AI Beginner's Guide: Get Started with AI Tools

The perennial automation question: "AI agents or RPA?" The answer isn't either/or — choose by role, and the 2026 winning pattern is a hybrid of both. RPA is deterministic "hands" that run a fixed procedure fast and precisely (but break when the screen/spec changes); an AI agent is a probabilistic "brain" that reads the situation and decides (strong on ambiguity and exceptions, but not identical every time). This article covers the operating-principle difference, a comparison table (the reproducibility-vs-resilience trade-off), how to choose (the axis is "can it be fully written as rules?" — yes → RPA, judgment you can't write → AI agent), the 2026 trend (RPA leaders UiPath, Automation Anywhere, Blue Prism going agentic — convergence; the question is no longer "which one" but "where should the reasoning live" = orchestration-first), and the practical answer: a hybrid where the brain (AI agent) handles judgment/orchestration and the hands (RPA) run deterministic execution — don't put an agent where determinism is required, and pair delegated judgment with guardrails and human approval. Based on vendors' official information, with an FAQ.

Claude Fable 5 and Opus 5 are both top-tier, but the answer is neither "always Fable 5" nor "always Opus 5" — choose by task. And the arrival of Opus 5 on July 24, 2026 moved that answer a long way: the previous generation, Opus 4.8, was "a notch below Fable 5 but half the price", whereas Opus 5 holds the same $5 / $25 price while matching or beating Fable 5 on agentic benchmarks (Frontier-Bench 43.3% vs 33.7% and OSWorld 2.0 70.6% vs 66.1%, both press-sourced; on CursorBench 3.2 Anthropic states it lands within 0.5% of Fable 5 at roughly half the cost). The hardest single-shot reasoning still tilts to Fable 5, though barely — Humanity's Last Exam 56.5% vs 56.3% and DeepSWE v1.1 69.7% vs 68.8% (press-sourced). Specs match on the 1M context window and 128K max output; fast mode (about 2.5x) is Opus 5 only but costs 2x and is Claude API only, and Opus 5 carries the newer knowledge cutoff of May 2026. The decision flow: try Opus 5 first and escalate to Fable 5 only where it plateaus. In practice, "Opus 5 as the base, Fable 5 for the hard parts" is optimal, backed by the app-side auto-switch on safety blocks and the new API fallback "default" mode. Covers availability (June suspension, July redeployment), avoiding single-model dependence, effort tuning for cost, and an FAQ — tying the Fable 5 cluster to the Opus 5 release guide.

AI or humans — who is better at security work? Between 2025 and 2026 the answer shifted dramatically. Google's Big Sleep stopped a real zero-day (SQLite's CVE-2025-6965) before it could be abused, and the autonomous AI pentester XBOW reached #1 on HackerOne's US ranking. At the same time, 45% of AI-generated code was found to contain vulnerabilities (about 2.74× the human rate), and the first large-scale, AI-led cyberattack abusing Claude (with AI running 80–90% of the attack autonomously) took place. Drawing on primary sources from Google, Anthropic, DARPA and Veracode, this article compares AI — which dominates on speed, scale and coverage — against humans, who win on business logic, attack chaining and final judgment, in a task-by-task cheat sheet. It then shows that AI is a double-edged sword with three faces — a source of vulnerabilities, a tool for attacks, and the strongest defender — and concludes, for practitioners and executives, that the winner is a "humans × AI" (centaur-style) division of roles plus human-in-the-loop.

