kafka exceptions

 When you attempt to produce to a Kafka topic and the broker is down or unreachable, the exact exception depends on your client library and configuration, but here's what typically happens:

Java/JVM Client (the reference client):

The producer operates asynchronously, so the behavior depends on timing:

• TimeoutException — This is the most common one. Specifically, if the producer can't get metadata or can't deliver the record within delivery.timeout.ms (default 120 seconds), you'll get an org.apache.kafka.common.errors.TimeoutException. The message is typically something like "Expiring X record(s) for topic-partition: delivery timeout".

• NetworkException — If there's an active connection that drops mid-send.

• DisconnectException — If the broker disconnects during the request.

• NotLeaderOrFollowerException — If the broker was up but the partition leader is unavailable.

How it surfaces:

• If you're using producer.send() with a callback, the exception is passed to the callback's onCompletion(metadata, exception) method.
• If you're calling producer.send().get() (synchronous), you'll get an ExecutionException wrapping the underlying cause (usually TimeoutException).

Key configs that control the behavior:

• delivery.timeout.ms (default 120s) — the upper bound on how long the producer will wait before giving up and throwing TimeoutException.
• request.timeout.ms (default 30s) — timeout for individual broker requests.
• retries / max.block.ms — control retry behavior and how long send() itself blocks waiting for metadata.

For other clients (Python's confluent-kafka, kafka-python, etc.), the pattern is similar — you'll generally see a delivery timeout or a KafkaError / NoBrokersAvailable exception if no brokers can be reached at all.

So the short answer: TimeoutException is the most common exception you'll encounter when a broker is down, after the delivery timeout period expires.

increase swap size

 sudo fallocate -l 4G /swapfile.new                                                                                                                                                                                                         
   sudo chmod 600 /swapfile.new                                                                                                                                                                                                               
   sudo mkswap /swapfile.new                                                                                                                                                                                                                  
   sudo swapoff /swapfile                                                                                                                                                                                                                     
   sudo swapon /swapfile.new                                                                                                                                                                                                                  
   sudo rm /swapfile                                                                                                                                                                                                                          
   sudo mv /swapfile.new /swapfile                                                                                                                                                                                                            
   free -h      

 

 

echo "username ALL=(ALL) NOPASSWD: ALL" | sudo tee /etc/sudoers.d/username-temp      

llama.cpp

 pip install llama-cpp-python

python -m llama_cpp.server \

  --model models/llama-3-8b.Q4_K_M.gguf \

  --host 0.0.0.0 \

  --port 8000

pyenv

 #!/bin/bash

# 1️⃣ Install required macOS libraries

brew install xz bzip2 zlib libffi

# 2️⃣ Set environment flags for pyenv

export LDFLAGS="-L$(brew --prefix xz)/lib -L$(brew --prefix bzip2)/lib -L$(brew --prefix zlib)/lib -L$(brew --prefix libffi)/lib"

export CPPFLAGS="-I$(brew --prefix xz)/include -I$(brew --prefix bzip2)/include -I$(brew --prefix zlib)/include -I$(brew --prefix libffi)/include"

export PKG_CONFIG_PATH="$(brew --prefix xz)/lib/pkgconfig:$(brew --prefix bzip2)/lib/pkgconfig:$(brew --prefix zlib)/lib/pkgconfig:$(brew --prefix libffi)/lib/pkgconfig"

# 3️⃣ Reinstall Python 3.11.14 via pyenv

pyenv uninstall -f 3.11.14

pyenv install 3.11.14

pyenv global 3.11.14

# 4️⃣ Verify Python and lzma

python3 --version

python3 -c "import lzma; print('lzma OK')"

# 5️⃣ Create a new virtual environment in the current project

python3 -m venv .venv

source .venv/bin/activate

# 6️⃣ Upgrade pip and install requirements if present

pip install --upgrade pip

if [ -f "requirements.txt" ]; then

    pip install -r requirements.txt

fi

echo "✅ Python 3.11.14 with lzma and .venv setup complete!"

echo "Activate your venv with: source .venv/bin/activate"

