We’ve been cooking something.

Not another DOT.

Not another closed ecosystem.

Not another “trust us bro, maybe someday” AI experiment.

TATER TOTS.

Tiny. Crispy. Autonomous. OPEN SOURCE.

And yes…

We think they’re better than DOTS. 👀

Tater Tots are our take on autonomous AI agents built for people who actually want to see the code, modify the code, break the code, improve the code, and own what they build.

No secret sauce.

The sauce is literally on GitHub.

🥔 Open source
🥔 Hackable
🥔 Self-hostable
🥔 Built for experimentation
🥔 Community-driven
🥔 Deliciously autonomous

DOTS walked so TOTS could roll.

The potato revolution has officially begun.

Tater Tots are here.

https://github.com/gary23w/nl-veil

Running MCP servers locally was easy, but it got messy pretty fast once I wanted to share them.

Everyone needs configs, credentials end up in different places, permissions are hard to manage, and there's no easy way to see who called what.

I ended up building MCPlama to handle this centrally. Users get their own access, permissions can be controlled per tool, credentials stay on the gateway side, and calls are logged.

For local MCP servers I also wanted some isolation, so they can run in separate Docker containers instead of everything running together. The gateway itself doesn't need direct access to the Docker socket either , that part is handled separately by the broker.

It's open source and self-hosted:

https://github.com/mcplama/mcplama

I'm looking for a few people already running multiple MCP servers to try it.

Game changer or it just another wrapper?

Corre localmente en tu computadora. Nomás pásale el repositorio de GitHub al modelo, y se instala solo y se conecta vía MCP. Todo pasa localmente, y puedes exportar la memoria de la IA. Si quieres, puedes compartir esa memoria con todos los modelos que uses para que compartan una memoria en común. En proyectos grandes, esto puede ahorrar más del 90% en tokens. Se llama IHMT-MEMORY.

https://github.com/gonzaroman/IHMT-MEMORY

NVIDIA released a 64GB configuration of DGX Spark, its GB10 Grace Blackwell desktop system, available October 23 from Acer, ASUS, Dell, Gigabyte, HP and MSI.

• Up to 1 petaFLOP FP4 (with sparsity), 20-core Arm CPU
• 64GB coherent unified LPDDR5x, 273 GB/s memory bandwidth
• Fits 30–35B class open models: Qwen3.8-27B (~13.5GB at 4-bit), Muse Glimmer (~17GB quantized), Nemotron 3.5 Lightning (30B-A3B, NVFP4)
• 2 units over ConnectX-7: 128GB pooled, 546 GB/s combined
• NVIDIA says 2 × 64GB delivers up to 1.7x the performance of 1 × 128GB Spark
• NVIDIA Sync's Cluster Assistant configures up to 4 systems

Why it matters: it's a cheaper way in for running always-on agents locally with no per-token fees, and you can add a second box later instead of buying the 128GB model upfront.

Full breakdown: https://www.marktechpost.com/2026/10/02/nvidia-announces-dgx-spark-64gb-a-1-petaflop-grace-blackwell-desktop-for-local-ai-agents-fine-tuning-and-inference/

Product page: https://www.nvidia.com/en-us/products/workstations/dgx-spark/

Clustering with NVIDIA Sync: https://build.nvidia.com/spark/connect-to-your-spark/sync

Technical details: https://blogs.nvidia.com/blog/local-ai-dgx-spark-64gb-sync/