AI Beginner's Guide: Get Started with AI Tools

When you work in Claude Code for a long stretch, the response can suddenly repeat the same word — "court court court…" — dozens to hundreds of times, then halt with "API Error: Response stalled mid-stream. The response above may be incomplete." This is not a mistake in your prompt; it is two separate known bugs chaining together: (1) the model emitting the same token over and over — repetition (degeneration), GitHub #68740/#65823/#66950 — and (2) "Response stalled mid-stream," where the flood of output or a connection problem stops the response stream (#70840 and the official error reference). "court" means nothing; the model just fixated on that token, and it carries the area:model label as a model-side bug. This article breaks down the two layers, how it differs from the "court/invoke tag leak" type (a separate article), the trigger conditions (long sessions, large context, prose right before a tool call, unstable connections, long output), how to stop it now (interrupt with Esc, escape to a new session or /clear, call tools with no preamble, split the task), developer measures (read timeout, repetition-detection guard, retry without keeping broken history, a max_tokens cap), how to tell it apart from Connection closed mid-response and Prompt is too long, and the official status as of July 2026, where no permanent fix is confirmed — all based on the official docs and the actual issues.

The "API Error: 400 Output blocked by content filtering policy" that suddenly appears in Claude Code and the API is not a usage limit and not a context overflow — it means the safety filter held back the "output" Claude was about to return. Its main purpose is preventing verbatim reproduction of existing copyrighted material, and it often trips a false positive with no ill intent — generating the full text of standard licenses like MIT/Apache, doing "match this existing source" work, or duplicating long documents. This article draws on Anthropic's official explanation (an output-stage filter that detects reproduction of copyrighted material and blocks it with a 400) and real Claude Code issue reports of false positives (initial OSS-repo setup, reconciling lists, and a 400 at the end of a long agent run misdiagnosed as a token limit), then lays out how to fix it now (don't make the model copy verbatim — fetch with a tool; rephrase the prompt toward generation/summarization; stop the retry loop with Esc; split the task; report false positives to support) and how to tell it apart from Prompt is too long, usage limit, 529 Overloaded, and max_tokens.

The biggest reason indie projects never get finished is "over-building." Piling on feature after feature makes it complex, and it vanishes before it ever launches. The one way to avoid that is to narrow the smallest product that conveys value—the MVP—to a single feature and ship it as fast as possible. This article explains, from the point of view of an indie developer who makes AI their partner, the right way to see an MVP, scope decisions for cutting features, two routes to build fastest with AI (vibe coding where you write no code, and the hands-on AI-editor route), judging "finished," and getting one person to use it after launch.

The most common failure in indie development is "I built it but no one uses it." But the real bottleneck isn't building skill—it's marketing. "Build something good and they'll come" is a fantasy. Build in Public to gather prospects before you build; get the first 10 by hand from people near you (do things that don't scale); find the first 100 by contributing where they gather (communities, social media) so they discover you; and create steady traffic with SEO/AEO/LLMO. This article lays out that order—using AI as your prep hand—from an indie developer's point of view.

Now that AI has a hand that writes code, one person can build a product and ship it. But the information is scattered by stage, so it is easy to get lost about where to start. This article is a full map (roadmap) from idea to design to implementation to launch to monetization, organizing solo development into five phases (Decide, Prepare, Build, Ship, Grow) and, for each stage, showing what to do and which tools to use, then sending you to a dedicated guide for the parts that need a deeper dive. It guides you along two lanes: a Beginner route where you barely write code, and a Hands-on route where you write code in an AI editor, so following whichever fits gets you to something that works without detours. It gathers spec-driven development, AI app builders, Claude Code and Cursor, wiring in AI features (API, RAG, gateway), deploy, SEO/AEO traffic, monetization, cost management, and five pitfalls of solo dev with AI onto one page, with links into the existing hands-on guides.

You hit Claude Code's weekly token limit, yet the allowance fully replenished before seven days passed — more than once. Online there are even "hidden-mechanism" write-ups claiming the weekly limit resets every 72 hours. Is that true? This article traces the phenomenon to primary sources. But Anthropic has not documented the internal reset mechanism, and the limits keep changing, so we clearly label three kinds of information: facts confirmable from official sources, events multiple users reproducibly observe but that Anthropic never addressed, and single-source unconfirmed speculation. The core: early full recovery is, in most cases, Anthropic's irregular global resets (announced repeatedly by @ClaudeDevs); the displayed reset time is also demonstrably unstable; and the circulating "72-hour cadence" is single-observer, unreproduced, and contradicted by another observation (24h), so it cannot be taken as fact. Where something can't be stated, we say so — a July 2026 investigation.

