6/ 3) We need to design UX for agents — not just for humans AlphaStudio was designed as an IDE for humans, with input fields, buttons, and a layout that visually organizes information. One of the biggest hurdles we encountered was defining the SQL queries, and we always thought, “Give it a couple more releases, and LLMs could handle that part of the process.” The config file was create intentionally as a single document: easy to parse, understand or generate by an LLM. We thought we were future-proof. As we continued developing AlphaStudio, we saw a massive shift in how we viewed software design. The focus was no longer on optimizing for human interaction but on making the experience seamless for the AI at the core. New tools like Cursor are a perfect example of this change. The majority of interaction now happens through chat interfaces, where we no longer need to click buttons. When we "vibe code", we might not even touch the keyboard — we simply use spoken natural language to define our intent. This realization caught us a bit off guard. AlphaStudio was always meant to be an intermediate step, a way to flesh out the core functionality before transitioning to a chat-based interface. The vision for 2.0 was a prompt-driven tool: “Build me an agent that trades breakouts based on RSI and Twitter sentiment. Here’s my API key.” However, as we realized the profound paradigm shift in how AI is interacting with software and MCP servers, we also recognized that by the time we fine-tuned some aspects of AlphaStudio, they might already be outdated. We think people won’t build agents by writing code or dragging blocks — they’ll describe intent.

3/ Next Steps: Arctis 2.0, Carbon Integration & the Future of Data Access We’ve made major improvements to Arctis, our data parsing engine, and recently integrated it with the Carbon framework by @SevenLabs_ — which is amazing and turned out to be an excellent drop-in replacement for several of our custom parsers and decoders. 🔥 If you're curious, you can check out our unit tests — Arctis 2.0 now correctly handles even complex multi-token, multi-pool swaps: 👉 github.com/AlphaArc4k/arctis… Over the past few weeks, we’ve been preparing the indexer to leverage this new parsing logic. Here's what we're currently doing: - We're re-indexing all raw blocks using Arctis 2.0 - These will be compressed into weekly(!) data chunks - After processing, raw block data will be discarded to eliminate redundant, uncompressed storage. Arctis 2.0 is currently covering north of 75% of all on-chain activity(!!) eliminating the need to redundantly store raw historical data. This new format has its own tradeoffs: ✅ More efficient for backtesting, cheaper to store ⚠️ More expensive to expose on-demand as user-facing API Importantly, paper trading will remain unaffected by this change. Looking ahead, we’re moving toward a new hybrid approach: - Real-time data <5m for active strategies - Snapshot-based historical data for fast, large-scale backtesting of the most promising strategies, score development and other initiatives We’re phasing out the in-between model of exposing historical data via API — it’s been abused by some users and isn’t sustainable to maintain at scale in the current market. Once re-indexing and restructuring are complete, we’ll begin exploring how to offer a dedicated paid backtesting service. We've already had promising conversations with other teams about building a MCP service powered by our data infrastructure — for all types of agents, in collaboration with some amazing projects. More on that in a future post.