AI Beginner's Guide: Get Started with AI Tools

GitHub Copilot launched in 2021 as smart code completion; by 2026 it is something else. Assign it a single GitHub Issue and walk away, and the AI writes the code, passes the tests, opens a pull request, and hands it back — the coding agent. GitHub Copilot is an AI coding-assistance service from GitHub (owned by Microsoft), with three ways to use it: completion, chat, and agent. Its defining trait is installing as an extension into existing editors like VS Code and JetBrains — you add AI without changing your usual editor. This article covers what Copilot can do, the 2026 headliner that is Agent Mode and the Coding Agent, Free/Pro $10/Pro+ $39 pricing and the June 2026 shift to usage-based billing (AI credits), how it differs in design philosophy from Cursor and Claude Code, who it fits, and how to get started — all with the latest information.

GPT-4 was trained on about 25,000 GPUs over months, and GPT-3's training alone burned 1,287 MWh (over a century of household power). Behind our casual "summarize this" lies a world of physics and cash. This article dissects an LLM from three directions: mechanism, power, and money. (1) Why can an LLM predict words from a pile of "weights (parameters)"? — next-token prediction, Transformer, Attention. (2) The two-stage learning of pre-training and RLHF. (3) Inference power of 0.43-33 Wh per query (inference is 80-90% of all AI power). (4) Is "frontier development is a money fight" true? — $200-500M per GPT-5-class run, $1-3B projected for 2027. (5) But the efficiency backflow (DeepSeek's floor reset) is strong too. (6) The coming physical wall of power, interconnect, and data scarcity. An intermediate guide to seeing an LLM not as a magic box but as an electricity-powered probability machine.

Japan's Big Five sogo shosha (Mitsubishi, Mitsui, Itochu, Sumitomo, Marubeni) again posted near-record FY2024 profits — Mitsubishi ¥1.2T, Mitsui ¥1T, Itochu ¥800B — and Berkshire Hathaway holds close to 10% of all five. Yet underneath that record, a structural shift is shaking the core business model. On May 19, 2026, Japan's ruling LDP adopted "Next-Generation AI × On-Chain Finance," driving automation of core sogo shosha work at the level of national policy. This article maps the historic moat ("information asymmetry") that AI is dissolving, four business areas hit by AI (trade execution 70% automation, investee operations, large investment judgment, relationship capital), side-by-side AI/DX strategy of the Big Five (Itochu leads, Mitsubishi reportedly drifts), the three survival strategies (investment-holding company, downstream expansion, AI-native organization), and the three-layer shosha-man career map (juniors at high risk, mid-level need AI-operator skills, seniors actually gain value) — all grounded in May 2026 data. "Getting a sogo shosha offer means a set career" is the biggest illusion of 2026 and beyond.

You have read enough "AI will take your job" takes. The WEF Future of Jobs Report 2025/2026 says the opposite: "92M displaced by 2030, but 170M created — net +78M." This article tilts positive: where to move your career. AI-resilient jobs share three principles (embodiment, high-accountability judgment, creativity x relationships) plus an ironic fourth category (the people operating AI: ML engineers, AI PMs, security specialists, exploding in growth). The article maps the 4 categories with concrete examples, lists 15 high-growth roles with US salary and growth data (nurse practitioner $130K +52%, electricians $200K+ in major cities, surgeons $400-700K+, ML engineers $250-500K+, AI safety $500K-1M+), and lays out four pivot moves (promote to AI operator, industry depth, re-evaluate embodied work, invest in relationship capital) — all grounded in WEF/BLS/BCG data as of May 2026. The 20th-century picture of "blue-collar at risk, white-collar safe" has completely inverted.

One five-person team reclaimed six to eight hours a week from file organization and report prep alone; one user cleared a 2,200-file Downloads folder in twenty minutes. Claude Cowork is the AI workspace Anthropic launched in 2026 to let AI directly touch your files, folders, and apps and run a full observe → plan → execute → steer loop. Any paid plan from Pro at $20 gets you in on macOS or Windows. Cowork plugs directly into Google Drive, Gmail, Slack, Jira, and DocuSign via official connectors, and the plugin layer lets organizations embed departmental knowledge. Enterprise adds RBAC, spend caps, and OpenTelemetry. You can touch Cowork from Pro $20, but Cowork tasks burn 50-100x more tokens than chat, so for daily use Max $100 is the realistic line. This article covers what Cowork does, why it was built, the four-step work loop, major connectors, plugins and enterprise features, the real cost line, and where Cowork fits vs Chat and Code — grounded in May 2026 reports.

In 2023 a New York lawyer cited six ChatGPT-generated precedents in court — all six were nonexistent. That is what AI trouble looks like. This article sorts the representative AI usage troubles into seven categories — hallucination, confidential leakage, copyright, prompt injection, overtrust, AI slop, and over-dependence — and walks through the typical incident (the Avianca and Samsung cases included), the cause, and the prevention. The root condenses into three: "convenience lowers our guard, we stop checking ourselves, responsibility blurs." So the countermeasures are shared: verify important info at a primary source, treat confidentiality at the weight of external email, leave final decisions to humans, take one AI-free day per week for core skills. For organizations: distribute an imperfect one-page AI-use guideline this week instead of waiting half a year for a perfect regulation. As of May 2026.

