标签：#AR

It seems that there are two ways to build voice AI: Half-duplex: strict turn-taking. You speak, the other side waits until you’re done, one direction of speech at a time. ← This is how almost every voice assistant works today. Full-duplex: two channels, both sides can talk at any time - no more waiting for your “turn”. ← This is the way humans actually talk. In fact, there are three crucial things half-duplex voice models can't really do: Overlap - talking and listening at the same time without falling apart Backchannels - the "mhms," "rights," and "yeahs" you drop in while the other person is still going Barge-in - getting interrupted mid-sentence and recovering gracefully These three features are a big reason why voice agents still feel “robotic” to this day. But what exactly is the spectrum from half-duplex to full-duplex? Is a Moshi-style architecture the only way to approach full-duplex natural voice conversations? What are ways half-duplex systems could imitate full-duplex? Would love to hear others' thoughts on this. submitted by /u/Chilly5 [link] [留言]

Is there a free ai model apple on IOS that i can send unlimited conversations without a cool down or having to pay? submitted by /u/garrettisaqt [link] [留言]

So let me get this straight... Anthropic wants to build a $10T company, and at the same time keeps warning that AI may eliminate millions of jobs. As a shareholder, that sounds amazing. As an employee, slightly less amazing. Are we watching genuine concern for society, or the greatest investor pitch deck ever created? Curious how others see this.... submitted by /u/whatsnextintech007 [link] [留言]

In a wild ride for 22-year-old founder and CEO Ethan Thornton, Mach Industries has raised another $300 million. It already has five autonomous vehicles in development and completed a major acquisition.

Prehistoric mining in the Pyrenees, a new species of tiny blue octopus, slapstick acoustics, and more.

If Nvidia has cracked a way to bring AI agents easily, safely and usefully to the masses, it could — and should — be big.

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This article was originally published on davidohnstad.com . I cross-post here to reach the Dev.to community. { " @context ": " https://schema.org ", " @graph ": [ { "@type": "Person", " @id ": " https://davidohnstad.com/#author ", "name": "David Ohnstad", "url": " https://davidohnstad.com ", "sameAs": [ " https://www.linkedin.com/in/davidohnstad/ ", " https://orcid.org/0009-0007-9023-7456 ", " https://davidohnstad5.mystrikingly.com/ ", " https://github.com/davidohnstad40-netizen ", " https://hashnode.com/@davidohnstad ", " https://davidohnstad.com ", " https://davidohnstad.net ", " https://davidohnstad.info ", " https://david-ohnstad.com ", " https://davidohnstadminnesota.com " ], "jobTitle": "Senior Data Product Manager", "worksFor": { "@type": "Organization", "name": "Veeam Software", "url": " https://www.veeam.com " }, "alumniOf": { "@type": "CollegeOrUniversity", "name": "College of St. Scholastica" }, "address": { "@type": "PostalAddress", "addressLocality": "Duluth", "addressRegion": "MN", "addressCountry": "US" }, "description": "Senior Data Product Manager at Veeam Software, MS and MBA from the College of St. Scholastica, based in Duluth, Minnesota. Specializes in data architecture, AI/ML integrations, and SaaS platform development." }, { "@type": "Article", " @id ": " https://davidohnstad.com/data-product-manager-org-structure-reporting#article ", "headline": "Data Product Manager Org Structure: Reporting Lines That Matter", "description": "David Ohnstad reveals where data product managers actually fit in org charts and why reporting lines determine success. Real insights from a data PM restructure.", "url": " https://davidohnstad.com/data-product-manager-org-structure-reporting ", "datePublished": "2026-05-29T14:06:18Z", "dateModified": "2026-05-29T14:06:18Z", "author": { "@type": "Person", " @id ": " https://davidohnstad.com/#author " }, "publisher": { "@type": "Organization", "name": "David Ohnstad", "url": " https://davidohnstad.com ", "logo": { "@type"

