Multimodality

Aug 7, 2026 · 21:14

The New Primitives: Building AI Native Software — Kwindla Kramer, Daily

The talk traces the 80-year history of digital computing—from Vannevar Bush's predictions in 1945 to modern AI agents—to argue that current 'agents' are merely a primitive. The speaker posits that just as web pages were superseded by full web and mobile applications, agents will eventually give way to a new fully AI native software layer. This next generation requires advanced primitives like asynchronous non-blocking context compression and dynamic interface generation.

Key takeaways

1. The Evolution of Primitives 6:52

   History shows that every major computing leap (e.g., web pages to mobile apps) renders the previous primitive insufficient for the next era. Agents are viewed as the 'web page' of the current AI age.

2. The Next Frontier: AI Native Software 20:05

   Building beyond agents requires mastering primitives such as asynchronous non-blocking context compression, long running subagents that share context, progressive skills loading, dynamic interface generation, and conversational voice.

3. The Role of Abstraction 17:15

   Historical examples like VisiCalc demonstrate how new abstractions (e.g., the spreadsheet) make vastly more complex work possible, creating entirely new categories of work rather than eliminating jobs.

• AI Engineer
• AI Agents
• Digital History
• Multimodality
• Software Architecture
• LLMs
• As We May Think
• Pipecat

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Jul 31, 2026 · 20:14

Agents at Scale: Inside MiniMax's Model and the Infrastructure Behind It — Olive Song

The discussion details the engineering stack and open-source philosophy behind MiniMax's model, M3. Olive Song emphasizes that the open weights approach allows the community to build upon and optimize the model, fostering widespread intelligence access. The technical focus covers advanced training techniques—including multimodality (text, image, video) and Reinforcement Learning (RL) for long-horizon tasks like replicating academic papers (12-hour runs)—and the complex infrastructure required for deployment. Key engineering challenges discussed include writing specialized GPU kernels, optimizing the inference stack from 'day zero,' managing KV cache growth in agentic workflows, and adapting to shifting workloads from chat-based to multi-turn, tool-calling agents.

Key takeaways

1. Open Weights Philosophy 2:07

   MiniMax advocates for open source because it aligns with their mission of making intelligence widely accessible. By releasing weights, they enable developers (like Together AI) to optimize the model's inference speed and capabilities through community contributions.

2. Multimodality Training 8:02

   MiniMax M3 is multimodal, understanding text, code, images, and videos. Crucially, it was trained multimodally from scratch to prevent 'training collapse,' ensuring that the modalities naturally interact (e.g., visual tokens attending to text tokens).

3. Agentic Workloads and Inference Shifts 13:40

   The workload is shifting from simple chat turns to complex agentic workflows involving hundreds of multi-turn tool calls. This requires significant optimization in the inference stack, particularly concerning KV cache management and routing.

4. Long-Horizon RL Tasks

   Training for complex tasks (e.g., replicating an ICLR paper over 12 hours) requires careful formulation of the problem, defining environments, and optimizing reward functions within the Reinforcement Learning framework.

• AI Engineer
• Large Language Models (LLMs)
• Reinforcement Learning (RL)
• GPU Optimization
• Inference Engineering
• Open Source AI
• Multimodality
• MiniMax M3
• OS World
• SVG bench / Kernel bench

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Jul 16, 2026 · 36:00

New Model: Inkling by Thinking Machine on Hugging Face

Thinking Machines announced Inkling, a massive open model with nearly one trillion parameters. It is designed as a natively multimodal architecture, accepting image, text, and audio inputs simultaneously. Key technical features include a Mixture-of-Experts (MoE) structure (975B total / 41B active params), a 1M token context window, and advanced deployment support across multiple frameworks like `transformers`, SGLang, vLLM, and `llama.cpp`. The model is available in BF16 and NVFP4 formats, facilitating high-performance inference on various hardware setups.

Key takeaways

1. Multimodal Capability & Scale 3:50

   Inkling is a true multimodal model that natively processes image, text, and audio inputs using a single architecture. It boasts an immense 1M token context window and was trained on 45T tokens.

2. Architectural Innovations 5:10

   The model utilizes relative attention (replacing RoPE) and a specialized SConv layer to efficiently aggregate hidden states, making it highly efficient for multimodal tasks compared to previous models.

3. Deployment Flexibility 7:10

   Inkling supports multiple deployment paths: BF16 (requiring ~2TB VRAM) and NVFP4 (600GB VRAM). It provides day-zero support for `transformers`, SGLang, vLLM, and `llama.cpp` (including GGUF quants via Unsloth), enabling diverse inference environments.

• Hugging Face
• Large Language Models
• Multimodality
• Mixture-of-Experts (MoE)
• Quantization
• Inference Optimization
• Reinforcement Learning (RL)
• Thinking Machines Inkling Model (BF16)
• Thinking Machines Inkling Model (NVFP4)
• GGUF Quants (Unsloth)
• Full Blog Post

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