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@faun shared a link, 3 days, 16 hours ago

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..

Open Source is one person
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@faun shared a link, 3 days, 16 hours ago

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..

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@faun shared a link, 3 days, 16 hours ago

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..

From Python to Go: Why We Rewrote Our Ingest Pipeline at Telemetry Harbor
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@faun shared a link, 3 days, 16 hours ago

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 ..

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@faun shared a link, 3 days, 16 hours ago

Bash Explained: How the Most Popular Linux Shell Works

Bash isn't going anywhere. It's still the glue for CI/CD, cron jobs, and whatever janky monitoring stack someone duct-taped together at 2am. If automation runs the show, Bash is probably in the pit orchestra. It keeps things moving on Linux, old-school macOS (think pre-Catalina), and even WSL. Stil..

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@faun shared a link, 3 days, 16 hours ago

Lessons learned from building a sync-engine and reactivity system with SQLite

A dev ditched Electric + PGlite for a lean, browser-native sync setup built aroundWASM SQLite,JSON polling, andBroadcastChannel reactivity. It’s running inside a local-first notes app. Changes get logged with DB triggers. Sync state? Tracked by hand. Svelte stores update via lightweight polling, wi..

Lessons learned from building a sync-engine and reactivity system with SQLite
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@faun shared a link, 3 days, 16 hours ago

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..

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@faun shared a link, 3 days, 16 hours ago

From GPT-2 to gpt-oss: Analyzing the Architectural Advances

OpenAI Returns to Openness. The company droppedgpt-oss-20Bandgpt-oss-120B—its first open-weight LLMs since GPT-2. The models pack a modern stack:Mixture-of-Experts,Grouped Query Attention,Sliding Window Attention, andSwiGLU. They're also lean. Thanks toMXFP4 quantization, 20B runs on a 16GB consume..

From GPT-2 to gpt-oss: Analyzing the Architectural Advances
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@faun shared a link, 3 days, 16 hours ago

Are OpenAI and Anthropic Really Losing Money on Inference?

DeepSeek R1 running on H100s puts input-token costs near$0.003 per million—while output tokens still punch in north of$3. That’s a 1,000x spread. So if a job leans heavy on input—think code linting or parsing big docs—those margins stay fat, even with cautious compute. System shift:This lop-sided ..

Are OpenAI and Anthropic Really Losing Money on Inference?
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@faun shared a link, 3 days, 16 hours ago

Some thoughts on LLMs and Software Development

Most LLMs still play autocomplete sidekick. But seasoned devs? They get better results when the model reads and rewrites actual source files. That gap—between how LLMs are designed to work and how prosactuallyuse them—messes with survey data and muddies the picture on real gains in code quality and..

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