Januda lelwala

CSE undergraduate, ML and Deep learning enthusiast.

[Januda lelwala @JanudaX]

AI research watch: new work in AI agents and reasoning: "Distributed Attacks in Persistent-State AI Control". Worth reading if you're tracking where deep learning is moving. https://t.co/1cYJouPsVg #AI #DeepLearning

[Claude @claudeai]

Announcing Built with Claude: Life Sciences, a global virtual hackathon.

Join us and @GladstoneInst for a week of researching and building with Claude Science and Claude Code, with a prize pool of $100k in credits.

[Januda lelwala @JanudaX]

@TTrimoreau Funding , Good decisions, technical understanding.

[Januda lelwala @JanudaX]

AI research watch: new work in AI evaluation: "Measuring the Gap Between Human and LLM Research Ideas". Worth reading if you're tracking where deep learning is moving. https://t.co/AfcPJ2x6ly #AI #DeepLearning

[Januda lelwala @JanudaX]

The bigger idea: deep learning does not have to mean dense floating-point activation everywhere. Spiking models point toward AI systems that are event-driven, adaptive, and closer to how biological neurons communicate: compute only when something meaningful happens.

[Januda lelwala @JanudaX]

Spiking neural networks (SNNs) are a different way to think about deep learning: instead of every neuron pushing continuous numbers every layer, neurons stay quiet until they emit a discrete spike. That tiny shift changes the whole compute model. 🧵

[Januda lelwala @JanudaX]

A useful mental model: SNNs trade easy dense math for temporal efficiency. They may not replace transformers on GPUs tomorrow, but they are promising where latency, power, and real-time sensory processing matter more than brute-force throughput.

[Januda lelwala @JanudaX]

@claudeai @machinelearnflx Les goooo

[Januda lelwala @JanudaX]

@TTrimoreau Vscode and conductor and nvim

[Januda lelwala @JanudaX]

AI research watch: new work in multimodal AI: "Introspective Coupling: Self-Explanation Training Tracks Behavioral Change Despite…". Worth reading if you're tracking where deep learning is moving. https://t.co/b46XXBSUUy #AI #DeepLearning

[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]

[Januda lelwala @JanudaX]

@dakshhhmehta Already bought it

[Januda lelwala @JanudaX]

🧠 Einstein World Models: LLMs with visual thought experiments.

The idea: an LLM calls a world-module to simulate short scene rollouts, inspect them, then improve its answer. Future agents may think in worlds, not only words.

https://t.co/CB7OulaJW6

#AI #LLM #AIAgents

[Januda lelwala @JanudaX]

AI research watch: new work in multimodal AI: "VLK: Learning Humanoid Loco-Manipulation from Synthetic Interactions in…". Worth reading if you're tracking where deep learning is moving. https://t.co/JoujSRBzBD #AI #DeepLearning

[Codez @0xCodez]

https://t.co/eJB3TgyJwV

[Januda lelwala @JanudaX]

@TTrimoreau Coding something else.

[Januda lelwala @JanudaX]

@eddaunizeau @TTrimoreau How do you find suitable people to dm apart from your friends?