Jutsu

𝐇𝐚𝐜𝐤𝐚𝐭𝐡𝐨𝐧 𝐒𝐮𝐜𝐜𝐞𝐬𝐬 𝐰𝐢𝐭𝐡 𝐀𝐈 𝐃𝐞𝐯𝐑𝐞𝐥: 𝐒𝐚𝐯𝐞 𝐌𝐨𝐧𝐞𝐲 𝐚𝐧𝐝 𝐓𝐢𝐦𝐞 𝐖𝐡𝐢𝐥𝐞 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫𝐬 𝐒𝐮𝐩𝐩𝐨𝐫𝐭 Hackathons are magic. Hundreds of developers build, test, and launch projects in hours. But when questions flood in about APIs, SDKs, Docs and Bugs - your communication channels explode. No human DevRel team can answer hundreds of questions at once. As a result momentum stalls, frustration rises and growth slips away. That’s why we built Jutsu AI DevRel - your always-on teammate. It plugs into Docs, GitHub, Discord, and Slack - giving instant, reliable answers 24/7. When it can’t answer, it escalates to your team - no question ever gets lost. The result? Developers get the support they need, instantly. We proved it at Stellar Protocol’s flagship hackathon during Meridian 2025 in Rio de Janeiro. The results spoke for themselves: -> 500+ developers actively built and launched projects -> 1,000+ questions answered in real time, across multiple channels -> 83% of developers reported a positive experience with the AI DevRel Agent Jutsu scales what humans can’t. It saves money, keeps engineers focused, and builds trust by never leaving a developer waiting. For founders, CTOs, and DevRel leaders: this is the future of Developer Relations. A future where your community scales without limits. https://x.com/tryjutsu https://jutsu.ai/ Join us in SF Tech Week: https://lnkd.in/gkBjzyBT #Meridian2025 #Stellar #Jutsu #DevRel

Developers move at AI speed. Traditional DevRel can’t keep up - docs go stale, support lags, and communities outgrow their teams. 🚀 AI DevRel flips the script: - Real-time docs & SDK support - AI copilots for instant answers - Community insights before issues escalate Automation so your team focuses on relationships, not tickets. At Jutsu, we help DevRel teams supercharge their developer communities and turning support into scale and feedback into growth. 👉 If you’re building in AI or Web3, the question isn’t if you’ll adopt AI DevRel, but when. Let’s talk.

Want to master agentic RAG apps? This session breaks down the concepts and code you need to build a full, industry-standard system from start to finish. Go Learn: https://lnkd.in/gzdJxzXV

Multi-Agent: Let Each Agent Focus on One Job Instead of one agent doing everything, split the work. Give each agent a role like researcher, planner, coder, reviewer, or project lead. They work together, share results, and improve the outcome by checking each other’s work. You don’t need anything complex. Just set clear roles, let them talk through a controller, and let the process run. This works best when the task has different parts. The back-and-forth between agents often leads to better and more thoughtful results

Google Just Gave Developers a Free AI Power Tool Google quietly launched Gemini CLI and it’s one of the most useful tools I’ve seen in a while. It gives you direct access to Gemini 1.5 Pro inside your terminal. That means you can fix bugs, build features, and even generate full apps from screenshots all by typing a single command. You get 1000 requests a day. No cost. No limits on creativity. I tested it on real projects: → Found and fixed a scroll bug in seconds → Built a memory feature for a chat app, end to end → Generated a landing page from scratch based on a YouTube channel If you're a developer and not trying this, you're already behind. Install it with one line: npm install -g @google/gemini-cli This is more than just code help. It’s a full-on AI teammate in your terminal.

Want to understand how Retrieval-Augmented Generation (RAG) really works? Build it yourself from the ground up. In this tutorial, you’ll create NutriChat, a RAG pipeline that lets users query a 1,200-page Nutrition Textbook PDF without relying on existing frameworks. This way, you’ll learn every piece of the system inside out.

ReAct: Let the Model Think While It Works ReAct means the model doesn’t just answer. It thinks, takes an action, checks the result, then decides what to do next. It’s useful for tasks that need steps. Like searching for invoices, noticing missing data, asking a follow-up, and trying again. To use ReAct, you need three things: - Tools to take actions - Memory to keep track - A loop that lets the model reason and adjust This pattern helps the model adapt instead of following a fixed script. If the task needs back-and-forth or updates along the way, ReAct is the way to go.

Most research tools feel the same but Google’s new NotebookLM is different. You upload your files and it turns them into a clear map of ideas that actually helps you understand what’s going on. You can also listen to two AI voices talk through your notes like a podcast and even ask them questions while they’re speaking. It saves time and helps you spot connections you might miss. The free version is already very useful and the paid one adds more features for teams. If you work with lots of documents or need to make sense of scattered info, this is worth trying.

Want to make large language models truly your own? Start here. This list offers step-by-step tutorials on different fine-tuning methods. Try them out and tailor LLMs to fit your needs with a strong foundation in customization. Go Learn: https://lnkd.in/dv4uWJun

Tool Use: Stop Expecting the Model to Know Everything The model doesn’t know your data, your files, or anything that changes often. To fix that, connect it to tools. Let it search documents, call APIs, or run functions. Now it fetches real answers instead of guessing. This is the only way to handle tasks that depend on fresh or private data. Even a simple setup can make results more accurate and reliable.