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Core Architecture:
Arena-restart: No Tracing GC. It prioritizes allocation speed (~2x faster than Lua). Memory is wiped instantly between tasks via a restart() mechanism.
Fast Compilation: 100k classes compile in ~888ms (Single-pass).
Lightweight: The VM is designed for a ~2ms startup target.
The Trade-off: Execution is currently 2x-5x slower than Lua as I'm still optimizing the bytecode dispatch (if-chains).
I\u2019d love to hear your thoughts on using Arena-restart for autonomous scripts and any advice on optimizing dispatch without bloating the startup time."},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["autolane"],"value":"Show HN: Autolang-A C++ VM with 2ms startup time and arena-restart memory model"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["autolane"],"value":"https://autolang.vercel.app/docs/introduction"}},"_tags":["story","author_hoansdz","story_47206397","show_hn"],"author":"hoansdz","created_at":"2026-03-01T13:11:47Z","created_at_i":1772370707,"num_comments":0,"objectID":"47206397","points":2,"story_id":47206397,"story_text":"Hi HN, I\u2019m a student and I\u2019ve spent the last few months building Autolang\u2014an embedded language and VM from scratch in C++.
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The Trade-off: Execution is currently 2x-5x slower than Lua as I'm still optimizing the bytecode dispatch (if-chains).
I\u2019d love to hear your thoughts on using Arena-restart for autonomous scripts and any advice on optimizing dispatch without bloating the startup time.","title":"Show HN: Autolang-A C++ VM with 2ms startup time and arena-restart memory model","updated_at":"2026-03-05T23:38:58Z","url":"https://autolang.vercel.app/docs/introduction"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"taubek"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["autolane"],"value":"The accountability problem exposed by the first Garmin Autoland 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I want to share something me and a few friends have been working on for a while now \u2014 Zeroshot, a web tool that builds image classifiers using text-image models and autolabeling. What does this mean in practice? You can put together an image classifier in about 30 seconds that\u2019s faster and more accurate than CLIP, but that you can deploy yourself however you\u2019d like. It\u2019s open source, commercially licensed, and doesn\u2019t require you to pay anyone per API call.
Here's a 2 minute video that shows it off: https://www.youtube.com/watch?v=S4R1gtmM-Lo
How/why does it work?\nWe believe that with the rise of foundation vision models, computer vision will fundamentally change. These powerful models will let any devs \u201ccompile\u201d a model ahead of time with a subset of the foundation model\u2019s characteristics, using only text and a web-tool. The days of teams of MLEs building complex models and pipelines are ending.
Zeroshot works by using two powerful pre-trained models, CLIP and DINOv2 together. The web-app allows users to quickly create our training sets via text search. Using pre-cached DINOv2 features, we generate a simple linear model that can be trained and deployed without any fine-tuning. Since you can see what\u2019s going into your training set, you can tune your prompts to get the type of performance or detail you want.
CLIP Small -- Size: 335 MB, Latency: 35ms
CLIP Large -- Size: 891 MB, Latency: 276ms
Zeroshot -- Size: 85 MB, Latency: 20ms
What\u2019s next?\nWe wanna see how people use or would use the tool before deciding what to do next. On the list: clients for iOS and NodeJS, speeding up GPU inference times via TensorRT, offering larger Zeroshot models for better accuracy, easier results refining, support for bringing your own data lake, model refinement using GPT-V, we\u2019ve got plenty of ideas."},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: Build an open-source computer vision model in seconds using text"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://usezeroshot.com/"}},"_tags":["story","author_nharada","story_38450690","show_hn"],"author":"nharada","children":[38453194,38453544,38455066,38458138,38458535],"created_at":"2023-11-28T20:18:32Z","created_at_i":1701202712,"num_comments":14,"objectID":"38450690","points":64,"story_id":38450690,"story_text":"Hello HN! I want to share something me and a few friends have been working on for a while now \u2014 Zeroshot, a web tool that builds image classifiers using text-image models and autolabeling. What does this mean in practice? You can put together an image classifier in about 30 seconds that\u2019s faster and more accurate than CLIP, but that you can deploy yourself however you\u2019d like. It\u2019s open source, commercially licensed, and doesn\u2019t require you to pay anyone per API call.
