Tokenless

Tokenless

Why Humans Can't Keep Up With AI-Written Code - Tariq Shaukat, Sonar

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
The Cheaper AI Model That Costs You More - Rayan Krishnan, Vals AI

The Cheaper AI Model That Costs You More - Rayan Krishnan, Vals AI

Rayan Krishnan of Vals AI explains why single benchmark scores in model cards hide a jagged frontier, using finance tasks like tax and Excel LBO modeling to show that the best model changes by task and that cheaper models can cost more. He covers benchmark design lessons (accuracy metrics, validity, reliability, reward hacking, multi-turn work), a case study in which a team cut token spend about 90% using internal evals built from its own GitHub history, and why a firm's own work history is its most valuable eval source.

Catch the Bugs Your Code Review Misses - Killian Carlsen-Phelan & Taylor Luttrell-Williams, Sonar

Catch the Bugs Your Code Review Misses - Killian Carlsen-Phelan & Taylor Luttrell-Williams, Sonar

A hands-on walkthrough of Gitar, a code change verification agent, applied to three pull requests in a sample Python ticketing app. It covers catching a bug outside the diff, enforcing a business rule through project configuration, and diagnosing a failing CI test before auto-merging, along with functional validation against Jira, learned conventions, and cross-repo and cross-PR analysis.

Why Humans Can't Keep Up With AI-Written Code - Tariq Shaukat, Sonar

Why Humans Can't Keep Up With AI-Written Code - Tariq Shaukat, Sonar

Sonar CEO Tariq Shaukat argues that agents have made code abundant but confidence scarce, and draws on Toyota's quality-first factory principles to show how to build software factories enterprises can trust. He covers five hard problems that break agentic development and presents Sonar's code confidence formula: intent, guide rails, verification, and evidence.

Technology

View All
Why Founders Are Betting Big on Factories, Mines, and Energy

Why Founders Are Betting Big on Factories, Mines, and Energy

Erin Price-Wright and Connor Love unpack the AD50 and the rebuilding of America's industrial base, covering how defense demand is financing manufacturing capacity for commercial markets, how AI is reshaping factory economics, how a new generation of founders applies lessons from SpaceX, Tesla, Anduril, and Palantir to mining, energy, and manufacturing, and why private capital is now willing to fund physical infrastructure before government demand is guaranteed.

Mark Cuban’s Plan to Make Healthcare Affordable

Mark Cuban’s Plan to Make Healthcare Affordable

Mark Cuban and Julie Yoo discuss how transparency, direct contracting, and AI could reshape healthcare economics, from Cost Plus Drugs and modular pod manufacturing to personalized medicine, the future of physicians, and advice for founders who want to use AI to learn rather than to stop thinking.

Exploits Now Beat Patches: Open Source Security in the AI Era - Patrick Smyth, Chainguard

Exploits Now Beat Patches: Open Source Security in the AI Era - Patrick Smyth, Chainguard

Patrick Smyth of Chainguard examines how frontier AI has compressed the time between vulnerability disclosure and exploitation, leaving 30-day remediation cycles obsolete. The talk covers AI-assisted attacks, waves of supply chain malware, four attack surfaces spanning malware, known CVEs, zero days, and side doors like CI/CD, developer laptops, and agent skills, and practical defenses including cooldown periods, faster CVE remediation, and signing artifacts with SLSA and Sigstore.

Recent Post

Front End Testing with GitHub Actions • Amy Kapernick • YOW! 2025

Front End Testing with GitHub Actions • Amy Kapernick • YOW! 2025

This presentation by Amy Kapernick at YOW! Australia 2025 details how to implement robust front-end testing using GitHub Actions. It covers the rationale for comprehensive front-end testing, the necessity of a live deployment preview, and practical steps for automating tests like Lighthouse for performance/accessibility and Playwright for UI validation. The talk also delves into continuous deployment workflows and explores GitHub Actions' broader utility for workflow automation beyond testing, such as generating issues from code comments.

Chai Discovery's Bitter Lesson: Drug Design Is Another Scaling Problem

Chai Discovery's Bitter Lesson: Drug Design Is Another Scaling Problem

Chai Discovery is revolutionizing drug discovery by treating biology as an engineering problem, leveraging AI—particularly diffusion models and the "bitter lesson" of scaling—to design molecules rather than merely discover them. Their approach has boosted antibody design hit rates from 0.1% to 16%, aiming for a "Molecular CAD" suite that collapses discovery timelines from months to days. They partner with pharma, building infrastructure and creating a data flywheel to develop higher-quality, more targeted medicines for previously undruggable diseases.

What Are Large Database Models? AI for SQL Data

What Are Large Database Models? AI for SQL Data

Martin Keen introduces Large Database Models (LDMs), a new AI paradigm that brings advanced analytical capabilities directly into SQL and relational databases. This allows for semantic queries on the 99% of enterprise data traditionally locked away, enabling faster, more secure insights without costly data movement.

The Constraint Your LLM Will Quietly Ignore (with Gurobi's Jerry Yurchisin)

The Constraint Your LLM Will Quietly Ignore (with Gurobi's Jerry Yurchisin)

Jerry Yurchisin from Gurobi Optimization explains mathematical optimization as an AI technology where constraints are hard guarantees, unlike LLMs which may ignore critical constraints. He outlines the three core building blocks of any optimization model: decision variables, constraints, and an objective function. The discussion highlights where optimization fits in the agentic AI era, with agents framing problems and generating code, then handing off to solvers like Gurobi via MCP servers. Jerry also covers advancements in non-linear solving, strategies for pitching optimization to stakeholders, and diverse case studies including energy grids, retirement planning, and USA Cycling's Paris 2024 gold medal.

Waymo Co-CEO Dmitri Dolgov: The Demo Is Only 1% Of The Work

Waymo Co-CEO Dmitri Dolgov: The Demo Is Only 1% Of The Work

Waymo co-CEO Dmitri Dolgov outlines seven crucial lessons from fifteen years of developing and scaling the Waymo Driver, the world's most advanced physical AI. He details the unique challenges of physical AI compared to digital, emphasizing the critical role of reliability, strategic technology choices, continuous innovation through foundation models, structure-augmented learning, high-fidelity simulation, AI flywheels, and robust evaluation frameworks to achieve superhuman safety and build trust in real-world autonomous systems.

Building Turbopuffer: Gergely Orosz (@pragmaticengineer ) × Simon Eskildsen (CEO)

Building Turbopuffer: Gergely Orosz (@pragmaticengineer ) × Simon Eskildsen (CEO)

Simon Eskildsen, CEO of Turbopuffer, discusses his unique path from early computer fascinations to founding an S3-native vector search company. He shares insights from his eight years at Shopify, detailing infrastructure scaling challenges and the development of Toxyproxy for fault injection. A core theme is his "napkin math" philosophy, using fundamental performance metrics to optimize systems and challenge benchmarks. He explains the impetus for Turbopuffer, its initial minimalist architecture, and how it achieved a 95% cost reduction for its first customer, Cursor. Simon also touches on the unexpected scarcity of CPUs due to AI workloads and his unconventional, principled approach to venture capital and building a remote-first company culture.

← PreviousNext →

Stay In The Loop! Subscribe to Our Newsletter.

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