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

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]

Claude playground

I’m creating Claude Playground — an interactive platform where developers can learn Claude and agent workflows by doing, not just reading. It helps you understand: • Which model to use and when • Expected response speed and delays • How different workflows behave in practice • AI architecture patterns before spending on API credits Currently in Phase 1, if you run it without a Claude API key, it provides dummy responses while still explaining exactly how the real workflow would behave behind the scenes. The goal is simple: Make learning AI workflows practical and hands-on. Try it here: https://www.claudeplayground.in/ Best experienced on desktop for now. submitted by /u/Zestyclose_Guitar951 [link] [comments]

Text-to-image is easy. Chaining LLMs to generate, critique, and iterate on images autonomously is a routing nightmare. AgentSwarms now supports Image generation playground and creative media workflows!

Hey everyone, If you’ve been building with AI agents, you know that orchestrating text is one thing, but stepping into multimodal workflows (Text + Image + Vision) is incredibly messy. If you want an agent to act as a "Prompt Engineer," pass that prompt to an "Image Generator," and then have a "Vision Agent" critique the output to force a re-roll—you are looking at hundreds of lines of Python boilerplate, messy API handshakes, and a terrible debugging experience when the loop breaks. I recently launched AgentSwarms, an in-browser sandbox for learning Agentic AI. Today, I am pushing a massive update: The Image Playground. What the feature actually does: Instead of fighting with code to test multimodal architectures, you can now drag, drop, and wire up text and image agents on a visual canvas to build creative workflows. Image Generation Nodes: Wire any text-output agent directly into an Image Node to autonomously generate visual assets. Vision AI Integration: Route generated images back into a Vision Node. You can instruct an agent to physically "look" at the generated image, evaluate it against your initial prompt, and trigger a loop to fix it if it hallucinated. Real-Time Data Flow: You can actually watch the payloads (the text prompts and the image outputs) flow across the node graph in real-time. submitted by /u/Outside-Risk-8912 [link] [comments]

I'm all in to try and learn about what the future will look like

I'm not a computer scientist and worry that I have a lot of catching up to do to become relevant in this world that is evolving too quickly. To that end I want to learn as much as I can on becoming ai native through Claude. I have two questions 1) if I want to run things smoothly are there specific laptop specs that can withstand a lot of work or is building a desktop the only way? 2) are there any good resources people recommend to start from the ground up? It feels like it's an endless playground but I don't want to drown in the sandbox Cheers! submitted by /u/BioLink25 [link] [comments]

Is the ds/ml slowly being morphed into an AI engineer? [D]

Agents are amazing. Harnesses are cool. But the fundamental role of a data scientist is not to use a generalist model in an existing workflow; it's a completely different field. AI engineering is the body of the vehicle, whereas the actual brain/engine behind it is the data scientist's playground. I feel like I am not alone in this realisation that my role somehow got silently morphed into that of an AI engineer, with the engine's development becoming a complete afterthought. Based on industry requirements and ongoing research, most of the work has quietly shifted from building the engine to refining the body around it. Economically, this makes sense, as working with LLMs or other Deep Learning models is a capital-intensive task that not everyone can afford, but the fact that very little of a role's identity is preserved is concerning. Most of the time, when I speak to data scientists, the core reply I get is that they are fine-tuning models to preserve their "muscles". But fine-tuning is a very small part of a data scientist's role; heck, after a point, it's not even the most important part. Fine-tuning is a tool. Understanding, I believe, should be the fundamental block of the role. Realising that there are things other than "transformers" and finding where they fit into the picture. And don't even get me started on the lack of understanding of how important the data is for their systems. A data scientist's primary role is not the model itself. It's about developing the model, the data quality at hand, the appropriate problem framing, efficiency concerns, architectural literacy, evaluation design, and error analysis. Amid the AI hype, many have overlooked that much of their role is static and not considered important. AI engineering is an amazing field. The folks who love doing amazing things with the models always inspire me. But somehow, the same attention and respect are no longer paid to the foundational, scientific side of data and modeling in the current industry. I realise it's not always black and white, but it's kind of interesting how the grey is slowly becoming darker by the day. Do you feel the same way? Or is it just my own internal crisis bells ringing unnecessarily? For those of you who have recognized this shift, how are you handling your careers? Are you leaning into the engineering/systems side and abandoning traditional model development? Or have you found niche roles/companies that still value the fundamental data scientist role (data quality, architectural literacy, statistical rigor)? I'd love to hear how you are adapting submitted by /u/The-Silvervein [link] [comments]

How do you use Claude AI without an account?

