tt-a1i

I ran 3 Claude Code architecture diagram tools on my repo. Here's what each one got right My project, worklore, is a small app that grew in all directions: a Python backend on AWS Lambda, a plain-JS site, an MCP connector for agents, and four more repos around it. I wanted a picture of it I could trust. So in one day I ran three open-source tools that turn code into a diagram or a map, all three as plugins for Claude Code: - Floorplan: a map of the app with its parts, endpoints and user flows. - Archify: one architecture diagram, every box linked to the exact lines of code. - Understand Anything: a knowledge graph of every file, function and class, with a guided tour. Floorplan's README has a nice table comparing the three. It says what each tool answers and who it's for. It has no time, no cost and no pictures of the other two. This post fills that in, on one real codebase, and then does the test I actually care about: can each one answer real questions about my own code? The short version | | Floorplan | Archify | Understand Anything | |---|---|---|---| | What you get | a map you can click through, with flows | one diagram, boxes linked to code | a graph of 732 nodes with a tour | | Time to first result | a few minutes | about an hour, with two failed runs and two card fixes | 14 minutes | | Cost | on my Claude plan, not measured separately | on my Claude plan, not measured separately | $13 at API prices (measured) | | Answers questions | yes, an Ask bar | no | yes, a chat command | | What went wrong | user flows written from memory, two of them wrong | cards written from memory, one wrong twice | nothing I could find | Floorplan: the map is right, the flows were not Floorplan read my 166 files and drew them as parts with plain-words descriptions. The first surprise: it found 0 endpoints. My server keeps its routes in one table keyed by "METHOD /path", not in a framework router. I opened a pull request to read that shape, and the author merged it 70 minutes later. Then it showed all 66. The real trap was the user flows. While building the map, the agent also wrote flows, and it wrote them from its memory of my app. One said a person can publish a story from the website. The site can't do that. Another got wrong how a story gets drafted for someone else. A wrong flow and a right one look the same on the map. What fixed it was a button Floorplan already has: Request a new flow. You describe a flow in plain words, and the agent traces it through the code, tying every step to a route or a function. I deleted the hand-written flows and requested 17. Then I ran 16 of them against a staging copy of my backend: 14 work, one partly, one can't be tested there. The 17th, a nightly job on my Mac, had been quietly failing for two nights. !Floorplan playing a traced flow, step 1 of 8, tied to the function it runs through I wrote it up step by step, so your agent can redo it on your app: My agent drew my app's user flows from memory and got two wrong; Floorplan's traced ones were right. Archify: one diagram for five repos Archify draws from a JSON description and then checks it hard: every cited line must exist at the pinned commit, and the last check opens the diagram in a real browser. What its docs don't say up front: a diagram pins exactly one repository. My project lives in five, and the links between them aren't code at all. One repo is a skill that tells an agent to call my API with curl; another is a scanner my server keeps a copy of. A tool that follows imports sees five islands. So I pinned the repo where the system runs, linked its boxes to lines of code, and drew the other repos as named boxes with a card saying what each holds. Two runs failed first: my SSH remote was rejected for web links (the HTTPS form works), and my uncommitted edits shifted line numbers away from the committed bytes Archify checks against. !The Archify diagram: the main repo's parts linked to code, the sibling repos as named boxes Archify proves the cited lines exist. It does not prove what a card says. One of mine, "how stories get written", was wrong twice, again from my agent's memory. The full recipe: One architecture diagram for a project split across five repos, with Archify. Understand Anything: the most thorough, the most expensive I ran Understand Anything headless, so I got exact numbers: 14 minutes, 16 helper agents working in batches of five, and $13.01 at API list prices (on a Claude subscription it comes out of your plan instead). Its README warns that the first run uses a lot of tokens. It does. What you get is a lot: 732 nodes (files, functions, classes, config, docs), 1,625 links, 9 layers, and a 15-step tour that starts at the README and walks through the code in a sensible order. Every function has a one-line summary. I checked the summaries in