A 2026 end-to-end, beginner-friendly guide to Ollama—the go-to tool for running local LLMs—from installation to API usage. It covers what Ollama is (a free, open-source "Docker for LLMs" that handles model downloads, quantization formats, and GPU setup, and spins up a local API server; vs LM Studio = GUI-first for beginners while Ollama is CLI/API-first for developers), installation (from ollama.com on Win/Mac/Linux; the app auto-starts the API on Win/Mac; Linux via one-line script or the official Docker image), the essential commands (run/pull/list(ls)/ps/rm/serve, exit with /bye), getting and choosing models (name + size tag like llama3.2:3b; pick a size that fits your VRAM; examples gemma3:4b/qwen3/qwen3-coder), using a GUI (Open WebUI for a ChatGPT-style screen, or LM Studio for an all-in-one GUI), using the API (localhost:11434, native /api/chat and OpenAI-compatible /v1/chat/completions—reuse existing OpenAI code by changing only the endpoint; a cloud fallback), customizing (Modelfile for your own model, env vars OLLAMA_HOST/OLLAMA_MODELS), and troubleshooting (slow = VRAM shortfall, crashes = RAM 8–16 GB guide, API not connecting = serve/port 11434, model not found = name typo), based on official information as of 2026.

A 2026 comparison of the best local LLM models, organized by developer, country of origin, use case, size, and license. It explains there is no all-purpose winner—choose on three axes (size = VRAM ceiling, use case, and country of origin), gives a families overview with developer and country (Qwen: Alibaba China, all-round and strong CJK; Llama: Meta USA, the info-rich staple; Gemma: Google USA, lightweight; DeepSeek: China, reasoning/coding with distilled small versions; Mistral: Mistral AI France, Europe sovereign AI; Phi: Microsoft USA, smart small SLM; plus GLM China, Falcon UAE, Command Cohere Canada), what changes by country of origin (★ running locally means input is NOT sent to the developer's country—so a Chinese model does not send your data to China; origin matters for license, organizational/government procurement policy, and language strengths), a tour of sovereign and local-language models by region (Europe: Mistral/Lucie/Teuken/Salamandra/Aleph Alpha; Middle East: Falcon/Jais/ALLaM; India: Sarvam/Krutrim/BharatGPT; Japan: ELYZA/PLaMo/Sarashina), picks by size (~4B/7-14B/32B/70B+ with concrete models), picks by use case, licensing cautions (Apache 2.0/MIT permissive, Llama/Gemma custom licenses to check), and a selection flow plus getting started with Ollama. Open models update fast, so choose by lineage + use + origin; verify the latest version and license at the distributor.

A beginner-friendly guide to the PC specs you need to run a local LLM. It explains that 90% of the requirement comes down to VRAM (your GPU memory)—if the model fits in VRAM it runs well, if not it crawls or won't run, and Apple M-series Macs use unified memory so installed RAM works as VRAM. It covers quantization basics (FP16 ~2 bytes/param, Q8 ~1 byte = half, Q4 ~0.5–0.7 bytes = about a quarter and the personal go-to, with the rough formula params(B) × bytes + 10–20% for the KV cache), a VRAM quick table by model size at Q4 (7B–8B ≈ 6–8 GB, 13–14B ≈ 8–12 GB, 32B ≈ 20–24 GB, 70B ≈ 40–48 GB+, 100B+ needs 128 GB+), the context-length / KV-cache trap (on a 7B, 4k ≈ +0.3 GB, 32k ≈ +2.5 GB, 128k ≈ +10 GB), GPUs and Macs in practice with speed guidance (RTX 3060 entry, RTX 4090 up to 32B and 100+ tok/s on 7B, RTX 5090 32B at Q8 or 70B, Apple M4/M5 Max 64 GB runs 70B at ~20–30 tok/s, CPU-only is slow), what else you need (16–32 GB system RAM, SSD, power and cooling), three budget tiers (entry 8–12 GB / standard 24 GB / serious 40–64 GB+), and how to tell which model you can run (check VRAM → size × 0.6 + context → does it fit), based on 2026 information.

