Ranit Karmakar

✅ Quality control of segmentation & ✨ Feature extraction (intensity, morphology, spatial features) by @RuzAntoine & @iRanitK 🔧 Workflow building with ZeroCostDL & Bioimage Zoo 📊 Data management strategies from @DrNickCondon (IMB, Australia)

Another year, another #BIC Workshop! This year at #ICCV 2025 in Honolulu, 🌺 🏄‍♀️ 🍹 🌼 Check it here: bioimagecomputing.com Submission deadline: July 4th Hey, you have a month left to submit your work!!! 👍

@DrAnneCarpenter NotebookLM is specifically designed for this task. While podcast generation feature received most of hype, it's an excellent tool for research. Never used it with more than 50 papers, but the 1M-token context window makes me optimistic about working with larger collections.

Help! Let’s say I have 200 scientific papers in my digital to-read pile I could stay in denial, or I could ask AI for help. Any workflows/tools to group them in some meaningful way? This alone would help mental load as I at least read abstracts And/or any good summary tools?

🚨 New paper out: SoftPQ – a robust metric for instance segmentation 🧠📊 Hard IoU thresholds miss the nuance. We propose: ✔️ Soft matching ✔️ Tunable thresholds ✔️ Sublinear penalties Rovust, interpretable evaluation. 📄 Paper: ⬇️ arxiv.org/abs/2505.12155 w/ @SimonFlyvbjerg

💻 Apply to our beginner-friendly Boston Bioimage Analysis Course! Get started with Python and learn to analyze fluorescence microscopy images—no coding experience required! Fee waivers available thanks to funding from @BioimagingNA! 🗓️ Apply June 1st! iac.hms.harvard.edu/bobiac/2025/

‼BREAKING‼ New funding secured for the Boston Bioimage Analysis Course (BoBiAC) this July at @harvardmed. Fees down 37%, with waivers for academic applicants, for an intensive hands-on intro to bioimage analysis with Python! Apply by June 1st! iac.hms.harvard.edu/bobiac/2025

Announcing the Boston Bioimage Analysis Course — BoBiAC! Join us this July at @harvardmed in Boston for a 6-day deep dive into bioimage analysis with Python, hosted by @HMS_IAC and @cite_at_hms. Apply by May 16th 2025!

A “10x engineer” — a widely accepted concept in tech — purportedly has 10 times the impact of the average engineer. But we don’t seem to talk about 10x marketers, 10x recruiters, or 10x financial analysts. As more jobs become AI enabled, I think this will change, and there will be a lot more “10x professionals.” There aren’t already more 10x professionals because, in many roles, the gap between the best and the average worker has a ceiling. No matter how athletic a supermarket checkout clerk is, they’re not likely to scan groceries so fast that customers get out of the store 10x faster. Similarly, even the best doctor is unlikely to make patients heal 10x faster than an average one (but to a sick patient, even a small difference is worth a lot). In many jobs, the laws of physics place a limit on what any human or AI can do (unless we completely reimagine that job). But for many jobs that primarily involve applying knowledge or processing information, AI will be transformative. In a few roles, I’m starting to see tech-savvy individuals coordinate a suite of technology tools to do things differently and start to have, if not yet 10x impact, then easily 2x impact. I expect this gap to grow. 10x engineers don’t write code 10 times faster. Instead, they make technical architecture decisions that result in dramatically better downstream impact, they spot problems and prioritize tasks more effectively, and instead of rewriting 10,000 lines of code (or labeling 10,000 training examples) they might figure out how to write just 100 lines (or collect 100 examples) to get the job done. I think 10x marketers, recruiters, and analysts will, similarly, do things differently. For example, perhaps traditional marketers repeatedly write social media posts. 10x marketers might use AI to help write, but the transformation will go deeper than that. If they are deeply sophisticated in how to apply AI — ideally able to write code themselves to test ideas, automate tasks, or analyze data — they might end up running a lot more experiments, get better insights about what customers want, and generate much more precise or personalized messages than a traditional marketer, and thereby end up making 10x impact. Similarly, 10x recruiters won’t just use generative AI to help write emails to candidates or summarize interviews. (This level of use of prompting-based AI will soon become table stakes for many knowledge roles.) They might coordinate a suite of AI tools to efficiently identify and carry out research on a large set of candidates, enabling them to have dramatically greater impact than the average recruiter. And 10x analysts won’t just use generative AI to edit their reports. They might write code to orchestrate a suite of AI agents to do deep research into the products, markets, and companies, and thereby derive far more valuable conclusions than someone who does research the traditional way. A 2023 Harvard/BCG study estimated that, provided with GPT-4, consultants could complete 12% more tasks, and completed tasks 25% more quickly. This was just the average, using 2023 technology. The maximum advantage to be gained by using AI in a sophisticated way will be much bigger, and will only grow as technology improves. Here in Silicon Valley, I see more and more AI-native teams reinvent workflows and do things very differently. In software engineering, we've venerated the best engineers because they can have a really massive impact. This has motivated many generations of engineers to keep learning and working hard, because doing those things increases the odds of doing high-impact work. As AI becomes more helpful in many more job roles, I believe we will open up similar paths to a lot more people becoming a “10x professional.” [Original text: deeplearning.ai/the-batch/issu… ]

Announcing new open-source Python package: aisuite! This makes it easy for developers to use large language models from multiple providers. When building applications I found it a hassle to integrate with multiple providers. Aisuite lets you pick a "provider:model" just by changing one string, like openai:gpt-4o, anthropic:claude-3-5-sonnet-20241022, ollama:llama3.1:8b, etc. pip install aisuite Open-source code with instructions: github.com/andrewyng/aisu… Thanks to Rohit Prsad, Kevin Solorio, @standsleeping, Jeff Tang and @Johnsanterre for helping build this!

Eager to learn how to train your own deep learning models for image segmentation? We got you covered! We are organizing a new workshop "Build Your Own Image Segmentation Pipeline in Python" on December 9th 2024 in-person @harvardmed. Apply here: iac.hms.harvard.edu/teaching/2024-…

Thanks to all the speakers, instructors and participants of the 2024 edition of #I2K2024! Follow us on social media and monitor our website to stay updated on the next course 👉 humantechnopole.it/en/external-tr…

I2K (i2kconference.org) group picture time!!! Look at this AMAZING crowd!!! 🤩 @humantechnopole #I2K2024

@BestBuySupport No response after a day. What is the point of asking to send a private message if you don't want to look at them? @BestBuy @BestBuy_Deals @ConsumerReports

@RanitKa87579204 Hello, Ranit, thank you for posting on Twitter. It's disappointing to hear that there was a misunderstanding regarding a recent purchase. I would like to assist you. To begin, send a DM including your full name, email, phone number, and the concerns about your purchase. ^Lisa twitter.com/messages/compo…