AAL VC

Engineers For Engineers Fund

[Steeve Morin @steeve]

We're releasing ZML/LLMD, our homegrown LLM server built on top of our homegrown high performance heterogeneous inference stack.

It ships with 5 architectures out of the box: NVIDIA, AMD, Metal, Intel and TPU. All transparent.

It supports DFlash, continuous batching, prefix caching, the whole deal.

Oh, and it's fast.

[Netris @netris_io]

Netris has raised its $15 million Series A, led by @a16z.

Guido Appenzeller (@appenz) led the round and is joining our board, with fellow a16z partners Martin Casado (@martin_casado) and Raghu Raghuram (@RaghuRaghuram) behind the investment. Together, they're the team behind the network virtualization that reshaped the modern data center — Nicira, NSX, VMware. They solved networking for the data center. Netris is doing it for AI.

Eight years ago, that bet looked wrong. We believed the network would become the hardest problem in computing, and that nobody outside the hyperscalers had the tools to solve it.

The hyperscalers had built that automation for themselves — armies of engineers, custom systems. Everyone else was stuck. So we built a platform to put it within reach of any operator.

Most investors didn't see it. Most of the industry didn't either. But the operators actually running networks did — they started choosing Netris long before AI was on anyone's roadmap.

Then the AI buildout hit, and the network became the bottleneck for the entire industry. A single GPU cluster runs across multiple network fabrics at once — Ethernet, InfiniBand, NVL72 — each with its own control plane, spanning hundreds or thousands of switches. None of it was built to work as one, and the automation tools that came before were never designed for it. One misconfiguration can take an entire cluster down.

It was the exact problem we'd spent years solving — and the platform was already built for it.

Netris automates network operations across every fabric and delivers true multi-tenancy through hard isolation (enforced on networking hardware). Operators can now run shared GPU clouds, maximize GPU utilization, and provision new tenants instantly.

In the last 12 months, Netris experienced 800% ARR growth and reached 35+ live deployments and growing — more live deployments than all other network automation vendors combined. The largest AI clouds in the world have chosen Netris: neoclouds, sovereign AI operators, and AI factories.

Huge thanks to the Netris team, our customers, investors, @nvidia, and our ecosystem partners, including @MirantisIT, @rafaysystemsinc, @RedHat, @spectrocloudinc, @vclusterlabs, and @HPE.

The AI buildout is just beginning. Netris has been ready for this for eight years.

Read the press release: https://t.co/hgd5f9UZQg

[Recraft @recraftai]

Two weeks since launch, and the public benchmarks are in. Recraft is officially the #1 independent image generation lab 🚀

[xmemory @xmemory_ai]

Today we’re sharing our first blog post: “Schema as the Core of Reliability.”

Our view is simple:

AI memory breaks down when facts are stored as unstructured text and reconstructed later. That approach can work for thematic recall, but it becomes fragile when systems need to answer exact questions, maintain state, support workflows, and drive automation.

In the piece, we explore why:
- Search can recover context, but memory must support facts
- Graph RAG is a meaningful step forward, but not the endpoint
- Reliable memory needs structure - types, relations, constraints, deduplication, provenance
- Schema is not decoration - it is the contract that makes memory observable, enforceable, and dependable

If AI systems are going to do more than sound coherent - if they are going to make decisions, trigger workflows, and operate over long horizons - memory has to become a governed system of facts.

That is where reliability starts.

Read the full post - link in the first reply.

[Latent Labs @Latent_Labs]

Today we announce results from our deployment of NVIDIA Blackwell B300 GPUs in partnership with @nvidia and @nebiusai, a leading AI cloud provider for training and inference, achieving over 4x training speedups and more than 60% faster inference for our generative models powering drug discovery.

[TheStage AI @TheStageAI]

Are you a big fan of jacket potato?

This is an open-source, real-time multilingual ASR for live speech.

It stays robust in heavy noise – even at SNR 0 dB.

That’s why it understands speech where people struggle to hear.

Use it for transcription, research, and multilingual apps

[Netris @netris_io]

622% ARR growth in 2025.

12% of all neoclouds globally rely on Netris for network automation and multi-tenancy — 15 operators onboarded in 10 months across 20+ live deployments.

Netris is now the essential networking foundation for AI operators worldwide.

Netris customers span the AI infrastructure landscape — high-growth neoclouds @STN_Inc , @tensorwave , and @BoostRunGPUs ; sovereign AI cloud providers @TELUS (Canada), @DCAIbyDynaChain (Denmark), and @YottaInfra (India); and leading AI platform provider @HPE .

In 2026, Netris is deepening collaboration with @nvidia and key PaaS/IaaS and compute partners, while continuing to expand solutions that streamline GPU adoption for telcos and enterprises.

Read the announcement: https://t.co/uGReGPb7Mq

[Recraft @recraftai]

We're excited to announce Recraft V4, our latest image model developed with designers to bring true visual taste to AI generation 🔥

Find out more about the model: https://t.co/0P1SCFqlrW

[Robert @skull8888888888]

Excited to share that @lmnrai has raised $3M to build open-source observability for long-running AI agents.

Laminar is how companies like @browser_use, @OpenHandsDev, and Rye see what their agents are doing, understand why they fail, and spot patterns across millions of runs.