Luisa.Tech

Futuristic Designer + Biz⚡️💎❤️

[Luisa.Tech @LuisaDotTech]

House I designed in midjourneyAI 📌 https://t.co/eDJ2sSSKkt

[ClaudeDevs @ClaudeDevs]

https://t.co/v694m8Eaj6

[Christopher Voss @fbinegotiator]

One question can completely change how an interviewer sees you: "How can I be guaranteed to be involved in projects that are critical to the strategic future of this organization?" It shows you're thinking beyond the job and focused on creating long-term impact.

[Quanta Magazine @QuantaMagazine]

Since 2003, we’ve known the sequence of the human genome’s 3 billion nucleotides. But within their tangled lineup lie many secrets.
https://t.co/xjFIdYqL9X

[Harvard Business Review @HarvardBiz]

Social media offers brands more opportunities to connect with customers. But it's not as easy as they hoped. https://t.co/VMGeEhDlv2

[McKinsey & Company @McKinsey]

Emotional connection is now the number one driver of luxury desirability, overtaking craftsmanship, heritage, and exclusivity.

The brands that win next won't just sell luxury, they'll create emotional connection. https://t.co/WDpDmQA0ez

[Andrew Ng @AndrewYNg]

“Loop engineering” is a hot buzzphrase after mentions of it by Boris Cherny (Claude Code’s creator) and Peter Steinberger (OpenClaw's creator) went viral on social media. Loops are now a key part of how we get AI agents to iterate at length to build software. In this letter, I’d like to share my 3 key loops, shown in the image below, for building 0-to-1 products. These loops guide not just how I build software, but also how I decide what software to build.

Agentic coding loop: Given a product specification and optionally a set of evals (that is, a dataset against which to measure performance), we can have an AI agent write code, test its work, and keep iterating until the code is bug-free and meets its specification. This idea of closing the loop took off around the end of last year, and it has been a game changer in enabling coding agents to work longer productively without human intervention. For example, over the weekend, I was building an app for my daughter to practice typing, and my coding agent could easily work for around an hour, using a web browser to check what it had built multiple times before getting back to me, without needing my intervention.

The engineering loop executes quickly. Every few minutes, the coding agent might build and test a new version of the software. I hear frequently from developers who are finding new ways to engineer more effective engineering loops. This is an active area of invention!

Developer feedback loop: In this loop, a developer examines the current product and steers the coding agent to improve it. Last year, a lot of developers (including me) were acting as the QA (quality assurance) function for our coding agents, manually finding bugs and then asking the agent to fix them. But with coding agents much more able to test their own code, the amount of time we need to spend on this function has decreased significantly. This allows us to make higher-level product decisions, such as what key features to offer, where the UI needs improvement, and so on.

The developer-feedback loop operates over time intervals between tens of minutes and hours — that's how frequently a developer might review a product and give feedback. In the case of the typing app, I changed my mind a few times about the visual design, what cat costumes she can unlock as she learns (she loves cats), and the user flow for a grown-up to log in and steer the child's learning experience.

When a developer has a clear vision for what to build, it is still a lot of work to translate that vision into a specification for a coding agent to implement. Further, after the developer has seen an implementation, they might update (or perhaps clarify) the spec to steer it toward what they want. If you find that the system repeatedly runs into certain problems, building a set of evals for the agent becomes useful.

AI-native teams are increasingly using AI to help shape product direction, for example, automating the gathering and analysis of usage data, summarizing written and verbal customer feedback, or carrying out competitive analysis. However, for pretty much all the products I’m involved in, I see humans as having a significant context advantage over current AI systems — we know a lot more than the AI system about the users and the context the product has to operate in — and thus humans play a critical role. Many people describe this human contribution as “taste,” but I prefer to think of it as humans having a context advantage, since that gives us a clearer path to helping AI systems get better. This also speaks to why this step can’t be automated: So long as the human knows something the AI does not, human-in-the-loop is needed to to inject that knowledge into the system.

