Creator perspective · MoneyHari Kumar identifies one of the biggest problems in hedge funds: an analyst works on an idea for a couple of days, it loses PM interest or something else takes precedence, and it doesn't come back for months — so the analyst misses a key price window. He recommends building agents that track all the notes an analyst has taken, even from a single day, against the market, and flash the idea as interesting again to the analyst, PM, and trader when it becomes interesting.
AI Engineer · Proving Alpha: How a Hedge Fund Measures AI's ROI - Hari Kumar, Contour Asset Management
Principle · MoneyThe hosts state that communicating a product is on-device is the most important trust message for local AI products. A founder who applied to YC with an open-source text message analyzer that read iMessages entirely locally used a hero saying 'open source, completely on device' and communicated that the internet doesn't touch the data — which they learned is what users really cared about. An 'on-device LLM' label is also a trust signal displayed on AI sites, though the host questions whether the wording is necessary.
Y Combinator · How To Stand Out In A World Of AI Designs
Principle · MoneyVague, generalized copy (e.g., 'institutional intelligence and decision infrastructure') leaves visitors unsure whether they need the product. Copy should use specific language about who should use the product and why, and give specific examples of what the product does and the problems it solves. Copy that lists claims without explaining what the product actually does fails the visitor — even after reading 'years of history, alive from day one, nothing installed, no interviews,' the reviewer still didn't know what the product does.
Y Combinator · How To Stand Out In A World Of AI Designs
Principle · MoneyIsabelle Williams argues that agents should operate using an agent token with scoped access issued from your application's backend, instead of taking the user's token or an API key, so agent actions can be audited, revoked, and permission-limited. Handing an agent the user's own token breaks auditing and attribution, prevents independent revocation of the agent without impacting the user's session and access, and prevents limiting the scopes of what the agent can do.
Commercial context: The creator describes this as their own product.
AI Engineer · How to Let AI Agents Sign Up for Your App Safely — Isabelle Williams, WorkOS
Principle · MoneyGiving an agent a user's API key instead of a scoped agent token means you cannot audit, revoke, or limit the agent's permissions.
AI Engineer · How to Let AI Agents Sign Up for Your App Safely — Isabelle Williams, WorkOS
Principle · MoneyGary Vaynerchuk advises that every episode of a live show should be post-produced into as much derivative content as possible, because failing to do so wastes content that was already created.
GaryVee · You Might Be Making Content Completely Wrong
Principle · MoneyRob Walling advises that if you're bootstrapping, you shouldn't compete head-on when replicating an existing idea. Instead, bring at least one differentiator: a unique focus or position (best X for Y while others chase the broad market), a unique traffic source such as ranking high in Google, or a lower price.
Startups For the Rest of Us · Episode 854 | How to Find Startup Ideas (Sneak Peek Chapter from My New Book)
Principle · MoneyGaryVee advises focusing on winning over the 10% of people who can detect your intentions. When they smell that you are not actually trying to sell them anything — that you are just listening to where they're at — you completely dominate the 90% and win over the 10% who are rarely won over, making you an unstoppable salesman.
GaryVee · The Biggest Opportunity Most People Are Ignoring
Creator perspective · MoneyAl Chen points out that most scanning tools (Sysdig, Wiz, Prisma) only check whether a package has been bumped to a specific version and say nothing about whether the exploit is still in the application code. One solution is creating a Harbor task — like a harness or world for an agent to try things out — containing instructions for the agent, an environment with the actual Docker images, and tests to run against the agent.
endorsed
AI Engineer · Shipping AI Into 5 Major Banks Without a 12-Month Compliance Wait — Al Chen, Cisco
Principle · MoneyWhen evaluating a predictive system, set the snapshot date to just before the outcome was announced. Live search leaks future knowledge into an evaluation of the past, so constraining the snapshot prevents the system from being informed by the confirmation of the outcome.
AI Engineer · Backtest Your Agent Against Yesterday's Web — Ryan Ahern, Exa
Creator perspective · MoneyNick Nisi recommends leaning away from relying on skills alone for verification because it's up to the model whether it follows that path; instead prefer CI enforcement of the commands. Verification checks can run regularly as part of CI. Hooks are another option — though a git hook is probably the last one he'd reach for — and hooks are relevant for local development across a whole team because they can be shared. He also suggests putting an agents.md (not a claudemd) in the app with wording like 'on change, verify things by running this' or 'before you open a PR, do this'; with that in place, agents have to go out of their way to ignore or avoid the verification instructions. One could even build a full pi extension to keep verification running in the background and feed in information.
