Playground AI

Playground AI AI

Playground AI AI

Playground AI AI

Playground AI AI

Playground AI AI

Playground AI AI

Playground AI AI

Playground AI AI

Playground AI AI

Playground AI AI

I used codex with gpt 5.5 from azure

how to reduce the input token on openai codex? submitted by /u/Signal_Tax241 [link] [comments]

I built a local MCP gateway (Conduit) almost entirely with Claude Code, what it does and what I learned

Since this is r/ClaudeAI, the how might be as useful as the what. I built this with Claude Code in the last 48 hours. What it is: a local desktop app that puts all your MCP servers behind one gateway, so they work across Claude Desktop, Claude Code, Cursor, and other tools. You set up and authenticate each server once instead of reconfiguring it in every client. It's free and open source (MIT), Windows now, macOS/Linux coming. Why I built it: I use Claude Desktop + Claude Code plus a couple other tools, and managing MCP servers across all of them was painful, the same servers configured in each app, API keys in plaintext, and every agent buried under hundreds of tool definitions. What it does: Lazy discovery: your agent sees 3 meta-tools instead of 400 and searches/calls on demand, so context stays small Keys live in your OS keychain, not config files Per-agent profiles, an audit log, and a built-in tool playground How Claude helped (the interesting part): It's a Tauri app, Rust gateway + React frontend, and I'm not a strong Rust dev. Claude Code wrote most of the gateway, the MCP protocol handling, the OAuth 2.1 flow, and the keychain integration. The hardest part was Windows-specific bugs, and it diagnosed each from the symptoms: MSIX path virtualization (packaged apps like Claude Desktop silently redirect %APPDATA% into a sandbox, so the app was reading the wrong config) a stdio server with no read timeout deadlocking the whole health check OAuth callback-port collisions when re-authing quickly What I learned: if you're routing many MCP servers through one place, "lazy discovery" (a few meta-tools the agent searches, instead of exposing all of them) is the thing that keeps context usable, most clients silently drop tools past ~40-128 anyway. Free to try: conduit.southforgeai.com · code: github.com/tsouth89/conduit Happy to answer anything about building it with Claude Code. And genuinely curious, what's the most annoying part of your MCP setup right now? edit: added a 30 second demo https://reddit.com/link/1uaj131/video/9b6vsvkvnf8h1/player submitted by /u/kydude [link] [comments]

Claude Sonnet hits 100% comprehension on a data format it's never seen. Opus scores 96.2%. We tested 10 models across 3 providers.

I built a wire format called GCF and tested whether LLMs could read and write it without any prior training. I sent 10 models the same payload: 500 symbols, 200 edges. Asked 13 extraction questions with no format instructions and no system prompt. Just the raw data and a question. Below are the results \"1,300+ evaluations across 10 models and 3 providers. GCF wins comprehension (90.7% vs 53.6% JSON) and generation (5/5 on every frontier model, zero training) Claude results (comprehension): Model GCF TOON JSON Claude Sonnet 4.6 100% 73.1% 53.8% Claude Opus 4.6 96.2% 84.6% 73.1% Claude Haiku 4.5 96.2% 69.2% 57.7% Sonnet hits 100% on every run. Opus and Haiku average 96.2%. JSON averages under 62% across the Claude family at 500 records. The failure modes are different too. When GCF gets an answer wrong, it's off by 1-2 (misread a section header count). When JSON gets an answer wrong, Opus spends 143 lines manually enumerating symbols and still gets the wrong number. The [full artifact is published](https://github.com/blackwell-systems/gcf/blob/main/eval/results/artifacts/opus-json-enumeration-failure.md). Generation (can the model write it?): Model GCF TOON JSON Opus 4.6 5/5 0/5 5/5 Sonnet 4.6 5/5 2-3/5 5/5 Haiku 4.5 5/5 1-3/5 5/5 All three Claude models produce valid, decoder-parseable GCF output with a 3-line primer. Zero prior training. Opus scores 5/5 with zero variance across 2 runs. Full results across all 10 models (3 providers): Metric GCF TOON JSON Average Accuracy 90.7% 68.5% 53.6% Input Tokens (500 symbols) 11,090 16,378 53,341 23 comprehension runs, 28 generation runs, 1,300+ total evaluations across Anthropic, OpenAI, and Google. Full methodology and raw logs published. Comprehension accuracy at 500 symbols across 10 models. Claude Sonnet hits 100%. GCF > TOON > JSON on every model. The eval is open source and reproducible: go test -run TestComprehension -v -timeout 0 [Full benchmark data](https://gcformat.com/guide/benchmarks.html) [GCF spec + 6 implementations](https://github.com/blackwell-systems/gcf) [Playground (live three-way comparison)](https://gcformat.com/playground.html) [Whitepaper (DOI: 10.5281/zenodo.20579817)](https://doi.org/10.5281/zenodo.20579817) submitted by /u/blackwell-systems [link] [comments]

