Claire Longo (@StatInStilettos) header
Claire Longo (@StatInStilettos) profile picture

Poker. Mathematics. AI.

Digital Nomad
Joined March 2016
Photo by Claire Longo (@StatInStilettos)Photo by Claire Longo (@StatInStilettos)Photo by Claire Longo (@StatInStilettos)Photo by Claire Longo (@StatInStilettos)Photo by Claire Longo (@StatInStilettos)Photo by Claire Longo (@StatInStilettos)Photo by Claire Longo (@StatInStilettos)Photo by Claire Longo (@StatInStilettos)
Where in the world is Claire? 🌵 ☀️ Working for a remote first tech company is amazing! Last week I was in Scottsdale and Sedona Arizona spending quality time with family, and working away on my latest research project on LLM evals. #remotejob #womeninscience (We're hiring!)
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Claire Longo retweeted
It's 2026 and we @Cometml decided to reward the community on working on some projects for themselves. We have a $30k USD cash prize pool for our three week virtual online hackathon. Spaces are filling up, get on the Tuesday kick off. luma.com/commit_to_change_ha…
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Claire Longo retweeted
AI agents fail for one quiet reason. Their prompts never improve. You write a prompt once, ship it, and hope it works. When it breaks, you manually tweak it again. This does not scale. And it is about to get worse. 👇
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This girl literally explained LLMOps better than most talks, in under 7 minutes.
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Replying to @gidim
I recently posted about this: teapawt.net/akshay_pachaar/status/… Great to see someone doing the work that actually matters!
I boosted my AI Agent's performance by 184% Using a fully open-source, automatic technique Here's a breakdown (with code):
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Claire Longo retweeted
Switching to GPT-5.2 won’t fix your broken agent. Neither will switching to Gemini 3, Claude 4.5, or Kimi-V100.72-Deep-Thinking-Pro-Flash-Nano. And the reason is simple: You don’t need OpenAI to train a better model. You need to train a better agent. 🧵
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Living in 2025 with - My @Spotify AI DJ hitting me with vibe evening tunes from @odesza - Got 4 tabs of @OpenAI codex running in my tmux window on another screen - @Grubhub keeping me fed from the local food spot Totally locked in ;) - Photo is from one my favourite trails along SF
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⭐⭐⭐New speakers announced for the 6th Annual @MLOpsWorld Summit (Oct 8-9, Austin). New sessions added from @pagerduty, @BlackRock, @Cometml, @patternhq, & @usebraintrust tackle testing, evals, and scaling. Early Bird ends Sept 8, 11:59PM ET → mlopsworld.com/speakers/?utm…
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Claire Longo retweeted
In Paris 🇫🇷 for #GTCParis and #VivaTech2025 and this place is an endless sea of AI companies, tech infrastructure and everything in between.
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Build your first AI agent + MCP Server in Python. Here is everything you need to build your first AI agent in less than 20 minutes. About the code you'll see here: 1. I used Google ADK with Gemini Flash to power the agent 2. The agent connects to an MCP server 3. It also uses two custom tools to do its work 4. You can see everything the agent does thanks to @Cometml's Opik library Here is the video, free for you to watch.
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Claire Longo retweeted
Advanced Hybrid RAG with miniCOIL, LangGraph, and @deepseek_ai 🚀 @TRJ_0751 shows how to build a hybrid Customer Support RAG chatbot using miniCOIL to augment sparse retrieval with semantic awareness ➡️ LangGraph by @langchain orchestrates the hybrid flow with MMR and re-ranking ➡️ Opik tracks and evaluates each step of the pipeline ➡️ DeepSeek-R1 by @SambaNovaAI delivers low-latency, focused answers 👉 Read it here: medium.aiplanet.com/advanced…
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I've been thinking a lot about Agent evals... while LLM as a Judge works great for text, how are y'all approaching evaluation metrics for voice, image and/or video? 🤔
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Claire Longo retweeted
That’s a wrap on #AWSSummit London! 🚀 Next stop: Amsterdam 🇳🇱 for an @AITinkerers on May 6th. Still time to snag a spot 👉 amsterdam.aitinkerers.org/p/…
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⭐️⭐️ Lecture 9 of @MITDeepLearning 2025 is now available online #FREE for ALL! Should there be a Hippocratic Oath for #AI? Tune in to hear about this from the amazing @DougBlank3 of @Cometml! 🚀 🔥 Lecture 👉 youtube.com/watch?v=CyCUZAf8… 🌐 Website 👉 IntroToDeepLearning.com
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Reinforcement Learning (RL) is quickly becoming the most important skill for AI researchers. Here are the best resources for learning RL for LLMs… TL;DR: RL is more important now than it has ever been, but (probably due to its complexity) there aren’t a ton of great resources for learning it online. I’ve been doing a lot of reading / learning on RL recently, so I wanted to share the best resources I’ve found. Links to all resources are provided in the image below. (1) RLHF book. Nathan is a long-time RL researcher and an expert on LLM alignment / post-training. He decided to write an entire book on (LLM-focused) RL techniques and has been slowly expanding / iterating on the book over the last year. This is the most comprehensive RL resource that is currently available, and it’s an especially great resource for those who are unfamiliar with RL and still need to learn the basics. (2) The Spinning up with Deep RL Course from OpenAI–despite being created in ~2018–has stood the test of time and is one of the best tutorials for learning RL. This course builds up to understanding PPO, which is one of the most widely used algorithms for RL with LLMs. Plus, understanding related algorithms (policy gradients, TRPO, etc.) will help a lot with gaining an understanding of new RL algorithms like GRPO. (3) PPO / GRPO blog. Jimmy Shi (DeepMind) recently wrote a great blog explaining both PPO (RL algo traditionally used for RLHF) and GRPO (RL algo used for reasoning models). This blog is great and it’s written in a way that is understandable for non-RL people. (4) HuggingFace RL. HuggingFace has also published numerous useful blogs on the topic of RL. Most recently, they published a blog that explains GRPO and PPO from the ground up (i.e., not assuming any background knowledge on RL). These blogs are inspired by the recent initiative from HuggingFace to create a fully open replication of DeepSeek-R1.
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👨‍💻 How do we improve AI Agent transparency and reliability? @StatInStilettos will dive into AI Agent observability, sharing how logging traces and monitoring help track Agent behavior and detect failures! 👉 April 15, 2025 | Register here: bit.ly/AgentsandGenAI
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6 years ago, I decided to open-source my Python code for a personal project I was working on, which led to numerous career successes that followed. Not just for myself but for some of the folks I met and collaborated with. Anyone else have a similar experience?
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Now, I'm working with Opik, Comet's open-source library for LLM Observability. If you want to build your resume by contributing to OSS, I can help you! Opik is a great place to start. on the issues page, you will find some low-hanging fruit. github.com/comet-ml/opik/iss…
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Creating and contributing to open-source projects helped me; 🔷 Build a developer portfolio to showcase my code 🔷 Be prepared for coding interviews 🔷 Connect with people who served as professional references
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Let's compare Llama 4 and DeepSeek-R1 using RAG:
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