Recommendation · MoneyThe source recommends that in defense, companies should not wait for the government to specify what to build. Instead, they should watch government signals and build in advance using private venture capital, then sell to the government once contracts appear. The traditional defense prime model—'tell me what you want, I'll develop after you give me the money'—is criticized as producing systems that are not technology-forward, developed for 10 years ago, and not valuable to the end soldier. Castelion is cited as an example: it built a reliable hypersonic missile in advance of government contracts using private venture capital, and the government then showed up and bought missiles. If this model repeats, new companies will take substantial market share from legacy primes, leading to better
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The a16z Show · The Startups Rebuilding American Industry | The AD50
RecommendationJimmy Carr recommends asking each day: what could I do today that I would be super happy I did tomorrow? Like giving a gift to your future self. He frames whether you look back on life with regret or nostalgia as depending on what you do today, and suggests chunking life down to a small unit of time you can think about and control — like tomorrow — instead of contemplating your whole life on your deathbed. He gives examples: write some jokes today and test them tonight, or eat right today and you'll feel better tomorrow.
PowerfulJRE · Joe Rogan Experience #2571 - Jimmy Carr
RecommendationJimmy Carr endorses practicing strategic ignorance: in a world of infinite information, pick some things in life to not give a fuck about, declare 'that's not going to be my thing' about most subjects, and focus on your chosen few.
PowerfulJRE · Joe Rogan Experience #2571 - Jimmy Carr
RecommendationFor the changes that actually scare people — sensitive grants, migration intent, and low-confidence verdicts — the rule is flag and never clear. The agent's job stops at surfacing the risk and the reasoning behind it; the decision stays with the person every time. Grants touching sensitive data always require a human decision no matter how confident the agent sounds, a migration that changes what the data means (not just how it is moved or stored) always requires a person, and if the agent is not confident in its own verdict it does not get to round up to approve — it escalates.
AI Engineer · 60% of Our Data PRs Now Merge With No Human Review - Chetas Joshi, Robinhood
Recommendation · MoneyJack Wotherspoon recommends building multi-agent workflows by pointing different agents at the same sandbox so they can write files and build on each other's work. He recommends passing the environment ID from one agent to the next so the new agent reuses the file system but gets a fresh context window and conversation, and having one agent write its output to a file so the next agent can pick up from that input.
AI Engineer · Build Your Own Team of Managed Agents - Jack Wotherspoon, Google DeepMind
Recommendation · MoneySam recommends moving control out of the agent's context or prompt and into the gateway so agents physically cannot perform unauthorized actions — a more sustainable, scalable, and secure approach than agent-internal controls.
AI Engineer · AI Agents Don't Read Your Policy Docs. They Hit Your APIs — Gravitee
Recommendation · MoneyTo scale past hand-auditing, give the agent a scheduled bedtime pass: it reads its own skills, finds stale ones and contradictions, and opens drafts for review — the system sleeps, cleans house, and wakes up sharper.
AI Engineer · Scale the Judgment, Not the Model — Andrew Orobator, Reddit
Recommendation · MoneyOrobator recommends making team judgment explicit in the mechanism that fits: a persona, a lint check, or a commit hook — not just a skill, which is only one implementation. He also suggests finding the judgment the team always asks the same person about as the starting point, and pushing the result org-wide so every team can encode its own taste for its own agents. Spread it by making it legible rather than mandating it.
AI Engineer · Scale the Judgment, Not the Model — Andrew Orobator, Reddit
Recommendation · MoneyTo shift from product to harness engineering, find the judgment your team always asks the same person about and make it explicit — don't just write a skill, which is only one implementation.
AI Engineer · Scale the Judgment, Not the Model — Andrew Orobator, Reddit
Method · MoneyTenev describes how agentic trading accounts are set up and operate: create a separate segregated account and move money into it, keep trade approvals on by default until you want the strategy to run autonomously, then run a daily agent loop that executes your strategy, rebalances the portfolio, and looks for opportunities — optionally connecting alternative data sources like satellite data via the app store. The agent is sandboxed and cannot access other accounts or move money outside the segregated account.
Method, not a verified protocol.