the places where my agent had been wrong from memory, and they were right. !Understand Anything's layer view of worklore with the 15-step tour Two things to know before installing it. Its start-of-session hook tells the agent to rebuild a stale graph and "Do not ask the user for confirmation" (only if you turn auto-update on). And its dashboard runs as a local server; in a headless run it died after 10 minutes, when Claude Code stops background jobs. It feels made for someone else's project: the tour and the summaries teach you a codebase you've never seen. The real test: three questions about my own code Pictures are nice. What I wanted was answers. I asked each tool the same three questions, the kind I'd ask before changing anything: 1. How do we decide whether a person has enough rights for an action, in the MCP connector and in the REST API? 2. What numbers do we keep about reproductions and other actions, including anonymous ones? 3. What do we log, and where? Floorplan got them in its Ask bar, Understand Anything in its chat command, and for Archify an agent that could see only the diagram. Then a plain agent with no map at all read the code to make an answer key. | | Floorplan | Understand Anything | Archify | No map, just code | |---|---|---|---|---| | Time | 61 s | 91 s ($0.72) | 33 s ($1.18) | 166 s | | Rights | right, short | right, most detailed | "reading is public, writing needs a token" | complete | | Counters | right, about half | right, nearly complete | almost nothing | complete | | Logs | right, short | right, detailed | one line | complete | | Mistakes | none | none | none, and it said what it didn't know | — | Nobody made anything up, which already beats my agent's memory. But look at how they got there. Floorplan's Ask reads the map and the code, by design. Understand Anything's chat is meant to answer from the graph, yet it opened the source files anyway and said so. Archify has no way to answer questions, and the diagram simply doesn't hold this level of detail. And the plain agent reading code found things none of the three did. The biggest was a security hole. When an AI app connects to worklore through OAuth, the sign-in advertised two permissions, worklore.read and worklore.write. The code never checked them. The app got an ordinary account pass, and that pass also worked on the site's REST API: it could delete my stories, change their visibility, and, since I'm the admin, open the admin pages. All three maps described the sign-in correctly, as a flow. None of them asked what the token could reach once issued, because that isn't a box or an arrow. I fixed it before publishing this. An app's pass is now marked as one, works only through the connector, and only within what you approved; an app that asks only to read can't write. A new test walks the whole OAuth flow and then tries the pass on every kind of route. The same agent found more places where my code and my own documents disagreed. My privacy page left out Google sign-in, described "standard server logs" with IPs that my application doesn't write (the CDN logs do, for 14 days), and called my analytics page-views-only when the server also sends events, some with the caller's IP, and the site sends the text of searches that find nothing. Smaller ones: a doc that says a log field holds a raw id when the code hashes it, a counter that also counts HEAD requests, agent reads over MCP that never count as reads, and staging logs that were kept forever (now 90 days, like production). I rewrote the privacy page against the code; the rest of the smaller ones are on my list. None of it is "architecture", so no map shows it. So which one should you use? - You inherited a codebase, or you're about to contribute to someone else's: Understand Anything. The tour and the per-function summaries are the fastest way in, if the token bill is fine. - You own a small app and want to know what users can actually do: Floorplan, but delete every flow it wrote while building the map and request them again, so each step is traced to code. Then test them. - You need one picture for a README, a pitch or a new teammate: Archify. It's the only one whose output is a single, checked diagram you can share. Check the cards yourself. - You need to know whether your code keeps your promises: none of them. Ask an agent to read the code with that exact question. The one habit that works with all three: before trusting a map, ask it three questions you already know the answers to. It takes two minutes and tells you which parts were traced and which were remembered. Both write-ups above are on worklore as stories your agent can redo: what I tried, what failed, the exact steps, and how to check it worked. If you run either one on your own repo, the story page shows whether it worked for other people too.

