V. Pan

Research: AI, Cognitive Agents, Transforming Transformers, Quantum Computing
Artificial * (Intelligence + Consciousness) = Artificial Wisdom

[V. Pan @AI_ML_iQ]

Next to Loops: Looping Loops!! Then Looping "Looping Loops" and so on... @AndrewYNg @AnthropicAI @sama @elonmusk @satyanadella @sundarpichai @ylecun @Meta
https://t.co/2ythaPqXZ8

[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]

[V. Pan @AI_ML_iQ]

@AndrewYNg Next: Looping Loops!! Then Looping "Looping Loops"...

[V. Pan @AI_ML_iQ]

@elonmusk Lol. Feedback of error during your 50% evaluation is part of training so training > 2%!! Depend, how you perform complete training loop.

[V. Pan @AI_ML_iQ]

@elonmusk Seems you need trillions antidollar to make antimatter!!

[V. Pan @AI_ML_iQ]

@GoogleResearch Compress it further. Transformer does not require separate query and key matrices. Both can be merged into one. See following paper: https://t.co/47l43vRfUi

[V. Pan @AI_ML_iQ]

@elonmusk 2-3 sentences are sufficient to change complete landscape of AI development! Would you @elonmusk like to know those sentences?

[V. Pan @AI_ML_iQ]

@newstart_2024 There is no "thought" in chain-of-thought. It is just chain-of-steps.

[V. Pan @AI_ML_iQ]

@elonmusk But what about intelligence of xAI which would be less when I am not part of xAI.😂

[V. Pan @AI_ML_iQ]

@elonmusk @luke_leisher_ Existence of different kind of ecosystem when atmospheric conditions are different, why should it be identical to Earth's ecosystem??

[V. Pan @AI_ML_iQ]

@elonmusk Any open position for open ended research on consciousness? OpenConsiousness😉

[V. Pan @AI_ML_iQ]

@elonmusk Is something wrong if people from countries colonized by the British migrate to UK and live there peacefully without committing any crime??

[V. Pan @AI_ML_iQ]

@elonmusk This is still missing secret algorithm(s) ingradients so to become anywhere near to AGI, A(ctual)H(uman)I. @elonmusk

[V. Pan @AI_ML_iQ]

@elonmusk Nothing can move at light speed except light.

[V. Pan @AI_ML_iQ]

@saylor Wrong. To be more precise, everything divided by 100,000,000 X 21 million Satoshis.

[V. Pan @AI_ML_iQ]

@DimaZeniuk @elonmusk
https://t.co/dKlMeafDRt

[V. Pan @AI_ML_iQ]

@elonmusk Are there advantages in catching the ship at tower instead of landing on hard surface?

[V. Pan @AI_ML_iQ]

@elonmusk Missing in video, clear representation of Mandelbrot fractal (e.g. fern plants) in fractal garden.

[V. Pan @AI_ML_iQ]

@SecScottBessent @RonWyden @SpeakerPelosi Also, ban on all type of short selling (in particular naked short selling, borrowing stocks for short selling) and multiple times reverse split of shares.

[V. Pan @AI_ML_iQ]

@elonmusk These are passive traditional type fins, not an impressive solution. Better solution can be invented.

[V. Pan @AI_ML_iQ]

@elonmusk Seems GMO rose plant without thorns!

[V. Pan @AI_ML_iQ]

@elonmusk Grok LLM has defect depending on how you write name, using capital letter or without. Telling you xDotai is not ready for AGI. @elonmusk please read my other messages if you care!