The people who gave up on drawing because they "can't draw" are exactly the ones for whom AI drawing is a playground. Put the picture in your head into words and it becomes art; a phone photo or a child's doodle turns into a piece in seconds. This is not a tool comparison or a serious course — it is a playbook of ideas for solo or family fun. From free tools to start (ChatGPT, Google Gemini, Microsoft Copilot, Canva) to 10 ideas (① doodle into art ② photo in a new style ③ your own character across scenes ④ a child's drawing as a storybook page ⑤ original emoji ⑥ four-panel comics ⑦ what-if mashups ⑧ your own coloring pages ⑨ room makeovers ⑩ drawing duels), small tricks to get better pictures, and three things to know before you play (don't use others' faces, check rights and commercial use, label it AI-made).

AI can be a know-it-all friend that keeps up with a child's "why?" and a storyteller before bed — but with kids, the safety setup matters before the fun. This article sets the safety promises first (① an adult operates and checks ② check age limits ③ enter no personal info ④ teach that AI can be wrong ⑤ set the tone and time), then shows 10 copy-paste activities: stories and words (a bedtime story where your child is the hero, a kids-decide story, a calm bedtime tale), learning and curiosity (a know-it-all professor, an animal & dinosaur quiz, a greetings teacher for other languages), and making and expression (draw an imagined creature, a coloring-page line drawing, craft ideas, a feelings & manners role-play). With tips by age (preschool / elementary / older kids), how to enjoy it more, what parents should know, and an FAQ. The basic rule: an adult operates the AI and checks the content before showing it. Works on free ChatGPT, Claude, or Gemini.

The most addictive way to play with AI is a text adventure with AI as your game master (GM): you declare an action, the AI describes the scene and the story branches, and it even handles wild actions outside the given choices. This article walks through how to build one from start to finish with copy-paste prompts: a starter prompt you just paste, the 5 parts that decide the fun (① setting ② tone ③ rules ④ state-tracking ⑤ pacing), genre presets (fantasy adventure, horror escape, mystery investigation, sci-fi survival, cozy slice-of-life), next-level tricks (dice for luck, stats and levels, save & resume), how to fix it when it goes wrong (too soft / runs on its own / on rails / forgets the state), and tips for long play. No programming, works on free ChatGPT, Claude, or Gemini. With cautions and an FAQ — the deep-dive companion to item ⑦ of "15 fun ways to use AI."

Stuck on "what am I even supposed to ask an AI?" This gives you 30 ready-to-paste prompts that get a fun reply back the moment you paste them. Six genres of five: get to know yourself (hidden strengths, shared values, which animal you are), spark ideas and what-ifs (unexpected uses for an everyday object, if gravity were halved, inventing a holiday), explanations that click (quantum mechanics for a 5-year-old, compound interest in three metaphors, the internet as pizza delivery), wordplay (rewrite in 10 styles, a two-sentence micro-story, epithets, palindromes), role-play and advice (a tough life coach, a sage who invents proverbs, a letter from your future self, a baffled alien), and useful-but-fun (turn an excuse into a maxim, five levels of saying no, fix a bland message, dramatic menu names, a fun detour in your to-do). Works on free ChatGPT, Claude, or Gemini. Includes tips (stack "more," add role and constraints, switch AIs), cautions, and a link to the sister article "15 fun ways to use AI."

Talk about AI and it is always the serious stuff, but AI might shine brightest at play. This article shows 15 fun ways to use AI, each with a copy-paste prompt you can try right now. Wordplay & comedy (① comedy battles ② parody-song/rap machine ③ naming factory), role-play & dialogue (④ talk to a historical figure ⑤ make two AIs debate ⑥ practice arguing with a devil's-advocate bot), games (⑦ text adventure with AI as game master ⑧ original quiz show ⑨ whodunit mystery game), creative (⑩ fictional creature field guide ⑪ a story where you are the hero ⑫ playing with image generation), and everyday & unexpected (⑬ leftovers chef ⑭ travel plans to ancient Rome or the Moon ⑮ entertainment fortune-teller). All work on free ChatGPT, Claude, or Gemini. The three keys to the fun — give it a role, bind it with constraints, stack "more" — double as the foundations of practical prompting. With cautions (fact vs. fiction, personal info, playing with kids) and an FAQ.

After you build an AI agent, you always hit the same wall: "OK, but is it actually working?" The mechanism for deciding whether a prompt or model change made things better or worse with data instead of gut feel is evals. LLMs produce different output every time for the same input, so exact-match unit tests don't fit. This article centers on the steps for actually building and running them, covering five ways to measure quality (① ground-truth matching ② rule-based checks ③ LLM-as-judge ④ regression testing ⑤ production monitoring), agent-specific evaluation (task success rate, correct tool calls, trajectory, cost), how to start small from 20 failure examples, common pitfalls, and key tools (Anthropic Console/Evals, OpenAI Evals, LangSmith, Langfuse, Ragas) — written for practitioners.