While the AI industry obsesses over ever-larger cloud models, Meta just made a somewhat contrarian bet with Llama 3.2. Instead of chasing GPT-4 with another massive, they’re going small and local — releasing lightweight AI models designed to run entirely on your phone. The technical achievement is genuinely impressive: vision-capable models that can analyze images and text, plus compact versions that “fit in as little as 1GB of memory.” But the real story might be more strategic. Meta is essentially arguing that the future of AI isn’t in OpenAI’s cloud-centric paradigm, but in edge computing where your data never leaves your device.

“The on-device models are designed to enable developers to build personalized experiences that don’t require an internet connection and keep your data private.”

There’s some irony here: Meta — a company built on harvesting user data — suddenly championing privacy. Besides the marketing speak, this makes perfect business sense. Edge AI could democratize access to AI capabilities, reduce infrastructure costs, and conveniently sidestep the regulatory scrutiny facing cloud AI providers. By giving away competitive AI models, Meta simultaneously weakens competitors’ moats while positioning themselves as the champion of AI democratization. It’s the classic platform play: make the complementary technology free to increase demand for your scarce resource—in this case, developer mindshare and ecosystem control.

Whether on-device models can match cloud performance remains to be seen. But Meta is betting that “good enough” plus privacy plus offline capability beats “perfect” in the cloud. In a world increasingly skeptical of Big Tech data practices, that might just be a winning hand.

(1) Many active funds hold concentrated portfolios. Flow-driven trading in these securities causes price pressure, which pushes up the funds’ existing positions resulting in realized returns. (2) The researchers decomposes fund returns into a price pressure (self-inflated) and a fundamental component and show that when allocating capital across funds, investors are unable to identify whether realized returns are self-inflated or fundamental. (3) Because investors chase self-inflated fund returns at a high frequency, even short-lived impact meaningfully affects fund flows at longer time scales. (4) The combination of price impact and return chasing causes an endogenous feedback loop and a reallocation of wealth to early fund investors, which unravels once the price pressure reverts. (5) The researchers find that flows chasing self-inflated returns predict bubbles in ETFs and their subsequent crashes, and lead to a daily wealth reallocation of 500 Million from ETFs alone. (6) Around 2% of all daily flows and 8-12% of flows in the top decile of illiquid funds can be attributed to “Ponzi flows”. The researcher estimate that every day around $500 Million of investor wealth is reallocated because of the price impact of Ponzi flows.

In active funds investors are unable to identify whether realized returns are self-inflated or fundamental. Here’s how the magic trick works: Many active funds hold concentrated portfolios. Flow-driven trading in these securities causes price pressure, which pushes up the funds’ existing positions resulting in realized returns. The mechanism is as follows: Fund managers pick concentrated positions, new money flows in, that money pushes up prices of the fund’s existing holdings, creating impressive returns that attract more money, which pushes prices higher still. As the researchers put it:

Via their own price impact, active funds effectively reallocate capital from late to early investors.

The numbers are staggering. Around 2% of all daily flows and 8-12% of flows in the top decile of illiquid funds can be attributed to Ponzi flows, with around $500 Million of investor wealth reallocated daily because of this price impact. Even more striking: funds with high Ponzi flows experience subsequent drawdowns of over 200%. This isn’t just academic theorizing—flows chasing self-inflated returns predict bubbles in ETFs and their subsequent crashes. The researchers propose a simple fix: a fund illiquidity measure that captures a fund’s potential for self-inflated returns.

OpenAI’s GPT-4o launch is a classic Silicon Valley competitive strategy disguised as a product announcement.

GPT-4o is 2x faster, half the price, and has 5x higher rate limits compared to GPT-4 Turbo

The real headline isn’t the multimodal wizardry — though watching an AI tutor walk through math problems or harmonize in real-time is genuinely impressive. It’s the economics. OpenAI is essentially paying developers to build on their platform while making it prohibitively expensive for competitors to match these specs profitably.

The free tier expansion is equally calculated. By giving ChatGPT’s 100+ million users access to frontier AI capabilities, OpenAI creates a consumer expectation that every other AI assistant will struggle to meet. It’s the Amazon playbook: lose money on the product, make it back on the ecosystem.

That being said, the technical achievement shouldn’t be understated—training a single model end-to-end across text, vision, and audio represents a genuine breakthrough in multimodal AI. Real-time voice conversation with natural interruptions moves us from “chatbot” to something approaching actual dialogue. Strip away the demos and you’re left with a company making an aggressive bet that they can outspend the competition into submission. Whether that works depends on how quickly Google, Anthropic, and others can respond—and whether OpenAI’s cash reserves outlast their patience. The AI wars just got expensive. For everyone except OpenAI’s customers.

Nothing says “we’re serious about dominating a market” quite like giving away breakthrough technology for free. Google’s latest move with AlphaFold 3 might be their most audacious version of this strategy yet.

“AlphaFold 3 can predict the structure and interactions of all of life’s molecules with unprecedented accuracy”

This isn’t just an incremental improvement - While previous versions of AlphaFold could predict protein structures, AlphaFold 3 models the interactions between proteins, DNA, RNA, and small molecules. It’s the difference between having a parts catalog and understanding how the entire machine works.

