Sardor Madaminov

Engineer & Builder, Shipped @fitlyai_co | NotedAI

[Rosinality @rosinality]

https://t.co/o4bRt58hDD

Using rejection sampling to consider accepted tokens as on-policy ones.

[Sardor Madaminov @sardormd]

@AniC_dev @florianldt @OpenAI @dev_korea @hashed_official Cool man, on luma it says: Sharp 7:00 pm, doors get closed, i could be 10 minutes late, i hope you let me in 😃

[Sardor Madaminov @sardormd]

@abdurshd Yeah, but it is hot outside thesedays (

[Sardor Madaminov @sardormd]

Everyone from ICML is hosting meetups but how am I supposed to attend all these meetups on the same day, same time ?

[Sardor Madaminov @sardormd]

@communidiyi @cerebras @OpenAI Just signed up

[Sardor Madaminov @sardormd]

@abdurshd Qoyilee, tinmaysiz 😃

[Sardor Madaminov @sardormd]

@aarush You should check out the Luma, it is full of ICML meetups now: https://t.co/d6hVTPk5HR

[Sardor Madaminov @sardormd]

@amir_nlp Amir, thanks for the talk, it was interesting enough to learn a lot from you !

[Sardor Madaminov @sardormd]

TL;DR

Loop engineering = is just wrapping conventional software-engineering discipline around feedback loops so AI Agents build with less hand-holding.

So 3 loops for 0-to-1 products:

> Coding loop (secs-mins): Agent writes, tests, and fixes itself. Runs ~1 hr solo.
> Dev loop (mins-hrs): You steer the big calls: features, UI, flow.
> External loop (hrs-weeks): Real users and A/B tests reshape the vision.

Key takeaway: Humans win on context, not "taste" so we know the users; the AI doesn't. Engineers are turning into part-PMs. Balance building vs. feedback. Do both.

[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]

[Claude @claudeai]

Introducing Claude Science, a new app designed with every stage of research in mind.

Artifacts traced to their code, environments managed on demand, and 60+ optional scientific databases that you can connect.

Available now in beta.

[Sardor Madaminov @sardormd]

@benjez31 Yeap, we've made decent progress this year. It's exciting to see what is next !

[Sardor Madaminov @sardormd]

Probably the hottest problem in ML right now: Continual Learning, and "Machine Studying" seems to frame it cleanly: not avoiding "forgetting", but learning a new domain from scratch the way humans actually study.

Interesting approach though.

[Jacob X. Li ✈️ ICML @jacobli99]

Continual learning is widely discussed right now, but mostly as improving on the job or avoiding catastrophic forgetting. But it has a different, difficult, and already urgent form:

Given nothing but a corpus of documents, how should AI systems develop expertise in a new, unfamiliar domain? We call this problem Machine Studying.

[Harrison Chase @hwchase17]

🧠Self-Harness: Harnesses that improve themselves

New paper on agents shaping their own harnesses to improve over time. Not from LangChain, but builds on top of DeepAgents! Three key steps:

1/ Weakness mining: find failure modes from traces
2/ Harness proposal: suggest changes to the harness
3/ Proposal validation: does regression testing on proposals and then accepts

Paper: https://t.co/yDd4TwPKwl

Builds on top of DeepAgents: https://t.co/a3Dv7ZVaSy

[Sardor Madaminov @sardormd]

@ConsciousRide @univerxe01 You may want to review these topics as an interview checklist

[Sardor Madaminov @sardormd]

@reinards @marclou Reinard, great meeting you at the run ! I will try out @tryjourneyai and I'll share my feedback once I do.

[Sardor Madaminov @sardormd]

@claudeai @kh_oybee you should check this out

[Sardor Madaminov @sardormd]

TL;DR:

PG's "How to Earn" in one idea: exponential growth is real, and small differences in growth rate compound into enormous differences in outcome.

So the formula behind everything Paul Graham has said:

• future = present * (1 + r)^N
• r = growth rate per period (0.15 for 15 %)
• N = number of periods (60 months = 5 years)

Paul Graham example:
• 1.15^60 = 4384,
so starting at $10k/month -> ~$44M/month in five years.

Ignore the noise from politics, focus on the one number that compounds. Growth is the whole game.

https://t.co/soqwwox3NM

[Sardor Madaminov @sardormd]

@abdurshd Raxmat )

[Sardor Madaminov @sardormd]

@marclou Thanks for making this happen, it was cool to see many people showed up today