Join us

ContentUpdates and recent posts about Vertex AI..
Link
@varbear shared a link, 4 months, 2 weeks ago
FAUN.dev()

Goodbye Microservices

Twilio Segment collapsed 140+ destination-specific microservices into asingle monolith, one repo, one set of dependencies, one test harness. They leveled out version sprawl and builtTraffic Recorder, a homegrown yakbak-based HTTP playback tool. That killed off hours-long test runs, dropping them to.. read more  

Link
@varbear shared a link, 4 months, 2 weeks ago
FAUN.dev()

Why I Didn’t Sign the Resonant Computing Manifesto: The Foundations Need Work

A sharp critique of theResonant Computing Manifestopushes it past vague ideals. It calls for real governance scaffolding, not just poetic prose. Without that? The manifesto risks becoming just another glossy PDF for entrenched players to wave around while changing nothing. Under the hood:What’s real.. read more  

Why I Didn’t Sign the Resonant Computing Manifesto: The Foundations Need Work
Link
@varbear shared a link, 4 months, 2 weeks ago
FAUN.dev()

Rust unit testing: file writing

To test file writes without hitting the disk, the author swaps in a closure that takes a file handle. That handle’s a test double, so after the code runs, you can crack it open and inspect what got written... read more  

Link
@varbear shared a link, 4 months, 2 weeks ago
FAUN.dev()

Full Unicode Search at 50× ICU Speed with AVX‑512

StringZilla v4.5drops a major speed bomb on Unicode text processing. Think10× faster tokenization and case folding. Up to150× faster for case-insensitive substring search. It leaves ICU and PCRE2 wheezing in the dust. Under the hood: SIMD all the way, AVX-512 on newer chips, plus script-aware SIMD k.. read more  

Full Unicode Search at 50× ICU Speed with AVX‑512
Link
@varbear shared a link, 4 months, 2 weeks ago
FAUN.dev()

pqr.sql: Generate QR Codes with Pure SQL in PostgreSQL

A developer jammed out aQR code generator in pure SQL, just PostgreSQL, no extensions or libraries. One gnarly single-statement query. It even runs faster onPostgreSQL 17than on 16, thanks to engine tweaks... read more  

pqr.sql: Generate QR Codes with Pure SQL in PostgreSQL
Link
@varbear shared a link, 4 months, 2 weeks ago
FAUN.dev()

5 engineering dogmas it's time to retire

Dependencies are risky, especially in smaller companies - avoid unnecessary packages to prevent security incidents and maintain code simplicity. Feature flags can become overwhelming if abused, leading to complex codebases and false sense of security - use them wisely. Commenting code is a balance -.. read more  

Link
@kaptain shared a link, 4 months, 2 weeks ago
FAUN.dev()

Dapr Deployment Models

Daprstarted as a humble Kubernetes sidecar. Now? It's a full-blownmulti-mode runtimethat runs wherever you need it,edge,VM, orserverless APIs. Diagrid’sCatalysttakes that further. It wraps Dapr in a fully managed API layer that’s detached from your app’s lifecycle. No infra lock-in, just token-based.. read more  

Dapr Deployment Models
Link
@kaptain shared a link, 4 months, 2 weeks ago
FAUN.dev()

v1.35: Job Managed By Goes GA

In Kubernetes v1.35,spec.jobControllerManagedByhits GA. That means full handoff of Job reconciliation to external controllers is now official. It unlocks tricks likeMultiKueue, where a single management cluster fires off Jobs to multiple worker clusters, without losing sight of what’s running where... read more  

Link
@kaptain shared a link, 4 months, 2 weeks ago
FAUN.dev()

Troubleshooting Cilium network policies: Four common pitfalls

Cilium’s Day 2 playbook covers the real work: dialing inL7 policy controls, tuningHubble observability, and wringing performance fromBPF. It's how you keep big Kubernetes clusters sane. The focus?Multi-tenant isolation,node-to-node encryption, and scaling cleanly withexternal etcdso the network does.. read more  

Link
@kaptain shared a link, 4 months, 2 weeks ago
FAUN.dev()

93% Faster Next.js in (your) Kubernetes

Next.js brings advanced capabilities to developers out-of-the-box, but scaling it in your own environment can be challenging due to uneven load distribution and high latency. Watt addresses these issues by leveragingSO_REUSEPORTin the Linux kernel, resulting in significantly improved performance met.. read more  

Vertex AI is Google Cloud’s end-to-end machine learning and generative AI platform, designed to help teams build, deploy, and operate AI systems reliably at scale. It unifies data preparation, model training, evaluation, deployment, and monitoring into a single managed environment, reducing operational complexity while supporting advanced AI workloads.

Vertex AI supports both custom models and foundation models, including Google’s Gemini model family. It enables organizations to fine-tune models, run large-scale inference, orchestrate agentic workflows, and integrate AI into production systems with strong security, governance, and observability controls.

The platform includes tools for AutoML, custom training with TensorFlow and PyTorch, managed pipelines, feature stores, vector search, and online and batch prediction. For generative AI use cases, Vertex AI provides APIs for text, image, code, multimodal generation, embeddings, and agent-based systems, including support for Model Context Protocol (MCP) integrations.

Built for enterprise environments, Vertex AI integrates deeply with Google Cloud services such as BigQuery, Cloud Storage, IAM, and VPC, enabling secure data access and compliance. It is widely used across industries like finance, healthcare, retail, and science for applications ranging from recommendation systems and forecasting to autonomous research agents and AI-powered products.