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🚀 RELIANOID is heading to Washington, DC!

From September 9–12, 2025, we’ll be at the 16th Billington CyberSecurity Summit — the premier government cybersecurity event gathering 2,500+ leaders and innovators to shape the future of AI, Zero Trust, cloud security, and critical infrastructure protection. Meet our team and discover how RELIANOID..

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Technical Content Writer, Mailtrap

Send emails with v0 and Mailtrap

Vercel Mailtrap.io

Learn how to integrate Mailtrap with your v0 application to send transactional emails and manage contacts without writing complex code.

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@faun shared a link, 3 months, 3 weeks ago
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We Needed Better Cloud Storage for Python so We Built Obstore

Obstoreis a new stateless object store that skips fsspec-style caching and keeps its API tight and predictable across S3, GCS, and Azure. Sync and async both work. Under the hood? Fast, zero-copy Rust–Python interop. And on small concurrent async GETs, it reportedly crushes S3FS with up to9x better .. read more  

We Needed Better Cloud Storage for Python so We Built Obstore
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@faun shared a link, 3 months, 3 weeks ago
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How Salesforce Delivers Reliable, Low-Latency AI Inference

Salesforce’s AI Metadata Service (AIMS) just got a serious speed boost. They rolled out a multi-layer cache—L1 on the client, L2 on the server—and cut inference latency from 400ms to under 1ms. That’s over 98% faster. But it’s not just about speed anymore. L2 keeps responses flowing even when the b.. read more  

How Salesforce Delivers Reliable, Low-Latency AI Inference
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@faun shared a link, 3 months, 3 weeks ago
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The unexpected productivity boost of Rust

Lubeno's backend is100% Rust, providing strong safety guarantees for refactoring confidence. Rust's type checker catches async bugs, unlikeTypeScript. Rust excels in tracking lifetimes and borrowing rules.Zig, on the other hand, can be alarming with its compiler choices, such as overlooking typos in.. read more  

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@faun shared a link, 3 months, 3 weeks ago
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From Python to Go: Why We Rewrote Our Ingest Pipeline at Telemetry Harbor

Telemetry Harbor tossed out Python FastAPI and rebuilt its ingest pipeline inGo. The payoff?10x faster, no more CPU freakouts, and strongerdata integritythanks to strict typing. PostgreSQL is now the slowest link in the chain—not the app—which is the kind of bottleneck you actuallywant. Means the s.. read more  

From Python to Go: Why We Rewrote Our Ingest Pipeline at Telemetry Harbor
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@faun shared a link, 3 months, 3 weeks ago
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Everything I know about good API design

This guide lays out the playbook for running tough, user-first APIs: no breaking changes, stick to familiar patterns, honor long-lived API keys, and make every write idempotent. It pushes cursor-based pagination for heavy data, rate limits that come with context, and optional fields to keep things .. read more  

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@faun shared a link, 3 months, 3 weeks ago
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Open Source is one person

New data from ecosyste.ms drops a hard truth:almost 60% of 11.8M open source projects are solo acts. Even among NPM packages topping 1M monthly downloads, about half still rest on one pair of hands. The world runs on open source. But the scaffolding seems shakier than anyone wants to admit—millions.. read more  

Open Source is one person
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@faun shared a link, 3 months, 3 weeks ago
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Developer's block

Overdoing “best practices” can kill momentum. Think endless tests, wall-to-wall docs, airtight CI, and coding rules rigid enough to snap. Sounds responsible—until it slows dev to a crawl. The piece argues for flipping that script. Start scrappy. Build fast. Save the polish for later. It’s how you d.. read more  

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@faun shared a link, 3 months, 3 weeks ago
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Go is still not good

Go’s been catching flak for years, and the hits keep coming: stiff variable scoping, no destructor patterns, clunky error handling, and brittle build directives. Critics point out how Go’s design often blocks best practices like RAII and makes devs contort logic just to clean up resources or manage .. read more  

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).