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Under the 30 connection rule, if the target is posting on LinkedIn you warm them up by engaging and commenting before pitching; if they're not posting, you hit them right with the pitch. For prospects with no LinkedIn activity or signals, Kade goes generic and sends a more standard message but still gives ideas and signals — though most of those people don't respond.
Prathik Naidu lays out a sequence for AI deployment in an acquired business: build your tech and capture the workflows, commit a lot of team time into making it work, partner very closely with the company's team and iterate to validate your product, potentially train new models, examine all the unique workflows and data within the business, and then put the unlocked capacity to work to generate real business impact.
Centralize verification authority in a web service when running multiple agents. Have agents report completed work through an MCP connection and ask a verifier 'Am I done? Please verify it'. Verify a PR was created correctly by going to the GitHub API and checking that the PR exists, is healthy, and all merge checks pass. When verification fails, redirect the agent with a list of the specific things that are still failing.
endorsed
Keep receipts from every run — this value cannot be overemphasized. For an e-commerce app, save receipts of the entire user journey: login, page hits, errors seen, catalog load time, cart actions, and cart abandonment. Every run receipt can be fed to LLMs, or you can set up a system running all night long that aggregates the latest receipts. An overnight aggregation system can identify patterns like eight failures in the caching module and issue a PR that night to fix it properly.
endorsed
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. Also build a foundational eval that shows what good looks like in your application.
endorsed
The speakers describe that a fixed app can still lie: after reloading, the page could say 'waitlisted' while the screenshot says 'you're in, your seat is confirmed' — a mismatch that is a failure. Confirmation text that breaks the contract (not exactly one-to-one with what the model saw) causes the judge to fail even when the feature worked. They recommend avoiding tightly coupling verification to the exact text on the screen and proving things worked without that tight coupling, noting the answer is system-one models.
If your web app lacks a bespoke CLI that lets any agent fully flex the functionality you care about verifying, you'll have a much rougher time using prompts alone. Build a bespoke CLI (like their 'Snack' tool, run with pnpm snack) that lets any agent verify the functionality you care about.
Build a script that proves the work deterministically across a number of checks and feeds output back to the agent. Pin verification to a single command backed by actual bash commands because LLMs are non-deterministic and 30 agents with the same prompt produce variance. The agent can run the script repeatedly — run, check, verify, run again — until there are no more errors.
Require actual screenshots, videos, test results, and smoke tests as proof that an agent did the work. Accepting an agent's verbal claim that 'it passes, it looks good' without backing evidence leaves you with nothing to verify the work.
endorsed
Give your agents the ability to see all aspects of performance — networking, traffic, rendering, etc. If you're shipping a web app, give your agent Chrome DevTools. If you're shipping native clients (iOS or OSX apps), attach alternative observability projects, with more coming out every day. The common foundation across all platforms is that you need to give your agent the ability to see absolutely everything you would normally see as a developer. The more eyes you give your agent, the more it will be able to take over from you.
endorsed
Denys recommends having technical implementations and detection systems to safeguard your own data cleanliness. Unless you have good systems to detect duplicate records, you just keep getting the same bad quality data. Once you can detect duplications, you no longer incentivize things like sending claims multiple times with different dates or the same claim. As an organization, you have to have lines of defense in order to make sure that missing records get flagged.
With claims data extracted, insurers can analyze what affects loss ratio, claim types, geographies, injury types, and doctors involved; map which lawyers, doctors, chiropractors, and physiotherapists work together; analyze which medical conditions co-occur (mapped to ICD codes) and compare write-ups from different doctors; analyze patient travel within provider networks; estimate treatment cost changes over time (e.g., a 10% year-over-year increase for a treatment type for a given doctor); and do counterfactual analysis on network changes, billing changes, and standard-of-care procedures.
Consequential creator advice requires independent verification before Today can promote it.
When a new model claim arrives via Slack or social media, Martone recommends not trusting public benchmarks. Instead, set the model up and run an internal experiment against your own benchmarks — ideally via a closed-loop system (e.g., a Kubernetes loop) that automatically benchmarks new claims on your own data.
endorsed advice
The agent's action log must be outside the agent's primary hands and brains because you can't trust the agent itself; you must be able to answer who did it, which is where the tamper-resistant auth log comes from.
The creator recommends putting the brain (model/LLM) in a sandbox with the tools inside it, and placing an egress proxy outside the sandbox as the authorization boundary. Building an agent loop with the brain inside the loop rather than outside the loop is easy to do and problematic. You can control what's inside the sandbox so it can't get API access on its own, and apply authorization at the egress proxy boundary rather than inside the agent.
Production agent teams should discover what's running and what the agent's capabilities are, validate inputs and outputs, and constrain agents using egress proxies and different networking rules.
The creator recommends keeping credentials and access separate from the agent itself so there is no broad standing access the agent has, noting that agents are deeply goal-seeking, so a random key found on a disk will be used by the agent.
Give agents a token that is both time-limited and budget-limited; access ends when the token expires or the budget is exhausted, and a supervisor — not blunt rate limiting — decides whether to grant more budget.
The creator recommends giving agents a token that is time-limited and also carries a budget of how much resource they can consume, and providing continual feedback to the agent about how much budget was consumed and how much budget remains so it can operate within a budget like a person would. Traditional rate limiting just cuts an agent off and the agent doesn't know why it got cut off.
Ian recommends using broad-based deterministic policy that says what must never happen, or what always requires a human to be involved. He describes that there's a big gap between the policy you can write as a human or organization and what you actually want the policy to be in the specifics of a specific task, and humans can't write it all. He recommends layering deterministic policy with user-or-agent-to-agent interaction that carries transactional intent between the two, plus the business's intent for the specific task.
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.
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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.
