标签：#AI

Last week, it came to light Cursor had mostly finished migrating from SolidJS to React . This migration happened about seven months ago. But it became a central focus of discussion following the Solid 2.0 RC release . Then yesterday, a week later, it came to my attention that the Anthropic docs example command for their large-scale migration feature is: I admit that my gut reaction was not great. Out of all the examples they could have chosen... Years of my work became a canonical example of the thing you migrate away from — in the same week we shipped the biggest release in the project's history — stung in a way I won't pretend it didn't. My second reaction was to assume that, like the other trickle-down posts I'd seen this week, this rode the same week-old news cycle. Then I checked the Internet Archive and realized this has been there since at least April 2026 . Four months before the Cursor story broke. At this point, the whole public footprint was a mention of an experiment sandwiched between bigger updates in a Cursor blog post posted in January. The kind of thing that no one outside the industry would even really pick up on. No reasoning, no benchmarks, no argument. Stop to think about what that means. I should be careful here because I can't prove anyone at Anthropic ever read that Cursor post. Nobody can. Maybe a docs writer saw the experiment. Maybe Claude drafted its own example. But think it through. Either it traveled from a buried line in one company's release notes into another company's official docs, or it needed no origin at all. It was already assumed before any public migration existed. Our industry has quietly started broadcasting conclusions where it used to transmit arguments. We couldn't have picked a worse time, because — as I'll get to — arguments are the only source that still matters. Why This Matters More Than It Used To It would be fair to ask, hasn't it always been like this? Teams cargo cult large players. Netflix or Facebook uses thi

The Linux Foundation has launched Akrites , an initiative intended to coordinate vulnerability discovery, remediation and disclosure for critical open-source software. The project arrives as AI-enabled vulnerability scanning changes the scale at which potential software flaws can be identified. Its central emphasis is not simply finding more issues, but getting fixes made upstream and patches deployed. Akrites is organized around a multi-stakeholder coalition that includes technology vendors, financial institutions and open-source foundations. The official Akrites open letter describes the effort under the message, “We All Depend on Open Source. We Will Defend It Together.” The initiative was launched on June 25, 2026 and is coordinated by the Linux Foundation. The publicly displayed letter includes organizations such as AWS, Anthropic, Chainguard, Cisco, Citi, Google, Microsoft and GitHub, JPMorganChase, IBM, NVIDIA and OpenAI, Endor Labs, Red Hat, the Rust Foundation, Sonatype, Vodafone and Zscaler. It also lists open-source groups including the Cloud Native Computing Foundation, OpenInfra Foundation, OpenJS Foundation, LF Energy, OpenSSF and the PyTorch Foundation. What Akrites is trying to change Akrites is focused on a practical security lifecycle: identifying vulnerabilities in critical open-source projects, helping drive remediation and handling disclosure. That focus matters because discovering a possible vulnerability is only an early stage of risk reduction. A finding has limited value if maintainers cannot address it, if the correction is not adopted upstream, or if downstream users do not deploy the available patch. The initiative therefore places upstream fixes and patch deployment at the center of its stated success measures. This is a meaningful distinction from approaches that judge progress largely by the number of vulnerabilities found or reports generated. The coalition combines several types of participants: Cloud, software and security vendors ,

Every hackathon has the same 90-second moment of dread: someone hands you a codebase you've never seen, and you have to make sense of it before the clock runs out. File trees don't help. grep doesn't help. You waste the first 30–60 minutes reading the wrong files, missing a hidden dependency, and stepping straight into a production trap nobody warned you about. In other words: before writing code, you spend half your time trying to figure out where the hell the code is. The idea wasn't "AI that writes your code." It was "AI that tells you where to look before you write it." For InnovaHack Chapter-1, my team built Waypoint — a dev onboarding platform. Point it at any GitHub repo or a local folder, describe a task like "Add a new global configuration flag to app.set()", and instead of you reading the whole codebase to figure out where that even goes, it hands you a Mission Brief : exactly which files you'll touch, the traps waiting in them, what to learn first, and the order to do it in. Waypoint made the Top 50 — one of the 50 chosen to advance to Round 2. Here's how it actually works under the hood, what it took to build, what's next for it — and since I don't believe in only posting the highlight reel, what happened after we placed that made us walk away from the next round. The problem: the cold-start tax Every time a developer joins a new codebase, or picks up an unfamiliar task in one they already know, there's a tax paid in wrong files read, missed dependencies, and traps hit blind. For me, that moment came when I wanted to contribute to Forem — the open-source project that actually powers DEV. I didn't know a line of Ruby on Rails, and between learning the language, understanding the framework, and preparing for interviews, I didn't have the time to read an entire unfamiliar codebase just to figure out where one feature belonged. I never ended up making that contribution. But the problem stuck with me. It's also a pattern for our team more broadly: we delibera

