SteveB (@bmtriet) header
SteveB (@bmtriet) profile picture

Building local AI agents and factory automation in Vietnam. 12 years shipping internal tools. Real tests, failures, and lessons.

Viet Nam
Joined September 2010
Photo by SteveB (@bmtriet)Photo by SteveB (@bmtriet)Photo by SteveB (@bmtriet)Photo by SteveB (@bmtriet)Photo by SteveB (@bmtriet)Photo by SteveB (@bmtriet)Photo by SteveB (@bmtriet)Photo by SteveB (@bmtriet)Photo by SteveB (@bmtriet)Photo by SteveB (@bmtriet)
We're giving away a Hogwarts Legacy custom wrapped NVIDIA GeForce RTX 5090 to celebrate its official release with DLSS 5 🪄 To enter: 🟢 Share this post 🟢 Comment #RTXON Terms and Conditions: nvidia.com/en-us/geforce/con…
69,444
59,246
5,639
58,730
4,603,210
SteveB retweeted
Introducing Jev @typesafeai powered social-api Try it at treg.to/social - One API to access any social data - Track every mention of you & your competitors - Find trending posts in your niche, filtered by Jev - Under $0.05 to pull 1,000 competitor ads Open source, 0% markup 👇 Repo link below
125
128
90
2,023
217,749
Today we release Mirage: A 22 MB visual-language model that runs scene descriptions + object detection + OCR + segmentation + spatial relations analysis + extractions + decisions, in a single pass. It can do all that in 74 ms on Samsung Galaxy S26 NPU or 107 ms on its CPU. Mirage has a 7.8M encoder + a 40.8M decoder at 2bits, and describes images in English, German, French, Spanish, Italian, Dutch and Polish. We trade general language capacity, to compete top small VLMs today (SmolVLM2-500M, LFM2.5-450M and Florence2-230m) on these tasks. On 256 VQAv2 questions, feeding Mirage's structured output and full trace to text-only Qwen3-0.6B raised its score from 9.3 to 31.3 out of 100, without fine-tuning either model. Read more and benchmark it yourself here: cactuscompute.com/blog/mirag…
58
183
22
1,667
126,519
Computer use 需要进化到鼠标集群
159
526
143
8,681
504,794
an absolutely beautiful and informative blog post! one of the biggest anti-patterns we see with jev is just plugging it in to make Big Decisions instead of having code do what it's best at and jev for just the parts that require judgment 🤌 THIS is how we transform software!
Why don't we at TypeSafe call Jev a decision model? If you think of Jev's questions as making *decisions*, I believe you'll use it at the wrong layer of abstraction and your reliability will suffer. I wrote all about this on our site 🧵👇
20
11
3
331
31,758
Voice direction & distance-aware UI. The demo shows Voice Ring + Libraries Voice Position model, where the UI reacts to voice direction and distance. The ring also changes its color based on speech emotion. Ready to use with SwiftUI on Libraries.dev
18
12
2
273
13,178
I turned my airpod case into a mouse and it works pretty well!
441
769
281
15,814
1,546,431
we made an open-source Grok Bot with unlimited usage! meet @mausbot_ use your existing subscriptions: Claude, Codex, or even local models. Try it here - mausbot.com
Replying to @DogecoinNorway
The cost of serving Grok Bot was unsustainably high relative to revenue
11
7
4
93
11,052
SteveB retweeted
LibreYOLO 1.7.0 is out, and this one is special: image to 3D! The first model is Speridlabs Idol by @speridlabs, and the quality is stunning. One photo in, a 3D Gaussian splat out, in three lines. Apache-2.0, weights straight from their Hugging Face. libreyolo.com/docs/models/sp…
6
67
1
474
19,778
SteveB retweeted
This quoted post is unavailable.
75
8
2
163
18,961
SteveB retweeted
Great to see Ming-3.0-tts-0.5B running locally on an RTX 3090! In @ItsmeAjayKV’s test, a 5-second sample of his own voice was enough for cloning—even with fan noise in the recording. 🫶 Thanks for sharing, AJ! We’d love to see what you build next.
