标签：#ai

OpenAI leaders think the company’s next generation model, which excels at computer use and coding, may mark a major milestone in AI development.

OpenAI claims that Astra represents "a new frontier on computer and browser use," and that it handles tasks with unmatched "speed, accuracy, and safety."

OpenAI's next big model is here: GPT-6 Astra. The company calls it a "generational leap in capability" for areas like cybersecurity, professional work, software engineering, science, and computer use. As OpenAI announced earlier this week, it's also the first model designated as meeting OpenAI's "critical cybersecurity capability threshold" - but the company promises that won't […]

Infostealer malware apparently had harvested active Claude login sessions from users' PCs.

The family-focused AI assistant wants access to the details of your everyday life, but says it won’t use that data to train AI models or share it with others.

A developer opens a pull request. The feature works. The tests are green. The implementation looks reasonably clean. Then they realize the PR changes 17 files for what should have been a relatively small feature. There is a new abstraction nobody on the team has seen before. A dependency has been added for functionality the project already had. Two similar utilities now exist in different parts of the codebase. The tests are extensive, but nobody is completely sure what assumptions they are actually testing. And the author didn't write most of it manually. AI did. This is one of the less obvious problems created by AI coding tools. The problem isn't necessarily that AI generates bad code. Sometimes it generates perfectly valid code. The problem is that it can generate code faster than a team can develop an understanding of it. That changes the bottleneck in software engineering. The Cost of Writing Code Has Changed For a long time, engineering teams were constrained by how quickly developers could implement things. A developer had to understand the requirement, design the solution, write the code, debug it, and test it. AI changes the economics of that process. A developer can now describe a feature, generate an implementation, ask for tests, refactor it, and generate supporting code in a fraction of the time it might previously have taken. That's useful. But the amount of code entering the system can increase faster than the team's ability to review and understand it. Consider a simple feature that requires modifying four files. An AI coding assistant might produce a solution that touches twelve. It may introduce a service layer, a helper abstraction, a configuration object, several interfaces, and a new package. None of these things are necessarily incorrect. But every additional abstraction creates another thing a future developer has to understand. This is where code familiarity becomes an engineering concern. Working Code Isn't the Same as Understandable Code A

NVIDIA's PAIR is a free, open-source tool that can distribute AI workloads across idle computers.

Google makes its suite of voice tools available for Gmail, Docs and Live - at least if you're a paying customer.

Learn how to run parallel agents in the GitHub Copilot app, and experience the moment it stops feeling scary and starts feeling powerful. The post GitHub Copilot app for Beginners: Run several agents at once appeared first on The GitHub Blog .

Your first AI patch should fail closed today. Do not ship a feature on day one. Prove one target file can revert cleanly now. You joined a messy repo this morning. The assistant wants a wide rewrite. Your job is a tiny reversible cut only. This drill gives you a blast-radius file first. You fill it before any model writes code. Then a short script checks the revert path. Why day-one AI diffs explode Cheap code is not cheap to unwind. One extra import can touch auth. One extra migration can lock deploys. You will not know the architecture yet. You also should not pretend otherwise. A blast-radius file makes unknowns explicit fast. If the file cannot name a revert, stop. You do not prompt for more code. You shrink the change until revert is boring. What you will build today You will add two artifacts on your branch. Keep both files in the first PR. blast_radius.py — the contract for this change. scripts/check_blast_radius.py — the fail-closed proof. The contract is the source of truth. The checker is the only merge gate. No green checker means no review yet. Step 1: Freeze one target file Pick one production file you can read. Do not pick a whole folder. Do not pick generated vendor code. git ls-files '*.py' '*.ts' '*.go' | head -n 40 TARGET = src/billing/invoice.py wc -l " $TARGET " git log -n 5 --oneline -- " $TARGET " Read the last five commits on that file. Write two plain sentences in notes. Pick a smaller file if you cannot yet. You now have a hard fence. Everything outside that fence is forbidden. Your assistant may not cross it. Step 2: Write the blast-radius contract Create blast_radius.py at the repo root. Keep the dict small. Fill every field with your own hands. # blast_radius.py # Day-one contract. Humans edit this. Models do not. BLAST = { " change_id " : " day-one-001 " , " intent " : " Add a fail-closed guard on invoice totals. " , " target_files " : [ " src/billing/invoice.py " , " tests/billing/test_invoice_flag_off.py " , " blast_radius.py " , " scr