We build GPTree with several coding agents working the same repository at once: Claude Code, Codex, and Cursor, each in its own git worktree. The failure that finally made us build tooling for it was small and completely silent. One session was told to replace PaymentService with a Stripe-specific implementation. Another was told to add PayPal support to PaymentService . Different worktrees. Different files. Zero textual conflict. Git merged both branches cleanly, and the second change now depended on an extension point the first had deleted. Nothing in the toolchain had an opinion about it at any moment. Git compares diffs. It cannot compare plans. Worktrees isolate files, not plans Worktrees became the standard answer to parallel agents for a good reason: two sessions editing one checkout will overwrite each other's files and poison each other's context. Isolated checkouts fix that completely. But three failure modes survive file isolation, because they were never about files: Destructive versus additive. One agent removes or replaces a thing another agent is building on. The example above. Merges clean, breaks the design. Duplicate work. Two agents solve the same problem from different angles because nothing assigned ownership. You pay twice and then pay again to reconcile. Contract drift. One agent changes an API, a schema, or a config contract while another codes against the old shape. Compiles, runs, disagrees at runtime. A shared task list helps with the second one, if every agent reads it, every time. Nothing in that setup catches the first or third, because the collision is between intentions, and intentions live in prompts, not in any file a tool can watch. Declare the work before doing it Foremerge is the internal tool we built for this, open-sourced this week. It is a coordination protocol that sits above Git: agents declare what they are about to do, before they do it, in a form precise enough to check. A declaration is an intent with one or more semant

I am not going to spend this whole article making the usual serverless argument. Yes, managed infrastructure is useful. Yes, automatic scaling is nice. Yes, not having to patch servers is a win. Yes, event-driven architectures can be a great fit for modern web applications. All of that is true, but it is not the thing I want to focus on here. The more interesting point is that serverless changes how useful agentic coding can be. It gives AI coding agents a better environment to work in. Not because the agents suddenly become smarter, but because the system they are working on becomes more explicit, more constrained, and easier to inspect. That matters more than I expected. When you build a web application, eventually it needs to be hosted somewhere. You can put it on a VPS, configure nginx, run your app with systemd or a process manager, add a database, bolt on a queue, and wire up whatever else you need. That is a completely valid way to run software. Plenty of serious production systems work that way. But once you start using coding agents, a problem appears. The agent may understand your application code, but not the environment around it. It may not know how your reverse proxy is configured. It may not know how background workers are started. It may not know which scripts run during deployment, which environment variables exist in production, which assumptions live in a README, or which parts of the setup are just tribal knowledge. So when you ask it to make a meaningful architectural change, it has to guess. Sometimes those guesses are fine. Sometimes they are not. The agent may invent a worker process that does not match how you deploy. It may reach for Redis because that is a common queueing answer, even though the rest of your system does not use Redis. It may assume local file storage is available. It may add a scheduler without understanding where that scheduler will actually run. That is where serverless starts to feel less like a deployment choice and mo

₹85,000 per month. That was the AI API bill sitting in my client's inbox when they called me in a mild panic last quarter. They run a mid-sized e-commerce operation in Pune — about 4,000 orders a day — and had integrated AI into customer support, product descriptions, and internal reporting. The AI was working beautifully. The invoice was not. Three weeks later, their monthly bill was ₹12,400. Same tasks. Same quality. No corners cut. Here's exactly what changed. The real problem: every task was using the most expensive model When I audited their setup, the issue was obvious within five minutes. Every single API call — whether it was classifying a customer complaint into one of 8 categories or generating a 2,000-word product description — was hitting the same premium model. It's the most common mistake I see with businesses adopting AI: they pick one model during the proof-of-concept phase and never revisit that decision as they scale. You wouldn't hire a senior chartered accountant to do data entry. But that's essentially what was happening — a top-tier reasoning model answering "Is this complaint about shipping or billing?" Fix 1: Model routing — the single biggest cost lever Model routing means sending each task to the cheapest model that can handle it at acceptable quality. I categorised their ~47 distinct API call types into three tiers. 68% of calls moved to the lightweight tier, 20% to mid-tier, only 12% stayed on premium. That single change dropped the bill from ₹85K to roughly ₹38K — no quality loss, verified with two weeks of A/B testing on customer satisfaction scores before switching fully. Fix 2: Prompt caching — stop paying for the same context twice Their support bot sent the same 1,200-token system prompt with every call — policies, tone, catalogue context, all identical across thousands of daily calls. Caching processes it once and references it cheaply on subsequent calls within the window. At ~6,000 support interactions a day, this alone saved ₹8,

The potential for AI to automate scientific research and manufacturing must be balanced with new risks, Anthropic says.

