→ Introduction to Agent Skills Crea instrucciones reutilizables que Claude aplica automáticamente cuando las necesita. Una vez lo configuras, trabaja solo. Eso es automatización real. https://
anthropic.skilljar.com/introduction-t
o-agent-skills
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ENTERPRISE AI
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Introduction to Claude Agent Skills and Reusable Instructions
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Introduction to Claude Cowork: collaborative AI for real projects
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→ Introduction to Claude Cowork Claude trabajando directamente sobre tus archivos y proyectos reales. No es un chatbot. Es un colaborador que entiende tu contexto. Aquí deja de ser un juguete y se convierte en herramienta de trabajo real. https://
anthropic.skilljar.com/introduction-t
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Genesis Energy Selects OPSWAT for Cybersecurity Solutions
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Genesis Energy chose OPSWAT after considering testimonials from customers nationally and internationally within the utility sector globally.
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Fine-tuning vs Retrieval: Fixing Hallucinations About Company Docs
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If your model is hallucinating about your company docs, fine-tuning is usually not the fix.
— Louis-François Bouchard 🎥🤖 (@Whats_AI) 4 avril 2026
That’s the trap.
A lot of teams see wrong answers about internal files and assume they need to retrain the model. But fine-tuning changes behavior, not factual recall of constantly… pic.twitter.com/LBVCwJbm3YIf your model is hallucinating about your company docs, fine-tuning is usually not the fix. That’s the trap. A lot of teams see wrong answers about internal files and assume they need to retrain the model. But fine-tuning changes behavior, not factual recall of constantly changing company knowledge. It can help with tone, structure, or broad domain patterns. It is not the best tool for making a model reliably remember your latest return policy, pricing sheet, or product catalog. For that, you usually want retrieval. In other words: fine-tuning teaches patterns, retrieval supplies facts. So if the issue is accuracy on specific documents, give the model better access to the right context instead of trying to bake those facts into its parameters. It is cheaper, easier to update, and much more controllable. Mixing those two up is one of the fastest ways to waste time and budget in AI. Have you seen teams make this mistake already?
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Forward Deployed Engineer Recruitment: Connecting AI Technology to Customer Business
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Forward Deployed Engineer is a bridge connecting customer business with Sakana AI's cutting-edge technology 🐟 For details and applications, please visit 👇
https://sakana.ai/careers/#forward-deployed-engineer This is a frontline role where you implement applications incorporating world-class generative AI and autonomous agents, breaking through challenges that were previously difficult to solve. [Translated from EN to English]→ View original post on X — @sakanaailabs, 2026-04-04 10:25 UTC
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Google Agent Skills: Engineering Best Practices für AI Coding Agents
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If you found this useful, a like or RT goes a long way 🦾 Follow me → @datachaz for insights on LLMs, AI agents, and data science! Charly Wargnier (@DataChaz) 🚨 You need to see this. @addyosmani from Google just dropped his new Agent Skills and it's incredible. It brings 19 engineering skills + 7 commands to AI coding agents, all inspired by Google best practices 🤯 AI coding agents are powerful, but left alone, they take shortcuts. They skip specs, tests, and security reviews, optimizing for "done" over "correct." Addy built this to fix that. Each skill encodes the workflows and quality gates that senior engineers actually use: spec before code, test before merge, measure before optimize. The full lifecycle is covered: → Define – refine ideas, write specs before a single line of code → Plan – decompose into small, verifiable tasks → Build – incremental implementation, context engineering, clean API design → Verify – TDD, browser testing with DevTools, systematic debugging → Review – code quality, security hardening, performance optimization → Ship – git workflow, CI/CD, ADRs, pre-launch checklists Features 7 slash commands: (/spec, /plan, /build, /test, /review, /code-simplify, /ship) that map to this lifecycle. It works with: ✦ Claude Code ✦ Cursor ✦ Antigravity ✦ … and any agent accepting Markdown. Baking in Google-tier engineering culture (Shift Left, Chesterton's Fence, Hyrum's Law) directly into your agent's step-by-step workflow! `npx skills add addyosmani/agent-skills` Free and open-source. Repo link in 🧵↓ — https://nitter.net/DataChaz/status/2040357775830814798#m
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Addy Osmani’s Agent Skills: Engineering Best Practices for AI Coding Agents
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🚨 You need to see this. @addyosmani from Google just dropped his new Agent Skills and it's incredible. It brings 19 engineering skills + 7 commands to AI coding agents, all inspired by Google best practices 🤯 AI coding agents are powerful, but left alone, they take shortcuts. They skip specs, tests, and security reviews, optimizing for "done" over "correct." Addy built this to fix that. Each skill encodes the workflows and quality gates that senior engineers actually use: spec before code, test before merge, measure before optimize. The full lifecycle is covered: → Define – refine ideas, write specs before a single line of code → Plan – decompose into small, verifiable tasks → Build – incremental implementation, context engineering, clean API design → Verify – TDD, browser testing with DevTools, systematic debugging → Review – code quality, security hardening, performance optimization → Ship – git workflow, CI/CD, ADRs, pre-launch checklists Features 7 slash commands: (/spec, /plan, /build, /test, /review, /code-simplify, /ship) that map to this lifecycle. It works with: ✦ Claude Code ✦ Cursor ✦ Antigravity ✦ … and any agent accepting Markdown. Baking in Google-tier engineering culture (Shift Left, Chesterton's Fence, Hyrum's Law) directly into your agent's step-by-step workflow! `npx skills add addyosmani/agent-skills` Free and open-source. Repo link in 🧵↓
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New comprehensive report covers AI and technology landscape
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read my new report: https://
x.com/Scobleizer/sta
tus/2040333538667667769?s=20
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Kimi K2.5 Hardware Requirements for Local Model Deployment
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It answered with a long post, but concludes: "I missed it because I was focused on models that run well locally. Kimi K2.5 technically runs locally but needs enterprise-class hardware to do so at useful speeds. It's now in the report with a full hardware breakdown."
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Scaling High Throughput AI Inference Infrastructure Challenges
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Sometimes I take for granted how quickly we can ship great product, vs how hard it is to tune a super high throughput inference + api stack. The scale makes the latter really hard. we’re working around the clock to make it better.