flight tokens during the 30–60 second Global Accelerator propagation window. That system is OpenCode — an AI-powered CLI assistant connected to our entire operational stack through the MC(Model Context Protocol). And it has fundamentally changed how a 20-

AI coding tools are incredible. But I noticed something — they ship code fast, skip the rules, and nobody catches it until it's already in production. That's exactly what GuardDuo is built to fix. The Problem We're in the age of vibe-coding. You describe what you want, the AI builds it, it works — and you ship it. But "works" and "correct" are two very different things. Imagine asking an AI to build a login form. It works perfectly. But under the hood it has hardcoded API keys, no input validation, missing aria-labels , and it's using fetch directly instead of your project's apiClient wrapper. Your Issue said none of that was allowed. Nobody caught it. That's the vibe-coding trap — and it's happening on every team using AI-assisted development right now. What is GuardDuo GuardDuo is a GitLab Duo Agent skill that acts as your AI guardian. Instead of just reviewing code in isolation, it cross-references your code changes against the actual intent of the linked GitLab Issue — using the Orbit Knowledge Graph , which is essentially the brain that knows your project's rules, requirements, and success criteria. In plain terms: GuardDuo reads what the Issue asked for , reads what the code actually does , and tells you exactly where they don't match. It audits across three dimensions: 🔐 Security — hardcoded secrets, SQL injection, missing input validation ♿ Accessibility — missing alt text, aria-labels , poor color contrast 📐 Standards — deviations from your project's established patterns and conventions And when it finds a problem, it doesn't just flag it — it fixes it. How It Works Just open GitLab Duo Chat or GitLab Agent Platform(on your choice of IDE) -> choose the agent as GuardDuo and type: Audit issue #[issue no.] — GuardDuo pulls the Issue context from Orbit, analyzes the code, and returns a structured report Fix issue #[issue no.] — GuardDuo generates a corrected implementation that satisfies all requirements Or paste any code snippet directly and ask it to audit o

Every serious AI workflow eventually runs into the same failure. The agent does useful work in one session. It learns the shape of the project. It figures out which assumptions were wrong. It follows a correction, makes a decision, and gets closer to the real work. Then the session changes. The next run starts too cold. Old context comes back without the correction that changed it. The agent asks for the same setup again. It repeats an assumption that was already fixed yesterday. You end up managing the memory of the work instead of moving the work forward. That is the problem Pith is built for. Pith gives AI agents durable project memory they can trust when facts change. It is not trying to make an agent remember everything. That would be the wrong goal. Real projects are messy. Facts change. Decisions get reversed. A note that was useful last week can become stale after a release, a migration, a new customer constraint, or one correction from the human operator. The harder problem is not recall. The harder problem is knowing which memory is still useful. Why Longer Context Is Not Enough Longer context helps, but it does not solve continuity by itself. A long prompt can carry more text into a single run. It cannot automatically decide which prior facts survived a correction, which decision is now superseded, or which evidence should come back when the project resumes three days later. Developers working with agents already feel this. The friction shows up as small taxes: repeating project background that the agent should already know; re-explaining decisions that were already made; correcting stale assumptions that were already corrected; losing the reason behind a prior choice; restarting from a cold state after every meaningful break. Those taxes compound. The more serious the workflow, the more expensive the memory gap becomes. If an agent is helping with a toy task, forgetting is annoying. If an agent is helping with a codebase, a release, a customer workflow,

The expensive, $27 million political proxy war between Anthropic and OpenAI came to a draw last night when Alex Bores, a New York state Assemblyman whose popularity surged after being targeted by a pro-AI super PAC, narrowly lost the Democratic primary to represent New York's 12th Congressional district. Prior to the race, Bores, a former […]

The new app, which is currently being tested with select creators, will have Facebook's recently launched AI creator assistant built into it.

Global music streaming service Deezer is taking a contrarian approach to AI, even as it adds a feature that lets fans remix songs.

If League of Legends and Valorant players have the right hardware and elect to opt into "pre-boot security mechanisms and Windows' own native protection features," then, starting today, they can switch the Vanguard anti-cheat software from always-on to one that's "on demand." With "Vanguard Pre-Check," the kernel-level driver won't launch when your system does, according […]

Agility Robotics, the humanoid robotics startup that spun out of Oregon State University in 2015, expects to generate $620 million in proceeds.

Figma's update adds a new code layer, support for motion and shaders, and the ability to create custom plug-ins for various tasks using AI.

Figma has revealed some new design and coding product updates at its annual Config conference that aim to help creatives "push their ideas further" and automate tedious tasks with AI. Part of this is a reimagined canvas that's now optimized for full-stack development, according to Figma, bringing teams, AI agents, tools, and materials "together in […]

As web developers, engineering workflows are heavily dependent on the NPM registry. However, the traditional process of searching, auditing, and integrating packages remains highly fragmented. Developers are routinely forced to hop between npmjs.com, GitHub source repositories, and external documentation tabs simply to verify bundle sizes, check dependency trees, or generate setup boilerplate. Following a strong reception on LinkedIn, X, and Facebook, the Motion Mind Team has introduced LibPilot to the dev.to community. LibPilot is not a traditional registry interface; it is an AI-powered search engine and discovery hub engineered to index, track, and analyze over 4,000,000 NPM packages in real time. Here is an architectural breakdown of how LibPilot restructures package exploration for modern developers and autonomous AI code agents. Intent-Based Discovery and Global Search Architecture Traditional search engines require users to input the exact name or strict keyword of a library. LibPilot introduces a dual-input architecture on its home page to eliminate this constraint: Direct Registry Querying: Users can input full or partial package names into the global search bar to instantly surface clean, structured, and typed suggestions directly from the live NPM ecosystem database. Contextual AI Recommendations: For scenarios where the ideal package is unknown, developers can type out a complete description of their project architecture or system constraints (for example: "a lightweight, typed state management engine that handles server-side rendering natively"). LibPilot's internal AI agent processes the functional requirements and suggests production-ready libraries suited for that stack. Continuous Context AI and Interactive Onboarding A core goal of the platform is reducing developer friction and maintaining deployment momentum. LibPilot transitions static package documentation into an interactive environment: Unlimited AI Chat Architecture: Once a library is select