I've heard good things about Claude AI but I'm certainly not super happy about having to give out my phone number in a not sure I want to yet. I mean I feel like that's a little bit personal I mean even chat GPT lets me use it with no account so does Google AI and many of the other AIS so is there a way to play around with Claude without having to give out such personal information the AI on Google when I did a search there's stuff called playground ai or something like that that let you access Claude without having to have an account but I don't know I just would like to experiment with it I'm not ready to give it a whole bunch of personal information like it's going to be my one and only a high just wanted to play with it submitted by /u/crazyhomlesswerido [link] [comments]

I reduced my token usage by 178x in Claude Code!!

Okay so, I took the leaked Claude Code repo, around 14.3M tokens total. Queried a knowledge graph, got back ~80K tokens for that query! 14.3M / 80K ≈ 178x. Nice. I have officially solved AI, now you can use 20$ claude for 178 times longer!! Wait a min, JK hahah! This is also basically how everyone is explaining “token efficiency” on the internet right now. Take total possible context, divide it by selectively retrieved context, add a big multiplier, and ship the post, boom!! your repo has multi thousands stars and you're famous between D**bas*es!! Except that’s not how real systems behave. Claude isn't that stupid to explore 14.8M token repo and breaks it system by itself! Not only claude code, any AI tool! Actual token usage is not just what you retrieve once. It’s input tokens, output tokens, cache reads, cache writes, tool calls, subprocesses. All of it counts. The “177x” style math ignores most of where tokens actually go. And honestly, retrieval isn’t even the hard problem. Memory is. That's what i understand after working on this project for so long! What happens 10 turns later when the same file is needed again? What survives auto-compact? What gets silently dropped as the session grows? Most tools solve retrieval and quietly assume memory will just work. But It doesn’t. I’ve been working on this problem with a tool called Graperoot. Instead of just fetching context, it tries to manage it. There are two layers: a codebase graph (structure + relationships across the repo) a live in-session action graph that tracks what was retrieved, what was actually used, and what should persist based on priority So context is not just retrieved once and forgotten. It is tracked, reused, and protected from getting dropped when the session gets large. Some numbers from testing on real repos like Medusa, Gitea, Kubernetes: We benchmark against real workflows, not fake baselines. Results Repo Files Token Reduction Quality Improvement Medusa (TypeScript) 1,571 57% ~75% better output Sentry (Python) 7,762 53% Turns: 16.8 to 10.3 Twenty (TypeScript) ~1,900 50%+ Consistent improvements Enterprise repos 1M+ 50 to 80% Tested at scale Across repo sizes, average reduction is around 50 percent, with peaks up to 80 percent. This includes input, output, and cached tokens. No inflated numbers. ~50–60% average token reduction up to ~85% on focused tasks Not 178x. Just less misleading math. Better understand this! (178x is at https://graperoot.dev/playground) I’m pretty sure this still breaks on messy or highly dynamic codebases. Because claude is still smarter and as we are not to harness it with our tools, better give it access to tools in a smarter way! Honestly, i wanted to know how the community thinks about this? Open source Tool: https://github.com/kunal12203/Codex-CLI-Compact Better installation steps at: https://graperoot.dev/#install Join Discord for debugging/feedback: https://discord.gg/YwKdQATY2d If you're enterprise and looking for customized infra, fill the form at https://graperoot.dev/enterprises submitted by /u/intellinker [link] [comments]

I built a desktop app to inspect, debug, and reuse the MCP tools you make with Claude

