Labor
4 items across 2 editions · appeared in the last 2 editions in a row. First seen Fri 11 Sep, last seen Sat 12 Sep.
- DLA Chief Information Officer Adarryl Roberts said: "We have approximately 185 to 190 bots that are running. I'd say about 90% to 95% of those are unattended bots." In 2025 the bots saved the agency an estimated 300,000 hours of work.
- Roberts spoke at DLA's Industry Collider Day on 9 September in Alexandria, Virginia. The agency employs roughly 25,000 military and civilian personnel.
- DLA staff reach military-specific versions of ChatGPT and Grok, along with Gemini, through the genai.mil platform for sensitive-but-unclassified information, and the agency runs "GenAI 101 training" before moving staff toward agentic systems.
- The 300,000-hour figure is the agency's own estimate and the reports do not say how it was calculated or what it is measured against.
- OpenAI published the post on 11 September at 16:00 GMT. Its own description reads: "GPT-6 Astra improves Devin's ability to test software and show that it works, with the goal of helping engineers review less code and ship more."
- The claim is about the weakest link in coding agents — not writing code but demonstrating it works — and OpenAI frames the benefit as reviewers reading less code rather than more throughput.
- The article page blocks our fetcher, so the wording above is taken verbatim from OpenAI's own RSS feed. No benchmark, defect-rate or review-time figures are given in that description.
- This is a vendor post about a customer deployment, with no independent measurement of the effect on code review or defect rates.
- TechCrunch reported on 11 September at 3:58 pm PDT that Mecka AI is nearing a Sequoia Capital-led round at a valuation of about $500 million; the round size is not disclosed and "terms of the deal are not final and could still change".
- That follows a $60 million Series A announced three months earlier, led by Framework Ventures with Menlo Ventures, SV Angel and Kindred Ventures.
- Mecka pays people to record themselves performing everyday tasks using body sensors and smartphones, and sells that motion data to train humanoid robots. It was founded in 2024 by Josh Gao, Mogen Cheng, Jason Chong and Duy Nguyen, and as of early June was projecting an annual run rate of $100 million by the end of 2026.
- TechCrunch names competitor XDOF as nearing a $1.2 billion valuation. The valuation and run-rate figures come from sources and company projections rather than filings.
- Announced 10 September: Skild AI's S1 learns new manipulation tasks from a single video demonstration using in-context learning, with no weight updates or task-specific retraining. NVIDIA reports roughly 66% per-step success on multistep tasks against about 9% for comparable systems, and that one video example is worth roughly 380 hands-on training examples — 50 to 100 hours of manual collection.
- S1 executes unfamiliar tasks up to 10 minutes long across dozens of steps, including potting plants, making pancakes, pour-over coffee and kit assembly. In one plant-potting test, the gap from recording the demonstration to autonomous execution on hardware was 11 minutes.
- Skild reports a $100 million annual revenue run rate ten months after launch and more than 60 deployment partnerships across manufacturing, logistics, inspection, security and food preparation. If the demonstration-efficiency claim holds outside curated tasks, the cost of teaching a robot a new job falls by orders of magnitude — which is the labour-substitution variable to watch.
- These are vendor figures published on a supplier's blog, not an independent benchmark. "Per-step" success is not end-to-end task success, and the 66% versus 9% comparison does not name the baseline systems.