Samir Abu Ghosh

Agilist, Futurist, Builder, CEO #Agility #AI #ML #Innovation — Signals no noise. First Principles. Always Be Closing (ABC)

[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]

[Sam Parr @thesamparr]

Replit's CEO on the months right before they blew up:

- Lost half the team. 120 people down to 60.
- Had just moved into a huge new office. Empty, cold, dark.
- Every morning he knew someone was gonna walk to his desk and quit.
- "You can see it in their eyes when they stop believing in you."

[Amjad Masad @amasad]

AI ate most of the software development lifecycle but maintaining live apps is still manual.

“DevSecOps” is the new bottleneck as software creation explodes.

Introducing Replit Auto-Protect: a 24x7 vulnerability scanner for your live apps.

[Dustin @r0ck3t23]

Jeff Bezos just delivered the clearest definition of what artificial intelligence actually is.

The market is still debating which department should own the AI budget.

They’re asking the wrong question entirely.

Bezos: “AI, modern AI is a horizontal enabling layer. It can be used to improve everything. It will be in everything. This is most like electricity.”

This isn’t a software product. It’s the new utility grid of the global economy.

Don’t treat it like a feature update. Treat it like the invention of alternating current.

When a horizontal layer hits the board, it doesn’t improve a single vertical. It violently rewrites the baseline physics of every industry it touches.

The companies that survive this decade won’t be the ones that bought a new AI tool.

They’ll be the ones that ripped out their entire infrastructure and rewired the execution engine to run on the new grid.

Bezos: “Because we are literally working on a thousand applications internally. I guarantee you there is not a single application that you can think of that is not going to be made better by AI.”

The standard enterprise strategy is to launch one or two safe, isolated AI pilots and test the waters.

You don’t pilot a horizontal enabling layer. You saturate the board immediately.

Amazon isn’t building a single monolithic chatbot. It’s deploying a thousand specialized execution loops across every friction point in the empire.

If your deployment strategy isn’t total saturation, you’re already bleeding margin to someone whose is.

Interviewer: “What is it that you’re doing at Amazon?”

Bezos: “AI. It’s 95% AI.”

The standard CEO delegates automation strategy to a mid-level committee while focusing on quarterly earnings.

The operator commanding a trillion-dollar supply chain is spending 95 percent of his personal bandwidth on a single vector.

That is the market signal.

If the leader of your organization isn’t driving algorithmic integration from the top down with everything they have, the company is already dead.

It just hasn’t received the memo yet.

[Financial Times @FT]

CEO of Microsoft AI Mustafa Suleyman joins FT editor Roula Khalaf to explain why most of the tasks accountants, lawyers and other professionals currently undertake will be fully automated by AI within the next 12 to 18 months https://t.co/yYKzS7NIOP

[All day Astronomy @forallcurious]

🚨: This is how many satellites are orbiting Earth right now

ITS ABSOLUTELY FRIGHTENING 🤯

[hinata @HinataMotivates]

Jeff Bezos: Future kids will build space companies from their dorm rooms.

[Luiza Jarovsky, PhD @LuizaJarovsky]

🚨 BREAKING: Singapore takes the lead again and publishes its Model AI Governance Framework for Agentic AI [Bookmark it below]. Other countries should take note:

As the document clarifies, the new components of an agent create new sources of risk.

"The risks themselves are familiar – fundamentally, agents are software systems built on LLMs. They inherit traditional software vulnerabilities (such as SQL injection) and LLM-specific risks (such as hallucination, bias, data leakage, and adversarial prompt injections). However, the risks can manifest differently through the different components."

Many countries and regions (including Europe) are still unsure how to apply their existing legal AI frameworks to agentic AI. Other countries seem to prefer the deregulatory trend.

Singapore understands that AI is evolving fast, and new risks are emerging, and the time to establish dynamic AI governance frameworks is NOW.

Bookmark the document below and don't miss pages 6-7, which cover agentic AI risks.

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👉 To learn more about AI governance, join my newsletter's 89,300+ subscribers and don't miss the 27th cohort of my AI Governance Training (links below).

[The Humanoid Hub @TheHumanoidHub]

China asserts dominance in the humanoid race

Yesterday, China's Ministry of Industry and Information Technology (MIIT) announced key 2025 achievements in AI and robotics:

- The embodied intelligence sector (AI-integrated robotics) attracted over 40 billion yuan ($5.7B) in financing and involved more than 350 companies.

- Humanoid robotics surged, with domestic manufacturers exceeding 140 and more than 330 humanoid models released.

