AI Agentic Frameworks

With prolification of AI Agents, it's only logical that there will be attempts at standardization and building protocols & frameworks:

• MCP covered previously
• Any-Agent from Mozilla.ai to switch between agents, vendors, clouds, etc
• Agent2Agent interoperability protocol

On Quantization

• Speed vs Accuracy trade off.
• Reduce costs on storage, compute, operations .
• Speed up output generation, inference, etc.
• Work with lower precision data.
• Cast/ map data from Int32, Float32, etc 32-bit or higher precision to lower precision data types such as 16-bit Brain Float (BFloat16) or 4-bit (NFloat)/ int4 or int8, etc.
• East mapping Float32 (1-bit Sign, 7-bit Exponent, 23-bit Mantissa) => BFloat16 (1-bit Sign, 7-bit Exponent, 7-bit Mantissa). Just discard the higher 16-bits of mantissa. No overflow!
• Straightforward mapping work out max, min, data distribution, mean, variance, etc & then sub-divide into equally sized buckets based on bit size of the lower precision data type. E.g int4 (4-bit) => 2^4 = 16 buckets. 
  
• Handle outliers, data skew which can mess up the mapping, yet lead to loss of useful info if discarded randomly.
• Work out Bounds wrt Loss of Accuracy.

LLMs, AI/ ML side:

• https://newsletter.theaiedge.io/p/reduce-ai-model-operational-costs

Lucene, Search side:

• https://www.elastic.co/search-labs/blog/scalar-quantization-101
• https://www.elastic.co/search-labs/blog/scalar-quantization-in-lucene

Speculative Decoding

• Ensemble of Weak + Strong model
  
• Weak model has a quick first go at generating tokens/ inference (potentials)
• Followed by the Strong, but slow model which catches up & uses the outputs of the weak model, samples them, grades them, accepting/ rejecting them to generate the final output
• Overall making inferences via LLMs quicker and cheaper

More to follow..

• https://pytorch.org/blog/hitchhikers-guide-speculative-decoding/ 
• https://www.baseten.co/blog/a-quick-introduction-to-speculative-decoding/
• https://research.google/blog/looking-back-at-speculative-decoding/
• https://medium.com/ai-science/speculative-decoding-make-llm-inference-faster-c004501af120

Revisiting the Bitter Lesson

Richard Sutton's - The Bitter Lesson(s) continue to hold true. Scaling/ data walls could pose challenges to scaling AI general purpose methods (like searching and learning) beyond a point. And that's where human innovation & ingenuity would be needed. But hang on, wouldn't that violate the "..by our methods, not by us.." lesson?

Perhaps then something akin to human innovation/ discovery/ ingenuity/ creativity might be the next frontier of meta-methods. Machines in their typical massively parallel & distributed, brute-force, systematic trial & error fashion would auto ideate/ innovate/ discover solutions quicker, cheaper, better. Over & over again.

So machine discoveries shall be abound, just not Archimedes's Eureka kind, but Edison's 100-different ways style!

Model Context Protocol (MCP)

Standardization Protocol for AI agents. Enables them to act, inter-connect, process, parse, invoke functions. In other words to Crawl, Browse, Search, click, etc. 

MCP re-uses well known client-server architecture using JSON-RPC. 

Apps use MCP Clients -> MCP Servers (abstracts the service)

Kind of API++ for an AI world!

Open Weight AI

Inspired by Open Source Software (OSS), yet not fully open...

With Open Weight (OW) typically the final model weights (& the fully trained model) are made available under a liberal free to reuse, modify, distribute, non-discriminating, etc licence. This helps for anyone wanting to start with the fully trained Open Weight model & apply them, fine-tune, modify weights (LoRA, RAG, etc) for custom use-cases. To that extent, OW has a share & reuse philosophy.
 
On the other hand, wrt training data, data sources, detailed architecture, optimizations details, and so on OW diverges from OSS by not making it compulsory to share any of these. So these remain closed source with the original devs, with a bunch of pros & cons. Copyright material, IP protection, commercial gains, etc are some stated advantages for the original devs/ org. But lack of visibility to the wider community, white box evaluation of model internals, biases, checks & balances are among the downsides of not allowing a full peek into the model.

Anyway, that's the present, a time of great flux. As models stabilize over time OW may tend towards OSS...

References

• https://openweight.org/    
• https://www.oracle.com/artificial-intelligence/ai-open-weights-models/
• https://medium.com/@aruna.kolluru/exploring-the-world-of-open-source-and-open-weights-ai-aa09707b69fc
• https://www.forbes.com/sites/adrianbridgwater/2025/01/22/open-weight-definition-adds-balance-to-open-source-ai-integrity/
• https://promptengineering.org/llm-open-source-vs-open-weights-vs-restricted-weights/
• https://promptmetheus.com/resources/llm-knowledge-base/open-weights-model
• https://www.agora.software/en/llm-open-source-open-weight-or-proprietary/

The Big Book of LLM

A book by Damien Benveniste of AIEdge. Though a work in progress, chapters 2 - 4 available for preview are fantastic. 

Look forward to a paperback edition, which I certainly hope to own...

Mozilla.ai

Mozilla pedigree, AI focus, Open-source, Dev oriented.

Blueprint Hub: Mozilla.ai's Hub of open-source templtaized customizable AI solutions for developers.

Lumigator: Platform for model evaluation and selection. Consists a Python FastAPI backend for AI lifecycle management & capturing workflow data useful for evaluation.