Data Scientist, Bird Lover, Math Lover and biologist
Joined January 2016
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FerDoran retweeted
Join us in this livestream next Tuesday, learn how to shrink models to fit in your laptop and make them go brrr 💨
We will have @mervenoyann @maximelabonne @TadimetiAditya @bartowski1182 and @mgoin_ giving a primer and know-hows on quantization with llama.cpp and vLLM ⚡️
FerDoran retweeted
"Although aesthetics is recognized as a reason for the protection of biodiversity, comparatively little weight has been accorded to the grandeur of insect life beyond butterflies." buff.ly/5sRZxFI
FerDoran retweeted
Don’t stop sharing! Indonesia is STILL on fire! The world’s most important rainforests are past their breaking point! 🌳🔥
Orangutans & tens of thousands of other species are in grave danger of disappearing forever! 🦧🐅🦜
Donate or even just talk about it, don’t let this fade!
FerDoran retweeted
The Hermes Agent Plugin Hackathon starts now!
It runs for 7 days, with $20,000 in cash prizes and more.
Plugins are the most powerful way to extend Hermes Agent, and we want this hackathon to show what they can do. A plugin can change anything from the model Hermes runs on, where it stores its memory, where it runs commands, which messaging apps it works in and what Hermes Desktop shows. Plugins in the marketplace already let Hermes do things like control a phone or turn a photo into a printable 3D model.
Every kind of plugin is eligible, so build whatever functionality you want Hermes to have. We'll list accepted entries in the new Hermes Marketplace, where every Hermes user can install them.
Prizes
1st: $10,000 + neon sign + merch + $1,000 creds
2nd: $5,000 + neon sign + merch + $1,000 creds
3rd: $2,500 + neon sign + merch + $1,000 creds
4th: $1,500 + merch + $500 creds
5th: $1,000 + merch + $500 creds
Runners up (x5): merch + $250 creds
Submissions are due 5PM EST on Nov 11th. To enter:
1. Open a pull request that adds your plugin to the Hermes plugin catalog: hermes-agent.nousresearch.co…
2. Post a demo video on X and tag @NousResearch
3. Submit both links through the form: form.typeform.com/to/FwJLvOl…
Your entry is the plugin itself. Judges score it on usefulness, reliability, polish and creativity, and they favor plugins that do more than a prompt or a skill could. Keep the demo under 90 seconds and show the plugin working.
The developer guide shows how to build and test a plugin: hermes-agent.nousresearch.co…
When submissions close, we'll open a thread for every entry in the Nous Research Discord, so you can browse all the plugins and talk to the people who built them.
You can submit the form as soon as your pull request is open. The catalog checks run once a maintainer starts them, and we review entries every few days.
If you have any questions stop by the Nous Research Discord: discord.gg/nousresearch
GLHF!
Plugins, connectors, and skills for Hermes Agent are now unified in a single marketplace so you can easily browse and extend your Hermes experience in a few clicks.
The new Hermes Marketplace has everything your agent needs:
hermes-agent.nousresearch.co…
FerDoran retweeted
Some 4.5 billion years ago, when a Mars-size object struck Earth in a collision that formed the Moon, it set our planet spinning in days that were just a few hours long.
Those short days also changed Earth’s shape. Rapid rotation squashed Earth into a flattened sphere, bulging at the equator, argues a new study.
As the Moon moved away from Earth, the planet began to slow and its crust—freshly crystallized out of a magma ocean—came under immense strain.
The resulting deformation may have driven melting and volcanism that helped produce the seeds of Earth’s first continents, setting the stage for the beginning of plate tectonics. Learn more: scim.ag/48vGFzn
ALT The Moon may have squeezed Earth’s first continents into existence As our young planet spun down, its bulging shape gave way to a sphere, driving the formation of continental crust
FerDoran retweeted
About 130 million years ago the first flowering plants emerged, eventually bearing a dazzling array of fruits that would grow alongside the dinosaurs. But researchers have long questioned whether dinosaurs ever made these earliest fruits a main course.
Now, a toothless, turkey-size oddity with a parrotlike beak may be the first known dinosaur frugivore.
Researchers report a newly discovered dinosaur that lived alongside Tyrannosaurus rex and Triceratops named Infernogallus jamesi—meaning “hell chicken”—that was fully adapted to a fruit-based diet.
The anatomy bears a striking resemblance to modern parrots, the authors say. Learn more: scim.ag/47HABDz @NewsfromScience
ALT The first dinos that only ate fruit munched like parrots Toothless, turkey-size oddity may have evolved alongside the emergence of flowering plants
FerDoran retweeted
Most people hear fine-tuning and assume we’re updating billions of LLM weights.
Often, we aren’t.
Modern fine-tuning can adapt a model while leaving almost all of its pretrained weights untouched.
If you’re moving from Data Scientist → AI Engineer, these 5 techniques are worth knowing:
1. LoRA — Learn the update, not the weights
Freeze W.
Train two small matrices:
ΔW = BA
So:
W' = W + BA
You’re learning a small correction to the model.
2. LoRA-FA — Freeze even more
Freeze W and A.
Only train B.
Same idea. Fewer trainable parameters.
3. VeRA — Don’t even learn the matrices
Freeze random A and B.
Only learn tiny scaling vectors that control their contribution.