A separate report by market research firm Omdia shows that global shipments of Chinese humanoids exceeded 13,000 units.

Here's a glimpse of a humanoid factory in Shanghai:

[McKinsey & Company @McKinsey]

AI adoption is widespread, but scaling it isn’t. New research highlights rising interest in AI agents and six insights on turning experimentation into enterprise value. https://t.co/RozwOv44po

[Rohan Paul @rohanpaul_ai]

Jensen Huang loves Claude and ChatGPT.

"Claude is incredible. Anthropic has made huge progress, a massive leap, in developing Claude. We use it all over our company. The coding capability, the reasoning capability, its overall ability is genuinely impressive.

Anybody who has a software company really ought to get involved and use it. On the other hand, ChatGPT is probably the most successful consumer AI in history. Its ease of use and approachability mean everybody should get involved.

Whether it’s someone in a developing country or a student, it’s very clear that learning how to use AI is essential. You need to know how to direct an AI, prompt an AI, manage an AI, guardrail an AI, and evaluate an AI."

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From 'World Economic Forum' YT channel

[Replit ⠕ @Replit]

AI builds web apps well. Mobile apps have been harder.

Now, the inventor of React (the technology that AI uses to build apps), has a new announcement.

[The Humanoid Hub @TheHumanoidHub]

"Everything that moves will be autonomous."

[Rohan Paul @rohanpaul_ai]

Jensen Huang on Nvidia vs Intel in 2009.

He called GPU vs. CPU a “battle for the soul” of computing, adding that GPUs would gain importance.

Intel’s valuation was $100B, Nvidia’s just $4B.

Now, Intel sits around $187B, and Nvidia crossed $5T a few days ago.
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Full video on 'Charlie Rose' YT channel

[Rohan Paul @rohanpaul_ai]

“The people who invented refrigeration made some money, but most of the money was made by Coca-Cola, who used refrigeration to build an empire.

LLMs are like as refrigeration. and the Coca-Cola has yet to be built”

~Chamath Palihapitiya

[Startup Archive @StartupArchive_]

Google founder Larry Page on how he learned to run a business

When asked how he learned to run a business, Google cofounder Larry Page responds:

“I read a lot of books.”

He joked:

“[When renaming Google to Alphabet] I read like three books on naming—which is more than anyone else had read. So I decided I was the expert . . . and actually that was useful. I recommend reading things.”

It’s an important mindset that is often overlooked. One of the richest ways to learn something is reading things written by people who deeply understand their subject matter.

Even Elon Musk was able to teach himself about the fundamentals of rocket design and astrodynamics by reading books. He is often quoted on this topic:

“I read books and talked to people. I mean that's kind of how one learns anything. There's lots of great books out there and lots of smart people.”

Building a startup is an infinite set of problems that are being thrown at you.

Next time you’re facing one of those problems, I’d recommend finding the best book or blog post you can on the topic and reading it.

You don’t need an MBA from a fancy school to be an expert in business or startups.

You just need to sit down and read.

Video source: @FortuneMagazine (2015)

[The Nobel Prize @NobelPrize]

"My journey began far from any laboratory. I grew up in Amman, Jordan, in a refugee family of ten children, in a home with no running water and no electricity, sharing our space with livestock, our family’s livelihood. Hardship was everywhere. My chances for success were slim—except for the surprising ways nature reveals itself and helps us overcome.

My turning point came at the age of ten, when I discovered drawings of molecules in my school library. Their beauty and mystery captivated me, and when I learned that they are the building blocks of everything, living and non-living, they ignited my passion for chemistry, and I was hooked forever. It became my escape and my direction."

In his Nobel Prize banquet speech, chemistry laureate Omar Yaghi spoke about his journey to becoming a scientist and laureate.

Read the full banquet speech: https://t.co/Wz517i2Lg9

[Andrew Lokenauth @FluentInFinance]

Sam Altman admits OpenAI is in trouble after Gemini 3 release in a leaked memo, per The Information.

Google has the World's data, its own chips, and unlimited cash. Google has YouTube, Search, Gmail, Maps, and Android. Billions of users. No other company has that combo.

OpenAI is projected to lose $7B by 2028. They're burning $8.5B a year trying to compete. OpenAI's valuation is $500B but its revenue is $13B. That's a 38x revenue multiple.

Google trades at 7x revenue with actual profits.

[Kshitij Mishra | AI & Tech @DAIEvolutionHub]

The future is here: Nvidia's NVLink Spine can now move more data than the entire internet.