@wownevarez
Account based inMexico
About this account
- Account based in
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They/Them. I craft software assets 🧙 Interested in AI, SaaS and cloud technologies.
Mexico
Joined December 2007
- Tweets6K
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- Followers359
- Likes15.1K
Oscar Nevárez retweeted
Instead of checking my phone first thing, I set up an agent to make a "morning newspaper".
It prints while I sleep. I used grokbot @bot to pull from my calendar and email
I made it available if you wanna try it 👇
Oscar Nevárez retweeted
If you're lamenting writing code by hand like something you love is being taken away from you, see if you can fall in love with solving problems instead. Maybe you'll find out that's what you really loved the whole time ❤️
"I thought that I loved coding. But really what I loved was solving problems"
🎙️ @kentcdodds @joelhooks
youtube.com/watch?v=0-FbGBb3…
Oscar Nevárez retweeted
Sometimes it's easier to show than tell. We're sure this update will help with that. 👀
GitHub CLI now has a repeatable --attach flag that uploads a local image or video. Reference it inline in an issue, pull request, or comment body.
Available now to all users on GitHub across all plans. 🎉
github.blog/changelog/2026-0…
Oscar Nevárez retweeted
The real breakthrough in the NVIDIA SANA team’s Sol Engine work on MiniMax H3！
By splitting generation into a 4-step low-res H3 draft and a 3-step LTX refinement pass at target resolution with Sol-Attn, they’ve crushed 10s 768p latency on a single GB200 from 414s down to 14.93s (27.7x speedup). Replacing heavy VAE decodes with TAEH3/TAEHV while holding the latents stable for refinement is a masterclass in co-designing sampling topology with hardware kernel acceleration.
When inference latency collapses this dramatically, unit economics fundamentally shift: a single node can suddenly serve 378K videos a month at 97%+ GPU margins. This is how high-fidelity AI video moves from asynchronous batch rendering to near-instant, interactive infrastructure. Huge respect to the team for setting a new engineering bar for our open-weights ecosystem! 🫡🩵
🚀 MiniMax H3 Super Acceleration in Sol-Engine🤩
We pushed H3 far beyond our previous 3–4× optimization regime — reaching 22.2× speedup for 5s video and 27.7× for 10s video vs. the published SGLang baseline.
On a single NVIDIA GB200:
• 5s 768p: 152.3s → 6.85s (22.2×)
• 10s 768p: 414.1s → 14.93s (27.7×)
But the more interesting part may be what this means economically.
Using MiniMax’s published H3 API price as a reference, we translate inference speed directly into production economics. Under an ideal fully utilized GB200 scenario, Sol-Super can serve about 525 five-second videos/hour, corresponding to roughly $210/hour of output value at the reference API price.
Assuming $5.50/GPU-hour, that implies a 97%+ GPU-only gross margin in the idealized model.
At full utilization, one GB200 could produce:
• 12.6K 5s videos/day
• 378K videos/month
• equivalent to 525 hours of finished video per month
For us, this is the bigger point of inference optimization: a 20×+ speedup does not just reduce latency — it can fundamentally change the unit economics, serving capacity, and viable business models of video generation.
🔗 nvlabs.github.io/Sana/Sol-En…
Oscar Nevárez retweeted
One pattern I find useful for working with LLMs is a nice long ramble session. Sometimes the LLM needs more bits to understand what you're trying to achieve, but you're too lazy to type them. In these cases I like to lean back, switch to /voice and just ramble for like 10 minutes, total mess, anything goes, full stream of consciousness. Sometimes I declare it up top, something like "switching to speech recognition sorry for any typos...". Sometimes I turn it into a small interview of a few turns. But I find that the LLMs are somehow very good at reconstructing long incoherent rambles and often their echo of your own tangle of thoughts comes out quite a bit cleaner than what you started with. The result is that you improve the mind meld and have to correct things less from that point on.
Oscar Nevárez retweeted
Had Claude Fable 5 log network packets and display them as cars on a highway, different car types = different packet types
Oscar Nevárez retweeted
In the age of AI slop, some of us are still making things by hand, I promise ❤️ Octocat for the new GitHub Copilot App was modeled, rigged, and animated in Blender...
Cooking up something new 🧑🍳
Join the waitlist for early access to technical preview of the GitHub Copilot app 👇
gh.io/github-copilot-app?utm…
Oscar Nevárez retweeted
Did you know your MacBook has a sensor that knows the exact angle of the screen hinge?
It’s not exposed as a public API, but I figured out a way to read it and make it sound like an old wooden door.
Oscar Nevárez retweeted
In times of tragedy, don't mistake inaction for apathy. It's often the result of empathy.
Empathic distress is feeling helpless in the face of suffering. It drains us until we're numb.
You never know whose pain others are carrying. They may need compassion more than you realize.
nytimes.com/2024/01/01/opini…
Oscar Nevárez retweeted
The source code for DragGAN has been released! Huge thanks to community contributor LeoXing1996 for the @gradio demo
try it now:
huggingface.co/spaces/radame…
Oscar Nevárez retweeted
Meta just released MusicGen, a simple and controllable model for music generation
MusicGen is a single stage auto-regressive Transformer model trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz. Unlike existing methods like MusicLM, MusicGen doesn't not require a self-supervised semantic representation, and it generates all 4 codebooks in one pass. By introducing a small delay between the codebooks, can predict them in parallel, thus having only 50 auto-regressive steps per second of audio
try out the @Gradio demo: huggingface.co/spaces/facebo…
Models on @huggingface: huggingface.co/models?other=…
github: github.com/facebookresearch/…