RAHUL SINGH (@gravity0890) header
RAHUL SINGH (@gravity0890) profile picture

Data Science at Adobe; trying to be more curious...and I also make good chai and snacks ☕ friends with @chewiesingh

San Francisco, CA
Joined May 2014
Photo by RAHUL SINGH (@gravity0890)Photo by RAHUL SINGH (@gravity0890)Photo by RAHUL SINGH (@gravity0890)Photo by RAHUL SINGH (@gravity0890)Photo by RAHUL SINGH (@gravity0890)Photo by RAHUL SINGH (@gravity0890)Photo by RAHUL SINGH (@gravity0890)Photo by RAHUL SINGH (@gravity0890)Photo by RAHUL SINGH (@gravity0890)
Every home has its own little rituals. Ours is simple: light an agarbatti, play bhajans, and let the day’s noise fade. I turned that feeling into a corner online. Presenting Bhajan Sandhya 🌺 Inspired by the creative wave @ybhrdwj set in motion🫰 bhajansandhya.online
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Happy Diwali to everyone celebrating 🌸🫰🪔
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In other words, readability is now a nicety, not a necessity.
We need to reconsider our priors on code readability. Humans, compared to agents, are pretty slow at understanding large quantities of text. So in the old days, readability was important so the human could efficiently grok what was going on. So readability now comes down to: 1. Can a human, if needed, jump in and understand the code 2. Does the code use the same domain language as the human describing the code? 1 helps with observability and the occasional spot checks you need to do on important PR's. 2 helps the agent translate the words you use into code changes. In other words, readability is now a nicety, not a necessity. The way agents 'read' code is different - good agents are all about gathering data about how the code works. They'll prod around with it, execute parts of it. So 'readability' to agents really means 'verifiability'. How easy it is to mess about with that code to see it working. Let's take this scenario: - You have a huge module in your codebase that uses all sorts of complex performance tricks - It has 100% test coverage A talented human would need an hour or two to get comfortable working in that module. An agent needs zero time. They verify it with the tests, poke about a bit, and they're good to go. The takeaways: - When judging a module (or a codebase), don't think about how easy the code is to read - Instead, think about how easy it is to verify - Design your codebase so modules can be tested and verified independently - Feel free to sacrifice a bit of readability for performance - Cyclomatic complexity and function size don't matter to agents
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RAHUL SINGH retweeted
Englishman in New York
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Definitely one of the most fun (and useful) use cases for AI by Matt Cortland. Cheers and enjoy reading about it🍻 ~thejournal.ie/ai-chatbot-pub… ~guinndex.ai/
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set this as a daily reminder...
🤖 Made with AI
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RAHUL SINGH retweeted
Introducing Contrastive Language Model (CLM): an ultra-fast System One Model trained with a contrastive learning objective that connects states and actions. CLM-8B is pre-trained on internet-scale data and delivers up to 9× faster inference than Jev ⚡ while achieving comparable performance across computer-use, gaming, and tool-calling tasks. With lightweight fine-tuning, CLM-8B sets a new SOTA on challenging agentic coding benchmarks, such as DeepSWE (81.6%) and Terminal-Bench 2.1 (87.6%). In contrast, Jev fails to serve as an effective verifier for these long-horizon tasks. We also build an efficient training and serving infra for CLMs by disaggregating states and actions, allowing their embeddings to be cached and reused independently. This substantially reduces inference latency in settings where the state evolves continuously while the action set remains fixed. Finally, we establish scaling laws for CLMs and show that the test contrastive loss decreases predictably as a power law in training compute, model size, and dataset size. 📄 Blog: contrastive-lm.notion.site 💻 Code: github.com/Contrastive-LM/CL… 🗣️ Discord: discord.gg/5dAQEDJBs 🤗 Data & Models: huggingface.co/Contrastive-L… More details on CLM’s architecture, data recipe, and scaling laws in the thread below 🧵
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RAHUL SINGH retweeted
Data Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician.
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One of my favorite use cases of @Muse is to have it handle the google reviews for my mum's business...
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Happy Dussehra, everyone! Shubho Bijoya! 🌸
🤖 Made with AI
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What tasks should AI be trusted with, and when should humans remain involved? Designing those decision rights is one of the defining challenges of the AI era. Researchers at the MIT Center for Information Systems Research have developed a framework to help. Learn more: bit.ly/4zed20M
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My therapist recently asked me to think of my life as a circle that can expand and shrink.
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I found that reassuring. Sometimes I compare how much I’m doing or how often I’m seeing friends with what I used to do, without accounting for everything else taking up energy in my life.
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I still care about those people, even when I have less energy to see them. For now, I’m okay with keeping my circle a little smaller, knowing I can expand it again when I have more room.
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RAHUL SINGH retweeted
Love this! If I had kids, I’d love to teach them this way. I’ve been exploring learning through play too! A Claude skill for making hand-drawn, interactive everyday objects. Here’s my little washing machine :) colorfigures.vercel.app
Here’s a prototype of the Kids version: “How does a pump fill a tyre?” I’m experimenting with different iterations to find the simplest way to help kids understand how things work, while still keeping enough detail to make the experience engaging, interactive, and educational
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Most aents bring so much creativity and speed, and can help push your thinking further. But an agent working without clear boundaries is often less useful than you would hope. One way I am trying to solve this by learning to find a balance. The balance between giving it enough context to do the work, while leaving room for it to explore ideas I had not considered. Finding the balance is hard. If I add too many constraints it can limit it's potential but on the other hand too few can send it in directions that don’t help me much. So next time, give your agent a jungle gym within an enclosed space 😉 🛝
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