Extremely parameter efficient.
4. Delta-LoRA — Let the base model evolve
Unlike standard LoRA, W can change too—but its updates are guided by the low-rank adaptation.
5. LoRA+ — Same LoRA, better optimization
Train A and B with different learning rates.
Typically:
ηB > ηA
The bigger idea:
Fine-tuning doesn’t have to mean retraining the model.
LoRA → 2 matrices
LoRA-FA → 1 matrix
VeRA → scaling vectors
LoRA+ → 2 matrices, different speeds
Delta-LoRA → low-rank updates + evolving weights
Five approaches to the same question:
What is the minimum we actually need to learn?
That’s parameter-efficient fine-tuning—and exactly the kind of concept Data Scientists need as they move into AI Engineering.
🚨 Want to learn how to build + ship AI and Data Science projects (that businesses actually want in 2026)?
On November 11th, I am hosting a free workshop to help you get started with AI + DS projects in Python (free).
Register here (500 seats): learn.business-science.io/ai…
FerDoran retweeted
Habitat fragmentation may be causing birds to lose genetic diversity far faster than previously estimated.
A new Science study of 62 songbird species in China found that all had lost nearly half their genetic diversity over roughly 50 years, including species considered common and at low risk of extinction. scim.ag/4AtWz9F
FerDoran retweeted
A 6 month plan for becoming a research engineer.
> Math and core ML
Linear algebra, probability, calculus and optimization, followed by loss functions, classification, metrics and evaluation. Evaluation needs more hours because a research engineer is usually the person who has to say whether a result is real.
> Deep learning from scratch
Neural networks, activations, optimizers, PyTorch and an autograd engine that you write yourself.
> Transformers and language models
Attention, embeddings and the full encoder block, then BERT and GPT pretraining.
> Post training
Fine tuning with LoRA and QLoRA, then SFT, reinforcement learning with verifiable rewards and how RL environments are built.
> GPUs and inference
GPU programming in CUDA and Triton, inference and KV caching, Quantization and speculative decoding. This stage lets you make your own experiments faster instead of waiting on someone else’s infrastructure.
Reproduce papers alongside all of it. Pick a recent one, implement it end to end, run the ablations and write up what matched and what did not. It is the closest thing to the actual job and it is what you can show in an interview.
Most of these stages are available on TensorTonic as problems where you implement the idea yourself and get graded against test cases, with GPUs provided for the heavy ones.
Start at tensortonic.com
FerDoran retweeted
Most of what science knows about sleep comes from studies of vertebrates. Jumping spiders sleep and may even dream—or, at least, exhibit REM-like behavior, including rapid retinal movements, twitching, and leg-curling. In PNAS: ow.ly/fzBA5102sRv
ALT Adult male P. fimbriata during sleep. CREDIT: Daniela C. Rößler.
FerDoran retweeted
There are some moments that stay with you forever, that change the course of history. These are not those moments – but we can't stop thinking about them anyway.
FerDoran retweeted
About 130 million years ago the first flowering plants emerged, eventually bearing a dazzling array of fruits that would grow alongside the dinosaurs. But researchers have long questioned whether dinosaurs ever made these earliest fruits a main course.
Now, a toothless, turkey-size oddity with a parrotlike beak may be the first known dinosaur frugivore.
Researchers report a newly discovered dinosaur that lived alongside Tyrannosaurus rex and Triceratops named Infernogallus jamesi—meaning “hell chicken”—that was fully adapted to a fruit-based diet.
The anatomy bears a striking resemblance to modern parrots, the authors say. Learn more: scim.ag/47HABDz @NewsfromScience
ALT The first dinos that only ate fruit munched like parrots Toothless, turkey-size oddity may have evolved alongside the emergence of flowering plants
FerDoran retweeted
I joined @GoogleDeepMind to develop a research program on the economics of AGI. Today, together with Andrew Koh (@andrewjkoh) and Shane Legg (@ShaneLegg), we are incredibly excited to release The Economics of Intelligence, the first in a series of projects from our group on how AGI will change the economy.
How do capability improvements transform the economy, and when? In this project, we define thresholds of economic capabilities, e.g., minimal AGI, based on how they transform the economy. We then track how the economy–market structure, employment, and wages–changes during the transition across capability regimes.
We have seen exponential increases in what AI agents can do but the broader economy seems almost remarkably unchanged. Part of the reason is that the solo AI agent is the wrong economic object; economic transformation is an equilibrium outcome where organizations compete. Looking at how intelligence functions within organizations, transformation will depend on when two key capability thresholds are crossed: competence (gains on individual skills, e.g., coding, math) and coordination (gains on ability of agents to cooperate and work together).
We show that what economic transformation looks like depends on whether AI becomes supercompetent, supercooperative, or both—the regime of artificial superintelligence (ASI). Tracking this transition shows why the real visible transformation may come from improvements in coordination—a capability most benchmarks don’t yet track. Past a certain threshold, AI capabilities can break the Coasian ceiling on firm size and lead to an economy that looks drastically different than the one we see today.
Two main takeaways:
1. To understand the path of transformation, we should be tracking agentic coordination more closely.
2. The impact of AI will likely show up in wages before market structure or employment.
We are really excited about this paper. Excited to hear people’s thoughts.
Link: institute.deepmind.com/essay…