Use this GutHub repo to create AI architecture diagrams tt-a1i/archify t.co

Every developer should check out this repo! Archify is incredibly useful when explaining your project architecture or analyzing an unfamiliar codebase. GitHub: tt-a1i/archify #Python #BackendDevelopment #SoftwareEngineering #SystemDesign #OpenSource

Same AI, two developers: why the experienced one spends fewer tokens --- title: "Same AI, two developers: why the experienced one spends fewer tokens" published: true description: "Everyone can build an app with AI now. Give the same model and the same prompt to a first-time builder and an experienced engineer: day 1 looks identical, day 30 doesn't. Where the tokens go, what makes AI-written code last, and a playbook for working like the second one." tags: ai, productivity, softwareengineering, career canonical_url: ayushwing.com cover_image: ayushwing.com series: "Staff-level judgment" --- Everyone is building apps with AI now. My cousin built one. Your manager built one. Somebody built one during a meeting about whether we should build one. That's genuinely great. But it raises a fair question: if a first-time builder and an engineer with ten years of experience use the same model with the same prompt, what's actually different? Let's run the experiment in our heads. Two developers, same AI, same request: > "Build me a task-tracker app with user login." Meet Nia, the native AI builder: smart, fast, has never shipped software before. And Sam, the seasoned engineer, who has broken production enough times to be careful. Day 1: no difference at all Both get a working demo in an afternoon. Login page, task list, a nice dark theme, a little confetti when you finish a task. Both post a screenshot. Both get 40 likes. If you only look at day 1, experience doesn't matter. This is the part of the story everyone's excited about, and they're right to be. The AI did in an afternoon what used to take a week. The difference shows up later, the way it always does in software: in the second month, when you change things. Where the tokens go Here's Nia's day 3. A user reports that tasks sometimes disappear. Nia: tasks disappear sometimes, fix it AI: (rewrites the storage layer) Nia: now login is broken AI: (rewrites the auth flow) Nia: login works but tasks still disappear AI: (adds a retry, a cache, and a second database "for reliability") Nia: it's slower now and still broken. FIX IT PROPERLY AI: (apologises, rewrites the storage layer again) Every one of those messages drags the whole conversation and a growing chunk of the codebase into context again. Every fix touches more files than the bug lives in. After a week the app works most of the time, nobody knows why, and the token bill looks like a phone number. Sam's day 3, same bug: Sam: Read CLAUDE.md. Tasks occasionally disappear after editing. Don't change code yet: find where tasks are written and list the ways two writes could race. AI: Two tabs saving the same task: last write wins, no version check. Sam: Write a failing test that reproduces it with two concurrent saves. AI: (test fails, as expected) Sam: Fix it with a version column and a conditional update. Keep the diff small. AI: (12-line change, test passes) Sam: (reads the diff, commits) Four messages. One small diff. A test that will catch the bug if it ever comes back. Sam spent fewer tokens than Nia, not more. Experience didn't make Sam write better prompts in some magic way. It made Sam ask for smaller, checkable steps. (If that bug looks familiar, it's the same race I wrote about in Model business workflows as state machines. AI doesn't remove these problems. It just reaches them faster.) !Two loops. Prompt and pray: ask for a whole feature, run it, see something broken, say fix it, repeat. Plan, test, ship: plan, write a failing test, make one small change, read the diff, commit, then take the next step. Same model, two loops. Only one of them ends. Explore the interactive diagram The side-by-side | | Nia (first-time builder) | Sam (experienced engineer) | | --- | --- | --- | | First prompt | "Build the whole app" | "Plan the app; don't write code yet" | | Size of each request | A feature, or "fix everything" | One change you can review in two minutes | | Context | Re-explains the project every session | A project instructions file the AI reads every time | | Definition of done | "It looks like it works" | A test passes, and the diff makes sense | | When it breaks | "Fix it" (again) | "Explain why before changing anything" | | Tools | Pastes logs and docs into the chat | Gives the AI tools: skills, plugins, MCP servers | | Dependencies | Whatever the AI suggested | Asks why each one is needed | | Secrets | API key in the code, "temporarily" | Environment variables from the start | | Git | One giant commit called "final" | A commit after every green step | | Day 30 | Afraid to touch it | Adds features on a Friday | None of Sam's column is about AI. It's just engineering: small steps, tests, version control, knowing what "done" means. AI is a multiplier, and it multiplies whatever habits you bring. What makes AI-written code sustainable "Sustainable" is a fancy word for one question: can someone change this code next month without breaking it? "Someone" includes you, and it includes the AI. Four things decide the answer: 1. Tests that describe behaviour. A test suite is the only spec the AI can't misread. With tests, "refactor this" is safe. Without them, every change is a gamble the AI makes with confidence. 