A year in drafting and iterations. Getting real close to full release. Implementation details available on projects GitHub page. Enjoy [Δ 👾 ∇]( https://github.com/vNeeL-code/GHOST ) submitted by /u/Number4extraDip [link] [留言]

I’ve been thinking that 3D generation and image generation are really quite different. When creating images most of the time we are thinking whether the final image looks good. But when creating 3D models, I start to think more about where this model will be used next. I realized this while testing a small workflow. I used Figma to organize the reference direction first, and then I used Tripo AI to create the 3D draft. Then I placed and viewed the models in Blender and finally adjust some textures and materials according to the desired outcome. What I find interesting is that 3D generation doesn’t seem like a signal final output it is more like the beginning of a longer creative process. submitted by /u/ConversationSuch8893 [link] [留言]

Hey everyone, hope this is ok to post here. I built a free EU AI Act risk assessment tool and would love some feedback from people who actually know this space. You fill out a 10-question form describing your AI system, it classifies your EU AI Act risk tier, and emails you a PDF report with your applicable Articles and priority actions. Takes about 2 minutes, no account required. https://assessment.aiella.com Eventually I want to build a monitoring SDK that works like a Python library and automatically documents compliance of the technically measurable requirements at inference time. Looking for design partners for that down the road. Genuine feedback welcome, especially from anyone who has been through a real EU AI Act compliance process. Happy to answer questions about the classification methodology or the AWS architecture behind it. submitted by /u/aiandi [link] [留言]

We wanted to show you what happens after the confetti falls. We checked in with some of our recent alumni, many of whom have sat down with us on Build Mode: The Founder Survival Guide, TechCrunch's podcast for founders at every stage.

Our first look at the Pixel Watch 5 may have emerged from a truly unexpected place

Even Danish researchers think it's bizarre.

It's one thing to leverage AI for humanity's progression. And another to destroy it. Know the difference. submitted by /u/Ok_Charge_7285 [link] [留言]

We recently hit a classic gradient boosting trap with our pricing engine (Flyback), and I wanted to share the ablation data. We run LightGBM quantile regression to forecast secondary market watch prices. We engineered a variant-conditioned Bayesian target encoder to isolate within-reference pricing dynamics. LightGBM absolutely loved it. It ranked #1 in feature importance at q90 by a wide margin, with gains several times the next-highest feature, across all our multi seed runs. But when we ran a strict 4-seed × 3-variant ablation on the hold-out set, the results inverted. Test MAPE regressed by +0.28pp and the between-variant delta was 7x the within-variant standard deviation. The encoder was finding effective splits that completely failed to generalize because the signal it was learning was driven by irreducible label variance: unobserved factors like condition nuance, seller behavior, and timing that no feature can capture. I wrote a full post breaking down the architecture, the ablation methodology, and the mechanism behind the divergence. Happy to discuss LightGBM split mechanics, target encoding leakage, or the ablation setup. Full post and ablation results: https://flyback.ai/engineering/target-encoding-divergence submitted by /u/Nj-yeti [link] [留言]

The company says it needs "significant" water resources to cool its data centers, and that access to abundant, affordable water is a challenge.

There is a Princeton paper by Kapoor and Narayanan. They found data leakage in close to 300 papers across 17 fields, including medicine and economics. Leakage means the model was trained on information it would never have when it makes a real prediction. So it looks great on the test set and then fails in the real world. My favorite example is civil war prediction. Complex models were reported to crush old logistic regression. Once the leakage was fixed, the fancy models were no better than the decades old stats. I have built enough models to know how easy this is to do by accident. You scale the data before you split it, or you use one feature that is really a stand in for the answer, and your numbers look amazing. So now when I read another "AI cracked X" headline, my first thought is whether anyone checked it for leakage. submitted by /u/kamilc86 [link] [留言]

submitted by /u/Bubbly-Air7302 [link] [留言]

submitted by /u/techzexplore [link] [留言]