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
Open Source Security in the Age of AI

Open Source Security in the Age of AI

AI is accelerating the discovery of open-source vulnerabilities, but finding flaws is only the beginning. Remediating, testing, validating, and deploying fixes safely across critical infrastructure is where the real bottleneck lies, and securing open source at AI speed requires upstream contributions and coordinated efforts like Lightwell and Akrites.

Why a Tenured Professor Went Back to School for AI (with John Renne)

Why a Tenured Professor Went Back to School for AI (with John Renne)

John Renne, a tenured professor of real estate and urban planning at Tulane University, explains how returning to school for a master's in AI transformed his research on transit-oriented development, from analyzing 4,500 U.S. train stations to a global database of over 110,000, and how computer vision can now rate how attractive a street is. He also covers teaching vibe coding to urban planning undergraduates and making the case for AI-led oral exams over AI detectors.

Why Are We Still Governing AI Agents Like Software? - Tim Tyler, Gravitee

Why Are We Still Governing AI Agents Like Software? - Tim Tyler, Gravitee

Tim Tyler of Gravitee introduces the Agent Accountability framework, which treats AI agents as a digital workforce governed through three stages: Assignment (identity, team, and a named sponsor), Alignment (role, mission, and least-privilege authority), and Administration (oversight scorecards, guardian agents, and graduated recourse when agents drift off course).

Technology

View All
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.

Built Together: Inside the Dell + CoreWeave Engineering Partnership Powering Next-Gen AI

Built Together: Inside the Dell + CoreWeave Engineering Partnership Powering Next-Gen AI

A candid panel between Dell and CoreWeave leaders on co-engineering GB200, GB300, and Vera Rubin rack-scale AI platforms. The discussion covers why early access and stabilization matter in the AI arms race, how the industry moved from server-level to rack-as-computer architecture, and how joint development, standardized Redfish tooling, and shifting diagnostics left have enabled scale. It closes with where rack-scale infrastructure is heading toward pods, denser heterogeneous systems, and the need for automation in liquid cooling and power.

Recent Post

How to Test AI Agents When No Benchmark Exists — Nathanael Weill, Air Canada

How to Test AI Agents When No Benchmark Exists — Nathanael Weill, Air Canada

Nathanael Weill of Air Canada explains how to evaluate AI agents when no public benchmark exists and expert time is scarce: start from a known answer, generate the input from it, and check whether the agent recovers it. He covers lessons from a telecom RAG project that moved from one expert-graded test a week to dozens a day, and five ways to build test sets for a supplier contract review agent, along with why code-based checks are preferred over LLM judges.

The AI Content System Behind 200M Views — Sarah Chieng, Cerebras & Cova Lee, OpenAI

The AI Content System Behind 200M Views — Sarah Chieng, Cerebras & Cova Lee, OpenAI

Sarah Chieng (Cerebras) and Cova Lee (OpenAI) walk through an agent-powered content system spanning ideation, scripting, recording, editing, repurposing, and publishing, emphasizing swipe files, voice-driven scripts, design systems, API-based editing, and weekly process reviews as ways to free creators to focus on craft over generic AI output.

Does Code Quality Still Matter? It Depends on This — Max Kanat-Alexander, Capital One

Does Code Quality Still Matter? It Depends on This — Max Kanat-Alexander, Capital One

Max Kanat-Alexander reframes the familiar "it depends" answer around two factors that govern all software work: understanding a system and safely changing it. He explains why code quality matters for durable software but not for a new, rapidly growing category of disposable software created by AI, and shows how to make systems more disposable and make rigor cheap through standardization, inward-flow tooling, easier testing, and maximum strictness.

One Day, $1,000 in Tokens: An AI War Room for Legacy Code — Ford Prior, GEICO

One Day, $1,000 in Tokens: An AI War Room for Legacy Code — Ford Prior, GEICO

Ford Prior, a senior staff engineer and tech evangelist in GEICO's claims technology group, describes an AI-native multiplayer war room built to unblock major, outer-loop software changes. The talk covers a one-day, $1,000 token experiment across rentals, damages, and salvage legacy domains, the FleetKit markdown rulebook that steers the agent fleet, the worksheets and design artifacts it produced, and the cost, model-selection, and tooling lessons that shape what comes next.

AI Cut Our Research Time From 7 Days to 15 Minutes — Gareth Yoder, Vanguard

AI Cut Our Research Time From 7 Days to 15 Minutes — Gareth Yoder, Vanguard

Gareth Yoder of Vanguard describes modernizing a cash ledger handling up to 30 million calls per day by running data, producer, and consumer migrations in parallel. Key themes include exposing legacy code to AI through an MCP server, reducing a seven-day research cycle to fifteen minutes, reimagining rather than lifting and shifting, and maintaining observability throughout the migration.

Is a Faster Model Actually Faster? Test the Whole Job — Morgan Suo, MiniMax

Is a Faster Model Actually Faster? Test the Whole Job — Morgan Suo, MiniMax

Morgan Suo of MiniMax tests the changes inside M3.1 Flash on a realistic analyst research task about circuit-board suppliers and AI-driven cash flow. She examines sparse attention, 4-bit KV caches, low-precision MoE experts, speculative decoding, serving concurrency, and reasoning budgets, showing how each saves compute while potentially costing accuracy or latency. She closes with a framework for judging a model switch by verifiable output, the full cost of a usable report, and repeatable results.

← PreviousNext →

Stay In The Loop! Subscribe to Our Newsletter.

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