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Mindset 5: 'I don't need to convince anyone of anything' — the upright apology

A major reason people apologize is to get forgiveness and absolve their guilt — children especially apologize to get something. The natural move after a cold reception is to double down on convincing the person you're sorry, but the coach does nothing; he hopes Ray accepts the apology without begging. Joe Hudson calls this an upright apology: neither begging for forgiveness nor escaping blame, just standing in it because you want to change. Dropping into convincing mode puts pressure on the other person instead of offering something, so adopt the mindset 'I don't need to convince anyone of anything.'

Charisma on Command · 6 Mindsets That Make People Respect You During Conflict

Excuses dodge responsibility; explanations don't

When people screw up, their instinct is to make excuses to protect their image. The host draws a line: an excuse gives a reason and then uses it to dodge responsibility. The recommended move is to give an explanation for what happened while still taking full responsibility — as the coach does — because blaming or deflecting would have made him look weak and defensive.

Charisma on Command · 6 Mindsets That Make People Respect You During Conflict

Mindset 1: 'No matter what, I will be okay'

Before entering a conflict, adopt the mindset 'no matter what, I will be okay.' The host says this is less positive thinking than flipping a switch in your brain: it reframes the situation from a threat to a challenge, so you approach the conflict instead of withdrawing.

Charisma on Command · 6 Mindsets That Make People Respect You During Conflict

Acknowledge the full scope of harm so the wronged party feels understood

The coach names not just the stolen goods but the inconvenience, time, and headache Ray's organization suffered. Apologies often fail because they don't acknowledge the full scope of the wrong — e.g., a partner not doing dishes makes you feel disrespected, not just burdened. Acknowledging the full depth makes the other person feel understood, like you share a reality, and therefore trustworthy.

Charisma on Command · 6 Mindsets That Make People Respect You During Conflict

When you don't know what to do, do nothing: get still before big decisions

Matt Paulson's counterintuitive advice is that when you don't know what to do, do nothing. Big inflection-point decisions don't get better by spending more time thinking about them, and seeking opinions from 15 different people drowns out your own voice. Instead, step back and get still through meditation, breathing exercises, music, or working out to remove noise and reconnect with your initial intuition. He notes there are neurons in your gut and heart, so information comes through outside your brain that you should listen to.

Foundr · The Near-Death Experience That Built a $400M Year Business

Workarounds and effort are the strongest evidence a problem is worth solving

Most of the time, the person with the problem you want to solve is not actively experiencing it at that moment, so they'll have to think back. You want to know what users do instead — their workarounds — and how much that workaround costs them and how often the problem happens. Find out how much effort the user is already putting into solving the problem: that is gold and really strong evidence the problem is worth solving. If the user can't remember the last time the problem happened, that's a signal — not the one you want, but one you want to have. Conversely, if users say they don't remember and just gave up, that's a signal they probably won't pay or change workflows. If they are actively experiencing the problem right now, that's a good signal and you'll get really direct answers.

AI Engineer · Build the Right Thing: Product Engineering (Part 1) — Kent C. Dodds, EpicProduct.engineer

Don't ask users to evaluate your idea or diagnose the problem (Mom Test)

Per the Mom Test, Kent C. Dodds says you don't want to ask users to evaluate your idea or diagnose the problem for you; people naturally look for someone to say the problem is huge and they'd spend untold sums to solve it, and talking about your specific solution before users agree the problem is worth solving is a mistake. Weak questions include 'Would you use this?', 'Is this a problem?', and 'What is the problem? Help me define the problem.'

AI Engineer · Build the Right Thing: Product Engineering (Part 1) — Kent C. Dodds, EpicProduct.engineer

The right number of credentials in a sandbox is always zero

Jim Clark's maxim: the right number of credentials to have in a sandbox is always zero — credentials should never be in there. If there are absolutely no credentials in a sandbox, the blast radius of an agent doing something incorrect is reduced. He also says never have any credentials anywhere in any harnesses.

AI Engineer · How Many Credentials Should Your AI Agent Have? Zero. — Jim Clark, Docker

Scope credentials to the moment of need: Git signing keys only when committing

Jim Clark notes that in an hour of agent work, the agent might need his Git signing keys for only about two minutes of that time. When committing, the agent needs the Git commit tree, the ability to make the commit, and signing keys — but nothing else. He would like the sandbox an agent runs a task in to be modeled after the intent of what the task is doing: if the agent is not making a commit, commit signing keys should not be inside that sandbox. If agents know the intent of what you're trying to do, use that intent to model the capabilities you give them.

AI Engineer · How Many Credentials Should Your AI Agent Have? Zero. — Jim Clark, Docker

Keep judge output simple: binary labels, not numeric scales

Keep judge output choices simple — binary is best, two clear labels. Numeric scales like 1–5 are tempting but LLMs are bad at them because they can't distinguish a three from a four. If you really need more categories, use a partial result like a 0.5 score, but by default go for zero or one. Under the hood the judge is given a tool it calls with correct or incorrect, so you don't need to specify output in the prompt.