Here's a 2 minute video that shows it off: https://www.youtube.com/watch?v=S4R1gtmM-Lo
How/why does it work?\nWe believe that with the rise of foundation vision models, computer vision will fundamentally change. These powerful models will let any devs \u201ccompile\u201d a model ahead of time with a subset of the foundation model\u2019s characteristics, using only text and a web-tool. The days of teams of MLEs building complex models and pipelines are ending.
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CLIP Small -- Size: 335 MB, Latency: 35ms
CLIP Large -- Size: 891 MB, Latency: 276ms
Zeroshot -- Size: 85 MB, Latency: 20ms
What\u2019s next?\nWe wanna see how people use or would use the tool before deciding what to do next. On the list: clients for iOS and NodeJS, speeding up GPU inference times via TensorRT, offering larger Zeroshot models for better accuracy, easier results refining, support for bringing your own data lake, model refinement using GPT-V, we\u2019ve got plenty of ideas.","title":"Show HN: Build an open-source computer vision model in seconds using text","updated_at":"2024-09-20T15:49:02Z","url":"https://usezeroshot.com/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"c1yd3i"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["autolane"],"value":"!PHL 01/165 PHL AD AP RDO ALTIMETER UNREL. AUTOLAND, HUD TO TOUCHDOWN, ENHANCED FLT VISION SYSTEMS TO TOUCHDOWN, HEL OPS REQUIRING RDO ALTIMETER DATA TO INCLUDE HOVER AUTOPILOT MODES AND CAT A/B/PERFORMANCE CLASS TKOF AND LDG NOT AUTHORIZED EXC FOR ACFT USING APPROVED ALTERNATIVE METHODS OF COMPLIANCE DUE TO 5G C-BAND INTERFERENCE PLUS SEE AIRWORTHINESS DIRECTIVES 2021-23-12, 2021-23-13 2201190501-2401190501"},"title":{"matchLevel":"none","matchedWords":[],"value":"NOTAM: Radio Altimeter Unreliable Due to 5G C-Band Interference"}},"_tags":["story","author_c1yd3i","story_29938406","ask_hn"],"author":"c1yd3i","children":[29938660],"created_at":"2022-01-14T18:46:19Z","created_at_i":1642185979,"num_comments":2,"objectID":"29938406","points":3,"story_id":29938406,"story_text":"!PHL 01/165 PHL AD AP RDO ALTIMETER UNREL. AUTOLAND, HUD TO TOUCHDOWN, ENHANCED FLT VISION SYSTEMS TO TOUCHDOWN, HEL OPS REQUIRING RDO ALTIMETER DATA TO INCLUDE HOVER AUTOPILOT MODES AND CAT A/B/PERFORMANCE CLASS TKOF AND LDG NOT AUTHORIZED EXC FOR ACFT USING APPROVED ALTERNATIVE METHODS OF COMPLIANCE DUE TO 5G C-BAND INTERFERENCE PLUS SEE AIRWORTHINESS DIRECTIVES 2021-23-12, 2021-23-13 2201190501-2401190501","title":"NOTAM: Radio Altimeter Unreliable Due to 5G C-Band Interference","updated_at":"2024-09-20T10:14:40Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"hammock"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["autolane"],"value":"In aviation we already have planes that can and regularly do land themselves without any pilot touching the controls. Landing being the most dangerous part of flying (don\u2019t try to compare it to auto-parallel park) It\u2019s called autoland. The plane\u2019s sensors gather left-right and up-down info and then control the plane down into the runway. We do this with planes carrying as many as 350+ souls on board. You would never even know if a pilot was using autoland, and you\u2019ve probably never heard of an autoland accident the way you have with self-driving.
The difference is, for a plane to do an auto-land (Cat III Landing) you need six things. The airport has to be certified, the crew needs to be certified, the aircraft needs to be certified, the company you are working with must be certified with its home civil aviation authority, the airport/country you are doing the cat III must certify the company as well, AND the weather should actually be low vis (meaning other types of landings like visual, VNAV and standard ILS are more difficult/impossible).
