AutoGen

AutoGen AI

AutoGen AI

AutoGen AI

AutoGen AI

AutoGen AI

AutoGen AI

AutoGen AI

AutoGen AI

AutoGen AI

AutoGen AI

IP Memorandum: Multi-Agent ("Agentic") AI Systems in Coding, Marketing, and Creation – Comprehensive 2026 Analysis. (Integrating Patentability, Hype vs. Reality, Human Dependency, and Cost Overruns)

​ \*\*Date:\*\* June 1, 2026 \*\*To:\*\* Interested Parties / Developers / Enterprises \*\*Re:\*\* Viability of Layered Agentic AI – IP Protectability, Practical Utility, and Economic Sustainability Without Substantial Human Creative Input \### Executive Summary The 2026 trend toward \*\*multi-agent ("agentic") AI systems\*\*—layering specialized agents via frameworks like CrewAI, LangGraph, and AutoGen—promises automated workflows for coding, marketing, and content creation. Promoters brag about superior implementation and reduced oversight, yet these systems remain "token-hungry," heavily dependent on human direction, and prone to producing generic outputs requiring extensive editing. \*\*Core Thesis\*\*: AI lacks independent creativity; it recombines human-provided inputs and training data. Layered agents amplify efficiency in structured tasks but do not yield broadly patentable inventions or customer-ready original works without differential human creative input. Recent corporate budget reversals—where AI costs exceeded human labor equivalents—highlight the gap between hype and sustainable value. This version fully integrates: (1) patentability and creativity concerns, (2) current agentic bragging, and (3) real-world budget cuts at Microsoft, Uber, and peers. \### Current Trends & Bragging on Agentic Formulas (2026 Landscape) Developers and vendors heavily promote multi-agent orchestration as the "next big thing": \- \*\*Shift to Layered Agents\*\*: Moving beyond single agents to coordinated teams (researcher + coder + reviewer + validator) for parallel, end-to-end workflows in coding and marketing. \- \*\*Key Frameworks & Claims\*\*: \- \*\*CrewAI\*\*: Role-based "crews" for quick multi-agent prototypes; touted for marketing teams and collaborative creation with minimal setup. \- \*\*LangGraph\*\*: Graph-based stateful orchestration for complex, traceable workflows; praised for production reliability in agentic coding. \- \*\*AutoGen\*\*: Conversation-driven multi-agent debates; marketed for autonomous coding and async tasks with reduced human supervision. \- \*\*Bragging Points\*\*: Claims of 50%+ efficiency gains, "death of the senior dev," full autonomy, and massive ROI through token-intensive inter-agent communication. High consumption is framed as essential for "superior workload implementation." These systems "suck tokens" via extensive prompting and iteration while promising independence—yet users remain tied to directing them. \### Patentability Analysis \- \*\*Patentable Elements\*\*: Narrow technical innovations—such as novel orchestration protocols, memory-sharing mechanisms, or domain-specific error-handling in multi-agent graphs—may qualify if they demonstrate novelty, non-obviousness, and utility. Human inventorship is required. \- \*\*Major Limitations\*\*: Broad "layered agents for coding/marketing" claims risk ineligibility under the \*Alice\* abstract idea doctrine. Crowded prior art from existing frameworks limits enforceability. AI-generated outputs alone are not patentable. \- \*\*Outcome\*\*: While specific implementations might secure protection, generic agentic layering is unlikely to produce strong, independent patents usable by customers without ongoing human differentiation and creative input. \### Copyright, Creativity, and Human Input Dependency AI excels at pattern synthesis but lacks true originality or aesthetic judgment. Multi-agent outputs are derivative of human prompts, context, and training data. U.S. law requires human authorship for copyright; raw agent-generated code, copy, or designs is generally unprotectable and may carry training-data risks. \*\*Reality Check\*\*: Even with 7, 28, or 100 agents, results tie directly to human instruction. Users face the scenario of editing days of output after short runtimes, undermining claims of full autonomy. \### Practical Usability for Customers & Cost Realities \*\*Strengths\*\*: Strong for boilerplate, data processing, and structured decomposition in hybrid teams. \*\*Weaknesses\*\*: Fragility on edge cases, silent failures, governance demands, and high token costs. Developers often rewrite large portions due to quality gaps and "cognitive debt." \*\*Recent Budget Cuts Due to Overruns\*\*: Major firms have slashed access after AI (especially Claude-powered agentic tools) burned through budgets faster than human equivalents: \- \*\*Microsoft\*\*: Canceled most internal Claude Code licenses for thousands of engineers in its Experiences and Devices division (Windows, M365, Outlook, Teams, Surface). Rolled out late 2025, it became too popular/costly ($500–$2,000+ per engineer/month in heavy use). Engineers redirected to cheaper GitHub Copilot CLI by June 30, 2026 fiscal year-end. Costs exceeded planned budgets despite productivity gains. \- \*\*Uber\*\*: Exhausted its entire 2026 AI coding budget in just four months (by April) due to rapid Claude Code adoption (84–95% of engineers). Mo