AI Engineer · From Vibes to Production: Evaluating and Shipping AI Agents That Work 101 — Laurie Voss, Arize AI

Don't use a correctness eval when the agent uses live data beyond the judge's training cutoff

In the demo, the correctness eval scored zero on every row — it completely failed. The correctness eval failed because it judges against the LLM's training data, while the agent used live web search for current 2026 stock prices the judge couldn't possibly know. Don't use a correctness eval when your agent uses live data beyond the judge model's training cutoff — it will complain about information from the future it couldn't know. Instead, use a faithfulness eval when your agent uses live data, because the judge actually has context and is on a level playing field with the agent.

AI Engineer · From Vibes to Production: Evaluating and Shipping AI Agents That Work 101 — Laurie Voss, Arize AI

Know which evals are guardrails (ship blockers) vs North Star metrics

Know which of your evals are guardrails (ship blockers) and which are North Star metrics (aspirational), because it changes how you act on results and set up production monitors. Some evals are guardrails and ship blockers: if an agent hallucinates a stock price, that is a hard fail — don't ship. A guardrail regression should page someone in the middle of the night. A North Star metric dip is something you look at in a weekly report.

AI Engineer · From Vibes to Production: Evaluating and Shipping AI Agents That Work 101 — Laurie Voss, Arize AI

Don't build a single 'God evaluator'; split into one evaluator per dimension

Don't build a single 'God evaluator' that checks accuracy, tone, completeness, policy compliance, and formatting all in one prompt — it is a nightmare to calibrate. A God evaluator that fails makes it impossible to know which of its five dimensions failed. Having a God evaluator emit 15 different labels becomes too complicated to judge or score by. Split evaluation into one evaluator per dimension instead of one mega evaluator; the combination gives a richer picture than any single eval.

AI Engineer · From Vibes to Production: Evaluating and Shipping AI Agents That Work 101 — Laurie Voss, Arize AI

Code evals: deterministic, cheap, and the first line of defense

The next step after knowing what's wrong is code evals — no model configuration, no API calls, just Python. A code evaluator is a simple Python function that returns an evaluation result with a label and a score; it needs no LLM and no API call and returns instantly. Code evaluators are completely deterministic functions that take input and output and decide whether the output is good according to some definition of good — anything with grading logic that always gives the same answer for the same input can be a code evaluator. A code eval doesn't have to be a simple string operation — the grading logic just has to be deterministic; it can query a database or hit an API. Use simple deterministic code evaluators as the first line of defense in your evaluation stack (Swiss cheese model).

AI Engineer · From Vibes to Production: Evaluating and Shipping AI Agents That Work 101 — Laurie Voss, Arize AI

Renewal is the prime expansion moment — but only with demonstrated value

Treat renewal as a commercial milestone where you present accumulated value evidence and ask for more (years, business units, products). Never ask for more money at renewal if you've only met the account cold — e.g., second meeting in eight months — without demonstrated value; that's like a stranger knocking on the door selling old brooms.

30 Minutes to President's Club | No-Nonsense Sales · #604 - Upsell & Expansion Masterclass: 7 Lever to Grow Any Account

Don't drop the bell — dropping ends the challenge

The single governing rule: the moment the kettlebell hits the floor, the challenge is over. Resting is allowed only by holding the bell (goblet hold or clean position), never setting it down.

iamariankanani · https://www.instagram.com/reel/Dcbq4SHSbew/

Four key valuation drivers: growth, ARR, churn, profit (in order)

The four key things that impact valuation for a B2B SaaS business, in order, are growth, ARR/revenue, churn, and profit. Founders should optimize them in this order.

MicroConf · When to Sell Your SaaS Business: How to Get a 5x Better Offer

The core of the PM role is hypothesis definition and testing, not trappings like docs and presentations

Tara Seshan states that the core of the PM role has always been about asking the most essential question about your product: what determines whether it works or not. Many trappings of the job (execution on time, writing docs, presentations) have fallen away, and the hypothesis definition and testing loop has become the most important thing at the company. Everyone (EMs, engineers, data scientists, designers) now focuses on this.

Lenny's Podcast · AI’s third era: the rise of persistent AI coworkers | Tara Seshan (OpenAI’s product lead)

Make your entire business accessible to agents via APIs

Lincoln Murr recommends that companies make their core business functionality accessible to agents via APIs, translating all human-available functionality into a world where agents can interact with it, so customers keep using the product.

AI Engineer · Most of Your Docs Readers Are Now AI Agents - Hahnbee Lee, Mintlify & Lincoln Murr, Coinbase

Create niche, single-purpose agents and break work into specific tasks per agent

Jack endorses creating very niche and specific targeted agents that have one core purpose they do really well, and breaking work into specific tasks per agent to keep things controllable and ensure tasks are done well. He discourages giving a super broad task to an agent, since it then has no way of measuring what it's actually doing.

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AI Engineer · Build Your Own Team of Managed Agents - Jack Wotherspoon, Google DeepMind