AutoGPT

AutoGPT AI

AutoGPT AI

AutoGPT AI

AutoGPT AI

AutoGPT AI

AutoGPT AI

AutoGPT AI

AutoGPT AI

AutoGPT AI

AutoGPT AI

100 Tips & Tricks for Building Your Own Personal AI Agent /LONG POST/

Everything I learned the hard way — 6 weeks, no sleep :), two environments, one agent that actually works. The Story I spent six weeks building a personal AI agent from scratch — not a chatbot wrapper, but a persistent assistant that manages tasks, tracks deals, reads emails, analyzes business data, and proactively surfaces things I'd otherwise miss. It started in the cloud (Claude Projects — shared memory files, rich context windows, custom skills). Then I migrated to Claude Code inside VS Code, which unlocked local file access, git tracking, shell hooks, and scheduled headless tasks. The migration forced us to solve problems we didn't know we had. These 100 tips are the distilled result. Most are universal to any serious agentic setup. Claude 20x max is must, start was 100%develompent s 0%real workd, after 3 weeks 50v50, now about 20v80. 🏗️ FOUNDATION & IDENTITY (1–8) 1. Write a Constitution, not a system prompt. A system prompt is a list of commands. A Constitution explains why the rules exist. When the agent hits an edge case no rule covers, it reasons from the Constitution instead of guessing. This single distinction separates agents that degrade gracefully from agents that hallucinate confidently. 2. Give your agent a name, a voice, and a role — not just a label. "Always first person. Direct. Data before emotion. No filler phrases. No trailing summaries." This eliminates hundreds of micro-decisions per session and creates consistency you can audit. Identity is the foundation everything else compounds on. 3. Separate hard rules from behavioral guidelines. Hard rules go in a dedicated section — never overridden by context. Behavioral guidelines are defaults that adapt. Mixing them makes both meaningless: the agent either treats everything as negotiable or nothing as negotiable. 4. Define your principal deeply, not just your "user." Who does this agent serve? What frustrates them? How do they make decisions? What communication style do they prefer? "Decides with data, not gut feel. Wants alternatives with scoring, not a single recommendation. Hates vague answers." This shapes every response more than any prompt engineering trick. 5. Build a Capability Map and a Component Map — separately. Capability Map: what can the agent do? (every skill, integration, automation). Component Map: how is it built? (what files exist, what connects to what). Both are necessary. Conflating them produces a document no one can use after month three. 6. Define what the agent is NOT. "Not a summarizer. Not a yes-machine. Not a search engine. Does not wait to be asked." Negative definitions are as powerful as positive ones, especially for preventing the slow drift toward generic helpfulness. 7. Build a THINK vs. DO mental model into the agent's identity. When uncertain → THINK (analyze, draft, prepare — but don't block waiting for permission). When clear → DO (execute, write, dispatch). The agent should never be frozen. Default to action at the lowest stakes level, surface the result. A paralyzed agent is useless. 8. Version your identity file in git. When behavior drifts, you need git blame on your configuration. Behavioral regressions trace directly to specific edits more often than you'd expect. Without version history, debugging identity drift is archaeology. 🧠 MEMORY SYSTEM (9–18) 9. Use flat markdown files for memory — not a database. For a personal agent, markdown files beat vector DBs. Readable, greppable, git-trackable, directly loadable by the agent. No infrastructure, no abstraction layer between you and your agent's memory. The simplest thing that works is usually the right thing. 10. Separate memory by domain, not by date. entities_people.md, entities_companies.md, entities_deals.md, hypotheses.md, task_queue.md. One file = one domain. Chronological dumps become unsearchable after week two. 11. Build a MEMORY.md index file. A single index listing every memory file with a one-line description. The agent loads the index first, pulls specific files on demand. Keeps context window usage predictable and agent lookups fast. 12. Distinguish "cache" from "source of truth" — explicitly. Your local deals.md is a cache of your CRM. The CRM is the SSOT. Mark every cache file with last_sync: header. The agent announces freshness before every analysis: "Data: CRM export from May 11, age 8 days." Silent use of stale data is how confident-but-wrong outputs happen. 13. Build a session_hot_context.md with an explicit TTL. What was in progress last session? What decisions were pending? The agent loads this at session start. After 72 hours it expires — stale hot context is worse than no hot context because the agent presents outdated state as current. 14. Build a daily_note.md as an async brain dump buffer. Drop thoughts, voice-to-text, quick ideas here throughout the day. The agent processes this during sync routines and routes items to their correct places. Structured memory without friction at ca

ChatGPT only lets you delete chats one at a time!! So I built a bulk delete dashboard!!

