base10

Fast Models Need Slow Developers — Sarah Chieng, Cerebras

May 22, 2026 · 18:02

Sarah Chieng from Cerebras argues that the 20x speed increase of models like Codex Spark (1,200 tokens/sec vs. 40-60 for Sonnet/Opus) forces developers to rethink workflows, or risk generating technical debt at unprecedented scale. She presents a practical playbook: validation and linting become free at every step, so must run continuously; developers can generate 75 component variations across five sub-agents and cherry-pick the best; and with context filling in 30 seconds instead of ten minutes, a four-file external memory system (agents, plan, progress, verify) maintains continuity between sessions. Chieng emphasizes real-time collaboration with the model rather than spawning agents and walking away, and advocates using a slower planner model with a fast executor to orchestrate agents effectively.

State of Startups and AI 2025 - Sarah Guo, Conviction

Aug 2, 2025 · 23:52

Sarah Guo of Conviction argues that AI's value creation is massive and early, with companies like Cursor reaching $100M ARR in 12 months and Harvey exceeding $70M ARR. She predicts that by end of 2026, AI agents will ship code directly to production, voice AI will replace text for most business communication, and inference costs will drop below a cent per million tokens. Reasoning is a new scaling vector unlocking higher-stakes use cases, and agent startups have increased 50% in the last year. Multimodal models from HeyGen and Eleven are already rocketing past $50M ARR. The model market is more competitive than ever, with GPT-4 costs falling from $30 to $2 per million tokens in 18 months and open-source like DeepSeek competing. Guo advises builders to focus on thick wrappers around LLMs, leveraging domain and workflow knowledge, and warns against generic text boxes: 'The prompt is a bug, not a feature.' Execution, not first-mover advantage, is the moat.

Agents reported thousands of bugs, how many were real? - Ian Butler and Nick Gregory

Jun 3, 2025 · 18:39

Ian Butler and Nick Gregory introduce SM-100, a benchmark of 100 real-world bugs across 84 repositories, and present their agent Bismuth as outperforming existing agents in detecting complex bugs—finding 10 needles vs 7 for the next best. However, even top performers struggle: Bismuth's true positive rate is only 25%, and Codex leads at 45%, while most agents (Cursor, Debian) report thousands of false positives (97% for basic loops). They argue that agents universally exhibit narrow thinking, missing simple bugs like a form state issue that only Bismuth and Codex caught, and that high SWE-bench scores do not translate to maintenance tasks. The episode concludes that software maintenance remains an unsolved problem requiring deeper reasoning and targeted search.