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Day 1 · Oct 13
Bridgewater built Vault, a governed 100-million-document platform for external and internal unstructured data, because frontier models fail on the open web — and the real prize is an always-on agent that captures what the firm learns back into the corpus.
Day 1 · Oct 13
Anthropic's Belinda Neal and Keon Shahab argue that model intelligence now runs far ahead of adoption in financial services, and they lay out a five-layer agent foundation (MCP connectors, skills, identity and permissions, approval, evals) plus Claude for Financial Advisors as the proof that the same pattern scales fro…
Day 1 · Oct 13
Two Sigma's John Feminella retells Citibank's $900 million Revlon wire error to show why agent safety and speed are not a tradeoff, then lays out five composable controls — an agent registry, risk-scored review, declared-purpose tool access, shared evals, and classifier-governed runtime — that let a regulated firm run…
Day 1 · Oct 13
The author of Code Simplicity reduces all software success to understanding and execution, splits software into durable versus disposable, and answers the title directly: quality matters for durable software, not for disposable software — and rigor does not have to be expensive.
Day 1 · Oct 13
Aparna Dhinakaran (Arize AI) argues the AI engineer's job has shifted from writing AI features to building the always-on loop — agents, evals, and telemetry — that writes, reviews, and improves those features, illustrated by Arize's Signal agent.
Day 1 · Oct 13
Wells Fargo pre-trained its own 1-billion-parameter transaction foundation model from scratch on billions of monthly customer transactions, and it beat the production XGBoost fraud model on detection rate, false positives, and latency.
Day 1 · Oct 13
Arca, an "AI native" wealth-management services firm, gives agents four primitives (signals, operational context, a harness with persistent memory, and human collaboration) so they own 10-day client goals end to end — and the firm steers the business by steering its agents.
Day 1 · Oct 13
A Keycard-moderated panel on runtime authorization for AI agents: approve each action against the agent's mission rather than standing OAuth scopes, using AAuth missions and budgets, supervising agents, egress proxies, and tamper-proof audit logs.
Day 1 · Oct 13
A certified financial planner at Range explains how non-coding planners wrote the Claude skills they use daily, and gives a five-step method (pick, teach, pack, test, ship) for getting domain experts to author their own agents.
Day 1 · Oct 13
A Keycard demo talk that first lets an overprivileged on-call triage agent leak customer PII and drop a production database, then rebuilds the same agent with OAuth 2.0 token exchange and intent-based authorization so every tool call gets an ephemeral, task-scoped, attributable credential governed by policy.
Day 1 · Oct 13
Branislav Brzak demonstrates how timing on shared GPUs can reveal model and workload information without reading another tenant’s memory. Reported results refer to distinct experimental setups.
Day 1 · Oct 13
Mahmoud Abdelwahab of Railway replaced his company's marketing SaaS with one agent-built internal tool.
Day 1 · Oct 13
Southbridge AI co-founder Hrishi Olickel argues that no universal schema exists — only four schemas that align solely at decision time — so build just-in-time ontologies, split intelligence into three pipeline roles, and let self-optimizing "Pareto surfing" pipelines drive an Ohio campaign-finance entity-resolution job…
Day 1 · Oct 13
An extra lunch talk from Perpetual's CTO arguing that reliable AI in regulated finance comes from deliberately placing the boundary between deterministic compiled workflows and minimal, typed model judgment - not from choosing "more agency" or "less agency".
Day 1 · Oct 13
Two Flywire engineers open their lunch talk on structured agentic workflow automation — Flywire's compliance context, the Victoria PaaS, and the Orbit platform — before the captured livestream audio cuts off after less than three minutes.
Day 1 · Oct 13
Modal CTO Akshat Bubna shows what agents actually need from infrastructure — sandboxes holding multiple containers and sidecars, shared volumes, snapshots, path-level network control, and agent-facing observability — backing workloads like Hebbia's agent-per-spreadsheet-cell at up to 10,000 sandboxes per second.
Day 1 · Oct 13
Rayan Krishnan of Vals AI argues that model cards hide a jagged, task-dependent frontier, so firms should mine their own work history into rigorous evals to pick models, cut spend, and codify their competitive advantage.
Day 1 · Oct 13
In a Kepler-hosted fireside, former Citadel PM and Candlestick Capital founder Jack Woodruff argues that AI will not change long-term stock picking or divergent-view alpha, but will flatten analyst prep work and turn investing into a time-management game where humans stay the bottleneck.
Day 1 · Oct 13
Revolut's David Abitbol explains how self-hosting GLM on 56 dedicated B200 GPUs cut the bank's token bill to one-tenth, lifted engineering lead time 70%, and still passed EU bank compliance through sandboxing and human-in-the-loop controls.
Day 1 · Oct 13
With AI access now universal, Sid Pednekar argues that advantage comes from judgment plus three pillars: design the right problem, build shared capabilities, and compound the advantage.
Day 1 · Oct 13
Coatue Head of AI Frank Long models a hedge fund as a function that compresses external and internal context into one tuple — selection, size, timing — and decomposes it into evaluable steps, each run by the best of four runner types: code, statistics, LLMs, or people.
Day 1 · Oct 13
General Intuition CEO Pim de Witte presents one model family that converts pixels to actions in real time — trained on Medal's game-play action data, it plays video games at the highest level and controls over 10 robot types, on the theory that scaling pixel space repeats Anthropic's bet on coding space.
Day 1 · Oct 13
In a 98-second closing Q&A, Pim de Witte distinguished General Intuition's action-label-trained foundation models from JEPA, calling JEPA "the more correct approach" in the limit but far less practical short-term.
Day 1 · Oct 13
Nous Research CEO Dillon Rolnick argues that commoditized models leave frontier labs two ways to recoup their trillion-dollar bets — trap business users at the agent layer or "build God" and aim it at lucrative problems like finance — so companies should build agents on open foundations they own, such as the MIT-licens…