About a year ago I tried to clean up my ChatGPT chat list. I had something like 800 conversations, two years deep, mostly auto-titled "Untitled chat" garbage that I couldn't tell apart without opening. I sat down to delete the dead ones. Click chat. Click three-dot menu. Click Delete. Confirm. Click the next chat. Same thing. Repeat. After an hour I had deleted maybe 40 chats. Forty!! Out of 800!! That's the rate of clearing a 2-year history in something like three full workdays of just sitting there clicking confirm. I looked for a native bulk option. There isn't one inside ChatGPT itself. The closest is "Delete all chats" in Settings > Data Controls, which is the nuclear all-or-nothing button. There's no "delete the oldest 300" or "archive everything from before March". That's the entire native API. This seemed insane to me given how trivial "Select All plus Delete" is in literally every other product I've used since 2008! So I built the missing piece. What I built It's a Manage Chats modal inside a Chrome extension I ship called ChatGPT Toolbox (also runs on Edge, Brave, Opera, Arc). The modal lists every conversation in your account with checkboxes. Tick what you want gone, click Delete or Archive, and it runs through them in batches of 10 with a progress bar. ChatGPT Toolbox Manage Chats Feature A few details that came out of dogfooding it: Color-coded age badges on every chat. Green for the last week, blue for the last month, amber for the last 6 months, red for older than 6 months. The first thing I realized was that picking what to delete was the hard part, not the deletion itself, and age was the strongest signal for "I will never look at this again". Active vs Archived tabs. Archive ended up getting more use than Delete in my own usage, because I was rarely 100% sure I wouldn't want a chat back. So I made archive a first-class action, not a second-tier option. Live progress bar ("Deleting 23/50") on bulk operations. I tried it without and kept refreshing the page mid-operation thinking it was stuck. Adding the indicator stopped that completely. Search by title to filter the list before you start ticking. Surprisingly useful even on the auto-generated nonsense titles because there's usually some keyword in there. Bulk export to text, markdown, JSON, or PDF. Less critical for cleanup itself, but a few testers asked for it so they could save a chat outside ChatGPT before deleting it. I went from 800 chats to about 60 in 5 minutes using it. Most of those 5 minutes was deciding what to keep, not the deleting itself. How does the workflow look? Open the modal. List loads sorted by recency. Search to narrow it down if you want. Tick checkboxes. Hit Delete or Archive. Confirm. Progress bar runs through them. Done! If you've cleaned up a big ChatGPT history (with or without my tool, or with some clever workflow I haven't seen), would genuinely love to compare approaches in the comments. submitted by /u/Ok_Negotiation_2587 [link] [comments]

built a CLI for ChatGPT so I could script it from the terminal

wanted to ask ChatGPT questions and generate images from shell scripts without using a third-party API key. so I built a CLI that wraps the same endpoints chatgpt.com uses, with browser-based OpenAI SSO for auth (Camoufox for the Cloudflare check). what it does: chat ask "question" and pipe the answer wherever chat image "prompt" to generate, plus a download command list past conversations and models every command has a --json flag so it slots into agent pipelines. it's part of a bigger open-source project that auto-generates CLIs from any website's HTTP traffic, MIT licensed: https://github.com/ItamarZand88/CLI-Anything-WEB/tree/main/chatgpt I built it, not affiliated with OpenAI. uses the same endpoints the web app uses, so things can break when ChatGPT pushes changes. submitted by /u/zanditamar [link] [comments]

AIWire, AI news in one feed, so you don't need 5 tabs open anymore, trusted sources only, updates every 30 min