We cut inter-agent token usage by 53-72%. Here is a lite version you can test yourself.

If you are building multi-agent systems, whether that is CrewAI, LangGraph, AutoGen, or a custom orchestration layer, you have probably noticed something wasteful: your agents talk to each other in full natural language. A simple task dispatch like "update row 39 in this Google Sheet to mark task E-4 as done, here are the credentials and auth details" eats 287 tokens. The actual information content of that message can be expressed in 89 tokens. Same information. Zero loss. 69% fewer tokens. That is one message. Multiply it across a session. In our multi-agent system (9 vertical team leads, dozens of specialist agents, a conductor orchestrating all of it), we exchange 40 to 80 inter-agent messages per session. At natural-language verbosity, that is 11,000 to 23,000 tokens spent on coordination alone. In a 200K context window, that is 5 to 11 percent of total capacity burned on overhead. Those wasted tokens are not just a cost problem. They are a capability problem. Every token spent on agent chatter is a token unavailable for actual reasoning, research, or output. At the margins, it is the difference between your agents completing a task and your system hitting a context wall mid-session. So we built a structured compression protocol for agent-to-agent communication. What we measured: We stress-tested it across six message categories. Every test maintained 100 percent information roundtrip fidelity. Test Case Before After Savings Fidelity Simple task dispatch 228 tok 126 tok 44.7% 100% Multi-step + dependencies 282 tok 146 tok 48.2% 100% Error escalation 234 tok 99 tok 57.7% 100% Status update 302 tok 136 tok 55.0% 100% Complex research 347 tok 132 tok 62.0% 100% Cross-team coordination 316 tok 150 tok 52.5% 100% Average 285 132 53.4% 100% For a heavy session (80 messages), that is roughly 13,600 tokens saved. Enough for 1-2 additional full agent interactions before the system needs to compact context. We also extended the approach to memory retrieval. Most agent memory systems serve full files into context when an agent queries for something. Ten results at 200-800 tokens each can load 7,600 tokens, most of it irrelevant to the current task. Our memory compression layer reduces that to about 670 tokens. A 91% reduction. The design draws from real research: TOON format, Google A2A Protocol, LangGraph, AgentPrune (ICLR 2025), LLMLingua-2 from Microsoft. It is not one novel idea. It is a practical synthesis of what the field already knows about structured compression. Try it yourself: We have published a lite version of the protocol on GitHub: https://github.com/puretechnyc/purebrain-skills/tree/main/skills/operations/liacl It covers the core message format for task dispatch and result returns, a subset of operation and domain shortcodes, and a quick-reference card you can paste into your agent prompts. Enough to test it in your own system and see what compression ratios you get. The full protocol (all message types, the complete shortcode library, memory compression, and ongoing improvements) is part of a larger agent orchestration platform we are building. But the lite version is genuinely useful on its own and free to use however you want. If you are running multi-agent pipelines and context management is a bottleneck, give it a shot. Curious to see what numbers other people get. submitted by /u/JaredSanborn [link] [comments]

