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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 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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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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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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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 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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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  

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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
BigQuery is a cloud-native, serverless analytics platform designed to store, query, and analyze massive volumes of structured and semi-structured data using standard SQL. It separates storage from compute, automatically scales resources, and eliminates the need for infrastructure management, indexing, or capacity planning.

BigQuery is optimized for analytical workloads such as business intelligence, log analysis, data science, and machine learning. It supports real-time data ingestion via streaming, batch loading from cloud storage, and federated queries across external data sources like Cloud Storage, Bigtable, and Google Drive.

Query execution is distributed and highly parallel, enabling interactive performance even on petabyte-scale datasets. The platform integrates deeply with the Google Cloud ecosystem, including Looker for BI, Vertex AI for ML workflows, Dataflow for streaming pipelines, and BigQuery ML, which allows users to train and run machine learning models directly using SQL.

Built-in security features include fine-grained IAM controls, column- and row-level security, encryption by default, and audit logging. BigQuery follows a consumption-based pricing model, charging for storage and queries (on-demand or reserved capacity).