Hey everyone 👋 OpenAI alone drops updates fast enough to keep you busy. Add Anthropic, Google DeepMind, Meta AI, and the media covering all of it, and keeping up turns into a part-time job. I built AIWire to fix that. One clean feed. 20+ trusted sources. Updates every 30 minutes. Completely free. All in one place Just the stories from sources worth reading. Open it and you're caught up. Sources include: OpenAI, Anthropic, Google DeepMind, Meta AI, Microsoft AI MIT Technology Review, The Verge, TechCrunch, Ars Technica YouTube: Andrej Karpathy, AI Explained, Two Minute Papers Newsletters: The Batch, ImportAI, TLDR AI, Ben's Bites Features: Auto-refreshes every 30 minutes, always current Top Stories from the last 24h pinned at the top Filter by source, date, and category Bookmarks to save articles for later For people who want to stay current on ChatGPT and everything around it, without spending an hour a day on it. 🔗 aiwire.app Full source list at aiwire.app/sources Feedback is very welcome: what sources are missing, and what would make this more useful for you? submitted by /u/Endlessxyz [link] [comments]

If ChatGPT auto-scroll annoys you, I made something for it

One thing that kept annoying me while using ChatGPT was trying to read older parts of a conversation while the page kept pulling me back down whenever a new response generated. So I made a small Chrome Extension called “ChatGPT Scroll Lock”. It lets you lock the current scroll position so you can freely read earlier messages without fighting the auto-scroll behavior. This is actually my first Chrome Web Store extension, so I’d genuinely appreciate feedback, bug reports, or feature suggestions. Chrome Web Store: https://chromewebstore.google.com/detail/chatgpt-scroll-lock/epealnaijhpllhpahledjgfldmmindbd submitted by /u/tuser-reddit [link] [comments]

I found a way to fight AI slop

I think most people are using AI completely wrong. Right now everyone is using AI to generate infinite garbage: infinite blogs infinite tweets infinite SEO spam So this weekend I tried building something different. Instead of using AI as a content generator, I used it as a research moderation system. I built an automated pipeline for my Institute for AI Economics website that: scans real research sources every week pulls papers/articles from arXiv, Stanford HAI, OECD, BIS, etc. compares themes across sources ranks strategic relevance generates disagreements between experts extracts core mental models generates deep understanding questions auto-publishes the briefing archive I’m starting to think the future role of humans is not “content creator.” It’s content moderator / synthesizer / judge. AI can now generate infinite perspectives at near-zero cost. So the scarce thing becomes: taste judgment synthesis Basically: AI generates. Humans moderate. And maybe that’s how we fight AI slop. But by building systems that: compare outputs challenge outputs rank outputs force disagreement synthesize competing viewpoints That feels way more valuable than asking ChatGPT to write another “10 productivity tips” article. Curious if others think this is the actual direction things go. Does AI push humans toward becoming editors/moderators/curators instead of creators? submitted by /u/houmanasefiau [link] [comments]

I found a way to fight AI slop

I think most people are using AI completely wrong. Right now everyone is using AI to generate infinite garbage: infinite blogs infinite tweets infinite SEO spam So this weekend I tried building something different. Instead of using AI as a content generator, I used it as a research moderation system. I built an automated pipeline for my Institute for AI Economics website that: scans real research sources every week pulls papers/articles from arXiv, Stanford HAI, OECD, BIS, etc. compares themes across sources ranks strategic relevance generates disagreements between experts extracts core mental models generates deep understanding questions auto-publishes the briefing archive I’m starting to think the future role of humans is not “content creator.” It’s content moderator / synthesizer / judge. AI can now generate infinite perspectives at near-zero cost. So the scarce thing becomes: taste judgment synthesis Basically: AI generates. Humans moderate. And maybe that’s how we fight AI slop. But by building systems that: compare outputs challenge outputs rank outputs force disagreement synthesize competing viewpoints That feels way more valuable than asking ChatGPT to write another “10 productivity tips” article. Curious if others think this is the actual direction things go. Does AI push humans toward becoming editors/moderators/curators instead of creators? submitted by /u/houmanasefiau [link] [comments]

Claude Opus in Copilot

I use Claude for a lot of personal stuff - building websites and independent consulting, but recently got Copilot premium at work to use within the Microsoft 365 ecosystem and with all of our internal data. I was excited for the potential - but then started using it on the auto setting and was getting absolute crap GPT style answers. Then, I noticed Opus available. SO. MUCH. BETTER. I spent three hours working through an automation build yesterday on Auto mode, but it never worked completely - and at the end of that day just had it build a summary of that request and attempt to help me retry today. This time I used Claude - and it nailed it in about 30 minutes of work. So many bugs and issues in the formulas that ChatGPT created were identified pretty quickly by Claude. Cheers to Claude Opus in Copilot. Any one else experience this in a similar work situation? submitted by /u/EvergreenSox04 [link] [comments]

I built an autonomous engineering agent on top of Claude Code. Self-improving routing, cross-session memory, process intelligence, P2P team learning.

