⚡Ratan

Product. Entrepreneur.
Curious about Next Big Thing, Startups, Crypto.
Was founder at revolutrapp

[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]

[Elon Musk @elonmusk]

@iam_smx *trillioniare

[Claude @claudeai]

Introducing Claude Fable 5: a Mythos-class model that we’ve made safe for general use.

Its capabilities exceed those of any model we’ve ever made generally available.

[Naval @naval]

Anthropic doesn’t have Claude, Claude has Anthropic.

[Naval @naval]

There is unlimited demand for intelligence.

[Ursula von der Leyen @vonderleyen]

Europe and India are making history today.

We have concluded the mother of all deals.

We have created a free trade zone of two billion people, with both sides set to benefit.

This is only the beginning.

We will grow our strategic relationship to be even stronger.

[RTSG News @RTSG_News]

🚨🇨🇳 BREAKING: China's Xaomi has unveiled a fully automated factory that makes 1 phone per second, runs 24/7, has no production workers, and operates in the dark.

Follow: @RTSG_News

[Vinod Khosla @vkhosla]

In 2013, I predicted data science & software will do more for medicine in the next decade than all biological sciences combined.

Today: Companies like @CuraiHQ, @Swordhealth, @AbridgeHQ, @SynchronInc, @CtheSignsSystem, @EllipsisHealth, @Limbic_ai, Radical Health, @a2zRadiologyAI, @RadAI, Travera, @ScienceCorp_ are proving this out across diagnostics, care delivery, mental health, radiology, and organ engineering.

[Vala Afshar @ValaAfshar]

Pressure is a privilege. It means things are expected of you.

[Naval @naval]

UI is pre-AI.

[⚡Ratan @ratandeshini]

@ycombinator @lkeshre @ishanishu @_harshjain26 @ycombinator investment returns is 28.9%

[Paul Graham @paulg]

Prediction: LLMs in their current form may not be able to do everything, but AI now has enough momentum that this won't matter. Beam engines couldn't do everything either, but they were enough to set off the Industrial Revolution.

[Naval @naval]

Plan as if you were going to live forever, act as if you were going to die tomorrow.

[Naval @naval]

AI lets you search by concepts instead of by keywords.

[Forbes @Forbes]

Next Billion-Dollar Startups 2025
https://t.co/mUWQZaQ3Pz

[Vala Afshar @ValaAfshar]

AI agents could generate around $450 billion in total economic value in the 14 countries surveyed by 2028, according to @Capgemini.

Organizations with scaled implementation are projected to generate around $382 million (2.5% of annual revenue) on average over the next three years, while we expect others to generate around $76 million (0.5% of annual revenue). This applies to an average organization with $15 billion in annual revenues. Capgemini expects that surveyed organizations will collectively achieve $19 billion in gains over the next 12 months, with this figure projected to increase to $92 billion by the third year.

Other predictions outside of Capgemini include:

Goldman Sachs predicts that Gen AI will drive a 6.1% rise in US GDP over the next decade -- by 2028, this translates to around $540 billion in the US.

IDC forecasts that AI technologies overall will influence 3.5% of global GDP by 2030 -- by 2028, this implies an impact of around $1.9 trillion globally.

MIT research estimates that a combination of AI capabilities could automate around one-fifth (slightly over 20%) of value-added tasks.

[Vala Afshar @ValaAfshar]

When someone helps you and they are struggling too, that is not help. That is love.

[Vala Afshar @ValaAfshar]

“Never quit something with great long-term potential just because you can’t deal with the stress of the moment.”

[Robert Reich @RBReich]

Intuit donated $1M to Trump’s inauguration. He's now planning to end the free IRS Direct File program.

Apple donated $1M. Trump exempted most of Apple’s imports from tariffs.

Coinbase donated $1M. Trump’s SEC dropped a major lawsuit against them.

See how this works?