Reading paths
26 guided paths for every profile.
Each path is a sequence of 7 entries curated for a specific audience — from prompt engineer to robotics engineer, from founder to IT manager — with one line on why each entry matters to you. Not a summary, a route.
For the IT Manager
Seven milestones to shape your IT department's AI strategy.
You are accountable for technology choices that impact security, cost and compliance. This path walks you from governance foundations (EU AI Act, RSP) through integration standards (MCP, function calling) to the 2025 key topic: on-prem AI for sensitive data, with no vendor lock-in.
Junior dev starting with AI
A chronological onboarding for devs who want to use AI professionally.
You are a developer with little AI exposure and you want to understand how it became a normal part of the job. This path walks you in chronological order from the first tools (Copilot, ChatGPT) to the 2025 agentic environments (Cursor, Claude Code), so you see why each step came after the previous one.
Sysadmin / DevOps going local
Milestones to run serious LLMs on your own servers, not someone else's cloud.
You are a sysadmin or DevOps engineer and you want to understand how we got to the point of hosting frontier-grade models on-prem. This path starts with the LLaMA leak that opened the open ecosystem and reaches the self-hostable reasoning of DeepSeek R1 and the quantizations that make it sustainable on real hardware.
AI Security & Policy
For CISOs, compliance officers and security engineers protecting AI systems.
You work in security, compliance or policy and you need the map of the moments that defined AI risk: from the first mainstream prompt injection (Bing/Sydney) to the safety frameworks of frontier labs, up to empirical evidence of scheming. You will leave with a sharper view of what to write in internal policies and what to demand from vendors.
Creator, marketing and content
From the first generated image to real-time AI video.
You are a designer, content creator, copywriter or marketer and you need to understand how generative AI is rewriting your workflow. This path covers the key jumps from image (DALL·E 2, Stable Diffusion, Midjourney) to voice (ElevenLabs) to video (Sora, Veo 3) and conversational multimodal (GPT-4o).
Executive / C-suite
The strategic backbone to understand where AI is heading.
You are a CEO, CTO or CIO and you don't have time for every paper, but you do need the map of the moments that reshaped the market and your competitors' strategies. Eight events that explain how we went from an academic curiosity (GPT-3) to a global industry able to move national budgets (Stargate, DeepSeek shock).
Prompt Engineer
Master advanced prompting techniques and injection defense.
For those who want to go beyond basic prompts: chain-of-thought, few-shot learning, system prompt design, and prompt injection defense. This path covers the key milestones that redefined how we communicate with and control the behavior of language models.
Data Scientist
Integrate LLMs into your workflow: RAG, embeddings, benchmarks, and fine-tuning.
For data scientists who want to use language models as real engineering components. This path traces the evolution of architectures, benchmarks, and tools that matter: from evaluating open-source models to building production-ready RAG pipelines.
Backend developer integrating LLMs
API calls, function calling, streaming, agents and MCP: the milestones that matter.
You are a backend developer who wants to bring AI into your applications in a robust way, not as a demo. This path takes you from foundational primitives (function calling, streaming) to the latest protocol standards (MCP, autonomous agents), keeping an eye on releases that changed what is actually buildable today.
ML engineer: training, optimization and infrastructure
GPUs, scaling laws, fast inference and quantization: the technical through-line.
You are an ML engineer who wants to understand the architectural and infrastructure decisions that drove the frontier model race. This path connects the foundational scaling papers with the hardware architectures that made them possible, and reaches the ultra-fast inference solutions that define the production cost of an LLM today.
AI Red Teaming & Agent Security
For penetration testers, red teams and security engineers attacking and defending AI systems.
You are an offensive or defensive security professional and you want to understand where vulnerabilities hide in AI systems: prompt injection, jailbreaks, autonomous agents with tool access, models that deceive their own evaluators. This path takes you from foundational alignment techniques to empirical evidence of scheming and operational frameworks for red teaming AI systems in production.
AI Governance & Compliance
For DPOs, compliance managers and legal counsel handling AI regulatory obligations.
You manage compliance, privacy or contracts and AI is becoming a priority regulatory front. This path traces the milestones that built the European regulatory framework and industry responses: from the EU AI Act across its application layers, to voluntary frameworks from frontier labs, to concrete implications for data governance, transparency obligations and vendor due diligence.
Videomakers and motion designers
From the first AI text-to-clip to cinematic generative photorealism.
You are a videomaker, motion designer, or creative director and want to understand how generative video is reshaping creative production. This path follows the key jumps: from Meta and Google's early text-to-video experiments to commercial models (Sora, Veo, Runway, Luma) and on to Veo 3's native synchronized audio, where the entire pipeline of a short film becomes accessible to a single author.
Audio engineers, podcasters and voice developers
From open-source speech recognition to real-time voice agents.