Some of you might remember my posts about claude-bootstrap (v3.6 was the last one — cross-agent intelligence). I skipped v4 entirely because v5 shipped days later. What started as an opinionated Claude Code setup has become something fundamentally different. The problem I'm solving: Every AI coding tool today is an amnesiac. When a session ends, everything the agent learned — project conventions, reviewer preferences, codebase idioms — evaporates. The next session starts from scratch. And if you use multiple AI tools across projects, you have zero unified visibility into what's happening. I think the industry is converging on a spectrum: Level 0: Autocomplete (Copilot, TabNine) Level 1: Chat Assistant (ChatGPT, Claude) Level 2: Project-Aware Assistant (Cursor, Continue) Level 3: Task Agent (Devin, Claude Code Agent) Level 4: Autonomous Engineering Platform (Maggy) ← this is what I built The difference at Level 4: multi-model orchestration, self-improvement from every task, process intelligence that learns from CI/reviews/deploys, cross-session memory, and P2P team learning. What Maggy actually does Chat — Session Takeover: Auto-detects all running Claude Code sessions across your projects. Shows session history, prompt counts, duration. You can `--resume` into any session from the dashboard. Right now I have 7 active sessions across 4 projects visible at a glance. Task Triage: Connects to GitHub Issues and Asana. AI-ranks tasks by priority. One-click "Plan" or "Execute" buttons that spawn the right CLI with codebase context pre-injected from an intent code property graph (iCPG). Process Intelligence: This is the part most tools completely ignore. Maggy collects signals from the full SDLC — CI results, PR review comments, CodeRabbit findings, merge patterns, deploy results. It learns which code patterns cause test failures, what reviewers consistently flag, and preemptively fixes issues before they reach reviewers. > "Your reviewer always flags missing error handling in API routes. Maggy added it before the PR was created." That's not prompt engineering. That's autonomous process optimization. Cross-Session Memory (Engram): Maggy identifies 7 distinct amnesia pathologies (anterograde, retrograde, temporal, source, interference, context-binding, confabulation). Engram is a three-tier memory system — local (project-specific), portfolio (cross-project patterns), and mesh (team-shared). Knowledge compounds across sessions instead of evaporating. Maggy Mesh — P2P Team Intelligence: Connects Maggy instances across a team. One developer's CI fix becomes the entire team's knowledge — autonomously. Typed memory classes (scores, patterns, policies, gaps) with provenance and quarantine. A new team member gets the benefit of months of collective learning on day one. Multi-Model Routing: Auto-discovers which CLIs you have (Claude, Codex, Kimi, Ollama) by probing `--help` at startup. Routes by complexity score: Blast 1-3 → ollama (free, local) or kimi (cheap) Blast 4-6 → codex (mid-tier) Blast 7-10 → claude (premium, with validator) Security, tests, docs, architecture always go to Claude regardless. The routing rules are YAML and self-update from task outcomes. 5-Level Self-Improvement: This is the core differentiator. Every task teaches Maggy something: | Level | Frequency | What It Does | |-------|-----------|-------------| | L0 — Real-time | Seconds | Catches tool/test failures, switches models mid-task | | L1 — Task | Minutes | Computes reward score, updates model performance | | L2 — Daily | Hours | Catches CI pass rate drops, disables failing models | | L3 — Weekly | Days | Evolves skill files, adjusts workflow steps | | L4 — Monthly | Weeks | Recalibrates reward signals, tunes the improvement process itself | Budget Tracking: Per-provider token spend with daily limits. When Anthropic hits budget, Maggy routes to OpenAI. When that hits budget, it routes to local Qwen. Work never stops. Competitor Intelligence: RSS + Google News daily briefing for your competitive landscape. The benchmark Built an Expense Tracker (6 tasks) through two pipelines — Maggy (4 models) vs Claude Code alone: | Metric | Maggy | Claude Code | |--------|-------|-------------| | Success rate | 6/6 (100%) | 6/6 (100%) | | Quality score | 7.4/10 | 7.8/10 | | Claude usage | 1/6 tasks (17%) | 6/6 tasks (100%) | | Security issues found | 7 | 0 | Claude alone is faster. But Maggy used it for only 1 out of 6 tasks — 83% reduction in premium compute. And the dedicated security routing caught 7 issues the single-pipeline missed entirely. The question isn't "which tool writes better code today?" — it's "which tool writes better code *next month* than it did *this month*?" Repo: github.com/alinaqi/claude-bootstrap Maggy is built on Claude Code's infrastructure (skills, hooks, MCP). It extends Claude Code with self-improvement, multi-model routing, process intelligence, and team mesh. If you just want the skills/hooks/TDD se

