9 theses on AI | Sarthak Munshi

kept by eddie aug 13

AI progress is constrained by long-task reliability, labor reallocation, cost inefficiencies of general APIs, the declining value of raw coding skills, inadequate benchmark testing, the limits of formal verification without strong specs, memory-bound local hardware advantages, the shift from data to environment-driven training, and the rising competitiveness of US open-weight models.

The recruitment company training AI to do your job
Tens of thousands of professionals are joining Mercor to train AI by performing tasks that mirror their own jobs, effectively teaching the technology to replace them.
also on Mercor, #machine-learning, #automation, #ai
Claude Security is now in public beta
Claude Security public beta lets Enterprise customers scan code for vulnerabilities and generate targeted patches using Opus 4.7, with integrations across partner platforms.
also on Palo Alto Networks, #enterprise, #ai
GitHub - foru17/neko-master at console.dev
Neko Master is a modern dashboard for real-time network traffic visualization and analysis, featuring multi-gateway monitoring, Docker-based deployment, and agent-assisted data collection from gateways like Clash and Surge.
also on Surge, #open-source, #developer-tools
Claude Science, an AI workbench for scientists \ Anthropic
Anthropic launched Claude Science, an AI workbench that integrates scientific tools, produces auditable artifacts, and manages compute for researchers.
also on Allen Institute, #ai, #developer-tools
AI's Bundling Moment
As AI model release cadence accelerates to every 42 days, companies like Harvey, Glean, and ElevenLabs are expanding from single workflow tools into broad platforms, reversing the SaaS-era unbundling strategy.
also on PwC, #enterprise, #ai