Cybersecurity new way to perform pen test scans workflow with @drost_ai xitter.cf/migtissera/status/2105…
ᕈablo ᑕarrera (パブロ) retweeted
I’m releasing Drost Edge today.
Drost Edge runs autonomous security engagements from your own computer. You can watch every decision, command and attack path as it happens, and your report is ready immediately after the engagement.
A year of R&D, and distilled down to less than 10MB binary. This is also the most capable version of Drost I can put directly in users’ hands, considering safety.
The public beta is now open for macOS and Linux. Every new signup gets $100 in promotional credits.
Sign up here: edge.drost.ai
🚀
ᕈablo ᑕarrera (パブロ) retweeted
Train in sim. Run it for real. Teach it new tricks. 🦆
This is the sim2real loop that powers Microduck.
RL stack open source here : github.com/pollen-robotics/m…
Buy it here : pollen-robotics.com/microduc…
Git : github.com/pollen-robotics/m…
Join our community : discord.com/invite/pollen-co…
ᕈablo ᑕarrera (パブロ) retweeted
We are excited to announce that we can successfully use Rust's portable SIMD on the GPU. This has never been done before.
vectorware.com/blog/simd-on-…
Supporting Rust's `core::simd` enables existing Rust code to work on the GPU and makes GPU programming more ergonomic.
ᕈablo ᑕarrera (パブロ) retweeted
Something that makes me really hopeful for US labs doing open models is that while a lot of people were so blackpilled about competing with OpenAi or Anthropic with whatever multi-trillion parameter monsters they have been putting out, DS4-flash shows how close you can get with a much smaller model. Thinking Machines used their old recipe and put a great model out - even if ALL labs do is now is follow the new recipe, they're going to get something really strong that they could train on their hardware
Very exciting to see!
ᕈablo ᑕarrera (パブロ) retweeted
Introducing Tess Server
Run frontier-scale open models entirely on your Mac.
▸ 300B-class MoE models on one M4 Max (128 GB)
▸ Up to 3.9× faster decode than stock llama.cpp
▸ Runs architectures stock can't load at all
▸ OpenAI-compatible API
▸ 100% local and secure — no cloud, no telemetry, no per-token cost
`npm install -g @trinity-cloud/tess-server`
ᕈablo ᑕarrera (パブロ) retweeted
Tess-4-27B is a pretty good model!
Multiple community-run evals are placing it #1 on LocalBench, which tests for reasoning, tool calling, CLI, instruction-following and agentic capabilities (OpenClaw, Hermes etc).
Following the community requests, I'm shipping two upgrades today:
EAGLE-3 Draft Head: 1.76× faster decode (up to 2.4× on reasoning), lossless.
NVFP4: 19 GB, Blackwell-native, calibrated on Tess's own reasoning.
Links below. Enjoy! 🚀
It's been a long time since I've released a new model, and timing feels very right.
Here's Tess-4-27B: huggingface.co/migtissera/Te…
Tess-4-27B is the first Tess release in two years, and the first that reasons.
It's built on Qwen/Qwen3.6-27B, and post-trained on a deliberate blend: 64K-token long-context agentic traces — real engineering work done with Fable-5, not synthetic generations — with a reasoning style approximated from Fable-5 by a three-model teacher ensemble (Opus-4.8, GPT-5.5, and GLM-5.2) fused into one coherent voice.
It's pretty good. Enjoy!
Four days ago, co-founder @nickfrosst warned about the dangers of subscriptions to proprietary LLMs on @MTSlive:
"We released [North Mini Code] because we think this technology needs to be sovereign. It needs to be owned, and controlled, by the people who use it."
Nick Frosst on MTS Live
Interview date: June 10th, 2026
ᕈablo ᑕarrera (パブロ) retweeted
You get a gpu. And….. You get a gpu.
One of the biggest barriers to frontier AI is compute and know-how.
This month we remove both as obstacles. Computes on us. AutoScientist is on your side.
Join our beta program now.
ᕈablo ᑕarrera (パブロ) retweeted
llama.cpp now has an official website: llama.app
Our goal is to make local AI accessible to everyone, and improving the user experience is a big part of that. On the new landing page you’ll find a single-line cross-platform installer. The installation provides a single unified `llama` entrypoint which you can use to run/serve models and interface with 3rd-party agentic applications.
While oriented towards simplified user experience, the new `llama` application also provides all the advanced functionality of the existing llama.cpp tooling with which experienced users are already familiar. Also note that all GGUF models that you might have already downloaded with llama.cpp in the past will be automatically available to use without downloading again (they are stored in the common HF cache on your machine).
We have many improvements in the pipeline both at the UX and at the engine level and we plan to iteratively ship new things over the coming months. One of the main focuses will be seamless integration with local-friendly 3rd-party agents (such as Pi). In the meantime, we’ll continue to listen for feedback from the community and adjust accordingly, so keep letting us know what you think and need.
ᕈablo ᑕarrera (パブロ) retweeted
Adapted my tennis AI pipeline for pickleball 🎾
-Audio based shot detection
-Top down court visualization
-Shot attribution
-Ball + player + pose tracking (RF-DETR + ViTPose)
ᕈablo ᑕarrera (パブロ) retweeted
llama.cpp adds MTP for the Qwen3.6 family
This is a significant milestone for the local AI ecosystem. The performance jump with these changes is massive and elevates local inference on commodity hardware further.
Special thanks to Aman Gupta for leading this development!
github.com/ggml-org/llama.cp…
Ha sido un día agitado me estoy muriendo de sed.
En la esquina vagancia tiene algo para beber.
Me dice que está mezcladito, agua de coco será?
Pero me pegó tan duro, debe tener algo más.
ᕈablo ᑕarrera (パブロ) retweeted
It's time to seize the means of computation! We have a live launch event coming up on April 21st at 10:30am Pacific where we'll be announcing the products we've been working on over the last 12+ months.





