Tested a batch of free AI tools this week, honest verdicts on Claude, MiniMax, K2Think, and a couple comparison playgrounds

Spent some time poking at free tiers across a few tools. Here's what actually held up and where the catches are. **Claude (Sonnet 4.6 on free tier)** Still the one I reach for when I want writing that doesn't read like a press release, or code that actually compiles. I trust it more for anything where being quietly wrong is worse than being loudly wrong. The catch: free tier is stingy. You hit limits fast on busy days, need a phone number to sign up, and there's no warning before it cuts you off. There's a browser extension that tracks usage so you can see the wall coming. My approach: use it for the hard 20% of the day, let a free model handle the rest. **MiniMax Agent** A free swing at what Devin and Manus charge for, give it a prompt and it writes, runs, and debugs the code itself. Replaces the copy-paste loop between ChatGPT and your editor for longer multi-step jobs. Catch: it burns credits fast, and complex tasks still go off the rails without warning. It's confidently wrong in ways that can cost you more time than just doing it yourself. Worth a few free runs to see if it actually finishes a task, but I wouldn't cancel anything for it yet. **K2Think** A 32B reasoning model from MBZUAI and LLM360, positioned as a free alternative to o1 / DeepSeek R1 for step-by-step reasoning, math, and logic. Note: this is NOT Kimi from Moonshot despite the name confusion. Honesty flag, the benchmark claims got real pushback, there's an HN thread literally titled "Debunking the Claims of K2-Think," so take the leaderboard numbers with salt. Still, a fully open 32B reasoning model is nice to have around. Try it on something gnarly and see if the reasoning holds. **Indic LLM Arena** A side-by-side chat playground from AI4Bharat (includes Gemini 3.5 Flash), built for benchmarking Indian languages. Usage is unlimited, which I double-checked because that's rare. No save history, and it's clearly tuned for Indic languages. If you write in Hindi, Tamil, or Bengali, easiest free way to see which model actually handles your language. **Together.ai playground** Rotating menu of open models in one place, GLM-5.1, Kimi K2.6, Deepseek-V4, so you're not juggling five tabs. Cap is 110 messages/day split across whatever models you pick. Plenty for tinkering, not enough to run a side project on. Got a 429 when I tried to load it, so expect occasional traffic jams. Worth a bookmark just to track which open model is winning this month. The one that actually made me cancel a paid subscription this batch was Claude replacing my main text workflow, which almost never happens. I write a weekly newsletter doing exactly this. DM me or drop a comment if you want the link. submitted by /u/Tall_Roof_4382 [link] [comments]

Building an observable MCP proxy with HITL and policy enforcement

We’ve been experimenting with a different direction for AI agents: trusted execution. Instead of only focusing on connecting more tools, we’re building a policy-aware MCP proxy layer that can: inspect tool calls validate execution apply policies support HITL approval trace agent workflows block unsafe actions before execution The goal is to create a safer execution boundary for MCP-based agents. Built with Spring AI. Local-first and self-hosted. Docs: https://spring-ai-community.github.io/spring-ai-playground/ submitted by /u/kr-jmlab [link] [comments]

Is this even real ?

I randomly came across this and honestly I can’t tell if it’s real or one of those AI demos that looks impressive but doesn’t actually work. From what I understand, it’s claiming you can fine-tune models, do image training, test them in a playground, and deploy them as an API from a phone. That sounds a little too convenient, which is why I’m skeptical. I haven’t tried it myself yet, but I’m curious if anyone here has. submitted by /u/Raman606surrey [link] [comments]

Building a monokernel for LLM inference on AMD MI300X - up to 3,300 output tokens/s per request [P]

We built a monokernel that runs the full decode sequence as one GPU-resident program on AMD MI300X, with some neat optimizations. The die topology is central to the result, we map memory access patterns to the physical layout, compute units group by their associated IOD, and the hardware runs at its full design performance. Up to 3,300 output tokens/s per request, batch size 1, no speculative decoding, no quantization, on 8x MI300X. This preview runs a small 2B coding model, and we plan to support large frontier MoE in the future. Technical deep dive: https://blog.kog.ai/building-a-single-kernel-latency-optimized-llm-inference-engine-on-amd-mi300x-gpus Try it: https://playground.kog.ai submitted by /u/averne_ [link] [comments]

