A practical blog series from Bian Jiang documenting real workflows for integrating generative AI into daily work, from writing to research to automation.
Why I recommend it: I keep pointing clients to concrete "here is how I actually use it" examples rather than hype. This series is calm, tactical, and honest about what works.
AI assistant for managing tasks, documents and connected services. Its pricing page lists a free plan and paid upgrades with higher usage; sign-in is required. Purchases or bookings made through the assistant can still cost money, and connecting accounts shares data under Hark’s policies.
A KDnuggets tutorial by Bala Priya C with five beginner Python scripts for automating repetitive tasks, including organizing files, processing images and simplifying everyday workflows, with code and explanations.
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.