Open YouTube to learn automation and you get n8n, Make, Zapier. The enterprise-market tools from the last piece (Boomi, Workato, UiPath) are almost nowhere. The job ads clearly want them; the content layer barely mentions them. The easy answer is views: beginners are the audience, so beginner tools win. That's half of it; the other half is where the money comes from. Follow who pays the teacher. The first money: affiliate commissions n8n runs an affiliate program for creators : 30% of referred cloud-subscription revenue, for twelve months. Every viewer who signs up through a creator's link becomes recurring income for that creator, and Make runs a similar program . Zapier has no affiliate program open to ordinary creators ; it holds the same shelf space through search dominance instead. Enterprise vendors don't pay creators this kind of commission. There is no per-signup commission open to individuals. Workato and UiPath do run partner revenue programs, but the money arrives after a partner agreement and a closed enterprise deal. A video can't collect on that, so a YouTuber teaching Workato has effectively nobody to collect from. Which means a creator's menu was set before the first video went up. The second money: the community business The monetization point in automation education now sits below the videos, on Skool, a community platform. The two biggest automation communities there, AI Automation Agency Hub (330,000+ members) and AI Automation Society (440,000+), are both free. The free tier brings people in, and what's sold behind it differs by operator. AI Automation Society sells a $129-a-month membership to its 440,000 free members; the AAA Hub side sells an accelerator priced above $5,000 to its 330,000 (pricing disclosed only on a sales call; that figure comes from student accounts). Where the published numbers allow an estimate, at AI Automation Society, the paid share sits under 1%: 447,000 free vs 3,700 paid is 0.8%. Strictly a paid-to-free count taken

How do you run an autonomous AI agent in the cloud 24/7 for just $5.70 a month? I recently wanted to build a background worker with persistent disk storage and an instant web dashboard, but I didn't want the headache of managing a virtual machine or paying a massive monthly bill. If you are building long-running agents, you know this exact cloud hosting dilemma: Standard serverless (like Cloud Run services or Lambda): When traffic stops, the container scales to zero — instantly killing your background loops and wiping your agent's active memory (RAM). On the flip side, a sudden traffic spike spins up multiple containers that can overwrite each other's state files and corrupt your data. (Note: Save state using JSON or Markdown files. Avoid SQLite, as Cloud Run volume mounts ) A regular virtual machine (like EC2 or Compute Engine): Keeps your agent running 24/7, but a standard 1-vCPU machine typically costs $15 to $25 a month even when idle. Even if you use a heavily-throttled fractional VM for $7/month, you are still stuck with the full infrastructure management overhead. Last year, I built a multi-agent Trend Spotter with ADK . It worked well, but I wanted to make it fully autonomous: a continuous, long-running agent that scans and summarizes tech feeds in the background without manual triggers or high hosting costs. Google Cloud's new Cloud Run instances primitive solves this exact problem. It gives you a single, always-on container that runs 24/7, costs $5.70 a month on a shared CPU, provides a free HTTPS endpoint, and lets you mount cloud storage like a normal local disk. Here is how to build and deploy a production long-running agent with this setup (you can follow along with the complete source code in the repo . What are we building? I want to stay up to date with what is happening in AI and agent engineering. But instead of manually opening 20 browser tabs across different websites every morning, I wanted to build my own long-running agent that updates me on

Design engineers increasingly work across two systems: the visual language of a product and the implementation details that make it usable. Skills for Design Engineers from ibelick focuses on that overlap, packaging practical guidance for building interfaces with stronger visual quality, clearer interaction patterns, and more consistent engineering decisions. The project is attracting attention, with +46 stars today . That momentum makes sense: design-focused AI workflows are moving quickly, but many generated interfaces still need human judgment around spacing, typography, responsive behavior, accessibility, and component reuse. The useful way to approach this project is not as a drop-in framework. Treat it as a reference layer for your development workflow. Read the relevant skill instructions, adapt them to your stack, and keep the resulting guidance close to the codebase so it can be applied consistently during implementation and review. A lightweight local setup might look like this: mkdir -p .ai/skills/design-engineering curl -L https://github.com/sponsors/ibelick \ -o .ai/skills/design-engineering/reference.html For a real team workflow, I would convert the useful parts into a checked-in Markdown file: .ai/ └── skills/ └── design-engineering/ ├── interface-quality.md ├── responsive-layouts.md └── review-checklist.md This keeps the process portable across editors and AI assistants instead of tying it to one tool. It also makes design decisions reviewable in pull requests, which is more valuable than keeping them inside an undocumented prompt. Before using the approach in production, watch for: Context drift: generic design guidance can conflict with an existing design system, so define project-specific tokens and component rules first. AI overconfidence: generated UI still requires manual checks for accessibility, keyboard navigation, mobile behavior, and performance. The strongest ROI comes from using these skills as repeatable engineering standards—not as a