Why We Build

One silver-lining to the dead internet we're living in, today, is that it's very quickly teaching us that we can't rely on our senses as much as we believe we can. It's not healthy to always live in skepticism, but it is necessary in a World where you don't know what's up or down anymore. That's why we need great minds to focus their attention on solving the problems associated with credible information sharing without it becoming some centralized playground designed to look like the free-flowing exchange of ideas. If we don't solve for that, then I guess we're heading into a future that a small handful of people want because elections or public opinion will no longer matter. One of the biggest focuses in AI should be in figuring out how to get it to provide deep credible knowledge in specific domains that can be best applied to the problems we're trying to solve. Sure, it can do this with enough fenagling, but what I really mean is having something easy for everyone to use like Perplexity or Gemini, only it doesn't simply find consensus information from the internet using all these black box methods that are owned by major corporations. Instead, it should use direct knowledge from domain experts who structure and cite their material and as users, we should be able to backtrack all of it, including the original author. And all of this should be achievable by simply engaging with a chatbot agent that can reliably go out and help me discover all of these things. Also, we shouldn't have to simply trust that the application works. We should be able to go in and see exactly how it's working. This way, the public can audit the systems we're relying on for grounding our worldviews. That, to me, is where we should be if we really want to break from the chains of propaganda and reclaim our genuine thoughts about how we ought to live. The alternative independent media space was co-opted long ago and now all of the feeds keep us in a state of perpetual dislocation from our friends, family, communities, new solutions, and better approximations to the truth. We exist in a walled-off digital pasture. But if regular people who are smart and capable enough decide to leverage this new technology, then we can break through the fencing and finally live in a world where discovery-based researching and learning can be easier than Google, which could eventually individuate society again, like how it was before, instead of keeping us clustered into specific groups based on our viewing preferences. That's why my brother and I got into this business. Yeah, sure, we also wanna make a buck so we can retire with dignity. That's true. But the drive has always stemmed from wanting to figure out a better way for people to share hidden insights and create things that are bigger than they thought they could handle. We have a long way to go, but we're making the first small steps, even if it isn't obvious, just yet. Bottom line, though? Humanity must figure out a way to help us master the means and methods of discovery-based knowledge acquisition, execution, and immediate distribution of information based on relevancy and needs from those who search instead of those who passively soak information in from the curated feeds. And all of this needs to be easy enough for a 12 year-old to do. If anyone else is working on this problem, we'd love to hear your thoughts, even if it's through a DM. We're living in the most exciting times, but with adventure, comes danger. So maybe, idk. Let's make it more fun and less hazardous, so that we can, at least, live long enough to re-tell this great story that we're all a part of. submitted by /u/CyborgWriter [link] [comments]

I built a zero-code visual client to test remote MCP servers instantly (Tested with Cloudflare’s free MCP).

Hey everyone, The Model Context Protocol (MCP) is amazing for standardizing how agents talk to data, but I got incredibly frustrated every time I wanted to quickly test a new remote MCP server. Writing custom client-side boilerplate or wrestling with CLI tools just to see if a tool actually exposes the right schema is a massive time sink. So, I built a native MCP client directly into the visual canvas of AgentSwarms. You can now test any remote MCP server entirely in the browser without writing a single line of code. Here is the workflow I just tested with Cloudflare: Cloudflare released a free MCP server for their documentation. Instead of building a local client to test it: I dropped their SSE URL into the new MCP Servers integration in AgentSwarms. The canvas immediately connected and extracted the available tools (e.g., cloudflare-docs-search). I wired that tool up to a basic agent and started asking complex infrastructure questions in natural language. The agent successfully used the MCP tool to pull live docs and synthesize an answer. Why this is useful for AI devs: If you are building your own MCP servers, you need a fast way to visually test if your endpoints are exposing tools correctly and if an LLM can actually route to them properly. This gives you an instant, visual debugging playground. It handles the SSE connection, tool extraction, and LLM routing automatically. It’s completely free to play with in the browser. I'd love for anyone building MCP servers right now to plug their endpoints in and see how it works. Link: https://agentswarms.fyi/mcp submitted by /u/Outside-Risk-8912 [link] [comments]

I built a zero-code visual client to test remote MCP servers instantly (Tested with Cloudflare’s free MCP).