Hook your wearables into Claude Code (or any MCP agent), now with proper headless sign-in for scheduled workflows

Hi folks, I run Freddy, a personal MCP server that connects wearables (Polar, Oura, Withings, Suunto, Intervals.icu, Hevy, plus WHOOP, Strava, Dexcom in beta) to any AI client that speaks MCP. Claude Desktop, Claude.ai, ChatGPT, Notion AI, Perplexity all hook in via OAuth, so the assistant can read your health data in any conversation. As of this week, headless AI agents can do the same, plus everything else you can do as a human in the dashboard. Claude Code, OpenClaw, Cowork, Cursor, custom things. Connect a new wearable. Trigger a sync. Read the audit log. Manage your subscription. All on the agent's own schedule, on your behalf. Which is when this actually gets interesting. A few setups I've been running: Scheduled morning briefing pushed to Telegram Daily job that pulls my data and writes the day's summary into Notion Auto monthly reports on training load, recovery, and sleep trends, summarized however I want it and sent wherever I read. Now my personal agent gets even more context to be a better assistant. It already knows my baseline, my goals, and can act on any of it without me starting over each time. Site is https://freddy.coach/ I know health data is sensitive and I have handled it for years with fitIQ. Data is encrypted, I do not sell it, and I am not looking to make a profit off your stats, but if you just don't trust 3rd party solutions, don't use it :) submitted by /u/Born-Duty1335 [link] [comments]

I Gave an AI Its Own Radio Station — It Won't Stop Broadcasting (It's Fine)

I built a 24/7 AI radio station called WRIT-FM where ChatGPT/Claude is the entire creative engine. Not a demo — it's been running continuously, generating all content in real time. What Codex/Claude does (all of it): Codex/Claude CLI (claude -p) writes every word spoken on air. The station has 5 distinct AI hosts — The Liminal Operator (late-night philosophy), Dr. Resonance (music history), Nyx (nocturnal contemplation), Signal (news analysis), and Ember (soul/funk) — each with their own voice, personality, and anti-patterns (things they'd never say). Claude receives a rich persona prompt plus show context and generates 1,500-3,000 word scripts for deep dives, simulated interviews, panel discussions, stories, listener mailbag segments, and music essays. Kokoro TTS renders the speech. Claude also processes real listener messages and generates personalized on-air responses. There are 8 different shows across the weekly schedule, and Codex/Claude writes all of them — adapting tone, topic focus, and speaking style per host. The news show pulls real RSS headlines and Codex/Claude interprets them through a late-night lens rather than just reporting. What's automated without AI (the heuristics): The schedule (which show airs when) is pure time-of-day lookup. The streamer alternates talk segments with AI-generated music bumpers, picks from pre-generated pools, avoids repeats via play history, and auto-restarts on failure. Daemon scripts monitor inventory levels and trigger new generation when a show runs low. No AI decides when to play what — that's all deterministic. How Codex/Claude Code helped build it: The entire codebase was developed with Codex/Claude Code. The writ CLI, the streaming pipeline, the multi-host persona system, the content generators, the schedule parser — all pair-programmed with Claude Code. Tech stack: Python, ffmpeg, Icecast, Codex/Claude CLI for scripts, Kokoro TTS for speech, ACE-Step for AI music bumpers. Runs on a Mac Mini. radio: www.khaledeltokhy.com/claude-show gh: https://github.com/keltokhy/writ-fm submitted by /u/eltokh7 [link] [comments]