If you have just one of these items not certified, then the pilot can\u2019t do an auto-land.
Until we have the same rigor in self-driving, I would not anticipate much success, for safety reasons."},"title":{"matchLevel":"none","matchedWords":[],"value":"Tell HN: Biggest self-driving challenge is not technology"}},"_tags":["story","author_hammock","story_39790764","ask_hn"],"author":"hammock","children":[39791122,39793361],"created_at":"2024-03-22T13:51:16Z","created_at_i":1711115476,"num_comments":2,"objectID":"39790764","points":2,"story_id":39790764,"story_text":"In aviation we already have planes that can and regularly do land themselves without any pilot touching the controls. Landing being the most dangerous part of flying (don\u2019t try to compare it to auto-parallel park) It\u2019s called autoland. The plane\u2019s sensors gather left-right and up-down info and then control the plane down into the runway. We do this with planes carrying as many as 350+ souls on board. You would never even know if a pilot was using autoland, and you\u2019ve probably never heard of an autoland accident the way you have with self-driving.
The difference is, for a plane to do an auto-land (Cat III Landing) you need six things. The airport has to be certified, the crew needs to be certified, the aircraft needs to be certified, the company you are working with must be certified with its home civil aviation authority, the airport/country you are doing the cat III must certify the company as well, AND the weather should actually be low vis (meaning other types of landings like visual, VNAV and standard ILS are more difficult/impossible).
If you have just one of these items not certified, then the pilot can\u2019t do an auto-land.
Until we have the same rigor in self-driving, I would not anticipate much success, for safety reasons.","title":"Tell HN: Biggest self-driving challenge is not technology","updated_at":"2024-09-20T16:42:26Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"ramacastro"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["autolane"],"value":"Hey everyone!
I created a startup with a friend to make it easier for anyone to create AIs without any prior experience or knowledge in the field. We started with computer vision, specifically object detection, but we plan to add all subfields of AI eventually, like natural language processing, speech and all the cool generative stuff.
You can do everything inside the platform: create datasets, train artificial neural networks and use them to give intelligence to your web apps, mobile apps, machines, security cameras, robots, drones, cars and everything else.
It is free to use forever without any credit card required. You can connect google colab notebooks to our platform and use their free GPUs for training. Forget about all the different labeling file formats and running complex code yourself, just connect colab to Theos, click the Start Training button and the metrics will stream directly to your browser, and we will automatically upload weights checkpoints every epoch so you don't have to worry about colab shutting down and losing all your AI training progress. Also, we have an autolabeler feature to let your current AI's version help you make a better version of itself 100 times faster than labeling manually. Finally, you can deploy your AI as a rest API and use it in your software with simple http requests.
Here's the getting started guide for object detection:
https://docs.theos.ai/get-started/object-detection
Please let me know if you find it helpful and what subfield of AI you think we should tackle next.
(the landing page is not finished yet, soon we will release a way better version, but for now you can sign up and use the app for free)
Thanks!"},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: Theos AI - development platform for AI"}},"_tags":["story","author_ramacastro","story_32038181","show_hn"],"author":"ramacastro","created_at":"2022-07-09T18:56:03Z","created_at_i":1657392963,"num_comments":0,"objectID":"32038181","points":1,"story_id":32038181,"story_text":"Hey everyone!
I created a startup with a friend to make it easier for anyone to create AIs without any prior experience or knowledge in the field. We started with computer vision, specifically object detection, but we plan to add all subfields of AI eventually, like natural language processing, speech and all the cool generative stuff.
You can do everything inside the platform: create datasets, train artificial neural networks and use them to give intelligence to your web apps, mobile apps, machines, security cameras, robots, drones, cars and everything else.
It is free to use forever without any credit card required. You can connect google colab notebooks to our platform and use their free GPUs for training. Forget about all the different labeling file formats and running complex code yourself, just connect colab to Theos, click the Start Training button and the metrics will stream directly to your browser, and we will automatically upload weights checkpoints every epoch so you don't have to worry about colab shutting down and losing all your AI training progress. Also, we have an autolabeler feature to let your current AI's version help you make a better version of itself 100 times faster than labeling manually. Finally, you can deploy your AI as a rest API and use it in your software with simple http requests.