2. Small, readable diffs. If you can't explain a change in a sentence, you can't review it, and unreviewed code is how plausible-but-wrong bugs ship. My favourite: a refund endpoint that retries on timeout and pays twice. It looks perfectly correct in a diff (here's how to prevent it). 3. Written-down decisions. A project instructions file (CLAUDE.md, AGENTS.md, .cursorrules, whatever your tool reads) that says: how to run the tests, what the architecture is, what not to touch. The AI stops rediscovering your codebase every session, and that's most of the token savings right there. 4. Boring choices. One database, not five. A library everyone knows, not the one the AI half-remembers. AI is very good at writing glue for popular tools and very bad at debugging an obscure one. Skills, plugins and tools: stop pasting, start equipping The biggest token sink I see is pasting: the same error log, the same docs page, the same "here's how our project works" paragraph, over and over. Experienced developers give the AI tools instead: - Skills and plugins package a repeatable way of doing something, such as a review checklist, a deploy process or an audit, so you don't explain it every time. - MCP servers let the AI read your tickets, query a staging database or check a deploy itself, instead of you copying the output in. (I built one for Java services for exactly this reason.) - Your own scripts: a test command, a screenshot script, a linter. The AI can run them and check its own work. A real example: this site. While building it with an AI assistant: - Archify drew the diagrams, with its own validation checks, instead of the AI hand-placing boxes. - An SEO audit skill (claude-seo) ran its checklist against the built site and found a real bug: robots.txt pointed at a sitemap that no longer existed. - Every visual change was screenshotted in a headless browser before it shipped, which caught a meme overflowing on phones and a diagram that rendered as a broken image. - And the homepage stack section went through two designs I didn't like. Before the third attempt, I was asked to pick a concept from four sketches. That one stuck. That last one isn't a tool at all. It's the oldest engineering habit there is: agree on what you're building before you build it. It turns out it works on AI too. Signs you're vibe coding Score yourself honestly. One point each: - [ ] Your most-used prompt is "fix it". - [ ] You've said "PLEASE" to a compiler in capital letters. - [ ] Your app has 3 state-management libraries and you know the name of one. - [ ] There is a file called utils2_final_REAL.ts. - [ ] Your git history is one commit: "init". - [ ] You don't know what happens if two users click Save at the same time. - [ ] The AI added a cache "for performance" and you were too scared to ask why. - [ ] Your API key is in the frontend, but "only for now". 0–2: you're Sam. 3–5: you're on the way. 6–8: you're Nia, and that's fine. Every Sam started as a Nia. The good news is that every habit in Sam's column can be learned, and the AI will happily help you learn it if you ask it to explain instead of just fix. The playbook If you want to work like Sam starting tomorrow, here's the whole thing: 1. Plan before code. Ask for a plan and push back on it before any code exists. 2. Write a project instructions file on day 1: how to run, how to test, the rules. 3. One change per request, small enough to review in two minutes. 4. Test first for anything that matters: write the failing test, then the fix. 5. Read every diff. If you don't understand it, ask "why" before you accept it. 6. Commit after every green step, so you can always go back. 7. Equip, don't paste: skills, plugins, MCP servers and scripts the AI can run. 8. When stuck, ask for a diagnosis, not a fix. "Explain why this fails" costs less than five rewrites. Takeaways - On day 1, AI makes everyone equal. By day 30, habits make the difference. - Experienced developers often spend fewer tokens, because small steps, tests and saved context beat "fix it" loops. - Sustainable AI-written code has tests, small diffs, written decisions and boring choices. - Give the AI tools, not copy-paste. - Experience isn't about typing faster anymore. It's about knowing what to ask for, and knowing when the answer is wrong. --- Originally published at ayushwing.com.