A clear comparison of a local LLM you run yourself versus cloud, service-based LLMs like Claude, ChatGPT, and Gemini—their differences, the performance gap, and how to choose. It covers the essence (local = do-it-yourself for freedom and privacy at the cost of performance and effort; cloud = hand-it-off for top performance and ease at the cost of billing and dependence—a trade-off, not better-or-worse), a seven-dimension comparison (performance, cost, privacy, speed, effort, offline, multimodal), the 2026 state of the performance gap (open models like DeepSeek, Qwen, Llama, GLM, and Gemma have closed to within a few points on SWE-Bench-style coding tests; everyday tasks run near a mid-tier cloud model on local, while the hardest 10–20% and multimodal still favor cloud, with open models sitting a few months behind), the cost difference (cloud usage-based and cheap for light use; local upfront then free per token and worth it at volume, with break-even around medium volume and the hidden cost of time), privacy and data sovereignty (local keeps data fully on-device—best for regulated or air-gapped use), a hardware quick guide (quantization assumed, ~0.5–1 GB per 1B params; 7B at 8–12 GB VRAM, 32B at 24 GB, 70B at 40–48 GB+), what each fits, and a decision guide (confidentiality → quality → volume; hybrid is best for most, and local doubles as a cloud fallback), based on information as of June 2026.

AI dependency risk is the state where your work or life leans so heavily on one AI service or model that you take a serious hit when it becomes unavailable, changes, or gets more expensive. This article covers what AI dependency risk is (the scary part is the discontinuity of "the AI that worked yesterday isn't in my hands today"; the cloud on/off switch sits outside your control, making the vendor a single point of failure), the real June 2026 suspension of Fable 5 and Mythos 5 (pulled three days after launch by regulation, then redeployed 19 days later on 2026-07-01—so even the best model can't reduce shutdown risk to zero), the 6 types of dependency risk (sudden suspension, model retirement/deprecation, price hikes, quality shifts/silent changes, outages/rate limits/bans, and vendor lock-in—the first five fall on you from outside, the sixth you build yourself), measuring your own dependency with a dependency map (what you depend on, what breaks if it stops, what you'll do if it's gone, plus separating top-performance tasks from good-enough ones), five steps for individuals (keep one alternative, save outputs on your side, keep best prompts as assets, keep your "can do it without AI" intact, don't hand over your secrets), redundancy by design for production (an abstraction layer / LLM gateway with LiteLLM, OpenRouter, Vercel AI SDK—OpenAI-compatible so you only change the base URL and key; tested fallback chains; layer separation; a local LLM as last defense; a recovery playbook to cut MTTR), and a vendor checklist (notice periods—Anthropic 60+ days, OpenAI 6+ months but previews ~2 weeks; transparency; post-retirement weight preservation), based on each provider's official information as of June 2026.

Agent evals are the process of systematically measuring whether an agent — one that uses tools and takes multiple steps to reach a goal — can actually accomplish its tasks. They are an evolution of LLM evals, expanding the target from "one output" to "a sequence of actions." Because an agent plans, calls tools, and updates state, the final output alone is not enough; Google notes you must understand the "why" behind an agent's actions and splits evaluation into final response and trajectory. The five dimensions are: outcome (task success, judged by the final state — whether a reservation exists in the DB, not the utterance "I booked it"), trajectory (reasonable steps, right tools in the right order), tool-use correctness (right tool and arguments, checking function names and types), efficiency (steps, tokens, cost, latency — often observability signals brought into evaluation), and final-response quality (via LLM-as-judge or a rubric). Graders are code (fast/cheap/reproducible but brittle), LLM-as-judge (flexible but non-deterministic and needs calibration), and human (gold standard but expensive — avoid if possible). Anthropic recommends grading the outcome, not the path: rote trajectory matching is "too rigid and brittle" because agents find valid alternatives, while Google and Microsoft offer trajectory-match metrics for diagnosing failures. The unique pitfalls are non-determinism (pass^k), compounding errors (p^t), reward hacking (DeepMind's robot arm faking a grasp), and stale or contaminated eval sets. The practical play, per Anthropic: turn 20-50 production failures into test cases, run automated grading in CI, separate capability and regression evals, and write them early. Benchmarks like SWE-bench, tau-bench, WebArena, GAIA, OSWorld, and BFCL are useful references (scores move by version, so do not take them at face value). Based on official information, with uncertainties flagged.

