1: Hermes now has the exit cleanup it was already trying to call. If the TUI quits abruptly, it has a better shot at putting your terminal back the way it found it. 2: Gateway restarts should stop killing themselves. The systemd template no longer uses the flag that turned a restart into a self-inflicted shutdown loop. 3: Telegram polling now uses the current asyncio pattern on Python 3.11 and newer. Narrow fix, but it keeps one noisy integration edge from breaking retries. 4: Desktop updates now close the success overlay when the backend finishes. If the backend never returns, the UI errors instead of leaving you staring at a ghost state. 5: Nix builds now verify cold npm installs and lockfile repairs against a real build. Less chance of a broken package path making it to main. github.com/NousResearch/herm…

If I had 6 months to become an Agentic AI Engineer. I'd do this. Stage 1: Python Async Foundations asyncio, FastAPI, event-driven architecture, error handling, API integration patterns. Stage 2: LLM Fundamentals for Agents Context management, model routing, token economics, latency tradeoffs, failure modes. Stage 3: Tool Calling Structured Outputs Pydantic validation, function calling schemas, error recovery, dynamic tool discovery. Stage 4: Memory State Management Short-term buffers, long-term vector recall, context compression, cross-session sync. Stage 5: Single Agent Workflows ReAct loops, plan-and-execute, self-reflection, iteration limits, graceful degradation. Stage 6: Multi-Agent Orchestration LangGraph/CrewAI, supervisor patterns, message passing, conflict resolution, handoffs. Stage 7: Human-in-the-Loop Systems Uncertainty detection, approval gates, audit trails, resume logic, intervention points. Stage 8: Evaluation Quality Assurance Automated eval harnesses, LLM-as-a-judge, regression testing, hallucination metrics. Stage 9: Observability Tracing Distributed tracing (LangSmith/Arize), cost dashboards, latency monitoring, alerting. Stage 10: Security Guardrails Prompt injection defense, output filtering, PII redaction, sandboxed execution, compliance. Stage 11: Production Deployment vLLM/SGLang, Kubernetes scaling, CI/CD for agents, canary releases, rollback strategies. Stage 12: Open Source Portfolio Ship autonomous agents publicly, write architecture docs, record demos, contribute to libs. Most people stay stuck watching tutorials. Builders get hired. (Bookmark it)

The Agentic AI roadmap hit 127k impressions. Top comment: "This is gold. But where do I actually learn this?" Every link below is direct, free (or freemium) & battle-tested. I verified these against the latest 2026 docs. Bookmark this. You won't need to Google again. 👇 Exact Resources for Each Stage 👇 Stage 1: Python Async Foundations • realpython.com/async-io-pyth… ( Hands-on asyncio walkthrough) • fastapi.tiangolo.com/ (Official FastAPI docs with interactive examples) Practice: Build a webhook handler that processes 100 req/sec Stage 2: LLM Fundamentals for Agents • huggingface.co/learn/llm-cou… (Free LLM course from Hugging Face) • youtube.com/@AndrejKarpathy (Andrej Karpathy's YouTube LLM series) Read: "Attention Is All You Need" (just the abstract diagrams) Stage 3: Tool Calling Structured Outputs • python.langchain.com/docs/ho… (LangChain tool calling patterns) • pydantic.dev/docs/concepts/j… ( Pydantic JSON schema mode guide) Practice: Build a weather agent that returns validated JSON Stage 4: Memory State Management • pinecone.io/learn/ (Vector DB 101 tutorials) • langchain-ai.github.io/langg… (LangGraph memory & state docs) Practice: Add long-term recall to a chatbot using Redis Stage 5: Single Agent Workflows • langchain-ai.github.io/langg… (LangGraph Academy (free tier)) • Paper: "ReAct: Reasoning Acting" arxiv.org/abs/2210.03629 Practice: Build a research agent that cites its sources Stage 6: Multi-Agent Orchestration • docs.crewai.com/ (CrewAI official docs quickstart) • Course: deeplearning.ai/short-course… (Search "Multi-Agent" (DeepLearning.AI)) Practice: Create a supervisor 2 worker agents that debate Stage 7: Human-in-the-Loop Systems • docs.smith.langchain.com/ (LangSmith Human Feedback & Tracing) • Tutorial: Search "Human in the Loop" on LangChain Blog Practice: Add a "pause for approval" step to a sensitive action Stage 8: Evaluation Quality Assurance • docs.ragas.io/en/stable/ (RAGAS documentation) • github.com/confident-ai/deep… (DeepEval GitHub repo (open-source)) Practice: Write 5 eval tests for your agent's outputs Stage 9: Observability Tracing • arize.com/docs/phoenix/ (Arize Phoenix tracing & evals) • docs.smith.langchain.com/ (LangSmith debugging guide) Practice: Add latency cost tracking to your agent Stage 10: Security Guardrails • nvlpubs.nist.gov/nistpubs/ai… (NIST AI Risk Framework (PDF)) • Repo: github.com/protectai/llm-pro… ( Prompt injection defense) Practice: Red-team your own agent with 10 attack prompts Stage 11: Production Deployment • docs.vllm.ai/ (vLLM official inference docs) • github.com/DataTalksClub/mlo… (MLOps Zoomcamp (free, full course)) Practice: Dockerize your agent deploy to Render/Railway Stage 12: Open Source Portfolio • Contribute: github.com/langchain-ai/lang… • Contribute: github.com/crewAIInc/crewAI Practice: Ship 1 public agent/month with a 2-min demo video (Bookmark this)

10 useful pytest plugins every Python developer should use: - pytest-xdist → Run tests in parallel using multiprocessing. - syrupy → Snapshot testing for API responses, CSV files, or emails. - freezegun → Control time inside your tests. pytest-env → Control environment variables for your test runs. - pytest-cov → Track code coverage of your tests. - pytest-asyncio → Test async code. - responses → Intercept and mock outgoing HTTP calls. - moto → Intercept and mock AWS API calls. - hypothesis → Property-based testing. - pytest-django → Testing Django apps.

Just built a pipeline that turns raw customer reviews into business intelligence using Python · asyncio · Groq · Pydantic · Scikit-Learn GitHub: github.com/Olami2596/custome…

The 4 challenges: 1️⃣ FastAPI Auth: Write a secure JWT login flow 5 robust pytest cases. 2️⃣ Traceback Debug: Untangle & fix a 3-level Python exception chain. 3️⃣ Async Refactor: Rewrite a 200-line sync class into asyncio with retry logic. 4️⃣ Schema Design: Convert a complex 5-table SQL schema into strict Pydantic v2 models.