Here's the getting started guide for object detection:
https://docs.theos.ai/get-started/object-detection
Please let me know if you find it helpful and what subfield of AI you think we should tackle next.
(the landing page is not finished yet, soon we will release a way better version, but for now you can sign up and use the app for free)
Thanks!","title":"Show HN: Theos AI - development platform for AI","updated_at":"2024-09-20T11:30:57Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"wilonth"},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: An \u201cautomatic\u201d way to learn Chinese"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["autolane"],"value":"https://play.google.com/store/apps/details?id=gupik.autolang.zh"}},"_tags":["story","author_wilonth","story_18463886","show_hn"],"author":"wilonth","children":[18464421,18465365,18465683,18465824,18465985,18466333,18467409,18467513,18467894,18468491,18468755,18468864,18469700,18469796,18470070,18472357,18473550,18473795,18476076],"created_at":"2018-11-15T22:04:32Z","created_at_i":1542319472,"num_comments":38,"objectID":"18463886","points":57,"story_id":18463886,"title":"Show HN: An \u201cautomatic\u201d way to learn Chinese","updated_at":"2024-09-21T17:04:19Z","url":"https://play.google.com/store/apps/details?id=gupik.autolang.zh"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"Nycto"},"story_text":{"matchLevel":"none","matchedWords":[],"value":""},"title":{"matchLevel":"none","matchedWords":[],"value":"A radically new type of engine"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["autolane"],"value":"http://www.autoblog.com/2009/11/30/autoline-on-autoblog-with-john-mcelroy/"}},"_tags":["story","author_Nycto","story_972846"],"author":"Nycto","children":[973089,973178,973248,973729,973806,973925,974088],"created_at":"2009-12-02T20:28:19Z","created_at_i":1259785699,"num_comments":14,"objectID":"972846","points":56,"story_id":972846,"story_text":"","title":"A radically new type of engine","updated_at":"2023-09-06T20:17:05Z","url":"http://www.autoblog.com/2009/11/30/autoline-on-autoblog-with-john-mcelroy/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"Shivetya"},"title":{"matchLevel":"none","matchedWords":[],"value":"The race for autonomous cars is over. 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1.0","updated_at":"2024-09-20T05:40:05Z","url":"https://towardsdatascience.com/5-new-features-in-pandas-1-0-you-should-know-about-fc31c83e396b?source=email-68bf0657a0cd-1580749467218-layerCake.autoLayerCakeWriterNotification-------------------------576a67b4_fcbd_4392_8ab6_11137f8f98b5&sk=e20b65134a9a908de33336e3889b09bd"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"chaostheory"},"title":{"matchLevel":"none","matchedWords":[],"value":"Ulteo Virtual Desktop: your Linux applications on Windows"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["autolane"],"value":"http://www.ulteo.com/home/en/virtualdesktop?autolang=en"}},"_tags":["story","author_chaostheory","story_194787"],"author":"chaostheory","created_at":"2008-05-20T06:35:17Z","created_at_i":1211265317,"num_comments":0,"objectID":"194787","points":1,"story_id":194787,"title":"Ulteo Virtual Desktop: your Linux applications on Windows","updated_at":"2023-09-06T19:15:10Z","url":"http://www.ulteo.com/home/en/virtualdesktop?autolang=en"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"arkhiver"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["autolane"],"value":"Age verification is just a precursor to automated attribution of speech"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://nonogra.ph/age-verification-is-just-a-precursor-to-attribution-of-speech-06-29-2026"}},"_tags":["story","author_arkhiver","story_48714529"],"author":"arkhiver","children":[48714628,48714661,48714668,48714676,48714696,48714699,48714701,48714704,48714719,48714739,48714758,48714762,48714764,48714771,48714858,48714920,48714953,48714968,48714973,48715014,48715076,48715081,48715091,48715127,48715183,48715230,48715347,48715478,48715485,48715556,48715700,48715710,48715799,48715940,48716285,48716381,48716604,48716735,48716759,48716946,48716961,48716964,48717081,48717218,48717344,48717542,48717693,48717717,48718065,48718082,48718155,48718209,48718499,48718683,48719039,48719201,48719249,48719696,48720360,48721378,48721469,48721643,48722115,48722173,48722717,48722722,48724463,48725360,48726243,48726348,48729341,48729946,48730032,48737714,48740292],"created_at":"2026-06-29T03:42:07Z","created_at_i":1782704527,"num_comments":631,"objectID":"48714529","points":1020,"story_id":48714529,"title":"Age verification is just a precursor to automated attribution of speech","updated_at":"2026-07-17T17:41:43Z","url":"https://nonogra.ph/age-verification-is-just-a-precursor-to-attribution-of-speech-06-29-2026"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"iandmacomber"},"story_text":{"matchLevel":"none","matchedWords":[],"value":"TL;DR: Turn hours of raw GoPro footage + a .fit file into a 60-second highlight reel with ride telemetry burned in. Every second of the ride is scanned by gemini-3.5-flash, as is the clip ranking + curation. The whole thing costs about $0.04 per ride and takes 10 minutes.