Hi everyone, If you use Claude Code or Claude Desktop with MCP tools, you’ve probably run into this problem. Claude is incredible at generating tool logic quickly. But as soon as the tool is created: Did it actually execute correctly, or is the AI hallucinating? What arguments did Claude actually pass to it? If it failed, why? How do I reuse this tool outside of this specific chat session? Debugging MCP tools just by retrying prompts in the chat interface is incredibly frustrating. To solve this, I built Spring AI Playground — a self-hosted desktop app that acts as a local Tool Lab for your MCP tools. What it does: Build with JS: Take the tool logic Claude just wrote, paste it in, and it works immediately. Built-in MCP Server: It instantly exposes your validated tools back to Claude Desktop or Claude Code. Deep Inspection: See the exact execution logs, inputs, and outputs for every single tool call Claude makes. Secure: Built-in secret management so you don't have to paste your API keys into Claude's chat. The goal is to give the tools Claude generates a proper place to be validated and reused, instead of staying as one-off experiments. It runs locally on Windows, macOS, and Linux (no Docker required). Repo: https://github.com/spring-ai-community/spring-ai-playground Docs: https://spring-ai-community.github.io/spring-ai-playground/ I'd love to hear how you are all currently handling tool reuse and debugging when working with Claude. submitted by /u/kr-jmlab [link] [comments]

GPT 5.4 vs GPT 5.4 Pro - SVG Generation Capability

SVGs are 'Scalable Vector Graphics' basically images written in code (XML). most of the top models are capable of writing a somewhat valid SVG that can do the job, but 5.4 Pro is getting to be next level. Granted, 5.4 pro took around 20x the time and over 10x the cost – if you need something done right, pro will do it right. playground/arena: svgBench.ai submitted by /u/lgats [link] [comments]

I don't use AI to write my reports. I built a system that remembers how to do it.

So I wrote a whole Medium post about this but like…5 claps lol after three days. Figured I'd share a shorter version here since I already put in the effort. Yes, I still write weekly reports in 2026. Very corporate, very dinosaur energy. But here's the thing: I don't mind writing reports (sort of like it as a signal of week end). What I mind is re-explaining the same context to ChatGPT every single week. You know the drill. Friday rolls around, you paste your notes into ChatGPT, and it goes: "Sure! What format would you like?" Didn't I tell you last week? ? So you dig up last week's report, copy-paste it as a reference, and spend 20 minutes babysitting the output because it forgot Feature X was supposed to ship last Tuesday. I did this for months. Then I realized why am I the one remembering things for an AI? Here's what I changed. I stopped relying on ChatGPT's memory and built a file-based system instead. I'm using Halomate, though the principles work with any AI tool that supports persistent workspaces. I actually tried Poe first but their memory resets between sessions so never worked out. Ok now all my past reports live as markdown files like below. My product roadmap is a file. Data analysis is a file. Everything's organized, not buried in some chat from three weeks ago. The Weekly Reports Project workspace: all files live in one shared space. I have an AI assistant I call Axel. His job on communication side, including writing reports. When I need a new one, I paste my messy notes and ask Axel to clean the notes and generate the weekly report. He reads last week's report from the actual file, not from fuzzy memory. He checks the roadmap file. He pulls in data analysis. Then writes the new report. Takes a few minutes now. The thing is, files don't forget but conversations do. ChatGPT's memory is fuzzy. It kind of remembers you like bullet points, thinks you mentioned something about a product launch but can't remember when. With files, there's no ambiguity. If I wrote "Feature X ships Tuesday" in Week_3_Report.md, Axel reads it and knows. If this week's notes don't mention Feature X, he flags it: "Last week we committed to Feature X, no update?" I also keep separate AI assistants for different jobs. Axel writes reports. Query handles data analysis. Leo maintains the product roadmap. Why separate? I want all my assistants to be specialist, and later on if I need them to other projects, they already know how. ah and also, save credits! When I need a quick chart, I don't want to load Axel's 52 weeks of report context. Query does the chart, saves it as a file, Axel references it later. Also, I can swap models without losing context. Most weeks I use Claude for Axel. Sometimes I want a second opinion, so I regenerate with GPT or Gemini. But Axel's personality or memory don't reset. Only the model underneath changes. Remember when OpenAI deprecated GPT-4o and people felt actual grief? I also migrated my old 4o persona here and built a new mate using that persona and memory. What I'm thinking is that if a model shuts down tomorrow, I switch engines and keep going. Now my actual Friday workflow: all week I keep rough notes. Friday I paste the mess and type: "Clean the notes and generate the weekly report." Axel reads last week's report, scans my notes, checks product roadmap and new data analysis, writes a new report for this week. Done. And maybe later I need a quarterly report? Axel will just read all 12 weekly reports and write a summary, and generate a decent report if needed. Something like this (all mock data). https://preview.redd.it/bv4w7ff64xqg1.png?width=720&format=png&auto=webp&s=732f82e8d029daead86c7d2e5905a7cf9654c421 I don't know if this is useful to anyone else. Maybe everyone's moved past weekly reports. But this mechanism could be applied to anything that you need to build over time. Anyway. If you're tired of re-explaining context every week, maybe this helps. submitted by /u/AIWanderer_AD [link] [comments]