Hey everyone, The Model Context Protocol (MCP) is amazing for standardizing how agents talk to data, but I got incredibly frustrated every time I wanted to quickly test a new remote MCP server. Writing custom client-side boilerplate or wrestling with CLI tools just to see if a tool actually exposes the right schema is a massive time sink. So, I built a native MCP client directly into the visual canvas of AgentSwarms. You can now test any remote MCP server entirely in the browser without writing a single line of code. Here is the workflow I just tested with Cloudflare: Cloudflare released a free MCP server for their documentation. Instead of building a local client to test it: I dropped their SSE URL into the new MCP Servers integration in AgentSwarms. The canvas immediately connected and extracted the available tools (e.g., cloudflare-docs-search). I wired that tool up to a basic agent and started asking complex infrastructure questions in natural language. The agent successfully used the MCP tool to pull live docs and synthesize an answer. Why this is useful for AI devs: If you are building your own MCP servers, you need a fast way to visually test if your endpoints are exposing tools correctly and if an LLM can actually route to them properly. This gives you an instant, visual debugging playground. It handles the SSE connection, tool extraction, and LLM routing automatically. It’s completely free to play with in the browser. I'd love for anyone building MCP servers right now to plug their endpoints in and see how it works. Link: https://agentswarms.fyi/mcp submitted by /u/Outside-Risk-8912 [link] [comments]

Introducing local SQL & BI Agent to AgentSwarms sandbox. Upload a CSV and chat with your data (Text-to-SQL + Auto-Charts).

Hey Everyone, A lot of you have been playing around with AgentSwarms (the Agentic AI learning platform We've been building). We wanted to add a fast way to test data-analysis without having to build a complex node graph, so We just shipped a dedicated SQL & BI Agent workspace right inside the app. You can drop in a CSV and just start asking questions about your dataset in natural language. Here is exactly what the agent does: Text-to-SQL: You ask a question (e.g., "What were the top 5 regions by revenue?"), and the agent translates your intent into an exact SQL query to run against your dataset. Auto-Visualization: Instead of just spitting out a raw JSON array or a boring text table, the BI agent analyzes the shape of the returned data, synthesizes a natural language summary, and automatically renders the appropriate visualization (bar chart, line graph, pie chart, etc.) right in the chat UI. Why I built this: I was tired of writing custom Pandas scripts or wrestling with Jupyter notebooks every time I just wanted to quickly visualize a dataset or test an AI's analytical capabilities. This gives you an instant playground to chat with your data and see immediate, visual results. It's free to play with right in the browser. I'd love for the data nerds here to try it out. What kind of complex aggregations or data questions do you usually struggle to get AI to answer correctly? submitted by /u/Outside-Risk-8912 [link] [comments]

Tired of Claude "losing the plot" in long Projects? I built a Context Snapshot tool to reset your threads.

I’ve been running into a massive issue with long-running Projects: the more I chat, the faster I hit my message limits, and Claude eventually starts forgetting the specific constraints I set 50 messages ago. I built a tool called Lakon to solve this. It basically acts as a "Save Game" button for your AI chats. You paste in your messy, bloated chat log, and it runs a Map-Reduce pipeline to extract: The exact project state and goals. Hard technical constraints and decisions made so far. Open tasks. It hands you back a dense "Continuation Prompt." You can then start a fresh thread, paste that prompt, and Claude is 100% caught up without the baggage of 100k tokens of old history. It saves your message limit and keeps the model focused. It’s completely free. I’d love to hear if this helps your workflow! Playground: Lakon Web submitted by /u/PriorNervous1031 [link] [comments]

Introducing AI finetuner, Source available and free Claude skill to fine tune your vibe coded UI with live preview

