Custom AI Inference Chips Are Eating the GPU Market
Discover why frontier AI labs are shifting to custom AI inference chips to solve memory bandwidth bottlenecks, reduce latency, and challenge GPU dominance.
PastAGI
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The End of AI Reasoning Transparency: When Chain of Thought Becomes a Summary
AI reasoning transparency is failing. Learn why models like Claude summarize their chain of thought, why raw logic is hidden, and how to evaluate black box AI agents.
Why AI Agents Leak Sensitive Data (and How to Stop Them)
Discover how autonomous AI agents leak sensitive enterprise data through reasoning traces. Learn practical architectures and sanitization frameworks to prevent exposure.
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Open Weights vs Frontier Models Is a 4.4 Month Call
The open weights vs frontier models gap has closed to 4.4 months at roughly 3x the cost. Here is the math that decides which workloads justify the premium.
Async GRPO Training on Serverless GPUs Cuts RL Cost
Async GRPO training with LoRA swaps NCCL for a bucket and proxy, turning RL fine-tuning from reserved-cluster spend into elastic spot-GPU economics.
AI Agent Monitoring Beyond the Dashboard
AI agent monitoring fails when dashboards track requests, not resolutions. This taxonomy maps silent failure modes to the signals that catch them.
Devin GPT-6 Astra Self-Testing Moves Review to Evidence
Devin GPT-6 Astra self-testing shifts code review from reading diffs to auditing evidence. Here is the pattern, its failure modes, and audit criteria.
Training a Diffusion Model From Scratch in 3.5 Days
Training a diffusion model from scratch can cost hundreds, not millions. The full math behind a 210M DiT built in 3.5 days on one RTX PRO 6000.
OpenAI Navier-Stokes, a Reported $40M Lesson in Verification
The OpenAI Navier-Stokes run reportedly burned $40M and 130 billion tokens yet produced no verified proof. Verification, not generation, now binds.
Netflix MAPS Teardown and What Actually Transfers
Netflix MAPS teardown: how multimodal asset personalization ranks artwork and previews per member, what results it drives, and which patterns transfer.
Meta's AI Second Brain Runs on a Knowledge Supply Chain
Meta's AI second brain shows why agents live or die on the knowledge supply chain, not the retrieval stack, and what small teams can copy first.
AI Text Watermarking in Python, Three Families Tested
Hands-on AI text watermarking in Python: how the three families work, what survives copy-paste, edits, and paraphrasing, and when to watermark or detect.
KV Cache Math for Million Token Agent Runs
KV cache math explains why every decoded token reads the whole context, and what eviction and quantization can cut from million-token agent bills today.
GPT-6 Astra Pricing at $6 an Hour, Audited
GPT-6 Astra pricing pitches an AI engineer under $6 an hour. We audit the real per-task costs, hidden token overhead, and what saturated benchmarks skip.
Prompt Dependency Graphs That Shrink Your Retest Set
Build a prompt dependency graph to compute the blast radius of any prompt change and rerun only the evals your multi-prompt LLM system needs.