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Building the system of record for onchain finance at Allium.
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Michael Ryaboy
Thanks to the resurgence of RL, LLMs are finally able to reliably coordinate tools and reasoning to do high-precision retrieval. Companies like Happenstance, Clado, and Mintlify have already shifted to agentic search, and it's only a matter of time until anything less feels broken to users. Link to full blog in comments.
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April Underwood
When Sara Beykpour started building Particle in early 2023, there was no playbook — no dashboards, no eval tools, no prompt libraries. Two years later, the AI stack looks entirely different. In our latest piece from our Adverb Ventures new series “Field Notes,” Sara Beykpour and Jennifer Jang share how they’re building their LLM stacks, what to buy, and what actually gives them an edge. They get into: 🧰 Build vs. buy → control vs. speed ⚙️ Prompt & eval design → how to engineer for reasoning 🧩 Evals vs. A/B tests → keeping your baseline solid while you climb higher It’s a candid look at the trade-offs founders are making as they scale AI products in real time. 👉 Read it here: https://lnkd.in/gCUXgEQv
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Vijay R.
A single plugin can move markets. Models / APIs → Workflows / Skills → Solutions / FDEs Foundation model companies are no longer just “plumbing.” When the model owner ships the workflow, value migrates up the stack. Anthropic’s launch of legal plugins for Claude wasn’t just a product update — it was a signal. Public legal and information-services companies sold off sharply, as investors repriced what had been viewed as “AI beneficiaries” into potential displacement targets. ▶️ So what changed? /review-contract /triage-nda /vendor-check /respond Claude introduced legal-specific plugins that automate real workflows: contract review, NDA triage, compliance checks, legal research assistance. These aren’t demos — they’re opinionated, end-to-end workflows, shipped directly into enterprises and bundled with the core model subscription: ▶️ Why the market reacted? ➖ Proprietary data alone is no longer a sufficient moat when competition shifts to the workflow layer ➖ The “ChatGPT for X” playbook just got much harder if foundation model companies ship vertical products themselves ➖ Investors may be pricing in a future where model vendors move beyond APIs into full solutions — potentially even with forward-deployed teams For founders and VCs, this forces a reset. The key question is no longer “does this use AI?”. It’s “what actually prevents the model provider from building this natively?” Overreaction — or the start of a structural shift? Do model companies stop at tools, or move decisively into solutions?
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