My thoughts on Fable 5 so far

The only disappointing thing so far was that I ran Fable’s plan through Gemini and ChatGPT for a second opinion, and when I brought the feedback back to Fable, it agreed with several of their suggestions. Other than that, Fable has been great. I burned through the $100 plan credits in under 10 minutes and upgraded to the $200 plan. The security review alone was worth it—it helped me close a lot of few loops. Plus, with Claude resetting limits today and tmw my limits reset feels like a double blessing. 🙌 submitted by /u/Jacob_gago [link] [comments]

I built and shipped a full iOS app to the App Store without writing a single line of code by hand — using Claude Code (here's the whole pipeline)

Quick context so this is honest: I'm not a developer. I've spent ~10 years in IT, but never in a dev role — I can read a stack trace and reason about systems, but I don't write Swift or Python by hand. I built this on nights and weekends around my 9-5. The app is dynaimic, an AI personal trainer for iOS that generates adaptive workouts based on your goals, experience, and performance during the session. It's live on the App Store and free to try (premium tier for unlimited generation etc., but the core loop is free). The point of this post isn't really the app — it's that every line of code was produced by Claude Code, not me. Over a month I built a pipeline around it that let a non-dev ship real, reviewed, production features. Sharing the whole thing because most of it is reusable. The /team agent workflow (the core of it) Instead of one big "build me a feature" prompt, I split development into four specialized subagents that hand off to each other, each with its own system prompt and tight permissions: Business Analyst — turns my brief into a requirements doc with explicit acceptance criteria. It's not allowed to write code — only to spec. Master Architect — reads the requirements and writes a technical implementation plan. Also can't write Swift. Software Engineer — implements the feature code only. No tests, no docs. QA — writes the XCTest/Swift Testing cases for every acceptance criterion, runs them, and reports back a pass/bug list. If the QA or architect review finds problems, it loops back to the engineer. Forcing that separation (spec → design → build → verify) is a big part of why a non-dev can trust the output — no single agent gets to be confidently wrong unchecked. Routines: an autonomous issue → fix → review loop My favorite part. I set up Claude Code Routines (scheduled recurring agents) as a closed loop: One routine continuously sweeps the codebase for quality issues and opens GitHub issues for what it finds. A second routine picks up open issues, solves them, opens a PR, and iterates until it gets approval from the reviewers — then moves to the next one. So the backlog partially fills and clears itself. I wake up to PRs that were filed, fixed, and review-approved while I was asleep. Branch management & automated PR review Every task runs on its own feature branch, and agents work in isolated git worktrees so parallel work doesn't collide. Flow is feature/* → dev → main — always PR into dev, promote to main as one merge. The part I like most: PRs get reviewed automatically by Gemini, Codex, and Copilot. Claude Code reads their comments and iterates until it gets approval from the bots before I even look. As a non-dev, having three independent AI reviewers gate every merge is what makes me comfortable shipping code I didn't write. UI testing with Maestro Maestro runs the end-to-end UI tests on the simulator — real flows, not just unit tests. Honest caveat: this only runs on my MacBook, and I haven't been able to fold it into the "cloud" workflow yet So UI testing is the one step that still pins me to the laptop. Mobile-only development (no MacBook open) Aside from Maestro, this surprised me the most. Using Claude Code from the mobile app plus auto-deployment via Xcode, I implemented and shipped features without opening my laptop. I'd describe a feature from my phone, the agents would build/test/PR it, the bots would review, and the build would archive and deploy. Genuinely shipped features from bed. App Store screenshots via a custom Skill The App Store screenshots are generated by an ASO image-generation Skill I keep in .claude/skills. It reads the actual codebase to discover the app's real benefits, pairs each with a proof point, and renders ASO-optimized screenshots (Nano Banana Pro). One command → store-ready marketing images that reflect what the app actually does. Coach art (the one non-Claude part) The app has 3 AI coach characters. Their portraits were made with ChatGPT (image gen) and composited/cleaned up in Canva — so the visual identity was AI-assisted too, just outside the code pipeline. Gamification & achievements There's a tiered achievement system (bronze/silver/gold medals) with unlock overlays and per-coach achievement views. The backend computes what's unlocked and returns display-ready state; the iOS client just presents it with haptics + an unlock animation. Keeping the rules server-side meant one source of truth instead of logic scattered across the client. Architecture iOS: SwiftUI, MVVM + service layer, iOS 17+, dark/OLED theme. Deliberately a thin client — presentation, animation, haptics only. Auth: Supabase (JWT, auto-refresh on 401, Keychain storage). Backend: FastAPI (Python) for workout generation, analytics, and all business rules. Build: XcodeGen, actor-based API client for thread-safe concurrent requests. A hard rule I gave Claude: push all business logic to the backend. Anything a future Android or web client would

