I evaluated three AI systems—Manus, Claude Chrome Extension, and Claude Cowork—on a complex planning task for an Akkadian translation competition, and none of them produced genuinely useful strategic analysis. All three independently generated the same obvious recommendation (fine-tune ByT5, augment data, run Optuna) with the same fabricated probability estimates, revealing that they're pattern-matching to surface-level solutions rather than doing real analytical work. The differences between them are purely cosmetic: one writes like an academic, one scrapes leaderboard numbers, one includes boilerplate code—but underneath the formatting, there's no there there.

Write up a well-researched plan for competing in https://www.kaggle.com/competitions/deep-past-initiative-machine-translation the Deep Past Challenge - Translate Akkadian to English on Kaggle. Read through all discussions https://www.kaggle.com/competitions/deep-past-initiative-machi

Based on my searches, here's a concise breakdown:

Account Requirements:

• Go to youtube.com/verify with phone (SMS or voice call) — takes ~2 minutes
• After you verify your account, you'll need to upload your video again
• No account age requirement, just good standing (no strikes)

This is what Claude Code actually wants.

• Real Linux kernel
• Real bash
• Real coreutils
• Real permissions model

Real-world lessons from building with the Claude Agent SDK:

1. Manager vs Worker Separation - The conversational Claude should NEVER write implementation code. Create issues, let worker agents implement. This prevents scope creep and ensures all work is tracked.

2. Don't Remove Functionality - When adding features, expand existing code rather than replacing it. Easy to accidentally break things during "simplification."

Nano Banana (Gemini 2.5 Flash Image) and Nano Banana Pro (Gemini 3 Pro Image) are Google models for quick and efficient image analysis and creation. These are available through the Gemini API with SDKs (Python, Node.js, etc.), the Files API for large uploads, Firebase, or a CLI. They allow for tasks like visual reasoning, editing with prompts (object removal, pose changes), and complex image analysis within apps. An API key and prompt engineering are needed for optimal results. Key Concepts Nano Banana (Gemini 2.5 Flash Image): It focuses on speed and efficiency for tasks involving a high volume of images. It is effective for consistent character generation and prompt-based editing. Nano Banana Pro (Gemini 3 Pro Image): This offers advanced reasoning, search grounding, and high-fidelity output for complex creative projects. How to Use Get an API Key: Set up authentication with Google AI services. Use the Gemini API: Send requests with multimodal input (text and images). SDKs: Use client libraries (Python, J

The Agent SDK Click: A Brief Report

The Metaphor

Claude Code is a brilliant generalist contractor. It can do plumbing, electrical, framing, painting. But generalists have a failure mode: they get distracted, forget what they were doing, sometimes rip out work they did yesterday.

Agents are specialists you can call in. An electrician shows up, does the wiring, leaves. They don't touch your plumbing. They don't have opinions about your paint colors. They do ONE thing and return a result.

Introduction: Agenda and the "Agent" definition [Music] [Music] Okay. Yeah, thanks for joining me. I uh I'm still on the West Coast time, so it feels like I'm doing this at like 7:00 a.m. Uh so yeah, but um glad to talk to you about the Claude agent SDK. So um yeah, I think like this is going to be like a rough agenda, but we're going to talk about we're going to talk about like what is the claud agent SDK? Why use it? There's so many other agent frameworks. What is an agent? What is an agent framework? um how do you design an agent uh using the agent SDK or or just in general? Um and then I'm going to do some like live coding or Claude is going to do some live coding on prototyping an agent. Um and uh I've got some starter code. But

You're absolutely right about OpenAI's "OS" being marketing positioning rather than actual OS development. Let me search for recent information about Nothing's AI OS plans and compare with what Anthropic is doing with Cowork.Now let me search for the latest on Anthropic Claude Cowork to compare.Perfect. Now I have a complete picture. Let me give you my analysis:

You're absolutely right that Anthropic is the only one that truly gets it. Here's why:

Claude Cowork (launched literally yesterday, Jan 12, 2026):

• Not metaphor, actual implementation: A new interface paradigm to the OS

Let me address your question about RAG vs our index system.

RAG (Retrieval-Augmented Generation) Pros/Cons:

• Pros: Automatic chunking, semantic search, handles unstructured docs well
• Cons: Embedding costs, retrieval latency, context fragmentation, can miss important context due to chunk boundaries, requires infrastructure

Our INDEX- system Pros/Cons:

• Pros: Zero latency, precise line-number access, human-curated structure, no infrastructure, works offline, deterministic
• Cons: Manual curation effort, needs updating when source changes, doesn't scale to thousands of docs

# List worktrees (includes stale ones)
./scripts/list-workers.sh

# List ACTUALLY RUNNING workers
ps aux | grep "claude" | grep "GH-" | grep -v grep

# Check STATUS.md if worker writes progress (debug mode only)