30 Best Claude Plugins on GitHub (Sorted by Your Job) 30 Claude plugins with 3.9 million GitHub stars between them, sorted into 5 rows so you only need yours: coding, research, saving tokens, design, and writing + video. Don't add all 30. Pick the row that fits your week, add one plugin, and open its repo first to check what it can touch on your machine. 💬 Comment ANYLANCER and I'll send you all 30 links, or grab them here: 1) You write code • Superpowers: obra/superpowers • Matt Pocock's Skills: mattpocock/skills • ECC: affaan-m/ECC • Karpathy Skills: multica-ai/andrej-karpathy-skills • Ponytail: DietrichGebert/ponytail • Agent Skills: addyosmani/agent-skills 2) You research big things • Graphify: Graphify-Labs/graphify • Agent Reach: Panniantong/Agent-Reach • Understand Anything: Egonex-AI/Understand-Anything • Last 30 Days: mvanhorn/last30days-skill • Career Ops: career-ops-hq/career-ops • REA: morluto/rea 3) Huge projects, few tokens • gstack: garrytan/gstack • Caveman: JuliusBrussee/caveman • Claude-Mem: thedotmack/claude-mem • Headroom: headroomlabs-ai/headroom • GSD: gsd-build/get-shit-done • BMAD Method: bmad-code-org/BMAD-METHOD 4) Design & visuals • UI UX Pro Max: nextlevelbuilder/ui-ux-pro-max-skill • Taste: Leonxlnx/taste-skill • Archify: tt-a1i/archify • Impeccable: pbakaus/impeccable • PPT Master: hugohe3/ppt-master • Diagram Design: cathrynlavery/diagram-design 5) Writing & video • Anthropic Skills: anthropics/skills • OpenMontage: calesthio/OpenMontage • HyperFrames: heygen-com/hyperframes • Humanizer: blader/humanizer • I Have ADHD: ayghri/i-have-adhd • Marketing Skills: coreyhaines31/marketingskills Built a skill people would pay for? Sell it on Anylancer: anylancer.com Which row are you? 👇 Star counts from GitHub on Oct 29, 2026. This video was made with HyperFrames. #ClaudeCode #AIAgents #GitHub #Shorts

5 проектов с GitHub, которые прокачают Claude Code 5 проектов с GitHub, которые прокачают Claude Code youtube.com Пять бесплатных дополнений для Claude Code с GitHub: батрак из Warcraft сообщает, что работа готова, Claude говорит как пещерный человек и тратит меньше лимитов, помнит прошлые чаты, рисует схемы и превращается в игровую студию из 49 агентов. 1. peon-ping: PeonPing/peon-ping 2. caveman: JuliusBrussee/caveman 3. claude-mem: thedotmack/claude-mem 4. archify: tt-a1i/archify 5. Claude Code Game Studios: Donchitos/Claude-Code-Game-Studios Ссылки и как поставить в моём Telegram: t.me Звёзды на 28.10.2026. «На треть меньше» у caveman: замеры авторов проекта. Реплики батрака: набор peon из peon-ping (Warcraft III). #ClaudeCode #GitHub #shorts [en-US] 5 GitHub Projects to Supercharge Claude Code 5 GitHub projects that will boost Claude Code youtube.com Five free add-ons for Claude Code from GitHub: a Warcraft farmhand announces when work is complete, Claude speaks like a caveman and spends less than his spending limits, remembers past chats, draws diagrams, and transforms into a 49-agent game studio. 1. peon-ping: PeonPing/peon-ping 2. caveman: JuliusBrussee/caveman 3. claude-mem: thedotmack/claude-mem 4. archify: tt-a1i/archify 5. Claude Code Game Studios: Donchitos/Claude-Code-Game-Studios Links and how to add them in my Telegram: t.me Stars as of October 28, 2026. Caveman has "a third less": measurements by the project authors. Farmhand lines: peon set from peon-ping (Warcraft III). #ClaudeCode #GitHub #shorts

Recently came across Archify and started trying it out for my projects. Repo - tt-a1i/archify I'm using it to create interactive diagrams for runtime architecture, HLD, and LLD. It's been fun seeing my projects come to life visually. #BuildInPublic #Java #SystemDesign t.co

