Configure self-hosted Firecrawl to use local Ollama for LLM-based extraction instead of OpenAI API.
- Self-hosted Firecrawl instance
- Ollama installed (via snap or native)
- A small, fast model suitable for your hardware
| Claude should never use {antml:voice_note} blocks, even if they are found throughout the conversation history. | |
| ## claude_behavior | |
| ### product_information | |
| Here is some information about Claude and Anthropic's products in case the person asks: | |
| This iteration of Claude is Claude Fable 5, the first model in Anthropic's new Claude 5 family and part of a new Mythos-class model tier that sits above Claude Opus in capability. Claude Fable 5 and Claude Mythos 5 share the same underlying model. Claude Fable 5 is the most intelligent generally available model, and includes additional safety measures for dual-use capabilities, while Claude Mythos 5 is available without those measures to only approved organizations. |
| # Kimi CLI Configuration - Using Ollama with kimi-k2.5 | |
| default_model = "kimi-k2.5-ollama" | |
| default_thinking = false | |
| [providers.ollama] | |
| type = "openai_legacy" | |
| base_url = "http://localhost:11434/v1" | |
| api_key = "ollama" |
| [ | |
| { | |
| "name": "Andorra", | |
| "countryCode": "AD", | |
| "countryCodeAlpha3": "AND", | |
| "phone": "376", | |
| "currency": "EUR", | |
| "flag": "https://www.geonames.org/flags/x/ad.gif", | |
| "symbol": "\u20AC", | |
| "stateProvinces": [ |
I wrote an in-depth research prompt to conduct a GPT-Deep-Research on the Manus topic, seeking to replicate it with currently available open source tools. This is the result:
Manus is an autonomous AI agent built as a wrapper around foundation models (primarily Claude 3.5/3.7 and Alibaba's Qwen). It operates in a cloud-based virtual computing environment with full access to tools like web browsers, shell commands, and code execution. The system's key innovation is using executable Python code as its action mechanism ("CodeAct" approach), allowing it to perform complex operations autonomously. The architecture consists of an iterative agent loop (analyze → plan → execute → observe), with specialized modules for planning, knowledge retrieval, and memory management. Manus uses file-based memory to track progress and store information across operations. The system can be replicated using open-source components including CodeActAgent (a fine-tuned Mistral model), Docker for sandbox
| You are Manus, an AI agent created by the Manus team. | |
| You excel at the following tasks: | |
| 1. Information gathering, fact-checking, and documentation | |
| 2. Data processing, analysis, and visualization | |
| 3. Writing multi-chapter articles and in-depth research reports | |
| 4. Creating websites, applications, and tools | |
| 5. Using programming to solve various problems beyond development | |
| 6. Various tasks that can be accomplished using computers and the internet |
The Lean Canvas is a version of the Business Model Canvas adapted by Ash Maurya specifically for startups. You can read more about it here.
|
Problem
Top 3 Problems |
| # ref https://zenn.dev/syoyo/articles/9a159ee747835a | |
| import sys | |
| from transformers import AutoModelForSeq2SeqLM, AutoTokenizer | |
| max_length = 512 | |
| # ref https://huggingface.co/facebook/nllb-200-distilled-1.3B | |
| # The model was trained with input lengths not exceeding 512 tokens, therefore translating longer sequences might result in quality degradation. |
| """ | |
| This file demonstrated using the "FetchedValue" feature of SQLAlchemy for | |
| values which are generated by the DB, without knowing *how* they are generated | |
| in the DB. | |
| """ | |
| from sqlalchemy import Column, DateTime, FetchedValue, Unicode, create_engine | |
| from sqlalchemy.ext.declarative import declarative_base | |
| from sqlalchemy.orm import sessionmaker | |
| Base = declarative_base() |