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 Claude Opus 4.8 and Claude Fable 5 on MineBench

Some Notes: Average Inference Time: 18m 04s (1,084.4s) Faster than Claude 4.8 Opus, which averaged 24m 48s / 1,487.9 seconds Surprising since in the Claude.ai web harness, Fable feels like it thinks for much longer, but through the API it averaged less total time than Opus 4.8 did Total Cost (for 15 builds): $54.93 More expensive than Opus 4.8, which was $41.52 for the same 15 builds Considering Fable’s API pricing is 2x more than Opus 4.8’s, the MineBench cost was only about 30% higher Fable is producing fewer total tokens overall it seems, which is likely contributing to the lower cost Furthermore, I think the quality of the model's builds was very surprising: they don't seem as big of a leap over GPT 5.5 Pro as the the official benchmark scores might suggest, but the model clearly has very high attention to detail. For example, this is the first model that in the Arcade Machine build, actually created a correctly detailed screen (of PacMan), including the full layout, a score, and even a "1UP" label. Though it seems the model was quite conservative with its interpretation of the system-prompt, and (subjectively) not all of its builds were clearly more impressive than 4.8. Still, the results were quite surprising, so I reached out to the VoxelBench team, who also confirmed in their tests the builds were of generally much smaller size. They mentioned adding these two lines to the template produced much better builds in their case: LEVEL OF DETAIL: MAXIMUM BOUNDING BOX: UNLIMITED Though I'm not changing the MineBench system-prompt to cater to any specific models, I do think it's worth noting that one might be able to achieve much better results with improved prompting. It's also interesting how the model was able to make these detailed builds while keeping the overall JSON size lower in comparison to Opus 4.8, and while thinking for less time. Pure speculation: I think this might indicate why Claude Fable is supposedly much better at coding-related tasks; it actually completes the task with an intuitive approach and without adding excess. Full release-notes/thoughts on the GitHub release 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 Opus 4.7 and Opus 4.8 Comparing GPT 5.4 and GPT 5.5 Comparing Kimi K2.5 and Kimi K2.6 Comparing Opus 4.6 and Opus 4.7 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]

Blast Arena: a Bomberman-style browser game built entirely with Claude Code

🎮 Play (free, nothing to install): https://bomberman-coral.vercel.app What it is: an 8-level arcade game where each level introduces a new mechanic — classic crate-bombing, enemies that pathfind toward you, ice you slide on, electric floors, a minion-spawning boss, teleport portals, enemies that plant their own bombs, and conveyor belts. Between levels you spend gold in an upgrade shop with an unlock tree (max one line to open advanced ones). There's a public per-level leaderboard with clear times, full touch controls on mobile, and procedurally drawn graphics/synthesized audio — zero asset files. How Claude helped: honestly, it built all of it. I steered with plain-language prompts ("make it look premium", "levels should get harder", "make it work on mobile", "my scores disappeared — fix it") and Claude Code wrote the vanilla JS/canvas engine, the particle and audio systems, the enemy AI, and the Vercel serverless leaderboard. The interesting parts were the bugs: it diagnosed a leaderboard race condition (concurrent score submissions silently overwriting each other through a CDN-cached read-modify-write) and redesigned the storage so every score is its own write-conflict-free record. It also wrote its own headless test harness that simulates full playthroughs, since there was no test framework in a plain HTML project. Transparency / security notes: No account, no login, no personal data requested. You pick a nickname to play — that nickname and your level clear times are publicly visible on the leaderboard, so don't use your real name. Game progress (gold/upgrades) is stored only in your browser's localStorage. The site uses Vercel Web Analytics (anonymous, cookie-free page counts). Nicknames are profanity-filtered server-side. Everything runs client-side except the leaderboard API (a small Vercel function). No downloads, no permissions, no credentials touched. Feedback very welcome — especially on difficulty balance in levels 5–8, which were tuned by an AI that can't actually feel panic. 😄 submitted by /u/igoroliveiragg [link] [comments]

This is How I Automated Tutorial Video Generation For My Web-Apps with Claude Code.

I've been building production-grade web apps at lightning speed for the last year using Claude Code. But every time a new app hits production, I need sales and tutorial videos — and making each one manually is painstaking. Tools like Supademo and Arcade ease the pain a lot, but you still have to record the steps and sync the voice-over by hand. I wanted something fully automated. Turns out you can just use Playwright with Claude Code to generate the whole thing. First, the result — here's a full walkthrough it produced for one of my apps (a real-estate CRM BricksDeck), start to finish with synced annotations, voice-over, background music, and a branded end card. Zero manual editing: ▶ Watch the demo: https://youtu.be/u-mql3q_jRU?si=Km1l5Ht-iRMPlotk And here's exactly how it's done: 1) Plan the script. Ask Claude Code to analyze the target pages of your app, give it the steps to perform, and have it write a single file with the steps + voice-over narration + the UI elements to annotate (buttons, cards, menus, KPIs). 2) Generate the voice-over with timestamps**.** Ask Claude to generate the VO with ElevenLabs (it returns word/character alignment), or use Gemini TTS + OpenAI Whisper to get an SRT. You need the timestamps so the spoken words can be aligned to the UI clicks/highlights. 3) Generate the Playwright driver. Ask Claude Code to write a Playwright script that performs the steps and annotates the UI elements — a moving cursor, border highlights + labels on the right button/card, and opening "Actions" menus. 4) Record, synced to the voice. Run that script. Playwright drives the real app and records natively (recordVideo), firing each annotation at its timestamp from step 2 — so every highlight lands on the exact word being spoken, and each screen holds for exactly its narration length. (Tip: flash a single coloured frame at t=0 as a sync marker — it makes lining up audio and video dead simple later.) 5) Stitch it into a produced video. Ask Claude to write the ffmpeg step: overlay the voice-over, add background music ducked under the narration (sidechain compression — this is the difference between "screen recording" and "video"), normalize loudness, and append a branded end card with your logo + CTA. Out comes a clean 1080p mp4. 6) (Bonus) Other languages, basically free. Because the voice-over is decoupled from the recording, translate the script, regenerate the VO in the new language, and re-stitch over the same run. I got a Hindi version of my demo in a few minutes — no re-shoot. The result: a full multi-screen walkthrough — cursor movements, synced annotations, real voice, music, end card — with essentially zero manual editing. Per-video cost is a few cents of TTS instead of a SaaS seat. Honest caveats (it's not magic): Claude nails the production; you still direct — which screens to feature, the script's tone, and a final watch-through. The script especially needs your eye (I caught it writing Hindi in English word order and had to fix it). Translate, don't transliterate. Expect a couple of iteration passes per app — selectors and timing always need a nudge. Gotchas that cost me time (in case they save you some): SPA auth in sessionStorage dies on browser restart → use a persistent profile + "Remember me" so tokens land in localStorage. networkidle never fires on long-polling SPAs → use domcontentloaded + URL waits, and cap the default timeout so a missing selector fails fast instead of stalling 30s. ffmpeg drawtext can't shape Devanagari/Arabic → keep on-screen text Latin and let the voice carry the language. I ended up wrapping the whole thing into a reusable Claude Code skill + subagent, so the next app is basically "point it at the screens and go." Happy to go deeper on any step. What would you point a pipeline like this at first? submitted by /u/SpeedyBrowser45 [link] [comments]

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]