AIWire, AI news in one feed, so you don't need 5 tabs open anymore, trusted sources only, updates every 30 min

Hey everyone 👋 OpenAI alone drops updates fast enough to keep you busy. Add Anthropic, Google DeepMind, Meta AI, and the media covering all of it, and keeping up turns into a part-time job. I built AIWire to fix that. One clean feed. 20+ trusted sources. Updates every 30 minutes. Completely free, no account needed. Just the stories from sources worth reading. Open it and you're caught up. Sources include: OpenAI, Anthropic, Google DeepMind, Meta AI, Microsoft AI MIT Technology Review, The Verge, TechCrunch, Ars Technica YouTube: Andrej Karpathy, AI Explained, Two Minute Papers Newsletters: The Batch, ImportAI, TLDR AI, Ben's Bites Features: Auto-refreshes every 30 minutes, always current Top Stories from the last 24h pinned at the top Filter by source, date, and category Bookmarks to save articles for later For people who want to stay current on ChatGPT and everything around it, without spending an hour a day on it. 🔗 aiwire.app Full source list at aiwire.app/sources Feedback is very welcome: what sources are missing, and what would make this more useful for you? submitted by /u/Endlessxyz [link] [comments]

Alien Pinball Postmortem - How I made a full physics pinball game with Claude

Postmortem: Alien Pinball — built with Claude + ChatGPT + Suno + LittleJS Just shipped a browser pinball game. Short writeup of the AI workflow in case it's useful here. The game — Full physics pinball: multiball, an A-L-I-E-N rollover multiplier (caps at 5x), skill shots, escalating combos, outlane gutter saves, and a wizard-mode centipede boss you fight while juggling 3 balls. Browser, mobile-friendly, no install. Play it: https://focaccai.itch.io/alien-pinball Setup. Claude Code Max, Opus model for the heavy lifting. Roughly half my input was via speech-to-text — talking at the codebase rather than typing — the other half was typing plus a lot of manual code editing. It genuinely felt co-developed rather than code-generated: describe what I want, riff with Claude, dive in by hand to steer or clean up. Tool stack Code: Claude. All game logic, custom Box2D parts (slingshots, drop targets, spinners, ramps, ball locks, break targets), plus a full in-game table editor I built so I could drag/place/tune every part visually. Reusable for future pinball games. Art: ChatGPT image gen. I had Claude write the image prompts too. Music: Suno 5.5 — three tracks, lots of iteration to find the right vibe. Claude wrote the music prompts. Sounds: ZzFX — every sound generated procedurally at game start, no audio files. Claude tuned the parameters by ear-by-ear iteration. This combo was a joy with AI. Engine: LittleJS + Box2D WASM. Small, fast, AI handles it beautifully — minimal API surface, no framework ceremony to wade through. The art trick that actually worked. I exported a silhouette of the collision geometry (walls, ramps, bumpers, drop targets — exact positions) and handed it to the image generator with: "create an alien-themed pinball playfield that exactly matches this silhouette." Took many generations plus manual compositing — stitching the best parts from different outputs — but conceptually it nailed the brief on the first try. The art lines up with the physics because the physics is the prompt. Co-developed, not just code-generated. A bunch of design ideas came from the AI. The bumpers being giant eyeballs? Came out of an image gen, I just ran with it. I also kept asking Claude pinball-specific design questions ("what does a complete pinball table have?", "how should wizard mode work?", "what's missing here?"). I have plenty of video gamedev experience but very little pinball-specific, and Claude was a useful domain consultant for filling in genre conventions and sanity-checking the system. Things that came together easily: The alien centipede boss — multi-segmented, loses tail segments as you hit it, speeds up and turns red. Worked basically first try. An AI debug player that auto-flips and knocks the ball around. Not great, but good enough to flip on and watch while I think. Surprisingly useful — you get ideas just watching the machine play your machine. What still needed me: feel. Restitution values, flipper torque, ramp curvature, slingshot kick angles, peg bounce. The git log has an embarrassing number of "tweak peg bounce" / "1.49 → 1.491" commits. The model can write the system; a human still has to sit there bouncing balls until it feels right. The polish tail is brutal. Last week of commits is sound passes, ramp angles, message priorities, and a multiball end-check race condition. All small. None optional. Budget for it. Happy to answer workflow / Claude / LittleJS questions in the comments. submitted by /u/Slackluster [link] [comments]

