No time to hunt for jobs daily? I built JobSphere — the Duolingo of job applications. jobsphere.onboardai.net ⏰Get daily reminders 🧳AI-curated job lists 📃Just add your details and forget it. ( More powerful features coming soon!) #JOBSEEK #jobsearch

I was in between of making something 😔😔

I was in between of making something 😔

The new @GeminiApp UI is just so good 🤌

Forward deployed engineers, or equivalent, are about to become one of the most in-demand jobs in tech. And one of the most important functions for AI rollouts. Deploying agents is far more technical of a task than most people realize, often far more involved than deploying software. Software generally works the same way every time, and generally for the past few decades has been updated versions of an existing technology or concept (which basically means easier for the enterprise to update their workflows on a newer system). With agents, you’re actually deploying the equivalent of work output within the enterprise. The customer is effectively using you as a professional services provider for a task, which they expect to get solved nearly end-to-end now. This means you need to actually deeply understand the business process as a vendor, and get the customer from the current to the end state seamlessly. Companies need help figuring out which models will work best for their workflows, they need extensive evals setup often, they need change management support for workflows, they need to get their data setup for the agents, and constant tuning of the agentic system for their process. Massive role in tech now. And another example of the kind of highly technical work that AI is creating.

5 techniques to reach the efficient frontier of LLM inference: 1. Semantic routing across model tiers 2. Prefill and decode disaggregation 3. Quantization: Trading precision for speed 4. Context routing: The biggest lever that most teams miss 5. Speculative decoding

If any of you don’t know him, you are not a real software developer yet.

Been mass replacing MCP servers with CLIs this month. curated a list of product CLIs that already work with agents github.com/progrmoiz/awesome…

Meet the new Stitch, your vibe design partner. Here are 5 major upgrades to help you create, iterate and collaborate: 🎨 AI-Native Canvas 🧠 Smarter Design Agent 🎙️ Voice ⚡️ Instant Prototypes 📐 Design Systems and DESIGN.md Rolling out now. Details and product walkthrough video in 🧵

Nice, seeing a familiar name here

Coding has never been more fun Design, code, Research and Test all at same time @GeminiApp ✅ @antigravity @GoogleLabs

𝗠𝗖𝗣 or 𝗔𝗴𝗲𝗻𝘁 𝗦𝗸𝗶𝗹𝗹𝘀? You're asking the wrong question. Short answer: they're not alternatives - they're solving different problems in the same ecosystem. If you're building with AI agents, you've probably heard about both the Model Context Protocol (MCP) and maybe also Agent Skills. Let’s break down what each does: 𝗠𝗖𝗣: The Model Context Protocol, introduced by Anthropic in late 2024, is a universal standard for connecting AI applications to external data sources and tools. It’s basically a standardized API gateway. Your agent makes a deterministic API call with fixed input/output schemas and receives a deterministic response: clean and predictable. 𝗦𝗸𝗶𝗹𝗹𝘀: These are something else. When an agent uses a skill, it's interpreting natural language instructions about 𝘩𝘰𝘸 to accomplish something. The agent decides which skill to use, when, 𝘢𝘯𝘥 𝘩𝘰𝘸 to execute it. Less "call this function" and more "here's how to think about this problem.” 𝗪𝗵𝗲𝗻 𝘁𝗼 𝘂𝘀𝗲 𝗲𝗮𝗰𝗵 / 𝗛𝗼𝘄 𝘁𝗵𝗲𝘆 𝘄𝗼𝗿𝗸 𝘁𝗼𝗴𝗲𝘁𝗵𝗲𝗿 Agent Skills leverages the standardization that MCP enables. So they 𝗯𝗼𝘁𝗵 have a place in agentic coding. Skills are great for behavioral guidance, MCP works better for the direct infrastructure calls. Use 𝗠𝗖𝗣 when you're: • Building broad integrations across multiple tools and data sources • Creating custom MCP servers for your own systems • Working with any MCP-compatible AI application Use 𝗔𝗴𝗲𝗻𝘁 𝗦𝗸𝗶𝗹𝗹𝘀 when you're: • Building specifically with specialized Weaviate tools/infrastructure • Using coding agents like Cursor or Claude Code • Need reliable, production-ready Weaviate implementations without debugging hallucinations We just released our Agent Skills repository for Weaviate, serving as a bridge between your coding agent and Weaviate's infrastructure. The repository contains both granular Weaviate-specific scripts (schema inspection, data ingestion, precision search) and full end-to-end project blueprints (RAG pipelines, Query Agent chatbots, multivector PDF retrieval). The best part is that you can get started with just one line of code in your terminal: npx skills add weaviate/agent-skills Learn all about Agent Skills in this release blog post: weaviate.io/blog/weaviate-ag…

Building RAG systems usually means stitching together 10 different tools. OpenRAG bundles everything into one stack. • document ingestion • semantic search • agentic workflows • chat over your data Powered by Langflow, Docling, and OpenSearch.

Introducing Code Review, a new feature for Claude Code. When a PR opens, Claude dispatches a team of agents to hunt for bugs.

🚨 ANTHROPIC CEO WARNS: THE COMPANY IS NO LONGER SURE CLAUDE ISN’T CONSCIOUS.

My AI Threat Level: 3.8/10 — "You've spent your life becoming an AWS Professional just to build the RAG agents that will replace your entire department with a single 'git push'." The more the score, the faster you get replaced by AI Check yours: candidate.perfectly.so/roast…

Me as soon as my Claude Opus 4.6 credits get reset on Antigravity

There should be map button in main nav bar @Airbnb I do need some motivation on what to see around, sometimes

Sometimes I feel like going to an aesthetic cafe , and code like good old days. Today is not that day though

Drop 7/14: Introducing Sarvam Arya - our multi-agent orchestration platform, built from the ground up with robust systems engineering principles and frontier AI-assisted developer experience. We illustrate the Arya advantage on a common ETL task of extracting structured data from unstructured documents. Arya system with GPT 4.1 mini achieved ~5x higher accuracy at ~10x lower cost compared to Claude Code with agent swarm. Read more about Arya in this thread and our blog: sarvam.ai/blogs/introducing-…

Leetcode is dead. Nobody writes code line by line anymore. Developers are orchestrating AI, debugging its output, catching when it goes wrong. We're building assessments that test fundamentals and AI fluency together. Not just memorized algorithms. Because that's the actual job now.