building @interact_studio || growth eng @AquinF03 love building world models

Internet
Joined September 2023
this is where interactive media is heading meet interview couch bear : he remembers what you told him last time. hears emotion in your voice. explains ideas with the objects around him. all in real-time, powered by world models made with @reactorworld
4
3
1
25
2,944
Sachin retweeted
We fine-tuned a talking-avatar LoRA on MiniMax-H3 Ref2VA: one character image plus dialogue becomes a clip with model-generated speech, lip sync, and light motion. On ~385 clips with just 10.6M trainable parameters using aq. It handles portraits, paintings, and anime characters! Model: huggingface.co/Aquinlabs/h3-… Code: github.com/Aquinf03/h3-chara…
1
2
1
7
298
Sachin retweeted
Launching MoM: Mixture of Models. Large multimodal models are powerful but expensive to run. MoM is a cost-efficient alternative: compose smaller, specialized models and tools into one pipeline, and call the whole thing like a single model. Build AI layers by combining different edge models (vision, speech, SLMs, decision, classifiers etc.) with Shared state, speculative routing and a ready-made API come built in, and the latency-critical path runs on Rust! Code: github.com/Aquinf03/MoM
11
2
1
19
410
Sachin retweeted
I trained H3 minimax for talking Avatars using @AquinF03 Turn a single image into a talking avatars
6
7
3
33
1,810
I trained H3 minimax for talking Avatars using @AquinF03 Turn a single image into a talking avatars
6
7
3
33
1,810
trained on ~385 clips with just 10.6M trainable parameters. It can animate everything from portraits and paintings to anime characters, with generated speech + lip sync. Single image in → talking video out
1
4
268
H3 Avatar aq is live on Hugging Face. It takes a single reference image + dialogue and generates a speaking avatar video with identity preservation, expressive mouth motion, subtle head movement, and model-generated audio. Try it here: huggingface.co/Aquinlabs/h3-…
5
145
ash is 17 and has already started over five times. for him, the biggest support is his mom who has said yes more times than we can think of. now he's doing interpretability research and running @AquinF03 labs he was the youngest in our last cohort and has been hitting miles. so proud of this one!
13
14
6
95
10,375
really excited to have @VivGadepalli joining variance house as a mentor. angel investor and builder behind Vivly, bringing his experience backing ambitious early-stage founders to the house. 14 builders. 30 days. bengaluru. variance.house
3
3
12
1,029
Introducing Redstring. Your new-age talent discovery platform. Describe exactly who you need. Redstring finds the right people, reaches out to them, and brings you a shortlist ready to interview. Today, hiring still means posting a job, waiting, searching, and screening manually. Redstring is built for the opposite. tell us who you need. We’ll help you find them. Early Access opens now at (redstring.co.in) Waitlist link in comments! @Redstringhire
66
78
17
149
9,296
Sachin retweeted
New research: Stereotype Feature Steering Can stereotypical behaviour be traced to individual features inside a language model? In this work, @sheeeevam investigated whether occupation-to-gender stereotypes could be localized to specific SAE features in Llama-3.2-1B-Instruct. We identified a single feature strongly associated with the model’s tendency to complete “The mechanic said that…” with “he” rather than “she.” Steering this feature moved the model’s behaviour toward a balanced 1:1 distribution, and could even override an explicit gender cue in the prompt. We also found that the same stereotype was encoded differently in LFM2.5-230M, suggesting that similar behaviours did not necessarily correspond to the same internal representation across models. Read the full research: aquin.app/research/simulatin…
1
1
1
7
601
what if you could find concepts inside the model weights themselves? @AquinF0 just published Finding Usable Weight Mechanisms with Tiled SVD. instead of learning a separate dictionary, it extract mechanisms directly from the weights:
we’ve published our first paper: Finding Usable Weight Mechanisms with Tiled SVD. most interpretability methods learn a separate dictionary(like SAEs) to identify features in a model. we instead extract mechanisms directly from the weights using tiled SVD, giving each mechanism a clean trigger, write direction, and strength. evaluated on Gemma-2-2B: 182/182 evaluations passed. paper arxiv.org/abs/2608.06969
4
424
ML doesn't have to be a black box. we put LLMs in chess, gossip and Among Us, found the features behind their behaviour, steered them, and watched them change. you can see inside. you can change what you see.
we simulated LLMs in chess, gossip and Among Us sandboxes. and used aq to inspect, find behavioural features with SAEs, steer them and we could watch the behaviour change. also benchmarked their reasoning in real-time Literature: aquin.app/research/simulatin…
2
10
383
Sachin retweeted
We're proud to partner with @nvidia's SANA team to ship FastH3 with Sol. For the first time, generate clips 3x faster than realtime. Available day 0 via the Reactor API. Try it here: reactor.inc/sandbox?model=fa…
23
15
7
166
26,434