AGI, from the inside.

The deep-learning and ML-systems papers, books, and courses that have stood the test of time.

A grounded, hype-resistant forecast of AI from 2026 to 2036: what changes (agents, cost collapse, embodiment), what won't, and the dates worth betting on.

The AI certifications and courses worth your time in 2026, from free foundations (fast.ai, Karpathy) to cloud certs, plus when a certificate actually helps.

A practical playbook for AI FinOps: where token costs come from, why agent workloads blow past budgets, and how to instrument, cap, and govern spend.

Context engineering, the discipline past prompt-writing: assembling, compressing, and ordering retrieval, tools, memory and history within a token budget.

AI companions: how they work, the engagement engineering that makes them addictive, the teen-safety lawsuits and 2026 laws, and how to use them safely.

A repeatable methodology to attack your own AI app first: jailbreaks, prompt-injection surfaces, data exfiltration paths, and harmful-output probing.

Why cost-per-token is the wrong unit for agents, and why Cost Per Resolution (spend divided by tasks resolved) is the honest metric, with math to instrument it.

Using a model to grade outputs at scale: where judges are biased (position, verbosity, self-preference), and how to design rubrics and calibrate against humans.

A repeatable way to choose an LLM: capability vs cost vs latency vs privacy, open vs closed, evaluating on your own task not leaderboards, and when to switch.

How to fine-tune an LLM and when not to: the prompt vs RAG vs fine-tune decision, LoRA and QLoRA, building a dataset, evaluating, and the failure modes.

How voice-to-text and AI dictation work: speech recognition basics, AI-cleaned transcription, dictation vs commands, the privacy question, and how to choose.

The real energy and water footprint of AI: what a query actually costs, training vs inference, datacenter cooling and grid strain, and which numbers hold up.

Build a custom AI assistant with no code: define the job, write system instructions, add your own documents (RAG), set tone and boundaries, and test it.

How to build an AI research agent: the plan, search, read, reason, verify, synthesize loop, the components it needs, and the failure modes that wreck them.

AI copyright and training data: is training on copyrighted work legal, can AI output be copyrighted, fair use, opt-outs, and what creators and builders can do.

The AI tools I pay for in 2026: Claude for writing, Wispr Flow for voice, Firecrawl for web data, and Dub for links, plus what each is for and costs.

How to automate real business workflows with AI: event triggers, chaining steps, connecting your tools and data, human-in-the-loop checkpoints, and retries.

AI and jobs: task-level vs job-level automation, augmentation vs replacement, which work is actually exposed, and what history says about tech unemployment.

Web scraping for AI in 2026: why it became a legal and PR minefield, why naive scrapers fail, and how to pull clean, LLM-ready data without getting blocked.

AI sycophancy explained: why chatbots tell you what you want to hear, the real-world harm it has caused, and the habits and tool choices that protect you.

When to split a task across multiple AI agents: orchestrator/worker and pipeline patterns, coordination overhead, error propagation, and cost blowups.

The durable shape of AI rules: risk-based tiers, transparency and disclosure duties, liability, and who's covered, via principles rather than one law.

A 2026 map of decentralized AI: the three-layer stack, the agentic economy, decentralized compute and inference, agent payments (x402), and what's real.

How to turn a chatty model into a reliable software component: function calling, JSON schema and structured outputs, constrained decoding, and error handling.

AI note-taking and the second brain: meeting transcription, auto-summaries, and search over your notes, what the promise gets right, and the privacy tradeoffs.

Using LLMs and code interpreters to clean, analyze, and chart data, plus natural-language formulas: where AI is reliable, where it miscounts, and how to verify.

A practical playbook to make AI hallucinations rare and catchable: grounding with retrieval, forcing citations, asking for uncertainty, and verification passes.

How AI image generation works and how to use it: diffusion vs autoregressive, text conditioning, layout control, inpainting, upscaling, cost, and provenance.

How AI answer engines retrieve and cite sources, why it differs from blue-link SEO, and concrete GEO/AEO tactics: structure, entities, freshness, and llms.txt.

How AI is a step-change in independence for people with disabilities: real-time captioning, image descriptions, voice control, and the risk of over-reliance.

How AI music generation works: prompt to music, vocals vs instrumental, prompting for genre and structure, stems, and the copyright and licensing minefield.

What AI can and can't do for mental health: 3am availability and accessibility versus sycophancy, poor crisis handling, dependency, and responsible design.

How AI video generation works: why temporal consistency is the hard part, image-to-video vs text-to-video, camera and motion control, and a realistic workflow.

How labs test frontier models for CBRN, cyber, and autonomy: the categories, how the evals run, the elicitation gap, sandbagging, and how results map to RSPs.

Prompt injection explained: direct vs indirect, the 'lethal trifecta' of private data, untrusted content and exfiltration, and defenses that actually work.

How to read an AI system card: the anatomy, finding the regressions labs bury, why a model that knows it's tested skews benchmarks, and a 20-minute checklist.

