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Article URL: https://www.jobyaviation.com/news/joby-completes-first-ever-fully-autonomous-flight-across-the-united-states

Comments URL: https://news.ycombinator.com/item?id=49868495

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Article URL: https://joplinapp.org

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I'm not a computer science guy, but I had a play with Jev and found it impressive. It allowed me to offer non-technical researchers the ability to classify arbitrary things their participants enter, without prior training or classification, at near instant speed, and negligible cost. My users may find that very helpful in the qualitative research world.

I showed them an innocuous working example of how they could classify thousands of participant entries as "mentioning Italian food" whether it mentioned "pasta", "penne", "rigatone", etc etc. Jev did this task with high confidence, and showed it could differentiate between the inverse, eg had very low confidence when I switch entry to mentioning "hamburgers". I find that a very useful tool to pass on to non-technical colleagues.

But on HN, there are lots of much more qualified people saying Jev is a con, a step backwards, or bonkers that people are impressed. Will one of them tell me plainly what software was doing this prior to Jev, and the like? I don't doubt it existed, but I never came across it. I'm interested to hear from people more expert than myself.

To be precise, software must be able to: - Classify thousands of entries with arbitrary themes or topics, even if entry doesn't mention that theme or topic directly, with high enough confidence to be useful. - Do it with thousands of entries in 0-3seconds. - Do it at negligible cost, eg sub 0.1cents

Comments URL: https://news.ycombinator.com/item?id=49868483

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Article URL: https://newhampshirebulletin.com/2026/09/24/as-states-seek-to-rein-in-flock-license-plate-cameras-new-hampshire-considered-a-model/

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Article URL: https://www.gingerbill.org/article/2026/05/13/aesthetic-namespacing/

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Article URL: https://www.bytestone.uk/posts/2026-09-27-elitech-temperature-logger/

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Article URL: https://ev12183725.substack.com/p/how-pink-is-pink-noise

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Article URL: https://www.storagereview.com/news/amd-posts-the-full-epyc-9006-sku-list-31-venice-parts-from-700-to-14904-across-sp7-and-sp8

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With effort Thinking: high on claude Misting… (10s · thinking with high effort) Misting… (57s · still thinking with high effort) · Misting… (1m 18s · thinking some more with high effort) Misting… (5m 18s · deep in thought with high effort) still no answers ...

You are in flow and the brain wants to move along but ai-inference becomes a bottleneck to the extend that we accept the garbage it gives as long the tests passes and loose the flow, get angry and then post on HN to see if everyone goes through this mind-numbing process (even after paying) ?

Comments URL: https://news.ycombinator.com/item?id=49868329

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Article URL: http://matt.colorado.edu/teaching/highcog/spr10/readings/cfg81.pdf

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Article URL: https://www.reddit.com/r/learnmachinelearning/comments/1wrd95w/statistical_tie_with_jev_on_banking77_9179_vs/

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Article URL: https://hkelkar.com/2026/09/27/the-agentic-web/

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Article URL: https://www.bleepingcomputer.com/news/artificial-intelligence/claude-opus-55-uses-95-percent-fewer-em-dashes-but-its-answers-are-getting-longer/

Comments URL: https://news.ycombinator.com/item?id=49867097

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Article URL: https://www.economist.com/science-and-technology/2026/09/27/alternatives-to-gps-are-around-the-corner

Comments URL: https://news.ycombinator.com/item?id=49867093

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We developed an early vector embedding model at Lawrence Berkeley National Lab and extended it called the Tuatara Vector Model. It's a blend and scored against Jev's 3,080 BANKING77 test messages resulting in 91.79% versus 92.40%, a statistical tie with some good cost savings.

As most may know, BANKING77 is a public dataset from PolyAI with 13,083 messages to a bank, each labelled with intents. The split was 10,003 messages for training and 3,080 for testing.

The run used the pinned model jev-1.13.0, all 77 intents as options in a single question, and up to 24 training examples retrieved for each message. Jev got 2,846 of 3,080 right.

Recomputing Jev’s accuracy from the file gives 92.40%, the same figure the experiment reports.

More stats: https://cymetica.com/blog/matching-jev-on-banking77-at-a-tho...

Comments URL: https://news.ycombinator.com/item?id=49867086

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