Library
Search signals and long-form analysis across research, systems, and applied cases.
Alberta reports large-scale agentic review of government code
Alberta says a human-supervised Claude workflow reviewed 466 million lines across thousands of repositories and generated testable fixes.
ChatGPT moves toward coordinated long-running knowledge work
OpenAI positioned ChatGPT for more ambitious work that coordinates tools and sustained tasks rather than isolated chat responses.
Claude Science packages agents, tools, compute, and auditable artifacts
Anthropic introduced a scientific workbench integrating databases, notebooks, packages, terminals, and flexible compute access.
Claude Sonnet 5 narrows the agentic capability and cost gap
Anthropic launched Sonnet 5 with stronger tool use, coding, reasoning, and configurable effort at a lower tier than Opus.
Anthropic proposes a severity scale for cyber jailbreaks
The draft Cyber Jailbreak Severity framework grades bypass outcomes from informational through critical instead of treating all jailbreaks equally.
Major EU AI Act rules approach their August 2026 application date
The Commission timeline identifies 2 August 2026 for broad enforcement, Annex III high-risk rules, and transparency obligations.
Gemini 3.5 expands across computer use, translation, and devices
Google's June release cycle moved Gemini 3.5 capabilities into computer-use, live translation, Android, and learning workflows.
GPT-5.6 reaches general availability as a three-model family
OpenAI released Sol, Terra, and Luna with different capability, latency, and cost positions for production selection.
Japan announces a national-scale Vera Rubin AI infrastructure build
NVIDIA and Noetra announced a national physical-AI facility using 13,750 Vera CPUs and 27,500 Rubin GPUs.
NVIDIA links AI factory deployment to new financing structures
NVIDIA described revenue-sharing and credit support intended to unlock large multi-tenant AI infrastructure for emerging providers.
OpenAI and Broadcom introduce a custom LLM inference accelerator
The Jalapeno accelerator is designed around LLM serving economics, utilization, memory movement, and multi-generation deployment.
Daybreak shifts AI cyber tooling from findings toward verified fixes
OpenAI combined cyber models, Codex Security workflows, partner access, and open-source patching around end-to-end remediation.
Balancing Cost, Latency, Reliability, and Security
Operational trade-offs must be measured at the successful user outcome, inside the same security and quality boundary.
AI in Clinical Decision Support: Evidence, Workflow, Failure, and Governance
Clinical value depends less on a model demonstration than on intended use, evidence quality, workflow fit, human control, monitoring, and accountable escalation.
The Complete Map of an LLM System
A production LLM system is a chain of contracts, not a model wrapped in a chat box.
Evaluating LLM Systems Without Guesswork
An evaluation is useful only when it changes a release, routing, or product decision.
Evaluation as a Control System for AI Products
Evaluation should connect product risks and user outcomes to measurable evidence, release policy, monitoring, and corrective action.
Inside vLLM: KV Cache, Scheduling, and the Model Runner
vLLM shows how memory allocation, request scheduling, model execution, and distributed coordination turn a language model into a serving system.
What Predictive Processing Can and Cannot Teach AI Engineers
Predictive processing offers a useful account of hierarchical inference and error correction, but it is not a shortcut from brain metaphor to system architecture.
Prompting vs RAG vs Fine-Tuning vs Tools
The right intervention follows the type of gap: instructions, knowledge, behavior, or action.
RAG from Ingestion to Grounded Citations
RAG quality is determined by the whole evidence path, not by adding a vector database.
Tokens, Context, Attention, and Inference
Four boundaries explain much of an LLM application's behavior: encoding, representation, finite context, and sequential generation.
Transformers as Systems: Tokens, Attention, Training, and Inference
A transformer becomes operationally understandable when architecture, training, inference, context, and serving constraints are traced as one system.