Inside the data labeling shops that still power every model
Automation was supposed to end the labeling industry. Instead the work got harder, more specialised and better paid.
Covers startups, venture funding and the European tech scene from Berlin.
Automation was supposed to end the labeling industry. Instead the work got harder, more specialised and better paid.
Dozens of competing orchestration libraries have collapsed into three serious options. Developers finally get to stop rewriting glue code.
Reinforcement learning from human feedback made assistants polite. It also made them hedge, flatter and over-explain.
Accelerators are finally available. The grid connections to run them are not.
Bootcamps and universities are rewriting curricula around tools that write most of the boilerplate.
Theorem provers, planners and rule engines are being bolted onto language models, and the combination is beating either alone.
Rust components now ship in mainstream operating systems, and the bug counts tell the story.
Investors have decided that breaches are inevitable and are backing the companies that help afterwards.
The first consumer notebooks on the open instruction set ship with working Linux and a long list of caveats.
Demand from accelerators ate the supply, and the fabs that could help are years from opening.
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