Ryan Spangler

Biotech Enthusiast 🧪 | Hobby Jogger 🏃| Investor/Trader 📈. Previously @broadInstitute and @NIH. Opinions are my own, not investment advice.

[Eric Topol @EricTopol]

Big progress vs cancer, folks.
The kind of event curves from randomized trials that we've not seen before for a couple of the most deadly cancers. Congrats to the oncology research community for getting these trial done. #ASCO26, @ASCO

[Parmita Mishra @parmita]

RAS finally getting drugged is one of the great stories in modern biology, and almost nobody outside oncology understands why it's such a big deal.

YOU'LL LEARN SOMETHING AWESOME TODAY.

i am going to keep this as understandable (and simple) as i can.

OPEN THE THREAD.

🧵

[Afshine Emrani MD FACC @afshineemrani]

1/5
I'm a cardiologist. I have spent twenty years watching cholesterol destroy arteries, trigger heart attacks, and kill people I care about.
Today, Eli Lilly presented data that may begin to end that era.
VERVE-102. A single infusion. One dose. It uses base editing to permanently turn off the PCSK9 gene in your liver.
Presented today at the European Atherosclerosis Society Congress:
88% reduction in PCSK9.
62% reduction in LDL cholesterol.
Sustained up to 18 months.
No treatment-related serious adverse events.
One infusion. Not daily pills you forget to take. Not monthly injections. One dose — and your cholesterol may stay low for the rest of your life.

[Andrej Karpathy @karpathy]

A few random notes from claude coding quite a bit last few weeks.

Coding workflow. Given the latest lift in LLM coding capability, like many others I rapidly went from about 80% manual+autocomplete coding and 20% agents in November to 80% agent coding and 20% edits+touchups in December. i.e. I really am mostly programming in English now, a bit sheepishly telling the LLM what code to write... in words. It hurts the ego a bit but the power to operate over software in large "code actions" is just too net useful, especially once you adapt to it, configure it, learn to use it, and wrap your head around what it can and cannot do. This is easily the biggest change to my basic coding workflow in ~2 decades of programming and it happened over the course of a few weeks. I'd expect something similar to be happening to well into double digit percent of engineers out there, while the awareness of it in the general population feels well into low single digit percent.

IDEs/agent swarms/fallability. Both the "no need for IDE anymore" hype and the "agent swarm" hype is imo too much for right now. The models definitely still make mistakes and if you have any code you actually care about I would watch them like a hawk, in a nice large IDE on the side. The mistakes have changed a lot - they are not simple syntax errors anymore, they are subtle conceptual errors that a slightly sloppy, hasty junior dev might do. The most common category is that the models make wrong assumptions on your behalf and just run along with them without checking. They also don't manage their confusion, they don't seek clarifications, they don't surface inconsistencies, they don't present tradeoffs, they don't push back when they should, and they are still a little too sycophantic. Things get better in plan mode, but there is some need for a lightweight inline plan mode. They also really like to overcomplicate code and APIs, they bloat abstractions, they don't clean up dead code after themselves, etc. They will implement an inefficient, bloated, brittle construction over 1000 lines of code and it's up to you to be like "umm couldn't you just do this instead?" and they will be like "of course!" and immediately cut it down to 100 lines. They still sometimes change/remove comments and code they don't like or don't sufficiently understand as side effects, even if it is orthogonal to the task at hand. All of this happens despite a few simple attempts to fix it via instructions in CLAUDE . md. Despite all these issues, it is still a net huge improvement and it's very difficult to imagine going back to manual coding. TLDR everyone has their developing flow, my current is a small few CC sessions on the left in ghostty windows/tabs and an IDE on the right for viewing the code + manual edits.

Tenacity. It's so interesting to watch an agent relentlessly work at something. They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day. It's a "feel the AGI" moment to watch it struggle with something for a long time just to come out victorious 30 minutes later. You realize that stamina is a core bottleneck to work and that with LLMs in hand it has been dramatically increased.

Speedups. It's not clear how to measure the "speedup" of LLM assistance. Certainly I feel net way faster at what I was going to do, but the main effect is that I do a lot more than I was going to do because 1) I can code up all kinds of things that just wouldn't have been worth coding before and 2) I can approach code that I couldn't work on before because of knowledge/skill issue. So certainly it's speedup, but it's possibly a lot more an expansion.

Leverage. LLMs are exceptionally good at looping until they meet specific goals and this is where most of the "feel the AGI" magic is to be found. Don't tell it what to do, give it success criteria and watch it go. Get it to write tests first and then pass them. Put it in the loop with a browser MCP. Write the naive algorithm that is very likely correct first, then ask it to optimize it while preserving correctness. Change your approach from imperative to declarative to get the agents looping longer and gain leverage.

Fun. I didn't anticipate that with agents programming feels *more* fun because a lot of the fill in the blanks drudgery is removed and what remains is the creative part. I also feel less blocked/stuck (which is not fun) and I experience a lot more courage because there's almost always a way to work hand in hand with it to make some positive progress. I have seen the opposite sentiment from other people too; LLM coding will split up engineers based on those who primarily liked coding and those who primarily liked building.

Atrophy. I've already noticed that I am slowly starting to atrophy my ability to write code manually. Generation (writing code) and discrimination (reading code) are different capabilities in the brain. Largely due to all the little mostly syntactic details involved in programming, you can review code just fine even if you struggle to write it.