Tested voice cloning with Ming-3.0-tts-0.5B by @AntLingAGI and it is even more insane. For a model of this size, voice cloning happens really well, i tested my own voice, half drunk, in a room full of noise (fan noise mostly) and it instantly cloned it. It needed just a 5-second sample of my voice, and it speaks faster than real time: about 4 seconds of speech in 2–3 seconds. All running locally on my RTX 3090. Ming takes about 3.3 GB of VRAM, and the whole model is under 3 GB on disk. Great time for Local AI.
3
8
2
85
5,964
Your Harness. You Define It. Meet Creator Mode in DeepSeek Harness. Turn ideas into plugins that you can use, from a design workspace to a sandbox game or a 3D LLM visualizer. What will you create?
745
2,108
951
25,192
2,467,583
Too fast,
A hall of fame product
20
Sound cool.. I will be the first person claim the "System Sub-zero"
a lot of decision models came out in the last two months. almost all of them work the same way. they read text, wait until the whole input has arrived, and then give you an answer that works for a support ticket. it doesn’t work for a robot, a phone call, or a video feed, where the input never really ends and the decision has to happen while it is still coming in so we spent half of our YC money training a model:) RightNow takes text, PDFs, images, voice, code, robot state and video, all in one model, and you can stream up to 1M tokens of context into it. it updates its decision as each piece arrives. on text it decides in 30 ms and we will make it less with our kernel optimization we trained it with what we call Reflex Training. the current decision models read everything first and answer once. RightNow is trained to decide on whatever has arrived so far, to hold back until the evidence is there, and then commit. it serves on what we call Reflex Architecture which keeps a probability for every option and updates it as each part of the input comes in we call this kind of model “System Zero”. system 1 is fast thinking and system 2 is slow reasoning. system zero is the step that comes before both, which is just deciding we will publish a technical paper on Reflex Training and Reflex Architecture. the model itself will stay closed source and available via RightNow API this is our first checkpoint and it’s still a little buggy. we launched it early because the fastest way to make it good is to have people use it:)
1
17
Introducing Granted! I bookmark a lot on X and almost never go back to it. So I built something that does. → Every night, agents read my X bookmarks → Research ideas, pick the best one → And build a working prototype. Every morning I wake up to a startup brief + a live app on my phone. Open Source, sharing soon!
48
15
3
418
12,858
12 years. One company. A 9–5 routine. I’m stepping away to build products at Orchexa. Our vision: an Agent OS where AI goes beyond chat—using real tools, running workflows, and working alongside humans to deliver outcomes. Less hype. More shipping. #BuildInPublic
1
14
SteveB retweeted
🎉 NVIDIA Nemotron 3.5 Transcribe is here—with day-0 serving through vLLM + NeMo Speech. 40 languages. Recordings up to an hour. Promptable transcripts, speaker labels and audio Q&A. Open weights in BF16, FP8 and NVFP4.
Meet NVIDIA Nemotron 3.5 Transcribe 🎙️ An open-weight, promptable 30B MoE model with ~4B active parameters that brings transcription in 40 languages, speaker attribution, translation, contextual prompting and audio understanding into one checkpoint. Watch it keep track of who said what when everyone’s talking at once. 1/ 🧵
19
24
297
20,289
So one of our ambassadors @divagr1925 has been working on something cool He found a way to use Qwen's Gated DeltaNet state as memory, and built a 32-bit RISC-V CPU directly into the model's weights. So instead of relying on predicting the answer to a calculation, the model can execute instructions internally. It can also run C and MicroPython programs. He calls it Qwenputer, love the name btw. The results so far are pretty impressive. On one arithmetic benchmark, accuracy jumped from 48.6% to 100%. Pretty cool to see developers experimenting with Qwen's architecture. Great work, my friend!
1/4 Meet Qwenputer: Qwen3.5-4B with a full RISC-V CPU inside its weights, using the model's own Gated DeltaNet layers as RAM. It runs C and MicroPython in its forward passes, no external runtime. On DeepMind math: base 55–90%, base + Python tool 95–100%, Qwenputer 97.5–100%.
114
374
92
3,927
192,164
@@
Introducing Reality-Aware Rendering in @Simulon, a completely new way to create with the real world. Place neural assets with your phone and act out the scene. When you stop recording, Simulon renders the take, reading everything in it (what you do, what you say and the place around you), and your assets respond to all of it. The final is regenerated from your recording: a new performance of the scene, not a copy of the footage. Available now on iPhone. Coming to Android soon.
9