Steady Lads Podcast
by @0xsteadylads
1 videos
Recent Posts
Is The Bottom In? What The Lads Are Buying!
Accumulate Bitcoin (BTC) on price dips as a hedge against currency debasement, using the firm $60,000 support level to target long-term cycle peaks between $125,000 and $185,000.
Rotate capital away from Ethereum (ETH), which continues to face persistent selling pressure and narrative dilution, into assets with stronger relative momentum.
Take partial profits on Hyperliquid (HYPE) to protect against downside risk following its rapid expansion toward a stretched $80 billion fully diluted valuation.
For asymmetric upside potential, allocate to Zcash (ZEC) as a high-reward privacy store-of-value play that shares Bitcoin’s fixed 21-million supply limit.
Capture the Artificial Intelligence (AI) boom by prioritizing cash-flowing Big Tech equities like Google and Meta, while restricting speculative crypto AI exposure to liquid names like NEAR and GRASS over struggling assets like Bittensor (TAO).
Top assets covered by Steady Lads Podcast
The 9 most-discussed assets across Steady Lads Podcast’s content on Kazuha.
Steady Lads Podcast’s sentiment — last 30 days
Aggregate of all sentiment-scored insights from Steady Lads Podcast in the last 30 days.
Frequently asked about Steady Lads Podcast
What does Steady Lads Podcast talk about on Kazuha?
Kazuha indexes 1 post from Steady Lads Podcast, with AI-extracted insights covering 9 distinct assets (stocks, ETFs, cryptocurrencies, and other investable assets).
Which assets does Steady Lads Podcast cover the most?
Steady Lads Podcast's most-discussed assets on Kazuha are ETH, TAO, META, HYPE, GRASS. See the "Top assets covered" section above for the full breakdown with sentiment.
Is Steady Lads Podcast bullish or bearish right now?
Mostly bullish. In the last 30 days, Steady Lads Podcast had 6 bullish, 3 bearish, and 0 neutral takes across all assets they discussed (per AI-extracted sentiment scoring on Kazuha).
Where does Kazuha get Steady Lads Podcast's insights?
Steady Lads Podcast's publicly available content (podcast episodes, YouTube videos, or X/Twitter posts) is transcribed and analyzed by an LLM that extracts the assets discussed and the speaker's sentiment toward each one. Each insight links back to the original source.