To become a great comic: travel with great comics or practice at a local club
Joe Rogan says that to become a great comic you must either travel on the road with great comics or practice at a local club with great comics.
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Joe Rogan says that to become a great comic you must either travel on the road with great comics or practice at a local club with great comics.
In the multi-agent workflow pattern, one agent interaction does work and saves a file; you then pass that environment ID to the next agent so it picks up the work with a fresh context window and conversation while reusing the same environment. Jack endorses this pattern, noting reusing the same environment ID lets subsequent agents pick up work from prior agents in the same sandbox. As a hypothetical example, one agent fetches annual reports and saves Q3 filings to the workspace, then a new agent reuses that environment with a fresh context window to do analysis.
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John Stecher advises getting your data story right before attempting to succeed with AI agents: move data out of transactional systems into a store that can be easily accessed, managed, and recalled from an agent perspective. He warns that companies that want to build many AI agents but lack a cohesive data strategy will fail.
John Stecher advises thinking through what your eval set is for testing your agents, and running evals consistently to measure the health of your AI system — he calls this paramount to success. In financial services the quality bar for what agents have to deliver is very high, and the duty to be accurate is paramount.
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Scott Worland recommends building a large set of evaluation samples — tens of thousands, hand-labeled by experts — so you can regression-test your AI system. When it became clear agents were viable, Norm AI was able to take this benchmark set and immediately run more agentic approaches on top of it. He also recommends working directly with domain experts (lawyers) to stand up these benchmarks and evaluations instead of a traditional roadmap, and building a scorecard that determines when an agent or human is successful at completing tasks across various dimensions, then watching it improve over time.
The speaker strongly advises always testing the whole workflow and not just the final answer. He illustrates with an anecdote: he asked a model to change only the background of an image, and the output changed the background to cream but added two handles — showing that checking only the final output misses workflow errors. He discourages looking only at the start point and end point of a workflow and assuming the objective was achieved, since the background may have changed but the objective may be broken. If you have a hundred steps in your workflow, each step has to be evaluated for what goes into that step, what comes out of it, and what the expected outcome is.
Jeff Smith describes piping agent access through organizational single sign-on (like Azure Entra ID), which returns a list of scopes and roles describing the user's role in the organization. The database can then be coded to enforce role-based access, e.g., an HR manager can see everyone's salary but no one outside that org can. When natural language questions are generated to SQL, the database dynamically strips data from the result set before it comes back to the user, enforcing data security.
Bakken endorses defining threshold effort as comfortably hard, around 80 to 87% of max heart rate, and, without advanced measurement, being able to say three to five words in a sentence before going after your breath.
Bakken recommends that with four or five running sessions per week, two of those sessions should be sub-threshold (threshold) work.
Bakken endorses keeping easy-run heart rate below 70% of max heart rate.
Bakken endorses a weekly split of threshold (sub-threshold) work for two sessions, one harder session, and easy running on the remaining runs.
Bakken endorses having control over the recovery window: making sure there is enough time for recovery after the hard X session so fatigue does not accumulate, and allowing two days of easy training afterwards.
Commercial context: The creator describes this as their own product.
Bakken recommends adding one session each week where you go harder, well above threshold, where you can push the pace, but limiting it to once a week.
Bakken endorses that with threshold work, one easy day after the session is usually sufficient, so you gain control over the recovery window.
When reaching out to an executive after meeting their team, open with a script that names the team members, summarizes their main hurdles, quantifies the impact, and asks to validate the perspective together.
Set a redline deadline — a set of deadlines for when the customer's legal team delivers first-round redlines, when you respond, and when they return their second response — to project-manage the vendor review stage. Be maniacal about project-managing the customer through the legal process once you send over a contract, because that stage is where sellers most often fail. Even without a commercial give-get to back it, set the deadline anyway: just stating it incentivizes the customer to hit the dates and gives your champion leverage to push their legal team.
Get the next meeting in the calendar before ending the conversation — if you are losing deals, this is probably why. Depending on your ICP, prospects will really respect the practice.
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Early in the sales process, meet with end users to learn what is working and what is not, then package those findings into a point of view that can be used as leverage to secure a call with the executive team.
The moment you are picked as vendor of choice, ask your champion to kick off procurement, security, and legal at the same time instead of sequentially — this alone can turn a six-week review into two weeks.
Daksh Gupta lays out a basic recipe for building a review agent: get yourself a sandbox (you can use Firecracker instead of buying one from a vendor), put an agent in the sandbox (the agent can be Codex or Claude Code; he personally recommends going model independent, using something like OpenCode or Pi as an open-source harness), connect your GitHub or GitLab with a PR webhook that sends the contents of a pull request when it is opened, give the agent a simple prompt like 'review this and send back some feedback', and wire the agent's feedback back using GitHub's review APIs. About five minutes later, you will get some feedback on your pull request.
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