Slopacolypse. I am bracing for 2026 as the year of the slopacolypse across all of github, substack, arxiv, X/instagram, and generally all digital media. We're also going to see a lot more AI hype productivity theater (is that even possible?), on the side of actual, real improvements.

Questions. A few of the questions on my mind:
- What happens to the "10X engineer" - the ratio of productivity between the mean and the max engineer? It's quite possible that this grows *a lot*.
- Armed with LLMs, do generalists increasingly outperform specialists? LLMs are a lot better at fill in the blanks (the micro) than grand strategy (the macro).
- What does LLM coding feel like in the future? Is it like playing StarCraft? Playing Factorio? Playing music?
- How much of society is bottlenecked by digital knowledge work?

TLDR Where does this leave us? LLM agent capabilities (Claude & Codex especially) have crossed some kind of threshold of coherence around December 2025 and caused a phase shift in software engineering and closely related. The intelligence part suddenly feels quite a bit ahead of all the rest of it - integrations (tools, knowledge), the necessity for new organizational workflows, processes, diffusion more generally. 2026 is going to be a high energy year as the industry metabolizes the new capability.

[Alex Svanevik 🐧 @ASvanevik]

a private equity fund that buys businesses and just forces everyone to use claude code

[Polymarket @Polymarket]

BREAKING: Elon Musk considers buying Ryan Air and replacing the CEO with someone named Ryan.

[Elon Musk @elonmusk]

@Ryanair Should I buy Ryan Air and put someone whose actual name is Ryan in charge?

[Ryan Spangler @RySpangler]

@gwei_sha 🔥🔥🔥

[Colossal Biosciences® @colossal]

SOUND ON. You’re hearing the first howl of a dire wolf in over 10,000 years. Meet Romulus and Remus—the world’s first de-extinct animals, born on October 1, 2024.

The dire wolf has been extinct for over 10,000 years. These two wolves were brought back from extinction using genetic edits derived from a complete dire wolf genome, meticulously reconstructed by Colossal from ancient DNA found in fossils dating back 11,500 and 72,000 years. This moment marks not only a milestone for us as a company but also a leap forward for science, conservation, and humanity. From the beginning, our goal has been clear: “To revolutionize history and be the first company to use CRISPR technology successfully in the de-extinction of previously lost species.” By achieving this, we continue to push forward our broader mission on—accepting humanity’s duty to restore Earth to a healthier state.

But this isn’t just our moment—it’s one for science, our planet, and humankind. All of which we love and are passionate about. Now, close your eyes and listen to that howl once more. Think about what this means for all of us.

[Todd White @DToddWhite]

https://t.co/7ZHLJNPC5c

[Alex Kosa @AlexKosa1]

A powerful conversation with @gwei_sha, Head of DeSci at @SeiNetwork, moderated by @_Chelseycrypto

They dive into decentralization, inclusion, and the future of open science in Web3.

Inspiring to see women leading critical discussions and shaping the next chapter of crypto
$SEI #DeSci #Web3

[Eleanor Davies @gwei_sha]

Probably nothing

[Brian Armstrong @brian_armstrong]

We have the potential to radically extend human healthspan in the coming decades using epigenetic reprogramming.

Check out @newlimit's 2025 Progress Update. Incredibly exciting.

[Tony Kulesa @kulesatony]

🚀 Fellowship applications are OPEN for Encode: AI for Science.

What if you could use AI to
- Design shape-shifting robots
- See through solid materials
- Decode language of the brain
- Create advanced materials

[Eleanor Davies @gwei_sha]

As the new Head of DeSci at @Sei_FND I’m excited to share my vision for DeSci V2.

The future, however, will be written by you: the builders and community members.

The infrastructure is ready. The capital is committed. Now it’s time to buidl 🧬

https://t.co/00g5FprxUw

[Ryan Spangler @RySpangler]

@gwei_sha @Sei_FND Ayoooooo 🔥🔥

[Ryan Spangler @RySpangler]

@metanova_labs 🔥🔥🔥

[METANOVA @metanova_labs]

introducing: NOVA ⚡️a global, decentralized engine for drug discovery

now live on #bittensor subnet 68!

NOVA pushes virtual drug screening beyond state-of-the-art models and transforms it into a high‑speed race for breakthroughs that rewards experts and enthusiasts alike for identifying synthesizable compounds with the highest affinity for key drug targets from a billion size database.

our subnet functions as a back to back hackathon, running 24/7. miners are encouraged to continuously develop and refine ai models and heuristic approaches that make drug hunting in a vast chemical universe more efficient.

🔗 gitHub: https://t.co/MM7hotwH0f
🔗 website: https://t.co/i3eijm174i

earn rewards for finding the best molecule the fastest, scoring submissions, or staking $TAO.

interested? you too can help shape the future of medicine.

[Bio Protocol @BioProtocol]

Introducing BioAgents: Accelerating Decentralized Science with AI

Sharing our 2025 vision for a decentralized fleet of BioAgents to accelerate scientific breakthroughs while equipping BioDAOs with agents to streamline ops

Learn more about BioAgents + our global AI hackathon ↓

[Ryan Spangler @RySpangler]

@gwei_sha @Sei_FND The new queen of DeSci 🔥🔥🔥