Longer version: Road cycling has been my primary form of exercise, social outlet, therapy, wardrobe expense, and personality trait for almost a decade. I ride most weekends, usually out of Manhattan and up 9W. By the end of a ride I have hours of GoPro footage and one .fit file with per-second speed, power, heart rate, cadence, and GPS. Absolutely no one wants to watch 3 hours of being stuck behind Citibikes on West Side Highway. It's fun to look through past footage, identify the fun parts, and put together a narrative to remember. But since cycling is already time consuming, manually editing a highlight reel edit per ride is a nonstarter. So I built and open-sourced https://github.com/ianmacomber/ride-recap.
I identify compelling moments from four separate sources:\n* Garmin telemetry via .fit file (speed, HR, power spikes, sprints, climbs)\n* Strava via API (popular segments)\n* Gemini vision scan + rating of individual frames\n* (optional) hand-labels via Streamlit app
The fusion step has a LLM narrative pass pick 20 clips to best tell the story of the ride, boosting \u201ccross-source agreements\u201d (if a human label + telemetry + Strava + Gemini all agree that a clip is interesting), with greedy re-ranking and a crowding penalty to avoid clips too close to something already selected.
Obvious in retrospect, but there\u2019s no substitute other than looking at the clips Gemini selects, being highly opinionated about what should / should not be included, being specific enough about why, and repeating until you can\u2019t think of anything more to improve. You cannot teach an LLM taste if you do not have taste yourself."},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["autolane"],"value":"Show HN: ride-recap, teaching a LLM my taste to automate cycling highlights"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://www.iandmacomber.com/blog/gopro-garmin-gemini-ride-recap/"}},"_tags":["story","author_iandmacomber","story_48957639","show_hn"],"author":"iandmacomber","children":[48958107,48958830],"created_at":"2026-07-18T12:41:43Z","created_at_i":1784378503,"num_comments":1,"objectID":"48957639","points":5,"story_id":48957639,"story_text":"TL;DR: Turn hours of raw GoPro footage + a .fit file into a 60-second highlight reel with ride telemetry burned in. Every second of the ride is scanned by gemini-3.5-flash, as is the clip ranking + curation. The whole thing costs about $0.04 per ride and takes 10 minutes.
Longer version: Road cycling has been my primary form of exercise, social outlet, therapy, wardrobe expense, and personality trait for almost a decade. I ride most weekends, usually out of Manhattan and up 9W. By the end of a ride I have hours of GoPro footage and one .fit file with per-second speed, power, heart rate, cadence, and GPS. Absolutely no one wants to watch 3 hours of being stuck behind Citibikes on West Side Highway. It's fun to look through past footage, identify the fun parts, and put together a narrative to remember. But since cycling is already time consuming, manually editing a highlight reel edit per ride is a nonstarter. So I built and open-sourced https://github.com/ianmacomber/ride-recap.
I identify compelling moments from four separate sources:\n* Garmin telemetry via .fit file (speed, HR, power spikes, sprints, climbs)\n* Strava via API (popular segments)\n* Gemini vision scan + rating of individual frames\n* (optional) hand-labels via Streamlit app
The fusion step has a LLM narrative pass pick 20 clips to best tell the story of the ride, boosting \u201ccross-source agreements\u201d (if a human label + telemetry + Strava + Gemini all agree that a clip is interesting), with greedy re-ranking and a crowding penalty to avoid clips too close to something already selected.