95% of the agents posted here would be dead within 24 hours of real production traffic and it's not the model's fault

I've spent 18 months building agent infrastructure and watched a lot of impressive demos. Here's the uncomfortable pattern: the demo works beautifully, the founder posts it, everyone claps and then it touches real users and quietly dies. Not because GPT-5 / Claude / whatever isn't smart enough. The model is almost never the problem anymore. It dies for three boring reasons nobody wants to talk about because they're not sexy: 1. AMNESIA. Your agent forgets everything the moment the process restarts. Crash, redeploy, pod cycle gone. So everyone hacks together a pickle file or a Postgres table, and it works until they have more than one agent and the memory needs to be shared. Then it's a mess. 2. SUICIDE BY LOOP. An agent has no idea it's in a loop. It will call the same tool with the same args 400 times and cheerfully burn $200 of tokens overnight, because it has no metacognition. It literally cannot detect its own failure. The defense has to live OUTSIDE the agent and almost nobody builds that. 3. NO BLACK BOX. The agent does something weird in front of a customer. They ask "why did it do that?" and you stare at logs that show inputs and outputs but no chain of reasoning. You have no answer. Trust evaporates. The whole industry is obsessed with the brain (the model and ignoring the nervous) system (memory, the immune system (loop detection), and the flight recorder (audit).) The unsexy truth: the next wave of agent winners won't have better prompts. They'll have better infrastructure. The model is commoditising. The reliability layer is where the actual moat is. I got annoyed enough about this that I built the layer myself persistent memory, automatic loop detection, and a tamper-evident audit trail, framework-agnostic (LangChain/CrewAI/AutoGen/OpenAI/MCP. It's at) octopodas.com if you want to tear it apart genuinely want feedback from people who've shipped agents and hit this wall. But honestly even if you never touch my thing: stop optimising the prompt and start thinking about what happens when your agent restarts, loops, or gets asked "why." submitted by /u/DetectiveMindless652 [link] [comments]

Multi-agent loop failures might be org-design failures, not prompt failures

Repo: https://github.com/jeongmk522-netizen/agentlas\_org\_chart Almost every multi-agent setup I have shipped or tested eventually hits the same wall. Agents bouncing between each other, reviewers asking for one more polish pass forever, research workers spawning indefinite subtopics, tool calls spiraling until the recursion limit kicks in. The framework docs usually call these "loops" and offer a max-iteration knob. I started suspecting the knob is treating a symptom, and the real issue is closer to how the agents are organized to begin with. The pattern that kept reappearing: when agents are designed as peers (researcher talks to analyst, analyst talks to writer, writer hands back to reviewer), nobody clearly owns the outcome. Every agent can keep asking another agent for more work. The graph has stop conditions on paper, but no single agent has the authority to declare "this is done, stop the run." That authority is implicit at best and gets diluted across the peer network. The hypothesis I am testing is that loop failures are organization-design failures more than prompt failures. The fix is to treat the agent network as an org chart with explicit reporting lines, not a chat room of peers. One accountable mission owner. One owner per workstream. Finite delegation depth. A typed return contract per worker (status, evidence, output, blockers, next action). Manager-only authority to reopen or terminate. Memory lives at the authority layers, specialists get scoped context only. The layers I have been working with are roughly chair, strategy office, division manager, team lead, and specialist worker, with QA and policy as separate staff offices that can reject and escalate but cannot themselves spawn unbounded new work. The reviewer-recursion failure mode in particular gets killed when verifiers are structurally allowed one reject pass, then must escalate. Frameworks already have most of the primitives. CrewAI has a hierarchical process where a manager validates worker output. LangGraph has supervisors, subagents, and an explicit recursion limit. OpenAI Agents SDK has manager-style orchestration distinct from peer handoffs. AutoGen has GroupChatManager. Anthropic's published research system is orchestrator-worker. What I think is underused is treating the manager not as a moderator for an open group chat but as a formal reporting line with authority to terminate. Two things I am unsure about. First, hierarchy can become its own bottleneck. If every decision routes upward, the chair agent becomes a single point of latency and a single point of failure. Second, escalation-as-feature only works if the top of the org chart has real stop authority. If the chair just calls another LLM that calls more LLMs, the loop just moved one floor up. submitted by /u/Hot-Leadership-6431 [link] [comments]

