Morgan
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brawnvivant.bsky.social
Morgan
@brawnvivant.bsky.social
430 followers 150 following 1.8K posts
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brawnvivant.bsky.social
Okay so you use it the same he way he did but his is invalid somehow? And like if you actually scroll back on the timeline there are a bunch of people in replies on the OP who knew exactly what he meant and reposted a confirmation of what he meant less than 10 minutes later.
October 16, 2025 at 8:41 PM
brawnvivant.bsky.social
Can I be into it *and* post cat pics?
October 3, 2025 at 10:00 PM
brawnvivant.bsky.social
Here, for example, it goes into great detail about nuanced reforms to USAID including how to use it to counter China's influence in the developing world. That starts on p 253 if you'd like to check it out for yourself static.heritage.org/project2025/...
September 30, 2025 at 6:52 PM
brawnvivant.bsky.social
USAID: return to 2019 funding levels, not completely destroying the agency
thefulcrum.us/governance-l...
Given Trump's statements while campaigning for President, none of this should be surprising. What is astonishing is that even the playbook that most Americans believed was President Trump’s roadmap—Project 2025—underestimated the stampede. We can now say, with some confidence, that Project 2025 represents a more moderate and gradual dismantling of “inside the beltway” customs compared to the President’s actual intentions. The next conservative Administration should scale back USAID’s global footprint by, at a minimum, returning to the agency’s 2019 pre–COVID-19 pandemic budget level. It should deradicalize USAID’s programs and structures and build on the conservative reforms instituted by the Trump Administration. This will require working closely with the U.S. Congress to make deep cuts in the international affairs “150 Account” while granting USAID greater flexibility in spending its appropriated funds to achieve better developmental outcomes.
September 30, 2025 at 5:23 PM
brawnvivant.bsky.social
Nostalgia is acceptable for one thing from 2002
September 29, 2025 at 3:53 PM
brawnvivant.bsky.social
August 29, 2025 at 11:07 AM
brawnvivant.bsky.social
What the fuck
August 15, 2025 at 2:07 PM
brawnvivant.bsky.social
August 10, 2025 at 1:36 AM
brawnvivant.bsky.social
This is a prototypical semicolon use case.
August 10, 2025 at 12:24 AM
brawnvivant.bsky.social
One can debate how much it matters but industrially-produced, shelf stable bread is made differently than what you make at home. (this is McDonald's bun ingredients FYI)
McDonald's bun ingredients: Enriched Flour (wheat Flour, Malted Barley Flour, Niacin, Reduced Iron, Thiamine Mononitrate, Riboflavin, Folic Acid), Water, Sugar, Yeast, Soybean Oil, Contains 2% Or Less: Salt, Dextrose, Corn Starch, Modified Food Starch, Wheat Gluten, Vinegar, Vegetable Proteins (pea, Potato, Rice And/or Faba), Sunflower And/or Canola Oil, Maltodextrin, Natural Flavors, Dough Conditioners (enzymes, Ascorbic Acid, Monoglycerides).
August 7, 2025 at 10:20 PM
brawnvivant.bsky.social
If you think this is sincere I think you have a very low opinion of whatever sort of people the singers are supposedly pretending to be
August 6, 2025 at 4:13 AM
brawnvivant.bsky.social
Okay fine here's a rare TL post. The new guy is settling in pretty well. Meet Ham and Beans.
Two black cats in different spots on a couch A small black cat reclines on a Birkenstock clog-clad foot, looking out at the view from a balcony Two black cats sitting near each other lazily on a couch A black cat lying on a pillow reaches out to gingerly touch the photographers arm
July 14, 2025 at 7:09 PM
brawnvivant.bsky.social
bsky.app/profile/braw...
June 3, 2025 at 12:09 AM
brawnvivant.bsky.social
Second edition had zero when I took it!
May 28, 2025 at 11:20 PM
brawnvivant.bsky.social
A follow-up based on what I actually ordered last night, following the Bishop recommendation and some reddit threads.