Obvious in retrospect, but there\u2019s no substitute other than looking at the clips Gemini selects, being highly opinionated about what should / should not be included, being specific enough about why, and repeating until you can\u2019t think of anything more to improve. You cannot teach an LLM taste if you do not have taste yourself.","title":"Show HN: ride-recap, teaching a LLM my taste to automate cycling highlights","updated_at":"2026-07-18T19:55:32Z","url":"https://www.iandmacomber.com/blog/gopro-garmin-gemini-ride-recap/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"Eridanus2"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["autolane"],"value":"Automated job application system, sponsored by the American Mathematical Society"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://www.mathjobs.org/jobs"}},"_tags":["story","author_Eridanus2","story_48951684"],"author":"Eridanus2","created_at":"2026-07-17T20:02:09Z","created_at_i":1784318529,"num_comments":0,"objectID":"48951684","points":4,"story_id":48951684,"title":"Automated job application system, sponsored by the American Mathematical Society","updated_at":"2026-07-18T17:40:16Z","url":"https://www.mathjobs.org/jobs"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"pm2222"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["autolane"],"value":"Chinese automakers are taking on the UK \u2013 and many Brits are embracing it"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["autolane"],"value":"https://www.cnbc.com/2026/07/17/chinese-automakers-uk.html"}},"_tags":["story","author_pm2222","story_48947805"],"author":"pm2222","created_at":"2026-07-17T14:29:16Z","created_at_i":1784298556,"num_comments":0,"objectID":"48947805","points":3,"story_id":48947805,"title":"Chinese automakers are taking on the UK \u2013 and many Brits are embracing it","updated_at":"2026-07-17T15:51:43Z","url":"https://www.cnbc.com/2026/07/17/chinese-automakers-uk.html"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"tedggh"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["autolane"],"value":"Some of my kitchen appliances are LG and I installed the LG ThinQ app on my phone.
Sometimes I like to leave a cold dish in the oven before I go out then remotely start it when I\u2019m on my way back home, so I arrive to a nice hot dinner.
After a recent HN post about LG malware and further research, I decided to kick my appliances out of the network.
I also tried to remove my user account just to find out that in order to use the app I need to first accept the new terms of use.
I asked ChatGPT for a summary. This seems a lot more aggressive than the standard terms of use of other products and services.
1. No arbitration opt-out. You give up court, jury trial, class actions, mass actions, and collective claims. Most disputes must go through individual arbitration, and LG says your only alternative is to stop using the service. Claims generally must be brought within one year. (Sections 20.b and 20.c)
2. Broad rights over anything you submit. LG receives a perpetual, irrevocable, transferable, worldwide licence to use, modify, sublicense, and commercially exploit your user content \u201cfor any purpose whatsoever,\u201d without payment. Avoid uploading photos, recordings, documents, or detailed personal information. (Section 9.b)
3. No privacy expectation for communications. LG states that it may monitor user content and that you have no expectation of privacy for in-app chat, text, or voice communications. (Section 9.e)
4. Voice capture can include other people. Voice-enabled products may record and analyse family members, children, guests, and bystanders. LG places responsibility on you to inform them and obtain any legally required consent. (Section 4.d)
5. AI may use appliance and usage data. LG says third-party AI systems may rely on data from your use of its products and services. The terms do not clearly describe exactly what is transmitted, how long it is retained, or whether it is used to improve models. That information should be in the separate privacy policy. (Section 4.a)
6. Marketing consent is bundled into use. By using the service, you agree to email, texts, calls, automated or prerecorded messages, and push notifications, including promotions. You can opt out, but you must do so separately. (Section 5)
7. Targeted advertising is permitted. LG reserves the right to show targeted third-party advertising based on user preferences. (Section 11.b)
8. Very low liability limit. For many claims, LG attempts to cap its liability at the greater of the amount involved in the transaction, $100, or a statutory remedy. It also broadly excludes responsibility for lost data and unauthorized access. Local law may limit these clauses. (Section 17)
9. LG can update services remotely. It may push over-the-air updates without further consent and can change or discontinue features. (Sections 3.c and 18)"},"title":{"matchLevel":"none","matchedWords":[],"value":"LG ThinQ Terms of Use"}},"_tags":["story","author_tedggh","story_48958273","ask_hn"],"author":"tedggh","children":[48958500,48958594,48958616,48958692,48958841,48958972,48959028,48959617,48959955,48961358,48961378,48962174],"created_at":"2026-07-18T14:04:55Z","created_at_i":1784383495,"num_comments":27,"objectID":"48958273","points":52,"story_id":48958273,"story_text":"Some of my kitchen appliances are LG and I installed the LG ThinQ app on my phone.
