Just read about Code2LoRA and it completely changed how I think about coding agents 🤯 We assumed AI models need massive context windows to understand your specific codebase. Turns out that's exactly what's been holding them back. Code2LoRA uses a hypernetwork to generate repository-specific adapters on the fly, deep contextual knowledge, zero extra tokens at inference time. What blew my mind: ↳ It's not just efficient, it's actually accurate, matching per-repo LoRA fine-tuning without the cost ↳ Code2LoRA-Evo updates adapters incrementally with each code diff, so the model evolves WITH your codebase The shift from "stuff the context window" to "generate the adapter" might be one of the most underrated breakthroughs in code AI right now. Highly recommend giving this one a read 👇 neuralnova.online/deep-dives… #AI #LLM #AIResearch #CodeGeneration #MachineLearning

Just read about StreamMA and it completely changed how I think about multi-agent AI 🤯 We always assumed AI agents need to finish thinking before passing info to the next agent. Turns out that's exactly what's been slowing t down. StreamMA lets agents stream their intermediate reasoning steps in real time, like an assembly line instead of a relay race. What blew my mind: ⚡ It's not just faster, it's actually more accurate 🧠 Early reasoning steps are more reliable, so sharing them early prevents errors from snowballing downstream 📈 Up to 22.4 pp performance boost on hard benchmarks And there's a brand new "step-level scaling law" meaning you can make systems better just by optimizing *how many steps* each agent takes. Wild. The shift from generate-then-transfer to real-time streaming might be one of the most underrated breakthroughs in multi-agent AI right now. Highly recommend giving this one a read 👇 neuralnova.online/deep-dives… #AI #LLM #AIResearch #MultiAgentAI

The dark side is so tempting

Introducing SubQ - a major breakthrough in LLM intelligence. It is the first model built on a fully sub-quadratic sparse-attention architecture (SSA), And the first frontier model with a 12 million token context window which is: - 52x faster than FlashAttention at 1MM tokens - Less than 5% the cost of Opus Transformer-based LLMs waste compute by processing every possible relationship between words (standard attention). Only a small fraction actually matter. @subquadratic finds and focuses only on the ones that do. That's nearly 1,000x less compute and a new way for LLMs to scale.

Working on an agentic memory optimization layer, so that agents can easily remember and manage long context in continuous workflows... Release and open source soon

3 months in creating agentic pipelines and I today got back to training models. Cant tell how much I missed this

Anthropic silently nerfed the weekly limits on Pro plan and thought we wouldn't notice. 3 Days left and I have already used 97% of my weekly limits apparently. No extra usage nothing What's even the point of 5 Hours session limit then?

What are your github stats for the month??

Quantum computing won’t just speed up AI, it will redefine it. From exponentially faster training to solving optimization problems current systems can’t touch, we’re entering a new intelligence paradigm. Read this latest research: Fundamentals of Quantum Machine Learning arxiv.org/abs/2602.20499 The real question isn’t if AI changes, but how fast we adapt.

Hot Take: $200 of Claude Code and zero employees won’t get you a complex production app.

People who get 0 likes and still keep posting are built different I am people, people is me 😭

These are the type of founders who then say "we have claude code max why do we need to hire Devs. The subscription is of better worth" Ironically they rarely grow to this level where they need actual scalability.

The job culture for beginners is the worst. Since the rise of ai nobody wants to bet on young minds 😔

Finding work or a job is more difficult than doing the actual thing ( even in a toxic environment) Maybe that's why some clients and founders treat Dev's this wayy....