AI Engineer · Teach Your Agent to Prove It's Done — Nick Nisi & Zack Proser, WorkOS
Creator perspective · MoneyNick Nisi recommends that if you have a web app and don't already have a custom verifier CLI, building one is one of the highest-leverage things you can start with for your codebase. He stresses that you must build tools that fit your specific application — without them the model has no way to prove correctness for your particular app — and that the tooling should produce detailed output about what happened and the state of the system so it can be audited by the application and the model.
AI Engineer · Teach Your Agent to Prove It's Done — Nick Nisi & Zack Proser, WorkOS
Principle · MoneyThe speakers warn that a successful screenshot is not proof: make sure it actually shows the state you expect and that the commands to reach that state actually ran, otherwise it might just be another lie. A screenshot might have taken a different path that's not actually possible within the UI or caused side effects — verify it ran start to finish.
AI Engineer · Teach Your Agent to Prove It's Done — Nick Nisi & Zack Proser, WorkOS
Creator perspective · MoneyThe speakers describe giving the agent a map with a verifiable destination — 'you must get here. You'll know you're here when this command returns this' — and note you don't necessarily have to prescribe how the agent gets there. If you prescribe an exact step-by-step path, agents may not sway from it, and you want them to sway a little while still reaching the right end goal; given a destination without a path, they may wander, catch more edge cases, figure something out, or ask a question that unlocks something.
AI Engineer · Teach Your Agent to Prove It's Done — Nick Nisi & Zack Proser, WorkOS
Creator perspective · MoneyNick Nisi emphasizes that the value of keeping receipts from every verification run cannot be overemphasized. For an e-commerce app, receipts should cover the full user journey: login, page hits, errors seen, catalog load time, cart actions, and cart abandonment. Those receipts can be fed to LLMs, or a system can run all night aggregating the latest receipts and reporting failure patterns — for example, eight failures in the caching module — and a PR can be issued that night to fix it, with proof included in the PR that the issue is fixed.
AI Engineer · Teach Your Agent to Prove It's Done — Nick Nisi & Zack Proser, WorkOS
Creator perspective · MoneyDavis says the more successful enterprises onboard agents like they onboard developers: agents do not get full commit access on day one and must add levels of capability as they mature. When determining an agent's autonomy level, enterprises should evaluate the context that makes the agent safe, including the environment around it, not just what the agent attests about itself. Whether an agent is running in a sandbox is a simple example of environmental context that can change the level of autonomy allowed.
None stated.
AI Engineer · An AI Agent Got Its User Banned From Resy — Keycard, JPMorgan Chase & AAuth
Creator perspective · MoneyDick believes there should be a separation of concerns between a worker agent that does the work and a separate supervising agent that decides what the worker agent gets to do; the supervisor cannot do any of the work itself. The supervising agent should be independent of the agent doing the work — it runs your policy and decides how things get done. Without an independent supervising agent, guardrail decisions about what an agent can and can't do get decided by the agent's vendor — and Dick does not want to cede that control. A request to read the CEO's email is outside the scope of an instance agent and should be rejected as intent from the user; a request to forward the CEO's email to the outside world is vaguer and should come back to a supervising agent, which would decide it is a bad
None stated.
AI Engineer · An AI Agent Got Its User Banned From Resy — Keycard, JPMorgan Chase & AAuth
Report · MoneyLaurie Voss argues you have to write down what good means before you measure it — you can't say your agent doesn't work if you haven't specified what working means. For a good financial analyst report: it should use real current data, be about the right company, stick to what it actually found, and (for his purposes) say what to do — buy, hold, or sell the stock — not just give analysis. Requirements sound obvious once written down, but before that they could be vague, or different people on your team could have different ideas about what a good agent would do. Writing requirements down turns vague disappointment into specific things that you can test.
AI Engineer · How to Know If Your AI Agent Is Any Good: Evals With Phoenix & Claude — Laurie Voss, Arize AI
Report · MoneyLaurie Voss insists you have to read your traces before you write a single eval: read a dozen or more of them end to end — what was the input, what was the output, what specifically is wrong. Manually inspecting traces sounds manual and old-fashioned, but it's an incredibly high-value thing you can do when building an agent. You have to manually inspect what is going on so you can find the unknown unknowns that you weren't expecting. Anthropic's team regularly sits down and reads eval transcripts by hand and has built a bunch of internal tooling to make that easy to do.
AI Engineer · How to Know If Your AI Agent Is Any Good: Evals With Phoenix & Claude — Laurie Voss, Arize AI
Creator perspective · MoneyLaurie Voss describes the three eval types (code, LLM judge, human), their individual flaws, and recommends using all three simultaneously because they are complementary.
AI Engineer · How to Know If Your AI Agent Is Any Good: Evals With Phoenix & Claude — Laurie Voss, Arize AI