Sometimes I like to leave a cold dish in the oven before I go out then remotely start it when I\u2019m on my way back home, so I arrive to a nice hot dinner.
After a recent HN post about LG malware and further research, I decided to kick my appliances out of the network.
I also tried to remove my user account just to find out that in order to use the app I need to first accept the new terms of use.
I asked ChatGPT for a summary. This seems a lot more aggressive than the standard terms of use of other products and services.
1. No arbitration opt-out. You give up court, jury trial, class actions, mass actions, and collective claims. Most disputes must go through individual arbitration, and LG says your only alternative is to stop using the service. Claims generally must be brought within one year. (Sections 20.b and 20.c)
2. Broad rights over anything you submit. LG receives a perpetual, irrevocable, transferable, worldwide licence to use, modify, sublicense, and commercially exploit your user content \u201cfor any purpose whatsoever,\u201d without payment. Avoid uploading photos, recordings, documents, or detailed personal information. (Section 9.b)
3. No privacy expectation for communications. LG states that it may monitor user content and that you have no expectation of privacy for in-app chat, text, or voice communications. (Section 9.e)
4. Voice capture can include other people. Voice-enabled products may record and analyse family members, children, guests, and bystanders. LG places responsibility on you to inform them and obtain any legally required consent. (Section 4.d)
5. AI may use appliance and usage data. LG says third-party AI systems may rely on data from your use of its products and services. The terms do not clearly describe exactly what is transmitted, how long it is retained, or whether it is used to improve models. That information should be in the separate privacy policy. (Section 4.a)
6. Marketing consent is bundled into use. By using the service, you agree to email, texts, calls, automated or prerecorded messages, and push notifications, including promotions. You can opt out, but you must do so separately. (Section 5)
7. Targeted advertising is permitted. LG reserves the right to show targeted third-party advertising based on user preferences. (Section 11.b)
8. Very low liability limit. For many claims, LG attempts to cap its liability at the greater of the amount involved in the transaction, $100, or a statutory remedy. It also broadly excludes responsibility for lost data and unauthorized access. Local law may limit these clauses. (Section 17)
9. LG can update services remotely. It may push over-the-air updates without further consent and can change or discontinue features. (Sections 3.c and 18)","title":"LG ThinQ Terms of Use","updated_at":"2026-07-18T22:17:33Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"felixdoerp"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["autolane"],"value":"Hi HN :) really excited to share this with you.
The one thing AI reliably does is generate noise. Half the tools I see launch are just machines for producing more noise across more channels. And people are starting to see this in the form of emails in their inboxes as spam filters are struggling.
There used to be a useful signal in email: the effort a sender put into customizing a message was a rough proxy for how relevant it actually was. AI killed that. Now it's customized slop with the appearance of effort with none of the cost. It is painful that the open internet / open channels have been abused like this.
Captchainbox applies the idea of proof-of-work to email. If a sender is willing to do a bit of work to reach you, the message is more likely to be worth your time and the sender more likely to be real. The work is a traditional captcha. You can also set a pay-to-deliver amount if you want more friction. The proceeds of the delivery payment after transaction costs go to the Internet Archive and the EFF. The tool currently works by authing with your Gmail or Outlook and during launch time I make this completely free as a lifetime deal (with optional payment if you wanna support).
