Research & Papers
Technical guides, research papers, and case studies
What Building Infrastructure Taught Us About Trust
Years of building infrastructure taught me that trust is earned quietly, honesty about limits beats big promises, and locking people in is a slow death.
The Hidden Energy Cost of AI, and What Low-Power Inference Could Look Like
The AI energy cost is treated as an inconsequential while the power demand is at the size of some countries and why efficiency, not scale, is the future.
Why Data Quality Decides Small-Model Quality
On saturated consumer hardware, you do not buy small-model quality with a faster framework. You buy it with better data and a better training objective, with numbers to back it.
LLM Decoding Without Changing Output Bytes
Speculative decoding and multi-token prediction cut byte-level generation latency without touching output quality. Here is what we measured on Veritate's models
Why Low-Bit QAT Needs to Happen In Training
Below roughly four bits per weight, post-hoc QAT on byte-level models is catastrophic, and quantization-aware training recovers nearly all of the lost quality
Making an LLM Efficient By Optimizing Thinking Time, Not Parameter Count
For a model that must fit on a small device, the parameter budget is fixed. The place to spend is inference-time compute.
Upcycling the Data Center: Doing More AI With Older Hardware
We throw away a staggering amount of perfectly useful compute. Here is why a lot of AI and cloud work can run on older, upcycled hardware, and where it honestly cannot.
Byte-Level Language Modeling Without a Tokenizer
Why our Veritate models read raw bytes instead of tokens, what the 256-symbol vocabulary costs, and what our 85M and 800M models tell us about the trade.
Open Weights and the Future of Private AI
Open-weight models let you run capable AI on hardware you control. Here is why I think that matters more than any leaderboard, for privacy, cost, and independence.
Activation Sparsity for Efficient Inference
You cannot retrofit activation sparsity onto a finished model, but a short, cheap retrain installs it, and the savings compose with weight pruning.
Why the Internet Forgot How to Be Efficient
We ship megabytes of script to display a paragraph of text, and we call it progress. I think we forgot that efficiency was ever a virtue.
The Cost of "Free" AI: Who Pays for Inference
Free AI is not a gift. Someone pays for every query, in energy, in data, in dependency. Here is who, and why I'd rather pay honestly for compute I control
React2Shell Explained: Carpathian's Incident Response
CVE-2025-55182 React2Shell: Critical Zero-Day RCE. Real Cyber Attack Analysis and Threat Detection, IOCs, Remediation Steps. React & Next.js Security.
Critical RCE in React Server Components
React2Shell (CVE-2025-55182): Critical RCE in React Server Components. Patch to React 19.0.1+ and Next.js 15.0.5+ immediately.
Rethinking AI Thinking
Can artificial intelligence truly think, or is it simply an extraordinarily sophisticated pattern-matching system? The people who succeed in the AI era won't be those who blindly adopt every new AI tool or those who resist all AI assistance. They'll be the ones who understand exactly what AI can and cannot do.