JGB

On December 3, 2025, Japanese Government Bonds (JGBs) are under significant selling pressure, driving yields to multi-year or record highs amid heightened expectations for a Bank of Japan (BOJ) rate hike at its December 18-19 meeting. This follows hawkish comments from BOJ Governor Kazuo Ueda on December 1, signaling the central bank will weigh "pros and cons" of raising the policy rate from 0.5% to 0.75%. The move is exacerbating concerns over Japan's massive public debt (over 250% of GDP) and aggressive fiscal stimulus under Prime Minister Sanae Takaichi's administration, which could fuel inflation and force further tightening. Despite BOJ interventions (including ¥1.1 trillion in purchases today), banks are offloading holdings, adding to supply and pushing yields higher. This has strengthened the yen, pressured equities (Nikkei down 3.2% in recent sessions), and raised global ripple risks, as Japan's low-yield bonds have long been a source of cheap capital for overseas investments like U.S. Treasuries.

Key Yield Movements TodayYields rose across the curve, with longer-dated bonds hit hardest due to auction caution and stimulus fears. Here's a snapshot of major JGB yields as of midday Tokyo trading:

Maturity	Yield (%)	Change (bps)	Notes
2-Year	1.015	+1	Near 17-year high; most sensitive to policy rate.
5-Year	1.385	+2	Highest since June 2008.
10-Year	1.89–1.90	+3–3.5	Highest since June 2008; benchmark borrowing cost.
20-Year	2.910	+3.5	Multi-year peak.
30-Year	3.43	+5	Record high (intraday hit 3.41% earlier).
40-Year	3.740	+5.5	Surged on long-end sell-off.

Data compiled from real-time quotes; the 10-year is ~0.84% higher year-over-year, reflecting the end of Japan's ultra-low rate era. Futures for December 2025 JGBs traded slightly lower at 134.51 (down 0.06%).Driving Factors

• BOJ Hawkishness and Rate Path Uncertainty: Markets now price an ~80% chance of a December hike, with January odds at 90%. Ueda's remarks triggered the heaviest JGB sell-off since July, as investors fret over hikes beyond December amid sticky inflation (core CPI at 2.8% in October).
• Fiscal Stimulus Concerns: Takaichi's plans for ¥20+ trillion in spending (via bonds) are seen as inflationary, clashing with debt sustainability. This has soured sentiment ahead of tomorrow's 30-year auction.
• Institutional Selling: Banks, insurers, and the BOJ itself dumped a record ¥10.7 trillion in JGBs in September; today, banks sought to offload ~¥2 trillion, but BOJ bought only ¥1.1 trillion—leaving excess supply for open-market sales. Major holders (BOJ ~50%, banks/insurers ~40%) prioritize liquidity over yield-chasing.
• Global Spillover: The yen carry trade unwind (borrowing cheap yen to fund higher-yield assets abroad) is accelerating, with USD/JPY nearing 150. Falling U.S.-Japan yield spreads (e.g., 30-year at March 2022 lows) could repatriate Japanese capital, pressuring U.S./global assets.

Market Sentiment and Outlook

• Positive Note: A recent 10-year auction saw strong demand (bid-to-cover ratio 3.59, above average), steadying the yen and Nikkei somewhat today.
• Bearish Vibes on X: Traders warn of "bond market rout" and "global liquidity crunch," with calls for 10-year yields >2% by Christmas. Some speculate Saudi or foreign bailouts, but focus remains on BOJ's balancing act between yen stability and growth.
• Forecast: Trading Economics sees 10-year at 1.79% by Q4 end, but upside risks loom if household spending data (due Friday) supports hikes. Watch for BOJ tapering yield curve control further—yields could test 2008 peaks if unchecked.

This volatility marks a pivotal shift: Japan's "zero-rate forever" playbook is obsolete, potentially reshaping global flows. For real-time updates, monitor BOJ announcements.

