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@faun shared a link, 8 months, 2 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, 8 months, 2 weeks ago
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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.. read more  

Lessons learned from building a sync-engine and reactivity system with SQLite
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@faun shared a link, 8 months, 2 weeks ago
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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.. read more  

From GPT-2 to gpt-oss: Analyzing the Architectural Advances
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@faun shared a link, 8 months, 2 weeks ago
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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 .. read more  

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@faun shared a link, 8 months, 2 weeks ago
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I set up an email triage system using Home Assistant and a local LLM, here's how you can too

A DIY email triage rig usingHome Assistant, IMAP, andOllamawires up local LLM smarts with YAML-fueled automation. At the core: an8B dolphin-llamamodel running on GPU, chewing through messy HTML emails, tagging them, and firing off priority-sorted summaries via notifications. Why it matters:A signal.. read more  

I set up an email triage system using Home Assistant and a local LLM, here's how you can too
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@faun shared a link, 8 months, 2 weeks ago
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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 .. read more  

Are OpenAI and Anthropic Really Losing Money on Inference?
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@faun shared a link, 8 months, 2 weeks ago
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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.. read more  

Effectively building AI agents on AWS Serverless
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@faun shared a link, 8 months, 2 weeks ago
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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.. read more  

Introducing AWS Cloud Control API MCP Server: Natural Language Infrastructure Management on AWS
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@faun shared a link, 8 months, 2 weeks ago
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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.. read more  

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@faun shared a link, 8 months, 2 weeks ago
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The Most Important Machine Learning Equations: A Comprehensive Guide

A new reference rounds up the core ML equations—Bayes’ Theorem, cross-entropy, eigen decomposition, attention—and shows how they plug into real Python code using NumPy, TensorFlow, and scikit-learn. It hits the big four: probability, linear algebra, optimization, and generative modeling. Stuff that.. read more  

BAO, c’est les chasseurs Tech et Produit qui apportent de la transparence au recrutement. Depuis Paris et Bordeaux, ils ont fait du monde startup leur terrain de jeu en décidant de ne travailler que sur très peu de postes à la fois. Pourquoi ? Parce qu’on voit la chasse comme un sprint dans lequel on travaille main dans la main avec nos clients : nous donnons un maximum de visibilité et de conseil à nos startups partenaires.

En créant BAO, en 2019, Baptiste et Lucas ont décidé de mettre l’écoute au cƓur de leur travail. Curiosité, sourire et empathie sont le seul trait commun à toute l’équipe !

Le bouche-à-oreille est au centre de leur manière de chasser : chaque recruteur entretient son réseau, conscient que la proximité amène à de belles rencontres.
Travailler chez BAO c’est avoir la volonté de rencontrer des personnes aux parcours passionnants et de tisser des liens avec eux. Mais c’est aussi gagner en autonomie tout en profitant d’une équipe dans laquelle les membres s’encouragent mutuellement.

C'est de la vente sans avoir à être agressif, des évolutions rapides au sein d’un écosystème passionnant et un environnement de travail ambitieux sans se prendre trop au sérieux.