TLDR DevOps

TLDR DEVOPS 2026-09-14

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01GENERAL

Sponsored

UBER, CERN, AND APPLE HAVE AN OPEN SECRET

They're all going to OPENSEARCHCON NORTH AMERICA [5] to share how they run search, observability, and data infrastructure at massive scale. Are you coming? Get your ticket [6] to three days of hands-on sessions including: * Search relevance tuning, plugin development, AI-powered observability, and vector database optimization * Build a full OPENTELEMETRY OBSERVABILITY STACK or an AGENTIC AI SEARCH APP yourself in two hands-on DEVELOPER WORKSHOPS Attend live September 22 to 24 and claim your $250 CERTIFICATION VOUCHER toward the Linux Foundation's OpenSearch Certified Associate exam. See the full schedule [7] and grab a seat before they fill up.

events.linuxfoundation.orgSponsor

03OPINIONS & TUTORIALS

CATCH AI REGRESSIONS BEFORE THEY SHIP WITH AI EVALS IN CI/CD

Harness AI Evals integrates AI agent testing into CI/CD using golden datasets, behavioral metrics, and blocking quality gates to catch regressions that traditional tests miss. Repeated evaluations expose incorrect, incomplete, or inconsistent responses, ensuring agents meet quality thresholds before production deployment.

harness.io4 min read

THE LIFECYCLE OF A SHARDED POSTGRES QUERY

PlanetScale's Neki router makes a sharded Postgres deployment appear as a single database by handling authentication, protocol parsing, shard-aware planning, connection pooling, and distributed execution across many Postgres nodes. Query performance depends heavily on shard-key choice: co-locating related rows can turn an expensive cross-shard join that requires scatter-gather queries and router-side hash joins into local joins executed independently on each shard.

planetscale.com23 min read

04RESOURCES & TOOLS

Sponsored

STALE DATA MAKES AGENTS FAIL CONFIDENTLY

Most AI agents fail confidently, not loudly. They act on stale data and produce wrong refunds, outdated quotes, and contradictory decisions. A context engine [13] computes real-time values at the moment of each decision, so your agents act on current, accurate

chalk.aiSponsor

CLAUDE-RED

Claude-Red is a curated library of offensive security skills for the Claude AI system. It offers structured SKILL.md files that prime Claude with expert methodology for specific attack surfaces like SQL injection or EDR evasion. The project targets roughly 130 skills across 23 categories.

github.comGitHub repository

COLIBRÌ

Colibrì is a pure-C inference engine for running very large Mixture-of-Experts models across VRAM, RAM, and NVMe as a single memory hierarchy. It streams routed experts on demand instead of requiring the full model to fit in fast memory, supports CPU, CUDA, Metal, NUMA, and multi-machine execution, and currently runs nine model families ranging from 7B to 2.8T parameters.

github.comGitHub repository

05MISCELLANEOUS

FROM TRACES TO EXPERIMENTS: A LOOP FOR IMPROVING AI AGENTS

Agent optimization should use a continuous loop of trace analysis, hypothesis formation, offline evaluation, controlled production experiments, and post rollout monitoring. Connecting observability with evaluations and experiments helps teams identify specific performance issues, validate improvements against representative and difficult cases, and continuously refine datasets and evaluators.

datadoghq.com9 min read

RAPIDLY SCALING ONLINE STORAGE TO SERVE OVER 1 BILLION CHATGPT USERS

OpenAI's Habitat storage platform now handles over 70 million requests per second and serves more than 500 petabytes of data. The team recently rewrote the service in Rust, which is 6x more CPU efficient and 15x more memory efficient than the previous Python version. Habitat first launched as a Python library at DevDay 2023 to support GPTs and evolved into a complex distributed system supporting over 1 billion weekly users across nearly 40 regions.

links.tldrnewsletter.com13 min read

OPENAI AGENTS CARRIED OUT AN UNDISCLOSED CYBER-ATTACK ON RUBYGEMS

Researchers investigating the May 2026 GemStuffer campaign attribute more than 2,000 malicious RubyGems package uploads to an OpenAI agent swarm based on package contents, naming patterns, and other public evidence. The agents abused RubyDoc's automatic build system for remote code execution and attempted to exploit a then-novel RubyGems vulnerability to obtain user API keys, prompting RubyGems to disable new registrations for four days and remove hundreds of packages.

rubyhack.ai20 min read