Welcome back to our series on building the ultimate AI research assistant for our AI agent podcast! In our first post, we built a fantastic agent that could search the web to find the latest AI agent news for the agent factory podcast . But what if we want to add more specialized skills, like getting the real pulse from developer communities on Reddit? To do that, we need to upgrade our agent's design. In this guide, we are going to level up our skills and refactor our simple agent into a powerful multi-agent system . We will build a "Manager" agent that directs a team of specialists, including one with a custom-built Reddit tool , to gather richer, more diverse insights. By the end of this post, you'll have an even more powerful Trend Spotter agent that gets information from multiple sources. More importantly, you will learn the advanced skills needed to build complex agents with ADK . You will know how to: Build a scalable multi-agent system. Build a custom tool from any Python function (like our new Reddit tool). Create an orchestrator agent that delegates tasks to a team of specialists. Write advanced prompts to manage a multi-step, multi-tool workflow. Debug a multi-agent system using the ADK's powerful Trace view. This architecture is the key to unlocking your agent's full potential. Let's get started! Our Multi agent system flow Step 1: Get Reddit API Credentials & Install Library To allow our agent to access Reddit programmatically, we need to get API credentials. This is free and only takes a minute. Navigate to Reddit Apps: Log in to your Reddit account and go to the app preferences page: https://www.reddit.com/prefs/apps . Create a New App: Scroll to the bottom and click the button that says "are you a developer? create an app…" . Fill out the form: name: Trend Spotter Agent Select the script option for the application type. about url: You can leave this blank. redirect url: You must enter http://localhost:8080 for this field. Click create app . You will

OpenAI's ChatGPT, xAI's Grok, and Anthropic's Claude are all experiencing issues. At around 11AM ET, ChatGPT started returning error messages for users trying to use the chatbot, with its status page saying there are currently "elevated errors across ChatGPT and Codex." In addition to preventing users from having conversations with ChatGPT, the outage is also […]

WeatherNext 3 is the latest wave of a sea change in meteorology brought out by deep learning techniques. Google says it will start feeding into weather information users see in search, Google Maps, and Gemini.

Google is rolling out an updated AI weather model that's supposed to be more accurate, especially when it comes to predicting rain and snowfall. In the announcement today, the company says it's now able to make forecasts with "unprecedented resolution" using its new WeatherNext 3 AI model. It can produce a global picture that's five […]

Nvidia says Hugging Face will stay open even as the chipmaker takes control of a key AI hub.

Most hackathons ask you to build whatever you want. Dogfood 2026 does the opposite. Everyone builds the same thing: a submission and judging platform for hackathons. The challenge is simple: Build the platform that will judge you. And there is a reason this is more interesting than it sounds. Hackathon Raptors has run 35 hackathons across 85+ countries since 2023. They have seen the same problems appear again and again: registrations, teams, submissions, judge assignments, scoring, normalization, results, certificates, and exports all becoming separate pieces of an increasingly messy workflow. Now they want to build the platform they actually wish they had. That is what Dogfood is about. About the Hackathon Dogfood 2026 is a 72-hour online hackathon organized by Hackathon Raptors . The event runs from September 25 to September 28, 2026 . At a glance 🌍 Online and global ⏳ 72 hours 💰 $2,500 prize pool 👥 Solo or teams of up to 4 💸 Free to participate 🔓 Open source 🐳 Self-hosted 🛠️ Build with the stack of your choice But this is not a normal platform-building challenge. The winning project is intended to be forked, self-hosted, and used for actual Hackathon Raptors events. So instead of building a demo that gets abandoned after the weekend, you are building something that could become real infrastructure. Why Build Another Hackathon Platform? Hackathon platforms already have most of the features organizers expect. Registration. Team formation. Project submissions. Public galleries. Judge scoring. Community voting. Organizer dashboards. CSV exports. So what is missing? The difficult part is not building another CRUD application. The difficult part is making the entire system reliable when real people start using it. Consider judging. Two judges can look at the same project and give completely different scores. One might give almost everything a 4 or 5. Another might rarely give anything above a 3. Simply averaging those scores can produce a ranking that reflects the judg

Disclosure up front: Mnemoverse publishes this post, and Mnemoverse is one of the seven tools on it, so read every row knowing the author holds a position. With that on the table, the honest answer to the question in the title has not changed all year: there is no single best AI agent memory in 2026. There is a best answer to one prior question, and it decides more than any feature list: how much of your application should the memory system own? This post turns that question into a decision map. The deep, dated per-system read lives in Mem0 vs Zep vs Letta vs Cognee vs Supermemory ; head-to-head pages live on the comparison hub . TL;DR No single best exists. The boundary question (how much of the app the memory system owns) sorts the field faster than any benchmark. Seven systems, seven different jobs: embeddable SDK, temporal fact graph, self-editing runtime, ingestion pipeline, managed context engine, framework primitive, cross-tool managed memory. A tool chosen by ranking gets replaced; a tool chosen by job stays. Every claim here was checked against the vendors' public pages in July and August 2026, and these products change fast: verify against their own docs before you commit. The decision map The boundary question is the one-sentence filter this map runs on: how much of your application should the memory system own? Answer it first, and most of the table collapses to one or two rows. Your job Start with The cost you accept Embed an open-source memory SDK inside one application you fully own Mem0 You wire it into each app yourself; Apache-2.0 self-hosting is real Track facts that change over time, with valid-from and valid-to history Zep You operate Graphiti with a Neo4j backend, or take the managed cloud Build an agent that curates and edits its own memory as first-class behavior Letta You adopt a full runtime from the MemGPT line, not just a memory API Turn documents and data sources into a queryable knowledge graph Cognee Pipeline thinking: Extract, Cognify