Tokenless

Tokenless

Open Source Security in the Age of AI

Interactive discovery

Explore the topic map

Follow the connections between themes, people, and ideas across the Tokenless archive in an interactive topic modeling map.

Open the map Browse posts

Machine Learning

View All
Judge a Transaction by the Company It Keeps — Somya Rai & MD Sharique, EXL

Judge a Transaction by the Company It Keeps — Somya Rai & MD Sharique, EXL

EXL developed an advanced fraud detection system that augments XGBoost with Graph Neural Network (GNN) embeddings. Traditional tabular models struggle to capture fraud signals residing in transaction neighborhoods. The system's innovative design includes meticulous sampling strategies, disjoint monthly graph construction for leakage control, and a GraphSAGE architecture that aggregates mean, max, and standard deviation of neighbor features to detect subtle fraud patterns. Emphasizing explainability for regulatory compliance and analyst trust, it also features a highly optimized serving architecture achieving 3ms P99 latency, pivoting to edge prediction to reduce cache footprint from 60GB to 2-3GB. This led to a 27-33% relative lift in true positive rates and a projected $65 million incremental fraud capture.

From 15% to 90% GPU Utilization: Fix the Data Pipeline, Not the Model

From 15% to 90% GPU Utilization: Fix the Data Pipeline, Not the Model

Tarun Sunkaraneni from Amazon AGI shows how a multimodal SFT pipeline that spent about 85% of its time waiting on data was brought to roughly 90% GPU utilization. He walks through concurrency with asyncio and Ray actors, prefetching, zero-copy transfer via Ray's object store, and a network-card bottleneck that appears only at scale, and argues that the hardest part of training throughput is often keeping GPUs fed rather than optimizing the model.

Small Models, Big Results: Training a Finance Agent for Under $500 — Charles Dickens, Snorkel AI

Small Models, Big Results: Training a Finance Agent for Under $500 — Charles Dickens, Snorkel AI

Snorkel AI and UC Berkeley's Sky Computing Lab show that a 4B Qwen3 model, trained with reinforcement learning for under $500 using the rLLM framework, beats its 235B sibling on expert-validated financial question answering built from SEC 10-K filings. The talk covers the FinQA dataset construction and verification, common failure modes of frontier models, transfer to multi-table and general tool-use tasks, and ablations showing that simple data and binary rewards outperform more elaborate setups, since the real bottleneck is disciplined tool use rather than reasoning depth.

Artificial Intelligence

View All
Stop Giving Your Agent Tools. Give It a Sandbox - Mikyo King, Arize AI

Stop Giving Your Agent Tools. Give It a Sandbox - Mikyo King, Arize AI

Mikyo King, head of open source at Arize AI, explains code mode, an agent architecture where the model searches for tools and composes them in a sandboxed programming environment instead of loading large tool catalogs into context. He covers why it works, how Arize built it into Phoenix with Bash, Python, SQL, and UI control, its hidden costs around secrets, permissions, durability, and double sandboxing, and Harbor evals showing 43% fewer tokens and 31% lower cost.

What a $900M Bank Error Teaches Us About AI Agents - John Feminella, Two Sigma

What a $900M Bank Error Teaches Us About AI Agents - John Feminella, Two Sigma

John Feminella of Two Sigma uses a 2020 Citibank payment error to show how AI agents can be given real autonomy through five composable controls: an agent registry, risk-based review, purpose-scoped tool access, shared evals, and classifiers that send humans only the approvals that matter.

The AI School Kids Love More Than Vacation | Alpha School's Joe Liemandt

The AI School Kids Love More Than Vacation | Alpha School's Joe Liemandt

Alpha School principal Joe Liemandt and Horowitz Andreessen Academy co-founder Gagan Biyani discuss how AI-powered learning could compress core academics into two hours a day, freeing the rest of school for teamwork, entrepreneurship, and real-world building, and what that model means for K-12 families, higher education, and the next generation of builders.

Technology

View All
Can Your AI Heat My House?

Can Your AI Heat My House?

A wide-ranging conversation on how AI inference is pushing cloud infrastructure, data centers, and hardware supply chains against physical limits, covering the rise of alt clouds, rack density and cooling, hardware lifecycles and circularity, talent shortages, heat reuse for communities, and what engineering leaders can do about resource-aware software.

What will computer chips look like in 2045?

What will computer chips look like in 2045?