After 6 months of running AI agents in production I think the framework you pick barely matters. The thing that kills them is something else.

Going to get downvoted for this but here we go. I've been running about 30 agents in production for paying customers for the last 6 months and I'm convinced the framework debate is mostly a distraction. LangChain, CrewAI, AutoGen, OpenAI Agents SDK. Pick whichever one your team already knows. It doesn't matter as much as you think. What actually decides whether your agent works in production is something almost nobody talks about on this sub, and it isn't in the framework. Here's what I've seen kill more agents than every framework bug combined. The agent gets stuck in a loop. It calls the same tool 200 times in 4 minutes because something downstream returned ambiguous data and the LLM decided to retry forever. Your OpenAI bill goes from $3 a day to $400 in one afternoon. By the time you notice you've burned a grand. You can't even tell which agent did it because there's no audit trail. Your VPS reboots overnight for kernel patches. Every agent that was mid-task loses everything. Tomorrow morning the support agent has no memory of yesterday's tickets, the research crew has forgotten what they were investigating, the pipeline agent restarts from scratch. None of these are framework problems. They're memory and state problems. A customer complains the agent gave them wrong info three days ago. You go to debug. There's no record of what the agent saw, what it decided, or which tool calls it made. The framework didn't log that because frameworks aren't observability tools. You shrug and refund. You scaled to 15 agents working together. Two of them have conflicting beliefs about the same customer because their memory isn't shared. The customer gets two different answers in the same conversation depending on which agent replies first. You've been around enough times to realize the part you actually need isn't in the framework at all. What I think the real stack is. The framework just orchestrates LLM calls. Use whatever your team likes. It's the cheap layer. A persistent memory layer that survives crashes, restarts, and redeploys, so the agent has actual continuity. This is the layer that decides whether your agent is a toy or a product. Loop detection at the runtime layer, not bolted on as a wrapper around the framework. Something that catches your agent making the same call too many times in a row and stops it before the bill explodes. An audit trail of every decision the agent made, with a hash chain so you can prove later what happened when the customer pushes back. Screenshots and logs aren't enough when ten thousand dollars is on the line. Shared memory between agents in the same team so they're not having different conversations about the same customer. Cost tracking per agent so you actually know which one ran away with your budget. When I look at what makes the agents that survive production look different from the ones that died, it's never that they picked the right framework. It's that they had this layer underneath, either built carefully in-house or borrowed from somewhere. Full disclosure I'm building one of these tools. There are others. Mem0 and Zep and Letta in the memory space. Helicone and LangSmith in the observability space. Mix and match. Use one or build your own. Just please stop arguing about whether LangChain or CrewAI is better when the thing eating your production agents has nothing to do with either of them. What's been your worst production agent failure? Curious what other people have actually hit. I built a free tool that aims to solve most of this issue, what do you think? submitted by /u/DetectiveMindless652 [link] [comments]

Am I stupid for pivoting to Transparency with Agents over Memory after 6 months?