​ Fine-tuning UI with AI right now: "Make the shadow softer." "Stronger." "No, less." "Go back." "A bit more." 17 messages later, you've spent more tokens than the shadow is soft. I built something that breaks the loop. AI Fine-Tuner — free, source-available — a plugin that teaches AI coding agents to stop chatting and hand you an actual GUI for your component. Sliders. Color pickers. Live preview. Drag until it feels right. The AI agent automatically opens the editor window for you on your default browser once ready. Then the magic part: you click one button. The tuner outputs a structured handoff with your exact tuned values mapped to their targets in your code. Paste it back to your AI — it reads the mapping, opens your source, and applies everything precisely. No CSS guesswork, no syntax translation, nothing for you to interpret. Why it's not just another slider playground: Bespoke controls — no raw CSS names Sliders are named in plain English: "Glow softness", "Card lift", "Hover intensity" — not "box-shadow-spread-radius" A single slider can drive multiple properties at once. The AI doesn't expose CSS to you; it wires meaningful, human-named controls to your element. 3 prebuilt editor templates — guaranteed polish, every time The AI doesn't design the editor. It picks one of three prebuilt templates and fills in your component: - single.html — 1 control, full-screen preview - small.html — 2-4 controls, preview + bottom grid - full.html — 5+ controls, grouped sidebar + preview Slider chrome, color picker, layout, animations, infinite canvas with zoom/pan — all pre-built. No "the AI generated an ugly panel" failure mode. And once it's open, you tune in pure browser JS — no AI sitting in the loop per drag. Color picker + hex paste Pick it or paste it. Done. Animation tuning Not just static styles — timing, easing, keyframes too. Works on ANY platform — language-agnostic Flutter, SwiftUI, React Native, Tailwind, vanilla CSS, SVG — the AI is meta-prompted to rebuild your component in HTML/CSS for the tuning preview (the web is where sliders work). When you copy back, the AI applies the tuned values to your real source, in your component's original framework. You never leave Flutter to tune Flutter. Infinite canvas + multiple previews Drop 5 variations side-by-side and tune them together. The template is a starting point — experiment freely. Contextually named presets Every tuner ships with thoughtful presets ("Subtle," "Bold," "Brutalist," whatever fits) so you can ping-pong through variations in one click. No new software It's a skill, not an app. Full install guides for Claude Code. One command and you're in. Website and Live demos: https://muhamadjawdatsalemalakoum.github.io/aifinetuner Free. Source-available. #AI #DeveloperTools #ClaudeCode #BuildInPublic #OpenSource #AITools #FrontendDev submitted by /u/keonakoum [link] [comments]

Testing AI modeling skills

So I am currently testing how useful AI models can be in day to day workflows, and went why not compare 3 models and see how good they are at replicating my work. The goal was simple they were asked to replicate one of the kitchen cabinets I am designing for kitchen project and well they all went differently about the task. Claude Sonnet 4.6: Claude was the fastest by a margin, it added the accessories on the first request and made user parameters, but they did not update the model when changed they were just there. Sadly I was testing the free version so immediatly ran out of limits after the one request. ( https://claude.ai/share/7d06b07c-b753-44cf-a253-f065e7a45448 ) ChatGPT 5.5 high ( in Codex ): Codex took a while and had trouble working in parametric mode so it decided to work in direct modelling, and well it did good adding the materials first try for wood (points for embedding its name on the front panel in a funny way). Sadly not including parameters was a let down and kinda useless for my workflow. Gemini 3.1 Pro ( Gemini CLI ): Now Gemini CLI had a lot of trouble getting to get the pro model working at one point it took 45 mins before I gave up and had to try few hours later. However, Gemini did the best job of all 3 it created user parameters, and changing them would update the model. Gemini did need a second prompt as it did not add the accessories on the first prompt and had to be asked to add them. Some Personal Takeaways: All 3 models did get the dimensions for each panel correctly and their relative location to each other. All 3 models did not look deeply enough at how the panels join to each other where the back panel is in the correct place, but its not cut correctly and not making the grooves on the side panels. Finally, I dont think AI will be able to model anytime soon but if used like a dynamic add on that can do tasks they work great. Ik claude opus is the superior model and I plan to test it soon enough in a different more refined workflow, this was just to mess around. The Prompt: I want you to create a replica from a file called BC_S1. do not copy and paste I want to test your modelling skills. The file should be saved in the AI playground project and I need you to add your AI name to the file. My Model Claude ChatGPT Gemini submitted by /u/Electrify338 [link] [comments]

Claude architecture mock test..

Built a new update for Claude Playground 🚀 Added Mock Tests for learners preparing for the Claude Architecture exam — users can now validate their understanding and test their learning directly on the platform. The goal of Claude Playground remains simple: Learn by doing, not just by reading. 🌐 Try it here: www.claudeplayground.in For the best experience, use desktop mode. Still in Phase 1, and I’m actively improving it based on feedback. Would love your thoughts if you try it out — your feedback will directly shape the next iterations. Claude Anthropic Anthropic #AI #Claude #LLM #RAG #EdTech #BuildInPublic submitted by /u/Zestyclose_Guitar951 [link] [comments]