InfoQ - AI, ML & Data Engineering

Presentation: What 25 Trillion Tokens Reveal About How Developers Actually Adopt AI Agents

Brendan O’Leary — Thu, 15 Oct 2026 11:00:00 GMT

Brendan O'Leary discusses how real-world AI adoption depends on building trust rather than chasing model benchmarks. Drawing from 40 trillion tokens of usage data, he explains the four rungs of the AI adoption ladder and analyzes key failure points in context construction, multi-model routing, and feedback loops to help engineering leaders successfully scale agentic workflows.

By Brendan O’Leary

Meta Extends Confidential Computing to AI Glasses with Private Processing

Leela Kumili — Wed, 14 Oct 2026 13:57:00 GMT

Meta has extended Private Processing to AI glasses, using confidential VMs, trusted execution environments, remote attestation, anonymous credentials, OHTTP routing, and TEE-based storage to process AI workloads and persistent user context in the cloud.

By Leela Kumili

Google Antigravity SDK Adds Local Model Support for Greater Token Efficiency and Code Privacy

Sergio De Simone — Wed, 14 Oct 2026 09:00:00 GMT

Google has introduced support for local models in the Antigravity SDK, enabling developers to build and execute agentic workflows directly on their own machines, while retaining the flexibility to mix in cloud-based models for higher-level tasks. This approach gives developers greater control over data privacy, inference costs, network dependencies, and computational workload distribution.

By Sergio De Simone

Production Patterns for Agents, Evals, and LLM Costs: QCon AI New York 2026

Artenisa Chatziou — Tue, 13 Oct 2026 12:00:00 GMT

Once AI is in production, code arrives faster than it can be reviewed, token costs rise, evals have to be good enough to act on, and agents need permissions and guardrails. QCon AI New York 2026 (December 15-16) has confirmed sessions from engineers sharing how they handle these constraints.

By Artenisa Chatziou

Spotify More Than Doubled Monthly Code Changes as AI Accelerated Development

Leela Kumili — Mon, 12 Oct 2026 13:57:00 GMT

Spotify reports that AI-assisted development more than doubled monthly merged changes, prompting a review of quality and reliability controls across its engineering organization. The company describes changes to testing, observability, rollback capacity, workload prioritization, and automated maintenance as it adapts to higher software change volume.

By Leela Kumili

Article: When Should You Use Liquid Clustering? Benchmarking Lakehouse Layout Strategies

Kushal Kumar Vishwakarma — Mon, 12 Oct 2026 11:00:00 GMT

Physical data layout is one of the most consequential decisions in lakehouse engineering. The author compares Liquid Clustering, ZOrdering, and partitioning across four workload types on Delta Lake, uncovering two undocumented failure modes that likely affect production systems today and offering a decision framework for choosing the right layout strategy.

By Kushal Kumar Vishwakarma

Podcast: What Does It Really Mean To Program With AI: A Conversation with Joy Ebertz

Joy Ebertz — Mon, 12 Oct 2026 11:00:00 GMT

In this podcast, Michael Stiefel spoke to Joy Ebertz about how the use of artificial intelligence is changing the way architects and developers do their jobs. Focusing on the long term success of the business is one of the most neglected tasks of an architect. This task becomes particularly important as the nature of artificial intelligence changes the landscape of software development.

By Joy Ebertz

Presentation: You Can't Trust Your AI Bill. And Neither Can I

Erik Peterson — Mon, 12 Oct 2026 11:00:00 GMT

Erik Peterson discusses why AI spend is eclipsing cloud bills, triggering a major software engineering cost crisis. He explains tokenmaxxing, unit economics, forced developer promotions, and why fully autonomous AI will write, deploy, and govern code by 2028.

By Erik Peterson

Context Language Models: Self-Managing Context to Improve Performance and Reduce Compute Costs

Sergio De Simone — Sun, 11 Oct 2026 14:00:00 GMT

Researchers from Meta, MIT, and the University of Washington introduce Context Language Models (CLMs), a new approach that enables language models to manage and edit their own context rather than relying on predefined mechanisms for summarization, compression, and information retrieval, reporting substantial gains in both performance and computational efficiency.

By Sergio De Simone

Cloudflare Traces Turns the Proxy Layer into OpenTelemetry Spans, with New Volume-Based Pricing

Steef-Jan Wiggers — Sat, 10 Oct 2026 07:12:00 GMT

Cloudflare has put Traces into open beta, extending automatic tracing from Workers to security rules, transformations, cache, routing and origin handling as OpenTelemetry spans. Traces accept and forward W3C traceparent headers, and Trace Rules allow targeted sampling. Pricing moves to ingestion and retention volume from December 1.

By Steef-Jan Wiggers

Android Bench 2 Adds Support for Long-Horizon Tasks, Agentic Evaluation, and Continuous Scoring

Sergio De Simone — Fri, 09 Oct 2026 17:00:00 GMT

Google has released Android Bench 2.0, a major update to its benchmark framework for evaluating AI models and agents on Android development tasks. The update introduces long-horizon tasks (LHTs), agent-based evaluation, and continuous scoring to better assess performance on complex, multi-step development tasks.

By Sergio De Simone

Github Migrates Copilot Runtime to Rust with AI-Assisted Rewrite

Leela Kumili — Fri, 09 Oct 2026 14:29:00 GMT

GitHub migrated more than 800,000 lines of Copilot runtime code from TypeScript and Node.js to Rust in about 14.5 weeks using AI-assisted development. The incremental migration used N-API interoperability, automated testing, human review, and 128 pull requests while continuing to ship releases.

By Leela Kumili

Presentation: Ontology‐Driven Observability: Building the E2E Knowledge Graph at Netflix Scale

Prasanna Vijayanathan, Renzo Sanchez-Silva — Fri, 09 Oct 2026 11:00:00 GMT

Prasanna Vijayanathan and Renzo Sanchez-Silva share how Netflix tackles observability across 38M events/sec. They discuss replacing reactive monitoring with an AI-driven operational ontology and agentic workflows using Claude and graph databases. They explain how unifying MELT telemetry into queryable knowledge graphs enables automated triaging, root-cause analysis, and self-healing systems.

By Prasanna Vijayanathan, Renzo Sanchez-Silva

Presentation: Multi-Agent Patterns from Spotify’s AI Powered Advertising Platform

Pratik Rasam — Thu, 08 Oct 2026 11:00:00 GMT

Pratik Rasam discusses how Spotify Ads Manager runs production-grade multi-agent systems at scale using Google ADK Java. He shares key architectural patterns, domain ownership models, deterministic guardrails, and tracing-based evaluation strategies, detailing hard-learned lessons on drawing agent boundaries, optimizing tool schemas, and managing costs while avoiding monolithic agent pitfalls.

By Pratik Rasam

Cloudflare Open Sources Decision Models for AI Agents

Renato Losio — Thu, 08 Oct 2026 06:54:00 GMT

During its recent “Birthday Week”, Cloudflare announced Clef, a set of open-weight AI models designed to choose between predefined options rather than generate text. Cloudflare released 9B- and 27B-parameter models, along with a platform for adapting them to specific decision-making tasks.

By Renato Losio