Arcade AI

Arcade AI AI

Arcade AI AI

Arcade AI AI

Arcade AI AI

Arcade AI AI

Arcade AI AI

Arcade AI AI

Arcade AI AI

Arcade AI AI

Arcade AI AI

Differences Between Opus 4.6 and Opus 4.7 on MineBench

Some Notes: You'll notice how sometimes it focused too much on the scenery (like the arcade or cottage builds), but the prompt has remained the same and Gemini 3.1 and GPT 5.4 were benchmarked with the same prompt The prompt encourages the model to decide when to focus more on scenery individually, which might indicate that Opus 4.7 isn't as good at creative / brainstorming tasks as Opus 4.6 was? It might also be the adaptive thinking mode causing inconsistencies, but Anthropic discontinued the default thinking mode for all models going forward so can't really test it EDIT: the inconsistencies with Opus 4.7 can probably be explained by its behavioral changes; they mention how 4.7 will tend to interpret prompts differently: More literal instruction following: Claude Opus 4.7 interprets prompts more literally and explicitly than Claude Opus 4.6, particularly at lower effort levels. It will not silently generalize an instruction from one item to another, and it will not infer requests you didn't make. The upside of this literalism is precision and less thrash. It generally performs better for API use cases with carefully tuned prompts, structured extraction, and pipelines where you want predictable behavior. A prompt and harness review may be especially helpful for migration to Claude Opus 4.7. Average Inference Time Per Build: ~2600 seconds (43ish minutes) Total cost was ~$275 I remember Opus 4.6 being a lot cheaper, though the benchmark has slightly evolved to favoring more tool usage and cached tokens since If you enjoy these posts please feel free to help fund the benchmark Benchmark: https://minebench.ai/ Git Repository: https://github.com/Ammaar-Alam/minebench Previous Posts: Comparing GPT 5.4 and GPT 5.4-Pro Comparing GPT 5.2 and GPT 5.4 Comparing GPT 5.2 and GPT 5.3-Codex Comparing Opus 4.5 and 4.6, also answered some questions about the benchmark Comparing Opus 4.6 and GPT-5.2 Pro Comparing Gemini 3.0 and Gemini 3.1 Extra Information (if you're confused): Essentially it's a benchmark that tests how well a model can create a 3D Minecraft like structure. So the models are given a palette of blocks (think of them like legos) and a prompt of what to build, so like the first prompt you see in the post was a fighter jet. Then the models had to build a fighter jet by returning a JSON in which they gave the coordinate of each block/lego (x, y, z). It's interesting to see which model is able to create a better 3D representation of the given prompt. The smarter models tend to design much more detailed and intricate builds. The repository readme might provide might help give a better understanding. (Disclaimer: This is a public benchmark I created, so technically self-promotion :) submitted by /u/ENT_Alam [link] [comments]

What's your "When Language Model AI can do X, I'll be impressed"?

I have two at the top of my mind: When it can read musical notes. I will be mildly impressed when I can paste in a picture of musical notes and with programming sets up instruments needed to play music, and then correctly plays the song it reads from the notes. My jaw will drop when finally with a simple prompt an AI can create a classic arcade style fully functioning and fun to play Pinball game. Each new version of models that become available I give that one a go. None have been even remotely close to achieving this goal. So what are your visions for what will impress you to some extent when an AI can make it for you? submitted by /u/KroggRage [link] [comments]

Built a WhatsApp AI assistant with Claude Code as an OpenClaw alternative

As a startup founder, I'm always looking for ways to improve my productivity. The promise of OpenClaw is enticing, however I couldn't get past the security model, or lack thereof. I was already using Claude Code heavily and am a heavy WhatsApp user, so I wanted something that brings both together: WhatsApp for messaging my AI assistant and Claude Code as the agentic brain. The benefit of using Claude Code: I'm already paying for a Claude Max subscription, so this covers the cost. Not to mention the fact I trust Anthropic's runtime more. The stack is a local relay server for WhatsApp webhooks, an MCP server bridging to Claude Code, and Arcade for scoped auth to Google Calendar, Gmail, and Slack. Here is the full working code https://github.com/manveer/whatsapp-assistant submitted by /u/manveerc [link] [comments]

I am doing a multi-model graph database in pure Rust with Cypher, SQL, Gremlin, and native GNN looking for extreme speed and performance

Hi guys, I'm a PhD student in Applied AI and I've been building an embeddable graph database engine from scratch in Rust. I'd love feedback from people who actually work with graph databases daily. I got frustrated with the tradeoffs: Neo4j is mature but JVM-heavy and single-model. ArcadeDB is multi-model but slow on graph algorithms. Vector databases like Milvus handle embeddings but have zero graph awareness. I wanted one engine that does all three natively. So I would like if someone could give me feedback or points to improve it, I am very open mind for whatever opinion I was working several months with my university professors and I decided to publish the code yesterday night because I guessed its more or less reddit to try it. The repo is: https://github.com/DioCrafts/BikoDB Guys, as I told you, whatever feedback is more than welcome. PD: Obviously is open source project. Cheers! submitted by /u/torrefacto [link] [comments]

Claude Code session dashboard - open source, 3 commands to install

I've been running 3–4 Claude Code sessions simultaneously and kept hitting the same problem: no combined cost view, no way to see which session is thinking vs idle vs waiting for input, no visibility into context window usage across sessions. So I built this: https://github.com/Stargx/claude-code-dashboard How Claude helped build it: The entire project was written using Claude Code. I described the problem, and Claude figured out that Claude Code writes JSONL session logs to ~/.claude/projects/ — then built the file watcher, the Express API, and the frontend in a single HTML file. I basically directed it and it did the heavy lifting. Felt very meta: using Claude Code to build a tool for watching Claude Code. What it shows per session: - Token usage and cost (with correct per-model pricing) - Status — thinking / waiting / idle / stale - Context window usage as a visual progress bar - Active subagents while they're running - Which files the session is currently working on - Expandable activity log - Git branch and permission mode (AUTO-EDIT / YOLO) How it works: Claude Code writes JSONL session logs to `~/.claude/projects/`. The dashboard watches those files and renders everything in a browser tab. No WebSockets, no build step, no cloud — just Node.js tailing local files and a single HTML file for the UI. Quick start: ``` git clone https://github.com/Stargx/claude-code-dashboard cd claude-code-dashboard npm install && npm start ``` Then open http://localhost:3001. Free and MIT licensed. Would love feedback — especially if you're on macOS or Linux and hit any issues with session detection. submitted by /u/ColdBeamGames [link] [comments]