built an open source memory layer for ai agents. thought the obvious feature people would care about was persistent memory across restarts and shared memory between agents. that was the whole pitch. few months of actual user data in. most of the api calls aren't about memory at all. they're hitting the audit trail (what did the agent do and when), the loop detector (catching when an agent is stuck doing the same thing 20 times in a row), and the per-agent performance dashboard (which agent is wasting tokens, which one keeps crashing, who's drifting off goal). basically people don't really care that their agent remembers stuff across restarts. they care that they can see what it did and pull the plug when it goes off the rails. so i'm wondering if i should just flip the pitch. lead with "observability and accountability for ai agents" instead of "memory for ai agents". memory is table stakes at this point and mem0/zep already dominate that framing. loop detection + audit trail + performance scoring per agent feels like open territory. am i stupid? or is this the obvious move i somehow missed for 3 months submitted by /u/DetectiveMindless652 [link] [comments]

Android Auto gets a massive AI-powered upgrade with YouTube, Dolby Atmos, and immersive 3D Maps | Google’s next-gen in-car software is getting smarter and slicker

submitted by /u/ControlCAD [link] [comments]

I made a Claude skill that stops it from cloning whole repos when I just want one function

Kept hitting the same friction with Claude Code. I'd point at a GitHub repo and say "look at how this handles agent handoffs" — meaning, borrow the idea. Claude would git clone the whole repo, read 50 files, and ask which __init__.py was interesting. Or worse — it'd add the library to my package.json as a dependency. For one function. Suddenly I own the transitive deps, the CVE notifications, and a version pin I'll never upgrade. The actual problem: "use this library", "borrow an idea from this library", and "just steal that one function" deserve totally different workflows, and nothing was telling Claude which one I meant. So I wrote a skill — a single SKILL.md (surgical-github-extraction) that auto-triggers when I drop a GitHub URL as inspiration. The rule: Read the README first to get the shape. Pull 1–3 source files via raw URLs to see how the pattern is wired — prompts, schemas, the orchestration file. Never the whole repo. Pin to a commit SHA, save to /tmp (or %TEMP% on Windows). Lift the smallest useful unit — a function, a prompt, or just the pattern. Rewrite in your style. Cite the source SHA. Two concrete cases this week: Pointed it at TradingAgents (a multi-agent trading repo) asking "can we use this pattern for a job-applier?" → README plus a few agent/prompt files, proposed an analogue (JobFitAnalyst + Critic arguing against). Nothing copied into my project. Asked it to "steal the exp backoff from litl/backoff" → fetched one file (_wait_gen.py), extracted the 8-line generator, rewrote inline in my style with a provenance comment. No pip install. Sibling skill: code-graft — for when a one-off snippet isn't enough but a runtime dep is too much. Vendor only the slice of a library you use into your project, trim the rest, re-sync selectively from upstream. Think "I want one tokenizer out of HuggingFace transformers without the 2GB." Why a Skill and not an MCP: Pure discipline on tools Claude already has (WebFetch, curl, gh, Read). MCPs ship new tools; Skills ship instructions. Same shape as Anthropic's own mcp-builder — that's a Skill, not an MCP. MIT-licensed, single file install: `mkdir -p ~/.claude/skills/surgical-github-extraction` curl -fsSL https://raw.githubusercontent.com/jeet-dhandha/jd-skills/main/skills/surgical-github-extraction/SKILL.md \ -o ~/.claude/skills/surgical-github-extraction/SKILL.md Both skills (jd-skills collection): https://github.com/jeet-dhandha/jd-skills Curious if anyone has hit this and solved it differently — especially failure cases where the skill picks the wrong path (concept vs. snippet vs. full vendor). Issues welcome. submitted by /u/hone_coding_skills [link] [comments]

5 enterprise AI agent swarms (Lemonade, CrowdStrike, Siemens) reverse-engineered into runnable browser templates.

