Join us

ContentUpdates and recent posts about Architect..
Link
@faun shared a link, 1 week 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
Link
@faun shared a link, 1 week 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..

Link
@faun shared a link, 1 week 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..

Link
@faun shared a link, 1 week 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
Link
@faun shared a link, 1 week ago

37 Things I Learned About Information Retrieval in Two Years at a Vector Database Company

A Weaviate engineer pulls back the curtain on two years of hard-earned lessons in vector search—breaking downBM25,embedding models,ANN algorithms, andRAG pipelines. The real story? Retrieval workflows keep moving—from keyword-heavy (sparse) toward embedding-driven (dense). Across IR use cases, the ..

Link
@faun shared a link, 1 week ago

Introducing AWS Cloud Control API MCP Server: Natural Language Infrastructure Management on AWS

AWS dropped theCloud Control API MCP Server, a mouthful of a name for a tool that makes 1,200+ AWS resources manageable through a standard CRUDL API—using natural language. Think: describe what you want, and tools like Amazon Q Developer turn it into actual infra code. It doesn’t stop there. It val..

Introducing AWS Cloud Control API MCP Server: Natural Language Infrastructure Management on AWS
Link
@faun shared a link, 1 week ago

Effectively building AI agents on AWS Serverless

AWS just dropped support for buildingserverless agentic AI systems. You’ll need the Strands Agents SDK, Bedrock AgentCore (preview), plus trusty tools like Lambda and ECS. What’s new? Agentic AI flips the script. Instead of dumb prompt-in, response-out bots, you getgoal-driven loopswith memory, too..

Effectively building AI agents on AWS Serverless
Link
@faun shared a link, 1 week 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?
Link
@faun shared a link, 1 week 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..

Link
@faun shared a link, 1 week ago

Combining GenAI & Agentic AI to build scalable, autonomous systems

Agentic AI doesn’t just crank out content—it takes the wheel. Where GenAI reacts, Agentic AI plans, perceives, and acts. Think less autocomplete, more autonomous ops. Hook them together, and you get a full-stack brain: content creation, real-time decisions, adaptive workflows, all learning as they ..

Combining GenAI & Agentic AI to build scalable, autonomous systems

This tool doesn't have a detailed description yet. If you are the administrator of this tool, please claim this page and edit it.