External feedback loop: This includes a wide range of tactics like asking a few friends for feedback, launching to alpha testers, or putting the code into production with A/B testing. These tactics are usually slow, rarely taking less than hours and sometimes taking days or even weeks. This data informs the developer vision, which in turn continues to drive the detailed product spec, which in turn drives the coding agent.

With coding agents speeding up software development, more engineers are starting to play a partial product management role. For many engineers who are growing into this role, the hardest part is shaping the product vision and striking a balance between building (bridging the gap between vision and spec) and getting user feedback to evolve the vision. It is important to do both!

I will write more about how to do this in future posts, but for now, I find it encouraging that engineers are playing an expanded role (just as product managers and designers now do more engineering).

[Original text: The Batch]

[The New York Times @nytimes]

Breaking News: A research program at the National Institutes of Health released the world’s largest database of human genomes, officials said, paving the way for a new era of study in personalized medicine.
https://t.co/N29D0YhJVp

[The Tennis Letter @TheTennisLetter]

Naomi Osaka says her Wimbledon outfit was inspired by Lucy Liu in Kill Bill:

"When you're coming out for a first round match. That striking amazing outfit you came out in... how much of a difference does that make and tell us about the inspiration for it.."

"Yeah, it makes a difference. For me, I find fashion to be really fun. You guys all look for beautiful and I kind of want to come out in something to. To me, my Japanese heritage means a lot. They said all white in Wimbledon and I thought it'd be really cute to come out in a Kimono. I just get inspired by a lot of different things.

One of my favorite movies is Kill Bill. I really love Lucy Liu's character, O-ren Ishii. She comes out in this really iconic white Kimono. I always tell people I like to be like a video character sometimes, I don't want to be myself when I'm playing on the court and I tried to embody her a little."

[Gary Vaynerchuk @garyvee]

I’m gonna …. Is often a bad way to start a sentence .. for the 10% that will execute against that statement … RESPECT .. to the 90% who won’t -
Watch this video every morning

[Harvard Business Review @HarvardBiz]

Go big on meaning: inspire your employees with a vision, set challenging goals, and pump up their confidence so they believe they can win. https://t.co/bkvfD9H508

[Quanta Magazine @QuantaMagazine]

Neutrinos have almost no mass and no charge. They can pass through Earth and everything on it, including our bodies, virtually unimpeded. So how do you catch one?

The answer so far has been to build some of the biggest, deepest, and most exotic experimental traps in scientific history … and then wait.

Scroll through the world’s most audacious detectors:

https://t.co/KG1Zz0qOUS

[Adam Grant @AdamMGrant]

When people are stressed, “You can handle this” is a better message than “It’s not a big deal.”

Data: Minimizing risks dismissing their experience. Encouragement builds their confidence.

The best way to support people is not to deny their pain—it's to help them navigate it.

[Luisa.Tech @LuisaDotTech]

Good youtube vid: I studied creativity for 365 days & here’s what I learned. https://t.co/ihQ6ALQcf3 via @YouTube

[Gary Vaynerchuk @garyvee]

The judgment in today’s world is quick, thoughtless and often grounded in the worst traits - envy jealousy, one’s own unhappiness … it continues to baffle me how many are effected by strangers $0.02 and comments and hot takes, in-fact I struggle with people’s judgment that are even acquaintances or people that even kinda know you … in the end …very few if anyone realllllllly knows what’s on your inside … so if you know your insides are sweet :) well intended and good … please tune out the noise and let them continue to judge while you continue to grow ❤️❤️❤️❤️❤️❤️

[Financial Times @FT]

Robots, not chatbots, will realise AI’s potential https://t.co/XA8R6wg3GX

[Nicholas Fabiano, MD @NTFabiano]

Being grateful rewires the brain.

Neuroplasticity in action.

[nature @Nature]

Scientists have used a machine-learning strategy to discover antibiotics with fresh mechanisms

https://t.co/qk33mqQQxD

[McKinsey & Company @McKinsey]

82% of advertisers plan to buy AI ad formats directly.

As AI reshapes discovery and purchasing, more value may flow to the platforms influencing consumer decisions. https://t.co/l4wJN4YzIF