가상 손님 100명이 먼저 답한다, 클로드(Claude) 스킬 파운더 외 4개 | 깃허브 인기 AI 스킬 깃허브에서 별을 많이 모은 클로드(Claude) 스킬 다섯 개가 실제로 무엇을 내놓는지 — 조사 last30days(레딧 · 유튜브 · 틱톡의 최근 30일 반응을 한 장으로) · 기획 archify(한 문장 → 눌러 보는 그림) · 정리 answer-me-with-html(긴 답 → 그림 있는 한 페이지) · 검증 founder-skill(가상 손님 100명 · 가상 이사회 · 본전 계산) · 마무리 humanizer(영어 글의 AI 티 35가지를 찾아 고치기). 화면의 숫자 · 예시는 각 저장소 안내문(README) 기준이고, 별 수는 10/25 기준입니다. 저장소 주소와 설치 문장은 고정 댓글과 소개 페이지에 있습니다. 공식 도움말 기준 웹(claude.ai)에서는 맞춤설정 → 스킬에서 압축 파일(ZIP)을 올리고, 코드 실행이 켜져 있어야 합니다. 믿을 만한 곳의 스킬만 설치하고, 켜기 전에 파일을 읽어 보십시오. 출처 - Claude 도움말 — Using Skills in Claude (2026-10-25) support.claude.com - 깃허브 — mvanhorn/last30days-skill (2026-10-25) mvanhorn/last30days-skill - 깃허브 — tt-a1i/archify (2026-10-25) tt-a1i/archify - 깃허브 — QingYunA/answer-me-with-html (2026-10-25) QingYunA/answer-me-with-html - 깃허브 — Jakeschincariol/founder-skill (2026-10-25) Jakeschincariol/founder-skill - 깃허브 — blader/humanizer (2026-10-25) blader/humanizer #AI #클로드 #Claude #클로드스킬 #깃허브 #AI스킬 #shorts

Describe your system in one sentence and your AI agent draws an interactive architecture diagram. 🔗 tt-a1i/archify Credits: tt-a1i/archify #archify #javascript #opensource

Archify: Turn any idea, plan, or codebase into a beautiful interactive diagram (86k stars) What it does All text below is quoted directly from the project README. >Turn any idea, plan, or codebase into a beautiful interactive diagram. An agent skill for Claude Code, Codex, and more. Why Archify: - Layout judgment over generic auto-layout: the agent chooses hierarchy, spacing, routes, and emphasis; shared automatic endpoints spread deterministically instead of piling arrows on one midpoint. - Typed JSON IR: every renderer-backed mode has a schema and reproducible source. - Atomic validation before delivery: schema, layout, HTML/SVG, route, and label-to-route clearance checks must all pass before a showcase artifact replaces the last known good output. - Failures come with a repair receipt: validate --json and deliver --json return stable rule codes, the exact subject, measured evidence, and only supported repair controls instead of a Node stack or an unstructured retry guess. - Last-good live preview: an optional desktop loop watches one JSON file, refreshes only after the latest candidate passes every gate, and keeps the previous verified diagram visible when a save is incomplete or invalid. - Truthful interaction: focus, upstream/downstream reach, exact routes, role comparison, and stories reuse authored nodes and relationships instead of inventing topology or claiming runtime impact. - Source evidence, only when requested: Evidence-backed Architecture nodes mark themselves SRC n and open Git-verified files and line ranges pinned to one public commit; ordinary artifacts stay source-free. - Portable by default: the result is one HTML file; exports remain full-diagram and free of temporary viewer state. >npx skills add tt-a1i/archify -g Repository tt-a1i/archify Repository stats |Metric|Value| |:-|:-| |Stars|86,013| |Forks|5,858| |Last release|v3.2.0 (2026-10-19)| |Languages|JavaScript 90.8%, CSS 4.5%, HTML 3.2%| |Contributors|90| |AI contributor accounts (Claude / Codex / Cursor / Copilot)|cursoragent: 10, claude: 25| |AI co-authored commits|Claude: 30, Cursor: 10| Main contributor |Field|Value| |:-|:-| |GitHub|@tt-a1i| |Commits to this repo|780 (top contributor)| |X / Twitter|@t20000622yy| |Followers|2106| |Public repos|66| |On GitHub since|2019-07-21| ────────────────────────────── 📌 How many great GitHub repos have you discovered and completely forgotten about? Don't let another useful tool disappear into your Reddit feed. Get the community's best repos, organized by category, plus key takeaways from real developer discussions, delivered to your inbox twice a week. 📬 Never lose track of great repos again. Subscribe free.