I made my agent 34.2% more accurate by letting it self-improve. Here’s how.

Edit: I rewrote everything by hand! Everyone I know collects a lot of traces but struggles with seeing what is going wrong with the agent. Even if you setup some manual signals, you are then stuck in a manual workflow of reading the traces, tweaking your prompts, hoping it’s making the agent better and then repeating the process again. I spent a long time figuring out how to make this better and found the problem is composed of the following building blocks with each having its technical and design complexity. Analyzing the traces. A lot can go wrong when trying to analyze what the failures are. Is it a one off failure or systematic? How often does it happen? When does it happen? What caused the failure? Currently this analysis step is missing almost entirely in observability platforms I’ve worked with and developers are resorting to the process I explained earlier. This becomes virtually impossible with thousands to millions of traces, and many deviations cause by the probabilistic nature of LLMs never get found because of it. The quality of the analysis can be/is a bottleneck for everything that comes later. Evals. Signals are nice but not enough. They often fail and provide a limited understanding into the system with pre-biasing the system, since they’re often set up manually or come generic out of the box. Evals need to be made dynamically based on the specific findings from step one in my opinion. They should be designed as code to run on full databases of spans. If this is not possible however, they should be designed through LLM as a judge. Regardless the system should have the ability to make custom evals that fit the specific issues found. Baselines. When designing custom evals, computing baselines against the full sample reveal the full extent of the failure mode and also the gaps in the design of the underlying eval. This allows you to reiterate on the eval and recategorize the failures found based on importance. Optimizing against a useless eval is as bad as modifying the agent’s behavior against a single non-recurring failure. Fix implementation. This step is entirely manual at the moment. Devs go and change stuff in the codebase or add the new prompts after experimenting with a “prompt playground” which is very shallow and doesn’t connect with the rest of the stack. The key decision in this step is whether something should indeed be a prompt change or if the harness around an agent is limiting it in some way for example not passing the right context, tool descriptions not sufficient etc. Doing all this manually, is not only resource heavy but also you just miss all the details. Verification. After the fixes, evals run again, compute improvements and changes are kept, reverted or reworked. Then this process can repeat itself. I automated this entire loop. With one command I invoke an agentic system that optimizes the agent and does everything described above autonomously. The solution is trace analyzing through a REPL environment with agents tuned for exactly this use case, providing the analysis to Claude Code through CLI to handle the rest with a set of skills. Since Claude can live inside your codebase it validates the analysis and decides on the best course of action in the fix stage (prompt/code). I benchmarked on Tau-2 Bench using only one iteration. First pass gave me 34.2% accuracy gain without touching anything myself. On the image you can see the custom made evals and how the improvement turned out. Some worked very well, others less and some didn’t. But that’s totally fine, the idea is to let it loop and run again with new traces, new evidence, new problems found. Each cycle compounds. Human-in-the-loop is there if you want to approve fixes before step 4. In my testing I just let it do its thing for demonstration purposes. Image shows the full results on the benchmark and the custom made evals. The whole thing is open sourced here: https://github.com/kayba-ai/agentic-context-engine I’d be curious to know how others here are handling the improvement of their agents. Also, how do you utilize your traces or is it just a pile of valuable data you never use? submitted by /u/Lucky_Historian742 [link] [comments]