AI and its impact on software engineering is becoming increasingly obvious. While initial tooling centered around basic code completion, software engineering agents are pushing towards large scale application development. We'll take a closer look at projects defining the SWE agent landscape below. The performance metrics around many of the latest tools are also interesting.

Data is the lifeblood of artificial intelligence. As LLM models grow exponentially in size, with newer models like GPT-4 having been trained on approximately 13T tokens, the need for training data is continuously growing. We understand that the “world model” we all possess so clearly as humans may still be out of touch for current AI models, whether that be due to a lack of high quality data, architectures, training strategies, or some combination of these. Even so, researchers remain fixated on the end form of AI: artificial general intelligence. Said pursuit of data has catalyzed advancements across AI, notably in natural language processing and image recognition. LLMs stand out in this field due to their ability to decode and replicate seemingly natural human communication. These models leverage transformer architectures which enable them to encode the context and meaning behind words into multi-dimensional vector spaces, transform these vectors in vector space, and decode them back into human readable language. Models like these perform as a function of the quantity and quality of training data provided. It’s no wonder that GPT-4's staggering consumption of over 10 trillion tokens has seemingly exhausted the internet's textual resources. These data appetites underscore the models' dependency on expansive corpora to refine their linguistic mimicry.

The quest for enhanced privacy and security continues to drive some of the most tech intense innovation in crypto. It’s important to remember that crypto once started as what was basically a science fair project wedding cryptography and a financial transaction platform. The rise in popularity of technology like FHE and ZK is a callback to the roots of blockchain.

Novel standards and new combinations of tokens always seem to energize digital assets. ERC-721s achieved mainstream attention shortly after the 2017 release and trickled into the NFT mania of 2021, ERC-1155s enabled a more semi-fungible experience and encouraged “casual” minting with open editions..etc. Just a few months ago, we explored 404s as they revealed a blend of ERC-20 and ERC-721 features. As it becomes clear that both NFTs and memecoins are the in-game currencies of the internet, experimental token implementations will play a key role in informing the products that connect these two categories.

The concept of “intent” has shifted from a crypto buzzword to a pillar of AI. LLMs and advancements in natural language processing have unlocked unprecedented opportunities for interaction, offering users the ability to naturally convey desires and the set of conditions for AI to navigate.

Community-based businesses aren’t a new concept, but their trajectory over the past few years feels notable. Many companies today, both in crypto and elsewhere, are tapping into communal / social networks to shape (or in some cases replace) their product offerings. This approach leverages the built-in trust and communication within these groups, allowing businesses to grow organically alongside the communities they’re tailored towards. Companies gain loyal customers, and community members find a sense of belonging. The end goal for members is a sense of ownership; a stake that drives them to contribute and stay engaged. While community has become a buzzword in crypto, early NFTs and DAOs exemplified this shift by transforming online gatherings into collectives where shared identities merged with financial incentives, challenging a number of traditional structures. The result was a class of companies that thrived on the involvement of their members.

Fairness has become an increasingly touchy point for many in the crypto industry. While the concern has been at the back of mind of many retail investors, it recently spilled into the public conversation with the release of Cobie’s substack article, uproar around token distribution models from protocols like Renzo, and the overall positive sentiment towards fair memecoin launches. As traditional projects often lean heavily on VC backing, a growing sentiment among retail investors has sparked a shift around fair launch mechanics.

Tokenized stocks are revolutionizing finance by turning traditional shares into highly fungible, extensible, and interoperable digital tokens with utility across the DeFi ecosystem. This shift promises greater accessibility, faster transactions, and lower fees. With the tokenization market projected to hit $24 trillion by 2027, the future of equity investment seems poised for a major transformation.

As Apple embraces OpenAI's ChatGPT, the landscape of artificial intelligence is entering a pivotal stage. The partnership not only signifies a massive leap forward in Apple’s ML capabilities, it solidifies a powerful partnership between the AI behemoth and the world's largest consumer brand and tech company leading many to wonder what this could mean for OpenAI’s dominance going forward.