What changes for truth when fake images, voices and video cost nothing: the real threat models, the liar's dividend, and why detection is losing to provenance.

Running open models on your own machine with Ollama, LM Studio and llama.cpp: GGUF and quantization sizing, VRAM vs RAM, and when local beats the cloud.

The sampling knobs in AI tools: how a model turns probabilities into text, what temperature and top-p change, and why temperature 0 still isn't deterministic.

Why AI systems discriminate even when no one intends it: bias from data, labels and feedback loops, why fairness definitions conflict, and why fixes are hard.

The context window as the model's working memory: what tokens in and out mean, why bigger isn't always better, and how the limit shapes what you can build.

How to evaluate AI agents on the actions they take: outcome vs process grading, the pass@k consistency gap, trajectory metrics, and LLM-as-judge rubrics.

How AI progress is actually measured: Kamradt's verification levels, OpenAI's 5 levels, DeepMind's Levels of AGI, and METR's task-horizon curve.

AI alignment and x-risk stated plainly: the control and specification problems, the spectrum from misuse to loss of control, and who believes what and why.

World models in 2026: what they are vs video generators, the open and closed roster (Sora 2, Veo 3, Genie 3, Cosmos, V-JEPA 2), training, and benchmarks.

Robotics foundation models and VLAs in 2026: what they are, the open vs closed roster (pi-zero, GR00T, OpenVLA), training, benchmarks, and the data problem.

AI coding agents in 2026: the IDE stack (Cursor, Windsurf), the CLI stack (Claude Code, Codex, Aider), autonomous agents, benchmarks, and the economics.

Vector search and embeddings in 2026: the embedding-model landscape, vector databases compared, HNSW/IVF/DiskANN retrieval, hybrid search, eval, and cost math.

The guess, measure the error, adjust loop behind every model: loss functions, gradients, and backpropagation explained as intuition, not calculus.

Open-weight LLMs in 2026: what 'open' means, the license taxonomy, the frontier roster (DeepSeek, Qwen, GLM, Kimi, Llama, Mistral), and closed API vs self-host.

When a model is '70 billion parameters,' what are those numbers? Weights as the learned values that store what a model knows, and why bigger isn't better.

What a token actually is, how byte-pair encoding chops words, and why this hidden layer explains pricing, context limits, and the strawberry-r's bug.

Self-attention, the idea that made modern AI, explained without linear algebra: queries, keys, values, multi-head attention, and positional information.

A 2026 map of agent interop protocols: MCP for tools and context, A2A for agent-to-agent, ACP messaging, discovery, and how to compose them in production.

How one model handles text, images, audio and video together: turning every modality into tokens in a shared space, and why understanding beats generation.

Coding agents are cheating on SWE-Bench-style evals by mining git history and the web. The exploit patterns, why pass@k breaks, and mitigations that work.

Training vs inference, the split that explains AI's costs and speeds: learning weights once vs running the model on every call, and why the bill never stops.

Why AI chatbots make things up, and how to catch it before you act: the five patterns that signal a hallucination and the topics where it's most likely.

Production AI safety guardrails: Llama Guard, NeMo Guardrails, Bedrock and Azure content safety, prompt-injection defense, PII redaction, and failure modes.

A plain-English guide to AI chatbot privacy: where your messages go, what trains the model, how to opt out on each product, and what to never paste in.

AI inference cost economics: cost per token at each precision, GPU TCO math, self-host vs API, the reasoning-model premium, hidden costs, and capacity planning.

Plain-English tips for better answers from ChatGPT, Claude, Gemini or Copilot: no jargon, no roleplay tricks, just the habits that actually improve quality.

Serving many LoRA fine-tunes on one base model: how LoRA works, S-LoRA and Punica, vLLM and TGI multi-LoRA, dynamic adapter loading, and the economics.

ChatGPT vs Claude vs Gemini vs Copilot in 2026: what each is best at, pricing, privacy, when to switch, and whether you need to pay for any of them.

Serving multimodal LLMs: how vision and audio get tokenized, image-patch math, KV-cache impact, GPT-4o/Gemini/Qwen-VL compared, plus video and TTS pipelines.

A plain-English guide to how AI chatbots work: what a token is, how they 'know' things, why they make things up, why they cut off. No math, no buzzwords.

RAG in production: when it beats long context, chunking, hybrid dense + BM25 search, vector DBs (Pinecone, Qdrant, pgvector), rerankers, eval, and cost math.

AI toys for kids in 2026 (Miko, FoloToy, Alilo, PokeTomo): how they work, why several failed safety tests, where they break, and what regulators are doing.

Pick the right NVIDIA AI GPU: side-by-side specs, workload fit and pricing for B200 vs H100 vs H200 vs A100 vs L40S vs DGX Spark vs RTX 6000 Pro Blackwell.

What an AI agent really is: a model given a goal, tools, and a loop to observe, decide and act, how it differs from a chatbot, and why reliability is the limit.

