Robo

Things have changed a ton for us over the past month — we've gone from quietly building the first version of our AI platform for biotech to being totally underwater trying to onboard new companies. Outside of product stuff what's bottlenecking us now is mapping out the details of a few specific BD/R&D workflows in biopharma. I need help from people who have: - Actual industry experience in biopharma in roles where they need to wrangle a bunch of scientific documents (abstracts, publications, trials, patents, etc etc) - Enough time messing with LLMs (even just ChatGPT) to understand what's possible and where the complexity lies - Deep interest + incentive to figure out how AI will transform the process of bringing drug candidates to market I'm specifically looking to find 1-2 advisors in each of these areas: - Oncology - Autoimmune conditions - Systemic inflammation Happy to talk details with anyone interested — referrals are much appreciated! DMs open or send me an email at joey (at) robo (dot) tech.

If you're in the oncology/biopharma world our AI agents are building a 𝐜𝐨𝐦𝐩𝐫𝐞𝐡𝐞𝐧𝐬𝐢𝐯𝐞 𝐝𝐚𝐭𝐚𝐬𝐞𝐭 𝐨𝐧 𝐀𝐀𝐂𝐑 𝟐𝟎𝟐𝟓 abstracts with 𝟔𝟎+ 𝐟𝐢𝐞𝐥𝐝𝐬 𝐨𝐟 𝐦𝐞𝐭𝐚𝐝𝐚𝐭𝐚. Happy to share free access for anyone doing something interesting with it💡 Includes: 📊 Authors & affiliations 💊 Drugs & targets 🎯 Indications 🔬 Antibody data 📈 Trial IDs 🧪 Study design 🧫 Cell line models Comment "AACR" for access and I'll ping you. All I ask in return is telling me: 1️⃣ How you'll use it 2️⃣ What other data you need

Over the weekend, I had a team of AI “consultants” digest 13,000 abstracts from the past 2 years of AACR— and produce a deep-dive report on the progress of ADCs.

What happens when you set AI agents loose on 20,000 AACR abstracts from the past 3 years? You get ... a lot of insights.

It took about 25 minutes for AI to process 7000 abstracts from last year's AACR conference and produce this network graph of all of the collaborations happening around antibody drug conjugates (ADCs). Unsurprisingly, academic institutions are much more densely interconnected than companies. Next up: mapping target antigens and digesting the data into some actionable insights for people going to this year's meeting.

Know an experienced engineer in Europe interested in AI for science? Send them our way 🫡

I've been testing using Deep Research to investigate the status of competitor drug candidates for a given biotech company and it already saves a huge amount of time. The limitation relative to what companies want is you can't compare it to your own candidate data (bc that data needs to be private). It also does make a few mistakes that need to be better handled in evals, but this is clearly the right architecture.

Biotech runs on BD and complex market/regulatory analyses, and most people seem to think that's out of scope for AI. I couldn't disagree more :)

We've built a pretty simple AI system that has totally transformed how we do market research in biotech. Here's our stack if you want to recreate it:

The clear opportunity in AI x Science isn't what you think: It's not about replacing scientists. It's about taking products that already work elsewhere and porting them into scientific workflows ⬇️

The transformation is happening across industries: • Sierra transforms luxury retail support • 8090 builds custom enterprise SaaS • Strange Loop automates financial workflows • We're building custom agents for biotech Full post here: blog.roboagencies.com/p/the-rise-of-…

Our latest on the rise of specialized AI agencies and why they matter: - Traditional software models (build vs buy) are breaking down as AI expands the surface area of opportunities for — and cost of — custom code - "Roboagencies" combine domain expertise with scalable infrastructure to deliver outcomes, not just code

@joey_debruin 🧑‍🔬🤖

And that's also why @robodottech is doubling down on biotech After years at neuroscience labs, ResearchGate, and Benchling, we know the opportunities for specialized AI agents to accelerate R&D. More thoughts here: flyingpenguins.io/p/software-is-…

The timeless rule is actually "don't outsource stuff that makes your beer taste better." Companies have always outsourced non-core work. What's new? Thanks to AI, the number of high potential opportunities for custom code has increased by 10X.

Everyone religiously says says "don't outsource custom software." But that only made sense when software was expensive. Why 2025 is the year of the roboagency 🧵

Full reveal of our not to secret plan here blog.roboagencies.com/p/introducing-…

Our marketing plan is very simple — launch cool products, continuously 🚀 Introducing Robodocs. Get a dashboard and a Claude based assistant on top of any Drive folder in seconds.

Such an inspiring conversation with Joey DeBruin at Glasp Talk! 🌟 His journey from neuroscience to empowering creators with AI-driven tools like Robo is fascinating. I loved hearing about his insights on leveraging AI to break down barriers for non-technical founders and the vision behind building modular workflows for scalable innovation. The concept of 'flying penguins' as agile digital organizations really resonates. Thank you, Joey, for sharing your story, and I'm so grateful to have had the opportunity to interview you! 🙌 #GlaspTalk @joey_debruin @roboagencies @_Glasp

🚀 Empowering Creators and Innovators with AI-Driven Tools 🎙️ Thrilled to announce the latest Glasp Talk #38 featuring an incredible conversation with Joseph DeBruin! Joey is a passionate entrepreneur, product builder, and writer on a mission to empower creators and innovators to make the world a better place. As the Co-founder and CEO of Robo, Joey is revolutionizing how founders build products by eliminating traditional barriers with the power of AI. 🎥 Watch the full interview on Glasp Talk #38 👉 Let’s learn from Joey's vision of creating impactful solutions and building the future of innovation! 🌍 #AI #Innovation #Entrepreneurship #GlaspTalk @joey_debruin @_Glasp @roboagencies

What if I told you that your idea for a viral product could be live by Christmas?