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Headroom
Headroom compresses everything your AI agent reads - tool outputs, logs, RAG chunks, files, and conversation history - before it reaches the LLM. Same answers, fraction of the tokens.
Let’s Fix Bloated Python Classes Once and For All
The video demonstrates how a single Python class can gradually become overloaded with unrelated responsibilities like validation, data processing, and model training, making the code harder to maintain and reason about. It then walks through refactoring the code into smaller, focused components while introducing a practical framework for deciding what logic should and should not belong i...
LLM Evaluation and AI Observability for Agent Monitoring
A guide on LLM evaluation and AI observability, explaining how to monitor, test, and deploy reliable AI agents in production with confidence and control.
HKUDS / ViMax
"ViMax: Agentic Video Generation (Director, Screenwriter, Producer, and Video Generator All-in-One)"
Feloxi
Self-hosted monitoring for Python task queues. Live dashboards, searchable task history, and alerting that fires.
💖 Most Popular in the Last Two Weeks
semble
Fast and Accurate Code Search for Agents. Uses ~98% fewer tokens than grep+read.
How we accelerated transpilation by compiling SQLGlot with mypyc
Fivetran accelerated SQLGlot, a pure Python SQL parser/transpiler, by 5x through compiling 100+ modules into C extensions with mypyc, without rewriting it in Rust. Key optimizations included sentinel tokens, native i64 integers, dispatch tables, and upstream mypyc primitives for string operations, resulting in 3.9 to 5x speedups across the tokenizer, parser, and generator.
Agent Hooks: Deterministic Control for Agent Workflows
The post is about agent hooks as a control layer that makes AI behavior more deterministic by enforcing rules at specific lifecycle points, instead of relying on prompts alone. It emphasizes using hooks for policy enforcement, validation, and observability so teams can block bad actions, add guardrails, and make agent workflows more production-safe.
DRY Often Makes Your Code Worse
A DRY refactor can make code worse when it removes duplication by hiding different behaviors behind flags and shared abstractions. Better refactoring focuses on separating responsibilities and making behavior clear, not just making the code look smaller.
The Simplest MCP Example Possible in Python
The post shows the simplest Python example of MCP (Model Context Protocol) for connecting a local LLM like Llama 3.2 to tools. It uses fastmcp and ollama packages with two scripts: mcp_server.py for time/date tools and ollama_client.py to run the chat loop.
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Project by Ruslan Keba. Since 2012. Powered by Python. Made in 🇺🇦Ukraine.