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Done-for you Audience Building & Content Writing for AI/ML Founders that frees up 100% of your time, so you can focus entirely on building your company Is your work too important to ignore? 💌 https://tally.so/r/wk69VJ (or shoot me a DM) 👉 It's 2025 and we're at the peak of the attention economy Where Authenticity is king, visibility is currency and people wanna buy from people Not faceless dehumanized brands So, they told you LinkedIn was "free marketing" you slapped some posts and called it strategy You've poured hours crafting the 'perfect' post, only to watch it sink without a trace.. Then it dawned on you, your technical brilliance isn't enough to draw a crowd Hell, maybe you've even tried mimicking influencers, scrambling for any traction.. Your post got 500 f*cking views... from your friends... Being ignored is soul-crushing It's not your fault tough When you started your company, you didn't sign-up for a degree in an Audience Building, Copywriting and LinkedIn strategy And here you are, sacrificing yet another late night for your vision "Am I just sabotaging my efforts?" "Is my life-work meaningless?" Getting traction feels impossible You hoped hiring some marketer would save your weekends but.. Tech is a code they can't crack They can only churn out the lame, soul-sucking crap we all ignore on sight With armies of founders flooding feeds, your voice dissolves into meaningless white noise But in the process, you've wasted more of your precious time explaining 101 tech concept and rewriting low-quality drafts Done being ignored? Let's make the world listen I can help you with: ✒️ No BS AI/ML Content ⭐ LinkedIn Profile Revamp 🎯 Website Copy Optimization I've done it before: ↳ For founders backed by YC, a16z, Sequoia, and Techstars ↳ For bootstrappers in LLMs, ML, Vector DBs, Data Analytics, and Engineering ↳ For AI and Machine Learning leaders at Google, Microsoft, Airbnb, and YouTube If they could rise above the noise, so can you Yeah, even if you're an introvert with a "boring" niche Still with me? Amazing—You're amazing! Cheers, Paolo My booking link: 💌 https://tally.so/r/wLW0oz (or shoot me a DM) 👉
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Most Agents are Stuck in Development Hell 💀 Here’s my 5-step roadmap to break out — and ship production-ready AI agents: 1️⃣ Master Python for Production First, you need to nail the basics that matter in prod: ○ FastAPI: For rapid, lightweight, production-grade APIs ○ Async Programming: For faster, concurrent agent tasks ○ Pydantic: For reliable data validation and settings management Check out these resources: 🔗 Fast API (1) https://lnkd.in/g7xiUur8 🔗 FastAPI (2) https://lnkd.in/gkviWXsn 🔗 Async Programming - https://lnkd.in/ghZkAseJ 🔗 Pydantic - https://lnkd.in/gMZ8ZQvB 🔗 Database Management (SQLAlchemy + Alembic) https://lnkd.in/gggR5_6P Start here, then move to Step 2. 2️⃣ Make Agents Stable Now you have two options: ○ Logging: Essential for tracing bugs in complex agents ○ Testing: Unit + integration tests prevent silent failures There's no wrong place to start, but you must do both Dive into the essentials with: 🔗 Logging - https://lnkd.in/gCTQDgX9 🔗 Unit Testing https://lnkd.in/gCTQDgX9 🔗 Integration Testing https://lnkd.in/gCJwzRmK 3️⃣ Go Deep on RAG RAG is a system built in two parts: ↳ RAG Fundamentals: ○ Understand what RAG is and why it matters ○ Learn how embeddings power semantic search ○ Use vector DBs for fast, scalable retrieval Databases ↳ RAG in Practice ○ Smart chunking = better context, better answers ○ Use LangChain to chain RAG components together ○ RAG Evaluations: Measure retrieval and answer quality Start with these: 🔗 Understanding RAG – https://lnkd.in/gBu_bEku 🔗 Text Embeddings – https://lnkd.in/g7F9RZ-d 🔗 Vector Database – https://lnkd.in/g96AXduj 🔗 Chunking Strategies – https://lnkd.in/gkjHe7fW 🔗 RAG with LangChain – https://lnkd.in/g9EFSvVQ 🔗 RAG Evaluations – https://lnkd.in/gNxz5Ndq 🔗 Advanced RAG Techniques – https://lnkd.in/ggDmV3-p 4️⃣ Define Agent Architecture Useful agents need structure, clarity, and memory Here's your toolkit: ○ LangGraph (or similar) – Framework for production-grade agents ○ Prompt Engineering – Write clear, effective instructions ○ SQLAlchemy + Alembic – Manage structured memory with confidence Don’t sleep on this — it’ll bottleneck everything down the line 🔗 Prompt Engineering Guide – https://lnkd.in/g29y6WXu 5️⃣ Monitor & Improve in Production This is where good agents become great: ○ Spot bugs early ○ Understand how user interact ○ Continuously improve your agent Use Langfuse to monitor, debug, and optimize in the wild 🔗 https://lnkd.in/g-cuPMuV No monitoring = no learning = no scale That's it Five steps, zero fluff. 🚀 Let’s go from stuck to shipped — fast. 💾 Save this post — it's your blueprint for building agents that work ♻️ Repost to help more builders escape development hell Post Inspiration Shirin Khosravi Image Credits Rakesh Gohel 🙌
