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A trillion dollars is a terrible thing to waste

OpenAI co-founder Ilya Sutskever just said the quiet part out loud: scaling laws are breaking down. Bigger models aren’t getting better at thinking, they’re getting worse at generalizing and reasoning. Now he’s eyeingneurosymbolic AIandinnate inductive constraints. Yep, the “just make it huge” era m.. read more  

A trillion dollars is a terrible thing to waste
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Prompts for Open Problems

The author, Ben Recht, proposes five research directions inspired by his graduate machine learning class, arguing for different research rather than just more. These prompts include adopting a design-based view for decision theory, explaining the robust scaling trends in competitive testing, and mov.. read more  

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Advancing Our Chef Infrastructure: Safety Without Disruption

Slack pulled back the curtain onSlack AI, its LLM-powered assistant built with a fortress mindset. Every customer gets their ownisolated environment. Any data passed tovendor LLMs? It'sephemeral. Gone before it can stick. No fine-tuning. No exporting data outside Slack. And there’s a wholemiddle-lay.. read more  

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Why we're leaving serverless

Every millisecond matters in the critical path of API authentication. After two years of battling serverless limitations, the entire API stack was rebuilt to reduce end-to-end latency. The move from Cloudflare Workers to stateful Go servers resulted in a 6x performance improvement and simplified arc.. read more  

Why we're leaving serverless
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Failure is inevitable: Learning from a large outage, and building for reliability in depth at

Datadog ditched its “never fail” mindset after a March 2023 meltdown knocked out half its Kubernetes nodes and took major user features down with them. The fix? A full-stack rethink built aroundgraceful degradation. The team addeddisk-based persistence at intake,live-data prioritization,QoS-aware re.. read more  

Failure is inevitable: Learning from a large outage, and building for reliability in depth at
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You’ll never see attrition referenced in an RCA

Lorin Hochstein argues that while high-profile engineer attrition is often speculated to contribute to major outages, it is universally absent from public Root Cause Analyses (RCAs). This exclusion occurs because public RCAs aim to reassure customers by focusing on technical fixes, whereas attrition.. read more  

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Declarative Action Architecture

The Declarative Action Architecture (DAA) is a scalable E2E testing pattern that separates concerns across three distinct layers. TheTest Layeris 100% declarative, statingwhatis being tested without any procedural logic, making tests read like documentation. The coreAction Layerimplements the execut.. read more  

Declarative Action Architecture
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Comparing AWS Lambda Arm64 vs x86_64 Performance Across Multiple Runtimes in Late 2025

A new open-source benchmark looked at 183,000 AWS Lambda invocations, andarm64 beats x86_64across the board in both cost and speed. Rust on arm64 with SHA-256 tuned in assembly? It clocks in 4–5× faster than x86 in CPU-heavy tasks. Cold starts are snappy too—5–8× quicker than Node.js and Python... read more  

Comparing AWS Lambda Arm64 vs x86_64 Performance Across Multiple Runtimes in Late 2025
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The story of how we almost got hacked

Team Invictus caught a BEC attempt using WeTransfer to slip in a fake Microsoft 365 login page powered byEvilProxy. Classic Adversary-in-the-Middle move, but dressed up with a slick delivery package. Digging deeper, the team mapped the attacker’s setup and found something bigger: a credential grab c.. read more  

The story of how we almost got hacked
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Agent Sandbox Brings Kernel-Level Guardrails to AI Agents on Kubernetes

Kubernetes gVisor Kata Containers Google Kubernetes Engine (GKE)

Agent Sandbox, a new Kubernetes primitive, was introduced at KubeCon NA 2025 to enhance AI agent management on Kubernetes and Google Kubernetes Engine.

Agent Sandbox Brings Kernel-Level Guardrails to AI Agents on Kubernetes
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).