Hey everyone, There is a massive disconnect right now between what indie devs are building with AI (mostly simple customer support chatbots) and what enterprise companies are actually deploying in production (complex, multi-agent swarms). I wanted to bridge this gap, so I spent the last few weeks analyzing case studies from massive tech companies to understand their multi-agent routing logic. Then, I recreated their architectures as runnable visual node-graphs inside agentswarms.fyi (an in-browser agent sandbox I’ve been building). If you want to see how the big players orchestrate agents without having to write 1,000 lines of Python, I just published 5 new industry templates you can run in your browser right now: 1. 🛡️ Insurance: Auto-Claims FNOL Triage Swarm Inspired by: Lemonade’s AI Jim, Tractable AI (Tokio Marine), and Zurich GenAI Claims. The Architecture: A multimodal swarm where a Vision Agent assesses uploaded images of car damage, a Policy Agent cross-references the user's coverage database, and a Fraud-Detection Agent flags inconsistencies before routing to a human adjuster. 2. ⚙️ Manufacturing: Quality / Root-Cause Analysis Swarm Inspired by: Siemens Industrial Copilot, BMW iFactory, Foxconn-NVIDIA Omniverse. The Architecture: A sensor-data ingest node triggers a diagnostic swarm. One agent pulls historical maintenance logs via RAG, while a SQL Agent queries the parts database to identify failure patterns on the assembly line. 3. 🔒 Cybersecurity: SOC Alert Triage & Response Inspired by: Microsoft Security Copilot, CrowdStrike Charlotte AI, Google Sec-Gemini. The Architecture: The ultimate high-speed parallel routing swarm. When an anomaly is detected, specialized sub-agents simultaneously investigate IP reputation, analyze the malicious payload, and draft an incident response ticket for the human SOC analyst to approve. 4. 📚 Education: Adaptive Socratic Tutor & Auto-Grader Inspired by: Khan Academy Khanmigo, Duolingo Max, Carnegie Learning LiveHint. The Architecture: A strict "No-Direct-Answers" routing loop. The Student Agent interacts with the user, but its output is constantly evaluated by a hidden "Pedagogy Agent" that ensures the AI is guiding the student to the answer via Socratic questioning rather than just giving away the solution. 5. 📦 Retail/E-commerce: Returns & Reverse-Logistics Swarm Inspired by: Walmart Sparky, Mercado Libre, Shopify Sidekick. The Architecture: A logistics orchestration loop that analyzes a customer return request, checks inventory levels in real-time, determines if the item should be restocked or liquidated (based on shipping costs vs. item value), and autonomously issues the refund. How to play with them: You don't need to spin up Docker containers or wrangle API keys to test these architectures. You can load any of these 5 templates directly into the visual canvas, see how the data flows between the specialized nodes, and try to break the routing logic yourself. Link: https://agentswarms.fyi/templates submitted by /u/Outside-Risk-8912 [link] [comments]

I built a Pokémon-styled multi-agent dashboard to manage all Claude Code sessions

Like many others here, I got frustrated with managing all my different claude/codex sessions, so i built Pokegents, which is an open source multi-agent workspace for coding agents. It has a Pokemon-themed dashboard/chat interface plus a local orchestration server for managing agent sessions (currently supports Claude Code in iTerm2, plus Claude and Codex through ACP-based chat runtimes), persistent agent identities, mcp messaging between agents, notifications, session cloning, and more. This was mostly a vibe-coded side project, but I've been using it constantly in my day-to-day workflow as an engineer, and its helped me parallelize a lot of my work. My coworkers make fun of me because it looks like I'm just playing Pokemon all day haha. I made it open source and sharing in case it might be useful or just fun for anyone to use (links in comment below). submitted by /u/girishkumama [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 hands-free voice AI that sends emails mid-conversation — and that's just one feature. Here's everything AskSary can do.