Some say "AI is plenty good for free" and others say "the free version is a non-starter." When the verdict splits this sharply even among people using the same ChatGPT, it is not about capability — it is about whether you know "where in the free tier you hit the wall." As of May 2026 the ChatGPT, Claude, and Gemini free tiers are all genuinely practical, but their shapes are completely different. ChatGPT has the widest feature set but the strictest top-model count limit (the wall recovers in a few hours). Claude has high-quality long-form analysis and writing but the lowest daily count, with a confusing dual short-window plus weekly-window cap. Gemini has the loosest usage limits and strong Google integration. This article sorts out why "free" means different things across the three, what each can do and where its wall is, a use-case quick-reference table, three tips to use the free tier wisely, and the signs it is time to consider a paid plan.

In 2025, one role's job-posting count grew by an extraordinary 1,165% year over year: the FDE — the Forward Deployed Engineer. Why has a quiet job that Palantir systematized over roughly 20 years suddenly become "the hottest title" in 2026? An FDE is "an engineer who carries their own company's product into the customer's site and personally owns observation, design, implementation, operation, and product feedback end to end." Generative AI carries a last mile of "the demo works but it doesn't work on site," and the FDE is the role that closes it with human hands. This article covers the definition, why the role exploded in 2026 (the OpenAI, Anthropic, and Google hiring rush), the 5-stage work loop, pay and career (Palantir average $238K, staff over $630K), the difference from SE / IT consultant / Applied AI Engineer, who fits and who does not, and how to get there from no experience — all with the latest May 2026 data.

Cold calls, first-touch emails, list building, meeting bookings — as of May 2026 these are no longer human work. The AI SDR market is forecast at $4.27B (2025) → $5.22B (2026) → $24.32B by 2034 (CAGR 21.2%). 11x.ai, Outreach, Salesforce Einstein SDR, Smartlead, and Amplemarket sell "all-AI SDR teams that work 24/7 without sleeping." Cost: human SDR $50K-$80K/year vs AI SDR $200-$2,000/month — 30x to 400x cheaper. This article covers the AI SDR boom, the 4-layer map of disappearing vs surviving sales (lists/qualification/closing/enterprise), seven major AI SDR tools compared, Gartner's prediction that 75% of B2B buyers will prefer human-prioritized sales by 2030, four reasons enterprise sales survives, three survival skill shifts (AI operator, industry depth, relationship capital), and what executives should do — all grounded in May 2026.

Type "build me a Todo app" and 10 minutes later you have a live URL and a GitHub repo — that's "vibe coding," and the 2026 top three are Vercel's v0, StackBlitz's Bolt.new, and Lovable. Lovable hit $20M ARR in two months (fastest in European startup history); Bolt reached $40M ARR in six months; v0 added Git, DB connectivity, and agentic workflows in February 2026. This article maps the essence of each (v0 = designer, Bolt = developer, Lovable = founder), runs a detailed feature/pricing/framework comparison, gives the right pick for six use cases, presents results from running the same prompt through all three, walks through the three production pitfalls (token burn, security holes, lock-in), and closes with a 5-minute decision flow — all grounded in May 2026 facts. Companion to the AI Recommends series.

You shipped on the OpenAI API and now want to try Claude and Gemini — and you've burned two hours rewriting against three different SDKs. The Vercel AI SDK (just "AI SDK" since 2026) collapses that into "one import, one function, every provider," with 20M+ monthly downloads and AI SDK 6 shipping Agents, MCP, tool approval, and DevTools — the de facto standard for unified LLM interfaces in 2026. This article covers what the AI SDK is, three practical reasons to use it (free switching, 1/3 the implementation, type safety), a 5-minute quickstart from generateText to streamText, type-safe structured output via generateObject and Zod, tool calling and agent loops, a 10-line React chat UI with useChat, switching between Claude/GPT/Gemini in 3 lines, and the three production pitfalls (provider feature gaps, stream-abort billing, type-inference overload) — all with working code grounded in AI SDK 6 as of May 2026.

Ask Claude Code or ChatGPT where to deploy a web app and you'll reflexively get "Push it to Vercel." But the May 2026 reality is more nuanced: Vercel is best DX for Next.js but overkill otherwise, the free Hobby plan forbids commercial use, Pro is $20/seat with $0.15/GB overage, there is no hard spending cap by design, and 2025–2026 produced multiple documented $23,000 DDoS bills. This article covers the 3 structural reasons AI defaults to Vercel, a 3-minute beginner explainer, a 5-minute 6-question decision flow, four use-case alternatives (Cloudflare Pages with unlimited bandwidth, Netlify with unlimited team members, Render with included PostgreSQL from $19, self-hosted VPS + Docker), the five pricing traps, and the three pitfalls every beginner hits (unbounded billing, function timeouts, lock-in) — all grounded in May 2026 facts. Third in the AI Recommends series.