Drug discovery typically costs billions and takes decades. If AlphaFold 3 can meaningfully accelerate that process - even by modest percentages—the value creation is staggering. Yet Google is handing it to researchers for free through the AlphaFold Server, with the predictable caveat of commercial restrictions. Is this Google’s cloud strategy playing out in life sciences? Establish the platform, get everyone dependent on your infrastructure, then monetize the ecosystem. The pharmaceutical industry, already grappling with AI disruption, now faces a world where molecular interactions can be predicted with “50% better accuracy” than existing methods.The real question isn’t whether AI will transform drug discovery - it’s whether Google will own that transformation.

My introduction to quantitative portfolio optimization happened during my undergraduate years, inspired by Attilio Meucci’s Risk and Asset Allocation and the convex optimization teachings of Diamond and Boyd at Stanford. With enthusiasm and perhaps more confidence than expertise, I created my first “optimal” portfolio. What struck me most was the disconnect between theory and accessibility. Modern Portfolio Theory had been established since 1990, yet the optimization tools remained largely locked behind proprietary software.

Nevertheless, only a few comprehensive software models are available publicly to use, study, or modify. We tackle this issue by engineering practical tools for asset allocation and implementing them in the Python programming language.

This gap inspired what would eventually be published as: A Python integration of practical asset allocation based on modern portfolio theory and its advancements.

My approach centered on a simple philosophy:

The focus is to keep the tools simple enough for interested practitioners to understand the underlying theory yet provide adequate numerical solutions.

Today, the landscape has evolved dramatically. Projects like PyPortfolioOpt and Riskfolio-Lib have established themselves as sophisticated open-source alternatives, far surpassing my early efforts in both scope and sophistication. Despite its limitations, the project yielded several meaningful insights: First, I set out to visualize Modern Portfolio Theory’s fundamental principle—the risk-return tradeoff that drives optimization decisions. This scatter plot showing the efficient frontier demonstrates this core concept. The results of my first optimization: maintaining a 9.386% return while reducing volatility from 14.445% to 5.574%, effectively tripling the Sharpe ratio from 0.650 to 1.684. By varying the risk aversion parameter (gamma), the framework successfully adapted to different investor profiles, showcasing the flexibility of the optimization approach. This efficient frontier plot with different gamma values illustrates how the optimization framework adapts to different investor risk preferences. Perhaps most importantly, out-of-sample testing across diverse market conditions—including the 2018 bear market and 2019 bull market—demonstrated consistent CVaR reduction and improved risk-adjusted returns.

We demonstrate how even in an environment with high correlation, achieving a competitive return with a lower expected shortfall and lower excess risk than the given benchmark over multiple periods is possible.

Looking back, the project feels embarrassingly naive—and surprisingly foundational. While it earned some recognition at the time, it now serves as a valuable reminder: sometimes the best foundation is built before you know enough to doubt yourself.

Somewhere, a peer reviewer just realized they may have been outsmarted by a machine.

Intology’s Zochi has achieved something unprecedented: becoming the first AI system to independently pass peer review at an A* scientific conference. Not just any conference—ACL, one of the most prestigious venues in computational linguistics.

“Zochi represents a significant step forward in AI-assisted research, demonstrating the ability to comprehend and analyze complex academic literature with remarkable accuracy.”

But that undersells what actually happened. This is academia’s Turing Test: when AI crossed the threshold from research tool to research colleague. If human experts can’t distinguish AI-generated research from human work, we’re facing fundamental questions about authorship, originality, and what constitutes scientific contribution. What are the implications. Will conferences soon be flooded with AI submissions? How do we handle attribution when an algorithm is the primary investigator? Could this democratize research globally, or will it devalue human scholarly work?

Similar to how Simon Willison describes his difficulties managing images for his approach to running a link blog I found it hard to remain true to pure markdown syntax but have images embedded in a responsive way on this site.

My current pipeline is as follows: I host my all my images in a R2 bucket and serve them from static.philippdubach.com. I use Cloudflares’s image resizing CDN do I never have to worry about serving images in appropriate size or format. I basically just upload them with the highes possible quality and Cloudflare takes care of the rest.

Since the site runs on Hugo, I needed a solution that would work within this static site generation workflow. Pure markdown syntax like ![alt](url) is clean and portable, but it doesn’t give me the responsive image capabilities I was looking for.

The solution I settled on was creating a Hugo shortcode that leverages Cloudflare’s image transformations while maintaining a simple, markdown-like syntax. The shortcode generates a <picture> element with multiple <source> tags, each targeting different screen sizes and serving WebP format. Here’s how it works: instead of writing standard markdown image syntax, I use {{ img src="image.jpg" alt="Description" }} in my posts. Behind the scenes, the shortcode constructs URLs for different breakpoints. This means I upload one high-quality image, but users receive perfectly sized versions - a 320px wide WebP for mobile users, a 1600px version for desktop, and everything in between. The shortcode defaults to displaying images at 80% width and centered, but I can override this with a width parameter when needed. It’s a nice compromise between the simplicity of markdown and the power of modern responsive image techniques. The syntax remains clean and the performance benefits are substantial - especially important since images are often the heaviest assets on any webpage.

(May 2025) Update: Completed migration to a fully custom Hugo build. Originally started with a fork of hugo-blog-awesome, but I’ve since rebuilt it from scratch.

(June 2025) Update: Added LaTeX math rendering using MathJax 3. Created a minimal partial template that only loads the MathJax library on pages that actually contain LaTeX expressions.

(June 2025) Update II: Enhanced SEO capabilities with comprehensive metadata support. Built custom Open Graph integration that automatically generates social media previews, plus added per-post keyword management.