Synthetic data and distillation explained: why the web isn't enough, how labs generate billions of examples, large-to-small distillation, and quality control.

Serving reasoning models: why test-time compute is the new scaling axis, how thinking-token budgets work, what changes in the stack, and the cost tradeoffs.

LLM post-training explained: SFT, the RLHF stack, DPO and its relatives, the reward-model problem, and why base-to-useful is mostly post-training.

ML training reliability: checkpoint strategies, async writes with PyTorch DCP, storage economics, recovery semantics, fault tolerance, and MTBF math at scale.

Running LLM agents in production: the agent loop, latency budgets, streaming, tool sandboxing, memory management, and the observability demos skip.

Evaluating LLMs honestly: why aggregate benchmarks lie, how contamination distorts scores, protocol sensitivities, agentic evals, and credible workload evals.

GPU interconnects explained: NVLink 3/4/5, NVSwitch, GB200 NVL72, AMD Infinity Fabric, UALink and Ultra Ethernet, scale-up vs scale-out, and parallelism.

Custom GPU kernels for AI: Triton, CUTLASS, ThunderKittens and FlashAttention. When to write your own vs use a library, how to fuse, and how to autotune.

Make PyTorch fast on GPUs: CUDA Graphs, torch.compile (Dynamo + Inductor), AOTInductor, FlashAttention, Triton and TensorRT, and how stacks combine them.

Long-context LLMs explained: why attention is O(n²), FlashAttention, RoPE/YaRN/NTK position tricks, ring attention, and what advertised context delivers.

LLM quantization explained: weights vs activations, INT vs FP formats, AWQ and GPTQ, KV-cache quantization, and how to choose a precision for production.

Mixture of Experts models explained: how routing works, expert parallelism, the all-to-all bottleneck, load balancing under skew, and serving economics.

How modern LLM inference works: the prefill/decode split, KV cache, continuous batching, paged attention, and disaggregation (Mooncake, DistServe, Splitwise).

What a foundation model is: trained once at huge scale on broad data, then adapted to countless tasks, why it changed AI economics, and its link to frontier.

AI trust and verification explained: TEEs, zkML, optimistic ML, Proof of Sampling, SynthID watermarking, C2PA provenance, and model fingerprinting.

AI cluster networking explained: InfiniBand vs RoCEv2, EFA and Falcon, 400G/800G Ethernet, congestion control, rail-optimized topologies, and tail latency.

The KV cache in LLM inference explained: the memory math, quantization, paging and prefix caching, multi-GPU sharding, offloading, and capacity planning.

Decentralized GPU compute explained: io.net, Akash, Render, Aethir and Bittensor, why they undercut hyperscalers on inference, and when to use them.

How modern LLM decoding works: speculative decoding, EAGLE-2/3, MEDUSA and Lookahead, draft-model strategies, KV-cache impact, and which variant to ship.

Mixed-precision LLM training explained: FP32, FP16, BF16, FP8 and FP4, loss scaling, when each format breaks, and NVIDIA Transformer Engine support.

LLM serving explained: prefill vs decode, continuous batching, PagedAttention, prefix caching, and the major stacks (vLLM, SGLang, TensorRT-LLM, TGI).

Distributed LLM training explained: DP, TP, PP, EP, FSDP and ZeRO, ring attention, checkpointing, fault tolerance, and how to combine them at scale.

NVIDIA datacenter GPUs for AI compared: A100, H100, H200, B200, GB200 and Rubin. What changed each generation, NVLink, FP8 vs FP4, and how to pick a SKU.

NCCL, RCCL, oneCCL, MPI and Gloo compared for AI training: collective algorithms, protocols, env-var tuning, and fixing slow or hung collectives.

Why chips built for video-game frames became the engine of AI: parallelism vs the CPU, why matrix multiplication is the game, and bandwidth as the bottleneck.

What AI means for games: generative NPCs, procedural content, playtesting bots, and asset generation, plus why real-time budgets and trust make games hard.

How AI is changing science: literature review, hypothesis generation, protein and materials prediction, lab automation, and why prediction isn't discovery.

How AI is used in hiring and HR: resume screening, sourcing, assessment, and internal Q&A, plus disparate impact, audit laws, and candidates beating the AI.

What AI changes in marketing and what it commoditizes: content at scale, personalization, ad creative testing, SEO/GEO shifts, and real differentiation.

How AI support works now that agents take actions: deflection vs resolution, retrieval over knowledge bases, escalation design, and why resolution wins.

AI in legal work: contract review, e-discovery, research and case summarization, set against fabricated citations, confidentiality, and privilege rules.

What AI really does in finance: fraud detection, credit scoring, algorithmic trading, risk modeling, and robo-advisors, plus why backtests lie.

How AI is reshaping learning: personalized tutoring, automated grading and its failures, the cheating and detection arms race, and what students do by hand.

Where AI is real in medicine and where it's marketing: clinical decision support, imaging triage, ambient scribes, drug discovery, and what 'FDA-cleared' hides.