"The company is experiencing strong demand for its AI solutions and services from enterprises and consumers, at levels that are exceeding the company’s available supply," Alphabet said in its statement.

Some report burning through their whole monthly "AI credit" allotment in a single day.

So let me get this straight... Anthropic wants to build a $10T company, and at the same time keeps warning that AI may eliminate millions of jobs. As a shareholder, that sounds amazing. As an employee, slightly less amazing. Are we watching genuine concern for society, or the greatest investor pitch deck ever created? Curious how others see this.... submitted by /u/whatsnextintech007 [link] [留言]

Hey. No new feature this time - just a pass through the corners before the next one. We had a list of nine bugs we'd written down and kept walking past. Most were small. One wasn't, and it was hiding behind a button. When you import a file the studio already has - same bytes - we ask whether to share the existing file or make an independent copy you can edit on its own. Pick "independent copy" and you expect exactly that: your own file, safe to change or delete without touching anything else. It mostly worked. But the new copy's internal name was built from how many copies already existed - copy 2, copy 3, and so on. The problem shows up after a delete. Say you had three, removed the middle one, then made another. The new one counted "two exist, so I'm number three" - but number three was already taken. The studio saw the clash, quietly kept the old file, and pointed your new scene at it. You thought you'd made a clean copy; you were sharing the original, and the real copy you just made was orphaned on disk with nothing pointing at it. Edit "your" copy later and you'd be editing the original too. Nothing crashed. Nothing warned you. That's the worst kind. The fix: stop counting, and instead look at which names are actually taken and pick the first free one - so a copy made after a delete always gets its own identity. We also made the studio shout in the logs if two files ever collide again, instead of silently dropping one. Better a loud bug than a quiet one. The rest were smaller. A menu element could jump for a single frame when you grabbed it (the drag started from where the element was saved , not where it was shown ). A countdown number sat blank for one frame before popping in. And the end screen had a leftover timing delay we fixed - which you'll never see, because that screen is solid black either way. Real bug, just invisible. The one we'd marked most important? We went to fix it and found a rebuild from two weeks ago had already solved it. We checked three

In a wild ride for 22-year-old founder and CEO Ethan Thornton, Mach Industries has raised another $300 million. It already has five autonomous vehicles in development and completed a major acquisition.

Prehistoric mining in the Pyrenees, a new species of tiny blue octopus, slapstick acoustics, and more.

Agent Orchestration & Workflow Automation: Dynamic Workflows, Robust Agent Patterns, and On-Commit AI Code Review Today's Highlights This week's highlights focus on advancements in AI agent coordination with Claude Code's new Dynamic Workflows, a pragmatic 6-file system for reliable agent state management, and the release of peektea v2 for on-commit AI code review. Claude Code Adds Dynamic Workflows for Parallel Agent Coordination (InfoQ) Source: https://www.infoq.com/news/2026/06/dynamic-workflows-claude-code/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global Anthropic has introduced Dynamic Workflows, a significant enhancement to Claude Code, designed to improve the coordination and efficiency of AI agents in complex tasks. This new capability enables developers to orchestrate multiple AI agents in parallel, allowing them to collaborate on different parts of a problem simultaneously. Unlike traditional sequential processing, Dynamic Workflows facilitate a more natural, concurrent approach, where agents can dynamically assign sub-tasks, share intermediate results, and adapt their strategies based on real-time progress. This is particularly beneficial for large-scale code generation, complex project management, and multi-stage data analysis where distinct competencies are required from different specialized agents. The core benefit of Dynamic Workflows lies in its ability to manage dependencies and synchronize agent activities, leading to faster execution and more robust outcomes. For instance, in a coding scenario, one agent might focus on generating unit tests while another refactors existing code, both operating in parallel and integrating their work seamlessly. This dynamic coordination mechanism moves beyond simple sequential chaining, offering a powerful paradigm for building sophisticated, multi-agent systems that mirror human team collaboration. Developers can leverage this to create more resilient and adaptive AI-driven workflows,

