XenZee

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XenZee

XenZee

@XenZeeCodes

System Engineer | C/C++ & AI Building Agentic infrastructure for complex C/C++ codebases 🛠️ AI | RAG | MCP | VS CODE

Katılım Ağustos 2025
122 Takip Edilen213 Takipçiler
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XenZee
XenZee@XenZeeCodes·
@ravixpanchal Always looking to vibe with people working on: - Agentic Workflows - MCP implementation - Optimizing RAG pipelines - gRPC Framework - Fine-tuning specialized models - VS Code ecosystem Let’s connect ! ⚡️
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XenZee
XenZee@XenZeeCodes·
@github @abhiaiyer Always looking to vibe with people working on: - Agentic Workflows - MCP implementation - Optimizing RAG pipelines - gRPC Framework - Fine-tuning specialized models - VS Code ecosystem Let’s connect ! ⚡️
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GitHub
GitHub@github·
Building AI apps in TypeScript just got easier. ⚡️ Tomorrow on Open Source Friday, learn all about @mastra, a TypeScript-first framework for building AI applications, directly from CTO @AbhiAiyer. Set a reminder and join the stream. 🔔👇 gh.io/mastra
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Suhas
Suhas@zuess05·
What is the actual difference between building an audience and building a customer base?
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XenZee
XenZee@XenZeeCodes·
@_devJNS Now it is just one subscription..
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JNS
JNS@_devJNS·
remember when knowing HTML, CSS and Javascript meant a $90k salary.
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Anupam Haldkar 
Anupam Haldkar @AnupamHaldkar·
You know you're a real engineer when You spend 2 hours debugging and the issue was: A missing semicolon.
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XenZee
XenZee@XenZeeCodes·
@Its_Nova1012 Nice nice....let's connect and share what we have together...
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NOVA
NOVA@Its_Nova1012·
73 days of showing up on X. Results: • 1.5K followers • 9.4M impressions • Monetized Consistency compounds.
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Shyam
Shyam@buildwithshyam·
What’s harder: A) Getting your first user B) Keeping your first user
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XenZee
XenZee@XenZeeCodes·
@buildwithshyam Right it is lesser than expected...BTW can we connect?
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Shyam
Shyam@buildwithshyam·
As a developer, how many hours do you actually sleep? > 4–5 😵‍💫 > 6–7 😌 > 8+ 😎
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Vivo
Vivo@vivoplt·
AI will literally never replace you if you do these things: • Installation & repair • Construction • Agriculture • Transportation • Production • Protective services • Food service • Ground maintenance • Personal care • Healthcare
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XenZee
XenZee@XenZeeCodes·
@e_opore Always looking to vibe with people working on: - Agentic Workflows - MCP implementation - Optimizing RAG pipelines - gRPC Framework - Fine-tuning specialized models - VS Code ecosystem Let’s connect ! ⚡️
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Dhanian 🗯️
Dhanian 🗯️@e_opore·
HOW AI AGENTS HANDLE UNCERTAINTY → Real-world environments are unpredictable and incomplete. → AI agents rarely have perfect information when making decisions. → Uncertainty arises from noisy data, missing inputs, and dynamic conditions. → Handling uncertainty is essential for building reliable and intelligent systems. SOURCES OF UNCERTAINTY → Incomplete Information → the agent does not have full visibility of the environment. → Noisy Data → sensor or input errors distort reality. → Dynamic Environments → conditions change over time. → Ambiguity → multiple interpretations of the same input. → Model Limitations → imperfect predictions from AI models. PROBABILISTIC REASONING → Agents use probability to represent uncertainty. → Instead of fixed answers, they assign likelihoods to outcomes. → Bayesian reasoning updates beliefs as new data arrives. Example → an agent estimates a 70% chance of rain and adjusts decisions accordingly. BELIEF REPRESENTATION → Agents maintain a belief state instead of a single known state. → This belief is a distribution of possible realities. → As new observations come in, beliefs are updated. This allows agents to act even when information is incomplete. DECISION-MAKING UNDER UNCERTAINTY → Agents evaluate possible actions based on expected outcomes. → They choose actions that maximize expected utility. → Trade-offs are made between risk and reward. Example → a self-driving car slows down when visibility is low. MARKOV DECISION PROCESSES (MDPs) → Used for decision-making in uncertain environments. → Defines states, actions, rewards, and transitions. → Helps agents choose optimal policies over time. PARTIALLY OBSERVABLE MDPs (POMDPs) → Extension of MDPs for incomplete information scenarios. → Agents rely on belief states instead of exact states. → Common in robotics and navigation systems. REINFORCEMENT LEARNING → Agents learn through trial and error. → Rewards guide behavior toward optimal decisions. → Exploration helps discover better strategies despite uncertainty. HEURISTICS AND APPROXIMATIONS → Agents use simplified rules to make faster decisions. → Reduces computational complexity. → Useful when exact solutions are too expensive. Example → rule-based shortcuts in real-time systems. MULTI-AGENT UNCERTAINTY HANDLING → Agents share information to reduce uncertainty. → Collaborative reasoning improves decision accuracy. → Distributed systems can handle complex, uncertain environments better. REAL-WORLD APPLICATIONS → Autonomous vehicles handling unpredictable traffic → Financial systems managing market volatility → Healthcare AI dealing with incomplete patient data → Robotics navigating unknown environments CHALLENGES → Balancing accuracy with computational efficiency → Avoiding overconfidence in uncertain predictions → Handling conflicting or ambiguous data → Ensuring safe decisions in critical systems TIP → AI agents handle uncertainty by combining probabilistic reasoning, belief updates, and adaptive decision-making. → Instead of relying on perfect information, they operate on likelihoods and continuously refine their understanding. → This capability is what enables AI systems to function effectively in real-world, dynamic environments. For a complete deep dive into building intelligent, uncertainty-aware systems → get the AI Agent Developer's Handbook here: codewithdhanian.gumroad.com/l/gfkbh
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Shubham Saboo
Shubham Saboo@Saboo_Shubham_·
JUST SHIPPED: Developer's Guide to AI Agent Protocols. Make sense of MCP, A2A, UCP, AP2, A2UI, AG-UI. My new blog is now live on Google for Developers.
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XenZee
XenZee@XenZeeCodes·
Looking to follow more people working on: 🤖 Agentic Workflows 🛰️ gRPC & Microservices 🔌 MCP Implementation 📝 Fine-tuning specialized LLMs 💻 VS Code Tooling If your feed is 90% tech and 10% 'what if we built this?', we belong in the same circle. Let's #connect guys...
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Aditi jaiswal
Aditi jaiswal@Aditibuildit·
I reached 100+ followers 😭😭 It's crazy I had 0, 4 days back genuinely grateful for every single one of you. 🙏
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DROID
DROID@droidbuilds·
I want to complete my 1K verified followers mark, If you're in tech and verified, let's support each other Comment "hi" and let's be mutual
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Priyansh Bhardwaj
Priyansh Bhardwaj@StackSamurai·
I'm 23 , a software developer. I want to connect with people who love:- - Coding - vibe coding - Full stack developer - software engineer - AI/ML IF YOU'RE INTO TECH.. LET'S CONNECT
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Mehetab Ali
Mehetab Ali@TabCrypt·
@StackSamurai let's connect for sure! coding is like therapy for me.. always learning something new 🙌 hit me up if you wanna vibe on some dev stuff! 🔥
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