The MCP Hype is Lying to You It’s filled with: ✨ Fancy acronyms solving imaginary problems ✨ Overengineered layers between agents and APIs ✨ Architecture decisions driven by hype, not need The reality of agents-to-APIs integration? You don’t need an MCP layer: 🛠️ Direct API calls are faster, clearer, and just work 🛠️ Less latency, less complexity 🛠️ Easier to debug, easier to scale, easier to maintain Stop adding layers just to follow the trend. If your API works, skip the MCP middleman. Less architecture theater. More systems that ships. That’s the real flex. Agree? Disagree? Let’s hear it 👇 Repost if you prioritize simplicity over AI infra hype! ♻️
3 Key Realizations From 8 Years of Debugging ML Systems in Production Over the last 8 years, I’ve been building and deploying ML systems at scale One pattern kept repeating: most production data issues weren’t bugs in code They were blind spots in the data itself And traditional monitoring tools were never built to catch them Here are my 3 key realizations: 👉 #1: You won’t catch what you’re not looking for Most data tests assume you know the problem in advance But in real-world pipelines, the biggest risks are unexpected Schema changes, freshness gaps, sudden volume drops.. Without observability, you’ll miss them 👉 #2: Metrics Observability is the missing layer Most data testing relies on predefined checks.. But the biggest issues in production are the ones you didn’t think to look for Observability flips that: it watches everything, learns what “normal” looks like, and flags when something drifts That gives you three big advantages: • You can cover far more data without writing custom tests • You stop guessing what to monitor, and start responding to real signals • You see problems the moment they appear, not after downstream damage 👉 #3: Feedback-aware AI is how detection keep improving Anomaly detection is only half the story What surprised me most was how much better things got once I could give feedback.. Confirm real issues, dismiss false positives, and let the system learn from it That loop made detection feel a lot less like noise, and more like guidance And it came with unexpected benefits: • Way fewer false alarms over time • Anomalies came with context—expected ranges, trends, impact • You could backfill historical data and surface issues we’d missed for months So if you’re serious about building resilient data pipelines, then don’t just test—observe If you’re looking for something to help with that, Soda has been surprisingly solid for me Setup took minutes, and you can backfill up to 1 year without extra configuration Might be worth checking out if you’ve run into similar blind spots.
ChatGPT bans at work are officially outdated—GPTGuard makes secure enterprise AI finally a reality 🔒💥 I just deployed a fully secure, enterprise-grade AI assistant—on-prem, PII-safe, and document-aware—in under a day. No compliance risks, no masking hacks, no LLM data leaks, no trust issues. Just a secure-by-design, context-aware, and privacy-compliant chat interface that actually works. GPTGuard changes everything. Repost to help someone whose company still bans ChatGPT 🙃 Link to try it: https://lnkd.in/gs9SCeEw Full demo in comments 👇
It’s the final day of Soda Launch Week—and if you haven’t checked it out yet—now’s the time The gap between what modern data systems demand and what legacy tooling can handle is growing fast 🤫 Quiet pipeline failures 🤖 Corrupted agent behavior 📉 Downstream metric chaos 🧬 Model drift that goes undetected Too many teams are flying blind in production.. This week’s launch was about solving that—introducing a new kind of data quality platform built for real-world complexity This week’s launch introduced a data quality platform designed for real-world complexity—built to solve challenges like: 📊 Monitoring systems even when ground truth isn't available 🎯 Cutting through the noise and surfacing what actually matters 🔗 Providing full traceability from data ingestion all the way to model predictions It’s not about more alerts. It’s about better ones—with context and consequences built in If your stack includes LLMs, real-time systems, or self-adaptive loops, don't sleep on this 🔗 Today's the final day to catch the launches, demos, and write-ups: https://lnkd.in/gp3MFSeU
Activation functions aren’t supposed to go viral But Damien Benveniste, PhD’s post did, and big time 4,500+ reactions, 450 reposts I reverse-engineered his approach into a template you can copy and use to write technical deep dives that gets noticed Now available inside The Tech Audience Accelerator👇 It’s time we raise the bar. Not just in what we build, but in how we share it. 🚀
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