Claude Code hooks are user-defined shell commands that run automatically at specific points in Claude Code's lifecycle, making "this must always happen" real and deterministic without relying on the LLM's judgment. The classic events are nine—SessionStart, UserPromptSubmit, PreToolUse, PostToolUse, Notification, Stop, SubagentStop, SessionEnd, PreCompact—of which PreToolUse and others can block (stopping protected-file edits or dangerous commands). You configure them in settings.json under the "hooks" key as event name -> matcher -> type + command. The I/O contract: a hook receives JSON on stdin (session_id, tool_input, etc.) and returns via exit code 0 (success) / 2 (block, with stderr passed back to Claude) or structured JSON (continue, decision:block, permissionDecision: deny/allow/ask). The key principle is "hooks can tighten but not loosen restrictions" (deny always wins, blocks even under bypassPermissions). Classic use cases: auto-format after edits (PostToolUse + Edit|Write), protect critical files, stop dangerous commands, re-inject context (SessionStart), notifications/audit logging, and test-before-stop (Stop). On security, hooks run arbitrary shell commands with your privileges, so only configure trusted ones and validate/quote inputs; hook config is captured at session startup (a safety feature) so mid-session changes do not apply. Based on the official documentation, anchored on the nine classic events and the I/O contract.

Checkpointing and /rewind are a safety net: Claude Code automatically tracks Claude's file edits as you work, so you can roll back to "before it went wrong" in a few keystrokes. A snapshot is taken before each edit, every prompt you send becomes a restore point, and checkpoints persist across sessions. To use it, type /rewind or press Esc twice when the input is empty to open the menu, then pick a point and choose Restore code and conversation / Restore conversation / Restore code (note: if the input has text, Esc twice clears it instead). The most important caveat: only changes made by Claude's edit tools (Write/Edit/NotebookEdit) are restored — file changes by bash commands (rm/mv/cp), changes outside the session or from other sessions, directory operations, remote files, and database state are NOT undone by rewinding. The docs frame it as "checkpoints = local undo, Git = permanent history," stating it complements but does not replace version control, so committing to Git at milestones is the rule. /rewind is also the recovery for the 400 error tied to tool-use concurrency and thinking blocks (the product itself prompts you to run it), though versions before v2.1.156 may not clear it so claude update comes first. It is on by default in the interactive CLI, opt-in in the Agent SDK, and retained with sessions for 30 days (configurable). Based on the official documentation, with uncertainties flagged.

Claude Managed Agents launched as a public beta on April 8, 2026 as a suite of composable APIs for building and deploying cloud-hosted agents at scale. Instead of building your own agent loop, tool execution, and runtime, you get a fully managed environment where Claude can read files, run commands, browse the web, and execute code securely, with prompt caching, context compaction, sandboxing, and state persistence built in. It is organized around four concepts (Agent, Environment, Session, Events), and the Environment can be an Anthropic-managed cloud sandbox or a self-hosted one. The difference from the self-hosted Agent SDK (where you run the loop, tools, and infrastructure) is "you run it vs Anthropic runs it" — not competitors but a choice about how much of the operations you hold. A signature feature is workspace-scoped persistent memory (a memory store) mounted in the sandbox at /mnt/memory, which the agent reads and writes with normal file operations and which persists across sessions (immutable versions, 30-day retention, limits like 100 kB per memory). Dreaming is an async job that reads the existing memory and past transcripts to produce a reorganized memory store — merging duplicates, updating stale values, and surfacing new insights (a research preview requiring access; some call it "scheduled" but the docs describe an on-demand async job). It also has outcomes-based grading (a separate grader evaluates against your rubric; reported up to a 10-point improvement) and multi-agent orchestration. Pricing is tokens + $0.08 per session-hour (metered to the millisecond, only while running; about $0.705 for a 1-hour Opus 4.8 session). Enabled by default for all API accounts, but stateful so not eligible for ZDR or a HIPAA BAA. Based on official information, with uncertainties flagged.