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The presenters recommend seeding a set of production traces (with scores) into your Langfuse instance to have more data to analyze, and using the production traces you find interesting — especially the problematic ones — to build up a dataset for your offline improvement process to make a better application.
The hosts describe a hypothetical starting sequence for someone starting tomorrow: take your repository and use the Langfuse skill to get set up with tracing so you really know what's happening, review traces manually to understand what people are doing, then get into the motion of building out the traces and eval setup for production traces and optimizing offline.
hypothetical framing by speakers
During contact exchange, keep the conversation going with no awkward silences — figure out where her friends are and what she'll be up to while solidifying plans for a potential meetup. Don't just get a quiet, silent phone number and nervously eject; stay in the interaction, solidify the plans, and end on a high note.
When she's leaving soon and going out that night, close by proposing a plan that fits her reality: mention you might be going out later and could meet up after dinner, or she and her friends could come party. The close should fit her archetype and her weekend reality.
Coach Kyle advises steering the conversation into a slightly more intentful, flirtatious direction gradually — for example, saying 'I like [her ethnicity] people, I think' instead of 'I like you, you like me' — and asking probes like 'did you have a good experience or bad experience with [ethnicity]?'
Laurie Voss recommends exporting your failing traces from AX along with their explanations and handing the whole batch to Claude Code, Cursor, or another coding agent as context. He calls this one of the highest-leverage things you can do once you have evals running, and notes the Arize Skills plugin works well because you don't have to know how to call the API — you can just tell Claude Code to pull down some traces, see what's wrong, and make the application better, and it works.
To debug voice agents you need to see the conversation beyond just the transcript: audio, transcript, and trace all in one place. View the session as a whole — multiple back-and-forth turns from the user in the same trace view — and replay the whole conversation: the session ID stitches every turn into a timeline with every WebSocket event tagged by speaker. Play audio directly from the trace in the UI with no external tooling or exports. Replaying calls lets you see who spoke when, when the agent paused, where it got cut off, where the user got cut off, and what tools the agent called. With a session view you can scrub the audio and find failure surfaces within the tools and what the agent is doing.
Replace a sales-led onboarding process with an AI-driven self-service experience. The user types in their business name, and the AI automatically crawls the web to find and fix issues, pulling in competitor data and showing a competitive landscape. This transforms the customer journey from a manual, sales-dependent process to an automated, AI-powered one.
Inspect for economic buyer access early in the deal cycle by pulling call data from Gong and CRM enrichment tools like Clay to verify contact with the decision-maker, not just the rep's claim. Build a mechanism to inspect how contact was made with the economic buyer (direct vs. indirect connection) to get deeper deal intelligence.
When inspecting pipeline, measure the window within which deals convert and exclude deals outside that window from coverage ratio to avoid diluting your view of real coverage.
Mark (host) recommends picking a metric you can actually move during the pilot, not a downstream metric with too many variables. Shaun Crimmins agrees, advising to pick a metric the buyer cares about that ties back to broader business impact (revenue or cost), move it during the pilot, then present the extrapolated annual value to the business champion. Don't try to tie the pilot metric directly to revenue or risk in the initial kickoff; start with the operational metric the buyer cares about and connect it to business impact later.
Shaun Crimmins recommends building two champions: a business champion responsible for the outcome and a technical champion living in the workflow every day. Identify both before entering a pilot. For the technical champion, learn how they do their work today, what metrics they care about, and how you can show tangible value to them in a short period of time. Deliver value to the technical champion to make their life easier so they are less likely to say the team can build the solution themselves. Consider the politics behind each champion: what they have to gain or lose by you coming into the organization.
Shaun Crimmins recommends agreeing on conversion criteria before the pilot starts: define success upfront and establish that hitting those outcomes should lead to a paid agreement. At the week-zero kickoff, ask the customer: 'If we achieve these agreed-upon success metrics, will you move forward?' If the customer answers 'no', it indicates the metrics don't truly matter, the deal is not a priority, or you are not talking to the right stakeholders. Use this question to surface hidden blockers such as politics, other stakeholders, or competing business priorities early in the pilot.
Rob Walling advises that if a founder is not willing to go full-time on their own startup, it is a vote of no confidence and the company is unlikely to succeed. As a non-founder employee in such a startup, you should leave—the slow traction is structural, not a product problem. He recommends either starting your own company or getting a job elsewhere. He describes a specific situation where a founder is part-time, has hired a remote CEO while the team is co-located, which he calls a structural disaster.
Kumar identifies a big hedge fund problem: an analyst works on an idea for a couple of days, then the idea loses the PM's interest or something else takes precedence, it doesn't come back for months, and the analyst misses a key price window. His response: deploy agents that track all of an analyst's notes against the market — even for ideas worked on for only a day — and when an agent detects a dormant idea has become interesting again, flash it not just to the analyst but also to the PM and the trader, prompting them to return to work they haven't looked at in weeks. He adds that timing is the most important thing when striking at an interesting point in the market, even more than which work item you prioritize, so agents should do more than idea generation: they should think about when,
Farza explains that most people scrolling only see the first three seconds of a video, so front-load the hook there. Purposely make the first frame show something your target audience recognizes — for Clicky, the first frame was Xcode because curious developers would be interested — and zoom into the part of the screen where your product is, especially when the product is small on screen.
The biggest cost savings in the market right now is deploying a dedicated instance if you know your base workload. Plan deployments around bursty traffic patterns with some base load; limit dedicated capacity and let spikes spill over to the public cloud. Mixing dedicated instances with burst to public cloud gives flexibility.
Give realtors a VIP/standard tier structure as the offer to present to their clients. The VIP tier costs about $250 more but feels significantly more premium than discounting $250 or giving $250 back to the realtor. If all movers are already master movers, there is no real cost difference between standard and VIP. Keep pushing the VIP price level up until people say no, since the standard tier is always a backup, and the people who have money will spend more. The word 'upgrade' is preferred over 'white glove,' and the VIP upgrade offer is described as the biggest lever.
Offer a free VIP upgrade (or 'free white glove') to anyone a realtor refers, so the realtor's customers think good things about them — the realtor needs to gain from referring you. Arm the referrer with a brag script ('my guy, I've got an amazing moving company, they give all my customers free upgrades') that makes the realtor look great. People go to realtors because realtors are supposed to know the area better and have done it more times — expertise is why the referral relationship exists.
Sexual desires and fantasies can be reached, but only through a calculated buildup: start with non-sexual desires, then biggest turn-offs, then what surprisingly turned her on. Abrupt jumps (e.g., favorite food to favorite sexual position) kill the vibe.
Use saved training trajectories and a controlled intervention to explain a failure, separating equilibrium theory from observed empirical stability.
Demonstrate all 3 repaired bugs and defend why a passed gradient check is necessary but not sufficient for generalization.
Use the tractable fixture and posterior predictive results to separate approximation, sampling and model errors; justify one revised modeling assumption.
Choose sources → to get a library of your own and a daily brief.
Map each implemented component to the original paper and explain 2 reduced-model failure modes without claiming full-scale reproduction.
Use calibration and shift measurements to justify a monitoring or abstention threshold, and give an explicit confounding counterexample to causal feature-attribution claims.
Defend a causal claim or refusal using the DAG, numerical estimates and a counterexample showing why predictive model fit cannot establish identification.