You are an audio engineer, podcaster, or voice application developer and want to map the trajectory of AI in spoken audio. This path starts with pre-Whisper self-supervised models (wav2vec, HuBERT), covers the Whisper breakthrough as a universal free transcriber, then climbs through real-time conversational voice (Moshi, OpenAI Realtime) up to the latest multilingual models and next-generation voice synthesizers like Sesame and Voxtral.
Tech founder building on AI
From GPT-3 to your product: APIs, costs, agents and MCP as infrastructure.
You are a founder, product manager or startup CTO who wants to build a real product on top of AI — not a demo. This path guides you through the foundational decisions (closed APIs vs open source, costs, vendor lock-in) all the way to modern agentic infrastructure, covering the milestones that concretely changed what you can ship today.
Open-source developer in the model era
Llama, Mistral, Gemma, DeepSeek: the history of open weights that matter.
You are a developer who does not want to depend on closed APIs and believes in the value of open-weight models: you can inspect the weights, fine-tune, and run everything locally. This path follows the open community's history — from EleutherAI and GPT-Neo through Llama 4 and DeepSeek — covering the milestones that redefined what you can do without paying tokens to anyone.
Cloud & solution architect: deploying AI on cloud infrastructure
AWS Bedrock, Azure Copilot, GPU scalability and LLM costs: the milestones that reshape architecture.
You are a cloud or solution architect responsible for bringing language models into production on managed cloud infrastructure. This path connects the hardware foundations (GPUs, dedicated chips) with the cloud services that expose them, up to the latest architectural standards for agents and integration, with close attention to costs and regulatory governance.
DevOps / LLMOps: inference, stack and production optimization
vLLM, Ollama, quantization and latency: the operational path for those who run LLMs for real.
You are a DevOps, MLOps or LLMOps engineer who needs to run language models in production with real requirements for latency, throughput and cost. This path follows the evolution of inference infrastructure: from the chips that define its physical limits, to the open-source stacks that push them to the maximum, to the quantization techniques that cut hardware requirements without sacrificing quality.
Frontend developer integrating AI into UIs
Streaming chat, voice UI, multimodal, Copilot in editor, MCP in the browser.
You are a frontend or fullstack developer who wants to go beyond simple API wrappers: you want to build interfaces that converse, listen, see and act. This path follows the releases that redefined what "intelligent UI" means — from editor autocompletion to agents that use the browser on behalf of the user.
Robotics engineer in the Physical AI era
Foundation models for robots, VLA, Pi0, Figure, Gemini Robotics: the milestones of embodied AI.
You are a robotics engineer or embodied AI researcher who wants to understand how foundation models are radically changing the design of robotic systems: from manual reward engineering to generalist policies trained on heterogeneous data. This path follows the releases that have shifted the boundary between simulation and real-world deployment.
AI Researcher & Academic
Foundational papers, architectures, scaling laws, reasoning, and Nobel prizes: the scientific thread.
You are a researcher, PhD student or academic who wants to reconstruct the scientific trajectory of modern AI through the contributions that actually moved the frontier. This path connects foundational papers on architectures and scaling with breakthroughs in reasoning and the institutional recognition that has marked the scientific legitimation of the field.
Healthcare IT: clinical AI, data, and regulation
AlphaFold, LLMs in clinical settings, EU AI Act for medical devices, and sensitive data management.
You are an IT manager in healthcare or a tech-savvy clinician who wants to understand where AI is changing diagnostics, proteomics and clinical data management — and which regulatory constraints apply today. This path connects AlphaFold's scientific breakthroughs with the evolution of general-purpose models in clinical contexts and the European regulatory framework that already classifies many medical AI systems as high-risk.
Product manager and AI product designer
When to integrate AI into your product, which features to build, how UX changes.
You are a product manager or designer who must decide which AI features are worth building, how to present them to users, and when the market is ready to adopt them. This path guides you through the launches that reshaped user expectations — from the first AI integrations in daily workflows to the agents and open standards that change product architecture.
Teacher and educator
AI without a technical background: impact on education, practical tools, AI literacy.
You are a teacher, trainer or simply someone who wants to understand artificial intelligence without reading technical papers. This path selects the milestones that genuinely changed how people learn, teach and access information — with explanations focused on real-world impact, not model architecture.
Python developer in the AI ecosystem
HuggingFace, LangChain, PyTorch, smolagents: the libraries that built modern AI.
You are a Python developer who wants to understand how open-source libraries have shaped the AI infrastructure you work with every day. This path follows the evolution of key tools — from Hugging Face Transformers to OpenAI and Anthropic SDKs, through next-generation agent frameworks — covering the milestones that matter for anyone writing real code, not just consuming products.
Multimodal AI specialist
Text, image, audio, video: the models that unify AI's senses.
You are a researcher or developer following the evolution of models capable of reasoning across multiple modalities simultaneously. This path starts with the contrastive foundations of CLIP and DALL-E, moves through the vision-language revolution of GPT-4V and Gemini, and reaches the natively audio and video models of 2025-2026 — where text, image, voice, and clips become a single cognitive surface for AI.