MCP Servers

 

Server Name	Description	Category
Airweave	A fully open-source context retrieval layer for AI agents. It connects to various apps, databases, and document stores, transforming their contents into a searchable knowledge base for AI consumption via a semantic search interface.	AI/Data Context
Atlassian	A secure bridge (like the Rovo MCP Server) that allows AI agents to read and interact with Jira, Confluence, and Compass data in real-time. It enables summarization, search, and ticket/page creation using natural language.	Project Management/DevOps
CodeMind	Not a specific tool, but often referenced in discussions about using Code Execution with MCP to make agents more efficient. The concept leverages the agent's ability to write code to interact with tools as APIs, filtering data and executing complex logic.	AI Framework/Execution
Dart	A server that exposes the Dart SDK commands to AI coding assistants. This allows the agent to perform code analysis, fix lints, format code, run tests, and manage packages for Dart and Flutter projects.	Programming Language/CLI
Figma Dev Mode MCP	A beta feature that surfaces rich design context from Figma (components, styles, variables, screenshots) into AI coding tools. This helps the AI generate code that is more consistent with the established design system.	Design-to-Code
GitHub	(General MCP context) Connects AI agents to GitHub repositories, issues, and pull requests to enable automated actions, such as analyzing code, suggesting fixes, summarizing PRs, and interacting with project boards.	Version Control/DevOps
Harness	Provides integration with Harness CI/CD, Chaos Engineering, and other platform tools. It allows agents to discover, run, monitor, and analyze pipelines and chaos experiments through natural language prompts.	DevOps/SRE
Heroku	Enables AI agents to interact with and manage apps hosted on the Heroku platform. It provides tools for deploying, managing configurations, scaling dynos, and defining agentic workflows within the Heroku ecosystem.	Cloud Platform/PaaS
Linear	Provides comprehensive project and issue management capabilities. The server exposes tools for listing, creating, updating, and summarizing Linear issues, projects, and team coordination directly through conversation.	Project Management
Locofy	Allows AI agents (specifically in AI-first IDEs) to retrieve, integrate, and extend code generated by the Locofy.ai platform. It helps sync Locofy-generated components and dependencies into a local codebase.	Design-to-Code
MongoDB	(General MCP context) Connects AI agents to MongoDB databases to perform data queries, structural analysis, and administration tasks using natural language instead of complex query language.	Database
Neon	A server that allows AI agents to interact with Neon Postgres databases using natural language commands, enabling project/branch management, SQL execution, database migrations, and query optimization.	Serverless Database
Netlify	(General MCP context) Connects AI agents to the Netlify platform to manage deployments, access logs, configure build settings, and perform platform actions via conversational prompts.	Web Hosting/DevOps
Notion	(General MCP context) Integrates AI agents with Notion databases and pages to search, summarize, create, and update documents, meeting notes, and knowledge bases using conversational commands.	Productivity/Knowledge Base
PayPal	(General MCP context) Typically exposes e-commerce and payment processing capabilities to AI agents, allowing them to integrate payment flows, manage transactions, or query account status.	Payment Processing
Perplexity Ask	Integrates the Perplexity search and research engine into AI agents. This allows the agent to perform deep, contextual research and quickly retrieve summarized, cited answers to complex queries.	Search/Research
Pinecone	Connects AI agents to a Pinecone vector database. This allows the agent to perform semantic searches, manage vector data, and retrieve highly relevant context for RAG (Retrieval-Augmented Generation) applications.	Vector Database
Prisma	(General MCP context) Exposes capabilities for managing database schemas and interacting with the ORM (Object-Relational Mapper), allowing the AI to query databases, perform migrations, and model data.	ORM/Database
Redis	(General MCP context) Allows AI agents to interact with Redis data structures (key-value store, cache, message broker) for tasks like accessing real-time data, managing caching, or queuing jobs.	Caching/Database
Sequential Thinking	A utility server that provides a structured dynamic and reflective problem-solving tool for the AI. It forces the agent to break down complex problems into manageable, revisable, and trackable thought steps.	AI Framework/Logic
SonarQube	(General MCP context) Integrates with the SonarQube platform for code quality and security. This allows AI agents to analyze code, retrieve vulnerability reports, and suggest code fixes based on SonarQube's findings.	Code Quality/Security
Stripe	(General MCP context) Exposes payment processing and subscription management tools to AI agents, allowing them to create customers, manage invoices, or check billing status.	Payment Processing
Supabase	(General MCP context) Connects AI agents to the Supabase platform (Postgres, Auth, Storage), allowing them to manage database tables, run queries, and interact with backend services.	Serverless Backend