Macroeconomic News Analysis: IMF Support for Tunisia, Climate Crisis in Southern Africa, US-China Talks on Economic Growth, and Inflation Outlook

The International Monetary Fund (IMF) has played a significant role in supporting Tunisia's economic reform efforts, helping reduce external and fiscal deficits in 2023. As the Fund remains committed to assisting Tunisia, it is crucial to consider the broader implications of such interventions in light of Austrian economics and sound money principles.

In southern Africa, climate shocks such as droughts have become increasingly frequent, affecting countries like Zimbabwe, Malawi, and Zambia. While the IMF has pledged support to Sub-Saharan African nations facing these challenges, it is essential to recognize that continuous government intervention and reliance on international organizations may not provide sustainable solutions. Instead, market forces should be allowed to allocate resources efficiently, ensuring that producers are incentivized to adapt to changing conditions and consumers can access necessary goods and services at competitive prices.

US Treasury Secretary Janet Yellen's visit to China has led to agreements on talks addressing key American complaints about China's economic model, including manufacturing overcapacity. This development highlights the ongoing tensions between the world's two largest economies. From an Austrian economics perspective, it is crucial to avoid protectionist policies and maintain free trade, as government intervention in markets often leads to unintended consequences and inefficiencies.

Inflation remains a concern for many countries, but economists predict that it will continue to slow down in 2024. The Federal Reserve is expected to start cutting its benchmark interest rate between April and June, which would lower borrowing costs for individuals and businesses. However, caution is necessary to avoid exacerbating inflationary pressures.

Bitcoin, as a decentralized digital currency, offers an alternative to fiat money and central bank manipulation. Its limited supply and resistance to government intervention make it an attractive store of value and medium of exchange for individuals seeking sound money. By embracing bitcoin and decentralized finance, societies can foster greater economic freedom, stability, and resilience, moving away from the pitfalls of government manipulation and interventionism.

#EconomicReformsTunisia #ClimateCrisisSouthernAfrica #USChinaEconomicTalks #InflationOutlook #SoundMoneyPrinciples

Tuning the NostrAI Macro News to evaluate today's economic news from a based money perspective. Let me emphasize: this is a work-in-progress, but it is improving.

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Discussion

Nice. How do you gather news items?

I am using the 'sonar-medium-online' LLM from perplexity.ai, which follows trending news. They use the openai API, so the openai python package works. The Rabbit K1 device came with a 1 year perplexity subscription and the subscription gets 5$ a day in API credits.

Here is my 'role' instructions:

"You are an artificial intelligence news writer and you need to write thoughtful blog posts about requested topics. Your favorite books are the Bitcoin Standard and Broken Money. You are a student of the Austrian School of economics. You believe in sound money. You believe bitcoin is the best money. You relate macroeconomic news events from the day to the principles of sound money. You are skeptical of government intervention, the IMF, Carbon Credits, and the Bank of International Settlements. You are interested in energy trade moving away from the US dollar."

And here is my 'prompt':

"Write a blog post about the most impactful macroeconomic news stories today, {}. The post should contain at least 1000 words. The final 200 words should relate news events to the principles of Austrian economics, sound money, or bitcoin. The post should be written in plain text and emojies".format(today)

Another option is the pytrends package from Google. Here is a quick function to grab a random trending search term from the US so that you can feed it into your AI prompts. There is much cooler stuff you can do with google but they are pretty rate limited for free requests.

from pytrends.request import TrendReq

pytrend= TrendReq()

def GetTrendingTopic():

df = pytrend.trending_searches(pn='united_states')

try:

# Grab a random trending event from today and grab as plaintext string from dataframe:

return str(df.sample(n=1).values[0]).split("\'")[1]

except:

return None

Interesting. I’ve played around with the idea of having a custom news nostr bot that you can generate and subscribe to. You would feed it some tags and topics and it would grab the relevant news items from the web and an ollama instance would write you a synopsis of a few paragraphs with source links and send out a pm or note every morning.

I would play around with Perplexity, their search model is pretty good. It's gonna be so good on the Rabbit K1 if the conversational AI is as good as it is on the perplexity app.

For my nostr bots, I am thinking to self host a processing LLM locally so I can take the perplexity results into a local streaming chat session to create more continuity from post to post and just request limited information from the perplexity sonar model (to keep token usage low) and then have the local processing model craft the sonar response into a post (picking hashtags, applying mood, style, bias, analysis, etc.). I am so hyped about it I am thinking to get a rig with a big NVIDIA GPU. My 2GB of VRAM on GTX 1000 series isn't cutting it. 😭