Personal essays from OpenAI's president on engineering careers, hiring, self-teaching and building technical teams.
Why I recommend it: His writing on how he taught himself and how he hires is the useful part — read those posts first.
This is the only entry from Greg Brockman's Blog in the hub so far.
Other resources that share this publication's topics.
A Substack newsletter on hiring trends and job market conditions written from inside recruiting, with posts on how employers screen and select candidates. Free posts are available to read and subscribe to by email.
RudderStack's free library of explainer articles on data engineering, data infrastructure and machine learning, covering topics such as generalization, deep learning and supplying customer data to LLM applications. RudderStack sells a customer data platform.
A KDnuggets guide by Vinod Chugani that goes round by round through current data science hiring processes, including AI-assisted coding rounds and generative AI evaluation questions, and lists ways candidates can prepare.
A Built In survey of job seekers on how AI tools shape which employers they find and consider. Among respondents who use AI for employer research, 66% say they applied to a company they had not considered and 53% say they decided not to apply somewhere because of what AI showed them. Built In sells employer branding services.
Artificial Analysis reports Muse Ember benchmark scores and task-cost estimates, including an Intelligence Index score of 36 and $0.14 per task in its evaluation. These are evaluator-specific results, not a verified Meta launch announcement or evidence of free model access.
Epoch AI explains an Inference Performance Index for estimating chip capacity across agentic workloads and output-speed requirements. Its modeled comparisons, expressed in GB300 equivalents, depend on workload and hardware assumptions and are not universal real-world performance guarantees.