Intel's leadership walks through how advanced process nodes are brought to volume, from yield learning and the lessons of 10nm to the shift from planar transistors to gate-all-around, High-NA EUV, backside power, and CFET. The discussion widens to the chip-to-chip bottleneck that hybrid bonding targets, the supply limits on EUV tooling as AI compute demand grows, and the economics that make foundry scale decisive.

Former Intel CEO: Why This is the Best Time to Build Hardware

Former Intel CEO: Why This is the Best Time to Build Hardware

Pat Gelsinger joins a16z's Raghu Raghuram and Guido Appenzeller to discuss how AI is reshaping chip design, why design speed alone will not fix semiconductor bottlenecks, and why power and energy capacity may ultimately limit AI growth. The conversation covers HBM and memory innovation, stacking limits, the shift from copper to optics, consolidation among AI chip startups, and what a VMware for AI agents might look like.

Recent Post

An AI Future Without the Lock-In — Remy Guercio, Tailscale

An AI Future Without the Lock-In — Remy Guercio, Tailscale

Remy Guercio of Tailscale argues that the era of unlimited AI budgets is over and that teams need to escape lock-in while proving real ROI. He breaks an internal AI deployment into four layers (models, data connectors, interfaces, and sandboxes) and presents an AI gateway as the way to keep each layer swappable. He demonstrates how Tailscale's Aperture uses tailnet identity instead of shared API keys, sets per-model budgets, serves as both an LLM and MCP gateway, centralizes authentication for coding agents, and provides logs for diagnosing agent behavior.

How MiniMax M3 Was Built: Sparse Attention and Native Multimodality — Olive Song

How MiniMax M3 Was Built: Sparse Attention and Native Multimodality — Olive Song

Olive from MiniMax walks through MiniMax M3, an open-weight model built for agentic coding, 1M token context, and native multimodality from the first training step. The talk centers on MSA, a GQA-aware block-level sparse attention design co-developed with GPU infrastructure in mind, and on why training text and vision together from scratch beats adding vision later.

AI Agents Don't Read Your Policy Docs. They Hit Your APIs — Gravitee

AI Agents Don't Read Your Policy Docs. They Hit Your APIs — Gravitee

Sam, CTO at Gravity, argues that AI agent governance must be enforced in infrastructure rather than in prompts or policy documents. Through five live demos against a hotel-booking agent, he shows a gateway blocking destructive tool calls, preventing data exfiltration, rate-limiting runaway agents, serving cached semantic responses, and rejecting oversized prompts before any LLM call. He also covers observability, vendor decoupling via LLM routing, trimming bloated MCP tool lists, and managing shadow AI.

Zero Cold Starts: Serverless AI Agents on Akamai Functions — Thorsten Hans

Zero Cold Starts: Serverless AI Agents on Akamai Functions — Thorsten Hans

Thorsten Hans shows how to build MCP servers and AI agents with Spin, the open-source WebAssembly framework, and run them on Akamai Functions, a globally distributed serverless platform with no cold starts. The talk explains why WebAssembly suits edge-native AI workloads, live-codes MCP servers with wasmcp in TypeScript and Rust, deploys them worldwide with one command, and closes with a Vercel AI SDK agent backed by a Linode GPU model and memory snapshots that keep apps warm.

From Your Laptop to the Pipeline: Scaling Custom Agents with GitHub Copilot

From Your Laptop to the Pipeline: Scaling Custom Agents with GitHub Copilot

Jose Palafox, a GitHub Field Copilot Specialist, walks through taking a Copilot agent from a single laptop to an organization-wide, pipeline-hosted system. Topics include how the Copilot harness and CLI work, building custom agents with the agent builder, sharing them through marketplaces, orchestrating cross-repository changes, and running headless agents in GitHub Actions through GitHub Agentic Workflows, with a focus on observability and cost control.

The Lethal Trifecta Is Already on Your Laptops — Michael Patterson, Coder

The Lethal Trifecta Is Already on Your Laptops — Michael Patterson, Coder

Michael Patterson of Coder explains Simon Willison's lethal trifecta: private data access, exposure to untrusted content, and external communication, and why coding agents running on developer laptops hit all three. He covers prompt injection, the organizational blockers to enterprise agent rollout such as shadow AI and alert fatigue, and three architectural guardrails: cloud development environments, a model proxy, and an agent firewall.

← PreviousNext →

Stay In The Loop! Subscribe to Our Newsletter.

Get updates straight to your inbox. No spam, just useful content.