No Priors: Artificial Intelligence | Technology | Startups · Tokenization, Agentic Trading, Harmonic: Robinhood's Mission to Expand Ownership with CEO Vlad Tenev
Recommendation · BodyDr. Søberg endorses a calculated cold exposure dose of 11 minutes in total per week — not in one session — corresponding to one to two minutes in cold water at a time across two to three visits per week. The sauna dose from her study was 57 minutes in total per week, divided across two to three days with two sessions each day of 10 to 15 minutes per session. She considers having such a low-threshold dose target good because it gives people something to aim for and prevents overdoing it.
Consequential creator advice requires independent verification before Today can promote it.
Andrew Huberman · Essentials: Cold & Heat Exposure for Metabolic Health | Dr. Susanna Søberg
Recommendation · MoneyThe speaker says it is extremely important to keep testing regression cases, known tasks, and past failures, and that fresh challenge cases at all points go back to setting the most ambitious target. You have to keep pushing your model and make sure it is challenged and has cases where it is not able to perform to expectations.
AI Engineer · If Your AI Scores 100%, Your Eval Is Wrong - Ramakrishnan Lokanathan, eslams.com
Recommendation · MoneyThe speaker identifies adversarial tests as the third part of a holistic evaluation system: find where you can break the workflow and put it into challenging situations. He advises choosing a test for the failures it can reveal — the idea of a test that can fight back.
AI Engineer · If Your AI Scores 100%, Your Eval Is Wrong - Ramakrishnan Lokanathan, eslams.com
Recommendation · MoneyThe speaker identifies two ways to get to an eval that is as holistic as possible: consistently human review your process, and ensure that your customer feedback is part of your eval pipeline. He explains these catch the last 5% edge cases that a model or an existing eval framework might not catch.
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AI Engineer · If Your AI Scores 100%, Your Eval Is Wrong - Ramakrishnan Lokanathan, eslams.com
Recommendation · MoneyThe speaker advises starting with the job and asking what must happen, what must never happen, and when it should stop, then extending these core questions into other WH questions and first principles to think about the most ambitious outcome. He also endorses aiming at the job you need, not the score you can get.
AI Engineer · If Your AI Scores 100%, Your Eval Is Wrong - Ramakrishnan Lokanathan, eslams.com
Recommendation · MoneyFor high-stake applications, the speaker's first recommendation is to define the most ambitious success before designing any test. He suggests assuming a world of AGI: what is your AI agent going to do, or what is the best outcome you can get out of it? Setting this most ambitious goal gives you the scope of what you can go ahead and design further.
AI Engineer · If Your AI Scores 100%, Your Eval Is Wrong - Ramakrishnan Lokanathan, eslams.com
Recommendation · MoneyThe speaker advises continuing human review and making sure the results make sense, and discourages relying on your agents to be the reviewer of your eval at any point, ever. He notes you can fast track your eval process with agents, but make sure you're doing human review.
AI Engineer · If Your AI Scores 100%, Your Eval Is Wrong - Ramakrishnan Lokanathan, eslams.com
Recommendation · MoneyThe speaker advises making automated checks and evals part of your CI pipeline, with eval questions asked to your agent every time they go through a production deployment process. He explains this ensures you're not just taking one eval score at the time you're deciding the model.
AI Engineer · If Your AI Scores 100%, Your Eval Is Wrong - Ramakrishnan Lokanathan, eslams.com
Recommendation · MoneyThe speaker advises continuously testing the workflow at every part of the workflow, discourages assuming things in the workflow or assuming an input and output to be the outcome of anything, and says to make sure your tests are designed for what that element of the workflow does. He notes the objective in that workflow process is to know your tool call ability and tool call accuracy at that point.
AI Engineer · If Your AI Scores 100%, Your Eval Is Wrong - Ramakrishnan Lokanathan, eslams.com
Recommendation · BodyDr. Marius Bakken recommends limiting harder-than-threshold running — the X session — to once per week, and allowing two days of easy training afterwards so fatigue does not accumulate. Not taking care of the days after the X session leads to trouble. Because of that recovery and the once-weekly limit, he argues it is difficult to do the X session wrong.
Garage Strength · The Genius Who Solved Endurance Training w/ Dr. Marius Bakken