How it works: Captchainbox builds a whitelist automatically from the metadata of your past correspondence. If you've emailed an individual address, that sender can reach you. If you talk to several people at the same domain, we whitelist the whole domain. If one transactional-looking sender has sent you more than 10 emails, we treat it as a transactional domain and let it through. This whitelist is for you to change whenever you want. It continues to build organically as you converse with more addresses.
Incoming mail is checked against that whitelist. Senders already on it land in your inbox as normal. Anyone else gets archived (never deleted) and is sent a challenge. This can be the captcha or the payment link. Once they solve it, their email is pulled out of the archive and put back into your inbox.
if you want to see what this looks like from a sender's point of view, send me an email here: doerpfelix15@gmail.com
The service only ever reads metadata, never message content. And since nothing is ever deleted, you can't lose a message. There is a legitimate risk / downside: if you sign up to a new service, these emails also land in the archive. Since we do not process the content, a first-time sender who can't solve the challenge (say an automated activation email) will sit in your archive until you spot it.
Happy to answer anything! :)"},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: Make senders work to get into your inbox"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://www.captchainbox.com"}},"_tags":["story","author_felixdoerp","story_48919489","show_hn"],"author":"felixdoerp","children":[48919640,48919648,48919660,48919692,48919697,48919701,48919729,48919822,48919853,48919860,48919911,48919930,48920030,48920065,48920127,48920141,48920153,48920179,48920220,48920260,48920367,48920405,48921541,48921706,48926177,48926849,48926986,48929270,48932176,48946897,48946956],"created_at":"2026-07-15T11:58:54Z","created_at_i":1784116734,"num_comments":68,"objectID":"48919489","points":44,"story_id":48919489,"story_text":"Hi HN :) really excited to share this with you.
The one thing AI reliably does is generate noise. Half the tools I see launch are just machines for producing more noise across more channels. And people are starting to see this in the form of emails in their inboxes as spam filters are struggling.
There used to be a useful signal in email: the effort a sender put into customizing a message was a rough proxy for how relevant it actually was. AI killed that. Now it's customized slop with the appearance of effort with none of the cost. It is painful that the open internet / open channels have been abused like this.
Captchainbox applies the idea of proof-of-work to email. If a sender is willing to do a bit of work to reach you, the message is more likely to be worth your time and the sender more likely to be real. The work is a traditional captcha. You can also set a pay-to-deliver amount if you want more friction. The proceeds of the delivery payment after transaction costs go to the Internet Archive and the EFF. The tool currently works by authing with your Gmail or Outlook and during launch time I make this completely free as a lifetime deal (with optional payment if you wanna support).
How it works: Captchainbox builds a whitelist automatically from the metadata of your past correspondence. If you've emailed an individual address, that sender can reach you. If you talk to several people at the same domain, we whitelist the whole domain. If one transactional-looking sender has sent you more than 10 emails, we treat it as a transactional domain and let it through. This whitelist is for you to change whenever you want. It continues to build organically as you converse with more addresses.
Incoming mail is checked against that whitelist. Senders already on it land in your inbox as normal. Anyone else gets archived (never deleted) and is sent a challenge. This can be the captcha or the payment link. Once they solve it, their email is pulled out of the archive and put back into your inbox.
if you want to see what this looks like from a sender's point of view, send me an email here: doerpfelix15@gmail.com
The service only ever reads metadata, never message content. And since nothing is ever deleted, you can't lose a message. There is a legitimate risk / downside: if you sign up to a new service, these emails also land in the archive. Since we do not process the content, a first-time sender who can't solve the challenge (say an automated activation email) will sit in your archive until you spot it.
Happy to answer anything! :)","title":"Show HN: Make senders work to get into your inbox","updated_at":"2026-07-18T17:13:15Z","url":"https://www.captchainbox.com"}],"hitsPerPage":50,"nbHits":61,"nbPages":2,"page":0,"params":"query=Autolane&tags=story&hitsPerPage=50&advancedSyntax=true&analyticsTags=backend","processingTimeMS":7,"processingTimingsMS":{"_request":{"roundTrip":26},"afterFetch":{"format":{"highlighting":3,"total":3},"merge":{"total":1},"total":1},"fetch":{"query":4,"total":5},"total":7},"query":"Autolane","serverTimeMS":12}