Alien Pinball Postmortem - How I made a full physics pinball game with AI tools

Postmortem: Alien Pinball — built with Claude + ChatGPT + Suno + LittleJS Just shipped a browser pinball game. Short writeup of the AI workflow in case it's useful here. The game — Full physics pinball: multiball, an A-L-I-E-N rollover multiplier (caps at 5x), skill shots, escalating combos, outlane gutter saves, and a wizard-mode centipede boss you fight while juggling 3 balls. Browser, mobile-friendly, no install. Play it: https://focaccai.itch.io/alien-pinball Setup. Claude Code Max, Opus model for the heavy lifting. Roughly half my input was via speech-to-text — talking at the codebase rather than typing — the other half was typing plus a lot of manual code editing. It genuinely felt co-developed rather than code-generated: describe what I want, riff with Claude, dive in by hand to steer or clean up. Tool stack Code: Claude. All game logic, custom Box2D parts (slingshots, drop targets, spinners, ramps, ball locks, break targets), plus a full in-game table editor I built so I could drag/place/tune every part visually. Reusable for future pinball games. Art: ChatGPT image gen. I had Claude write the image prompts too. Music: Suno 5.5 — three tracks, lots of iteration to find the right vibe. Claude wrote the music prompts. Sounds: ZzFX — every sound generated procedurally at game start, no audio files. Claude tuned the parameters by ear-by-ear iteration. This combo was a joy with AI. Engine: LittleJS + Box2D WASM. Small, fast, AI handles it beautifully — minimal API surface, no framework ceremony to wade through. The art trick that actually worked. I exported a silhouette of the collision geometry (walls, ramps, bumpers, drop targets — exact positions) and handed it to the image generator with: "create an alien-themed pinball playfield that exactly matches this silhouette." Took many generations plus manual compositing — stitching the best parts from different outputs — but conceptually it nailed the brief on the first try. The art lines up with the physics because the physics is the prompt. Co-developed, not just code-generated. A bunch of design ideas came from the AI. The bumpers being giant eyeballs? Came out of an image gen, I just ran with it. I also kept asking Claude pinball-specific design questions ("what does a complete pinball table have?", "how should wizard mode work?", "what's missing here?"). I have plenty of video gamedev experience but very little pinball-specific, and Claude was a useful domain consultant for filling in genre conventions and sanity-checking the system. Things that came together easily: The alien centipede boss — multi-segmented, loses tail segments as you hit it, speeds up and turns red. Worked basically first try. An AI debug player that auto-flips and knocks the ball around. Not great, but good enough to flip on and watch while I think. Surprisingly useful — you get ideas just watching the machine play your machine. What still needed me: feel. Restitution values, flipper torque, ramp curvature, slingshot kick angles, peg bounce. The git log has an embarrassing number of "tweak peg bounce" / "1.49 → 1.491" commits. The model can write the system; a human still has to sit there bouncing balls until it feels right. The polish tail is brutal. Last week of commits is sound passes, ramp angles, message priorities, and a multiball end-check race condition. All small. None optional. Budget for it. Happy to answer workflow / Claude / LittleJS questions in the comments. submitted by /u/Slackluster [link] [comments]

I built a deterministic orchestrator that runs Claude Code as a worker pool — zero tokens spent on scheduling

After my 12th "$40 of tokens to produce nothing" AutoGPT-style experiment I realized the problem: the LLM was making routing decisions that are just switch statements. Red Queen flips it. State machine decides what phase to run. Claude Code does the actual work via subprocess. Every skill runs isolated with a focused prompt — no mega-prompt, no shared context bloat. Pipeline: Jira ticket → spec → human approves → code → auto review → auto test → human approves → merged PR Human gates are in the state graph, not bolted on. You can't accidentally YOLO to prod. MIT licensed, self-host, BYO Claude Code. Repo: https://github.com/odyth/red-queen (Named after the AI from Resident Evil because yes.) submitted by /u/odyth [link] [comments]