Your coding agent talks to a model provider over HTTPS. That connection is a straight line: the agent asks, the provider answers, the answer lands in your editor. Nothing in the middle looks at what came back. For most of what an agent produces, that's fine. For the rest of it — the query built by string concatenation, the API key the model helpfully echoed back into a code sample, the eval() on user input — you find out later, in review, or in a scanner run, or never. This is a walkthrough of putting a policy layer in that line: a local proxy your agent points at instead of the provider, which inspects the response stream and decides allow , redact , or block before the text reaches you. I'll use Cencurity Engine because it's the one I build, it's Apache-2.0, and it runs entirely on your machine. The pattern generalises — if you're building your own gateway, the steps below are still the shape of the problem. What you need first Go installed (the engine is a Go binary you run from source) An API key for whatever provider your agent already uses An agent or IDE that lets you override the API base URL That last one is the real prerequisite. If your tool hardcodes the provider endpoint, none of this applies to it. Most don't: Roo Code, Continue, Claude Code and Gemini CLI all expose a base URL, and anything reading OPENAI_API_BASE will work too. Step 1: Start the gateway Clone the repo, open a terminal in it, and run: go run ./cmd/cast serve \ --listen :8080 \ --upstream https://api.openai.com \ --policy ./cast.rules.example.json Three flags, and each one is doing something you should understand before moving on: --listen is where the gateway accepts traffic. Local only. --upstream is your real provider base URL. Swap it for https://api.anthropic.com , https://api.deepseek.com , https://api.x.ai — whatever you actually use. --policy is the rule file. cast.rules.example.json ships in the repo and is a working starter set, not a placeholder. Note what is not in that com

Most package tracking flows make the customer do the work. You get a tracking number. You open a page. You refresh it. Maybe you get a generic text that says the package is out for delivery. If you need to ask a real question, you usually end up somewhere else entirely. I wanted to build the opposite shape: what if the package itself had an agent? The shipment-agent example is a Python and Flask app that treats a shipment as a durable AI entity. It can send proactive SMS updates, understand customer replies with Telnyx AI Inference, and answer inbound calls with shipment context. Code: https://github.com/team-telnyx/telnyx-code-examples/tree/main/shipment-agent What it builds The app centers around a ShipmentAgent . The agent owns: shipment status carrier and tracking context customer phone number interaction history messaging and voice behavior Instead of a stateless chatbot waiting in a web page, the agent lives alongside the shipment lifecycle. Carrier update -> Flask webhook -> ShipmentAgent updates state -> SMS customer Customer SMS reply -> Telnyx Messaging webhook -> AI Inference response -> SMS reply Customer phone call -> Telnyx Call Control -> ShipmentAgent answers with context Why this is useful Shipment status is not just data. It is a customer communication problem. People want to know: Is my package delayed? Can I leave delivery instructions? Did it already arrive? Who do I call if something looks wrong? Traditional tracking pages are good at showing status, but not at handling conversation. This example shows how to turn the shipment into a small communications agent that can respond across SMS and voice. The main flow When a carrier status changes, the app receives a webhook. For example: out_for_delivery delayed delivered The ShipmentAgent updates its internal state and sends a message to the customer through Telnyx Messaging. If the customer replies, the app passes the message and shipment context to Telnyx AI Inference. That lets the response incl

In the last article, I wrote about a release story that was weaker than the engine underneath...

Some of the world's largest tech companies and AI startups have come together to decry the current state of cybersecurity and to advertise a new solution that they say can ward off a new generation of cyber threats.

"Everyone wants to take that aircraft up now..."

Code reviewed by WIRED reveals the company is developing a feature that enables Codex to continue working proactively until it is “put to sleep.”

On Nvidia's earnings call Wednesday, CEO Jensen Huang casually announced the company had "achieved AGI," one of the tech industry's ultimate goals some of its biggest players have spent years chasing. Almost immediately, Huang dismissed the coveted milestone as "senseless." He's right. For the supposed finish line of the AI race, there is no consensus […]

Google AI Mode is getting a flight price tracker, hotel booking and a way to view the cost in miles or points.

The updates indicate that Google is looking position to AI Mode as an AI travel agent of sorts, as it's moving beyond simply helping users find information to actually handling parts of the trip-planning and booking process itself.

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