https://reddit.com/link/1symbsj/video/k2no3zfgq1yg1/player Been building AskSary solo for a while. Just shipped hands-free voice email - you're mid-conversation with an AI and you say "send an email to [john@example.com](mailto:john@example.com) subject X body Y" and it pre-fills the Gmail modal automatically. One tap sends. Powered by OpenAI Realtime API, works in 22 languages. But that's just the latest feature. Here's the full picture: Every major model in one place GPT-5-Nano, GPT-5.2, GPT-5.2 Pro, O1 Reasoning, Claude Sonnet 4.6, Grok 4, Gemini 2.5 Flash, Gemini 3.1 Pro, Gemini Ultra, DeepSeek V3, DeepSeek R1 - with smart auto-routing or manual override. Pro-Active Personalisation On every login the AI reads your previous conversations and sends the first message itself - asking if you want to continue or start fresh. Before you type a single word. Persistent Cross-Model Memory Start a conversation with Claude on your phone, open your laptop, switch to GPT-5.2 - it already knows what you discussed. No copy-pasting, no summaries. Just works. Knowledge Base - RAG Upload docs up to 500MB per file, unlimited uploads, chat with them across any model via OpenAI Vector Store. Your files stay in context forever. Integrations Google Drive, Gmail, Google Calendar, Notion - access files, get email and calendar summaries, use them in chat or push them to your Knowledge Base. Generation Tools Image Gen - GPT-Image-1 and Nano Banana Pro Flux Image Editor - full editing suite with visual history Video Studio - Luma Dream, Veo 3.1, Kling 1.6 / 2.6 / 3, up to 10 second AI videos with audio Music Studio - 30 second tracks with custom or AI lyrics via ElevenLabs, visualizer built into chat 3D Model Studio - Meshy with STL export (deploying soon) Video Analysis - upload up to 500MB or paste a YouTube link Developer and Builder Tools Vision to Code - screenshot any UI, get live editable code Web Architect - build full web apps from a single prompt Game Engine - build and prototype games with AI Code Lab - split screen live coding with SQL Architect, Bug Buster, Git Guru, Regex Generator, Test Genie and more Tavily web search across all models Voice and Audio Real-time 2-way voice chat - 8 voices, near-zero latency WebRTC Podcast Mode - two AI voices, switchable, near-zero latency, downloadable as MP3 Voiceover Studio, Voice Notes, Voice Tuner Productivity and Content Slides, Docs and File Tools Pro Writer and Content Library Social Tools - Hook Generator, Video Script, Hashtag Creator, Idea Spark Business Suite - Pitch Deck Builder, Deep Analytics, Legal Eagle, Maths Solver Daily Briefing and Market Watch CV Creator, Email Polisher, Cover Letter Builder, TL;DR Bot Share conversations or snippets with anyone Platform Extras 30+ live interactive wallpapers and themes Custom Agents and Personas Folder organisation and Smart Search across chat history Media Manager Gallery - all your generated content in one place Fully customisable UI in 26 languages with full RTL support The Stack Frontend: Next.js, Capacitor (iOS + Android), Vanilla JS / React Backend: Vercel serverless, Firebase / Firestore, Firebase Admin SDK AI: OpenAI, Anthropic, Google, xAI, DeepSeek Generation: Luma AI, Kling via Replicate, Veo via Replicate, ElevenLabs, Flux via Replicate, Meshy Integrations: Google Drive, Notion, Tavily, OpenAI Vector Store, Stripe, CloudConvert, Sentry Rendering: Mermaid, MathJax Platforms: Web, iOS, Android, Apple Vision Pro What you get free just for creating an account (1,000 credits/month, rolling): Unlimited chat on GPT-5 Nano, Gemini Flash and DeepSeek V3 - no daily limits, zero credit charge 25 image generations via GPT-Image-1 and Nano Banana Pro - 40 credits each 8 image edits via Flux Studio - 80 credits each 2 song generations via ElevenLabs - 350 credits each 2 video generations via Luma Dream and Kling - 350 credits each ~70 messages on Claude Sonnet 4.6, GPT-5.2, Grok 4, Gemini 3.1 Pro and DeepSeek R1 - 15 credits each No credit card required. Built entirely solo. No CS degree, no team, no funding. Started because I asked an AI to build me a chatbot and it failed - so I built my own. Accepted to LEAP 2026 in Saudi Arabia along the way. Happy to answer anything about the build. asksary.com submitted by /u/Beneficial-Cow-7408 [link] [comments]