- Deep Learning (Bishop 2024)
- Mathematics for Machine Learning
- Introduction to Statistical Learning (Python)
Screenshot of a conversation with Claude: Returning to deep learning and LLMs, I saw some recommendations for Mathematics for Machine Learning by Deisenroth, Faisal, and Ong as a good overview of the relevant math. I decided to pick up that, Introduction to Statistical Learning with Python, and the newer book Deep Learning: Foundations and Concepts by Bishop. What do you think about that as a starting point? What will I be missing? Edit That's an excellent and well-balanced selection! You've chosen three books that complement each other very well and will give you a strong foundation. Let me break down what each contributes and what gaps might remain: Your Selected Books - Strengths Mathematics for Machine Learning (Deisenroth, Faisal, Ong) Excellent bridge between pure math and ML applications Covers linear algebra, calculus, and probability with ML context More focused and practical than pure math texts Free online version available Introduction to Statistical Learning with Python (James, Witten, Hastie, Tibshirani) Perfect practical introduction to ML concepts Excellent balance of theory and hands-on coding Strong statistical foundation (the Python version is newer and excellent) Less intimidating than Elements of Statistical Learning while still rigorous Deep Learning: Foundations and Concepts (Bishop, 2024) This is Bishop's new book, likely more accessible than his classic PRML Should provide modern treatment of deep learning with solid mathematical grounding Bishop is excellent at mathematical exposition What This Combination Gets You Right Mathematical foundations without getting lost in pure math Statistical learning principles with practical implementation Modern deep learning from a respected mathematical perspective Progression from general ML to specialized deep learning Python implementation throughout Potential Gaps & Additions For LLM-Specific Knowledge: Transformer architecture details - You'll still need the "Attention Is All You Need" paper and Karpathy's videos NLP fundamentals - Consider adding Speech and Language Processing by Jurafsky & Martin (free online) Information theory concepts - Might need Elements of Information Theory by Cover & Thomas for deeper understanding of entropy, mutual information, etc. For Practical Implementation: Hands-on transformer building - Definitely still do Karpathy's "Let's build GPT" series Modern deep learning frameworks - You might want Deep Learning with Python by François Chollet for practical Keras/TensorFlow skills For Optimization Deep Dives: Your selection might be light on optimization theory - the Deisenroth book covers some, but for deeper understanding, Boyd & Vandenberghe's Convex Optimization remains valuable For Current Research: Scaling laws, emergence, alignment - These require reading recent papers since they're evolving rapidly Recommended Learning Sequence Start with Mathematics for Machine Learning (2-3 months) - Get the math foundation solid Work through Introduction to Statistical Learning with Python (3-4 months) - Build ML intuition with hands-on practice Parallel activity: Karpathy's video series - Start building GPT from scratch while learning theory Deep Learning: Foundations and Concepts (4-6 months) - Deep dive into neural networks and modern architectures Paper reading and implementation - Attention Is All You Need, recent LLM papers Additional Recommendations Consider Adding: The Elements of Statistical Learning (Hastie, Tibshirani, Friedman) - Keep this as a reference for when you want to go deeper on specific topics Pattern Recognition and Machine Learning (Bishop's classic) - Also good as a reference, though his new book might supersede this need For Staying Current: Follow key researchers on Twitter/X Subscribe to ML newsletters like The Batch, AI Research Join communities like r/MachineLearning or ML Twitter Assessment Your selection is actually better than many traditional paths because: You avoid getting bogged down in overly theoretical pure math You get modern Python implementations rather than older R-focused books Bishop's new book is likely more accessible than his classic while maintaining rigor The progression from general ML to deep learning is logical The main thing you'll be "missing" is the transformer-specific knowledge, but that's best learned through papers and video tutorials anyway. Your foundation will be solid enough to tackle those effectively. This is a smart, efficient path that should get you to deep LLM understanding in 12-18 months of focused study!
May 26, 2025 at 4:37 PM
brawnvivant.bsky.social
Shit, why am I asking you guys? Claude 4 is out. Apparently it'll only take me 8-15 months of serious study! (this seems like more than really necessary)
May 26, 2025 at 7:40 AM
brawnvivant.bsky.social
L rather than R
May 24, 2025 at 6:58 PM
brawnvivant.bsky.social
I think he probably knows more than two things about computing and some kind of a limited shell prompt seems like an appropriate way for a language model to run commands to me.
May 24, 2025 at 5:05 PM
brawnvivant.bsky.social
bsky.app/profile/thel...
May 12, 2025 at 4:01 PM
brawnvivant.bsky.social
Literally the second and third paragraphs
May 9, 2025 at 10:02 PM
brawnvivant.bsky.social
Siri was pretty widely talked about as an implementation of AI prior to the release of ChatGPT www.google.com/search?q=sir...

Also OpenAI was founded two years before the publication of the foundational paper in LLM development.
May 7, 2025 at 6:51 PM
brawnvivant.bsky.social
I feel like this really needs the first and last paragraphs of Wendy
May 7, 2025 at 1:29 PM
brawnvivant.bsky.social
May 1, 2025 at 4:25 PM
brawnvivant.bsky.social
I'd be happy to explain what this economic report means. This is information about the Institute for Supply Management (ISM) Manufacturing Index for April 2025, which is an important economic indicator. Here's what the different parts mean: "ISM not as weak as feared" - The overall manufacturing index came in at 48.7, which was better than economists' expectations of 47.9, though still below 50 (which is the dividing line between expansion and contraction). "Prices lower than expected" - The prices component of the report showed lower inflation in manufacturing than analysts anticipated, which is generally positive news for inflation concerns. "New orders and employment higher than expected but both of those are in contraction" - While these components beat expectations, they're still below 50, indicating that both new orders and manufacturing employment are contracting, just not as severely as economists predicted. "US APRIL ISM MANUFACTURING INDEX FALLS TO 48.7; EST. 47.9" - This confirms the headline number - manufacturing activity is contracting (below 50) but not as badly as expected. "US ISM FACTORY PRODUCTION GAUGE DROPS TO LOWEST SINCE MAY 2020" - This is more concerning - it indicates that actual factory output has fallen to levels not seen since the early COVID pandemic period, suggesting significant weakness in manufacturing production. In summary, this report shows the manufacturing sector is still contracting, but slightly better than economists expected. The most worrying part is the factory production component hitting a nearly 5-year low, which points to significant weakness in actual manufacturing output. This mixed economic data is important for understanding the current state of the US economy and potential future Federal Reserve actions.
May 1, 2025 at 3:27 PM
brawnvivant.bsky.social
I'm sure this isn't what you're referring too but lol
April 27, 2025 at 9:59 PM