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OpenAI to acquire Astral

OpenAI will acquire Astral, pending regulatory close. It will fold Astral's open-source Python tools —uv,Ruff, andty— intoCodex. Teams will integrate the tools.Codexwill plan changes, modify codebases, run linters and formatters, and verify results acrossPythonworkflows. System shift:This injects pr.. read more  

OpenAI to acquire Astral
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Scaling Karpathy's Autoresearch: What Happens When the Agent Gets a GPU Cluster

A team pointedClaude Codeatautoresearchand spun up 16 Kubernetes GPUs. The setup ran ~910 experiments in 8 hours.val_bpbdropped from 1.003 to 0.974 (2.87%). Throughput climbed ~9×. Parallel factorial waves revealedAR=96as the best width. The pipeline usedH100for cheap screening andH200for validation.. read more  

Scaling Karpathy's Autoresearch: What Happens When the Agent Gets a GPU Cluster
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OpenClaw Tutorial: AI Stock Agent with Exa and Milvus

An autonomous market agent ships. OpenClaw handles orchestration. Exa returns structured, semantic web results. Milvus (or Zilliz Cloud) stores vectorized trade memory. A 30‑minute Heartbeat keeps it running. Custom Skills load on demand. Recalls query 1536‑dim embeddings. Entire stack runs for abou.. read more  

OpenClaw Tutorial: AI Stock Agent with Exa and Milvus
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Building AI Teams with Sandboxes & Agent

Docker Agentruns teams of specialized AI agents. The agents split work: design, code, test, fix. Models and toolsets are configurable. Docker Sandboxesisolate each agent in a per-workspacemicroVM. The sandbox mounts the host project path, strips host env vars, and limits network access. Tooling move.. read more  

Building AI Teams with Sandboxes & Agent
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OpenClaw is a great movement, but dead product. what's next?

After talking to 50+ individuals experimenting with OpenClaw, it's clear that while many have tried it and even explored it for more than 3 days, only around 10% have attempted automating real actions. However, most struggle to maintain these automations at a production level due to challenges with .. read more  

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How to Host your Own Email Server

This guide shows how to self-hostSMTPon a cheapVPS. It runs DockerizedPostfixand bundlesopendkimfor DKIM signing. It skipsIMAPand inbound SMTP and relies on registrar email forwarding. It configures reverse DNS plusSPFandDMARCDNS records. It checks port 25 reachability, maps host port 1587 to contai.. read more  

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Rocky Linux 9 on AWS EC2: Best Practices for Production

Rocky Linux 9 pairs RHEL-9 binary compatibility and modern kernels with AWS EC2 features:cloud-init,ENA,NVMe,gp3. The guide recommendsM6i/M7ifor general servers. It favorsC‑seriesfor heavy compute andio2for databases. PreferXFS. KeepSELinuxenabled. Use immutable AMIs. Automate withAnsible... read more  

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California’s AB 1043 Could Regulate Every Linux Command, and the Open Source World Is Too Quiet

California'sAB 1043requires operating systems to collect age/DOB at account setup and expose anAPIthat returns anage bracket signal. Apps must request that signal on launch and restrict access by bracket. EffectiveJan 1, 2027, vague definitions could sweepapt,flatpak,snap, and package managers into .. read more  

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How we fixed Postgres connection pooling on serverless with PgDog

A startup swappedSupavisorandPgBouncerforPgDogonEKS. The swap stopped serverless deploy connection spikes. A multi-threaded, colocated pooler handled the bursty traffic. PgDogneeded fixes forPrismaprepared-statement handling. The team shipped those.PgDognow exports metrics viaOpenMetricstoPrometheus.. read more  

How we fixed Postgres connection pooling on serverless with PgDog
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New Malware Highlights Increased Systematic Targeting of Network Infrastructure

The enterprise attack surface has changed, with threat actors increasingly targeting network infrastructure. Eclypsium recently captured new malware samples, including CondiBot and "Monaco," both impacting network devices such as Fortinet products. The rise in network device attacks poses serious th.. read more  

GPT-5.4 is OpenAI’s latest frontier AI model designed to perform complex professional and technical work more reliably. It combines advances in reasoning, coding, tool use, and long-context understanding into a single system capable of handling multi-step workflows across software environments. The model builds on earlier GPT-5 releases while integrating the strong coding capabilities previously introduced with GPT-5.3-Codex.

One of the defining features of GPT-5.4 is its ability to operate as part of agent-style workflows. The model can interact with tools, APIs, and external systems to complete tasks that extend beyond simple text generation. It also introduces native computer-use capabilities, allowing AI agents to operate applications using keyboard and mouse commands, screenshots, and browser automation frameworks such as Playwright.

GPT-5.4 supports context windows of up to one million tokens, enabling it to process and reason over very large documents, long conversations, or complex project contexts. This makes it suitable for tasks such as analyzing codebases, generating technical documentation, working with large spreadsheets, or coordinating long-running workflows. The model also introduces a feature called tool search, which allows it to dynamically retrieve tool definitions only when needed. This reduces token usage and makes it more efficient to work with large ecosystems of tools, including environments with dozens of APIs or MCP servers.

In addition to improved reasoning and automation capabilities, GPT-5.4 focuses on real-world productivity tasks. It performs better at generating and editing spreadsheets, presentations, and documents, and it is designed to maintain stronger context across longer reasoning processes. The model also improves factual accuracy and reduces hallucinations compared with previous versions.

GPT-5.4 is available across OpenAI’s ecosystem, including ChatGPT, the OpenAI API, and Codex. A higher-performance variant, GPT-5.4 Pro, is also available for users and developers who require maximum performance for complex tasks such as advanced research, large-scale automation, and demanding engineering workflows. Together, these capabilities position GPT-5.4 as a model aimed not just at conversation, but at executing real work across software systems.