LLMs & Models
Large language models: GPT, Claude, Gemini, Mistral and open weights.
97 published articles
Multi-Agent Collaboration
The group chat just became AI's most dangerous proving ground
Alibaba Cloud's AgentTeams and Anthropic's Claude Tag are turning group chats into the proving ground for multi-agent collaboration. The shift from one-on-one to many-to-many conversations introduces complex challenges in context management, permission governance, and memory, and a new paradigm for how AI works inside organizations.
2026-07-16
Systems Design / LLM Infrastructure
The real cost of model routing has nothing to do with sticker price
Across 417 agentic tasks, GPT-4.1 cost nearly double Claude Sonnet despite lower sticker pricing, because caching rewrote the math. A deep-dive on why model routing fails as classification, and how optimization-based routers beat difficulty-based ones.
2026-07-15
Artificial Intelligence
Inkling is open-source AI's $53 billion reality check
Inkling is the first openly available near-1T parameter model with native audio, image, and text input alongside a 1M-token context window. The raw benchmark scores are strong. The real story is how the open-source ecosystem has moved from playing catch-up to competing at the frontier, and where Inkling fits on that new map.
2026-07-15
AI Development
Vibe coding is fast. Shipping what it builds is where the real work begins.
Vibe coding accelerates prototyping and lowers the barrier for beginners, but its hidden technical debt, security risks, and lack of reasoning traceability challenge assumptions that AI-generated code is ready for production. Developers must weigh speed against control.
2026-07-14
LLM Performance
A Chinese video-generation startup just quietly beat Claude Opus at coding
MiniMax's M2.7 scores 56.22% on SWE-Pro, matching near-Claude Opus performance, while touting 97% skill adherence on complex tasks and superior office productivity editing. The model signals a shift from benchmark chasing to real-world agent deployment.
2026-07-14
Google DeepMind
Gemma 4 just made every other open-weight model look 10x too big
Google DeepMind's Gemma 4 natively multimodal open-weight family introduces thinking mode, encoder-free architecture, and MoE options. The 2.3B model matches Gemma 3's 27B performance. The 31B model tops open-weight leaderboards.
2026-07-13
Remote Sensing AI
Smarter tokens just cut satellite AI costs by 3x without losing accuracy
Ai4earth's OlmoEarth v1.1 cuts compute costs by up to 3x over v1 for satellite image analysis, using a smarter token merging technique that maintains performance. The updated models enable cheaper planet-scale map refreshes for partner organizations.
2026-07-13
Lean 4
The $4 theorem prover that embarrasses $300 competitors and found bugs the tests missed
Leanstral 1.5, a 6B active-parameter model, saturates miniF2F, solves 587 PutnamBench problems, and uncovers 5 previously unreported bugs in open-source repositories. At roughly $4 per problem, it undercuts Seed-Prover by 75x and Aleph Prover by 15x, challenging the assumption that formal verification requires massive compute budgets.
2026-07-12
Systems optimization
DeepSeek just proved most AI inference speed tricks are working against each other
DeepSeek's DSpark paper reveals that naive speculative decoding degrades throughput under high concurrency. Its solution, confidence-scheduled verification, adapts block length per request and shifts the Pareto frontier of serving performance.
2026-07-12
Long-context LLMs
Jet-Long's bifocal attention just killed the fixed-scaling trade-off for long-context LLMs
Jet-Long adapts RoPE rescaling dynamically per sequence length, using a local window and a long-range window merged via inclusion-exclusion. On Qwen3 models up to 128K context, it beats existing zero-shot baselines by over 2 percentage points on RULER and achieves the lowest perplexity on PG-19, all while adding less than 4% generation overhead.
2026-07-12
Synthetic Data Strategy
Nvidia's data atlas shows why synthetic data matters more than model weights
Nvidia's Nemotron Post-Training v3 Prompt Atlas provides an interactive map of billions of synthetic data samples, highlighting how open synthetic data is the missing layer for building reliable AI agents. The company argues that agent behavior must be inspectable and that synthetic data, released openly, is the only way to preserve proprietary signals without exposing trade secrets.
2026-07-12
Open Source Infrastructure
Your AI search pipeline is broken. This open-source framework fixes the plumbing.
Teams building AI search infrastructure still spend too much time on plumbing. Search Toolkit unifies ingestion, retrieval, and evaluation into a single open-source framework, eliminating the weeks of integration work needed to stitch together separate tools. It's designed for enterprise use cases like RAG quality, domain-specific retrieval, and agentic search.
2026-07-11