PostgreSQL 19 Graph Queries & REPACK; SQLite Advanced SQL Patterns Today's Highlights PostgreSQL 19 introduces major features with SQL/PGQ for graph queries on relational data and a new REPACK command to combat table bloat. Meanwhile, the SQLite community explores advanced SQL patterns for complex 'first match' selection logic, pushing the boundaries of efficient embedded database querying. SQL/PGQ in PostgreSQL 19: Graph Queries Without the Graph Database (Planet PostgreSQL) Source: https://postgr.es/p/9kW PostgreSQL 19 is set to integrate native graph querying capabilities through the introduction of SQL/PGQ (Property Graph Queries) and the GRAPH_TABLE construct. This innovative feature allows users to perform sophisticated graph-like analytics, such as pathfinding and pattern matching, directly on their existing relational datasets without requiring data migration to a specialized graph database. The syntax employed for defining graph patterns closely resembles that of Cypher, making it intuitive for developers familiar with graph query languages. Developers will be able to define 'nodes' and 'edges' from their relational tables and then utilize the GRAPH_TABLE function within standard SQL queries to traverse and extract complex, interconnected information. This effectively transforms PostgreSQL into a multi-model database capable of handling both traditional relational workloads and demanding graph analytics side-by-side, within a single consistent environment. This significant enhancement expands PostgreSQL's utility across a wide range of applications, including social network analysis, fraud detection, and supply chain optimization, where understanding relationships between data points is critical. By embedding graph capabilities, PostgreSQL removes the need for separate graph database infrastructure, simplifying application architectures and leveraging the robustness and familiarity of the PostgreSQL ecosystem for new and existing projects. Comment: This is

If Nvidia has cracked a way to bring AI agents easily, safely and usefully to the masses, it could — and should — be big.

This week's Java roundup for May 25th, 2026, features news highlighting: lifecycle changes with two of the JEPs that were targeted for JDK 27; the GA release of Koog 1.0; point releases of Hazelcast, Quarkus, Hibernate and JHipster; the eighth milestone release of Spring AI 2.0; and introducing Endive, a JVM-native WebAssembly (Wasm) runtime. By Michael Redlich

I recently read Jérémie Rodon's excellent article On-Demand Archives on S3 , where he describes an elegant Rust solution for zipping 3,000 × 5MB files from S3 within a single Lambda function. His approach is impressive: streaming a ZIP archive through a custom Rotating Slab Buffer, saturating bandwidth with concurrent downloads, all within 512MB of RAM. The result: 3 minutes 35 seconds . I thought it was a good challenge to reach better performance. His article ends with an open invitation: "do you think you can do better with your favorite language?" Well, my favorite language is not Rust nor Go nor.. however, I'm fluent in serverless ;) so I took a different angle entirely. A Different Approach: Why Not Parallelize the Problem? Jérémie's constraint was a single Lambda. That's elegant, but it means you're bound by one machine's network bandwidth (~600 Mbps). No matter how perfect your streaming is, physics wins: 15GB at 600 Mbps ≈ 200 seconds minimum. My question was: what if we break that single-machine bottleneck? The key insight is that ZIP files in STORE mode (no compression) have deterministic byte offsets . Each entry is exactly 50 + len(filename) + filesize bytes (local header + ZIP64 extra field + data). If you know all filenames and sizes upfront, you can pre-calculate exactly where every file will land in the final archive, before downloading a single byte. This means independent workers can each build their portion of the zip in parallel, and S3's multipart upload lets them write their chunks independently (parts can be uploaded in any order by different processes sharing the same upload ID). Architecture Planner Lambda → Step Functions Distributed Map → N Worker Lambdas → Finalizer Lambda │ │ │ │ │ │ CreateMultipartUpload │ │ │ UploadPart (parallel) │ CompleteMultipartUpload ▼ ▼ ▼ ▼ ▼ S3 Output Bucket Planner : Lists all source files, computes zip byte offsets, initiates multipart upload, divides work into balanced batches (equal data volume per worker)