LinkedIn Content Strategy & Writing Style
No BS AI/ML Content | ML Engineer with a Plot Twist 🥷100M+ Views 📝
1 person tracking this creator on Viral Brain
Paolo Perrone positions himself as a high-signal practitioner who bridges the gap between academic AI research and production-grade engineering. His content strategy centers on debunking "shallow" AI hype by providing deep-dive technical breakdowns of agentic architectures, open-source repositories, and cost-efficient training methods. He is notable for his "no-BS" technical transparency, often critiquing popular but fragile prompt-engineering trends in favor of robust software principles like CI/CD, event-driven runtimes, and explicit planning. Perrone’s work sits at a sharp intersection of elite education and developer utility, where he democratizes high-level Stanford or Karpathy-led insights while simultaneously curating the specific tools—like ADK or MCP servers—needed to build enterprise-ready systems.
119.7K
4.7K
343
—
15.3
33
3
Stop paying for AI engineering bootcamps. The fundamentals can be learned in 2-3 weeks. For free. Here's everything you need: → https://lnkd.in/gCFQ69G9 — Andrew Ng's bite-sized courses → huggingfac…
Google just open-sourced their MCP server for databases. Connect any database to any AI agent in 10 lines of code. It's called MCP Toolbox. What it handles: → Connection pooling → Authentication →…
Google's new agent framework makes LangChain look like a toy. It's called ADK. It treats agent development like software development Not prompt engineering. What it is: → Code-first agent developmen…
30B parameters running on 24GB. Not a typo. NVIDIA AI dropped a banger MoE model. Nemotron 3 Nano. Runs on 24GB. Only 3.6B active during inference. 1M context window. I ran it on my DGX Spark. Her…
They told you 70B models need 80GB VRAM. They lied. AirLLM runs Llama 70B on a 4GB GPU. No quantization. No distillation. No pruning. The trick: layer-wise inference. Instead of loading the entire…
Perplexity just got embarrassed by a database company. Airtable just dropped Superagent. Their first standalone product. I tested both on the same research task. The difference? → Perplexity: An…
15.3 posts/week
Posts / Week
0.5 days
Days Between Posts
3
Total Posts Analyzed
HIGH
Posting Frequency
342.7%
Avg Engagement Rate
STABLE
Performance Trend
230
Avg Length (Words)
HIGH
Depth Level
ADVANCED
Expertise Level
0.82/10
Uniqueness Score
NO
Question Usage
0.25%
Response Rate
Writing style breakdown
<start of post>
Your AI agent isn’t “failing.”
It’s doing exactly what you built: generating text with no feedback loop.
Can it take actions?
Can it check if those actions worked?
Can it recover when it breaks?
Most “agents” can’t.
They have prompts. Not systems.
→ Tool access (real actions, not just suggestions)
→ State (what happened, what’s pending, what changed)
→ Evaluation (did the tool call succeed? did it hit the goal?)
→ Guardrails (what it must never do)
→ A retry policy (how it behaves when tools fail)
If you’re missing 3 of those 5, your agent is just a chatbot wearing a toolbelt.
1️⃣ Plan
→ write down the next 1–3 actions in plain language
→ keep it short enough that a human could sanity-check it
2️⃣ Act
→ call one tool
→ do not chain 6 tools in a row without checking anything
3️⃣ Verify
→ look at tool output and score it (pass/fail or 0–1)
→ if fail, generate a corrected action, not a new essay
4️⃣ Commit
→ update state (what was done, what is still true, what is now false)
→ store the minimal artifacts you’ll need later (IDs, URLs, file paths)
That’s the difference between “agentic UX” and an agent.
→ Did the tool output contain the expected artifact?
→ If you expected a file, does the file exist?
→ If you expected an email, is there a message ID?
→ If you expected a DB write, did rows change?
No artifact = no success.
Add this and your agent stops hallucinating progress.
Goal: “Unsubscribe from newsletters I never read.”
→ drafts 12 unsubscribe emails
→ marks task “done”
→ you still get 50 emails tomorrow
→ finds sender domains
→ opens unsubscribe links (browser tool)
→ verifies the preference center updated OR a confirmation page appeared
→ logs the result per sender: success / blocked / needs human
Same LLM. Different loop.
People treat “memory” like the solution.
Memory is not the solution.
Memory is a liability unless you have evaluation.
If the agent can’t tell what’s true, it will remember the wrong thing forever.
pip install your-agent-framework
objective
current_step
completed_actions[]
artifacts[]
last_tool_result
confidence_score
→ tool error: retry up to 2x, then escalate
→ low confidence: ask one clarifying question, then act
→ repeated failure on same step: switch strategy, don’t repeat the same call
→ separate “read-only tools” from “write tools”
→ require verification before any write tool can run twice
Because “full system access” without checks is not autonomy.
It’s just faster mistakes.
Real agents reduce human decisions.
Fake agents just move the work into verification, at 2am, when it breaks.
→ https://lnkd.in/agent-loop-checklist
💾 Save for when your “agent” starts shipping confident nonsense
♻️ Repost if your team is still calling prompts “autonomous systems”
<end of post>
Sign in to unlock the full writing analysis
Nail your LinkedIn strategy with ViralBrain.
Analyze and write in Paolo Perrone's style. Grow your LinkedIn to the next level.